Arash Veshkini

Harald M Hammon
email: hammon@fbn-dummerstorf.de

Laura Vogel

Didier Viala

Mylène Delosière

Arnulf Tröscher

Sébastien Déjean

Fabrizio Ceciliani

Helga Sauerwein

Muriel Bonnet
email: muriel.bonnet@inrae.fr

Didier Viala

Plasma proteomics reveals crosstalk between lipid metabolism and immunity in dairy cows receiving essential fatty acids and conjugated linoleic acid

scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Differential plasma proteome during the antepartum and postpartum period. From day 21 antepartum (AP) to day 1 postpartum (PP), the relative abundance of 14 proteins was increased, and of 49 proteins decreased (Log2 fold change range from -4 to + 2, p-value ≤ 0.05) in the plasma regardless of treatment (Fig. 1A, Supplementary S2, Supplementary Table S3). The KEGG analysis mapped differentially abundant proteins (DAP) to cholesterol metabolism and complement and coagulation cascades pathways. Underabundant proteins were annotated by biological process (BP) gene ontology (GO) terms mainly related to immune regulation and glucose homeostasis, particularly through activation of immune response GO:0002253, acute-phase response GO:0006953, inflammatory response GO:0006954, complement activation GO:0006958, regulation of glucose metabolic process GO:0010906, regulation of tumor necrosis factor production GO:0032680, cellular response to insulin stimulus GO:0032869 (Fig. 1B). Overabundant proteins were annotated by BP GO terms including, response to stress GO:0006950, acute-phase response GO:0006953, cellular oxidant detoxification GO:0098869, and lipid transport GO:0006869 (Fig. 1B). The DAP were assigned to the extracellular region and lipoprotein particles based on the cellular component (CC) GO category (Supplementary S2, Supplementary Tables S4, S5, S6, S7, S8).

From day + 1 to day + 28 PP, 29 proteins were over-and 10 proteins were underabundant. Over and underabundant proteins are displayed in the Volcano plot (Fig. 1C, Supplementary S3, Supplementary Table S9). The DAP were mapped to the phagosome, NF-kappa B signaling pathway, complement and coagulation cascades, and toll-like receptor signaling pathway using the KEGG pathway analysis (Supplementary S3, Supplementary Table S10). The GO analysis showed enrichment of similar BP terms by both up-and downregulated proteins, including response to stress GO:0006950, regulation of cytokine production GO:0001817, inflammatory response GO:0006954, and cellular oxidant detoxification GO:0098869 (Fig. 1D) (Supplementary S3, Supplementary Tables S11, S12, S13, S14).

Twenty-five proteins, including one over and 24 under-abundant ones, were identified as DAP in d + 63 over day + 28 (Fig. 1E, Supplementary S4, Supplementary Table S15). In total, three KEGG pathways, including complement and coagulation cascades, Staphylococcus aureus infection, and thyroid hormone synthesis, have annotated the DAP. Insulin-like growth factor-binding protein 2 (IGFBP2) as the only over-abundant protein was not annotated by a GO term, but under-abundant proteins were annotated by GO BP terms related to the regulation of immune response GO:0050776, regulation of response to stimulus GO:0048583, homeostatic process GO:0042592, GO:0010951 negative regulation of endopeptidase activity and regulation of primary metabolic process GO:0080090 (Fig. 1F) (Supplementary S4, Supplementary Tables S16, S17, S18).

Differential plasma proteome between EFA + CLA and control groups. Overlap between differ-

entially abundant proteins in response to the fatty acid treatment at different timepoints. The Venn diagram in Fig. 2A represents the intersection of proteins identified in all timepoints in response to the EFA + CLA supplementation. The relative abundance of apolipoproteins (APO) C3 (APOC3), APOA1, and APOA4 was greater at all timepoints in response to EFA + CLA supplementation as compared to the CTRL group ("overlapping

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Content courtesy of Springer Nature, terms of use apply. Rights reserved proteins") (details in Supplementary S5, Supplementary Table S19). Also, APOC4 and hemoglobin subunit alpha (HBA) were more abundant in the EFA + CLA group in the whole PP period. These APO were mapped to KEGG pathways such as the PPAR signaling pathway, fat digestion and absorption, and cholesterol metabolism (Fig. 2B, Supplementary S6, 7, 8, and, 9, Supplementary Tables S21, S25, S29, S33). Moreover, APO proteins were annotated by enriched BP GO terms, including triglyceride homeostasis (GO:0070328), cholesterol homeostasis (GO:0042632), lipid transport (GO:0006869), plasma lipoprotein particle assembly (GO:0034377), regulation of lipoprotein lipase activity (GO:0051004), and lipoprotein metabolic process (GO:0042157) (Supplementary S6, 7, 8, and, 9, Supplementary Tables S22, S26, S30, S34). At the CC level, localization of DAP was extracellular region (GO:0005576), extracellular space (GO:0005615), high-density lipoprotein particle (GO:0034364), chylomicron (GO:0042627), and very-low-density lipoprotein particle (GO:0034361) (Supplementary S6, 7, 8, and, 9, Supplementary Tables S23, S27, S31, S35). In association with EFA + CLA supplementation, the same KEGG pathways and BP and CC GO terms were enriched in all time points ("overlapping pathways"), therefore to avoid repetition, we did not mention them in each timepoint.

Differentially abundant proteins in response to the fatty acid treatment at specific timepoints around parturition. On day 21 AP, seven proteins were differentially abundant between treatments (Volcano plot, Fig. 2C, Supplementary S6, Supplementary Table S20), in which, beside overlapping proteins, proteoglycan 4 (PRG4), and apoN protein (APON) were more abundant. At the same time, argininosuccinate synthase (ASS1) and phosphatidylinositol-glycan-specific phospholipase D (GPLD1) were less abundant in the EFA + CLA compared to the CTRL group. On day 21 AP, DAP were annotated only by pathways identified for the overlapping proteins (Supplementary S6, Supplementary Tables S21, S22, S23).

An overview of the proteomic variability between the treatment groups at day 1 PP is given in Fig. 2D (Supplementary S7, Supplementary Table S24). The relative abundance of 14 proteins was greater and two proteins were lower in the EFA + CLA than in the CTRL group. The overabundant proteins, were annotated by enriched BP GO terms comprised negative regulation of inflammatory response (GO:0050728), and regulation of immune system process (GO:0002682). There were no enriched pathways for the two underabundant proteins in the EFA + CLA group (Supplementary S7, Supplementary Tables S25, S26, S27).

At day 28 PP, beside overlapping proteins, PRG4, hemoglobin subunit beta (HBB), insulin-like growth factor-binding protein acid-labile subunit (IGFALS), plasma amyloid P-component (APCS), transthyretin (TTR), WAP domain-containing protein (LOC100850808), and matrix Gla protein (MGP) were more abundant in the EFA + CLA group as compared to the CTRL group (Fig. 2E). The PCYOX1 and filamin A (FLNA) were less abundant in EFA + CLA than in CTRL (Fig. 2E, Supplementary S2, Supplementary Table S28). Herein in response to EFA + CLA supplementation, only negative regulation of the immune system process (GO:0002683) was enriched. The GO analysis was not significant for those proteins that were less abundant in the EFA + CLA group than in the CTRL group (Supplementary S8, Supplementary Tables S29, S30, S31).

On day 63 PP, we identified 13 DAP, containing overlapping proteins, coagulation factor XIII A chain (F13A1), C3/C5 convertase (C2), and PRG4 with greater abundance, and LDL receptor-related protein 1 (LRP1), carboxypeptidase A1 (CPA1), adenosylhomocysteinase (AHCY), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and epoxide hydrolase (EPHX1) with lower abundance in the EFA + CLA group as compared to the CTRL group (Fig. 2F, Supplementary S9, Supplementary Table S32). Again at this timepoint, only overlapping pathways have been enriched (Supplementary S9, Supplementary Tables S33, S34, S35).

Discussion

This study investigated the plasma proteome of dairy cows supplemented with EFA + CLA during the periparturient period. The DAP and their associated pathways are discussed separately during the transition from late pregnancy to early lactation and between treatment groups. Time-affected DAP will be discussed prior to treatment to gain a deeper understanding of dairy cows' metabolic and immune status while confirming some physiological adaptations that increase the reliability of proteomic data. Then FA supplementations will be discussed as they may affect these metabolic adaptations. Protein-protein interaction network analysis depicts that among DAP identified in this study, some belonged to immune system and lipid metabolic process.

From day -21 to + 1 of parturition, various immune-related proteins involved in the regulation of pro-inflammatory proteins, such as TNFα, were found to be DAP. This list includes CD14 and adiponectin (ADIPOQ), complement and coagulation cascades such as clusterin (CLU), complement C1q A Chain (C1QA), coagulation factor VII (F7), MASP1, component C2 (C2), CFI, mannose-binding protein C (MBL1), acute-phase response

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Content courtesy of Springer Nature, terms of use apply. Rights reserved and inflammation comprising haptoglobin (HP), lipopolysaccharide-binding protein (LBP), alpha 2-Heremans-Schmid glycoprotein (AHSG), fibronectin 1 (FN1), and alpha-1-acid glycoprotein 1 (AGP1), and cellular oxidant detoxification protein (GPX3). The involvement of these immune-related proteins is likely due to the activation of the immune system and the development of a systemic inflammation (SI) which is typical in periparturient dairy cows [START_REF] Horst | Invited review: The influence of immune activation on transition cow health and performance-A critical evaluation of traditional dogmas[END_REF] , regardless of their health status, to support a new physiological state.

An excessive SI in early PP is generally associated with massive fat mobilization in conjunction with the release of pro-inflammatory cytokines, TNFα, and interleukin IL6 [START_REF] Trevisi | Metabolic stress and inflammatory response in high-yielding, periparturient dairy cows[END_REF][START_REF] Bradford | Invited review: Inflammation during the transition to lactation: New adventures with an old flame[END_REF][START_REF] Pascottini | Effect of anti-inflammatory treatment on systemic inflammation, immune function, and endometrial health in postpartum dairy cows[END_REF] which in turn alter the secretion of acute-phase proteins (APP). Herein, from day -21 to + 1 relative to parturition and in response to SI, we observed increased abundance of positive APP (HP and LBP) and decreased abundance of negative APP (AHSG and FN1). In addition, AGP1 which is considered a moderate positive APP decreased. Following our results, it is well documented that HP increased from AP to early PP and then gradually decreased [START_REF] Hernández-Castellano | Endocrine and metabolic changes in transition dairy cows are affected by prepartum infusions of a serotonin precursor[END_REF][START_REF] Saremi | Bovine haptoglobin as an adipokine: Serum concentrations and tissue expression in dairy cows receiving a conjugated linoleic acids supplement throughout lactation[END_REF] . In this regard, both HP and LBP are involved in the anti-inflammatory reactions, which are essential to maintain the balanced inflammatory response (for review 1 ). Moreover, AHSG, apart from being an APP, is a free fatty acid transporter that enhances cellular lipid uptake and lipogenesis during NEB in dairy cows [START_REF] Strieder-Barboza | Fetuin-A: A negative acute-phase protein linked to adipose tissue function in periparturient dairy cows[END_REF] . Therefore, it seems logical to expect ASHG reduction at the onset of lactation when the lipogenesis pathways were downregulated in favour of energy production. It must be said that the observed AGP1 protein abundances are not in supports of AGP being a positive APP; AGP has been demonstrated to increase during the first and second week of lactation in cows [START_REF] Cairoli | Serum protein pattern during cow pregnancy: Acute-phase proteins increase in the peripartum period[END_REF] and water buffalos [START_REF] Sauerwein | Acute phase proteins and markers of oxidative status in water buffalos during the transition from late pregnancy to early lactation[END_REF] , respectively. However, there is no comparable study reporting the AGP1 differences between AP and PP in dairy cows. Remarkably, AGP1 has been reported to act as an anti-inflammatory protein [START_REF] Ceciliani | The immune functions of α(1) acid glycoprotein[END_REF] , thus being crucial for maintaining immune tolerance. Moreover, it has been reported that both AHSG and AGP1 were reduced before the onset of ketosis in dairy cows [START_REF] Gao | Significant changes in ITIH4, AHSG, ORM1, and CD46 content in milk fat globule membrane proteins of ketotic dairy cows[END_REF] . Considering that dairy cows are prone to develop (subclinical) ketosis during the early PP phase, reduced abundance of AHSG and AGP1 in our study is in line with this report.

On the other side, the relative abundance of several complement proteins was reduced at calving. Proteins of the complement system comprise a complex enzymatic cascade that possesses anti-inflammatory functions [START_REF] Nesargikar | The complement system: History, pathways, cascade and inhibitors[END_REF] . The literature on complement proteins in dairy cows plasma, especially during the periparturient period, is scarce. Nevertheless, downregulation of the complement cascade while dairy cows were in SI, implies reduced immune system responsiveness during the early PP period. These results are supported by previous studies that reported immunosuppression in dairy cows before or around calving [START_REF] Wankhade | Metabolic and immunological changes in transition dairy cows: A review[END_REF][START_REF] Hammon | Neutrophil function and energy status in Holstein cows with uterine health disorders[END_REF] .

During transition from AP to PP, five proteins namely phospholipid transfer protein (PLTP), LRP1, and a cluster of APO containing APOE, APOH, and APOB were annotated by the cholesterol metabolism pathway. The relative abundance of all these proteins, except APOE, was downregulated. In accordance, increased ApoE and decreased ApoB100 mRNA abundance have been reported in the liver of dairy cows during the transition from d -35 AP to d 3 PP, whereby APOB was correlated with decreased cholesterol plasma levels [START_REF] Bernabucci | Abundance of mRNA of apolipoprotein B 100 , apolipoprotein E, and microsomal triglyceride transfer protein in liver from periparturient dairy cows*[END_REF] . Reduced cholesterol synthesis is also a part of the APP response 1 and is typically observed during early lactation [START_REF] Kessler | Cholesterol metabolism, transport, and hepatic regulation in dairy cows during transition and early lactation[END_REF] .

Hepatic and whole-body cholesterol homeostasis are regulated by the different lipoprotein classes and their APO particles [START_REF] Bionaz | Advances in fatty acids nutrition in dairy cows: From gut to cells and effects on performance[END_REF] . Concerning the role of the DAP in cholesterol metabolism, PLTP stimulates phospholipid transfer from very-low-density lipoproteins (VLDL) to the high-density lipoprotein (HDL), and its deficiency is associated with decreased plasma HDL, APOA1, and APOB levels [START_REF] Yazdanyar | Liver-specific phospholipid transfer protein deficiency reduces high-density lipoprotein and non-high-density lipoprotein production in mice[END_REF] . Also, LRP1 deficiency reduced plasma clearance of APOE-containing lipoproteins [START_REF] Paththinige | Genetic determinants of inherited susceptibility to hypercholesterolemia-A comprehensive literature review[END_REF] and thereby accelerated hepatic steatosis [START_REF] Hamlin | Low-density lipoprotein receptor-related protein-1 dysfunction synergizes with dietary cholesterol to accelerate steatohepatitis progression[END_REF] . In this regard, ApoE which acts as a receptor-binding ligand is highly associated with both VLDL and HDL in the plasma [START_REF] Paththinige | Genetic determinants of inherited susceptibility to hypercholesterolemia-A comprehensive literature review[END_REF] , and its high level leads to the development of lipid-related disorders such as fatty liver in periparturient cows [START_REF] Takahashi | Bovine apolipoprotein E in plasma: Increase of ApoE concentration induced by fasting and distribution in lipoprotein fractions[END_REF] . In addition, decreased ApoB100 is associated with decreased synthesis and secretion of VLDL during the periparturient period. A positive correlation (r = 0.65) has been confirmed between ApoB100 and plasma cholesterol in early PP dairy cows [START_REF] Bernabucci | Abundance of mRNA of apolipoprotein B 100 , apolipoprotein E, and microsomal triglyceride transfer protein in liver from periparturient dairy cows*[END_REF] . Furthermore, ApoH reduces the intracellular accumulation of cholesterol and blocks the oxidation of LDL [START_REF] Paththinige | Genetic determinants of inherited susceptibility to hypercholesterolemia-A comprehensive literature review[END_REF] , which in turn inhibits steatosis development.

In line with our proteomics findings, we have previously reported [START_REF] Vogel | Effects of abomasal infusion of essential fatty acids together with conjugated linoleic acid in late and early lactation on performance, milk and body composition, and plasma metabolites in dairy cows[END_REF] that the plasma concentration of total cholesterol, LDL-cholesterol, HDL-cholesterol as well as energy balance were at a minimum level on day 1 PP. Decreased APO and lipoproteins suggested reduced cholesterol metabolism (synthesis and reverse transport) and probably initiation of steatosis by the hepatic accumulation of triglycerides (TG) at the onset of lactation.

Interestingly, on day 28 of lactation, some of the immune-related DAP showed the reverse trend compared to d1 PP and tended back to AP levels. In this sense, the APP HP, ITIH4, LBP, and cellular oxidant detoxification protein (PRDX2) were downregulated, and CD14, ADIPOQ, and two proteins related to activation and regulation of the immune system process (HRG, MBL2) were upregulated. Taken together, these observations suggest that the compromised immune system at the beginning of lactation was gradually recovering during the first weeks of lactation. These results support the concept that dairy cows have to adapt to profound metabolic and immune challenges around parturition related to a state of SI. However, they usually recover within few weeks by dampening the inflammatory reaction.

From day 28 to 63 PP, only slight fold changes in protein abundance were observed; the relative abundance of alpha-1-antiproteinase (SERPINA1), alpha-2-macroglobulin (A2M), complement factor B (CFB), protein HP-20 homolog (C1QC) associated with complementing and coagulation cascades were decreased. These results further confirm our previous conclusion that suboptimal immune and metabolic functions are largely limited to early lactation and then gradually improve.

In response to EFA + CLA supplementation as compared to the CTRL group, APOC3, APOA1, and APOA4 had greater abundances at all timepoints. Genes encoding all these proteins have been shown to share a common enhancer sequence, meaning they operate in synergy [START_REF] Lin | Decreased expression of the APOA1-APOC3-APOA4 gene cluster is associated with risk of Alzheimer's disease[END_REF] probably under the control of transcription factors like PPAR [START_REF] Veshkini | Longitudinal liver proteome profiling in dairy cows during the transition from gestation to lactation: Investigating metabolic adaptations and their interactions with fatty acids supplementation via repeated measurements ANOVA-simultaneous component analysis[END_REF] . They are predominantly transcribed, translated, and secreted by hepatocytes in many species including dairy cows [START_REF] Liao | Dyslipidemia, steatohepatitis and atherogenesis in lipodystrophic apoE deficient mice with Seipin deletion[END_REF] . These exchangeable APO have a pivotal role in cholesterol homeostasis as a major component of circulating lipoproteins (chylomicrons, VLDL, LDL, and HDL) [START_REF] Bionaz | Advances in fatty acids nutrition in dairy cows: From gut to cells and effects on performance[END_REF] and immune cell function [START_REF] Bonacina | Myeloid apolipoprotein E controls dendritic cell antigen presentation and T cell activation[END_REF] , also exerting antiand pro-inflammatory effects [START_REF] Zewinger | Apolipoprotein C3 induces inflammation and organ damage by alternative inflammasome activation[END_REF][START_REF] Roula | Apolipoprotein A-IV acts as an endogenous anti-inflammatory protein and is reduced in treatment-naïve allergic patients and allergen-challenged mice[END_REF][START_REF] Georgila | Apolipoprotein A-I (ApoA-I), immunity, inflammation and cancer[END_REF] . Considering the differential expression of APO during the periparturient period and between treatments, as well as their involvement in both lipid metabolism and the immune system, this study suggests APO (as well as their associated molecules lipoproteins) as potential mediators for crosstalk between these two systems.

Besides, fat digestion and absorption and the peroxisome proliferator-activated receptor (PPAR) signaling pathway were among the most important KEGG pathways associated with the identified APO. In that sense, previous studies have demonstrated that PPAR [START_REF] Staels | Fibrates downregulate apolipoprotein C-III expression independent of induction of peroxisomal acyl coenzyme A oxidase. A potential mechanism for the hypolipidemic action of fibrates[END_REF] and insulin [START_REF] Chen | Transcriptional regulation of the apoC-III gene by insulin in diabetic mice: Correlation with changes in plasma triglyceride levels[END_REF] are inhibitors of ApoC3 gene expression, whereas FA [START_REF] Pavlic | Triglyceride-rich lipoprotein-associated apolipoprotein C-III production is stimulated by plasma free fatty acids in humans[END_REF] induce ApoC3. It has also been shown that a high-fat diet rich in monounsaturated and omega-3 FA reduced APOC3 in human patients with hypertriglyceridemia and/or hyperlipidemia [START_REF] Pieke | Treatment of hypertriglyceridemia by two diets rich either in unsaturated fatty acids or in carbohydrates: Effects on lipoprotein subclasses, lipolytic enzymes, lipid transfer proteins, insulin and leptin[END_REF] . In this study, we did not observe any changes in the plasma concentration of insulin by treatment; therefore, it may be proposed that the supplemented FA directly or indirectly increased the abundance of APOC3.

Among APO, APOA4 is associated with the formation and secretion of chylomicrons which are an essential component in the transportation of dietary FA from the intestine to the circulation [START_REF] Bionaz | Advances in fatty acids nutrition in dairy cows: From gut to cells and effects on performance[END_REF] . Also, APOA4 has been shown to regulate TG-rich lipoprotein secretion from mice hepatocytes by stimulating APOB activity and enhancing APOB-containing particle expansion in the secretory pathway, although the underlying mechanisms are not clear [START_REF] Qu | Apolipoprotein A-IV: A multifunctional protein involved in protection against atherosclerosis and diabetes[END_REF] . More specifically, studies on human hepatocytes and transgenic mice have shown that APOA1 [START_REF] Guo | Overexpression of apolipoprotein A-I alleviates endoplasmic reticulum stress in hepatocytes[END_REF] and APOA4 [START_REF] Qu | Apolipoprotein A-IV: A multifunctional protein involved in protection against atherosclerosis and diabetes[END_REF] could reduce hepatic TG accumulation by suppressing endoplasmic reticulum stress and thus restraining the development of steatosis. In this sense, APOA1 is a major component of HDL, and as such involved in bringing cholesterol from peripheral tissues back to the liver (reverse cholesterol transport in dairy cows) [START_REF] Oikawa | Decreases in serum apolipoprotein B-100 and A-I concentrations in cows with milk fever and downer cows[END_REF] . It has been reported that cows with fatty liver and ketosis had lesser serum concentrations of APOA1 which was associated with increased NEFA and decreased cholesterol and phospholipid concentrations [START_REF] Oikawa | Decreases in serum apolipoprotein B-100 and A-I concentrations in cows with milk fever and downer cows[END_REF] . Also, APOC3 is mainly involved in VLDL production and thus it may reduce intracellular TG accumulation [START_REF] Qin | Missense mutation in APOC3 within the C-terminal lipid binding domain of human ApoC-III results in impaired assembly and secretion of triacylglycerol-rich very low density lipoproteins: Evidence that ApoC-III plays a major role in the formation of lipid precursors within the microsomal lumen[END_REF][START_REF] Norata | Apolipoprotein C-III: From pathophysiology to pharmacology[END_REF] . Recently, it has been proposed that the downregulation of plasma APOC3 protein in over-conditioned cows is associated with an impaired assembly of VLDL contributing to hepatic TG accumulation [START_REF] Ghaffari | Plasma proteomic profiling and pathway analysis of normal and overconditioned dairy cows during the transition from late pregnancy to early lactation[END_REF] .

These results could be a possible explanation for previous observations of specific FA being capable of reducing TG accumulation in cows' liver [START_REF] Moallem | Invited review: Roles of dietary n-3 fatty acids in performance, milk fat composition, and reproductive and immune systems in dairy cattle[END_REF] . Since the ruminants` ability to export TG as a constituent of VLDL from the liver is limited as compared to non-ruminant species [START_REF] Drackley | Biology of dairy cows during the transition period: The final frontier?[END_REF] , these APO may accomplish TG transport from the liver. Following this background, the PP concentrations of NEFA in plasma and of TG in the liver were lower in the EFA + CLA than in the CTRL group [START_REF] Vogel | Effects of abomasal infusion of essential fatty acids together with conjugated linoleic acid in late and early lactation on performance, milk and body composition, and plasma metabolites in dairy cows[END_REF] . Our results support the common concept that APOC3, APOA1, and APOA4 are essential in the metabolism and transport of supplemented FA to the target organs. In this regard, decreased plasma concentrations of APOB-100, APOA1, and APOC3, along with the induced secretion of HP, were reported to be associated with the development of fatty liver [START_REF] Katoh | Relevance of apolipoproteins in the development of fatty liver and fatty liver-related peripartum diseases in dairy cows[END_REF] . Our results, therefore, suggest that EFA + CLA supplementation was increasing the abundance of proteins that may avoid fatty liver. Our results are in line with previous results stating that CLA supplementation decreased the liver and plasma TG concentrations during the PP period [START_REF] Vogel | Effects of abomasal infusion of essential fatty acids together with conjugated linoleic acid in late and early lactation on performance, milk and body composition, and plasma metabolites in dairy cows[END_REF] . Moreover, other studies reported that dietary CLA alleviates non-alcoholic fatty liver disease in humans [START_REF] Abedi | The effect of conjugated linoleic acid (CLA) on inflammatory factors in Non-Alcoholic Fatty Liver Disease (NAFLD): A randomized controlled clinical trial[END_REF] and rats [START_REF] Nagao | Dietary conjugated linoleic acid alleviates nonalcoholic fatty liver disease in Zucker (fa/fa) rats[END_REF] . On the other hand, there are reports, indicating that CLA supplementation induced fatty liver in mice [START_REF] Nakanishi | γ-Linolenic acid prevents conjugated linoleic acid-induced fatty liver in mice[END_REF] . This inconsistency can be due to different FA profiles, isomers, and dosage of administrated FA to the animal along with liver physiological differences between species.

Apart from the APOA1-APOC3-APOA4 cluster, a new member of the APO family, APOC4, was found overabundant exclusively in EFA + CLA treated cows when compared to the CTRL cows during the PP period. APOC4 is primarily synthesized in small intestine enterocytes, but its function in hepatocytes is still uncertain [START_REF] Su | The exchangeable apolipoproteins in lipid metabolism and obesity[END_REF] . The APOC4 belongs to the APOC family, making another gene cluster together with APOE, APOC1, and APOC2 [START_REF] Pirim | Apolipoprotein E-C1-C4-C2 gene cluster region and inter-individual variation in plasma lipoprotein levels: A comprehensive genetic association study in two ethnic groups[END_REF] . Both APOC4 and APOE were detected in this study and are reportedly associated mainly with VLDL and TG metabolism in humans [START_REF] Kamboh | DNA sequence variation in human apolipoprotein C4 gene and its effect on plasma lipid profile[END_REF][START_REF] Getz | Apoprotein E as a lipid transport and signaling protein in the blood, liver, and artery wall[END_REF] . As mentioned earlier, the plasma concentration of TG was lower in the EFA + CLA group (data published elsewhere [START_REF] Vogel | Effects of abomasal infusion of essential fatty acids together with conjugated linoleic acid in late and early lactation on performance, milk and body composition, and plasma metabolites in dairy cows[END_REF] ), and these differences were significant on days 14, 28, and 32 PP. There are no reports on APOC4 in cattle, and its roles are yet to be elucidated in dairy cows. Given that APO and probably their associated lipoproteins are increased, it can be concluded that EFA + CLA supplementation elevated the capacity of transferring supplemented FA to the peripheral tissues including the liver, of hepatic TG export, and of reverse cholesterol transport.

At the onset of lactation, coagulation factors 9 and 7, and serum amyloid A (SAA) were more abundant in the EFA + CLA group than in the CTRL group. SAA increases its plasma concentration in cows during the systemic reaction to inflammation [START_REF] Ametaj | A new understanding of the causes of fatty liver in dairy cows[END_REF] , although it is worth mentioning that SAA also plays a role in the reverse cholesterol transport as a minor constituent of HDL, and diseases come secondary after a substantial increase in SAA (inflammation) that displaces APOA1 from HDL and form acute-phase HDL (HDL-SAA) (for review [START_REF] Wang | down-regulation and aggregation of serum amyloid A[END_REF] ). Increased immune-related proteins with FA supplementation coincide with PP immune suppression and may provide another piece of this complex puzzle of the way FA may induce immune-modulatory effects. However, the exact related pathways yet have to be elucidated.

On day 28 PP, the serum amyloid P component (SAP, also known as APCS) was more abundant in the EFA + CLA than in the CTRL group. The SAP is primarily secreted by hepatocytes and macrophages into the circulation and acts as a soluble pattern recognition receptor of the innate immune system [START_REF] Ozawa | Multifaceted anti-amyloidogenic and pro-amyloidogenic effects of C-reactive protein and serum amyloid P component in vitro[END_REF] . Database searches and functional enrichment analysis demonstrated that SAP associated with HDL might play a role in cholesterol removal from cells [START_REF] Xi | SAP: Structure, function, and its roles in immune-related diseases[END_REF] . Furthermore, there is mounting evidence that in mice treated with SAP, the plasma paraoxonase1 (PON1) activity was increased [START_REF] Xi | Serum amyloid P component therapeutically attenuates atherosclerosis in mice via its effects on macrophages[END_REF] . In this regard, a significantly greater paraoxonase activity level was observed in the EFA + CLA group, albeit limited to day 49 [START_REF] Gnott | Changes in fatty acids in plasma and association with the inflammatory response in dairy cows abomasally infused with essential fatty acids and conjugated linoleic acid during late and early lactation[END_REF] .

At the last timepoint (day 63 PP), concurrent with the shift to a positive EB in the EFA + CLA group, no other pathways were detected or activated apart from common APO pathways (overlapping pathways). This suggests that the beneficial effects of supplemented FA were more relevant during physiological and immunological challenges in the early PP phase.

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Conclusion

The plasma protein profiles during the periparturient period provided molecular insight into the systemic inflammatory state, reduced responsiveness of immune response, and impaired cholesterol metabolism at the onset of lactation in dairy cows, which gradually recovered with time PP by reduced their inflammatory response. In addition, this study provides novel knowledge on how EFA + CLA supplementation and the related changes in plasma proteome may act in the crosstalk between lipid metabolism and immune responsiveness in dairy cows. The underlying mechanism might be partly associated with the functions of the APO in regulating hepatic cholesterol and TG metabolism and their emerging role in modulating immune functions in a time-dependent manner. The over-abundance of APOA1-APOC3-APOA4 was explicitly related to EFA + CLA supplementation (regardless of time), although APOC4 was only elevated during lactation. Collectively, our results suggest a beneficial effect of EFA + CLA supplementation on the prevention of hepatic lipid accumulation during the periparturient period. However, future research should integrate proteomics with other results on specific markers of fatty liver to develop reliable metaphylactic and therapeutic strategies.

Materials and methods

Animals, treatments, and experimental design. All the experimental procedures were carried entirely under animal welfare guidelines and were approved by the ethics of the State Mecklenburg-Western Pomerania, Germany (LALLF M-V/TSD/7221.3-1-038/15). This study was in the frame with a recent comprehensive project described in detail by Vogel et al. [START_REF] Vogel | Effects of abomasal infusion of essential fatty acids together with conjugated linoleic acid in late and early lactation on performance, milk and body composition, and plasma metabolites in dairy cows[END_REF] . In brief, 16 Holstein dairy cows in their second lactation from 63 days before to 63 days after parturition were abomasally (10 cm center diameter rumen cannula; Bar Diamond Inc., 106 Parma, ID) infused with one of the two treatments, 1: CTRL, (n = 8; coconut oil, Bio-Kokosöl #665, Kräuterhaus Sanct Bernhard, KG, Bad Ditzenbach, Germany; 76 g/day), this supplement was formulated to compensate for energy intake of EFA treatment and provided no EFA and CLA, 2: EFA + CLA, a combination of linseed oil (DERBY Leinöl #4026921003087, DERBY Spezialfutter GmbH, Münster, Germany; 78 g/day), safflower oil (GEFRO Distelöl, GEFRO Reformversand Frommlet KG, Memmingen, Germany; 4 g/day) and Lutalin (CLA, n = 8; cis-9, trans-11, 10 g/day trans-10, cis-12 CLA, BASF SE, Lampertheim, Germany; 38 g/day). During the dry period, each dose was halved. Oil supplements were provided twice daily at 0700 and 1630 h in equal portions through abomasal infusion lines (Teflon tube [i.d. 6 mm] with two perforated Teflon flanges [o.d. 120 mm], directly into abomasum to avoid ruminant degradation of the fatty acids. The cows were housed in free-stall barns with ad libitum access to a corn silage-based total mixed ration (TMR), formulated according to recommendations provided by the Society for Nutrition Physiology (GfE, 2001 [START_REF] Gfe | Empfehlungen zur Energie-und Nährstoffversorgung der Milchkühe und Aufzuchtrinder (Recommended energy and nutrient suply of dairy cows and growing cattle)[END_REF] , 2008 [START_REF] Gfe | New equations for predicting metabolisable energy of grass and maize products for ruminants. Communications of the Committee for Requirement Standards of the Society of Nutrition Physiology[END_REF] , 2009 [START_REF] Gfe | New equations for predicting metabolisable energy of compound feeds for cattle. Communications of the Committee for Requirement Standards of the Society of Nutrition Physiology[END_REF] ) and Deutsche Landwirtschaftliche Gesellschaft (DLG, 2013), for AP and PP [START_REF] Dlg | Leitfaden zur Berechnung des Energiegehaltes bei Einzel-und Mischfuttermitteln für die Schweine-und Rinderfütterung[END_REF] . No clinical signs of disease were observed in any of the dairy cows during the experiment.

Ingredients, the chemical composition of the experimental diets, the amount and FA compositions of lipid supplements, FA composition of the experimental diets, and FA composition of the daily infused supplements during lactation are given in Supplementary S10, Supplementary Tables S36, S37, S38, and S39. The concentrations of NEFA, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC) in blood plasma and TG in the liver can be found in Vogel et al. [START_REF] Vogel | Effects of abomasal infusion of essential fatty acids together with conjugated linoleic acid in late and early lactation on performance, milk and body composition, and plasma metabolites in dairy cows[END_REF] .

Blood sampling and plasma preparation for proteomics analysis. Blood (about 5 mL) was sampled from each cow on days -21, + 1, + 28, and + 63 relative to calving. Sampling was performed immediately after the morning milking and before feeding by jugular vein puncture using a Vacuette system (Greiner Bio-One International GmbH, Kremsmünster, Austria) containing K 3 EDTA (1.8 g/L) as an anticoagulant. Immediately after collection, the samples were cooled on crushed ice and centrifuged at 1500×g (4 °C, 20 min). The supernatant was harvested and stored at -80 °C until analysis.

Before protein extraction, plasma samples were thawed slowly in the fridge (2 to 8 °C) and then centrifuged at 10,000×g for 10 min at 4 °C to precipitate cells, debris, and aggregated proteins in the bottom. Enrichment of low-abundance plasma proteins was performed using the ProteoMiner small capacity kit (#163-3006, Bio-Rad Laboratories, Inc., CA, USA), according to the manufacturer's instructions.

Before digestion, the protein concentration was measured by the bicinchoninic acid assay (BCA) kit (Thermo Scientific, Rockford, IL, USA) using bovine serum albumin as a reference protein standard. Protein digestion was performed in the S-Trap filter according to the manufacturer's procedure. Briefly, 100 μg of extracted proteins were first mixed with 4% SDS and 20 mM DTT (final concentrations) and boiled for 10 min at 95 °C. After cooling at RT, proteins were alkylated by adding 50 mM iodoacetamide, followed by incubation in the dark for 45 min. The procedure was followed by acidification of samples to a final concentration of 1.2% phosphoric acid (~ pH 2) for better adhesion to the filter. After that, six volumes of S-Trap binding buffer (90% methanol; 100 mM triethylammonium bicarbonate, TEAB; pH 7.1) were added to the samples. Following a gentle mixing, the protein solution was loaded to a S-Trap filter, centrifuged at 4000g for 10 min at RT, and the flow-through collected and reloaded onto the S-trap filter. This step was repeated two times, and then the filter was washed with 150 μL of binding buffer (two times). Finally, protein digestion was performed by adding 2 μg of sequencing-grade trypsin and 150 μL of digestion buffer (50 mM TEAB) to the filter for overnight at 37 °C.

After the digestion step, peptides were eluted in two steps: in the first step, 40 µL of 50 mM TEAB, 0.2% formic acid in H 2 O, and the second step, 50 μL 50% acetonitrile 0.2% formic acid in H 2 O were applied. Before injection, peptides were dried in a Speed Vac (Eppendorf AG, Hamburg, Germany) for 1 h and suspended in 20 µL of equilibration solution (H 2 O/Trifluoroacetic Acid -99.95/0.05).

Liquid chromatography-mass spectrometry analysis. The plasma peptide mixture was analyzed by label-free LC-MS/MS quantitative proteomics approach using an Ultimate 3000 RSLCnano system (Thermo Content courtesy of Springer Nature, terms of use apply. Rights reserved Fisher Scientific)) coupled to an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific) with a Nanospray Flex Ion Source, according to the previously described method by Ref. [START_REF] Santos | Listeria monocytogenes biofilm adaptation to different temperatures seen through shotgun proteomics[END_REF] . Briefly, 1 μL of hydrolyzate was first preconcentrated and desalted at a flow rate of 30 µL/mn on a C18 pre-column 5 cm length X 100 µm (Acclaim PepMap 100 C18, 5 µm, 100A nanoViper) equilibrated with Trifluoroacetic Acid 0.05% in water to remove contaminants that could potentially disrupt the efficiency of the mass spectrometry analysis. After 6 min, the concentration column was switched online with a nano debit analytical C18 column (Acclaim PepMap 100-75 µm inner diameter × 25 cm length; C18-3 µm-100 Å-SN 10711311) operating at 400 nL/ min equilibrated with 96% solvent A (%99.5% H 2 O, 0.5% formic acid). The peptides were then separated according to their hydrophobicity, thanks to a gradient of 4 to 20% solvent B (99.5 ACN, 0.5% formic acid) in 60 min.

For MS/MS analysis, eluates were electro-sprayed in positive-ion mode at 1.6 kV through a nanoelectrospray ion source heated to 250 °C. The Orbitrap Q Exactive HF-X MS was used in HCD top 18 mode (i.e. 1 full scan MS and the 18 major peaks in the full scan were selected for MS/MS). Higher-energy C-trap dissociation (HCD): HCD refers to a CID variation that uses a higher RF voltage to retain fragment ions in the C-trap. Mass spectrometry analysis parameters were as follows: the parent ion is selected in the orbitrap cell (FTMS) at a resolution of 60,000 with an injection time of 50 ms on a mass range from 375 to 1600 m/z. Each MS analysis is followed by 18 MS/MS with analysis of MSMS fragments at a resolution of 15,000 with an injection time of 100 ms.

Data processing, statistical analysis, and functional enrichment analysis. Each acquired raw MS/MS spectrum was first aligned to the reference sample (assigned automatically with having the highest of peptide ions coverage) and then processed for peptide ions identification using Progenesis QI software (version 4.2, Nonlinear Dynamics, New Castel upon Tyne, UK), with the default parameter settings (ion charged set to five and Ions ANOVA P-value < 0.05). The identified and the quantified peptide ions were then searched against Bos taurus decoy database (Uniprot, download date: 2019/11/07, a total of 37,513 entries) in MASCOT (version 2.5.1) interrogation engine, with the following setting: trypsin as an enzyme digest of a protein, tryptic specificity to cleavage C-terminal after lysine or arginine residues, allowing two missed cleavages, variable modification to carbamidomethylation (C) and oxidation (M), mass tolerance to 10 ppm for precursor ions and 0.02 Da for fragment ions, and FDR < 0.01. Accordingly, the corresponding proteins with at least two peptides and two unique validated peptides were identified and quantified based on their intensities.

Before statistical analysis, logarithmic transformation was applied to protein intensities, and missing intensities (with the frequency of less than 50% of samples) were imputed and replaced by the 1/5 of the minimum positive value of each variable in the original dataset. DAP were identified and investigated separately during the time and between treatments. The time associated DAP were identified using paired t-test between each consecutive time-point to cope with carryover effects.

At each timepoint, the most important proteins (VIP) involved in the discrimination of the CTRL and EFA + CLA groups was identified using Partial Least Square Discriminant Analysis (PLS-DA) analysis (mixOmics package in R). The VIP contributed to the projection scores (> 1.5) of the first two components (PC1 and PC2) and to cluster separation between treatment groups were ranked. A permutation test (100 random computations) was applied to disprove the over-fitting of the PLS-DA model. However, the significance of permutation test is more relevant for a prediction purpose, which is not the goal of our study. The permutation test was not significant at all timepoints and there was a possibility for false positive detection. Therefore, the second filtration step was applied and only VIP with P-value < 0.05, and log2 (fold change) > 1.3 (metaboanalyst R package) were maintained and considered as differentially abundant proteins (DAP) for gene ontology functional enrichment analysis. The P-value was assessed by Student's t-test. Statistical analysis was performed in mixOmics and metaboanalyst R-packages in R statistical software (R version 4.0.0). DAP were visualized according to their expression (by Volcano plot, EnhancedVolcano R package). The GO categorization, including BP, MF, and CC, and KEGG (www. kegg. jp/ kegg/ kegg1. html) [START_REF] Kanehisa | KEGG: Kyoto encyclopedia of genes and genomes[END_REF] pathway enrichment analysis were conducted using the web-based tool String version 11.0, summarized in REVIGO web-based tool (http:// revigo. irb. hr/) and visualized in Cytoscape software (https:// cytos cape. org, version 3.8). The B. taurus interaction map was set as a background list, and pathways (false discovery rate < 0.05) with at least two protein hits were considered as enriched pathways. Protein-protein interaction networks (PPI) were constructed by inputting the time and treatment DAP to proteINSIDE (V 2.0) tool. PPI were searched in Homo sapiens to take advantage of the well-characterised physical interactions between proteins, and PPI agreed by curator review were used to construct PPI networks (Supplementary Fig. S1). Within the network, proteins that interact to contribute to cellular or metabolic processes have been highlighted using betweeness or closeness centralities as previously described [START_REF] Kaspric | ProteINSIDE to easily investigate proteomics data from ruminants: Application to mine proteome of adipose and muscle tissues in bovine foetuses[END_REF] .

Proteomics data matched the classical measurements of proteins and metabolites. To validate the proteomics results, we integrated the serum proteome data with plasma metabolites and proteins 6,70 using sparse partial least squares (sPLS) regression model (mixOmics package in R). With X (proteomics data) and Y (classical plasma proteins and metabolites data) as input matrices (logarithmic transformed values), the sPLS model controls many noisy, collinear (correlated) and missing variables in omics datasets, and relates multiple outcome variables across X and Y. Positive and significant correlations were observed between 1-proteomics and ELISA-measured HP; 2-proteomics and Western blot-measured IGFBP2; 3-NEFA with LBP and IGFBP2; 4-LDL with APOB, APOC4, and APOC3 proteins (Supplementary Fig. S2A,B). Although, ADIPOQ ELISA results were not correlated (r = 0.05, P-value = 0.66) with proteomics results. Also, using the bovine AHSG ELISA Kit (Catalog No: DL-aHSG-b, DLdevelop, Wuxi, China), and testing a series of different plasma dilutions, we could not detect AHSG in our plasma samples. This was probably attributable to an inadequate binding specificity of the antibody used in the AHSG ELISA kit. Additionally, despite the accuracies of the mass spectrometry Content courtesy of Springer Nature, terms of use apply. Rights reserved 1.

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Figure 1 .

 1 Figure 1. (A) Volcano plot representing differentially abundant proteins (DAP) between day -21 and + 1 relative to parturition; increased (red dots in top right) and decreased (blue dots in top left) proteins in d + 1/d -21 are highlighted (P < 0.05 and log2 fold change (FC) > 1.3). (B) Biological Process for the DAP Proteins after semantic synthesis by Revigo; bars indicate proportional to the false discovery rate (FDR) adjusted P-value, the intensity of color bars is gene count (GC) represents the amount of differentially abundant proteins enriched in the pathway. (C) Volcano plot representing DAP between day + 1 and + 28 postpartum. (D) Biological Process for the DAP between day + 1 and + 28 postpartum. (E) Volcano plot representing DAP between day + 28 and + 63 postpartum. (F) Biological Process for the DAP between day + 28 and + 63 postpartum (For the high quality figure, the reader is referred to the web version of this article).

Figure 2 .

 2 Figure 2. (A) Venn diagram representing the overlap between differentially abundant proteins (DAP) in response to EFA + CLA supplementation identified at day -21, + 1, + 28, and + 63 relative to parturition. (B) KEGG pathways map 14 , biological process (BP), and cellular component (CC) gene ontology terms of DAP after semantic synthesis by Revigo (overlapping pathways). Bars represents the -log10 (adjusted P-value); the intensity of color bars is gene count (GC) represents the amount of differentially abundant proteins enriched in the pathway. (C) Volcano plot representing DAP between Control (CTRL) and essential fatty acid (EFA) + conjugated linoleic acid (CLA) in day 21 antepartum; increased (red dots in the top left) and decreased (blue dots in the top left) proteins in EFA + CLA group are highlighted (P < 0.05 and log2 fold change (FC) > 1.5). (D) Volcano plot representing DAP between CTRL and EFA + CLA in day 1 postpartum. (E) Volcano plot representing DAP between CTRL and EFA + CLA in day 28 postpartum. (F) Volcano plot representing DAP between CTRL and EFA + CLA in day 63 postpartum (For the high quality figure, the reader is referred to the web version of this article).

Scientific Reports | (2022) 12:5648 | https://doi.org/10.1038/s41598-022-09437-w

Acknowledgements

The authors acknowledge A. Delavaud (INRAE) for his technical assistance in protein extraction, quantification, and concentration for mass spectrometry analyses.

Funding

Open Access funding enabled and organized by Projekt DEAL. This project has received funding from the European Union's Horizon 2020 research and innovation programme H2020-MSCA-ITN-2017-EJD: Marie Skłodowska-Curie Innovative Training Networks (European Joint Doctorate)-Grant agreement nº: 765423. The animal study was supported by BASF SE (Ludwigshafen, Germany).

Data availability

The data and the related analyses are available through the link https:// doi. org/ 10. 15454/ ZIIL2H. Received: 12 October 2021; Accepted: 14 March 2022

Ethics declaration. All the experimental procedures were carried entirely under animal welfare guidelines (including ARRIVE guidelines) and were approved by the ethics of the State Mecklenburg-Western Pomerania, Germany (LALLF M-V/TSD/7221.3-1-038/15).

Author contributions

H.H. and A.T. suggested the main idea of the basic animal study; A.T. supplied materials. H.H. designed and performed the animal study and collected performance and various blood and tissue data. H.H., F.C., and H.S. elaborated the concept for the current analyses, contributed to the design and implementation of the research and supervised the related works. M.B. designed and supervised the Proteomics study and bioinformatics analysis, provided laboratory and informatics resources and supervised the related works. S.D. designed and supervised the statistical analyses. L.V., D.V., M.D., and A.V. planned and performed the farm trial, sample preparation, laboratory analysis and provided methodologies respectively. A.V., curated, analyzed and visualized the proteomics data and wrote the paper. M.B., S.D., H.H., F.C., and H.S. revised the manuscript. All authors reviewed the results and approved the final version of the manuscript.

Competing interests

The authors declare no competing interests.


Jameel Malik

Ahmed Elhayek

Fabrizio Nunnari

Kiran Varanasi

Kiarash Tamaddon

Alexis Heloir

Didier Stricker

DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth

Articulated hand pose and shape estimation is an important problem for vision-based applications such as augmented reality and animation. In contrast to the existing methods which optimize only for joint positions, we propose a fully supervised deep network which learns to jointly estimate a full 3D hand mesh representation and pose from a single depth image. To this end, a CNN architecture is employed to estimate parametric representations i.e. hand pose, bone scales and complex shape parameters. Then, a novel hand pose and shape layer, embedded inside our deep framework, produces 3D joint positions and hand mesh. Lack of sufficient training data with varying hand shapes limits the generalized performance of learning based methods. Also, manually annotating real data is suboptimal. Therefore, we present SynHand5M: a million-scale synthetic dataset with accurate joint annotations, segmentation masks and mesh files of depth maps. Among model based learning (hybrid) methods, we show improved results on our dataset and two of the public benchmarks i.e. NYU and ICVL. Also, by employing a joint training strategy with real and synthetic data, we recover 3D hand mesh and pose from real images in 3.7ms.

Introduction

3D hand pose estimation is essential for many computer vision applications such as activity recognition, humancomputer interaction and modeling user intent. However, the advent of virtual and augmented reality technologies makes it necessary to reconstruct the 3D hand surface together with the pose. Recent years have seen a great progress in the pose estimation task primarily due to significant developments in deep learning and the availability of low cost commodity depth sensors. However, the stated problem is still far from being solved due to many challenging factors that include large variations in hand shapes, view point changes, many degrees of freedom (DoFs), constrained parameter space, self similarity and occlusions.

Large amounts of training data, enriched with all possible variations in each of the challenging aspects stated Figure 1: Real hand pose and shape recovery: We describe a deep network for recovering the 3D hand pose and shape of NYU [START_REF] Tompson | Real-time continuous pose recovery of human hands using convolutional networks[END_REF] depth images by learning from synthetic depth. Note that we infer 3D pose and shape even in cases of missing depth and occluded fingers. above, are a key requirement for deep learning based methods to generalize well and achieve significant gains in accuracy. The recent real dataset [START_REF] Yuan | Bighand2. 2m benchmark: Hand pose dataset and state of the art analysis[END_REF] gathers a sufficient number of annotated images. However, it is very limited in hand shape variation (i.e. only 10 subjects). Progress in essential tasks such as estimation of hand surface and hand-part segmentation is hampered, as manual supervision for such problems at large scale is extremely expensive. In this paper, we generate a synthetic dataset that addresses these problems. It not only allows us to create virtually infinite training data, with large variations in shapes and view-points, but it also produces annotations that are highly accurate even in the case of occlusions. One weakness of synthetic datasets is their limited realism. A solution to this problem has been proposed by [START_REF] Shrivastava | Learning from simulated and unsupervised images through adversarial training[END_REF][START_REF] Mueller | Ganerated hands for realtime 3d hand tracking from monocular rgb[END_REF], where a generative adversarial training network is employed to improve the realism of synthetic images. However, producing realistic images is not the same problem as improving the recognition rates of a convolutional neural network (CNN) model. In this paper, we address this latter problem, and specifically focus on a wide variation of hand shapes, including extreme shapes that are not very common (in contrast to [START_REF] Romero | Embodied hands: Modeling and capturing hands and bodies together[END_REF]). We present SynHand5M: a new million scale synthetic dataset with accurate ground truth joints positions, angles, mesh files, and segmentation masks of depth frames; see Figure 2. Our SynHand5M dataset opens up new possibilities for advanced hand analysis.

Currently, CNN-based discriminative methods are the state-of-the-art which estimate 3D joint positions directly from depth images [START_REF] Guo | Two-stream convolutional neural network for accurate rgb-d fingertip detection using depth and edge information[END_REF]21,4,27]. However, major weakness of these methods is that the predictions are coarse with no explicit consideration to kinematics and geometric constraints. Sinha et al. [35] propose to estimate 3D shape surface from depth image or hand joint angles, using a CNN. However, their approach neither estimates hand pose nor considers kinematics and physical constraints. Also, these methods generalize poorly to unseen hand shapes [52].

On the other hand, building a personalized hand model requires a different generative approach, that optimizes a complex energy function to generate the hand pose [29,[START_REF] Oikonomidis | Efficient model-based 3d tracking of hand articulations using kinect[END_REF][START_REF] Qian | Realtime and robust hand tracking from depth[END_REF]40,[START_REF] Tang | Opening the black box: Hierarchical sampling optimization for estimating human hand pose[END_REF]. However, person specific hand model calibration clearly restricts the generalization of these methods for varying hand shapes. Hybrid methods combine the advantages of both discriminative and generative approaches [START_REF] Ge | Robust 3d hand pose estimation in single depth images: from single-view cnn to multi-view cnns[END_REF]34,[START_REF] Oberweger | Training a feedback loop for hand pose estimation[END_REF][START_REF] Tang | Latent regression forest: Structured estimation of 3d articulated hand posture[END_REF][START_REF] Sridhar | Real-time joint tracking of a hand manipulating an object from rgb-d input[END_REF][START_REF] Ye | Occlusion-aware hand pose estimation using hierarchical mixture density network[END_REF]54]. To the best of our knowledge, none of the existing works explicitly addresses the problem of jointly estimating full hand shape surface, bone-lengths and pose in a single deep framework.

In this paper, we address the problem of generalizing 3D hand pose and surface geometry estimation over varying hand shapes. We propose to embed a novel hand pose and shape layer (HPSL) inside deep learning network to jointly optimize for 3D hand pose and shape surface. The proposed CNN architecture simultaneously estimates the hand pose parameters, bones scales and shape parameters. All these parameters are fed to the HPSL which implements not only a new forward kinematics function, but also the fitting of a morphable hand model and linear blend skinning to produce both 3D joint positions and 3D hand surface; see Figure 3. The whole pipeline is trained in an end-to-end manner. In sum, our contributions are: (c) Linear blend Skinning algorithm to animate the 3D hand surface; see Section 4.2.

2. A novel end-to-end framework for simultaneous hand pose and shape estimation; see Section 3.

3.

A new 5 million scale synthetic hand pose dataset that offers accurate ground truth joint angles, 3D joint positions, 3D mesh vertices, segmentation masks; see Section 5. The synthetic dataset will be publicly available.

Related Work

Depth-based hand pose estimation has been extensively studied in the computer vision community. We refer the reader to the survey [START_REF] Supancic | Depth-based hand pose estimation: data, methods, and challenges[END_REF] for a detailed overview of the field. Recently, a comprehensive analysis and investigation of the state-of-the-art along-with future challenges have been presented by [52]. The approaches can be roughly divided into generative, discriminative and hybrid methods. In this section, we briefly review the existing hand pose benchmarks. Then, we focus our discussion on CNN-based discriminative and hybrid methods.

Existing Benchmarks. Common shortcomings in existing real hand datasets are low variation in hand shape, inaccurate ground truth annotations, insufficient amount of training data, low complexity (e.g. occlusion) of hand poses, and limited view point coverage. Most commonly used benchmarks are NYU [START_REF] Tompson | Real-time continuous pose recovery of human hands using convolutional networks[END_REF], ICVL [START_REF] Tang | Latent regression forest: Structured estimation of 3d articulated hand posture[END_REF] and MSRA15 [START_REF] Sun | Cascaded hand pose regression[END_REF]. NYU hand pose dataset uses a model-based direct search method for annotating ground truth which is quite accurate. It covers a good range of complex hand poses. However, their training set has single hand shape. ICVL dataset uses a guided Latent Tree Model (LTM) based search method and mostly contains highly inaccurate ground truth annotations. Moreover, it uses only one hand model [START_REF] Yuan | Bighand2. 2m benchmark: Hand pose dataset and state of the art analysis[END_REF]. MSRA15 employs an iterative optimization method [START_REF] Qian | Realtime and robust hand tracking from depth[END_REF] for annotating followed by manual refinement. It uses 17 hand poses, however, it has large view-point coverage. The major limitation of this dataset is its limited size and low annotation accuracy. Recently, Yuan et al. [START_REF] Yuan | Bighand2. 2m benchmark: Hand pose dataset and state of the art analysis[END_REF] propose a million scale real hand pose dataset, but it has low variation in hand shape(i.e. only 10 subjects). Some other very small real hand pose datasets such as Dexter-1 [START_REF] Sridhar | Interactive markerless articulated hand motion tracking using rgb and depth data[END_REF], ASTAR [START_REF] Xu | Estimate hand poses efficiently from single depth images[END_REF] , MSRA14 [START_REF] Qian | Realtime and robust hand tracking from depth[END_REF] are not suited for largescale training. Several works focused on creating synthetic hand pose datasets. MSRC [START_REF] Sharp | Accurate, robust, and flexible real-time hand tracking[END_REF] is a synthetic benchmark however, it has only one hand model and limited pose space coverage. In [35,[START_REF] Mueller | Real-time hand tracking under occlusion from an egocentric rgb-d sensor[END_REF], medium-scale synthetic hand datasets are used to train CNN models, but they are not publicly available. Given the hard problem of collecting and annotating a large-scale real hand pose dataset, we propose the first million scale synthetic benchmark which consists of more than 5 million depth images together with ground truth joints positions, angles, mesh files, and segmentation masks.

CNN-based Discriminative Methods. Recent works such as [START_REF] Moon | V2v-posenet: Voxelto-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map[END_REF]2,[START_REF] Wang | Region ensemble network: Towards good practices for deep 3d hand pose estimation[END_REF]9,[START_REF] Ge | 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images[END_REF]27] exceed in accuracy over random decision forest (RDF) based discriminative methods [START_REF] Sharp | Accurate, robust, and flexible real-time hand tracking[END_REF][START_REF] Sun | Cascaded hand pose regression[END_REF][START_REF] Wan | Hand pose estimation from local surface normals[END_REF][START_REF] Xu | Lie-x: Depth image based articulated object pose estimation, tracking, and action recognition on lie groups[END_REF][START_REF] Li | 3d hand pose estimation using randomized decision forest with segmentation index points[END_REF]. A few manuscripts have used either RGB or RGB-D data to predict 3D joint positions [START_REF] Zimmermann | Learning to estimate 3d hand pose from single rgb images[END_REF][START_REF] Dibra | How to refine 3d hand pose estimation from unlabelled depth data? In 3DV[END_REF][START_REF] Simon | Hand keypoint detection in single images using multiview bootstrapping[END_REF][START_REF] Mueller | Real-time hand tracking under occlusion from an egocentric rgb-d sensor[END_REF]. In [START_REF] Ge | 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images[END_REF], Ge et al. directly regress 3D joint coordinates using a 3D-CNN. Recently, [START_REF] Moon | V2v-posenet: Voxelto-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map[END_REF] introduced voxel-to-voxel regression framework which exploits a one-to-one relationship between voxelised input depth and output 3D heatmaps. [9, 47] introduce a powerful region ensemble strategy which integrates the outputs from multiple regressors on different regions of depth input. Chen et al. [2] extended [START_REF] Wang | Region ensemble network: Towards good practices for deep 3d hand pose estimation[END_REF] by an iterative pose guided region ensemble strategy. In [35], a discriminative hand shape estimation is proposed. Although the accuracy of these methods is the state-of-the-art, they impose no explicit geometric and physical constraints on the estimated pose. Also, these methods still fail to generalize on unseen hand shapes [52].

CNN-based Hybrid Methods. Tompson et al. [START_REF] Tompson | Real-time continuous pose recovery of human hands using convolutional networks[END_REF] employed CNN for estimating 2D heatmaps. Thereafter, they apply inverse kinematics for hand pose recovery. In extension to this work, [START_REF] Ge | Robust 3d hand pose estimation in single depth images: from single-view cnn to multi-view cnns[END_REF] utilize 3D-CNN for 2D heatmaps estimation and afterwards regress 3D joint positions. Oberweger et al. [START_REF] Oberweger | Training a feedback loop for hand pose estimation[END_REF] utilize three CNNs combined in a feedback loop to regress 3D joint positions. The network comprises of an initial pose estimator, a synthesizer and finally a pose update network. Ye et al. [START_REF] Ye | Spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation[END_REF] present a hybrid framework using hierarchical spatial attention mechanism and hierarchical PSO. Wan et al. [START_REF] Wan | Crossing nets: Combining gans and vaes with a shared latent space for hand pose estimation[END_REF] implicitly model the dependencies in the hand skeleton by learning a shared latent space. In [START_REF] Zhou | Modelbased deep hand pose estimation[END_REF], a forward kinematics layer, with physical constraints and a fixed hand model, is implemented in an end-to-end training framework. Malik et al. [14] further extend this work by introducing a flexible hand geometry in the training pipeline. The algorithm simultaneously estimates bone-lengths and hand pose. In [START_REF] Wan | Dense 3d regression for hand pose estimation[END_REF], a multi-task cascade network is employed to predict 2D/3D joint heatmaps along-with 3D joint offsets. Dibra et al. [5] introduce an end-to-end training pipeline to refine the hand pose using an unlabeled dataset. All of the above described methods cast the problem of hand pose estimation to 3D joints regression only. Our argument is that given the inherent 3D surface geometry information in depth inputs, a differentiable hand pose and shape layer can be embedded in the deep learning framework to regress not only the 3D joint positions but also, the full 3D mesh of hand.

Method Overview

We aim to jointly estimate the locations of J = 22 3D hand joints , and ϑ = 1193 vertices of hand mesh from a single depth image D I . Our hand skeleton in rest pose is shown in Figure 3(b). It has J hand joints defined on 26 DoFs. The hand root has 6 DoF; 3 for global orientation and 3 for global translation. All other DoFs are defined for joints articulations. The 26 dimensional pose vector is initialized for the rest pose, called θ init . Any other pose Θ can be constructed by adding change δθ to the rest pose i.e. Θ = θ init + δθ. The bone-lengths B, are initialized by averaging over all bone-lengths of different hand shapes in our synthetic dataset. In order to add flexibility to the hand skeleton, 6 different hand bones scales, α, are associated to bone-lengths. Our hand mesh has ϑ vertices and 1184 faces. The neutral hand surface is shown in Figure 3(b). We use 7 hand shape parameters β which allow to formulate the surface geometry of a desired hand shape in reference pose; see Section 5.

Our pipeline is shown in Figure 3(a). Firstly, a new CNN architecture estimates δθ, α and β given a depth input D I . This architecture consists of PoseCNN which estimates δθ and ShapeCNN which estimates α and β. Thereafter, a new non-linear hand pose and shape layer (HPSL) performs forward kinematics, hand shape surface reconstruction and linear blend skinning. The outputs of the layer are 3D joint positions and hand surface vertices. These outputs are used to compute the standard euclidean loss for joint positions and vertices; see Equation 2. The complete pipeline is trained end-to-end in a fully supervised manner.

Joint Hand Pose and Shape Estimation

In this section, we discuss the components of our pipeline which are shown in Figure 3(a). We explain the novel Hand Pose and Shape Layer (HPSL) in detail because it is the main component which allows to jointly estimate hand pose and shape surface.

The CNN Architecture

Our CNN architecture comprises of three parallel CNNs to learn δθ, α and β, given D I . The PoseCNN leverages one of the state-of-the-art CNN [9] to estimate joint angles δθ. However, the CNN was originally used to regress 3D hand joint positions; see Section 2. We refer the reader to [9] for network details of Region Ensemble (REN). In our implementation, the final regressor in REN outputs 26 dimensional δθ. The ShapeCNN consists of two simpler CNNs similar to [22]; called α-CNN and β-CNN. Each of them has 3 convolutional layers using kernels sizes 5,5,3 respectively. First two convolution layers are followed by max pool layers. The pooling layers use strides of 4 and 2. The convolutional layers generate 8 feature maps of size 12

Hand Pose and Shape Layer (HPSL)

HPSL is a non-linear differentiable layer, embedded inside the deep network as shown in Figure 3(a). The task of the layer is to produce 3D joint positions P ∈ R 3xJ and vertices of hand mesh V ∈ R 3xϑ given the pose parameters Θ, hand bones scales α and shape parameters β. The layer function can be written as:

(P,V) = HPSL(Θ, β, α) (1)
We compute the respective gradients in the layer for back-propagation. The Euclidean 3D joint location and 3D vertex location losses are given as:

L J = 1 2 P -P GT 2 , L V = 1 2 V -V GT 2 (2)
Where L J and L V are the 3D joint and vertex losses respectively. P GT and V GT are vectors of 3D ground truth joint positions and mesh vertices, respectively. Various functions inside the layer are detailed as follows:

Hand
Ψ(β) = b 0 + 7 t=1 β t (b t -b 0 ) (3)
Forward Kinematics and Geometric Skinning: To estimate the 3D hand joints positions and surface vertices, we implement forward kinematics and geometric skinning functions inside our HPSL. As this layer is part of our deep network, it is essential to compute and back-propagate the gradients of these functions. The rest of this section addresses the definition of these functions and their gradients. The deformation of the hand skeleton from the reference pose θ init to the current pose Θ can be obtained by transforming each joint j i along the kinematic chain by simple rigid transformations matrices. In our algorithm, these matrices are updated based on bones scales α and the changes in pose parameters δθ which are learned by our ShapeCNN and PoseCNN, respectively. The kinematics equation of joint j i can be written as:

j i = F j i (Θ, α) = M j i [0, 0, 0, 1] T = k∈Sj i [R φ k (θ k )] × [T φ k (αB)] [0, 0, 0, 1] T (4)
where M j i represents the transformation matrix from the zero pose (i.e. joint at position [0, 0, 0, 1]) to the current pose. S j i is the set of joints along kinematic chain from j i to the root joint and φ k is one of the rotation axes of joint k.

For animating the 3D hand mesh, we use linear blend skinning [START_REF] Lewis | Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation[END_REF] to deform the set of vertices ϑ according to underlying hand skeleton kinematic transformations. The skinning weights ω i , define the skeleton-to-skin bindings. Their values represent the influence of joints on their associated vertices. Normally, the weights of each vertex are assumed to be convex (i.e. n i=1 ω i = 1) and ω i > 0. The transformation of a vertex v κ ∈ Ψ can be defined as:

v κ = Υ vκ (Θ, β, α) = i∈Pv κ ω i C j i v κ (β) = i∈Pv κ ω i C j i (b vκ 0 + 7 t=1 β t (b vκ t -b vκ 0 )) (5)
where P vκ is the set of joints influencing the vertex v κ and C j i is the matrix of each joint j i from its reference pose θ init to its actual position in the current animated posture. C j i can be represented as:

C j i = M j i M j * i -1 (6)
where M j * i -1 defines the inverse of reference pose transformation matrix.

Gradients computation: For backward-pass in the HPSL, we compute gradients of the following equation with respect to the layer inputs:

HPSL(Θ, β, α) = ( F(Θ, α) ,Υ(Θ, β, α) ). (7)
Each vertex v κ = HPSL vκ (Θ, β, α) in the reconstructed hand morphable model Ψ is deformed using Equation 5. Hence, its gradients with respect to a shape parameter β t can be computed as:

∂(HPSL vκ ) ∂β t = i ω i C j i (b vκ t -b vκ 0 ) for t = 1, 2, . . . , 7
According to Equation 7, bones scales influence the joints positions and vertices positions. Hence, the resultant gradient with respect to a hand scale parameter α s , can be calculated as:

∂(HPSL) ∂α s = ∂F ∂α s + ∂Υ ∂α s for s = 1, 2, . . . , 6
To compute the partial derivative of F with respect to α s , we need to derivate each joint with respect to its associated scale parameter. The gradient of a joint with respect to α s , can be computed by replacing the scaled translational matrix containing α s by its derivative and keep all other matrices same; see Equation 2 in supplementary document. In a similar way, the gradient of a vertex v κ with respect to α s can be computed by:

∂Υ vκ ∂α s = i ω i ∂C j i ∂α s v κ = i ω i [M j i (M j * i -1 ) + (M j i ) M j * i -1 ]v κ
Likewise, for the pose parameters Θ, we compute the following equation:

∂(HPSL) ∂θ p = ∂F ∂θ p + ∂Υ ∂θ p for p = 1, 2, . . . , 26
Accordingly, the derivative of a joint with respect to a pose parameter θ p , is simply to replace the rotation matrix of θ p by its derivation; see Equation 5 in supplementary document. And, the derivative of a vertex v κ with respect to θ p is computed by:

∂Υ vκ ∂θ p = i ω i ∂C j i ∂θ p v κ = i ω i [(M j i ) M j * i -1 ]v κ for p = 1, 2, . . . , 26
More details about the gradients computation can be found in the supplementary document.

Synthetic Dataset

There are two main objectives of creating our synthetic dataset. First is to jointly recover full hand shape surface and pose provided that there is no ground truth hand surface information available in public benchmarks; see Section 6.2. Second objective is to provide a training data with sufficient variation in hand shapes and poses such that a CNN model can be pre-trained to improve the recognition rates on real benchmarks; see Section 6.3. This problem is different from generating very realistic hand-shape, where a real-world statistical hand model [START_REF] Romero | Embodied hands: Modeling and capturing hands and bodies together[END_REF] can be applied. However, the variation in shape is more challenging for realworld databases e.g. BigHand2.2M [START_REF] Yuan | Bighand2. 2m benchmark: Hand pose dataset and state of the art analysis[END_REF] database was captured from only 10 users, and the MANO [START_REF] Romero | Embodied hands: Modeling and capturing hands and bodies together[END_REF] database was built from the contribution of 31 users. Instead, we generate a bigger hand shape variation which may not be present in a given cohort of human users.

Our SynHand5M dataset offers 4.5M train and 500K test images; see Figure 2(a) for SynHand5M components. SynHand5M uses the hand model generated by Manuel-BastionLAB [15] which is a procedural full-body generator distributed as add-on of the Blender [START_REF]Blender[END_REF] 3D authoring software. Our virtual camera simulates a Creative Senz3D Interactive Gesture Camera [START_REF] Creative | Senz3d interactive gesture camera[END_REF]. It renders images of resolution 320x240 using diagonal field of view of 74 degrees.

In the default position, the hand palm faces the camera orthogonally and the fingers point up. We procedurally modulate many parameters controlling the hand and generate images by rendering the view from the virtual camera. The parameters characterizing the hand model belong to three categories: hand shape, pose and view point.

Without constraints the hand generator can easily lead to impossible hand shapes. So, in order to define realistic range limits for modulating hand shapes, we relied on the DINED [START_REF] Molenbroek | Dined, anthropometric database[END_REF] anthropometric database. DINED is a repository collecting the results of several anthropometric databases, including the CAESAR surface anthropometry survey [START_REF] Robinette | The CAESAR project: a 3-D surface anthropometry survey[END_REF]. We manually tuned the ranges of the 7 hand shape parameters (see Section 4.2) in order to cover 99% of the measured population in this dataset; see supplementary document for more details.

To modulate the hand pose, we manipulate the 26 DoFs of our hand model; see Figure 3(b). For each finger, rotations are applied to flexion of all phalanges plus the abduction of the proximal phalanx. Additionally, in order to increase the realism of the closed fist configuration, the roll of middle, ring, and pinky fingers is derived from the abduction angle of the same phalanx. The rotation limits are set to bring the hand from a closed fist to an over-extended aperture, respecting anatomical constraints and avoiding the fingers to enter the palm.

The hand can rotate about three DoFs to generate different view points: roll around its longitudinal axis (i.e. along the fingers), rotate around the palm orthogonal axis (i.e. rolling in front of the camera), and rotate around its transversal axis (i.e. flexion/extension of the wrist).

Experiments and Results

In this section, we provide the implementation details, quantitative and qualitative evaluations of the proposed algorithm and the proposed dataset. We use three evaluation metrics; mean 3D joint location error (JLE), 3D vertex location error (VLE) and percentage of images within certain thresholds in mm.

Recent CNN-based discriminative methods such as [START_REF] Ge | 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images[END_REF][START_REF] Wang | Region ensemble network: Towards good practices for deep 3d hand pose estimation[END_REF][START_REF] Moon | V2v-posenet: Voxelto-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map[END_REF]27] outperform CNN-based hybrid methods; see Section 2. However, due to direct joints regression, discriminative methods neither explicitly account for the hand shapes nor consider kinematics constraints [START_REF] Zhou | Modelbased deep hand pose estimation[END_REF]14]. Moreover, in contrast to hybrid methods, discriminative methods generalize poorly to unseen hand shapes; see [52]. Our proposed hybrid method does not exceed in accuracy over recent discriminative works but, it does not suffer from such limitations. Therefore, it is not fair to compare with these methods. However, we compare with the state-of-the-art hybrid methods and show improved performance. Notably, we propose the first algorithm that jointly regresses hand pose, bone-lengths and shape surface in a single network. To fine-tune, we pretrain DeepHPS on our Syn-Hand5M. Our results on ICVL show improved accuracy over the state-of-the-art hybrid methods (e.g. LRF [START_REF] Tang | Latent regression forest: Structured estimation of 3d articulated hand posture[END_REF] and DeepModel [START_REF] Zhou | Modelbased deep hand pose estimation[END_REF]). On NYU, the results are better than the state-of-the-art hybrid methods (e.g. DeepPrior[22], DeepPrior-Refine[22], Feedback [START_REF] Oberweger | Training a feedback loop for hand pose estimation[END_REF], DeepModel [START_REF] Zhou | Modelbased deep hand pose estimation[END_REF] and Lie-X [START_REF] Xu | Lie-x: Depth image based articulated object pose estimation, tracking, and action recognition on lie groups[END_REF]). The curves show the number of frames in error within certain thresholds.

Implementation Details

For training, we pre-process the raw depth data for standardization and depth invariance. We start by computing the centroid of the hand region in the depth image. The obtained 3D hand center location (i.e. palm center) is used to crop the depth frame. The camera intrinsics (i. We use Caffe [START_REF] Jia | Caffe: Convolutional architecture for fast feature embedding[END_REF] which is an open-source training framework for deep networks. The complete pipeline is trained end-to-end until convergence. The learning rate was set to 0.00001 with 0.9 SGD momentum. A batch size of 256 was used during the training. The framework is executed on a desktop equipped with Nvidia Geforce GTX 1080 Ti GPU with 16GB RAM. One forward pass takes 3.7ms to generate 3D hand joint positions and shape surface. For simplicity, we name our method as DeepHPS.

Algorithm Evaluation

In this subsection, we evaluate our complete pipeline using the SynHand5M. Moreover, we devise a joint training strategy for both real and synthetic datasets to show qualitative hand surface reconstruction of real images. Evaluation on the synthetic dataset: The complete pipeline is trained end-to-end using SynHand5M for pose and shape recovery. For fair comparison, we train the stateof-the-art model based learning methods [START_REF] Zhou | Modelbased deep hand pose estimation[END_REF]14] on Syn-Hand5M. [14] works for varying hand shapes in contrast to Figure 5: Real hand pose and shape recovery: More results on hand pose and surface reconstruction of NYU [START_REF] Tompson | Real-time continuous pose recovery of human hands using convolutional networks[END_REF] images. Despite of unavailability of ground truth hand mesh vertices, our algorithm produces plausible hand shape. the closely related method [START_REF] Zhou | Modelbased deep hand pose estimation[END_REF]. The quantitative results are shown in Table 1. Our method clearly exceeds in accuracy over the compared method and additionally reconstructs full hand surface. The qualitative results are shown in Figure 6. The estimated 22 joint positions are overlaid on the depth images while the reconstructed hand surface is shown using two different views named as 3D View1 and 3D View2. For better visualization, view2 is similar to ground truth view. The results demonstrate that our DeepHPS model infers correct hand shape surface even in cases of occlusion of several fingers and large variation in view points. Evaluation on the NYU real dataset: In order to jointly train our whole pipeline on both real and synthetic data, we found 16 closely matching common joint positions in Syn-Hand5M and the NYU dataset. These common joints are different from the 14 joints used for the public comparisons [START_REF] Tompson | Real-time continuous pose recovery of human hands using convolutional networks[END_REF]. The loss equation is;

L = L J + 1L V (8)
where 1 is an indicator function which specifies whether the ground truth for mesh vertices is available or not. In our setup, it is 1 for synthetic images and 0 for real images. For real images, backpropagation from surface reconstruction part is disabled.

The qualitative pose and surface shape results on sample NYU real images are shown in Figure 1 and5. Despite of the missing ground truth surface information and presence of high camera noise in NYU images, the resulting hand surface is plausible and the algorithm performs well in case of missing depth information and occluded hand parts.

Comparison on Public Benchmarks

The public benchmarks do not provide ground truth hand mesh files. Therefore, we provide quantitative results for pose inference on two of the real hand pose datasets (i.e. NYU and ICVL). For comparisons, NYU dataset use 14 joint positions [START_REF] Tompson | Real-time continuous pose recovery of human hands using convolutional networks[END_REF] whereas ICVL dataset [START_REF] Tang | Latent regression forest: Structured estimation of 3d articulated hand posture[END_REF] 19.72mm Crossing Nets [START_REF] Wan | Crossing nets: Combining gans and vaes with a shared latent space for hand pose estimation[END_REF] 15.5mm Feedback [START_REF] Oberweger | Training a feedback loop for hand pose estimation[END_REF] 15.9mm DeepModel [START_REF] Zhou | Modelbased deep hand pose estimation[END_REF] 17.0mm Lie-X [START_REF] Xu | Lie-x: Depth image based articulated object pose estimation, tracking, and action recognition on lie groups[END_REF] 14.5mm DeepHPS:NYU [Ours]

15.8mm DeepHPS:fine-tuned [Ours] 14.2mm [START_REF] Tang | Latent regression forest: Structured estimation of 3d articulated hand posture[END_REF] 12.57mm DeepModel [START_REF] Zhou | Modelbased deep hand pose estimation[END_REF] 11.56mm Crossing Nets [START_REF] Wan | Crossing nets: Combining gans and vaes with a shared latent space for hand pose estimation[END_REF] 10.2mm DeepHPS:ICVL [Ours] 10.5mm DeepHPS:fine-tuned [Ours] 9.1mm

Table 3: Quantitative comparison on ICVL [START_REF] Tang | Latent regression forest: Structured estimation of 3d articulated hand posture[END_REF]: The DeepHPS model fine-tuned on the ICVL dataset outperforms the state-of-the-art hybrid methods.

Our DeepHPS algorithm is trained on NYU and ICVL individually, called DeepHPS:NYU and DeepHPS:ICVL models. Then, we fine-tune the pre-trained DeepHPS (on SynHand5M) with the NYU and ICVL, we call DeepHPS:fine-tuned models. The 3D joint location errors of the trained models are calculated on 8252 NYU and 1596 ICVL test images respectively. The quantitative results are shown in Figure 4 and Tables 2 and3. DeepHPS:fine-tuned models achieve an error improvement of 13.3% and 10.12% over DeepHPS:ICVL and DeepHPS:NYU models respectively.

On the ICVL and NYU datasets, we achieve improvement in the joint location accuracy over the state-of-the-art hybrid methods. Failure case: Our framework works well in case of missing depth information and occlusions. However, under severe occlusions and a lot of missing depth information, it may fail to detect the correct pose and shape; see Figure 7.

Conclusion and Future Work

In this work, we demonstrate the simultaneous recovery of hand pose and shape surface from a single depth image.

For training, we synthetically generate a large scale dataset with accurate joint positions, segmentation masks and hand meshes of depth images. Our dataset will be a valuable addition for training and testing CNN-based models for 3D hand pose and shape analysis. Furthermore, it improves the recognition rate of CNN models on hand pose datasets. In our algorithm, intermediate parametric representations are estimated from a CNN architecture. Then, a novel hand pose and shape layer is embedded inside the deep network to produce 3D hand joint positions and shape surface. Experiments show improved accuracy over the state-of-the-art hybrid methods. Furthermore, we demonstrate plausible results for the recovery of hand shape surface on real images. Improving the performance of CNN-based hybrid methods is a potential research direction. These methods bear a lot of potential due to their inherent stability and scalability. In future, we wish to extend our dataset with wider view points coverage, object interactions and RGB images. Another aspect for future work is predicting fine-scale 3D surface detail on the hand, where real-world statistical hand models [START_REF] Romero | Embodied hands: Modeling and capturing hands and bodies together[END_REF] possibly give better priors.

1 .

 1 A novel deep network layer which performs: (a) Forward kinematics using a new combination of hand pose and bone scales parameters. (b) Reconstruction of a morphable hand model from hand shape parameters and the morph targets.

Figure 2 :

 2 Figure 2: The SynHand5M dataset contains 5 million images. (a) The dataset ground truth components: hand poses (joints angles and 3D positions), depth maps, mesh files, and hand parts segmentation. (b) Samples illustrating the big variation in shape.

  Figure 3: (a) An overview of our method for simultaneous 3D hand pose and surface estimation. A depth image D I is passed through three CNNs to estimate pose parameters δθ, bones scales α and shape parameters β. These parameters are sent to HPSL which generate the hand joints positions P and hand surface vertices V . (b) Our hand model with 26 DoFs overlaid with the neutral hand shape b 0 . The bone colors illustrate 6 bone-length scales α.x 12. Lastly, the two fully connected (FC) layers have 1024 neurons each with dropout ratio of 0.3. After the second FC layer, the final FC layers in α-CNN and β-CNN output 6 dimensional α and 7 dimensional β parameters respectively. All layers use the ReLu as activation function.

  Skeleton Bone-lengths Adaptation: In order to adapt bone-lengths of hand skeleton during training over varying hand shapes in the dataset,[14] propose various bone-length scaling strategies. Following the similar approach, we assign a separate scale parameter for bonelengths in palm s p and 5 different scales for bones as shown in Figure3(b). The HPSL acquires the scaling parameters α = [s p , s 1 , s 2 , s 3 , s 4 , s 5 ] from the ShapeCNN during the training process. Morphable Hand Model Formulation: Given the shape parameters β learned by our ShapeCNN, we reconstruct the hand shape surface by implementing a morphable hand model inside our HPSL. A morphable hand model Ψ ∈ R 3xϑ is a set of 3D vertices representing a particular hand shape. Any morphable hand model can be expressed as a linear combination of principle hand shape components, called morphable targets b t[START_REF] Lewis | Practice and theory of blendshape facial models[END_REF]. Our principle hand shape components are defined for Length, Mass, Size, Palm Length, Fingers Inter-distance, Fingers Length and Fingers Tip-Size. They represent offsets from a neutral hand shape b 0 similar to one shown in Figure3(b). Each learned shape parameter β t defines the amount of contribution of a principle shape components b t towards formulation of final hand morphable model. Hence, a hand morphable model Ψ can be formulated using the following Equation:

Figure 4 :

 4 Figure 4: Quantitative evaluation. (a) show the results of our algorithm (DeepHPS) on ICVL test set, when trained on ICVL and fine-tuned on ICVL. (b) is the same but with NYU.To fine-tune, we pretrain DeepHPS on our Syn-Hand5M. Our results on ICVL show improved accuracy over the state-of-the-art hybrid methods (e.g. LRF[START_REF] Tang | Latent regression forest: Structured estimation of 3d articulated hand posture[END_REF] and DeepModel[START_REF] Zhou | Modelbased deep hand pose estimation[END_REF]). On NYU, the results are better than the state-of-the-art hybrid methods (e.g. DeepPrior[22], DeepPrior-Refine[22], Feedback[START_REF] Oberweger | Training a feedback loop for hand pose estimation[END_REF], DeepModel[START_REF] Zhou | Modelbased deep hand pose estimation[END_REF] and Lie-X[START_REF] Xu | Lie-x: Depth image based articulated object pose estimation, tracking, and action recognition on lie groups[END_REF]). The curves show the number of frames in error within certain thresholds.

  e. focal length) and a bounding box of size 150, are used during the crop. The pre-processed depth image is of size 96 x 96 and in depth range of [-1, 1]. The annotations in camera coordinates are simply normalized by the bounding box size and clipped in range [-1, 1].

Figure 6 :Figure 7 :

 67 Figure6: Synthetic hand pose and shape recovery: We show example estimated hand poses overlaid with the preprocessed depth images from our SynHand5M. We show the reconstructed surface from two different views (yellow) and the ground truth surface (gray). 3D View2 is similar to the ground truth view. Our algorithm infers correct 3D pose and shape even in very challenging condition, like occlusion of several fingers and large variation in view points.

Table 1 :

 1 use 16 joint positions. Quantitative Evaluation on SynHand5M: We show the 3D joint and vertex locations errors(mm). Our method additionally outputs mesh vertices and outperforms model based learning methods [55, 14].

	Method \Error(mm) 3D Joint Loc. 3D Vertex Loc.
	DeepModel [55]	11.36	-
	HandScales [14]	9.67	-
	DeepHPS [Ours]	6.3	11.8
	Methods	3D Joint Location Error
	DeepPrior [22]		20.75mm
	DeepPrior-Refine [22]

Table 2 :

 2 Quantitative comparison on NYU[START_REF] Tompson | Real-time continuous pose recovery of human hands using convolutional networks[END_REF]: Our finetuned DeepHPS model on the NYU dataset shows the stateof-the-art performance among hybrid methods.

	Methods	3D Joint Location Error
	LRF

Acknowledgements

This work was partially funded by NUST, Pakistan, the Federal Ministry of Education and Research of the Federal Republic of Germany as part of the research projects DY-NAMICS (Grant number 01IW15003) and VIDETE (Grant number 01IW18002).