Denis Rg

Magnan

Le Stunff

Increased levels of inflammatory plasma markers and obesity risk in a mouse model of Down syndrome

Keywords: Down syndrome, inflammation, obesity, leptin, ghrelin, resistin, interleukin-6, galectin-3, HSP72 Down syndrome, Gal-3: galectin-3, GIP: glucose dependent insulinotropic polypeptide;

Down syndrome (DS) is caused by the trisomy of human chromosome 21 and is the most common genetic cause of intellectual disability. In addition to the intellectual deficiencies and physical anomalies, DS individuals present a higher prevalence of obesity and subsequent metabolic disorders than healthy adults.

There is increasing evidence from both clinical and experimental studies indicating the association of visceral obesity with a pro-inflammatory status and recent studies have reported that obese people with DS suffer from low-grade systemic inflammation. However, the link between adiposity and inflammation has not been explored in DS. Here we used Ts65Dn mice, a validated DS mouse model, for the study of obesity-related inflammatory markers. Ts65Dn mice presented increased energy intake, and a positive energy balance leading to increased adiposity (fat mass per body weight), but did not show overweight, which only was apparent upon high fat diet induced obesity. Trisomic mice also had fasting hyperglycemia and hypoinsulinemia, and normal incretin levels. Those trisomy-associated changes were accompanied by reduced ghrelin plasma levels and slightly but not significantly increased leptin levels. Upon a glucose load, Ts65Dn mice showed normal increase of incretins accompanied by over-responses of leptin and resistin, while maintaining the hyperglycemic and hypoinsulinemic phenotype. These changes in the adipoinsular axis were accompanied by increased plasma levels of inflammatory biomarkers previously correlated with obesity galectin-3 and HSP72, and reduced IL-6. Taken together, these results suggest that increased adiposity, and pro-inflammatory adipokines leading to low-grade inflammation are important players in the propensity to obesity in DS. We conclude that DS would be a case of impaired metabolic-inflammatory axis.

Introduction

Overweight and obesity are acquiring epidemic proportions worldwide, causing alarming increases of various associated health problems. Individuals with Down syndrome (DS) are a particularly vulnerable population, since they present significantly higher prevalence of obesity than the general population. DS is caused by trisomy of chromosome 21 and is associated to multiple endocrine and nervous system alterations [START_REF] Lott | Cognitive deficits and associated neurological complications in individuals with Down's syndrome[END_REF], that may contribute to obesity driving metabolic complications, insufficient exercising and poor eating habits [START_REF] Temple | Physical activity and persons with intellectual disability: some considerations for Latin America[END_REF] [3] [START_REF] Grammatikopoulou | Nutrient intake and anthropometry in children and adolescents with Down syndrome--a preliminary study[END_REF]. DS is associated with high body adiposity [START_REF] Loveday | Bioelectrical impedance for measuring percentage body fat in young persons with Down syndrome: validation with dual-energy absorptiometry[END_REF] [START_REF] Gonzalez-Aguero | Accuracy of prediction equations to assess percentage of body fat in children and adolescents with Down syndrome compared to air displacement plethysmography[END_REF] suggesting that a propensity for fat accumulation could be a major contributor of DS obesity.

In the general population, high adiposity is associated with inflammation and oxidative stress markers [START_REF] Hruskova | A new "immunological" role for adipocytes in obesity[END_REF] [START_REF] Fernández-Sánchez | Inflammation, Oxidative Stress, and Obesity[END_REF]. Indeed, visceral fat accumulation contributes to pro-oxidant and pro-inflammatory states, as well as to alterations in glucose and lipid metabolism [START_REF] Fernández-Sánchez | Inflammation, Oxidative Stress, and Obesity[END_REF].

DS individuals show increased susceptibility to autoimmune disorders, hypothyroidism, celiac disease and diabetes mellitus compared to non-DS individuals [START_REF] Cuadrado | Immune dysfunction in Down's syndrome: primary immune deficiency or early senescence of the immune system?[END_REF], all conditions having in common inefficient immune responses or abnormally increased inflammation [START_REF] Giménez-Barcons | Autoimmune Predisposition in Down Syndrome May Result from a Partial Central Tolerance Failure due to Insufficient Intrathymic Expression of AIRE and Peripheral Antigens[END_REF]. However, the potential link between adiposity and inflammation has not been explored. Here we used the Ts65Dn mice, a validated DS mouse model, for the study of the effect of trisomy 21 on adiposity, energy balance and inflammatory status in non-obesogenic (standard rodent chow) and obesogenic conditions (high fat chow).

Methods

Animals and housing

4-6 moths-old male Ts(1716)65Dn (Ts65Dn) and wild-type (WT) mice were used in this study. Mice were obtained through crossings of a B6EiC3Sn a/A-Ts (1716)65Dn (Ts65Dn) female to B6C3F1/J males purchased from The Jackson Laboratory (Bar Harbor, ME). Genotyping was performed by amplifying genomic DNA obtained from the mice tail as previously described [START_REF] Liu | Quantitative PCR genotyping assay for the Ts65Dn mouse model of Down syndrome[END_REF]. It has been shown that there is a sex-dimorphism in the effect of high fat diet metabolic effects in rats and that it might be influenced by gonadal cycles [START_REF] Priego | Sex-associated differences in the leptin and ghrelin systems related with the induction of hyperphagia under high-fat diet exposure in rats[END_REF]. Here we used males for simplifying the interpretation of the results. However, the gender dimension of the genotypes studied should be addressed in future studies. The experiments were performed in the Animal Facilities in the Barcelona Biomedical Research Park Supplementary Table 1 shows the composition of the different experimental diets.

Experiment 1: Ts65Dn phenotypes under standard chow

WT and Ts65Dn mice were given standard chow (SC; SDS, UK) for the assessment of the genotype-dependent differences in body weight, body composition, metabolic phenotyping and plasma biomarkers in non-obesogenic conditions.

Body weight and body composition analysis

Non-invasive analysis of body composition was estimated by nuclear magnetic resonance (NMR) technology using a scanner EchoMRI 900 (Echo Medical Systems, USA), that creates contrast between soft tissues taking advantage of the differences in relaxation times of the hydrogen spins and/or hydrogen density. We obtained separate measures for lean and fat mass in grams (WT, n = 10; Ts65Dn, n = 8). Fat is defined as the mass of all the fat molecules in the body expressed as equivalent weight of canola oil. Lean is muscle mass equivalent to all the body parts containing water, excluding fat, bone minerals, and other substances or tissues that do not contribute to the NMR signal, such as hair.

Metabolic phenotyping

Metabolic phenotyping was performed using calorimetric cages (Labmaster, TSE Systems GmbH, Bad Homburg, Germany). Mice were individually housed in the calorimetric cage in controlled laboratory conditions with the temperature maintained at 22 ºC ± 1 o C on a 12 hours light/dark cycle and acclimated to the chambers for 48 h before experimental measurements. Spontaneous feeding and locomotor activity (beam breaks/hour) were measured using automated feeding and activity recording in a Labmaster apparatus (TSE Systems). Whole energy expenditure was calculated according to the Weir equation respiratory gas exchange measurements [START_REF] Weir | New methods for calculating metabolic rate with special reference to protein metabolism[END_REF]. Sensors were previously calibrated with a mixture of known concentrations of O 2 and CO 2 (Air Liquide, Paris, France).

Energy intake (Kcal lean body mass/h), locomotor activity (infrared breaks/h), respiratory exchange rate (respiratory quotient (RQ) = VCO 2 /VO 2 ) and energy expenditure (Kcal/h) were recorded during 72 h. Representative 24 h values were calculated as the mean of the three consecutive experimental days. Raw values of VO 2 consumed, VCO 2 production (expressed in ml/h), and energy expenditure (Kcal/h), were collected every 15 min. Subsequently, each value was expressed by whole lean tissue mass obtained from the EchoMRI analysis. No practical methods of estimation of the resting metabolism are available presently [START_REF] Even | Indirect calorimetry in laboratory mice and rats: principles, practical considerations, interpretation and perspectives[END_REF]. However, an estimation of resting metabolism (Kcal/h) can be made on the following basis: data of energy expenditure were considered as the best estimation of resting metabolism if spontaneous activity was less than 5% of the highest daily value during the 30 minutes previous to the measurement, and if animals ate less than 0.1 g of food within the last hour.

Bio-Plex Pro Mouse Diabetes Assay

Glucose tolerance test was performed in WT (n = 11) and Ts65Dn (n = 7) mice on a standard chow diet. Briefly mice were food deprived for 5 hours prior to an oral administration of 2 g/Kg of 30% glucose (Lavoisier, France). Blood was collected from the tail vein before (T 0 ) and 30 minutes after (T 30 ) glucose administration, to evaluate the response to glucose load of incretin activity and related metabolic parameters after food deprivation and after the glucose load. We measured the levels of basal (T 0 ) glucose stimulated (T 30 ) insulin, anorexigenic and orexigenic signals (leptin and ghrelin), and incretins (glucose dependent insulinotropic polypeptide (GIP), glucagon-like peptide-1(7-36) amide (GLP-1) and glucagon).

Plasminogen activator inhibitor-1 (PAI-1) and resistin were also measured since they are implicated in the release of fatty acids, are associated with insulin resistance as well as with pro-inflammatory cytokines, and are increased in obese insulin resistant individuals. Determinations were carried out using suspension bead array immunoassay kits following manufacturer's specifications (Bio-Plex Pro Mouse Diabetes Assay 8-plex, Biorad, France). Upon blood centrifugation, plasma was stored in presence of Linagliptin, a DPP-4 inhibitor (Selleckchem, USA) and protease inhibitor (Roche, France) to inhibit the degradation of incretins. The samples were measured using a Luminex 200 apparatus (Luminex, Austin, TX). All assays were performed in duplicate.

Inflammatory markers

Galectin-3, HSP72 and interleukin-6 were measured from blood in mice feed with standard chow (WT, n = 6; Ts65Dn, n = 5). Blood was obtained by retro-orbital sinus sampling with heparinized capillaries into the tubes containing a 1/10 volume of 3.8% sodium citrate. Plasma was isolated by centrifugation (2500 x g for 15 min at 4°C). Plasma galectin-3 (Abcam ELISA kit, France), HSP72 (Enzo Life sciences ELISA kit, Germany) and IL-6 (Enzo Life sciences ELISA kit, Germany) were assessed using sandwich ELISA. After removal of unbound conjugates, bound enzyme activity was assessed by use of a chromogenic substrate for measurement at 450 nm by a microplate reader (Flex Station 3, Molecular Devices, Ltd., UK).

Hepatic toxicity assays

Alanine aminotransferase (ALT) and Triacylglycerol (TG) were measured from blood in mice feed with standard chow (WT, n = 6; Ts65Dn, n = 5). Blood was obtained by retro-orbital sinus and plasma was isolated by centrifugation (2500 x g for 15 min at 4°C). ALT activity was assayed using the Alanine Aminotransferase Activity Assay Kit (Sigma-Aldrich, France) determined by a coupled enzyme assay, which results in a fluorometric (λex = 535/λem = 587 nm) product, proportional to the pyruvate generated. One unit of ALT is defined as the amount of enzyme that generates 1.0 μmole of pyruvate per minute at 37 °C. TG levels were measured according to the manufacturer's instructions (Sigma-Aldrich, France). Briefly, the assay involves enzymatic hydrolysis by lipase of the triglycerides to glycerol and free fatty acids. All assays were performed in duplicate.

A pyruvate tolerance test was performed by injecting sodium pyruvate (Sigma, 1 g/kg, i.p.) after a 6 h of fasting. Blood samples were collected from the tail tip [START_REF] Taveau | Vasopressin and hydration play a major role in the development of glucose intolerance and hepatic steatosis in obese rats[END_REF] and glucose concentrations (Diagnostic Glucometer, Lifescan Laboratories, France) were measured at 5, 10, 15, 30, 60, 90 and 120 min after the injection.

The pyruvate tolerance test was performed in SC conditions and after 8-weeks of HF exposure (WT = 10; TS = 7).

Experiment 2: high fat diet induced obesity

Some parameters were also studied as described above upon ad libitum access to a high fat diet (High Fat/DIO, Test Diet, USA) for 8 weeks. These experiments were performed in a separate group mice: WT, n = 24; Ts65Dn, n = 21. Specifically we analysed changes in body weight and body composition (WT, n = 6; Ts65Dn, n = 6), plasma levels of Galectin-3, interleukin-6, and HSP72, as inflammatory biomarkers previously correlated with obesity and hepatic function markers such as ALT and triacylglycerol (WT, n = 8; Ts65Dn, n = 4), to discard diet-induced liver damage.

Statistical analyses

Sample size was estimated based on our previous experience with similar experiments. Differences between genotypes and diet groups were determined using 2-tailed unpaired Student's t test. Repeated measures ANOVA was used for the analysis of the differences in locomotor activity and pyruvate tolerance test. Two-way ANOVA with Bonferroni post hoc test to correct for multiple comparisons was used to test diet and genotype interaction. Levels of statistical significance were set at P<0.05. The statistical analyses were performed using the by using Statistical Package for Social Science software (SPSS version 17.0, IBM Corporation, USA).

Results

Increased adiposity and positive energy balance in Ts65Dn mice

In Experiment 1, we observed that 5 months-old Ts65Dn mice fed with standard chow showed similar body weight than WT littermates (Figure 1 A), but Ts65Dn mice presented a significant reduction of body length (t (1, 16) = 4.23, P< 0.01, Figure 1B). Regarding energy intake, Ts65Dn mice showed significantly higher mean daily energy intake compared to WT (t (1, 9) = -3.23; P<0.01) due to significantly increased energy intake during the active (dark) (t (1, 9) = -3.58; P<0.01; Figure 3A). Locomotor activity was similar in both genotypes during the resting phase, but we detected a slight hyperactivity in Ts65Dn mice during specific periods of the dark phase that did not reach statistical significance (Figure 3B ANOVA (dark phase) F (1,8) = 3.017; P>0.05). Energy expenditure (EE) was significantly higher in Ts65Dn mice both in the light and the dark phases of the circadian cycle compared to WT (t (1,9) = -3,40; P<0.01 (light) and t (1,9) = -3.52; P<0.01 (dark); Figure 3C). This increased EE in Ts65Dn mice might be related to a non-significant higher total oxygen consumption and carbon dioxide production (ml/Kg LM/h) compared to WT mice (TS [4567, 49 ± 383,44] 3D). Estimated resting metabolism (ERM) of Ts65Dn mice was significantly higher compared to WT mice, (t (1, 9) = -3.47; P<0.05; Figure 3E), [START_REF] Luhrmann | Effects of fat mass and body fat distribution on resting metabolic rate in the elderly[END_REF]. Even so, Ts65Dn mice still showed a significantly more positive energy balance during the dark phase than WT mice (t (1, 9) = -2.43; P<0.05, Figure 3F).

Altered levels of metabolic and inflammatory biomarkers in Ts65Dn mice

To study the effect of the trisomy on energy balance, inflammation and oxidative stress, we obtained the basal (t 0 , 5-hour after starvation) and postprandial (t 30 , 30 minutes after an oral glucose load) glycemic phenotype and metabolic profile of selected markers in Ts65Dn and WT (Table 1).

In basal conditions (t 0 ), Ts65Dn mice showed higher plasma glucose compared to WT mice (t (1, 17) = -2.59; P<0.05) along with significantly lower insulin levels (t (1, 17) = 2.40; P<0.05) suggesting an insulin-resistant phenotype in trisomic mice or a deficient insulin secretion. We also analysed ghrelin and leptin, which are key appetitive and satiety signals. The plasma levels of ghrelin were significantly lower in Ts65Dn than in WT mice (t (1, 16) = 3.17; P<0.01) while leptin levels were similar (Table 1) suggesting that the increased energy intake in trisomic mice could be related to an altered balance of anorexigenic and orexigenic signals. After glucose load (t 30 ), the levels of glucose increased similarly in both genotypes (Figure 4), and thus remained significantly higher (t (1, 17) = -2.26, P<0.05) in Ts65Dn compared to WT mice (Table 1). The levels of insulin also increased similarly in both genotypes (Figure 4). Both genotypes increased plasma leptin levels that were higher in Ts65Dn (t(1,17) = -2.40; P <0.05) though the absolute levels were not significantly different from those of WT mice (t (1, 17) = -2.09; P = 0.052; Table 1).

Incretins are implicated in glucose homeostasis since they stimulate insulin secretion. We did not observe genotype-related differences in the levels of glucose dependent insulinotropic polypeptide (GIP), glucagon-like peptide-1(7-36) amide (GLP-1) or glucagon at t 0 or t 30 nor in their percentage of change upon glucose load (Table 1, Figure 4). Plasminogen activator inhibitor-1 (PAI-1) and resistin were also measured since they are implicated in the flow of fatty acids from plasma and are associated with insulin resistance as well as with pro-inflammatory cytokines [START_REF] Viitanen | Association of angiotensin converting enzyme and plasminogen activator inhibitor-1 promoter gene polymorphisms with features of the insulin resistance syndrome in patients with premature coronary heart disease[END_REF] [START_REF] Nogueiras | Resistin: regulation of food intake, glucose homeostasis and lipid metabolism[END_REF]. Ts65Dn and WT mice showed similar levels of PAl-1 and resistin at t 0 and t 30 . However, Ts65Dn mice showed a significantly higher increase of resistin upon glucose load (t (1,17) = -2.26; P <0.05) that have been previously associated with impaired insulin-stimulated glucose intake in in vitro adipocytes studies [START_REF] Steppan | The hormone resistin links obesity to diabetes[END_REF].

Body weight increase, body composition and indirect calorimetry upon high fat diet access

In Experiment 2, we studied the effect of 8-weeks of exposure to high fat (HF) diet in obesity development and inflammatory status. We first observed that both Ts65Dn mice fed with HF showed higher energy intake than WT mice both during the light and dark periods, being significant the mean daily energy intake (t (1,10) = -4.01; P<0.01; Figure 6A). While the ambulatory activity was similar between genotypes (Figure 6B), with only a slight hyperactivity in specific time-points, again trisomic mice showed increased energy expenditure in both the light and dark phases (t (1,10) = -3.07; P<0.05 (light) and t (1,10) = -2.97; P< 0.05 (dark); Figure 6C). It might be related to the significant higher oxygen consumption and carbon dioxide production in Ts65Dn relative to WT mice (TS vs. WT (VO 2 ) t (1,8) = -3.7; P<0.01; TS vs. WT (VCO ), t (1,8) = -3.81; P<0.01) although the metabolic exchange (VCO 2 /VO 2 was similar between genotypes (Figure 6D). Again, Ts65Dn mice showed a significant increased estimated resting metabolism (t (1,10) = -3.57; P< 0.01 (light) and t (1,10) = -2.50; P<0.05 (dark; Figure 6D) but both genotypes had negative energy balance during light phase and positive during the dark period (Figure 6C). Student's t test for independent samples ** P <0.01. LM: lean mass; EE: energy expenditure; ERM: estimated resting metabolism.

Ts65Dn show higher plasma levels of galectin-3 and HSP72 and dietassociated changes in interleukin-6

Chronic low-grade inflammation is observed in subjects with obesity and represents a mechanistic link between obesity, insulin resistance and type 2 diabetes mellitus [START_REF] Hersoug | The link between the epidemics of obesity and allergic diseases: does obesity induce decreased immune tolerance?[END_REF]. Hence, plasma levels of molecules such as galectin-3, HSP72, and interleukin-6 (IL-6) have been shown to positively correlate with human obesity [START_REF] Rheinheimer | Current role of the NLRP3 inflammasome on obesity and insulin resistance: A systematic review[END_REF].

Independently of the diet (Experiment 1 and 2), Ts65Dn showed higher levels of galectin-3 and HSP72 compared to WT (genotype effect for galectin 3, ANOVA, F

Ts65Dn mice do not present compromised liver function

Insulin resistance is usually associated to intra-or extra-hepatic injury that can be determined by measuring the circulating levels of alanine amino transferase (ALT) activity [START_REF] Schindhelm | Alanine aminotransferase as a marker of nonalcoholic fatty liver disease in relation to type 2 diabetes mellitus and cardiovascular disease[END_REF] and triglycerides levels in plasma [START_REF] Petersen | Roles of Diacylglycerols and Ceramides in Hepatic Insulin Resistance[END_REF]. Several human reports have shown an altered lipid profile associated to trisomy 21 [START_REF] Buonuomo | Lipid profiles in a large cohort of Italian children with Down syndrome[END_REF]. Therefore, we here checked the ALT and triacylglycerol (TG) in standard chow and high fat diet conditions to determine the possible implication of hepatic dysfunction in the insulin-resistance and increased pro-inflammatory profile detected in Ts65Dn mice (Experiment 1 and 2). In non-obesogenic conditions, ALT levels were similar in WT and Ts65Dn mice and upon HF access, mice of both genotypes showed higher ALT circulating levels (diet effect, ANOVA F(1,1) ) 5.45, P<0.05; Figure 8A). The opposite was detected for triacylglycerol plasma levels with HF mice showing lower circulating TG in both genotypes compared to SC fed mice, without differences between genotypes (diet effect, ANOVA F(1,1) ) 13.34, P<0.01;Figure 8B). Finally, we performed a pyruvate tolerance test to address the possible effect of the trisomy on hepatic glucose homeostasis. It has been shown that the development of insulin resistance in the liver is accompanied by a progressive deterioration of the control of hepatic glucose production leading to excessive hepatic glucose output through increased glycogenolysis and gluconeogenesis [START_REF] Sharabi | Molecular pathophysiology of hepatic glucose production[END_REF]. In non-obesogenic conditions, the injection of pyruvate led to a similar rise of plasma glucose levels in both WT and Ts65Dn. Upon HF diet, both genotypes displayed a greater increase in plasma glucose levels upon pyruvate which indicates increased gluconeogenesis (diet effect, ANOVA F(1,32) = 6.48; P<0.05; Figure 8C).

Discussion

Obesity in Down syndrome (DS) has been mainly attributed to insufficient exercising and reduced energy metabolism or to poor eating habits [28] [29]. Here we show that visceral fat accumulation contributes to pro-inflammatory states, as well as to alterations in glucose metabolism in Ts65Dn mice that show increased plasma levels of inflammatory biomarkers previously correlated with obesity such as galectin-3 and HSP72.

In the general population, obesity and propensity to weight gain have been associated with low resting metabolism and reduced energy expenditure [START_REF] Weyer | Energy expenditure, fat oxidation, and body weight regulation: a study of metabolic adaptation to long-term weight change[END_REF]. In our experiments, Ts65Dn mice showed higher energy intake but also energy expenditure (EE), and higher estimated resting metabolism (ERM) compared to WT. Even so, trisomic mice present a marked positive energy balance. The contribution of energy metabolism and energy expenditure in DS obesity is controversial. Some reports have shown that resting metabolism is reduced in DS [START_REF] Allison | Decreased resting metabolic rate among persons with Down Syndrome[END_REF] [32] [START_REF] Bauer | Energy expenditure in neonates with Down syndrome[END_REF], possibly due to defects in thyroid gland function, which is prevalent in this population [START_REF] Graber | Down syndrome and thyroid function[END_REF]. Instead, in our experiments, Ts65Dn mice presented an increased resting metabolism, which may contribute to their hyperphagic phenotype, which could lead to the significantly increased fat mass we observed in trisomic mice. Also, we replicated previous observations in Ts65Dn mice that showed hyperglycemia with concomitantly reduced insulin plasma levels in fasting and glucose loaded conditions. Approximately 60-70% of insulin secretion is dependent on the release of the incretin GLP1 and GIP [START_REF] Defronzo | Type 2 diabetes mellitus[END_REF] but in our experiments, we did not detect trisomy-dependent changes in fasting or glucose-stimulated incretin plasma levels. However, other factors could be contributing to this hypoinsulinemic phenotype. Perturbed glucose homeostasis has been previously described in Ts65Dn that was ascribed to functional changes in pancreatic β-cells possibly dependent of RCAN1 triplication [START_REF] Peiris | A Syntenic Cross Species Aneuploidy Genetic Screen Links RCAN1 Expression to β-Cell Mitochondrial Dysfunction in Type 2 Diabetes[END_REF], but recent studies also point to the adipose tissue, that we found increased in Ts65Dn, as a highly active endocrine organ secreting a range of hormones such as resistin and leptin. Resistin is an adipocyte-specific hormone that has been related to impaired glucose tolerance, and reduced insulin plasma levels [START_REF] Nogueiras | Resistin: regulation of food intake, glucose homeostasis and lipid metabolism[END_REF]. Studies in adipocytes have shown that resistin antiserum enhances insulin-stimulated glucose uptake and blunts insulin action [START_REF] Steppan | The hormone resistin links obesity to diabetes[END_REF]. Conversely, exogenous administration of resistin impairs glucose tolerance upon a glucose load in mice [START_REF] Peiris | A Syntenic Cross Species Aneuploidy Genetic Screen Links RCAN1 Expression to β-Cell Mitochondrial Dysfunction in Type 2 Diabetes[END_REF]. We found significantly higher increases in resistin levels upon glucose load in Ts65Dn compared WT mice. This resistin over-response could contribute to hypoinsulinemic phenotype of trisomic mice through insulin-secretion independent mechanisms. We only found one report in human showing that the levels of resistin in amniotic fluid of DS pregnancies are lower compared to non-affected pregnancies [START_REF] Vrachnis | Resistin in mid-trimester amniotic fluid in trisomy 21[END_REF].

On the other hand, leptin, a key hormone secreted by adipocytes that leads to higher energy expenditure and reduces food intake [START_REF] Park | Physiology of leptin: energy homeostasis, neuroendocrine function and metabolism[END_REF] was increased, though not significantly, in Ts65Dn mice, whereas the level of the orexigenic hormone ghrelin was decreased compared to WT. Ts65Dn consumed more calories, suggesting that the satiating effects of leptin would be inefficient. Leptin levels correlate with adiposity in DS children and adolescents [START_REF] Yahia | Leptin, insulin and thyroid hormones in a cohort of Egyptian obese Down syndrome children: a comparative study[END_REF] [40] and in free feeding conditions, and leptin levels are higher in DS compared to unaffected siblings [41] [42] possibly due to the greater leptin production per unit of adipose tissue [START_REF] Magge | Leptin levels among prepubertal children with Down syndrome compared with their siblings[END_REF]. Two studies have reported lower leptin levels in DS adults [START_REF] Proto | Free and total leptin serum levels and soluble leptin receptors levels in two models of genetic obesity: the Prader-Willi and the Down syndromes[END_REF] and foetus [START_REF] Radunovic | Fetal and maternal plasma leptin levels during the second half of normal pregnancies and those with Down syndrome[END_REF], but the majority of the existing reports support a leptin resistance phenotype. We only found one report of ghrelin levels in DS individuals showing no differences compared to typical developing peers [START_REF] Magge | Leptin levels among prepubertal children with Down syndrome compared with their siblings[END_REF] but in the general population obese individuals usually have lower plasma levels of ghrelin than lean individuals [START_REF] Buss | Associations of ghrelin with eating behaviors, stress, metabolic factors, and telomere length among overweight and obese women: preliminary evidence of attenuated ghrelin effects in obesity?[END_REF].

Even though we did not explore directly the leptin resistance in our mice, the finding of obese-like hyperleptinemia and hypoghrelinemia along with hyperphagia in Ts65Dn mice lead to speculate that diminished anorexigenic/orexigenic sensitivity might increase the risk of obesity development when exposed to an obesogenic environment. In fact, we detected a higher body weight gain and increased energy intake in Ts65Dn mice compared to WT littermates upon exposure to high fat diet. This result would be in line with previous reports showing that rats having greater absolute leptin response to intravenous glucose administration, also had increased energy intake and gained more weight upon 6 weeks of high fat diet [START_REF] Levy | Leptin responses to glucose infusions in obesity-prone rats[END_REF].

It may be argued that leptin/insulin resistance could be driven by hepatic dysfunction. Diet-induced obese mice show elevated blood levels of alanine aminotransferase (ALT) activity and triglycerides [START_REF] Tomizawa | Elevated levels of alanine transaminase and triglycerides within normal limits are associated with fatty liver[END_REF] that are indicators of liver damage. Thus, we measured ALT activity, as a marker of hepatic injury, and circulating triacylglycerols (TG) that might reflect a high fat-induced partitioning of lipids in the liver, either by reduced hepatic triglyceride production or by increased triglyceride clearance [START_REF] Podrini | High-fat feeding rapidly induces obesity and lipid derangements in C57BL/6N mice[END_REF]. In non-obesogenic conditions, ALT activity was similar in WT and Ts65Dn mice and upon HF access, mice of both genotypes showed higher ALT activity. The opposite was detected for plasma TG in HF mice, showing lower circulating TG compared to SC fed mice, without differences between genotypes. Finally, it has been shown that the development of insulin resistance in the liver is accompanied by a progressive deterioration of the control of hepatic glucose production leading to excessive hepatic glucose output through increased glycogenolysis and gluconeogenesis [START_REF] Sharabi | Molecular pathophysiology of hepatic glucose production[END_REF]. We explored this phenotype by performing the pyruvate tolerance test in WT/Ts65Dn mice in both non-obesogenic and obesogenic conditions. According to our results, gluconeogenesis is neither impaired as a result of the trisomy nor trisomy leads to an increased vulnerability to hepatic injury upon diet-induced obesity.

Leptin is a pro-inflammatory adipokine, implicated in the release of inflammatory cytokines [START_REF] Febbraio | Role of interleukins in obesity: implications for metabolic disease[END_REF] while ghrelin acts as an anti-inflammatory signal [START_REF] Baatar | The effects of ghrelin on inflammation and the immune system[END_REF]. We thus speculated that hyperleptinemia and hypoghrelinemia could produce increased inflammation that may contribute to obesity development. In the general population there is a strong association between inflammation and increased propensity for fat accumulation and obesity [21] [50]. Also in DS there is a link between adiposity and autoimmune diseases [START_REF] Cuadrado | Immune dysfunction in Down's syndrome: primary immune deficiency or early senescence of the immune system?[END_REF][START_REF] Febbraio | Role of interleukins in obesity: implications for metabolic disease[END_REF]. Specifically, we found that Ts65Dn mice had higher levels of pro-inflammatory and pro-obesogenic markers such as plasma galectin-3 and HSP72 compared to WT mice both in non-obesogenic conditions and upon high fat diet. Galectin-3 is a lectin which levels are highly increased during inflammation in both humans and obese mice [START_REF] Weigert | Serum galectin-3 is elevated in obesity and negatively correlates with glycosylated hemoglobin in type 2 diabetes[END_REF]. Moreover, its administration causes insulin resistance and glucose intolerance in mice [START_REF] Li | Hematopoietic-Derived Galectin-3 Causes Cellular and Systemic Insulin Resistance[END_REF]. Therefore, our result suggests that galectin-3 could be a new interesting target for DS. Also, high levels of the stress heat shock protein 70 (HSP72) in plasma has been associated

with autoimmunity [START_REF] Millar | Hsp70 promotes antigen-presenting cell function and converts T-cell tolerance to autoimmunity in vivo[END_REF] [54] and accumulating data show that HSP72 extracellular levels correlate with oxidative damage [55] [56]. According to our results, trisomy 21 seems the first natural example of increased plasma level of HSP72, which is in accordance with the high prevalence of autoimmune diseases in DS individuals.

Finally, Interleukin-6 (IL-6) is positively correlated with fat mass and inflammation in the general population and also in DS individuals [START_REF] Pal | From cytokine to myokine: the emerging role of interleukin-6 in metabolic regulation[END_REF] [58] [START_REF] De Winter | Prevalence of cardiovascular risk factors in older people with intellectual disability[END_REF]. However, in our hands, Ts65Dn fed with SC had lower plasma IL-6 level compared to WT mice.

This would parallel the depressed levels of IL-6 described before in DS children [23] [60]. In fact, it has been shown that mice lacking IL-6 expression develop systemic insulin resistance and late-onset obesity [61] [62]. Taken together, our results suggest that trisomy 21 is associated to a disrupted adipoinsular axis leading to abnormally increased levels of pro-inflammatory mediators sustaining a low-grade inflammatory state.

In conclusion, in this study we found that Ts65Dn mice have increased fat mass and energy intake and impaired glucose homeostasis. We also detected an increased glucose-stimulated response of pro-inflammatory adipokines along with significant increases of plasma galectin-3 and HSP72 that might potentially explain their increased obesity propensity. Taken together, these results suggest that increased adiposity, and pro-inflammatory adipokines leading to low-grade inflammation are important players in the propensity to obesity in DS. We conclude that DS would be a case of impaired metabolic-inflammatory axis.

(

  PRBB, Barcelona, Spain, EU) and the Functional & Physiological Exploration Platform (FPE) of the Functional & Adaptive Biology (BFA) Unit of the University Paris Diderot-Paris 7, CNRS UMR 8251, in controlled conditions of temperature (22 ºC ± 1 o C) and humidity at 55 ± 10% on a 12 hours light/dark cycle. Food and water were available ad libitum except during food deprived experimental sessions. All experimental procedures were approved by the local ethical committees [Comité Ético de Experimentación Animal del PRBB (CEEA-PRBB); procedure number MDS-12-1464P3 and Animal Care Committee of the Paris Diderot University (CEEA40)]. Diets Depending of the experiment, WT/Ts65Dn mice had ad libitum access to water and standard chow (Experiment 1: WT, n = 26; Ts65Dn, n = 20) or to high fat diet for 8 weeks (High Fat/DIO, Test Diet, USA; Experiment 2: WT, n = 24; Ts65Dn, n = 21).

Figure 1 .

 1 Figure 1. Somatometric characteristics of wild type (WT) and Ts65Dn (TS) mice fed with standard chow (SC). (A) Body weight (grams) and (B) body length (cm) in WT mice (n=10) and TS mice (n=8) fed with standard chow (SC). Student's t test for independent samples ** P<0.01.

Figure 2 .

 2 Figure 2. NMR analysis of body mass composition in wild-type (WT) and Ts65Dn (TS) mice. Bar blots depict (A) lean mass (LM) and fat mass (FM) represented in grams, and (B) LB and FB corrected for body weight (BW) in WT (n = 10) and TS (n = 8) mice. Data expressed as mean ± SEM. Student's t test for independent samples ** P<0.01.

Figure 3 .

 3 Figure 3. Indirect calorimetry in wild-type (WT) and Ts65Dn (TS) mice fed with standard chow Bar blots depict (A) energy intake (kcal/ LM/h) during the inactive (light phase), active (dark phase) period of the 12 hours light/dark cycle and the daily mean intake. (B) Locomotor activity (beam breaks/h) during 24 h. (C) Energy expenditure, EE (Kcal/Kg LM/h). (D) Respiratory exchange ratio (VCO 2 /CO 2 ). (E) Estimated resting metabolism, ERM (Kcal/Kg LM/h). (F) Energy balance (energy intakeenergy expenditure). WT (n = 5) and TS (n = 6) data are expressed as mean ± SEM. Student's t test for independent samples * P<0.05; ** P<0.01. LM: lean mass; EE: energy expenditure; ERM: estimated resting metabolism.

Figure 4 .

 4 Figure 4. Percentage of change of obesity biomarkers in plasma levels in wild type (WT) and Ts65Dn (TS) mice from basal (t 0 ) levels to 30 minutes after a glucose load (t 30 ). Data are expressed as mean ± SEM. WT (n = 11) and TS (n = 7). WT vs. TS; Student's t test for independent samples * P< 0.05.

  Figure 5A; fat mass: (t (1, 10) = -3.40; P< 0.01; Figure 5B).

Figure 5 .

 5 Figure 5. Body weight gain and body composition in wild type (WT) and Ts65Dn (TS) mice fed with high fat diet. (A) Body weight gain upon 8 weeks of HF (expressed as the difference from baseline body weight in grams). (B) Lean mass (LM) / body weight (g) and fat mass (FM) /body weight (g) in the 8th week. WT (n = 6) and TS (n = 6). Data expressed as mean ± SEM. Student's t test for independent samples ** P<0.01.

Figure 6 .

 6 Figure 6. Indirect calorimetry in wild-type (WT) and Ts65Dn (TS) mice fed with high fat diet (HF). Bar blots depicted (A) intake (Kcal/ Kg LM/h) during the inactive (light phase) and active (dark phase) period of the 12hours light/dark cycle and the daily mean intake. (B) Daily locomotor activity (beam breaks/h). (C) Energy expenditure, EE (Kcal/Kg LM /h). (D) Respiratory exchange ratio (VCO 2 /CO 2 ). (E) Estimated resting metabolism, ERM (Kcal/Kg LM /h). (F) Energy balance (food intakeenergy expenditure). WT (n = 5) and TS (n = 6) data are expressed as mean ± SEM.

( 1 ,

 1 23) = 23.31, P<0.001; Figure 7A; and for HSP72, ANOVA, F (1, 23) = 7.2, P<0.05; Figure 7B). As previously shown in DS patients [23] plasma levels of IL-6 were lower in Ts65Dn compared to WT mice (Figure 7C) but increased significantly upon HF diet in both genotypes (diet effect, ANOVA F (1, 23) = 18.25, P<0.001).

Figure 7 .

 7 Figure 7. Levels of circulating markers of inflammation in wild type (WT) and Ts65Dn (TS) mice in non-obesogenic (standard chow, SC) and obesogenic (high fat diet, HF) conditions. (A) galectin-3 (Gal-3), (B) Heat shock protein HSP72 (HSP72) and (C) interleukin-6 (IL-6). D-E) SC: standard chow; HF: high fat diet. Data are expressed as mean ± SEM. WT (SC) = 6, WT (HF) = 8; TS (SC) = 5, TS (HF) = 4. Two-way ANOVA. # P< 0.05; ## < 0.01; ### P< 0.001 genotype effect; & P< 0.05 diet effect.

Figure 8 .

 8 Figure 8. Hepatic glucose homeostasis though plasma biomarkers of liver injury and in vivo glycogenesis determination in wild type (WT) and Ts65Dn (TS) mice in non-obesogenic (standard chow, SC) and obesogenic (high fat diet, HF) conditions. A-B) Hepatic markers: (A) alanine aminotransferase (ALT) activity and (B) triacylglycerols (TG). (C) Pyruvate tolerance test (PTT). SC: standard chow; HF: high fat diet. Data are expressed as mean ± SEM. For metabolic parameters: WT (SC) = 6, WT (HF) = 8; TS (SC) = 5, TS (HF) = 4. For PTT: WT (SC) = 10, WT (HF) = 10; TS (SC) = 7, TS (HF) = 7. A,B) Two-way ANOVA; & P< 0.05; && P< 0.01 diet effect. C) ANOVA Repeated measures.

Table 1 . Plasma levels of obesity biomarkers markers in wild type (WT) and Ts65Dn (TS) mice in basal conditions (t 0 ) and 30 minutes after a glucose load (t 30 ).

 1 Data are expressed as mean ± SEM. WT (n = 11) and TS (n = 7). WT vs. TS; Student's t test for independent samples.

		Glucose	Insulin	Ghrelin	Leptin	Glucagon	GIP	GLP-1	PAI-1	Resistin
		(mg/dl)	(ng/ml)	(pg/ml)	(pg/ml)	(pg/ml)	(pg/ml)	(pg/ml)	(pg/ml)	(pg/ml)
	WT	195.5 ± 5.86	3658.11 ± 122.07	5595.74 ± 360.27	7193.91 ± 541.18	387.07 ± 15.62	518.16 ± 12.48	55.43 ± 2.28	2876.12 ± 154.90	50159.32 ± 1696.01
	T0
	TS	251.33 ± 4.55	2528.17 ± 135.78	1540.14 ±2.41.39	10173.25 ± 848.14	386 ± 19.51	584.42 ± 30.78	54.53 ± 2	2605.35 ± 221.52	48765.63 ± 1810.21
		P< 0.05 P< 0.05	P< 0.01
	WT	340 ± 13.72	6332.15 ± 200.37	6382.22 ± 476.85	12763.55 ± 1004.05	696.22 ± 37.63	575.37 ± 11.42	121.15 ± 7.41	5155 ± 254.42	79604.79 ± 2515.79
	T30
	TS	470.67 ± 22.03	4709.02 ± 193.55	3421.21 ± 327.81	20783.37 ± 1174.96	511.03 ± 22.28	673.40 ± 31.77	135.86 ± 6.26	4416.92 ± 635.62	97005.72 ± 3480.49
		P< 0.05

Acknowledgements

This work was supported by the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017', the 'CERCA Programme / Generalitat de Catalunya' and the CIBER of Rare Diseases. We acknowledge DIUE de la Generalitat de Catalunya (Grups consolidats SGR 2014/1125), MINECO (SAF2013-49129-C2-1-R), CDTI ("Smartfoods"), INSERM (LR) and EU (Era Net Neuron PCIN-2013-060). We acknowledge EMBO for granting Marta Fructuoso with a short term EMBO fellowship for doing part of this work. The authors also acknowledge the technical platform Functional & Physiological Exploration Platform (FPE) of the Unit "Biologie Fonctionnelle et Adaptative", (Université Paris Diderot, Sorbonne Paris Cité, BFA, UMR 8251 CNRS, F-75205 Paris, France) for metabolic analysis and Karine BAILLY of the Cochin Cytometry and Immunobiology Facility for the luminex 200 analysis.

Highlights

In non-obesogenic conditions, Ts65Dn mice present increased body adiposity and hyperglycemic/hypoinsulinemic phenotypes.

Upon high fat free choice access, Ts65Dn mice gained more weight than WT mice.

Ts65Dn mice present increased glucose-stimulated responses for pro-inflammatory adipokines, resistin and leptin that might account for the observed increase of energy intake.

Independently of the diet, Ts65Dn mice show increased plasma levels of galectin-3 and two inflammatory biomarkers previously correlated with obesity.


Tilahun Yeshambel
email: tilahun.yeshambel@uog.edu.et

Josiane Mothe
email: josiane.mothe@irit.fr

Yaregal Assabie
email: yaregal.assabie@aau.edu.

Amharic Semantic Information Retrieval System

Keywords: Amharic Semantic Information Retrieval System Semantic information retrieval, Query expansion, Complex morphology, Amharic IR resources

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Introduction

Searching information on a huge corpus is one of the common tasks nowadays. Information Retrieval (IR) focuses mainly on the process of matching user queries terms to index terms in order to locate relevant documents from Web or corpus. Matching query terms with index terms is one of the main challenges of IR in many languages [START_REF] Xu | Query expansion using local and global document analysis[END_REF]. Linguistic variation of a natural language and term mismatch affects the effectiveness of IR system. As a result of linguistic variation, some relevant documents for a user need will be omitted from a search result. Natural language processing (NLP) has significant role in many languages IR systems for extracting index and query terms. It is applicable to reduce the space required for indexing and maximizing the retrieval effectiveness by conflating variants of words to a common form [START_REF] Ben | A new stemmer to improve information retrieval[END_REF]. Stemming is one of the typical NLP techniques to handle morphological variants in many languages and is still an active research topic specifically for under resourced languages [START_REF] Coustié | Meting: a robust log parser based on frequent n-gram mining[END_REF][START_REF] Jabbar | Empirical evaluation and study of text stemming algorithms[END_REF]. Furthermore, NLP techniques are employed to handle term matching for semantically related concepts. Semantic text matching in IR is the task of finding semantic similarity between query and document text. Sometimes, since a user information need is imprecise, incomplete and semantically ambiguous, a retrieval system cannot retrieve relevant documents to a query. Query expansion is the task of adding semantically related terms to a given query for improving the performance of IR system. Different approaches have been proposed for expanding query terms such as relevance feedback [START_REF] Lavrenko | Relevance based language models[END_REF], the use of Wikipedia [START_REF] Xu | Query dependent pseudo-relevance feedback based on Wikipedia[END_REF], or resources like thesauri or WordNet [START_REF] Harb | Semantic retrieval approach for web documents[END_REF], and neural network (NN) to capture semantic relationships between words [START_REF] El-Mahdaouy | Semantically enhanced term frequency based on word embedding for Arabic information retrieval[END_REF].

Research and development on Amharic IR lags behind because of morphological complexity of the language, lack of usable NLP tools, resources, and test collection. Despite many works on IR for many languages, few researches have been conducted on Amharic IR. The existing Amharic IR systems face challenges in searching relevant documents because of the morphological complexity and semantic richness of the language. Amharic exhibits complex morphology that poses challenges in NLP and IR [START_REF] Abate | Development of Amharic morphological analyzer using memorybased learning[END_REF][START_REF] Yeshambel | Morphologically annotated Amharic text corpora[END_REF]. The base of Amharic word can be stem or root. The morphological structure (root or stem) one should choose for indexing, matching, and resource construction is an open question in Amharic IR. Relevant documents for an Amharic query may not be retrieved as a result of term mismatch between index terms and query terms. Amharic stem-based indexing and term matching misses some relevant documents because multiple stems exist for variants. For example, the variant , , and have the stems , , and , respectively. On the other hand, a document can possibly be relevant to a user query even if they share semantically similar terms that may even be different variants. For example, 'union'/, 'unity'/, 'fellowship'/, 'coalition'/, 'cooperation'/ and 'collaboration'/ are semantically related words. Both cases, i.e. same variants with different stems and similar semantics with different variants, lead to poor IR performance as relevant documents are missed. Therefore, Amharic retrieval system needs to identify optimal representative of variants and reformulate a user query by expanding initial user query in order to retrieve more relevant documents. In this paper, we investigate the characteristics of the language and suggest root forms of words to handle morphological variations to increase the quality of indexing and the probability of matching between index and query terms. Furthermore, we investigate the impact of query expansion on Amharic semantic IR using word embedding and WordNet.

The rest of this paper is organized as follows. Section 2 briefly describes the characteristics of Amharic language. Section 3 discusses related work. Section 4 presents the design of Amharic semantic IR system whereas Sect. 5 presents Amharic resources that we constructed. Experimental results are discussed in Sect. 6. Finally, conclusion and future research directions are forwarded in Sect. 7.

Amharic Language

Amharic is the official language of Ethiopia that has a population of over 118 million at present [START_REF]Countrymeters: Ethiopian population[END_REF]. It has been used as a working language of the government of the country for a long time. As a result, its rich literary heritage has endowed the language with huge written resources and it serves as a lingua franca of the country. Amharic belongs to Semitic language families and has its own script which has alphabet, numbers and punctuations. The alphabet has 33 basic characters and each of them has 7 different forms representing consonant-vowel combination. The vowels are , , and

. For example, consonant-vowel combination of the base character has the following modifications:

, and . Furthermore, the alphabet has labialized characters such as , and . Their structure is consonant-vowel-vowel combinations.

Amharic is morphologically rich and complex agglutinative language. It is considered as one of the most prolific languages [START_REF] Abate | Development of Amharic morphological analyzer using memorybased learning[END_REF]. Clitics such as prepositions, articles, conjunctions, and pronouns are glued to nouns, verbs, and adjectives. The internal structure of a word may include the base (i.e. stem or root), affixes, and patterns. Amharic word may contain many affixes which are attached in complex rules. An Amharic word can represent a sentence in another language. For example, 'she broke them'/ is an agglutination of the verbal stem ( 'broke'/), subject marker pronoun (

), and object marker pronoun ( ). Amharic words can be classified as derived and non-derived. Derived words are formed from other word classes through derivational process. The word formation in both cases usually involves change in one or more characters of a stem or root. The change arises as a result of making a word for case, gender, number, tense, person, mood, etc.

Related Work

Conventional Information Retrieval

Although Amharic is widely used in Ethiopia, the status of IR system development for the language is relatively at rudimentary level. The retrieval effectiveness of stembased and root-based text representations on Amharic language are studied in [START_REF] Alemayehu | The effectiveness of stemming for information retrieval in Amharic[END_REF]. Experiments were carried out by running 40 queries on 548 documents using OKAPI system and the study concluded that stem-based retrieval is slightly better than rootbased one. Amharic search engine was developed using stems and tested by running 11 queries on 75 news documents [START_REF] Mindaye | Design and implementation of Amharic search engine[END_REF]. The average precision and recall values were 0.65 and 0.95, respectively using OR operator for query terms, and 0.99 and 0.52, respectively using AND operator. Arabic is a Semitic language for which relatively more IR research is conducted. For example, Al-Hadid et al. [START_REF] Al-Hadid | Arabic information retrieval system using the neural network model[END_REF] developed a neural network-based model where documents and queries are represented using stems and their similarity is computed using cosine similarity. The effectiveness of Arabic word-based, stem-based, and root-based representation of documents and queries was investigated by Musaid [START_REF] Musaid | Arabic information retrieval system-based on morphological analysis (AIRSMA): a comparative study of word, stem, root and morpho-semantic methods[END_REF].The word-based and stem-based representations miss relevant documents while root-based one retrieves non-relevant documents. The effects of stem and root on Arabic search engine was also compared by Moukdad [START_REF] Moukdad | A comparison of root and stemming techniques for the retrieval of Arabic documents[END_REF].The results indicated that stemming is more effective than root. A comparison between stem-based and root-based Arabic retrieval was made by Larkey et al. [START_REF] Larkey | Light stemming for Arabic information retrieval[END_REF]. The finding indicates that light stemmer outperforms root analyzer and other stemmers which are based on detailed morphological analysis. Ali et al. [START_REF] Ali | An intelligent use of stemmer and morphology analysis for Arabic information retrieval[END_REF] investigated the effect of morphological analysis on Arabic IR.

A rule-based stemmer was used to extract the root/stem of words to be used as indexing and searching terms. The results showed slight improvement on IR effectiveness due to the stemmer. Hebrew is one of the Semitic languages spoken mainly in Israel. Ornan [START_REF] Ornan | A morphological, syntactic and semantic search engine for Hebrew texts[END_REF] designed Hebrew search engine by applying a rule-based morphological analysis. The design of the search engine takes into account the construction of a morphological, syntactic and semantics analyzer. Words unsuited for the syntax and the semantic of a sentence were removed.

Semantic Information Retrieval

Amharic semantic-based IR using BM25 was developed [START_REF] Getnet | Amharic information retrieval based on query expansion using semantic vocabulary[END_REF]. Documents and queries were processed by a stemmer. The system was evaluated by running 10 queries on 8,759 documents and performed an average recall and precision of 0.84 and 0.23, respectively. Fang [START_REF] Fang | A re-examination of query expansion using lexical resources[END_REF] developed and evaluated English semantic retrieval system using WordNet and dependency-thesaurus on TREC test collections. Query terms are expanded considering term relationships and synset definition of a word in the WordNet and mutual information in the collection. The retrieval results indicated that significant improvement was achieved after query expansions using both methods. Better retrieval result was obtained using synset definition of terms. Retrieval based on thesaurus is less effective than definition-based retrieval. The impact of integrating word embedding and entity embedding with and without interpolation within the adhoc document retrieval task was studied and evaluated on TREC collections (ClueWeb'09B and 100 ClueWeb'12B) [START_REF] Bagherid | Neural word and entity embeddings for Ad hoc retrieval[END_REF]. The authors reported that word embedding do not show competitive performance to any of the baselines (relevance model, sequential dependence model and entity query feature expansion) even after interpolation. CBOW method showed better performance than skip-gram for the adhoc document retrieval task. Entity-based embedding performed better than word-based embedding.

Evaluation of Amharic IR Corpora, Resources and NLP Tools

Few studies have been conducted to develop NLP tools and create Amharic corpora resources although IR test collections are required for automatic evaluation of IR system. We can quote a few such studies. Demeke and Getachew [START_REF] Demeke | Manual annotation of Amharic news items with part-of-speech tags and its challenges[END_REF] created Walta Information Center news corpus; Yeshambel et al. [START_REF] Yeshambel | 2AIRTC: the Amharic Adhoc information retrieval test collection[END_REF] built 2AIRTC; and Yeshambel et al. [START_REF] Yeshambel | Morphologically annotated Amharic text corpora[END_REF] created stem-based and root-based morphologically annotated Amharic corpora semiautomatically. The sizes of corpora created by Demeke and Getachew [START_REF] Demeke | Manual annotation of Amharic news items with part-of-speech tags and its challenges[END_REF], Yeshambel et al. [START_REF] Yeshambel | 2AIRTC: the Amharic Adhoc information retrieval test collection[END_REF] and Yeshambel et al. [START_REF] Yeshambel | Morphologically annotated Amharic text corpora[END_REF] are 1,065, 12,586, and 6,069 documents, respectively. Mindaye et al. [START_REF] Mindaye | Design and implementation of Amharic search engine[END_REF] and Samuel and Bjorn [START_REF] Samuel | Classifying Amharic news text using self-organizing maps[END_REF] created Amharic word-based stopword list whereas Alemayehu and Willett [START_REF] Alemayehu | Stemming of Amharic words for information retrieval[END_REF] built stem-based stopwords list. NLP tools such as stemmer and morphological analyzer have crucial role for processing text documents and user information need. Alemayehu and Willett [START_REF] Alemayehu | Stemming of Amharic words for information retrieval[END_REF] and Alemu and Asker [START_REF] Alemu | An Amharic stemmer: reducing words to their citation forms[END_REF] developed rule-based Amharic stemmers. Sisay and Haller [START_REF] Sisay | Application of corpus-based techniques to Amharic texts[END_REF] and Amsalu and Gibbon [START_REF] Amsalu | Finite state morphology of Amharic[END_REF] developed Amharic morphological analyzers using Xerox Finite State Tools (XFST) method. Gasser [START_REF] Gasser | HornMorpho: a system for morphological processing of Amharic, Oromo, and Tigrinya[END_REF] developed rule-based morphological analyzer for Amharic, Tigrignya and Afaan Oromo languages. Mulugeta and Gasser [START_REF] Mulugeta | Learning morphological rules for Amharic verbs using inductive logic programming[END_REF] also developed a morphological analyzer using supervised machine learning approach whereas Abate and Assabie [START_REF] Abate | Development of Amharic morphological analyzer using memorybased learning[END_REF] developed morphological analyzer using memory-based supervised machine learning approach.

In this work, we assess the accessibility, quality, and usability of the existing accessible Amharic IR corpora, resources and tools with the purpose of highlighting the status of Amharic language processing applications. The majority of them are not accessible and have limited functionality and size. They are also inconvenient to use or to integrate in Amharic IR experiments. The existing Amharic NLP tools are not full-fledged systems as they are under prototype stages. For example, the stemmer developed by Alemayehu and Willett [START_REF] Alemayehu | Stemming of Amharic words for information retrieval[END_REF] and Gasser's morphological analyzer [START_REF] Gasser | HornMorpho: a system for morphological processing of Amharic, Oromo, and Tigrinya[END_REF] over-stem and under-stem many words. Moreover, these NLP tools extract basic stems of some words and derived-stems of other words. We tested them using a dataset that contains 200 words from different word classes. The stemmer and the analyzer performed 41.4% and 47.6%, respectively. From our experiments, we observed that the performance of Amharic NLP tools on verbs is less than on other word types. Many of the existing test collections are simply sets of documents without topics and relevance judgment. Furthermore, they are small in size compared to test collections created for other languages. Consequently, they would not be used for accurately testing the performance of IR techniques. In our previous work [START_REF] Yeshambel | 2AIRTC: the Amharic Adhoc information retrieval test collection[END_REF], we developed an Amharic IR test collection that consists in a corpus, topic set and the associated relevance judgment. It allows researchers to evaluate retrieval system automatically though the size is still small relative to standard test collections. The test collection is accessible freely at https://www.irit.fr/AmharicResources/.

Design of Amharic Semantic IR System

Considering the morphological characteristics of the language, we propose a design for Amharic semantic IR system. In the proposed design, the morphological analysis is carried out before stopword removal (see Fig. 1). Morphological analysis is among the key tasks in our IR system as it helps to select index and query terms from documents and queries.

Preprocessing

Preprocessing includes character normalization as well as tag removal and punctuation mark removal. Character normalization is made to represent various characters having similar pronunciation using a single grapheme. The characters and their modifications are normalized to their corresponding modifications of . The character and its modifications are normalized to their corresponding modifications of . The character and its modifications are normalized to their corresponding modifications of . The character and its modifications are normalized to their corresponding modifications of . The fourth orders and are normalized to whereas the fourth orders are normalized to . After character normalization, we segment sentences and tokenize words. Sentence segmentation is carried out using punctuation marks used for marking sentence boundaries whereas word tokenization is performed using space, tags and punctuation marks. Tags and punctuation marks are removed after sentence segmentation and tokenization.

Morphological Analysis

Documents and user information need should be represented appropriately using terms that will be used later for matching query with documents. It is to be noted that indexing terms are weighted based on the word frequency. In IR, most often, the variants of a word are conflated during indexing into a single form. It has the advantage of making the calculation of indexing term frequency straightforward. Therefore, in this research, we study the feasibility of stem-based and root-based document representation with respect to their effectiveness for Amharic IR. Since well-designed Amharic stemmer and morphological analyzer are not available yet, we design a semi-automatic morphological processor to segment words into their morphemes so that the base of words (i.e. stem and root) could be extracted easily and quickly. The two morphological analyses performed in this work are stem-based and root-based morphological analysis using lexicons created by Yeshambel et al. [START_REF] Yeshambel | Morphologically annotated Amharic text corpora[END_REF]. The lexicons are constructed from a corpus. The stem-based morphological process segments stem of a word from the rest of morphemes whereas the root-based morphological process segments root from the rest of morphemes of a word. For example, the stem-based and root-based morphological segmentation of the verb and the noun are presented in Table 1. The morphological annotation of different word classes is further presented in Sect. 5.1.

In Amharic, roots are the base of stems. Multiple verbal stems can be generated from an Amharic verbal root (see Table 2). The stems are generated by using different patterns that insert different vowels between root radicals. However, variants of words that are not derived from verbal roots have only one stem which has the same representation as its root. The stems and roots of words are extracted from stem-based and root-based morphologically annotated corpora, respectively, using Algorithm 1.

Stopword Removal

Stopwords are words that evenly occur in many documents and serve as purpose rather than content. Thus, as they are non-content bearing terms, they are removed from documents and queries in IR systems. As shown in our proposed design (Fig. 1) Amharic stopwords are removed after morphological analysis is carried out on documents and queries, which is different from the design of IR for morphologically simple languages. In morphologically simple languages like English, stopword identification and removal is made before stemming by using stopword list. The conventional trend applied so far for removing Amharic stopwords is also to use a stopword list, and it is carried out before stemming or morphological analysis. However, taking the characteristics of the language into consideration, this is certainly not the most appropriate way. Indeed, Amharic stopwords are characterized by the following three morphological features: (i) they do not necessarily exist as standalone words; (ii) they can accept prefixes and suffixes; and (iii) they may exist as part of Amharic words and serve as prefix or suffix. For these reasons, it is not possible to find and remove all Amharic stopwords unless the morphological structure of words is known. For example, the stopwords 'about'/, 'from'/, and 'not'/ do not appear as a standalone word as shown in the following sample words. The word 'since he brought'/, 'from heart'/, 'did not come'/ are equivalent to , and , respectively. As there could be several sequences of affixes representing various linguistic functions, words can appear in various morphological structures. As a result, Amharic stopwords usually have many variants. For example, the stopword 'other'/ has variants , etc. This indicates that stopword identification and term representation in Amharic IR demands a different consideration than the conventional trend. It means that one could not work with the surface forms of words to identify and remove stopwords. Therefore, we removed them after applying morphological analysis on documents and queries using a stopword list. The stopword list itself is constructed from a corpus after applying morphological analysis. We removed stopwords from stem-based and root-based corpora using our stem-based and root-based stopword lists, respectively.

Indexing

In our system, document processing involves text preprocessing, morphological analysis, stopword removal and indexing. As a result of these processes, we obtain indexed documents. To test the impact of morphological analysis on Amharic IR, word-based, stem-based and root-based indexes were created using Lemur 1 toolkit. The stem-based index was created using basic stems of words while the root-based index was created using the root of words. The number of root-based index terms is less than or equal to stem-based index terms. However, the frequency of a root is greater than or equal to the frequency of the corresponding stem as root form conflates all variants of a word to a single common form. Accordingly, the frequency of terms accurately computed using root forms, which means that index term selection could be appropriately made by making use of the root forms of words.

Word Embedding

One of the main objectives of this work is to create an efficient model on a large Amharic dataset and investigate the impact of query expansion using term embedding on Amharic IR retrieval effectiveness. To this effect, we propose four neural network models using word2vec: stem-based with Continuous Bag of Words (CBOW), stem-based with skipgram algorithm, root-based with CBOW and root-based with skip-gram algorithm. Accordingly, four vector space models are generated, which are used for expanding stem-based and root-based query terms based on semantic similarity of words in stembased and root-based corpora, respectively. The similarity sim between a query term q and a corpus word d is computed using cosine similarity as shown in Eq. [START_REF] Xu | Query expansion using local and global document analysis[END_REF].

sim(q, d ) = i qi.di i qi 2 i di 2 (1)
where qi is vector representation of the i th query term and di is the vector representation of the i th word in the corpus. The top 5 most related terms are used to expand query terms.

Query Expansion

The root-based morphological analysis addresses variation among word variants during exact matching between Amharic documents and queries. However, matching only keywords may not accurately reveal the semantic similarity between a query and a document. To resolve this issue and optimize Amharic IR system, we performed query expansion using vector space model and WordNet. Semantically related terms to each non-stopword of user query are identified based on the word vector space and WordNet. Since there is no publicly available Amharic WordNet, we build the resource to be used only for the title of the topics from 2AIRTC [START_REF] Yeshambel | 2AIRTC: the Amharic Adhoc information retrieval test collection[END_REF]. The WordNet is organized to include terms' synonyms, hypernyms, and hyponyms relationships. The stem-based and rootbased morphological analyses are carried out on words included in the WordNet and a user query. For query expansion, the stem or root of semantically related words from the WordNet are added to the original set of query term(s).

Matching and Ranking

Query term vector for searching is constructed after a query is subjected to preprocessing, morphological analysis, and stopword removal. Here, we applied both semantic-based and exact vocabulary term matching. The system searches documents that contain query terms and semantically related words (i.e. expanded terms). Searching for relevant documents is carried out by matching query terms (representing information need of users) with index terms (representing documents). As documents and query terms are represented using stem and root forms of words, the stem-based query terms are matched against stem-based index terms whereas root-based query terms are matched against root-based index terms. In IR, a given user information need does not uniquely identify one document in the corpus. Instead, many documents might match a query but with different degree of relevancy. For a given query Q and a collection of retrieved documents D, the Lemur toolkit ranks retrieval results based on their possible relevance. The document length and number of matching query terms are taken into consideration. OKAPI BM25 score ranks documents based on Eq. [START_REF] Ben | A new stemmer to improve information retrieval[END_REF].

score (D, Q) = n i=1 IDF(qi). f (qi, D).(k1 + 1) f (qi, D) + k1. 1 -b + b. |D| avgdl ( 2
)
where f (qi,D) is qi's term frequency in the document D, |D| is the length of the document D in words, and avgdl is the average document length in the text collection from which documents are drawn. The variables k1 and b are free parameters whereas IDF (qi) is the inverse document frequency weight of the query term qi. For language modeling, the similarity between a document D and a query Q is measured by the Kullback-Leibler (KL) divergence between the document model Dθ and the query model Qθ.

The KL divergence ranking function captures the term occurrence distributions and it is computed using Eq. ( 3) as:

KL (Qθ, Dθ ) = w∈V p(w|Qθ) log p(w|Qθ) p(w|Dθ) (3)
where w is word, v is word vector, p(w|Qθ ) is estimated query term, p(w|Dθ) is the smoothed probability of a term seen in the document. The ranking of the results of the proposed IR system was evaluated by precision, recall, mean precision and normalized discounted cumulative gain (NDCG). Precision is used to measure how many of retrieved documents are relevant and it is computed as:

Precision = relevant item retrieved retrieved items (4)
Recall measures the ability of an IR system to retrieve all relevant items and it is computed as:

Recall = relevant item retrieved relevant items (5)
Mean Average Precision (MAP) indicates a single-figure measure of quality across multiple queries. The MAP value is obtained by taking the mean of the Average Precision Pav(qi) over all the queries in the set Q and it is computed as:

MAP = 1 |Q| qieQ Pav(qi) (6)
Normalized Discounted Cumulative Gain (NDCG) is used to measure the position of relevant documents in the retrieval set. It is calculated as:

NDCG = DCG IDCG ( 7
)
where DCG is discount cumulative gain, IDCG is the ideal discounted cumulative gain and it is the maximum possible value. These measures are valued between 0 and 1.

Construction of Amharic IR Resources

Context-Based Morphologically Annotated Corpora

Segmenting a word into its morphemes and extracting its base is crucial in many applications. In this work, Amharic surface words are segmented into their morphemes by analyzing the internal structure of words and their contexts. The annotation is made semi-automatically using Amharic lexicons built by Yeshambel et al. [START_REF] Yeshambel | Morphologically annotated Amharic text corpora[END_REF]. For comparison of stem-based and root-based text representations, we created the stem-based and root-based corpora from the same document collection. Words are morphologically segmented into affixes and basic stems or roots. The general annotation structure for a word W is represented as:

p_ * w[ _s] *
where p is a prefix morpheme, '_' is a morphological segment marker, w is the root or stem of W, s is a suffix morpheme, […] denotes optionality, and * denotes the possibility of multiple occurrence. For example, the word can be annotated as follows.

A single word may have multiple annotations when annotated with a single base form. However, among multiple annotations, only one of them could be relevant in a given context. For example, the root-based annotation of the word could be 'their name'/, person having a name 'Simachew'/, 'you listen them'/, and 'having that she kissed them'/. This may lead to incorrect retrieval results. Thus, we identify the context of the word in a sentence during the annotation process. We annotate each word in the corpus with a single annotation by taking the context into consideration and context-based morphologically annotated corpora is constructed for stem-based and root based text representation. Depending on morphological structures, Amharic words can be categorized as derived and nonderived from verbs. Words derived from verbs may have various word classes, but they are morphologically generated from verbal stems or roots. The root forms of such words are represented only by radicals. On the other hand, words that are non-derived from verbs have root forms that contain radicals and vowels. The process of context-based morphological annotation of different word classes is presented as follows.

Stem-Based Morphologically Annotated Corpus. The stem-based morphological annotation segments word forms into more general representation known as basic stem and affixes. The majority of Amharic words are composed of stems and attached affixes. IR systems use the stems of words during indexing and term matching, and thus, we segment the stems of words from the rest of morphemes.

Stem-Based Annotation of Words Derived from Verbs:

The base of many verbs, nouns, adjectives, and adverbs are verbal roots. Stems can be generated from a single verbal root and many words can be generated from a single stem by attaching affixes. For example, the verb 'since they haven't been fast'/, the noun 'from our speed'/, the adjective 'like the fast ones'/ and the adverb 'quickly'/ are derived from the verbal root . These words are generated from three stems ( and ) and their stem-based annotation is shown below.

Stem-Based Annotation of Words Not Derived from Verbs:

Amharic words may also be generated from primary nouns, adjectives, adverbs and functional words. Such words are not formed from verbal roots, and their stem representation is different from that of verbal stems as variants of words that are not derived from verbal stems have a single common basic stem. For example, words derived from the primary noun 'country'/ include 'for our country'/, 'about the country'/, 'national'/, etc. The stem-based annotations of these words are presented as follows.

Root-Based Morphologically Annotated Corpus. Root-based morphological annotation segments the roots of words from other affixes. The annotation helps to investigate the impact of root-based text representation on Amharic IR. The root annotation process for verbal words differs from that of others as presented below.

Root-Based Annotation of Words Derived from Verbs. Verbal words are words derived from verbal roots by inserting new character, palatalizing one or more characters, changing the shape of one or more characters, or adding affixes. For example, the verb 'since they haven't been fast'/, the noun 'from our speed'/, the adjective 'like the fast ones'/ and the adverb 'quickly'/ are derived from the verbal root . The root-based annotations are shown below.

Root-Based Annotation of Words Not Derived from Verbs:

The root and stem forms are the same for words that are generated from primary nouns, adjectives, adverbs and functional words. The root forms may contain vowel in addition to radicals. For example, the words 'for our country'/, 'about the country'/, 'national'/, etc. are derived from the primary noun 'country'/. The root-based annotations of these words are presented as follows.

Stopword List Construction

We remove Amharic stopwords from the vocabulary using a predefined list constructed based on stem and root forms. For the sake of comparison between stem-based and rootbased text retrieval, both types of stopword lists were created from the annotated corpora based on morpheme statistics involving frequency, mean, variance, and entropy. The values of frequency, variance, entropy and mean of each morpheme in the corpus were used while constructing the stopword list. The top 250 morphemes based on the values of frequency, variance, entropy and mean are selected to create corpus-based stopword lists. However, the final stopword list also contains a few words which were selected manually from other sources considering the nature of the language. In total, 222 morphemes are included in each stopword list. The identified stopwords include prepositions (e.g. 'to'/, 'about'/, 'up to', 'by'/, 'from'/, etc.), conjunctions (e.g. 'and'/, 'however'/, 'here'/, etc.), negation markers ( 'not'/), indefinite articles ( 'an'/), auxiliary verbs ( 'say'/, 'was'/, etc.), 'and so on'/, etc. The stem-based stopword list may contain multiple stems for variants of a word while the root-based list contains only one root form for variants of a word.

Experiment

Implementation

We carried out different experiments on 2AIRTC collection [START_REF] Yeshambel | 2AIRTC: the Amharic Adhoc information retrieval test collection[END_REF]. Python was used to implement preprocessing tasks whereas Lemur toolkit was used for indexing and retrieval. The retrieval effectiveness was evaluated automatically using trec_eval tool which can compute many evaluation measures 2 . LM and BM25 models were used as retrieval models.

Experimental Results

Retrieval with LM and BM25. LM is a popular model for the development of IR systems, but it has not been used in previous Amharic IR ones. Many of them are rather based on vector space model [START_REF] Mindaye | Design and implementation of Amharic search engine[END_REF][START_REF] Getnet | Amharic information retrieval based on query expansion using semantic vocabulary[END_REF][START_REF] Argaw | Amharic-English information retrieval[END_REF][START_REF] Munye | Amharic-English bilingual Web search engine[END_REF]. Here, we investigated the effect of LM model on Amharic IR and compared it with BM25. As shown in Table 3, LM performs slightly better than BM25 model. This is potentially because of the capability of LM to capture term dependency and estimate the probability distribution of a query in each document. This means LM is more suitable retrieval model for Amharic language. The Effect of Stopword Removal on Amharic IR. Experiments were conducted to investigate the effect of morpheme-based stopword removal on Amharic IR. The retrieval effectiveness of stem-based and root-based text representations with and without stopwords are shown in Table 4. As shown in the table, removing stopwords has a positive impact both in stem-based and root-based retrieval using LM.

Retrieval Without Query Expansion. The retrieval effectiveness of the proposed Amharic IR model without query expansion using LM is presented in Table 5. As shown in the table, root-based retrieval is better than stem-based retrieval as the root-based text representation maps all variants to a single common form and can reject non-relevant documents better than stem-based and word-based text representations. The word-based and stem-based methods miss more relevant documents since they cannot handle some morphological variations. The retrieval effectiveness of the three text representations decreases from precision @5 documents to precision @20 due to scarcity of relevant documents in the test collection. The overall recall and precision values of stem-based and root-based text representations are shown in Fig. 2 which has been taken from our previous study [START_REF] Yeshambel | Amharic document representation for adhoc retrieval[END_REF]. The blue line depicts the root-based retrieval effectiveness whereas the red line represents the stem-based retrieval results without query expansion. It can be seen that the retrieval effectiveness of root-based representation outperforms stem-based one.

Semantic Retrieval Using Word Embedding. CBOW and skip-gram learning algorithms were trained by adjusting the parameter settings into the following same values: vector size (300), min_count [START_REF] Harb | Semantic retrieval approach for web documents[END_REF], iter (400), alpha (0.05) and negative [START_REF] Getnet | Amharic information retrieval based on query expansion using semantic vocabulary[END_REF]. By experiment, we found that the best performing window size of CBOW (resp. skip-gram) are 3 (resp.7). Recall and precision of semantic retrieval using word embedding technique on stem-based and root-based corpora are shown in Figs. 3 and4. The green curve depicts stem-based (Fig. 3) and root-based retrieval (Fig. 4) without query expansion and the remaining are after query expansion using word embedding with CBOW and skip-gram algorithms. The retrieval effectiveness of stem-based query expansion based on both algorithms are almost similar. In the case of root-based representation, CBOW model slightly outperforms skip-gram method. However, the retrieval effectiveness after query expansion is reduced in stem-based and root-based representation. A statistical test is made for root-based query expansion using CBOW and Skip-gram models (see Table 6). Semantic Retrieval Using WordNet. The retrieval effectiveness of stem-based and root-based text representations are investigated with the application of query expansion using WordNet. Experimental results are shown in Figs. 5 and6. The red curve is retrieval without query expansion and the others are semantic retrieval with query expansion using WordNet.

Discussion

Comparison of Root and Stem for Retrieval. Both stem-based and root-based text representations improve Amharic retrieval effectiveness in comparison to word-based text representation. Root-based retrieval is better than stem-based possibly due to the following three reasons. First, variants of a word have a single common root but might have more than one stem. For example, the stems of morphological variants , , and are , and , respectively. This creates term mismatch with each other. As a result, stem-based text representation is unable to retrieve all relevant documents and compute the actual term frequency which results in loss of the rank of retrieved relevant documents. However, all variants have one common root 'strong'/. the root-based representation can return more relevant documents than stem-based representation and can compute and increase the actual term frequency which usually leads to better retrieval result at correct rank.

Second, root forms do not conflate semantically unrelated words to a common form. However, the stem-based text representation sometimes conflates semantically unrelated words. For example, is the stem of the verb 'upright' or 'become jealous'/ and the noun 'days'/. However, their roots are and , respectively. Many cases like these occur in the language, and stem-based text representation increases word ambiguities than root-based text representation. Thus, the stem-based text representation is unable to filter out some non-relevant documents.

Third, stem-based retrieval depends largely on user query formulation. Different users will certainly construct the same information need using different word variants. For example, the query 'deforestation' can be constructed as or . After the stembased morphological analysis, the two queries have stem terms and , respectively. a result of variation of the second term, the system will return different results in different ranks. Therefore, stem-based text representation performs differently in our test collection. However, the root-based representation performs equally for all the variants of the query terms as the two queries have the same root terms .

Comparison with Other Amharic IR Systems. Few Amharic IR systems have been developed so far. Some of them are based on stems [START_REF] Mindaye | Design and implementation of Amharic search engine[END_REF][START_REF] Getnet | Amharic information retrieval based on query expansion using semantic vocabulary[END_REF][START_REF] Munye | Amharic-English bilingual Web search engine[END_REF]; while some others are based on citation forms [START_REF] Argaw | Amharic-English information retrieval[END_REF]. The effects of stem-based and root-based text representations are investigated on Amharic IR [START_REF] Alemayehu | The effectiveness of stemming for information retrieval in Amharic[END_REF]. They found that the stem-based representation is better than the root-based representation. They stated that root-based representation maps semantically unrelated Amharic words. However, roots were represented incorrectly in their research. For example, the word 'cotton'/ and 'drink'/ were represented incorrectly as even though their correct roots are and , respectively. Furthermore, the roots of some verbal stems are represented incorrectly. For example, 'hit'/ and 'die'/ were mapped incorrectly to a common root even though the correct roots are and , respectively. On the other hand, they remove vowels from all types of words leading to conflation many semantically unrelated words to the same form. The Google Amharic search engine retrieves different documents in different ranks for basic stems and their derived stems though they are morphological variants. For example, Google search results of the queries 'being broken'/ and 'the process of being broken'/ are different though the same concept is expressed via these two variants. In our work, on the contrary, the stem-based text representation considers only basic stems and provides the same retrieval results for both basic stems and derived stems. We use root-based text representation as it conflates all variants of words to a single common form. In summary, previous studies that recommended the use of stems made their conclusions without thorough investigation on the applicability of roots. Many of them suggested stem-based as the best option. However, due to the complexity of the language stembased representation does not work well. In this work, we have shown that the roots are better than stems for Amharic IR. This is a new finding which was not looked at in previous work.

Comparison of Conventional and Semantic Amharic IR. Even though the proposed morphological analysis (i.e. root-based) has positive impact on Amharic IR, query expansion using word embedding and WordNet does not improve the performance of the system due to term ambiguity. Ambiguous terms are prevalent in the language due to its complex morphology. For example, the term can mean 'the world', 'they targeted', 'they dreamed', 'they developed' or a person with the name 'Alemu'. Accordingly, the term needs to be expanded based on the context of the term in a given query. This affects the retrieval effectiveness of the proposed Amharic IR system. Since Amharic word sense disambiguator is not available yet, we did not integrate disambiguator into our proposed semantic retrieval system. Moreover, stembased representation may not expand query terms though its semantically related words are in the WordNet. This is because a word can have several stems but there could be only one form representing the word in the WordNet. In this the plausible way to organize Amharic WordNet is to make use of root form as it can represent all variants of a word by a single common form. This could not be achieved by stem-based representation. Thus, root-based expansion could work well if word sense disambiguator was integrated in this work. Moreover, the case of word embedding, variants of a given word in the corpus might co-occur with variants of a given semantically related word in different forms. For example, the word might co-occur with , ,

, and within a specified window size. As these words have different stems, the actual co-occurrence frequency based on stem could not be computed correctly. As a result, the similarity between a query term and its semantically related word is lower which affects stem-based semantic retrieval. Furthermore, some expanded terms are related to a query term syntactically rather than semantically. For instance, the expanded terms for the proper noun are , and where their meanings are completely different. Consequently, retrieval using expanded terms sometimes returns more non-relevant documents than the original query retrieval. The overall retrieval effectiveness using expanded terms is lower than retrieval with only original query terms. The other possible reason for lower performance could be the small size of the corpus. However, a promising result was reported in previous Amharic IR research even using stems [START_REF] Getnet | Amharic information retrieval based on query expansion using semantic vocabulary[END_REF].

Comparison of Our Stopword List with Others. Few researches were conducted to build Amharic stopwords. However, classical methods that have been used in many morphologically simple languages such as English are applied without considering the characteristics of Amharic. For example, stopword lists constructed by Mindaye et al. [START_REF] Mindaye | Design and implementation of Amharic search engine[END_REF] and Samuel and Bjorn [START_REF] Samuel | Classifying Amharic news text using self-organizing maps[END_REF] contain variants of a word. However, it is challenging to list all the variants of stopwords. Alemayehu and Peter [START_REF] Alemayehu | Stemming of Amharic words for information retrieval[END_REF] created stopword list based on stem. Though stems are better than word forms to construct Amharic stopwords, it is not the plausible way because of the existence of multiple stems for variants of a stopword. In our case, all the variants of stopword have a single common form. For example, the stopword list created by Alemayehu and Peter [START_REF] Alemayehu | Stemming of Amharic words for information retrieval[END_REF] would contain two stems (

) which are variants of a single word. However, in our case all variants of the stopword are represented by single root 'was'/.

Conclusion

Amharic has complex morphology which poses tremendous challenges for NLP and IR.

In this work, we evaluate the existing Amharic NLP tools and resources, and investigate the implications of the morphological complexity on Amharic IR. After analyzing the gaps, we constructed standard resources and proposed a new Amharic IR system that takes the morphology of the language into consideration. The resources that we constructed are Amharic stopword list and context-based morphologically annotated corpora. They are made publicly accessible to the research community. Furthermore, stembased and root-based morphological features were considered to construct resource, corpora, and develop Amharic IR system. Our findings indicate that root is the optimal form of word representation for Amharic IR development and resource construction. We also investigated semantic-based query expansion based on word embedding and Word-Net. We exploited the deep learning models (i.e. CBOW and Skip-gram) and WordNet to deal with term mismatch in Amharic IR though negative results were obtained due to prevalent term ambiguity. Further research on Amharic IR needs to be conducted by integrating Amharic word sense disambiguation so that only relevant terms are considered during query expansion of words having multiple interpretations.

Fig. 1 .

 1 Fig. 1. Design of Amharic semantic IR system.

Fig. 2 .

 2 Fig. 2. Recall-precision curves of stem-based and root-based retrieval [33].

Fig. 3 .

 3 Fig. 3. Recall-precision curves of stem-based semantic retrieval using word embedding.

Fig. 4 .

 4 Fig. 4. Recall-precision curves of root-based semantic retrieval using word embedding.

Fig. 5 .

 5 Fig. 5. Recall-precision curves of stem-based retrieval using WordNet.

Fig. 6 .

 6 Fig. 6. Recall-precision curves of root-based retrieval using WordNet.

Table 1 .

 1 Sample stem-based and root-based morphological segmentation. 1 1: first person, 3: third person s: singular, p: plural, f: feminine, pre: preposition, foc: focus, pas: passive, nom: nominative, conj: conjunction, neg: negative, gen: genitive, def: definite marker, adj: adjectivizer, pos: possessive, acc: accusative, pal: palatalizer, comp: complement.

	Delete from an annotated document
	End if
	End for
	End for

Input: Affix lists and Annotated corpora Output: Stem-based and root-based Corpora Step 1: Open affix lists and annotated corpora Step 2: For each document in the annotated corpus:

For each word in a document: Segment a word into morphemes using '_' If a morpheme is in affix lists Algorithm 1. Extracting stem and root from corpus.

Table 2 .

 2 Sample of Amharic verbal roots and basic stems of variants.

Table 3 .

 3 Comparison of LM and BM25.

	Model Average	R-precision NDCG Bpref
		precision
	BM25 0.67	0.64	0.83	0.64
	LM	0.70	0.65	0.86	0.66

Table 4 .

 4 Stem-based and root-based retrieval with and without stopwords on 2AIRTC.

	Metrics	Stem-based		Root-based
		With stopword	Without stopword	With stopword	Without stopword
	AMP	0.14	0.51	0.24	0.70
	NDCG	0.37	0.71	0.50	0.86
	Bpref	0.15	0.48	0.27	0.66
	R-prec	0.17	0.49	0.29	0.65

Table 5 .

 5 Retrieval effectiveness based on the three text representations.

	Text	Precision
	representation

Table 6 .

 6 Statistical test for root-based query expansion using CBOW and Skip-gram models.

	Statistical test	Average precision R-precision NDCG Bpref P@5	P@10 P@20
	t-test	0.7123	0.9564	0.7711 0.7778 0.6487 0.8413 0.9282
	Randomized test 0.7043	0.9597	0.7643 0.7763 0.6647 0.8443 0.9345
	Sign test	0.5270	0.9924	0.7718 0.9183 0.6847 0.7576 0.8356

http://www.lemurproject.org.

http://trec.nist.gov/trec_eval.