Nadjet Zemirline
email: nadjet.zemirline@supelec.fr

Yolaine Bourda
email: yolaine.bourda@supelec.fr

Chantal Reynaud²
email: chantal.reynaud@lri.fr

Fabrice Popineau
email: fabrice.popineau@supelec.fr

MESAM: A Protégé Plug-in for the Specialization of Models

Introduction

Nowadays, several efforts are focused on re-using generic platforms to create new systems, in order to make the design process easier and faster. Often, the designer has his own models and resources and would like to reuse the generic system over his resources. That means, he has to integrate his models and resources in the system, and then to directly reuse the generic system. But many problems occur. One of them is that the designer needs to translate his models into the specific format that understood by the system and to use the vocabulary specific to that system. Furthermore, he also needs to translate all the instantiations of his models (i.e. the resources and their metadata). We think that this task is tedious and time-consuming and we want to avoid it. Our objective is to allow the designer to reuse his models (his vocabulary) and his models' instantiations without any change of format or vocabulary. For example, a generic Adaptive Hypermedia System (AHS) is made of a generic adaptation model relying on generic user and domain models. The designer would like to integrate his models and instances in the generic models in order to reuse the generic adaptation engine.

Specific systems can be obtained by specializing the generic models. However, this specialization process is not always easy to perform. It has to be supported to make the design process easier and faster. This paper focuses on assisting designers to specialize generic models using their own models. We aim to automate this process which has been so far entirely manual. Our objectives are twofold: to create a support for defining mappings between elements in generic models and elements in the designer's personal models and to help creating consistent and relevant models integrating the generic and specific ones and taking into account the mappings between them. The proposed approach relies on OWL1 , a W3C standard and SWRL 2 , a W3C proposal.

MESAM plug-in

The approach and MESAM Plug-in functionality

Given two models, a generic model used in a generic system and a specific model provided by a designer, we propose an approach to support the construction of a model that would integrate all the particularities of the specific model and be usable in the generic system. The approach needs the definition of mappings between elements of both models and a validation process at the structural and semantic level. It relies on the designer who has a very good understanding of his model. He will be responsible for semantic validation while all the structural verifications will be done automatically by the plug-in. The main steps of the approach are described below, for more details see [START_REF] Zemirline | A Pattern and Rule-Based Approach for Reusing Adaptive Hypermedia Creator's Models[END_REF]:

1. Specification, by the designer, of the equivalence and specialization mappings between classes of the generic model and the specific model, merging the whole generic model and the mapped classes of the specific model (together with the associated mapping links) in order to obtain a new model.

Automatic computation of additional mappings between classes, the mappings and the linked classes being

added in the model being built. 3. Automatic computation of mappings between elements different from classes, i.e. between attributes and between relations. 4. Validation by the designer of the deductions made by the system in step 3. The designer interacts with the system in steps 1 and 4, these are detailed in section 3.

In order to propose a generic solution that can be used whatever the generic and specific models are, we modelled structural knowledge in a meta-model based on the OWL meta-model 1 , and we performed inferences on knowledge modelled in the meta-model using SWRL rules. The meta-model is loaded inside the plug-in, and the specific and generic models are transformed in instances of the meta-model. We have chosen to keep these processes invisible to the designer. So, in our plug-in, the designer will have the illusion of working on the specific and generic models.

As described in the Figure 1, the plug-in includes two parts. First, a knowledge part gathers the metamodel and deduction rules, for more details see [START_REF] Zemirline | A Pattern and Rule-Based Approach for Reusing Adaptive Hypermedia Creator's Models[END_REF]. All these components are reusable across applications. Second, the processing part is made of some components performing interaction with an inference engine (in our case Jess) and the OWL Protégé editor. We have used the OWL Protégé API to manipulate OWL models, as editing OWL models or the generation of meta-model instances from OWL models, and the SWRL Jess Bridge 2 to execute SWRL rules using the Jess inference engine 3 .

Interaction with MESAM Plug-in

Here, we describe how a designer can interact with the plug-in, what facilities the plug-in proposes to guide him in order to obtain a consistent merged model. At first, the developed plug-in provides an interface allowing the designer to indicate the OWL files of both the generic and specific models to be merged (cf. (1, 2) Fig. 2). Then, it guides the designer through the other steps of the process. These steps are described below.

Specification of equivalence or specialization mappings

The developed plug-in proposes a vision of the reused generic and specific models in separate windows, as it can be seen in (cf. (3) Fig. 2). The designer can specify either equivalence or specialization mappings between the classes of the two models (cf. (4) Fig. 2). Given these correspondences, additional correspondences between classes, properties or relations are generated using a reasoning module. The hypothesis underlying this work is that it is much easier for designers to specify simple correspondences from small numbers of classes in the models, and then evaluate mappings returned by the system. The consistency of the merged model is automatically checked.

Validation of structural deductions

After running the deduction process (cf. ( 5) Fig. 2), the plug-in deduces mappings and inconsistency problems.

The different deductions are presented in separate windows as it can be seen in (cf. (6, 7, 8) Fig. 2). The designer can confirm, choose the right one among several solutions, and view inconsistency problems that are in the specific model to eventually modify it outside the plug-in. Problems can be exported in text file (cf. (9) Fig. 2). If no inconsistency problems are deduced, the plug-in proposes the creation of the merged model (cf. (10) Fig. 2).

Figure 2 describes the different mappings we defined and the results obtained by the deduction process. Specialization mappings have been defined by us between the classes Historical_Representation and History, and between User_representation and User (cf. (4) Fig. 2). The deduction process has deduced new additional mappings and inconsistency problems. One of these mappings is the relation has_Required_Attribute, a sub property of the relation has_First_Name (cf. (6) Fig. 2). An inconsistency problem has been discovered between the relations has_Acquired_attribute and has_Optional_attribute (a cardinality problem) (cf. (7) Fig. 2) and a missing mapping is indicated for the relation of the generic model has_Knowledge_attribute (cf. (8) Fig. 2). The user can save all inconsistency problems and missing mappings in a text file.This text file will contain the defined mappings between classes and all found problems.

Related works

There are several approaches for performing a semantic integration depending on the degree of integration usually referred to as ontology mapping, aligning or merging. The process of ontology merging takes as input two (or more) source ontologies and returns a merged ontology based on the given source ontologies. Several systems and frameworks for supporting the knowledge engineer in the ontology merging task have been proposed [START_REF] Stumme | FCA-MERGE: bottom-up merging of ontologies[END_REF][START_REF] Gomez-Perez | A survey on ontology tools[END_REF][START_REF] Noy | The PROMPT Suite: Interactive Tools for Ontology Merging And Mapping[END_REF]. These approaches are based either on instances of the two given ontologies that are to be mapped (bottom-up) or on concepts (top-down). None of them have been used to merge abstract models with specialized ones. In this paper, we focus on this specific point. The models to be merged are relatively small. The merging process is performed once at the design time. Generic models are composed of abstract classes which have no instances. The designer of the system knows the models to be integrated in the system very well and can then provide simple correspondences between their elements.

Conclusion and future works

Throughout the MESAM plug-in, we have proposed a solution enabling the designer to reuse its own models and consequently his own resources and their metadata (the instantiations of the models) to be reused by the generic system. The MESAM plug-in allowed us to make some experiments in Adaptive Hypermedia Systems. The first one has been done in the e-learning domain aiming at building a user's model, which is usable in the GLAM system [START_REF] Jacquiot | GLAM: A generic layered adaptation model for adaptive hypermedia systems[END_REF]. We have personally played the role of a designer. A user model developed by our team at Supélec [START_REF] Jacquiot | GLAM: A generic layered adaptation model for adaptive hypermedia systems[END_REF] has been chosen as a specific model. As future work, we plan to add several extensions for the plug-in. First, we intend to consider inconsistency problems and to help the designer in resolving them. Then, we intend to extend our plug-in, taking into account disjunctions.

2Fig. 1 Fig. 2

 12 Fig. 1 Architecture of the MESAM plug-in

http://www.omg.org/docs/ad/05-09-08.pdf inria-00432999, version 1 -19 Nov

Author manuscript, published in "International Protege Conference (2009)"


Clement Delmas

François Roubille

Nicolas Lamblin

Laurent Bonello

Guillaume Leurent

Bruno Levy

Meyer Elbaz

Nicolas Danchin

Sebastien Champion

Pascal Lim

Clement Delmas1

François Roubille2

Nicolas Lamblin3

Laurent Bonello4

Guillaume Leurent5

Bruno Levy6

Meyer Elbaz1

Nicolas Danchin7

Sebastien Champion8

Pascal Lim9

Francis Schneider10

Alain Cariou11

Hadi Khachab12

Jeremy Bourenne13

Marie- France Seronde14

Guillaume Schurtz3

Brahim Harbaoui15

Gerald Vanzetto16

Charlotte Quentin17

Xavier Delabranche18

Nadia Aissaoui19

Nicolas Combaret20

Stephane Manzo- Silberman21

Danka Tomasevic22

Benjamin Marchandot23

Benoit Lattuca24

Patrick Henry21

Edouard Gerbaud25

Eric Bonnefoy22

Etienne Puymirat7

Baseline characteristics

Keywords: , management, and predictors Cardiogenic shock, Epidemiology, Mortality, Organ support

Introduction

Cardiogenic shock (CS) is a syndrome caused by a primary cardiovascular disorder in which inadequate cardiac output results in life-threatening tissue hypoperfusion associated with tissue oxygen metabolism impairment and hyperlactataemia which, depending on the severity, can result in multi-organ dysfunction and death.1 While in clinical practice there is a spectrum of presentations, clinical trials have used specific haemodynamic parameters to define CS. The common definition of CS in the SHOCK trial2 requires the presence of three major haemodynamic parameters: (i) persistent hypotension defined as systolic blood pressure (SBP) <90 mmHg (or mean arterial pressure 30 mmHg below the baseline), (ii) decreased cardiac index (<1.8 L/min/ m 2 without support or <2.2 L/min/m2 with support) with, (c) adequate or elevated filling pressure (left ventricular end-diastolic pressure >18 mmHg or right ventricular end diastolic pressure >10 to 15 mmHg). These haemodynamic parameters, while necessary when enrolling patients in clinical trials, may not be universally applicable in clinical practice.

Inclusion criteria used to define CS in a study are not the only parameters of a study population to be considered. The study setting for prehospital, intensive cardiac care unit (ICCU) or general intensive care unit (ICU) provides patients with different characteristics and severity; hence the interest of studies on CS that recruit patients from different settings.

Ischaemic CS that complicates approximately 3-9% of the cases of acute myocardial infarction (AMI) was reported to be the most common cause of CS3-7 and confers a severe prognosis associated with a high hospital mortality rate of 24.6-51.0%.7-9 Data regarding non-ischaemic CS are limited9 probably in part because all the definitions of CS used are based only on AMI CS and the major studies have focused on ischaemic CS to evaluate management (myocardial revascularization, circulatory support and medications).2,3 However, patients with non-ischaemic CS could represent more than 50% of all the cases of CS and may be associated with a better prognosis. [START_REF] Schrage | Patient characteristics, treatment and outcome in non-ischemic vs. ischemic cardiogenic shock[END_REF][START_REF] Schrage | Application of the SCAI classification in a cohort of patients with cardiogenic shock[END_REF][START_REF] Jentzer | Cardiogenic shock classification to predict mortality in the cardiac intensive care unit[END_REF][START_REF] Berg | Epidemiology of shock in contemporary cardiac intensive care units[END_REF][START_REF] Yandrapalli | Cardiogenic shock during heart failure hospitalizations: age-, sex-, and racestratified trends in incidence and outcomes[END_REF] The aim of this analysis is to report the baseline characteristics, management and independent correlates of 30 day mortality in patients with CS in routine clinical practice included in the FRENSHOCK multicentre registry, regardless of the aetiology and the initial place of admission.

Methods

Patient population

FRENSHOCK is a prospective multicentre observational study conducted in metropolitan France during a 6 month period between April and October 2016 in ICU and ICCU (NCT02703038). The methods used for this registry have been previously described. [START_REF] Delmas | Design and preliminary results of FRENSHOCK 2016: a prospective nationwide multicentre registry on cardiogenic shock[END_REF] Briefly, the primary objective was to evaluate the characteristics, management, and outcomes of CS patients, with a new modified definition of CS (Supporting information, Table S1) as seen in routine clinical practice, on a nation-wide scale.

All adult patients (≥18 years old) with CS were prospectively included in this registry if they met at least one criterion of each of the following three components: (i) haemodynamic criteria, defined as a low SBP <90 mmHg and/or the need for maintenance with vasopressors/inotropes and/or a low cardiac index <2.2 L/min/m2; (ii) left and/or right heart pressure elevation, defined by clinical signs, radiology, blood tests, echocardiography, or signs of invasive haemodynamic overload; and (iii) signs of organ malperfusion, which could be clinical and/or biological. Patients admitted after cardiopulmonary resuscitation were included if they fulfilled previously defined CS criteria. Patients could be included regardless of CS aetiology, and whether CS was primary or secondary. Exclusion criteria were refusal or the inability to consent and a diagnosis of CS refuted in favour of alternative diagnoses, such as septic shock, refractory cardiac arrest and post-cardiotomy CS. [START_REF] Delmas | Design and preliminary results of FRENSHOCK 2016: a prospective nationwide multicentre registry on cardiogenic shock[END_REF] All institutions were invited to participate in the study, including university teaching hospitals, general and regional hospitals, public and private hospitals that manage CS patients (ICCUs, surgical ICUs, medical ICUs and general ICUs). The study was conducted in accordance with the guidelines for good clinical practice and French law. Written consent was obtained for all the patients. The data recorded and their handling and storage were reviewed and approved by the CCTIRS (French Health Research Data Processing Advisory Committee) (n° 15.897) and the CNIL (French Data Protection Agency) (n° DR-2016-109).

Data collection

Data on baseline characteristics, including demographics (age, gender, body mass index, social status), risk factors (hypertension, diabetes, current smoking, hypercholesterolemia, family history of coronary artery disease), and medical history [cardiomyopathy, myocardial infraction (MI), stroke, peripheral artery disease (PAD), chronic kidney disease (CKD), active cancer, chronic obstructive lung disease], were collected as previously mentioned.15 Clinical, biological, and echocardiographic data within the first 24 h after admission were collected. Up to three CS triggers were determined for each patient by the local investigator, that is, ischaemic (Type 1 or Type 2 AMI according to European guidelines); ventricular and supraventricular arrhythmia; conduction disorder; infectious disease; non-compliance (poor compliance with medical treatment or hygiene and diet rules, for example, stopping or skipping an angiotensin-converting enzyme inhibitor or beta blocker treatment, deviation from a low sodium diet, etc.); or iatrogenesis. Investigators could also note other existing factors or aetiologies. Such triggering factors were indicated as 'other'. Information regarding the use of cardiac procedures, that is, coronary angiography and/or percutaneous coronary intervention (PCI); right heart catheterization; the need for medications (inotropes, vasopressors, diuretics, and fibrinolysis) and organ replacement therapies such as mechanical ventilation (invasive or non-invasive); temporary mechanical circulatory support [intra-aortic balloon pump (IABP); extracorporeal membrane oxygenation or Impella® (Abiomed, Danvers, MA, USA)]; and renal replacement therapy (continuous or intermittent) were collected.

In-hospital complications, such as stroke, bleeding and transfusions, haemolysis, thrombocytopenia, nosocomial infections, vascular complications, and death, were noted. Information on mortality was obtained directly by the local investigators (cause and date).

Statistical analysis

Continuous variables are reported as means (SD) or medians and interquartile ranges when appropriate. Discrete variables are described in numbers and percentages. Groups (30 day survivors and non-survivors) were compared by analysis of variance for continuous variables and χ2 or Fisher's exact test for discrete variables. Odds ratios (ORs) are presented with their 95% confidence intervals (CIs).

To determine independent predictors of in-hospital all cause mortality, binary logistic regression analyses were used, with a threshold <0.10 for variable elimination. Variables included in the final models were selected ad hoc based on their physiological relevance and potential to be associated with outcomes. Two multivariable analyses were conducted. The first included only variables available on admission: age, gender, type of institution, risk factors, comorbidities, and causes of CS. A sensitivity analysis was performed, and lactate peak was added to the covariates in the main analysis. The second model added in-hospital management variables (first place of admission; respiratory, circulatory, or renal support; use of inotropes, vasopressors, diuretics, and fibrinolysis in the first 24 h; and myocardial revascularization). This analysis was repeated on the subset of patients with ischaemic CS.

Analyses were repeated using forward stepwise analysis to assess the consistency of results. Collinearity was assessed by calculating variance inflation factors. Statistical analyses were performed using IBM SPSS 23.0 (IBM SPSS Inc.). For all analyses, two-sided P values <0.05 were considered significant.

Results

Study population

A total of 772 CS patients were included in 49 centres. Clinical characteristics are presented in Table 1. CS criteria used to define CS are reported in Table S2. The main criteria were hypotension and/or echocardiographic parameters for the haemodynamic criteria, clinical parameters for the overload criteria, and biological parameters for the organ malperfusion criteria. Less than 8% of the patients were diagnosed based on invasive parameters of CS. Mean age of the population was 65.7 (±14.9) years with a predominance of men (71.5%). The rate of hypertension, dyslipidaemia, diabetes mellitus, and current smoking were high, respectively, 47.2%, 35.9%, 28.1%, and 26.7%. A history of cardiac disease was reported in 56.1% (29.8% coronary artery disease), previous PCI in 21.5%, previous stroke in 8.0%, PAD in 11.8%, and CKD in 21.2%.

At admission, mean SBP was 101 (±25) mmHg and mean heart rate was 96 (±30) bpm (Table 2). Clinical signs of left and right heart failure were frequent with 72.5% and 50% respectively. Mottling was reported in 37.5% of the cases.

The main triggers of CS (not mutually exclusive) were ischaemic (36.3%), supra ventricular arrhythmias (13.3%), ventricular arrhythmias (12.6%), and infectious disease (11.9%) (Figure 1). Cardiac arrest occurred in 10.3% of patients. Most patients had multiple organ failure as evidenced by kidney dysfunction, hepatic cytolysis and cholestasis, and lactate elevation.

Seven hundred and sixty patients (98.5%) had an echocardiography at admission. Left ventricular ejection fraction (LVEF) <40% was reported in 80.7% of the patients and <30% in 61.4%. S3.

Vitals parameters at 24 h after admission are detailed in Table

In-hospital management

In-hospital management is reported in Table 3. Most patients were directly admitted in ICCU (56.1%); 23.1% were admitted in ICU, and 13.3% were transferred from another centre (emergency or another department).

Intravenous diuretics were used in 82% of the cases. Dobutamine was the most used inotrope (81.9%) at low to intermediate doses (5-10 μg/kg/min). Norepinephrine was used in 53.1% of the patients, a dobutamine-norepinephrine combination in 45.6%, and epinephrine in 12.4%. Use of levosimendan (7.4%), dopamine (0.3%), milrinone (1.8%), and enoximone (0.4%) was minimal. Use of invasive and non-invasive ventilation was 37.9% and 25.9%, respectively. Circulatory support was provided in 18.6% of the cases. Extracorporeal life support was the most frequent type of assistance (85/143, 59.4%). Finally, renal replacement therapy was provided in 15.8% of the cases. Overall, 41.7% had no organ support (no respiratory, or circulatory support, and no renal replacement therapy).

Coronary angiography was performed in 399 patients (51.7%), 63.7% of whom had significant coronary disease (one-vessel, two-vessel, and three-vessel disease in 20.1%, 22.8%, and 21.8%, respectively). A PCI was performed for 54.4% of those who had a coronary angiogram.

Thirty-day outcome and correlates

In-hospital complications are listed in Table S4. Pneumonia was the most frequent infection reported (19.7%) and the rate of severe bleeding was 12.4% (mainly digestive bleeding) (Figure 2). At Day 30, two patients were lost to follow-up (0.26%). Mortality rate at 30 days was 26.0% in the overall population and differed according to initial place of admission [22.2% for ICCU, 31.5% for ICU, and 36.4% for patients transferred from another centre (emergency or another department); P = 0.04]. Mortality at 30 days was numerically higher but non-significant with a higher number of CS triggers (21.4%: absent, 27.3%: one, 27.5%: two, and 37.5%: three or more).

Baseline characteristics and 30 day mortality

Clinical characteristics according to vital status at 30 days are presented in Table 1. Overall, non-survivors at 30 days were older (70.4 ± 14.3 years vs. 64.0 ± 14.8 years) with similar gender ratios. No significant difference was observed in risk factors and comorbidities except for CKD (30 day survivors: 18.2%; 30 day non-survivors: 29.9%).

Cardiogenic shock mortality also depended on the trigger (Figure 1). In the overall population, ischaemia was the most frequent CS trigger while infectious disease was more frequent in non-survivors (18.9% vs. 9.5%). The proportion of cardiac arrests was numerically higher in non-survivors. In resuscitated cardiac arrest patients, mortality was 31.6% (and 48.8% in non-ischaemic cardiac arrest).

At admission, non-survivors had a lower SBP and diastolic blood pressure, a higher heart rate and less sinus rhythm (Table 2). They had a lower LVEF (24.5% ± 13 vs. 27% ± 13.5) and more severe aortic stenosis (7.1% vs. 3.9%). They had higher arterial blood lactate and inflammatory markers (C-reactive protein), more severe kidney failure and lower haemoglobin.

Outcomes in relation to early management

In-hospital management according to vital status at 30 days is presented in Table 3. The rate of intravenous diuretics was numerically higher in survivors group (83.9 vs. 76.6%). Dobutamine was prescribed for 85.6% of the non-survivors compared with 80.6% of the survivors, for whom numerically, higher doses (>15 μg/kg/min) were fewer. Epinephrine and norepinephrine were used more often in non-survivors. Invasive respiratory support was more frequent in non-survivors (46.2% vs. 35%) as were renal replacement therapy and circulatory support.

Coronary angiography was performed in 55.7% of survivors and 40.3% of non-survivors (P < 0.001). However, the number of diseased vessels and the rate of PCI were similar in both groups.

Most in-hospital complications were similar in survivors and non-survivors, except for stroke (non-survivors: 9%; vs. survivors: 3.2%) (Table S4). Pneumonia was the most common infectious complication in both groups (19.7%).

Independent correlates of 30 day mortality

Factors related to 30 day mortality are reported in Tables 4 and5. At admission, age (per year: OR 1.03, 95% CI: 1.02-1.05), infectious trigger (OR 2.10, 95% CI: 1.26-3.50), and LVEF <30% (OR 1.79, 95% CI: 1.21-2.64) were independently associated with higher mortality at 30 days. High lactate levels (>4 mmol/l) were also associated with higher mortality (OR 2.07, 95% CI: 1.19-3.58).

With full adjustment (Model 2), independent factors associated with mortality at 30 days were (Table 5): age [per year (OR 1.06, 95% CI: 1.04-1.08)], LVEF <30% (OR 2.15, 95% CI:

1.40-3.29), circulatory support (OR 1.92, 95% CI: 1.12-3.29), renal replacement therapy (OR 2.72, 95% CI 1.65-4.49), and the use of norepinephrine (OR 2.55, 95% CI: 1.69-3.84) and of diuretics (OR 1.74, 95% CI: 1.05-2.88). In addition, invasive (OR 1.14, 95% CI: 0.68-1.90) and non-invasive respiratory support (OR 0.83, 95% CI: 0.51-1.34), PCI (OR 0.64, 95% CI: 0.41-1.01) and dobutamine (OR 1.27, 95% CI: 0.74-2.19) were not associated with 30 daymortality in our analysis.

Similarly, in patients with ischaemic CS, the use of PCI was not associated with a significantly lower mortality (OR 0.63, 95% CI: 0.32-1.25).

Table S5 describes all ORs and 95% CIs of all variables tested in Models 1 and 2.

Discussion

To date, the FRENSHOCK registry is the largest European prospective, observational multicentre study on CS that describes a contemporary cohort of unselected patients with CS, from a broad spectrum of aetiologies. The main findings and the originality of this study are the diversity of patient profiles and CS aetiologies linked to the inclusion of patients in different departments (ICU, ICCU, etc.). Although ischaemia remains the primary trigger, it represented only 36.3% of the causes of CS. Secondary, mortality rate at 30 days was 26.0% in the overall population but varied according to the trigger and the first place of admission (ICU, ICCU, transfer from another hospital), and ranged from 16.7% to 48.0%. Finally, six independent factors (age, LVEF <30%, circulatory support, renal replacement therapy, the use of norepinephrine and diuretics) were associated with higher mortality at 30 days.

An inadequate definition of cardiogenic shock in current practice

Numerous definitions of CS have been suggested, but it is broadly recognized as a state of low cardiac output resulting in end-organ hypoperfusion. The definition of CS has evolved over the years from persistent hypotension (starting with SBP < 80 mmHg, followed by SBP < 90 mmHg or use of pressors to maintain a SBP > 90 mmHg) to several haemodynamic parameters described in the SHOCK trial.2 These haemodynamic parameters may not be universally applicable in clinical practice. In our registry, the first mean SBP was high for a CS population (101 ± 25 mmHg) with a high rate of patients with SBP ≥ 90 mmHg (507/772). Of these, 463 patients (91.3%) had vasopressor or circulatory support. This indicates that patients were included at different times after CS.

The common definition of CS is binary (CS present or absent), while in fact CS should be considered as a continuum from mild haemodynamic perturbations observed in pre-shock or mild shock, progressing to shock, profound shock, and finally refractory shock which invariably results in death. [START_REF] Bellumkonda | Evolving concepts in diagnosis and management of cardiogenic shock[END_REF] A new five-stage CS classification was recently proposed by the Society for Cardiovascular Angiography and Interventions (SCAI) (A: 'At risk'; B: 'Beginning'; C: 'Classic'; D: 'Deteriorating'; E: 'Extremis') to depict the entire spectrum of CS and predict the risk of death in ICCUs.12,17 It may be difficult to use a posteriori in our registry and would therefore require adaptation as in the recent Cardiogenic Shock Working Group publication.18 Our pragmatic and practical definition of CS is based on simple criteria available at the patient's bedside in any centre, regardless of the level of expertise, allowing rapid recognition of CS from among the different CS aetiologies and presentations without excluding non-ischaemic aetiologies or unusual presentations (low cardiac output without severe hypotension, right ventricular failure) which may concern almost 40 to 50% of CS cases.

Clinical characteristics compared with previous European observational studies

Clinical characteristics of the FRENSHOCK population show similarities with previous large European multinational observational studies (219 patients in 6 centres in the CardShock study, and 195 patients in 211 centres in 21 countries in the ESC Heart Failure Long-Term Registry).19,20 Table S6 compares main characteristics of these studies. Mean age (66 vs. 67 years), percentage of men (72% vs. 60 to 74%), risk factors, and medical history are similar. As in the CardShock study, our patients were included from emergency departments, ICCU, and ICU; although in other studies inclusions were only in cardiology departments.11,12,20 But inclusion criteria were different and more restrictive in CardSHOCK with exclusion of CS caused by ongoing haemodynamically significant arrhythmias, although these are frequent in our registry (13.3% supra ventricular arrhythmias and 12.6% ventricular arrhythmias as CS triggers), and easily reversible causes potentially associated with a better prognosis. Moreover, in CardShock, patients were enrolled within 6 h of detection of CS, which explains the difference regarding biological and clinical presentation, especially haemodynamic parameters (e.g. SBP).

Cardiogenic shock triggers

Cardiogenic shock was long forgotten by cardiology research before being restricted to the form secondary to MI.4-7 This can be explained by the fact that the underlying pathology and management differ from non-ischaemic cases. The rate of ischaemic CS (36% in our study) is variable according to the study, but classically stays the main aetiology (57.9 to 80.8%)19,20 even if other studies have indicated more heterogeneous and non-ischaemic aetiologies (42.3 to 56.9%).11-13 Non-ischaemic CS can be caused by a variety of diseases or triggers which lead to severe myocardial dysfunction (either through acute decompensation in chronic heart failure or de novo).12,20 In our population, ventricular and supra-ventricular arrhythmias and infectious triggers were frequent and concerned respectively 12.6%, 13.3%, and 11.9% of our CS cases (CS precipitants in respectively 18.9%, 26.2%, and 24.6% in the ESC Heart Failure registry).20 Unfortunately, the causes of non-ischaemic CS are rarely specified in the available studies and registers, and direct comparison is therefore impossible.11-13

Mortality and predictors of hospital death

The mortality rate observed in the FRENSHOCK registry (26%; range 16.7% to 48% according to the cause of CS) is lower than in previous studies where hospital mortality was between 30% and 40%.13,18-21 The comparatively low short-term mortality observed in FRENSHOCK can be explained by several factors. First, it could be secondary to the FRENSHOCK definition used, but our 30 day mortality was quite similar regardless of the usual CS definitions used (respectively 26.8% and 28.3% for patients meeting the ESC-Heart failure and IABP-Shock2 definitions). Second, our inclusions were in 2016 and more recent than previous retrospective analyses which included patients for longer periods between 2005 and 2017.11,12,20 Third, our lower mortality is related to differences in presentation, especially less severe baseline biological and haemodynamic parameters. This was in part related to the timing of inclusion relative to the time of shock onset, and to the variety of recruiting units (ICCU and ICU), as illustrated by the lower mortality in ICCU. In addition, our registry does not make it possible to differentiate isolated CS from mixed shocks, which may explain part of the observed differences in mortality compared with previous studies. The wider use of RHC could have helped to better classify these patients, but remains little carried out in France to date, even if recent US data found a possible link between its use and the short-term prognosis of CS patients.22 Another factor might be that the in-hospital mortality rate for patients with non-acute coronary syndrome CS decreased between 2005 and 2014 from 42.4% to 23.3% as suggested in a large database (8 333 752 hospitalizations for heart failure) in the United States (P value for trend <0.001).14 Data related to the prognosis of CS according to ischaemic or non-ischaemic trigger have shown conflicting results.10,13,14,21 However, ischaemic CS is mainly associated with lower in-hospital mortality except in the CardShock study.19 An improvement in survival has been observed over the past two decades, attributed to the introduction of routine percutaneous revascularization in AMI and modern intensive care. But in our analyses, ischaemic cause was not associated with improved mortality and the impact of PCI on survival at 30 days was not significant as in the CardShock study.

In the multivariate analysis, four independent factors at admission (age, LVEF <30%, lactate ≥4 mmol/L, infectious trigger) were associated with higher mortality at 30 days. In the CardShock study, the predictors of in-hospital death were different and included: prior coronary artery bypass surgery, ACS aetiology, confusion, previous MI, blood lactate, LVEF, age and SBP. [START_REF] Harjola | CardShock study investigators; GREAT network. Clinical picture and risk prediction of short-term mortality in cardiogenic shock[END_REF] Other previously published studies have identified diverse factors associated with short-term mortality but these were related to CS aetiologies (mainly or only based on ischaemic CS) and the patient inclusion settings (mainly or exclusively in general ICUs).19,23,24 Thus, faced with very similar initial characteristics on admission, it is difficult to predict on simple static elements, the prognosis of patients presenting for miscellaneous CS. In this sense, the use of the recent SCAI classification taking into account the evolution under treatment seems advantageous.11,12

Finally, the level of invasiveness of the treatment reflects the severity of the patient's presentation and the advanced shock state (norepinephrine, renal replacement therapy, and mechanical circulatory support) and is correlated with short-term prognosis without any significant difference depending on the type of support used (IABP, Impella or VA-ECMO for example).

Limitations

As in any observational study, there are limitations to our analysis. First, inclusions were not exhaustive and probably not consecutive in all centres. Moreover, non-inclusions and the reasons for non-inclusion were not presented. Second, patients were enrolled from ICU and ICCU (directly or after transfer from another centre) and we cannot exclude the possibility that severe comorbid, older, or most severe cases with multiple organ failure could have not been transferred for futility even if the older patient included was 98 years old. Moreover, data for patients who died early (before informed consent was obtained) were not collected and recorded in the database because of administrative regulations. This could be a source of bias resulting in an underestimation of mortality. Third, certain aetiologies or triggers of CS were not recorded in the electronic reported form and were therefore not collected (takotsubo, pulmonary embolism, acute decompensation of chronic heart failure, aetiologies of infectious triggers, etc.). Subsequently, a conclusion could not be drawn regarding them. In addition, causality between CS and 30 day mortality cannot be demonstrated. However, we adjusted our results based on well-recognized determinants of short-term outcome and sensitivity analyses confirmed our main findings. The period between admission and CS onset and from CS onset to enrolment in the registry were not recorded which may explain why some parameters described were not as high as expected (e.g. lactate levels could have been high at CS onset and not at inclusion). Finally, other previously identified risk indicators (e.g. confusion, medications, timing of revascularization, and organ support) were not recorded in our database.

Conclusions

Cardiogenic shock is characterized by its diversity in terms of aetiologies and severity. Ischaemic CS remains the main CS trigger but non-ischaemic causes accounted for more than 60% of all the cases. CS is still associated with significant but variable short-term mortality according to the cause and first place of admission, despite more frequent use of haemodynamic support and organ replacement therapies.

Acknowledgements

FRENSHOCK is a registry of the French Society of Cardiology, managed by its Emergency and Acute Cardiovascular Care Working Group. Our thanks go out to all the devoted personnel of Société Française de Cardiologie who actively participate in the upkeep of the registry especially N. Naccache, E. Drouet, and V.Bataille. The authors are deeply indebted to all the physicians who took care of the patients at the participating institutions.

The study was sponsored by the Fédération Française de Cardiologie and was funded by unrestricted grants from Daiichi-Sankyo and Maquet SAS. Complementary grants will be sought for dedicated research projects within the main study.

Funding

Conflict of interest

The authors declare that they have no conflict of interest.


Shuvankar Gupta

Sudip Chakraborty

Vidha Bhasin

Santanu Pakhira

Shovan Dan

Celine Barreteau

Jean-Claude Crivello

S N Jha

Maxim Avdeev

D Bhattacharyya

V Paul-Boncour

Chandan Mazumdar

CoMnCrGa: a novel ferromagnetic material with high spin-polarization for room temperature spintronics

I. INTRODUCTION

Spintronics is a promising area of research for creating more efficient and powerful nanoelectronics. It aims to reduce power consumption and enhance memory and processing capabilities [1][2][3][4]. Materials with high spin-polarization are considered ideal for use in spintronics technology [5]. Half-metallic ferromagnetic (HMF) materials offer 100% spin polarization because of their unique band structure, where one sub-band conducts like a metal and the other acts like a semiconductor [6][7][8][9]. This unique band structure of HMF have made them attractive for a wide range of applications, including spintronics, data storage, magnetic sensors [5], spin valves [10], spin injectors [11], and magnetic tunnel junctions [12]. Out of different existing classes of HMF materials, Heusler alloys occupy a very special place as their electronic structure can be tuned over a wide range by appropriate elemental substitution at different crystal sites. Heusler alloys are generally denoted by the formula X 2 YZ, where X and Y are transition elements and Z is from the s-p group element. These alloys have a L2 1 crystalline structure (Cu 2 MnAl-type), in which the X, Y, and Z atoms occupy 8c, 4b, and 4a sites, respectively, in the Fm 3m space group (no. 225) [START_REF] Felser | Heusler alloys[END_REF]. If one of the X atoms from X 2 YZ is swapped with a different transition element * chandan.mazumdar@saha.ac.in (X ′ ), it will result in a quaternary Heusler alloy with a Y -type crystal structure (LiMgPdSn type, space group: F 43m, no. 216) [START_REF] Dai | [END_REF][15][16][17]. As a result of this substitution, the crystal structure transforms to a lower symmetry space-group, where the 8c site in Fm 3m space group splits into 4c and 4d sites in F 43m space group. Recently, quaternary Heusler alloys attracted a lot of attention of the research community due to their HMF [18][19][20][21][22], spin-gapless semiconductors [23][24][25][26][27][28], spin-semimetal [29], spin glass [30], re-entrant spin glass [31] and bipolar magnetic semiconducting properties [32]. However, the structural disorder, which is quite inherent for Heusler alloy, is known to have a significant impact on the magnetic as well as the electronic structure. The electronic structure of a material is altered by disorder, which changes the density of states, generates local magnetic moments, and narrows the band gap in the half-metallic ferromagnetic materials. These changes result in a reduced magnetic moment, lower magnetic ordering temperature [31,33], weakened magnetic anisotropy, and decreased electrical conductivity. It makes sense that in order to preserve the half-metallic ferromagnetic properties, either the compound must be synthesized without defects or one must find compounds where the properties are resilient to disorder. However, it is crucial to note that while theoretical predictions are promising [22,[34][35][36][37][38][39], there are many more factors that are to be considered before the experimental realizations of materials that can have potential for technological applications. Additionally, many of the predicted systems do not even form in single phase, and to the best of our knowledge, only few such compounds have been experimentally realized so far. Recently, FeM-nVAl [19] and FeMnVGa [20] were found to show robust half-metallicity even in the presence of structural disorder. However, the Curie temperature for FeMnVAl is 213 K and for FeMnVGa is close to room temperature (293 K). For practical applications at room temperature, compounds having high transition temperature are highly desirable. In this study, we have reported a novel quaternary Heusler alloy CoMnCrGa, having the valence electron count (VEC) 25. Combining different experimental tools and theoretical DFT calculations, we have shown that the compound exhibits half-metallic ferromagnetic character despite having site disorder between Mn and Cr atomic sites. The compound orders ferromagnetically at a very hight temperature, 807 K, and hence is perfectly suitable for room temperature practical application in spintronics.

II. METHODS

A. Experimental Methods

The CoMnCrGa polycrystalline material was synthesized through an arc melting process utilizing high purity (>99.9%) constituent elements. An extra 2% of Mn was added to make up for its evaporation during the process. To enhance homogeneity, the sample was melted 5-6 times in an argon environment and turned over after each melting. Both neutron and X-ray diffraction techniques were used to characterize the sample's crystal structure at room temperature. The powder neutron diffraction data with λ = 2.43 Å was collected at 300 K at the ECHIDNA beamline in ANSTO, Australia [40]. Room temperature powder X-ray diffraction pattern was obtained using a X-ray diffractometer (TTRAX-III, Rigaku Corp., Japan) with Cu-Kα radiation. The single-phase nature and crystal structure of the sample were determined through Rietveld refinement [41] of both the neutron and X-ray diffraction data using the FULLPROF software. The transmission electron microscopy (TEM) measurement was performed in a FEI, TECNAI G 2 F30, S-TWIN microscope operated at 300 kV and equipped with a GATAN Orius SC1000B CCD camera. Extended X-ray absorption fine structure (EXAFS) was obtained at the Energy Scanning EXAFS beamline (BL-9) located at the Raja Ramanna Centre for Advanced Tech- [20,31]. High-temperature VSM (Model EV9, Mi-croSense, LLC Corp., USA) was used to measure the magnetization (M) vs. temperature (T) in the range of 300-900 K. M vs. field (H) measurement was carried out using superconducting quantum interference vibrating sample magnometer (Quantum design Inc.,USA) in the magnetic fields up to ±70 kOe at 5 K. Four-probe resistivity measurement in the absence of magnetic field was carried out in a Physical Property Measurement System (Quantum Design Inc., USA).

B. Computational

The enthalpy of formation, electronic structure and spin polarization at 0 K had been determined through DFT calculations using the projector augmented wave (PAW) method [42] implemented in the Vienna ab initio simulation package (VASP) [43,44]. The exchangecorrelation was described by the generalized gradient approximation modified by Perdew, Burke and Ernzerhof (GGA-PBE) [45]. Calculations parameters were discussed in detail elsewhere in literature [19,20,31].

III. RESULTS AND DISCUSSION

A. Electronic band structure calculations

There are four different crystallographic sites in a quaternary Heusler XX ′ YZ : 4a (0,0,0), 4b (0.5,0.5,0.5), 4c (0.25,0.25,0.25) and 4d (0.75,0.75,0.75). In most cases, the sp element Z occupies the 4a position and the transition metals are located on the other three sites. In total, 6 configurations are possible but the permutation of the atoms in the 4c and 4d positions leads in energetically invariant configurations. Hence, only three independent configurations have been calculated. These configurations are defined as Type-1, 2 and 3 and described in Table I. The DFT calculations allow to determine the most stable structure by comparing their enthalpies of formation.

According to our calculations on the ordered structure, Type-2 is the most stable configuration, with Ga in the 4a position and Mn in the 4b position, Cr at 4c and Co at the 4d position. Fig. 1 shows for the calculated spin-polarised band structure and the density of states (DOS) for the Type-2 configuration. As shown in the DOS, the spin-down band exhibits a band gap at the Fermi level (E F ) where the spin-up band is typical of a metal. This unique band structure corresponds to that of a Half-Metallic Ferromagnetic material. This result is confirmed by the very high polarisation of the compound, P = DOS ↑ (EF)-DOS ↓ (EF) DOS ↑ (EF)+DOS ↓ (EF) = 96.1%. The total magnetic moment is estimated to be 1.06 µ B which is in accordance with the Slater-Pauling rule [46][47][48]. All the constituents elements (Co, Mn, Cr and Ga) of the studied compounds belongs from the same row of periodic table and have close-by X-ray scattering factors. As a result, X-ray diffraction (XRD) data is not likely to be reliable for determining the actual crystal structure (discussed in detail in Sec. III C). Extended X-ray fine structure (EXAFS) measurement, however can satisfactorily provide the information about the crystalline disorder, particularly when the disorder is of B2 type [49,50]. On the other hand, EXAFS fails when the disorder is of DO 3 type [51]. Neutron diffraction have already proved their merits for such difficult cases [20,31,33,52,53]. Fig. 3 represents the powder neutron diffraction pattern taken at 300 K. For Heusler alloys, presence of (111) and (200) Bragg peaks in the diffraction pattern are generally considered to be indicative of well-ordered crystal structure. As it can be clearly seen in the diffraction pattern of CoMnCrGa (Fig. 3), both the above mentioned peaks are present, indicating towards an ordered structure. However, despite the presence of ( 111) and (200) Bragg peaks, the intensities of all the experimental data cannot be described by a simple ordered Type-2 structure, as proposed by DFT calculation (Sec. III A) in which Ga is at 4a, Mn at 4b, Cr at 4c and Co at 4d (space group: F 43m, no. 216). We have tried different combinations of anti-site and swap disorder to fit the diffraction data. A swap disorder of 55:45 between Mn (4b) and Cr (4c) sites in the space group: F 43m fits the experimental data very well. The disordered structure is presented in Fig. 4. The shape of the peak is not very well fitted, this can result from some anisotropic peak broadening related to the Mn/Cr disorder. It may however be noted that, as we will see later (Sec. III F), the compound orders magnetically at 807 K, and hence the ND spectra at 300 K would also consists of magnetic contribution. However, for a VEC 25 compound, as is the case of CoMnCrGa, the S-P rule suggests the maximum magnetic moment to be 1 µ B /f.u. (Sec. III F). The contribution to magnetic Bragg peaks are not known to yield more than 2-3% of nuclear Bragg peaks [20,31,33]. Therefore, our analysis of ND spectra at 300 K considering only the lattice Bragg contributions would not be far off from that expected for a paramagnetic state. We have also measured the XRD pattern at room temperature and analysed the data, to check the structural model derived from the neutron diffraction result. Moreover, the shorter wavelength used in XRD measurement, in comparison to neutron diffraction measurement, also helps us to determine the lattice parameter more precisely. Very interestingly, the (111) and (200) Bragg peaks, which are generally considered to be indicative of crystal order and prominently present in the ND data (Fig. 3), are almost completely absent in XRD data. A simulation of XRD pattern considering the perfectly ordered Type-2 structure (Fig. 5) also does not show these Bragg peaks any prominently due to the close by X-ray scattering factors of the constituents elements. The scattering factor for (111) and (200) planes can be written as [30]

F 111 = 4(f Z -f Y ) -i(f X -f X ′ )) F 200 = 4[(f Z + f Y ) -(f X + f X ′ )]
Since all the elements of CoMnCrGa belongs to the same row of the periodic table

, f Z -f Y ≈ 0, f X -f X ′ ≈ 0, f Z + f Y ≈ f X + f X ′ .
Because of the very faint or nondiscernible nature of (111) and (200) peaks in the simulated diffraction pattern of the ordered structure, the experimental XRD pattern can be explained well with ordered structure. However, the XRD analysis also shows almost equally good fit, if not slightly better, if we use the structural model obtained from neutron diffraction analysis. Since the disordered structure can simultaneously explain both the ND as well as XRD data, one is inclined to rely on the structural model obtained from analysis of ND data. Fig. 5 represents the Rietveld refinement of the powder XRD data assuming the disordered structure taken at room temperature. The lattice parameter is found to be a = 5.841(3) Å. 6(c). For a detailed distribution of atomic content, elemental mapping of Co, Mn, Cr and Ga were performed using drift corrected EDX spectrum imaging using STEM-HAADF mode. The STEM-HAADF image in Fig. 6(c) and the corresponding chemical maps from orange-boxed region (marked 3 in Fig. 6(c)) for Co, Mn, Cr and Ga acquired using Co-L, Mn-K Cr-K and Ga-K energy and overlay of three images are presented in Fig. 6(f), respectively. Composite maps of Co, Mn, Cr and Ga were presented in Fig. 6(f) confirm the single phase nature of the material with stoichiometry Co 0.98 MnCr 0.9 Ga 0.92 .

E. EXAFS

In our studied compound, the structural disorder is neither B2-type nor DO 3 -type. To get any insight of structural disorder between Mn (4b) and Cr (4c) atoms, we have probed the system using EXAFS, which in contrast to XRD, concentrates on the atomic surroundings of the chosen atoms. In order to study the local environment of the Co, Mn, and Cr edges, we performed EXAFS measurements on CoMnCrGa.

The normalized EXAFS ((µ(E) versus E) spectra, measured at the Co, Mn, and Cr edges, are shown in Fig. 7. The conventional technique was followed in processing the analysis of the EXAFS data [54]. Briefly, the absorption spectra (µ(E) vs E ) have been transformed to the absorption function χ(E) given as follows to get quantitative information on the local structure.

χ(E) = µ(E) -µ 0 (E) ∆µ 0 (E 0 ) (1)
where µ 0 (E 0 ) is the background made up entirely of bare atoms, ∆µ 0 (E 0 ) is the absorption edge step in µ(E ) value, and E 0 is the absorption edge energy. The following relation converts the energy dependent absorption coefficient χ(E ) to the wave number dependent absorption coefficient:

K = 2m(E -E 0 ) ℏ 2 (2)
where m is the mass of an electron. To increase the oscillation at high k, χ(k) is weighted by k 2 , and the resulting χ(k)k 2 functions are then Fourier converted in R space to produce the χ(R) versus R plots in terms of the actual distances from the absorbing atom's center. The aforementioned data reduction, which includes background reduction and Fourier transformation, was carried out using the ATHENA subroutine, which is a component of the Demeter software package [55]. The CoMnCrGa sample's Fourier converted EXAFS spectra at the Co, Mn, and Cr edges are presented in Fig. 8 as χ(R) versus R plots. The goodness of fit has been determined by the value of the R f actor defined by:

R f actor = [Im(χ dat (ri)-χ th (ri)] 2 +[Re((χ dat (ri)-χ th (ri)] 2 ] 2 [Im(χ dat (ri)] 2 +[Re(χ dat (ri)] 2
(3) where, χ dat and χ th refer to the experimental and theoretical values respectively and Im and Re refer to the 4 dered Type-2 structure (Fig. 3) also does not show these Bragg peaks any prominently due to the close by X-ray scattering factors of the constituents elements. The scattering factor for (111) and (200) planes can be written as [24]

F 111 = 4(f Z -f Y ) -i(f X -f X ′ )) F 200 = 4[(f Z + f Y ) -(f X + f X ′ )]
Since all the elements of CoMnCrGa belongs to the same row of the periodic table

, f Z -f Y ≈ 0, f X -f X ′ ≈ 0, f Z + f Y ≈ f X + f X ′ .
Because of the very faint or nondiscernible nature of (111) and (200) peaks in the simulated diffraction pattern of the ordered structure, the experimental XRD pattern can be explained well with ordered structure. However, the XRD analysis also shows almost equally good fit, if not slightly better, if we use the structural model obtained from neutron diffraction analysis. Since the disordered structure can simultaneously explain both the ND as well as XRD data, one is inclined to rely on the structural model obtained from analysis of ND data. Fig. 3 represents the Rietveld refinement of the powder XRD data assuming the disordered structure taken at room temperature. The lattice parameter is found to be a = 5.841(3) Å.

D. EXAFS

In our studied compound, the structural disorder is neither B2-type nor DO 3 -type. To get any insight of structural disorder between Mn (4b) and Cr (4c) atoms, we have probed the system using EXAFS, which in contrast to XRD, concentrates on the atomic surroundings of the chosen atoms. In order to study the local environment of the Co, Mn, and Cr edges, we performed EXAFS measurements on CoMnCrGa.

The normalized EXAFS ((µ(E) versus E) spectra, measured at the Co, Mn, and Cr edges, are shown in Fig. 4. The conventional technique was followed in processing the analysis of the EXAFS data [42]. Briefly, the absorption spectra (µ(E) vs E ) have been transformed to the absorption function χ(E) given as follows to get quantitative information on the local structure.

χ(E) = µ(E) -µ 0 (E) ∆µ 0 (E 0 ) (1)
where µ 0 (E 0 ) is the background made up entirely of bare atoms, ∆µ 0 (E 0 ) is the absorption edge step in µ(E ) value, and E 0 is the absorption edge energy. The following relation converts the energy dependent absorption coefficient χ(E ) to the wave number dependent absorption coefficient: imaginary and real parts of the respective quantities. The Demeter software package's ATOMS and ARTEMIS subroutines have been used to generate theoretical routes from crystallographic structures and fit experimental data to theoretical simulations, respectively. The best fit of theoretical spectra and experimental data are shown together in Fig. 8, and the best fit parameters are included in Table II. EXAFS data can be fitted very well assuming Type-2 ordered structure. As mentioned earlier (Sec. III B), since the structural disorder we derive from the analysis of ND data is not of B2 type, EXAFS analysis can not detect the atomic disorder present in the system due to the close by scattering factors between Mn and Cr atoms.

K = 2m(E -E 0 ) ℏ 2 (2)

F. Magnetic properties

DFT calculations suggest that the ground state of CoMnCrGa is half-metallic ferromagnetic. To check whether the compound really orders ferromagnetically or not and also to determine the magnetic ordering temperature, if any, we have measured temperature dependence of the magnetization (M ) in presence of 100 Oe applied magnetic field. Indeed, the compound orders ferromagnetically, and the Curie temperature is found quite high, ∼807 K, which making it suitable for any ferromagnetic application at room temperature. The important parameters related to magnetism and spin polarization of CoM-nCrGa are given in Table III.

The other theoretical prediction, i.e. the presence of HMF state can be also checked by measuring the saturation magnetization at low temperature. All the known Heusler based HMF materials are known to follow the Slater-Palling (S-P) rule, which relates the total valence electron count (VEC) with the saturation magnetization. The S-P rule states that the total magnetic moment for Heusler alloy can be expressed as m = (VEC-24) µ B . Since CoMnCrGa has VEC 25, the S-P rule predicts the saturation moment to be (25-24 =) 1 µ B /f.u. The experi-mentally observed saturation magnetization measured at 5 K (Fig. 10) indeed matches very closely to the theoretically predicted S-P rule with 1 µ B /f.u. This value is also in agreement with the DFT calculation (Sec. III A), suggesting CoMnCrGa to have a half-metallic ferromagnetic ground state.

G. Resistivity

HMF materials are also known to show their signatory imprint on the electrical transport properties [19,20,56]. We have measured resistivity, ρ(T), in the temperature regime 5-300 K (Fig. 11). The residual resistivity ratio (RRR=ρ 300K /ρ 5K ) found to be quite low, ∼1.05, similar to that reported earlier in many other HMF Heusler alloys [19,20,57,58]. However, in the present case, the ρ(T) behaviour shows an additional broad minima near 60 K, followed by a slow upturn at further lower temperatures. Such behaviour is generally attributed to the disorder-induced coherent scattering of conduction electrons, known as weak localization [59], and also reported in several Heusler alloys [60][61][62][63].

To analyze the ρ(T) data over the whole temperature range, we have followed the Matthiessen's rule covering various contributing factors in a ferromagnetic material [15]. The said rule considers all the scattering processes to be independent of one another and additive. Accordingly, the total resistivity can be expressed as

ρ(T ) = ρ 0 + ρ P (T ) + ρ M (T ) (4)
where ρ 0 is the residual resistivity resulting from lat- where m is the mass of an electron. To increase the oscillation at high k, χ(k) is weighted by k 2 , and the resulting χ(k)k 2 functions are then Fourier converted in R space to produce the χ(R) versus R plots in terms of the actual distances from the absorbing atom's center. The aforementioned data reduction, which includes background reduction and Fourier transformation, was carried out using the ATHENA subroutine, which is a component of the Demeter software package [43]. The CoMnCrGa sample's Fourier converted EXAFS spectra at the Co, Mn, and Cr edges are presented in Fig. 5 as χ(R) versus R plots. The goodness of fit has been determined by the value of the R f actor defined by:

R f actor = [Im(χdat(ri)-χth(ri)] 2 +[Re((χdat(ri)-χth(ri)] 2 ] 2 [Im(χdat(ri) 2 ]+[Re(χdat(ri) 2 ]
(3) where, χ dat and χ th refer to the experimental and theoretical values respectively and Im and Re refer to the imaginary and real parts of the respective quantities. The Demeter software package's ATOMS and ARTEMIS subroutines have been used to generate theoretical routes from crystallographic structures and fit experimental data to theoretical simulations, respectively. The best fit of theoretical spectra and experimental data are shown together in Fig. 5, and the best fit parameters are included in Table II. EXAFS data can be fitted very well assuming Type-2 ordered structure. As mentioned earlier (Sec. III B), since the structural disorder we derive from the analysis of ND data is not of B2 type, EXAFS analysis can not detect the atomic disorder present in the system due to the close by scattering factors between Mn and Cr atoms.

E. Magnetic properties

DFT calculations suggest that the ground state of CoMnCrGa is half-metallic ferromagnetic. To check whether the compound really orders ferromagnetically or not and also to determine the magnetic ordering temperature, if any, we have measured temperature dependence of the magnetization (M ) in presence of 100 Oe applied magnetic field. Indeed, the compound orders ferromagnetically, and the Curie temperature is found quite high, ∼807 K, making it suitable for any ferromagnetic application at room temperature. The other theoretical prediction, i.e. the presence of HMF state can be also checked by measuring the saturation magnetization at low temperature. All the known Heusler based HMF materials are known to follow the Slater-Palling (S-P) rule, which relates the total valence electron count (VEC) with the saturation magnetization. The S-P rule states that the total magnetic moment for Heusler alloy can be expressed as m = (VEC-24) µ B . Since CoMnCrGa has VEC 25, the S-P rule predicts the saturation moment to be (25-24 =) 1 µ B /f.u. The experimentally observed saturation magnetization measured at 5 K (Fig. 7) indeed matches very closely to the theoretically predicted S-P rule with FIG. 8. Fourier transformed EXAFS spectra of CoMnCrGa taken at Co, Mn and Cr edges. tice flaws, irregularities, etc., and ρ P (T) and ρ M (T) are temperature-dependent factors, representing phonon scattering and magnon scattering, respectively. The phonon scattering term is generally written as

ρ P = A T Θ D 5 Θ D T 0 x 5 (e x -1)(1 -e -x ) dx ( 5
)
where A is the phonon scattering constant and Θ D is the Debye temperature [64]. The standard magnon contribution due to spin-flip scattering (∼T 2 ) are generally not found for HMF materials, as one of the sub-spin band do not have any influence to charge conduction [19,20].

Considering the aforementioned information and taking the magnon contribution to be zero, we have first tried to fit the resistivity data using eqn. 4. The theoretically generated data fit the experimental data very well in the region 300-100 K, however it fails to trace the experimental data in the low temperature region (5-100 K).

The non-adherence of ρ(T) to eqn. 4 is due to the development of another contribution to resistivity at lower temperature that causes a slow upturn in ρ(T), below 60 K as described earlier in the beginning of this section.

To fit the low temperature resistivity data we have used another following equation

ρ(T ) = ρ 0 -BT 1/2 + CT 5 + DT 2 (6)
where -BT 1/2 arises due to the disorder increased coherent scattering of conduction electrons [59], CT 5 is the low temperature phonon contribution (can be easily derived from eqn. 5 for low temperature approximation) and DT 2 is the electron-electron scattering. The eqn. 6 fits the experimental data well in the low temperature regime (inset of Fig. 11). The absence of magnon contribution in the temperature dependent resistivity data indirectly suggests towards presence of half-metallic ferromagnetic ground state.

H. Anomalous Hall measurement

Hall measurements were conducted over a temperature range spanning from 5-300 K to investigate the charge transport characteristics of CoMnCrGa. Generally, the total Hall resistivity (denoted as ρ H ) can be divided into two components: the ordinary Hall effect and the anomalous Hall effect (AHE). Mathematically, it can be expressed as [65]:

ρ H = R 0 H + R S M S (7)
Here, R 0 represents the ordinary Hall coefficient, R S is the anomalous Hall coefficient, and M S signifies the saturation magnetization. The product R S M S quantifies the magnitude of the anomalous Hall resistivity (ρ AH ). Field-dependent data for ρ H were recorded at various temperatures, up to a maximum magnetic field of 70 kOe, as depicted in Fig 12(a). The ρ AH exhibits a sharp increase up to a 3 kOe magnetic field due to the AHE. At higher magnetic field strengths (> 3 kOe), ρ AH exhibits a linear increase with a positive slope, which is attributed to the ordinary Hall effect. To distinguish between the ordinary and anomalous Hall contributions, we applied a fitting procedure using Eqn. 7 in the high-field region. This fitting yielded the values of R 0 and R S M S , corresponding to the slope and intercept on the y-axis of the fitted line, respectively. The consistently positive value of R 0 across the entire temperature range (5-300 K) suggests that holes are the dominant charge carriers in the transport process. The carrier concentration (n), determined by the expression n = 1 eR0 , was found to be 10 22 , and its temperature dependence is illustrated in the inset of Fig 12(a). The Hall conductivity (σ H ) was determined using the equation [66]:

σ H = ρ H (ρ H 2 +ρ xx 2 ) (8)
The field-dependent Hall conductivity taken at various temperatures are presented in Fig 12(b), where the anomalous Hall conductivity (AHC) were determined by extrapolating the high-field Hall conductivity data to zero field. The obtained value of AHC is 22 S/cm at 5 K. To discern the individual contributions of extrinsic and intrinsic mechanisms to the overall AHC, we analyzed the ρ AH ρxx versus ρ xx data (depicted as blue triangles in Fig 12(d)) and fitted it with the following relation [67]:

ρ AH ρ xx = a + bρ xx (9)
In this equation, the parameters a and b contain information about extrinsic skew scattering and the combined effects of extrinsic side jump and intrinsic contributions, respectively. The fitting yielded values of a = -0.001 and b = 28 S/cm. The negative value of coefficient a implies that the extrinsic skew scattering contribution opposes both the side jump and intrinsic contributions due to momentum space Berry curvature. The parameter b encompasses the contributions to AHC from both the side jump and momentum space Berry curvature. By using these coefficients a and b, we calculated the skew scattering term (aρ xx ) and the intrinsic plus side jump term (bρ xx

2 ) and plotted them on the same scale, as shown in Fig 12(c). It is evident that the side jump, in conjunction with the intrinsic contribution, dominates over the skew scattering contribution in the overall AHE within the temperature range of 5-300 K. The AHC arising from the side jump mechanism can be approximated using the expression e 2 ha . Eso E F where E so represents the spin-orbit interaction energy, and E F is the Fermi energy [68,69]. It is worth noting that Eso E F is typically less than 0.01 for ferromagnetic metals, indicating that the intrinsic Berry-phase-driven contribution predominantly governs the AHC, which is also evident from Fig 12(c).

IV. CONCLUSION

To summarize, the structural, electronic, magnetic, and transport properties of a new quaternary Heusler alloy CoMnCrGa were investigated using both experimental and theoretical methods. First principle calculations reveals half-metallic ferromagnetic ground state in this material. Neutron diffraction experiment reveals that the compound crystalizes in disordered structure in which Mn (4b) and Cr (4c) atoms mixes with each other in 55:45 ratio. Despite the structural disorder, the material is found to order ferromagnetically at a very high temperature (∼807 K) and the total saturation magnetization follows the S-P rule, commensurate with the HMF characteristics predicted from DFT calculation. The ab-sence of magnon contribution in the resistivity data further supports the presence of HMF state. High value of the T C along with high spin polarized ferromagnetic state in this structurally disordered compound makes it perfectly suitable for for practical application in the field of room temperature spintronics. The low magnetic moment of CoMnCrGa is also advantageous for spintronics applications. It results in a negligible stray magnetic field, which is desirable in devices where magnetic interference needs to be minimized. Additionally, a lower switching current is required to flip the spin direction of this alloy, enhancing its energy efficiency.

  FIG. 1. Spin-polarized band structure and density of states of CoMnCrGa in ordered Type 2 structure: (a) minority spin band (b) density of states, (c) majority spin band.

FIG. 2 .

 2 FIG. 2. Electronic DOS (total and partial) of CoMnCrGa.

Fig. 2 FIG. 3 .

 23 FIG.3. Rietveld refinement of the neutron diffraction pattern (taken at 300 K) of CoMnCrGa, considering the crystal structure only.

FIG. 4 .

 4 FIG. 4. Atomic arrangements in disordered structure. Grey, blue, olive and blue color represents Ga, Co, Cr and Mn atoms respectively.

  FIG. 5. Rietveld refinement of the powder XRD pattern of CoMnCrGa assuming disordered structure & ordered structure measured at room temperature. The two fits got overlapped and remain essentially indistinguishable. Bragg peaks are mentioned in vertical first bracket.

FIG. 6 .

 6 FIG. 6. (a) TEM image (thickness fringes running over the image) (b) Selected area electron diffraction (c) Scanning TEM-High angle annular dark-field image (d) Energy dispersive X-ray (EDX) spectrum from point 1 (e) EDX line-profile from line 2 in (c) (f) Elemental mapping from area 3 in (c).

Fig. 6 (

 6 Fig. 6(a) represents Transmission Electron Microscopy (TEM) image, replete with discernible thickness fringes traversing the micrograph. This portrayal exposes intricate nanoscale structural features. The selected area electron diffraction (SAED) pattern from a region marked by a dotted circle is shown in Fig. 6(b), which indicates the single crystalline nature of the material. SAED was indexed using the lattice parameters of cubic crystal structure (lattice parameters, a = 5.841 Å). To investigate the chemical composition of the compound, we have performed energy dispersive X-ray spectroscopy (EDX) in high-angle annular dark field (HAADF) scanning transmission electron microscopy (STEM-HAADF) mode. Fig. 6(c) shows the STEM-HAADF image, and Fig. 6(d) showing EDX spectrums take at point 1 indicated in Fig. 6(c), clearly showing the presence of Co, Mn, Cr and Ga. Fig. 6(e) represents the EDX line-profile analysis performed along Line 2 in image Fig. 6(c). For a detailed distribution of atomic content, elemental mapping of Co, Mn, Cr and Ga were performed using drift corrected EDX spectrum imaging using STEM-HAADF mode. The STEM-HAADF image in Fig. 6(c) and the

FIG. 4 .FIG. 7 .

 47 FIG. 4. Normalized EXAFS of CoMnCrGa taken at (a) Co edge (b) Mn edge and (c) Cr edge. FIG. 7. Normalized EXAFS spectra of CoMnCrGa taken at (a) Co edge (b) Mn edge and (c) Cr edge.

C 3 )R a d i a l d i s t a n cFIG. 5 .

 35 FIG. 5. Fourier transformed EXAFS spectra of CoMnCrGa taken at Co, Mn and Cr edges.

FIG. 9 .FIG. 10 .

 910 FIG. 9. Temperature dependence of magnetic susceptibility of CoMnCrGa measured in a 100 Oe applied magnetic field. Curie temperature, TC, is determined from the minima in dM/dT vs. T plot.

FIG. 11 .

 11 FIG. 11. Temperature dependence of the electrical resistivity measured in the absence of magnetic field in the temperature range 5-300 K.

FIG. 12 .

 12 FIG. 12. (a) Field-dependent Hall resistivity curves at different temperatures. Inset shows temperature variations of carrier concentration (b) Field-dependent Hall conductivity curves at different temperatures. (c) Different contributions in ρAH with temperature are plotted on the same scale. (d) Fitting of the ratio of anomalous Hall resistivity and longitudinal resistivity.

TABLE I .

 I Different types of ordered structure of CoMnCrGa 4a 4b 4c 4d Type 1 Ga Cr Mn Co Type 2 Ga Mn Cr Co Type 3 Ga Co Mn Cr nology (RRCAT) in Indore, India. Standard EXAFS measurements protocols have been used as detailed elsewhere

TABLE II .

 II Bond length (R), coordination number (N), and Debye-Waller or disorder factor (σ 2 ) obtained by EXAFS fitting for CoMnCrGa at Co, Mn and Cr edge.

		Co edge			Mn edge			Cr edge
	Path	R ( Å)	N	σ 2	Path	R ( Å)	N	σ 2	Path	R ( Å)	N	σ 2
	Co-Cr 2.42±0.01 4 0.0277±0.0030 Mn-Ga 2.42±0.01 4 0.0143±0.0022 Cr-Mn 2.42±0.01 4 0.0143±0.0022
	Co-Ga 2.42±0.01 4 0.0060±0.0009 Mn-Cr 2.42±0.01 4 0.0277±0.0030 Cr-Co 2.42±0.01 4 0.0277±0.0030
	Co-Mn 2.82±0.01 6 0.0062±0.0010 Mn-Co 2.82±0.01 6 0.0062±0.0010 Cr-Ga 2.81±0.01 6 0.0062±0.0010
	Co-Co 4.10±0.01 12 0.0168±0.0024 Mn-Mn 4.14±0.01 12 0.0182±0.0043 Cr-Cr 4.12±0.04 12 0.0182±0.0043
	Co-Cr 4.88±0.04 12 0.0109±0.0068 Mn-Ga 4.92±0.01 12 0.0160±0.0033 Cr-Mn 4.89±0.02 12 0.00160±0.0033
	Co-Ga 4.88±0.04 12 0.0091±0.0050 Mn-Cr 4.92±0.01 12 0.0083±0.0012 Cr-Co 4.89±0.02 12 0.0083±0.0012

TABLE III .

 III Curie temperature, coercivity & saturation magnetisation (Msat) at ∼5 K, Slater-Pauling value (MS-P) for saturation magnetisation and spin polarisation value of CoMnCrGa. For comparison, the same set of parameters of other two such known Heusler alloys, FeMnVAl and FeMnVGa, are also provided.

	Compound Curie Temperature Coercivity Msat	MS-P Spin Polarization reference
		(K)	(Oe)	(µB/f.u.) (µB/f.u.)	(%)
	CoMnCrGa	807	50	1	1	96.1	This work
	FeMnVAl	213	125	0.84	1	92.4	ref [19]
	FeMnVGa	293	54	0.92	1	89.9	ref [20]

ACKNOWLEDGEMENTS

S.G and S.C would like to sincerely acknowledge SINP, India and UGC, India, respectively, for their fellowship. The authors would like to sincerely thank Prof. Biswarup Satpati, SINP, for the TEM & EDX measurements. DFT calculations were performed using HPC resources from GENCI-CINES (Grant 2021-A0100906175). Work at the Ames Laboratory (in part) was supported by the Department of Energy-Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-07CH11358.

AUTHOR CONTRIBUTIONS

S. Gupta

CONFLICTS OF INTEREST

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.