Miracle-dz/UniFAR-FTUs huggingface dataset:
Geophysical signature of the transition zone between the sedimentary cover and the basement: an analogue approach to help de-risking geothermal prospects.

Within the frame of the ANR-funded CANTARE-Alsace project, we have undertaken a multi-scale and multidisciplinary approach to increase our knowledge of the transition zone between the sedimentary cover and the basement and provide fundamental knowledge for the assessment of its geothermal potential. In this paper, we report out the results of a study performed on an exhumed transition zone in the Ringelbach area in the Vosges Mountains, on the flank of the Rhine graben. In this analogue of a deeply buried transition zone of the Rhine Graben, a thin layer of Triassic sandstones is still present on the top of the fractured and altered granitic basement providing the opportunity to study in-situ the physical properties of this transition zone. In this paper, we focused on electrical and acoustic properties of the transition zone as they are the main physical parameters usually assessed with the help of geophysical methods during the exploration phase of a geothermal project.
Introduction to Gauge Theory of Gravitation

The fundamental interactions of nature, the electroweak and the quantum chromodynamics, are described in the Standard Model by the Gauge Theory under internal symmetries that maintain the invariance of the functional action. The fundamental interaction of gravitation is very well described by Einstein's General Relativity in a Riemannian spacetime metric, but General Relativity has been over time a gravitational field theory apart from the Standard Model. The theory of Gauge allows under symmetries of the group of Poincare to impose invariances in the functional of the action of the spinor field that result in the gravitational interaction with the fermions. In this approach the gravitational field, besides being described by the equation similar to General Relativity, also brings a spin-gravitational interaction in a Riemann-Cartan spacetime.
Effective Implementation of collaborative care for depression: what is needed?

OBJECTIVES To identify the care model factors that were key for successful implementation of collaborative depression care in a statewide Minnesota primary care initiative. STUDY DESIGN We used a mixed-methods design incorporating both qualitative data from clinic site visits and quantitative measures of patient activation and 6-month remission rates. METHODS Care model factors identified from the site visits were tested for association with rates of activation into the program and remission rates. RESULTS Nine factors were identified as important for successful implementation of collaborative care by the consultants who had trained and interviewed participating clinic teams, and rated according to a Likert Scale. Factors correlated with higher patient activation rates were: strong leadership support (0.63), well-defined and -implemented care manager roles (0.62), a strong primary care physician champion (0.60), and an on-site and accessible care manager (0.59). However, remission rates at 6 months were correlated with: an engaged psychiatrist (0.62), not seeing operating costs as a barrier to participation (0.56), and face-to-face communication (warm handoffs) between the care manager and primary care physician for new patients (0.54). CONCLUSIONS Care model factors most important for successful program implementation differ for patient activation into the program versus remission at 6 months. Knowing which implementation factors are most important for successful activation will be useful for those interested in adopting this evidence-based approach to improving primary care for patients with depression.
The Roles of Intracellular Chaperone Proteins, Sigma Receptors, in Parkinson’s Disease (PD) and Major Depressive Disorder (MDD)

Sigma receptors, including Sigma-1 receptors and Sigma-2 receptors, are highly expressed in the CNS. They are intracellular chaperone proteins. Sigma-1 receptors localize mainly at the mitochondria-associated endoplasmic reticulum (ER) membrane (MAM). Upon stimulation, they translocate from MAM to plasma membrane (PM) and nucleus, where they interact with many proteins and ion channels. Sigma-1 receptor could interact with itself to form oligomers, its oligomerization states affect its ability to interact with client proteins including ion channels and BiP. Sigma-1 receptor shows high affinity for many unrelated and structurally diverse ligands, but the mechanism for this diverse drug receptor interaction remains unknown. Sigma-1 receptors also directly bind many proteins including G protein-coupled receptors (GPCRs) and ion channels. In recent years, significant progress has been made in our understanding of roles of the Sigma-1 receptors in normal and pathological conditions, but more studies are still required for the Sigma-2 receptors. The physiological roles of Sigma-1 receptors in the CNS are discussed. They can modulate the activity of many ion channels including voltage-dependent ion channels including Ca2+, Na+, K+ channels and NMDAR, thus affecting neuronal excitability and synaptic activity. They are also involved in synaptic plasticity and learning and memory. Moreover, the activation of Sigma receptors protects neurons from death via the modulation of ER stress, neuroinflammation, and Ca2+ homeostasis. Evidences about the involvement of Sigma-1 receptors in Parkinson’s disease (PD) and Major Depressive Disorder (MDD) are also presented, indicating Sigma-1 receptors might be promising targets for pharmacologically treating PD and MDD.
On-line human action recognition based on P-LDCNF

To better describe the substructures and external dynamics of human action, a probabilistic method of human action recognition is presented that is based on an improved conditional random field (CRF) model. In our method, feature functions are redefined to describe the conversions between the substructures, and a neural network layer is used to perform a pretreatment process. A probabilistic latent-dynamic conditional neural fields (P-LDCNF) model with transition probabilities is proposed in our method, which can be used for online human action recognition. Specifically, spatial-temporal interesting points are extracted as features in the model. The efficiency of the proposed method was evaluated using unsegmented action sequences from the Weizmann and KTH datasets. The experimental results showed that P-LDCNF compares favorably with CRF, hidden conditional random field (HCRF), and latent-dynamic conditional random field (LDCRF) models for online human action recognition tasks.
Signal intensity of superficial white matter on phase difference enhanced imaging as a landmark of the perirolandic cortex

Background The superficial white matter (SWM), which fills the space between the deep white matter and the cortex, has not been well characterized. Purpose To determine whether the assessment of the relative signal intensity (SI) of the SWM in the precentral and postcentral gyri on phase difference enhanced (PADRE) images contributes in establishing anatomical landmark. Material and Methods The study population consisted of 43 normal subjects (28 women, 15 men; mean age, 52.9 years; age range, 22–90 years). By the consensus of two observers, the precentral gyri, postcentral gyri, and superior frontal cortex (SFC) were identified based on the established anatomical methods. The SI of the SWM in the precentral and postcentral gyri on PADRE images was divided into three grades in comparison with that of the SFC: Grade I, isointense; Grade II, slightly hypointense; and Grade III, markedly hypointense. Results The SWM in the precentral and postcentral gyri showed hypointensity on PADRE images. In the SI analyses of the PADRE images, the Grade I, Grade II, and Grade III appearances were found in one (1%), 20 (23%), and 65 (76%) of the 86 precentral gyri (43 subjects), respectively, and in one (1%), 23 (27%), and 62 (72%) of the 86 postcentral gyri, respectively. Conclusion On PADRE images, the perirolandic SWM showed hypointensity compared to other cerebral cortices, which probably reflects differences in the concentrations of the nerve fibers, as well as the higher myelin content. PADRE may be useful for the identification of the central sulcus by assessing the SI of the SWM.
Development of an Advanced Force Field for Water using Variational Energy Decomposition Analysis.

Given the piecewise approach to modeling intermolecular interactions for force fields, they can be difficult to parameterize since they are fit to data like total energies that only indirectly connect to their separable functional forms. Furthermore, by neglecting certain types of molecular interactions such as charge penetration and charge transfer, most classical force fields must rely on, but do not always demonstrate, how cancellation of errors occurs among the remaining molecular interactions accounted for such as exchange repulsion, electrostatics, and polarization. In this work we present the first generation of the (many-body) MB-UCB force field that explicitly accounts for the decomposed molecular interactions commensurate with a variational energy decomposition analysis, including charge transfer, with force field design choices that reduce the computational expense of the MB-UCB potential while remaining accurate. We optimize parameters using only single water molecule and water cluster data up through pentamers, with no fitting to condensed phase data, and we demonstrate that high accuracy is maintained when the force field is subsequently validated against conformational energies of larger water cluster data sets, radial distribution functions of the liquid phase, and the temperature dependence of thermodynamic and transport water properties. We conclude that MB-UCB is comparable in performance to MB-Pol, but is less expensive and more transferable by eliminating the need to represent short-ranged interactions through large parameter fits to high order polynomials.
Michael addition-type 4-hydroxy-2-nonenal adducts in modified low-density lipoproteins: markers for atherosclerosis.

It has been proposed that plasma low-density lipoprotein (LDL) undergoes oxidative modification before it can give rise to foam cells in atherosclerosis. Oxidation of LDL generates a variety of reactive aldehyde products including 4-hydroxy-2-nonenal (HNE), which may covalently attach to the LDL apolipoproteins. We here present direct evidence that HNE derivatization of LDL forms Michael addition-type adducts of HNE with histidine and lysine residues of apolipoprotein B-100 (apoB) and also demonstrate the utility of an antibody specific to the HNE adducts generated in the LDL treated with HNE or oxidatively modified by Cu2+ or cultured endothelial cells. HNE adducts present in the LDL that had been treated with HNE were attested to be Michael addition-type adducts on the basis of the fact that incubation of LDL with 1 mM HNE (2 h, 37 degrees C) resulted primarily in the formation of Michael addition-type HNE-histidine (39.9 mol/mol of LDL) and HNE-lysine (19.3 mol/mol of LDL) adducts. An enzyme-linked immunosorbent assay (ELISA) and an SDS-polyacrylamide gel electrophoresis (SDS-PAGE)/immunoblot analysis of HNE-modified LDL demonstrated that these HNE adducts were detectable with the HNE-specific antibody affinity-purified with the Michael adduct (HNE-histidine) as a ligand. The following lines of evidence indicated the presence of Michael addition-type HNE adducts in the oxidatively modified LDL in vitro: (i) Amino acid analysis of LDL that had been treated with Cu2+ (24 h, 37 degrees C) demonstrated the presence of a Michael addition-type HNE-histidine adduct (7-9 mol/mol of LDL). (ABSTRACT TRUNCATED AT 250 WORDS)
Breather induced quantized superfluid vortex filaments and their characterization.

We study and characterize the breather-induced quantized superfluid vortex filaments which correspond to the Kuznetsov-Ma breather and super-regular breather excitations developing from localised perturbations. Such vortex filaments, emerging from an otherwise perturbed helical vortex, exhibit intriguing loop structures corresponding to the large amplitude of breathers due to the dual action of bending and twisting of the vortex. The loop induced by Kuznetsov-Ma breather emerges periodically as time increases, while the loop structure triggered by super-regular breather---the loop pair---exhibits striking symmetry breaking due to the broken reflection symmetry of the group velocities of super-regular breather. In particular, we identify explicitly the generation conditions of these loop excitations by introducing a physical quantity---the integral of the relative quadratic curvature---which corresponds to the effective energy of breathers. Although the nature of nonlinearity, it is demonstrated that this physical quantity shows a linear correlation with the loop size. These results will deepen our understanding of breather-induced vortex filaments and be helpful for controllable ring-like excitations on the vortices.
How Openness Enriches the Environment: Read More

The recently proposed OFCI model and specifically the Environmental Enrichment Hypothesis state that Openness positively influences the development of cognitive abilities (Ziegler et al., 2012). It is assumed that Openness leads to engagement in more learning activities through creating an enriched environment (e.g., reading). However, despite positive evaluations of the OFCI model in general, there is little empirical research on this specific hypothesis. The current paper used a longitudinal design to test the positive impact of Openness on the frequency of reading activities in general and in the specific case of periods of unemployment. PIAAC (Programme for the International Assessment of Adult Competencies) data were used to fit structural equation models. The results show that Openness fosters greater engagement in reading activities over 3 years; a buffering function in case of unemployment could not be found. Theoretical and practical implications are discussed.
Cross-referencing Using Fine-grained Topic Modeling

Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge of the entire corpus, and second, a focused search through the corpus specifically to find such useful connections. Due to this, cross-reference resources are prohibitively expensive and exist only for the most well-studied texts (e.g. religious texts). We develop a topic-based system for automatically producing candidate cross-references which can be easily verified by human annotators. Our system utilizes fine-grained topic modeling with thousands of highly nuanced and specific topics to identify verse pairs which are topically related. We demonstrate that our system can be cost effective compared to having annotators acquire the expertise necessary to produce cross-reference resources unaided.
Enabling Computer Vision Driven Assistive Devices for the Visually Impaired via Micro-architecture Design Exploration

Recent improvements in object detection have shown potential to aid in tasks where previous solutions were not able to achieve. A particular area is assistive devices for individuals with visual impairment. While state-of-the-art deep neural networks have been shown to achieve superior object detection performance, their high computational and memory requirements make them cost prohibitive for on-device operation. Alternatively, cloud-based operation leads to privacy concerns, both not attractive to potential users. To address these challenges, this study investigates creating an efficient object detection network specifically for OLIV, an AI-powered assistant for object localization for the visually impaired, via micro-architecture design exploration. In particular, we formulate the problem of finding an optimal network micro-architecture as an numerical optimization problem, where we find the set of hyperparameters controlling the MobileNetV2-SSD network micro-architecture that maximizes a modified NetScore objective function for the MSCOCO-OLIV dataset of indoor objects. Experimental results show that such a micro-architecture design exploration strategy leads to a compact deep neural network with a balanced trade-off between accuracy, size, and speed, making it well-suited for enabling on-device computer vision driven assistive devices for the visually impaired.
Predictive variables for major bleeding events in patients presenting with documented acute venous thromboembolism. Findings from the RIETE Registry

Summary A score that can accurately determine the risk of major bleeding during anticoagulant therapy may help to make decisions on anticoagulant use. RIETE is an ongoing registry of consecutive patients with acute venous thromboembolism (VTE). We composed a score to predict the risk for major bleeding within three months of anticoagulant therapy. Of 19,274 patients enrolled, 13,057 (67%) were randomly assigned to the derivation sample, 6,572 to the validation sample. In the derivation sample 314 (2.4%) patients bled (fatal bleeding, 105). On multivariate analysis, age >75 years, recent bleeding, cancer, creatinine levels >1.2 mg/dl, anemia, or pulmonary embolism at baseline were independently associated with an increased risk for major bleeding. A score was composed assigning 2 points to recent bleeding, 1.5 to abnormal creatinine levels or anemia, 1 point to the remaining variables. In the derivation sample 2,654 (20%) patients scored 0 points (low risk); 9,645 (74%) 1–4 points (intermediate); 758 (5.8%) >4 points (high risk). The incidences of major bleeding were: 0.3% (95% confidence interval [CI]: 0.1–0.6), 2.6% (95% CI: 2.3–2.9), and 7.3% (95% CI: 5.6–9.3), respectively. The likelihood ratio test was:0.14 (95% CI:0.07–0.27) for patients at low risk;2.96 (95% CI:2.18–4.02) for those at high risk. In the validation sample the incidence of major bleeding was:0.1%,2.8%,and 6.2%,respectively. In conclusion, a risk score based on six variables documented at entry can identify VTE patients at low, intermediate, or high risk for major bleeding during the first three months of therapy.
An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations

Motivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the associations among diseases could help researchers gain a deep insight of human diseases, since similar diseases are usually caused by similar molecular origins or have similar phenotypes, which is beneficial to reveal the common attributes of diseases and improve the corresponding diagnoses and treatment plans. Some previous are proposed to measure the disease similarity using a particular biomedical ontology during the past few years, but for a newly discovered disease or a disease with few related genetic information in Disease Ontology (i.e., a disease with less disease-gene associations), these previous approaches usually ignores the joint computation of disease similarity by integrating gene and phenotype associations. Results: In this paper we propose a novel method called GPSim to effectively deduce the semantic similarity of diseases. In particular, GPSim calculates the similarity by jointly utilizing gene, disease and phenotype associations extracted from multiple biomedical ontologies and databases. We also explore the phenotypic factors such as the depth of HPO terms and the number of phenotypic associations that affect the evaluation performance. A final experimental evaluation is carried out to evaluate the performance of GPSim and shows its advantages over previous approaches.
Efficient block pivoting for multibody simulations with contact

Simulating stiff physical systems is a requirement for numerous computer graphics applications, such as VR training for heavy equipment operation. However, iterative linear solvers often perform poorly in such cases, and direct methods involving a factorization of the system matrix are typically preferred for accurate and stable simulations. This can have a detrimental impact on performance, since factorization of the system matrix is costly for complex simulations. In this paper, we present a method for efficiently solving linear systems of stiff physical systems involving contact, where the dynamics are modeled as a mixed linear complementarity problem (MLCP). Our approach is based on a block Bard-type algorithm that applies low-rank downdates to a Cholesky factorization of the system matrix at each pivoting step. Further performance improvements are realized by exploiting low bandwidth characteristics of the factorization. Our method gives up to 3.5x speed-up versus recomputing the factorization based on the index set. Various challenging scenarios are used to demonstrate the advantages of our approach.
Abnormal heart rate recovery after maximal cardiopulmonary exercise stress testing in young overweight women with polycystic ovary syndrome

Objective Heart rate recovery (HRR) is a measure derived from exercise test, defined as the fall in heart rate during the first minute after maximal exercise. Abnormal HRR is a measure of autonomic dysfunction associated with an increased mortality. This study was performed to evaluate the HRR in polycystic ovary syndrome (PCOS).
Chemodiversity of the Glucosinolate-Myrosinase System at the Single Cell Type Resolution

Glucosinolates (GLSs) are a well-defined group of specialized metabolites, and like any other plant specialized metabolites, their presence does not directly affect the plant survival in terms of growth and development. However, specialized metabolites are essential to combat environmental stresses, such as pathogens and herbivores. GLSs naturally occur in many pungent plants in the order of Brassicales. To date, more than 200 different GLS structures have been characterized and their distribution differs from species to species. GLSs co-exist with classical and atypical myrosinases, which can hydrolyze GLS into an unstable aglycone thiohydroximate-O-sulfonate, which rearranges to produce different degradation products. GLSs, myrosinases, myrosinase interacting proteins, and GLS degradation products constitute the GLS-myrosinase (GM) system (“mustard oil bomb”). This review discusses the cellular and subcellular organization of the GM system, its chemodiversity, and functions in different cell types. Although there are many studies on the functions of GLSs and/or myrosinases at the tissue and whole plant levels, very few studies have focused on different single cell types. Single cell type studies will help to reveal specific functions that are missed at the tissue and organismal level. This review aims to highlight (1) recent progress in cellular and subcellular compartmentation of GLSs, myrosinases, and myrosinase interacting proteins; (2) molecular and biochemical diversity of GLSs and myrosinases; and (3) myrosinase interaction with its interacting proteins, and how it regulates the degradation of GLSs and thus the biological functions (e.g., plant defense against pathogens). Future prospects may include targeted approaches for engineering/breeding of plants and crops in the cell type-specific manner toward enhanced plant defense and nutrition.