Miracle-dz/UniFAR-FTUs huggingface dataset:
An ELISA-based method for detection of rabies virus nucleoprotein-specific antibodies in human antemortem samples

Rabies is a fatal encephalitic disease in humans and animals caused by lyssaviruses, most commonly rabies virus (RABV). Human antemortem diagnosis of rabies is a complex process involving multiple sample types and tests for the detection of antibodies, antigen (protein), and nucleic acids (genomic RNA). Serological diagnosis of human rabies includes the detection of either neutralizing or binding antibodies in the cerebrospinal fluid (CSF) or serum samples from unimmunized individuals without prior rabies vaccination or passive immunization with purified immunoglobulins. While neutralizing antibodies are targeted against the surface-expressed glycoprotein (G protein), binding antibodies to viral antigens are predominantly against the nucleoprotein (N protein), although there can be antibodies against all RABV-expressed proteins. To determine N protein-specific antibody responses in the CSF and serum during RABV infection, we developed an enzyme-linked immunosorbent assay (ELISA) with purified recombinant N protein expressed in E. coli. N protein-specific immunoglobulin (Ig) subtypes IgG and IgM were detected in the CSF or serum of previously diagnosed human rabies cases. In addition, anti-N protein seroconversion was demonstrated over the course of illness in individual rabies cases. We compared the N protein ELISA results to those of an indirect fluorescent antibody (IFA) test, the current binding antibody assay used in diagnosis, and show that our ELISA is consistent with the IFA test. Sensitivity and specificity of the N protein ELISA ranged from 78.38–100% and 75.76–96.77% with respect to the IFA results. Our data provide evidence for the use of an N protein ELISA as an additional option for the detection of RABV-specific IgG or IgM antibodies in human CSF or serum specimens.
Behavioral activation and behavioral inhibition sensitivities in patients with substance use disorders: A study from India

Introduction: Addictive disorders are an epiphenomenon of underlying vulnerabilities. Research over the past decades into these vulnerabilities has distinguished internalizing and externalizing spectra as two distinct personality factors underlying substance use disorders (SUDs). In this study, we explore the behavioral activation and inhibition factors in patients with SUD. Materials and Methods: A total of 240 patients with SUD were recruited for the study. Behavioral inhibition system-behavioral activation/approach system (BIS-BAS) scale was used to assess the three domains of the behavioral activation, namely drive, fun seeking and reward responsiveness, and the behavioral inhibition as a single domain. BIS and BAS subscale total scores, inter-domain correlation, factor structure, and difference in the early-onset and late-onset SUD subgroup scores were calculated. Results: The drive, fun seeking, and reward responsiveness showed a moderate degree of correlation among each other ranging from 0.30 to 0.36. The behavioral inhibition subscale had a modest correlation r = 0.26 with the reward responsiveness subdomain of behavioral activation. The factor structure remained valid at two- and four-factor solutions apart from few items with inconsistent loading. The early-onset n = 209 (87.1%) and late-onset n = 31 (12.9%) SUD subgroup analysis showed a statistically significant difference in the mean scores of drive and fun-seeking subscales with P < 0.05. Discussion and Conclusions: Behavioral activation and inhibition remain two valid personality factors in patients with SUDs. Patients with early onset of SUD have a significantly higher behavioral activation scores in the drive, and fun-seeking subfactors suggesting a higher externalizing tendency.
Deep FisherNet for Image Classification

Despite the great success of convolutional neural networks (CNNs) for the image classification task on data sets such as Cifar and ImageNet, CNN’s representation power is still somewhat limited in dealing with images that have a large variation in size and clutter, where Fisher vector (FV) has shown to be an effective encoding strategy. FV encodes an image by aggregating local descriptors with a universal generative Gaussian mixture model (GMM). FV, however, has limited learning capability and its parameters are mostly fixed after constructing the codebook. To combine together the best of the two worlds, we propose in this brief a neural network structure with FV layer being part of an end-to-end trainable system that is differentiable; we name our network FisherNet that is learnable using back propagation. Our proposed FisherNet combines CNN training and FV encoding in a single end-to-end structure. We observe a clear advantage of FisherNet over plain CNN and standard FV in terms of both classification accuracy and computational efficiency on the challenging PASCAL visual object classes object classification and emotion image classification tasks.
FXR and TGR5 Agonists Ameliorate Liver Injury, Steatosis, and Inflammation After Binge or Prolonged Alcohol Feeding in Mice

Bile acids (BAs) activate various dedicated receptors, including the farnesoid X receptor (FXR) and the Takeda G protein‐coupled receptor 5 (TGR5). The FXR agonist obeticholic acid (OCA) is licensed for the treatment of primary biliary cholangitis and has shown promising results in NASH patients, whereas TGR5 agonists target inflammation and metabolism. We hypothesized that FXR and/or TGR5 agonists may be therapeutic in early alcoholic liver disease (ALD) in mice, in which hepatic inflammation plays a major role. OCA, INT‐777, and INT‐767 are BA derivatives with selective agonist properties for FXR, TGR5, or both, respectively. These compounds were tested in two mouse models (3‐day binge model and prolonged Lieber DeCarli diet for 12 days) of early ALD. Serum alanine aminotransferase and liver histology were used to assess liver injury, Oil Red O staining of liver sections to assess steatosis, and real‐time polymerase chain reaction to assess changes in gene expression. In the ethanol binge model, treatment with OCA and INT‐777 decreased hepatic macrovesicular steatosis and protected from ethanol‐induced liver injury. After prolonged ethanol administration, mice treated with OCA, INT‐767, or INT‐777 showed decreased hepatic steatosis, associated with reduced liver fatty acid synthase protein expression, and protection from liver injury. Treatment with BA receptor agonists in both models of ethanol administration modulated lipogenic gene expression, and decreased liver interleukin‐1β mRNA expression associated with increased ubiquitination of NLRP3 inflammasome through cyclic adenosine monophosphate–induced activation of protein kinase A. Conclusion: OCA, INT‐767, or INT‐777 administration is effective in reducing acute and chronic ethanol‐induced steatosis and inflammation in mice, with varying degrees of efficacy depending on the duration of ethanol administration, indicating that both FXR and TGR5 activation can protect from liver injury in ALD models.
Facial Landmark Detection for Manga Images

The topic of facial landmark detection has been widely covered for pictures of human faces, but it is still a challenge for drawings. Indeed, the proportions and symmetry of standard human faces are not always used for comics or mangas. The personal style of the author, the limitation of colors, etc. makes the landmark detection on faces in drawings a difficult task. Detecting the landmarks on manga images will be useful to provide new services for easily editing the character faces, estimating the character emotions, or generating automatically some animations such as lip or eye movements. This paper contains two main contributions: 1) a new landmark annotation model for manga faces, and 2) a deep learning approach to detect these landmarks. We use the "Deep Alignment Network", a multi stage architecture where the first stage makes an initial estimation which gets refined in further stages. The first results show that the proposed method succeed to accurately find the landmarks in more than 80% of the cases.
Nomograms to predict naming decline after temporal lobe surgery in adults with epilepsy

Objective To develop and externally validate models to predict the probability of postoperative naming decline in adults following temporal lobe epilepsy surgery using easily accessible preoperative clinical predictors. Methods In this retrospective, prediction model development study, multivariable models were developed in a cohort of 719 patients who underwent temporal lobe epilepsy surgery at Cleveland Clinic and externally validated in a cohort of 138 patients who underwent temporal lobe surgery at one of 3 epilepsy surgery centers in the United States (Columbia University Medical Center, Emory University School of Medicine, University of Washington School of Medicine). Results The development cohort was 54% female with an average age at surgery of 36 years (SD 12). Twenty-six percent of this cohort experienced clinically relevant postoperative naming decline. The model included 5 variables: side of surgery, age at epilepsy onset, age at surgery, sex, and education. When applied to the external validation cohort, the model performed very well, with excellent calibration and a c statistic (reflecting discriminatory ability) of 0.81. A second model predicting moderate to severe postoperative naming decline included 3 variables: side of surgery, age at epilepsy onset, and preoperative naming score. This model generated a c statistic of 0.84 in the external validation cohort and showed good calibration. Conclusion Externally validated nomograms are provided in 2 easy-to-use formats (paper version and online calculator) clinicians can use to estimate the probability of naming decline in patients considering epilepsy surgery for treatment of pharmacoresistant temporal lobe epilepsy.
Time to progression of mantle cell lymphoma after high‐dose cytarabine‐based regimens defines patients risk for death

The current standard treatment of younger patients with mantle cell lymphoma (MCL) includes rituximab and highdose cytarabine (HD-AraC), usually followed by autologous stem cell transplantation (ASCT). This approach has improved the long-term outcome of these patients. However, no plateau in survival curves has been observed, and virtually all patients will experience disease recurrence for which no standard therapy exists (Dreyling et al, 2017). Allogeneic stem cell transplantation (AlloSCT) remains the only option with curative potential (Robinson et al, 2015; Tessoulin et al, 2016; Rule et al, 2017). The data included in the largest report on MCL patients who relapsed after ASCT was obtained between 2000 and 2009, but only 50% of cases had documented exposure to rituximab and HD-AraC before ASCT (Dietrich et al, 2014). Inclusion criteria for this retrospective study of firstrelapsed or -refractory MCL patients from 26 centres associated to the Fondazione Italiana Linfomi (FIL) were: (i) upfront treatment with intensive regimens including rituximab and HD-AraC, defined as cytarabine with a single dose >1 g/m; (ii) MCL diagnosed between 1 January 2007 and 31 June 2016. Upfront regimens were stratified into three categories: high dose sequential therapy followed by ASCT (Geisler et al, 2008; Magni et al, 2009); R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisolone) alternating with R-DHAP (rituximab, dexamethasone, HD-AraC, cisplatin) followed by ASCT (Hermine et al, 2016); R-HyperCVAD/MTXHDAC (rituximab, hyperfractionated cyclophosphamide, vincristine, doxorubicin, dexamethasone, high-dose methotrexate/cytarabine) followed by
Mindfulness and psychoeducation to manage stress in amnestic mild cognitive impairment: A pilot study

Abstract Objectives: Amnestic mild cognitive impairment (aMCI) often corresponds to the prodromal stage of Alzheimer disease (AD). The aMCI stage represents a crucial time window to apply preventive interventions in an attempt to delay cognitive decline. Stress, one of AD’s modifiable risk factors frequently co-occurring with aMCI, stands out as a key intervention target. The goal of this study was to assess the impacts of two non-pharmacological interventions, mindfulness and psychoeducation, on stress at the psychological and physiological levels among aMCI older adults. Methods: Forty-eight aMCI participants were randomized between a mindfulness-based intervention (MBI) and a psychoeducation-based intervention (PBI) for eight weekly sessions. Anxiety symptoms, perceived stress levels, cortisol awakening response (CAR), and coping strategies were assessed pre- and post-intervention. Mindfulness attitudes and time dedicated to at-home meditative practices were evaluated in the MBI group. Results: The main results revealed a slight reduction of the CAR among MBI participants who practiced meditation at home the most and a decrease in perceived stress levels in the PBI group. Both interventions enhanced problem-focused coping strategies. Conclusion: In sum, this pilot study supports the potential of MBI and PBI to reduce stress at the physiological and psychological level, respectively, and increase coping strategies in older adults at risk for AD.
Emotional Intelligence and Mindfulness: Relation and Enhancement in the Classroom With Adolescents

Emotional intelligence (EI) and mindfulness are two constructs that have been separately studied, and the relation between them still remains unclear. Research in this area has not attempted to go further into how enhancing EI and mindfulness together can achieve better improvements in this ability to attend mindfully. To bridge this knowledge gap, our research goal was to study the relationship between EI and the mindfulness competence in our study sample and to assess the impact of implementing EI and a mindfulness competence developmental program (SEA) about participants’ mindfulness competence. The sample consisted of 156 students aged 11–14 years old from a Spanish public high school. One hundred and eight participants were randomly assigned to the experimental condition, and the remaining 48 were to the control condition. The instruments used to evaluate EI were the CDE-SEC, EQi-Youth Version and the General Empathy Scale. Mindfulness on the School Scope Scale was used to assess mindfulness competences. Social adaptation was evaluated by using the social abilities and adjustment questionnaire BAS3. All the instruments where answered by the participants and have been adapted to a sample of youths with such age specifications. The results showed that EI and mindfulness were related to many of the variables measured by the instruments. Showing a good mindfulness competence was particularly related to having a good general level of the EI trait, and to many of the assessed social and emotional variables. The data indicated a significant relation between the mindfulness competence and having better general empathy skills or being better socially adjusted to the school context. The data also indicated a significant effect on participants’ interior and kinesthetic mindfulness competence after implementing the SEA Program. These findings corroborate the relationship between EI and mindfulness, and the possibility of enhancing mindfulness by applying a direct intervention program in the classroom.
Properties of Sediment Trap Catchment Areas in Fram Strait: Results From Lagrangian Modeling and Remote Sensing

Vertical particle fluxes are responsible for the transport of carbon and biogenic material from the surface to the deep ocean, hence understanding these particle fluxes is of climate relevance. Sediment traps operated in Fram Strait in the framework of the Arctic long-term observatory FRAM provide an estimate of vertical particle fluxes in a region of high CO2 uptake. Until now the source area (catchment area) of trapped particles is unclear; however, lateral advection of particles is supposed to play an important role. This study presents a Lagrangian method to backtrack the origin of particles for two Fram Strait moorings equipped with sediment traps in 200 m and 2300 m depth by using the time-dependent velocity field of a high-resolution, eddy-resolving ocean-sea ice model. Our study shows that the extent of the catchment area is larger the deeper the trap and the slower the settling velocity. Chlorophyll-a concentration as well as sea ice coverage of the catchment area are highest in the summer months. The high sea ice coverage in summer compared to winter can possibly be related to a weaker across-strait sea level pressure difference, which allows more sea ice to enter the then well-stratified central Fram Strait where moorings are located. Hence, highest vertical particle fluxes may be expected in late summer and autumn. Furthermore, a backward sea ice tracking approach shows that the origin and age of sea ice drifting through Fram Strait, partly responsible for vertical particle fluxes, varies strongly from year to year, pointing to a high variability in the composition of particles trapped in the moorings.
Risk factors for polypharmacy in older adults in a primary care setting: a cross-sectional study

Purpose Polypharmacy (PP) is a clinical challenge in older adults. Therefore, assessment of daily drug consumption (DDC) and its relationships is important. First-line health services have a crucial role in monitoring and preventing PP. In this study, we aimed to assess DDC and investigate the risk factors for higher DDC among older adults in a primary care setting. Patients and methods A total of 1,000 patients aged ≥65 years who visited Melek Hatun Family Practice Center between December 1, 2014, and August 1, 2017, were enrolled in the study. All patients were seen either at the center or in their homes, and informed consent was obtained. Comprehensive geriatric assessment was performed for each subject. Data were analyzed using SPSS software (version 17). The daily number of medicines that each patient used (DDC) regardless of whether they were prescribed was the dependent variable. Relationships between DDC and other continuous variables were examined using Pearson’s correlation. For between-group comparisons of DDC, Student’s t-tests were performed. Results Univariate tests showed relationships between DDC and various demographic and clinical parameters. The variables that remained significant at the last step of a stepwise linear regression analysis were metabolic syndrome, chronic pain, incontinence, increased serum creatinine level, increased Geriatric Depression Scale scores, reported gastric disturbances, and neutrophil/lymphocyte ratio. Conclusion Along with certain chronic conditions, depressive symptoms and an inflammatory marker (neutrophil/lymphocyte ratio) were significantly and independently related to higher DDC. Longitudinal and larger studies are needed to further explore the multifaceted relationships of PP.
Strategic and Dynamic Temporal Weighting for Perceptual Decisions in Humans and Macaques

Abstract Perceptual decision-making is often modeled as the accumulation of sensory evidence over time. Recent studies using psychophysical reverse correlation have shown that even though the sensory evidence is stationary over time, subjects may exhibit a time-varying weighting strategy, weighting some stimulus epochs more heavily than others. While previous work has explained time-varying weighting as a consequence of static decision mechanisms (e.g., decision bound or leak), here we show that time-varying weighting can reflect strategic adaptation to stimulus statistics, and thus can readily take a number of forms. We characterized the temporal weighting strategies of humans and macaques performing a motion discrimination task in which the amount of information carried by the motion stimulus was manipulated over time. Both species could adapt their temporal weighting strategy to match the time-varying statistics of the sensory stimulus. When early stimulus epochs had higher mean motion strength than late, subjects adopted a pronounced early weighting strategy, where early information was weighted more heavily in guiding perceptual decisions. When the mean motion strength was greater in later stimulus epochs, in contrast, subjects shifted to a marked late weighting strategy. These results demonstrate that perceptual decisions involve a temporally flexible weighting process in both humans and monkeys, and introduce a paradigm with which to manipulate sensory weighting in decision-making tasks.
Improving Multi-Person Pose Estimation using Label Correction

Significant attention is being paid to multi-person pose estimation methods recently, as there has been rapid progress in the field owing to convolutional neural networks. Especially, recent method which exploits part confidence maps and Part Affinity Fields (PAFs) has achieved accurate real-time prediction of multi-person keypoints. However, human annotated labels are sometimes inappropriate for learning models. For example, if there is a limb that extends outside an image, a keypoint for the limb may not have annotations because it exists outside of the image, and thus the labels for the limb can not be generated. If a model is trained with data including such missing labels, the output of the model for the location, even though it is correct, is penalized as a false positive, which is likely to cause negative effects on the performance of the model. In this paper, we point out the existence of some patterns of inappropriate labels, and propose a novel method for correcting such labels with a teacher model trained on such incomplete data. Experiments on the COCO dataset show that training with the corrected labels improves the performance of the model and also speeds up training.
Contrastive explanation: a structural-model approach

Abstract This paper presents a model of contrastive explanation using structural casual models. The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust and understanding of intelligent decision-making. While different sub-fields of artificial intelligence have looked into this problem with a sub-field-specific view, there are few models that aim to capture explanation more generally. One general model is based on structural causal models. It defines an explanation as a fact that, if found to be true, would constitute an actual cause of a specific event. However, research in philosophy and social sciences shows that explanations are contrastive: that is, when people ask for an explanation of an event—the fact—they (sometimes implicitly) are asking for an explanation relative to some contrast case; that is, ‘Why P rather than Q? ’. In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical problems in artificial intelligence: classification and planning. We believe that this model can help researchers in subfields of artificial intelligence to better understand contrastive explanation.