Miracle-dz/UniFAR-FTUs huggingface dataset:
Gadd 45 α activity is the principal effector of Shigella mitochondria-dependent epithelial cell death in vitro and ex vivo

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Gadd45α activity is the principal effector of Shigella mitochondria-dependent epithelial cell death in vitro and ex vivo. L. Lembo-Fazio, G. Nigro, G. Noël, G. Rossi, F. Chiara, K. Tsilingiri, M. Rescigno, A. Rasola, M. L. Bernardini
Patterns of Injury Severity and its Likelihood in Two-Vehicle Crashes

Understanding injury severity patterns in roadway crashes is important not only from the view point of treating crash victims, but also for directing the crash avoidance efforts of traffic safety agencies and motor vehicle manufacturers. The factor that is most discussed in this context is vehicle incompatibility. However, there are other vehicle-, occupant-, and roadway-related factors, too, that play roles in injury severity. In order to investigate these factors in relation to the injury severity, this paper considers a two-vehicle crash as a ‘system’ with its elements: vehicles, drivers, and roadway. Some of the possible inputs (contributing factors) to this system are considered with a focus on injury severity of the driver as an outcome. The differences in weights, heights, and shapes, etc. of the crash-involved vehicles, vehicle speed, drivers’ ages and genders are the factors in question. Data mining the crash databases compiled by the National Highway Traffic Safety Administration (NHTSA) makes many important revelations. The association between the subject variables and driver injury severity is studied through contingency analysis. Configuration frequency analysis helps to identify patterns of injury severity. The main objective of the study is achieved by building a logit model that can be used to predict the likelihood of injury severity from a given set of vehicle-, driver-, and roadway-related crash characteristics.
Deep Learning-based framework for Autism functional MRI Image Classification Cover

The purpose of this paper is to introduce the deep learning-based framework LeNet-5 architecture and implement experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.
MFM Neural Architecture MFM Generative Network ( b ) MFM Inference Network Inference Network Generative Network

Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) models must learn the complex intra-modal and cross-modal interactions for prediction and 2) models must be robust to unexpected missing or noisy modalities during testing. In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels. We introduce a model that factorizes representations into two sets of independent factors: multimodal discriminative and modality-specific generative factors. Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction. Modality-specific generative factors are unique for each modality and contain the information required for generating data. Experimental results show that our model is able to learn meaningful multimodal representations that achieve state-of-the-art or competitive performance on six multimodal datasets. Our model demonstrates flexible generative capabilities by conditioning on independent factors and can reconstruct missing modalities without significantly impacting performance. Lastly, we interpret our factorized representations to understand the interactions that influence multimodal learning.
The Causal Closure of What? An Epistemological Critique of the Principle of Causal Closure

The paper analyses the so-called causal-closure thesis: “Pick any physical event (.) and trace its causal ancestry or posterity as far as you would like; the principle of causal closure of the physical domain says that this will never take you outside the physical domain. Thus, no causal chain involving a physical event will ever cross the boundary of the physical into the nonphysical” (J. Kim, Philosophy of Mind [1996], chap. 6, p. 147). I focus on two problems concerning this thesis: the first, the argument from the “open-ended character” of physics, is already well known. The other one, is more neglected, but it seems to cast a relevant shadow of circularity on the use of the causal closure thesis as an empirical principle. I end with some meta-philosophical remarks, arguing that we should not even accept causal physicalism as a philosophical 'stance': paraphrasing Wittgenstein, the limits of our physicalistic language (as well as those of our scientific language in general) do not mean the limits of our world.
Structural and Functional Characterization of IS1358 from Vibrio cholerae

ABSTRACT The new epidemic serovar O139 of Vibrio cholerae has emerged from the pandemic serovar O1 biotype El Tor through the replacement of a 22-kbp DNA region by a 40-kbp O139-specific DNA fragment. This O139-specific DNA fragment contains an insertion sequence that was described previously (U. H. Stroeher, K. E. Jedani, B. K. Dredge, R. Morona, M. H. Brown, L. E. Karageorgos, J. M. Albert, and P. A. Manning, Proc. Natl. Acad. Sci. USA 92:10374–10378, 1995) and designated IS1358O139. We studied the distribution of the IS1358 element in strains from various serovars by Southern analysis. Its presence was detected in strains from serovars O1, O2, O22, O139, and O155 but not in strains from serovars O15, O39, and O141. Furthermore, IS1358 was present in multiple copies in strains from serovars O2, O22, and O155. We cloned and sequenced four copies of IS1358 from V. cholerae O22 and one copy from V. cholerae O155. A comparison of their nucleotide sequences with those of O1 and O139 showed that they were almost identical. We constructed a transposon consisting of a kanamycin resistance gene flanked by two directly oriented copies of IS1358 to study the functionality of this element. Transposition of this element from a nonmobilizable plasmid onto the conjugative plasmid pOX38-Gen was detected in an Escherichia coli recA donor at a frequency of 1.2 × 10−8. Sequence analysis revealed that IS1358 duplicates 10 bp at its insertion site.
CSE 291 : Communication Complexity , Winter 2019 Deterministic protocols

The model of communication complexity was introduce by Yao in the late 1970s [8] as an abstract way to capture communication bottlenecks in algorithms. In this model, there are several players, each with their own input. Their goal is to perform some distributed task based on their inputs, while minimizing communication. Importantly, we assume the players have unbounded computational model, and focus only on the communication. In this first part of the class, we focus on the simplest setting: deterministic protocols between two players, which are typically called Alice and Bob.
Multivariate approach to the analysis of correlated RNA-seq data

High-throughput RNA-seq technology has emerged as a powerful tool for understanding the molecular basis of phenotype variation in biology, including disease. Recently, some correlated RNA-seq datasets started to be generated. While there have been several approaches proposed for identifying the differentially expressed genes (DEGs), not many methods can analyze correlated RNA-seq data. We expect the simultaneous analysis of correlated RNA-seq data to increase of power of detecting DEGs. We propose a multivariate method to find DEGs on correlated RNA-seq data based on the Generalized Estimating Equations (GEE) approach. The advantage of the proposed method is to consider correlated RNA-seq data simultaneously while accounting for correlations. Through real data analysis and simulation studies, we show that our multivariate approach has higher power of detecting DEGs than the existing methods.
Bank Risk During the Financial Crisis: Do Business Models Matter?

We exploit the 2007-2009 financial crisis to analyze how risk relates to bank business models. Institutions with higher risk exposure had less capital, larger size, greater reliance on short-term market funding, and aggressive credit growth. Business models related to significantly reduced bank risk were characterized by a strong deposit base and greater income diversification. The effect of business models is non-linear: it has a different impact on riskier banks. Finally, it is difficult to establish in real time whether greater stock market capitalization involves real value creation or the accumulation of latent risk.
Evaluation of Theophylline-Stimulated Changes in Carnitine Palmitoyltransferase Activity in Skeletal Muscle and Liver of Rats

The effect of theophylline treatments on the activity of carnitine palmitoyltransferase (CPT) in skeletal muscle and the liver of rats was investigated. Theophylline was administered at 100 mg/kg bw/day and effects were monitored after a treatment period that lasted between a week and five weeks. Results showed that a significant increase in the activity of CPT was observed in skeletal muscle of theophyl-line-treated groups as compared to either control or placebo groups. However, there was no significant change in the activity of CPT in the hepatic tissues of theophylline-treated groups. The observed discrepancies in activity of CPT might be due to the presence of two isoenzymes, the muscle type (M-CPT) and liver type (L-CPT); it is possible that theophylline affects only M-CPT activity.
Transfer learning based on graph ranking

A fundamental assumption in machine learning is that the data distributions of the training and the test sets should be identical. When the assumption does not hold, the traditional machine learning algorithms might perform worse. In this paper, we tackle this transfer learning problem by implementing a general graph ranking framework for a sentiment classification task. We construct a fusion graph model by using the in-domain and the out-of-domain data. The in-domain data can help us to get pseudo labels of the out-of-domain data. The out-of-domain data can help us to update the labels and can get the convincing prediction labels. Experimental results show the significant improvements in accuracy and demonstrate the effectiveness of this algorithm.
Influenza A Virus as a Predisposing Factor for Cryptococcosis

Influenza A virus (IAV) infects millions of people annually and predisposes to secondary bacterial infections. Inhalation of fungi within the Cryptococcus complex causes pulmonary disease with secondary meningo-encephalitis. Underlying pulmonary disease is a strong risk factor for development of C. gattii cryptococcosis though the effect of concurrent infection with IAV has not been studied. We developed an in vivo model of Influenza A H1N1 and C. gattii co-infection. Co-infection resulted in a major increase in morbidity and mortality, with severe lung damage and a high brain fungal burden when mice were infected in the acute phase of influenza multiplication. Furthermore, IAV alters the host response to C. gattii, leading to recruitment of significantly more neutrophils and macrophages into the lungs. Moreover, IAV induced the production of type 1 interferons (IFN-α4/β) and the levels of IFN-γ were significantly reduced, which can be associated with impairment of the immune response to Cryptococcus during co-infection. Phagocytosis, killing of cryptococci and production of reactive oxygen species (ROS) by IAV-infected macrophages were reduced, independent of previous IFN-γ stimulation, leading to increased proliferation of the fungus within macrophages. In conclusion, IAV infection is a predisposing factor for severe disease and adverse outcomes in mice co-infected with C. gattii.
Black Box Testing Technology Based on Virtual Reality

This paper is focused on the concept of virtual testing technology based on VR, putting its focus on the systematic study of virtual testing technology based on VR from a macro perspective, so as to establish the basic concept and theoretical model as well as implementation methods, the specific content can be divided into three parts: theoretical basis, technology of realization, examples of application. Thus the application of virtual reality technology to black box testing is studied systematically in this paper.
The 33-item Hypomania Checklist (HCL-33) - a study of the consistency between self - and external assessments in Polish bipolar patients.

OBJECTIVES The Hypomania Checklist (HCL) has become an important tool for the assessment of hypomanic symptoms in patients with mood disorders and in the general population. The HCL-33 scale, containing 33 symptom items, is a new instrument which, in addition to the self-administered questionnaire, has a version for external rating. The aim of this study is to evaluate the consistency between the self - and external assessments using the HCL-33 in Polish patients with bipolar disorder. METHODS The data from 81 euthymic bipolar patients recruited in Poznan and Krakow centers were analyzed. All the patients filled out the HCL-33 questionnaire, and, for each patient, the HCL-33 questionnaire-external assessment was completed by his/her significant other. RESULTS Of the 33 symptom items, sufficient agreement (significance of kappa factor < 0.05) was found for 13 out of the 19 questions of the "active/elated" (factor 1) and for all 14 items of the "irritable/risk-taking" (factor 2). Insignificant consistency was found for 6 items of factor 1 and the question regarding the longest period of hypomania. The inter-rater agreement between patient and significant other was not affected by gender, living together or subtype of relationship with the patient. CONCLUSIONS The results show significant consistency between self - and external assessments for 27 symptom items (82%) of the HCL-33. The future status of the items showing insufficient consistency should be discussed. Limitation of the study is a small number of subjects recruited from only two centers which may not be representative for the Polish population.
The efficacy of bupropion in winter depression: results of an open trial.

BACKGROUND Seasonal affective disorder (SAD) refers to regularly recurring episodes of affective illness bearing a fixed relationship to season. Wintertime depression is its most widely recognized form. This study was undertaken to assess the efficacy of bupropion as a treatment for this disorder. METHOD Fifteen consecutively presenting patients were treated with bupropion (200 to 400 mg/day). All met DSM-III-R criteria for major depression with a seasonal pattern. All were moderately to severely depressed. A modified version of the Hamilton Rating Scale for Depression (mHAM-D) including ratings of hypersomnia, increased appetite and carbohydrate craving, and weight gain was used to quantify the severity of illness. Up to 5 weeks of treatment was allowed before the subjects were categorized as nonresponders, partial responders, or responders. RESULTS The mean +/- SD mHAM-D scores before and after treatment were 25.5 +/- 6.4 and 4.1 +/- 3.1, respectively. Ten (66.7%) of the subjects had a complete response to treatment (mHAM-D score less than or equal to 5). The other 5 (33.3%) had a partial response (mHAM-D score = 6-10). Five of the subjects had chronic pain and 3 had panic attacks restricted to episodes of depression. These problems resolved simultaneously with the symptoms of depression. CONCLUSION The results of this open trial suggest that bupropion is an effective treatment for winter depression. However, controlled studies are required to confidently determine whether this is the case.
Estimation of Surface Spectral Reflectance on 3 D Painted Objects Using a Gonio-Spectral Imaging System

This paper reports estimation of surface spectral reflectance on three dimensional (3D) painted objects using a gonio-spectral imaging system developed by the authors, which consists of two robot arms, a monochrome digital camera, a lighting system with eight spectral channels, and a personal computer. The most remarkable feature of this system is that it can measure surface spectral reflectances of objects with colored texture individually at each surface points. This feature makes the system suitable to reflectance measurement of painted objects with complex color texture. The system acquires gonio-spectral images of 3D painted objects for different combination of incident and viewing angles of light and for each spectral channel of the light source. The intensities of the captured images are sampled at each surface point of the painted object. From these intensities, parameters of spectral reflectance models are estimated at each individual surface points. The obtained reflectance models are used for rendering the painted objects for arbitrarily chosen illumination and viewing conditions. Experiments are carried out for real painted objects. The results confirm that surface spectral reflectance is successfully modeled by the proposed method.
APPLICATION OF RUSSIAN NAMED ENTITY RECOGNITION AND COREFERENCE RESOLUTION IN THE OIL INDUSTRY

This paper describes the application of named entity recognition and coreference resolution algorithms in the oil industry. Oil industry researchers and businesses generate large amounts of content every day. Managing them correctly is very important to get the most use of each article and document. Named entity recognition algorithms can automatically scan entire articles and reveal the most significant people, organizations, and places discussed in them, while coreference resolution combines each entity mention into clusters of mentions. Each cluster represents one entity across one document. These methods allow to simplify the analysis of large numbers of documents and articles for researchers, managers, engineers, etc.
Alcohol disorders and re-employment in a 5-year follow-up of long-term unemployed.

AIMS To establish whether the high prevalence of alcohol abuse among unemployed people is explained by alcohol abuse causing unemployment, or vice versa. DESIGN A 5-year postal follow-up survey of a community sample of unemployed from Grenland, southern Norway. PARTICIPANTS Two hundred and twenty-eight unemployed people, registered for more than 12 weeks, aged 16 to 63 years. Response rate 74%. MEASUREMENTS The Alcohol Use Disorder Identification Test (AUDIT) and DSM-III diagnoses of alcohol disorders in medical examinations. FINDINGS At the 5-year follow up, 23% of those still unemployed and 12% of those re-employed scored higher than the AUDIT cut-point of 10. Re-employment reduced the chance of scoring positive on the AUDIT to 34% of the chance for those still unemployed. Significant selection to long-term unemployment according to AUDIT score was not demonstrated. None of the 7% who had a DSM-III diagnosis of an alcohol disorder had a job 5 years later, however, suggesting that alcohol-related selection to unemployment does occur. CONCLUSION The high prevalence of harmful drinking among Norwegian unemployed is explained mainly by unemployment causing alcohol abuse rather than vice versa. Reducing unemployment should contribute to reduced alcohol problems in Norway.
Incident tuberculosis and risk factors among HIV-infected children in Dar es Salaam , Tanzania

Corresponding author: Christopher Duggan, M.D., M.P.H, Christopher.Duggan@childrens.harvard. edu, Address: 300 Longwood Ave, Boston, MA 02115. Christopher Duggan is the author to whom requests for reprints should be made. Role of authors Nan LI: data analysis, writing, literature search. Karim P. MANJI: literature search, editing. Donna SPIEGELMAN: data analysis, editing. Aisa MUYA: literature search, editing Ramdhani S MWIRU: editing. Enju LIU: data analysis, editing. Guerino CHALAMILLA: data collection, editing. Wafaie W FAWZI: data analysis, editing. Christopher DUGGAN: data analysis, literature search, editing HHS Public Access Author manuscript AIDS. Author manuscript; available in PMC 2016 February 05. Published in final edited form as: AIDS. 2013 May 15; 27(8): 1273–1281. doi:10.1097/QAD.0b013e32835ecb24. A uhor M anscript
Self-Preservation Mechanisms for Cognitive Software Agents

Humans and other animals have a sense of selfpreservation that motivates them to take appropriate actions to preserve themselves. Intelligent software systems must have built-in or learnt functionality to detect emergencies in their domains, and must be able to act appropriately to save their state and data, and shutdown cleanly if necessary. They must also be able to restart and restore themselves to their most recent state once their computing environment comes back online. We have designed such mechanisms for an Intelligent Distribution Agent (IDA) built for the U.S. Navy. IDA implements Baars’ Global Workspace Theory of consciousness. As a result, she can react to novel and problematic situations in a more flexible, more human-like way than traditional AI systems. We hypothesize that software agents like IDA must have self-preservation mechanisms to adapt and survive in their domains. This paper presents a design for self-preservation mechanisms consistent with IDA’s cognitive cycle which can be implemented in intelligent software systems.
Britt Dijkstra The effects of weather circumstances on the onset of influenza-like illness using the Grote Griepmeting data

As the gathering of data becomes easier, by for instance using computers and the internet, datasets keep becoming larger. This makes it more difficult to find an appropriate way to analyze the data. In this thesis I have analyzed the data of de Grote Griepmeting (influenzanet), which is a dataset that contains over 300,000 measurements. The goal of this thesis is to find out if weather circumstances have an effect on the incidence of influenza. To do this the data of de Grote Griepmeting are combined with weather variables gathered from the KNMI. The data of the Grote Griepmeting contains repeated measurements, multicollinearity, the covariates are likely to be nonlinear to the response variable and because influenza is a contagious disease most likely there will be dependence between the subjects. These issues all need to be accounted for in the analysis. In this thesis several possibilities to analyze the data are considered; the Cox proportional hazards model, a logistic regression model, generalized estimating equations and the generalized linear mixed model. Finally, it was decided to use a logistic regression, with a lasso penalty to account for multicollinearity and Bsplines for nonlinearity. For using the B-splines a lot of extra variables need to be created, so the data expand even more. Computations become bothersome, and a trick from the medical field that is used in case-control studies is introduced to reduce computational time.