Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidants in dermatology*

The skin cells continuously produce, through cellular respiration, metabolic processes or under external aggressions, highly reactive molecules oxidation products, generally called free radicals. These molecules are immediately neutralized by enzymatic and non-enzymatic systems in a physiological and dynamic balance. In situations where this balance is broken, various cellular structures, such as the cell membrane, nuclear or mitochondrial DNA may suffer structural modifications, triggering or worsening skin diseases. several substances with alleged antioxidant effects has been offered for topical or oral use, but little is known about their safety, possible associations and especially their mechanism of action. The management of topical and oral antioxidants can help dermatologist to intervene in the oxidative processes safely and effectively, since they know the mechanisms, limitations and potential risks of using these molecules as well as the potential benefits of available associations.
Topic segmentation with an aspect hidden Markov model

We present a novel probabilistic method for topic segmentation on unstructured text. One previous approach to this problem utilizes the hidden Markov model (HMM) method for probabilistically modeling sequence data [7]. The HMM treats a document as mutually independent sets of words generated by a latent topic variable in a time series. We extend this idea by embedding Hofmann's aspect model for text [5] into the segmenting HMM to form an aspect HMM (AHMM). In doing so, we provide an intuitive topical dependency between words and a cohesive segmentation model. We apply this method to segment unbroken streams of New York Times articles as well as noisy transcripts of radio programs on SpeechBot, an online audio archive indexed by an automatic speech recognition engine. We provide experimental comparisons which show that the AHMM outperforms the HMM for this task.
GPS-Free node localization in mobile wireless sensor networks

An important problem in mobile ad-hoc wireless sensor networks is the localization of individual nodes, i.e., each node's awareness of its position relative to the network. In this paper, we introduce a variant of this problem (directional localization) where each node must be aware of both its position and orientation relative to the network. This variant is especially relevant for the applications in which mobile nodes in a sensor network are required to move in a collaborative manner. Using global positioning systems for localization in large scale sensor networks is not cost effective and may be impractical in enclosed spaces. On the other hand, a set of pre-existing anchors with globally known positions may not always be available. To address these issues, in this work we propose an algorithm for directional node localization based on relative motion of neighboring nodes in an ad-hoc sensor network without an infrastructure of global positioning systems (GPS), anchor points, or even mobile seeds with known locations. Through simulation studies, we demonstrate that our algorithm scales well for large numbers of nodes and provides convergent localization over time, even with errors introduced by motion actuators and distance measurements. Furthermore, based on our localization algorithm, we introduce mechanisms to preserve network formation during directed mobility in mobile sensor networks. Our simulations confirm that, in a number of realistic scenarios, our algorithm provides for a mobile sensor network that is stable over time irrespective of speed, while using only constant storage per neighbor.
False Information on Web and Social Media: A Survey

False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient detection algorithms and tools for early detection. A recent surge of research in this area has aimed to address the key issues using methods based on feature engineering, graph mining, and information modeling. Majority of the research has primarily focused on two broad categories of false information: opinion-based (e.g., fake reviews), and fact-based (e.g., false news and hoaxes). Therefore, in this work, we present a comprehensive survey spanning diverse aspects of false information, namely (i) the actors involved in spreading false information, (ii) rationale behind successfully deceiving readers, (iii) quantifying the impact of false information, (iv) measuring its characteristics across different dimensions, and finally, (iv) algorithms developed to detect false information. In doing so, we create a unified framework to describe these recent methods and highlight a number of important directions for future research.
A physical synthesis flow for early technology evaluation of silicon nanowire based reconfigurable FETs

Silicon Nanowire (SiNW) based reconfigurable field-effect transistors (RFETs) provide an additional gate terminal called the program gate which gives the freedom of programming p-type or n-type functionality for the same device at runtime. This enables the circuit designers to pack more functionality per computational unit. This saves processing costs as only one device type is required, and no doping and associated lithography steps are needed for this technology. In this paper, we present a complete design flow including both logic and physical synthesis for circuits based on SiNW RFETs. We propose layouts of logic gates, Liberty and LEF (Library Exchange Format) files to enable further research in the domain of these novel, functionally enhanced transistors. We show that in the first of its kind comparison, for these fully symmetrical reconfigurable transistors, the area after placement and routing for SiNW based circuits is 17% more than that of CMOS for MCNC benchmarks. Further, we discuss areas of improvement for obtaining better area results from the SiNW based RFETs from a fabrication and technology point of view. The future use of self-aligned techniques to structure two independent gates within a smaller pitch holds the promise of substantial area reduction.
A 1H NMR-Based Metabonomic Investigation of Time-Related Metabolic Trajectories of the Plasma, Urine and Liver Extracts of Hyperlipidemic Hamsters

The hamster has been previously found to be a suitable model to study the changes associated with diet-induced hyperlipidemia in humans. Traditionally, studies of hyperlipidemia utilize serum- or plasma-based biochemical assays and histopathological evaluation. However, unbiased metabonomic technologies have the potential to identify novel biomarkers of disease. Thus, to obtain a better understanding of the progression of hyperlipidemia and discover potential biomarkers, we have used a proton nuclear magnetic resonance spectroscopy (1H-NMR)-based metabonomics approach to study the metabolic changes occurring in the plasma, urine and liver extracts of hamsters fed a high-fat/high-cholesterol diet. Samples were collected at different time points during the progression of hyperlipidemia, and individual proton NMR spectra were visually and statistically assessed using two multivariate analyses (MVA): principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Using the commercial software package Chenomx NMR suite, 40 endogenous metabolites in the plasma, 80 in the urine and 60 in the water-soluble fraction of liver extracts were quantified. NMR analysis of all samples showed a time-dependent transition from a physiological to a pathophysiological state during the progression of hyperlipidemia. Analysis of the identified biomarkers of hyperlipidemia suggests that significant perturbations of lipid and amino acid metabolism, as well as inflammation, oxidative stress and changes in gut microbiota metabolites, occurred following cholesterol overloading. The results of this study substantially broaden the metabonomic coverage of hyperlipidemia, enhance our understanding of the mechanism of hyperlipidemia and demonstrate the effectiveness of the NMR-based metabonomics approach to study a complex disease.
Antiviral Therapy in Steroid-refractory Ulcerative Colitis with Cytomegalovirus: Systematic Review and Meta-analysis

Background:The role of antiviral therapy in patients with ulcerative colitis (UC) with cytomegalovirus (CMV) remains unclear. We therefore performed a systematic review and meta-analysis to assess the association between antiviral therapy and the risk of colectomy. Methods:Multiple electronic databases were searched systematically through July 2014 for studies reporting the risk of colectomy in patients with UC with CMV stratified by treatment with antiviral agents. Colectomy rates were assessed for the overall cohort and stratified by corticosteroid (CS) refractoriness. We estimated summary odds ratios and 95% confidence intervals, using random-effects model, and used Grading of Recommendations Assessment, Development, and Evaluation criteria to appraise the quality of evidence. Results:Fifteen observational studies (333 patients with UC with CMV, 43.2% treated with antiviral agents) were identified, of which 8 stratified patients according to CS-refractory disease (55.4% treated with antiviral agents). Antiviral therapy resulted in a significantly lower risk of colectomy in patients with CS-refractory disease (odds ratio, 0.20; 95% confidence interval, 0.08–0.49; I2 = 0%) but not in the overall population of patients with UC (odds ratio, 0.92; 95% confidence interval, 0.31–2.76; I2 = 65). The quality evidence was low. The results were stable when restricting the analysis to patients with a tissue diagnosis of CMV and studies that defined CS-refractory disease as a failure to respond to intravenous CS. Conclusions:Antiviral therapy may benefit a subgroup of patients with UC who are refractory to CS. Further prospective trials are required to confirm these findings.
Detecting Personality Unobtrusively from Users' Online and Offline Workplace Behaviors

Personality affects various social behaviors of an individual, such as collaboration, group dynamics, and social relationships within the workplace. However, existing methods for assessing personality have shortcomings: self-assessed methods are cumbersome due to repeated assessment and erroneous due to a self-report bias. On the other hand, automatic, data-driven personality detection raises privacy concerns due to a need for excessive personal data. We present an unobtrusive method for detecting personality within the workplace that combines a user's online and offline behaviors. We report insights from analyzing data collected from four different workplaces with 37 participants, which shows that complementing online and offline data allows a more complete reflection of an individual's personality. We also present possible applications of unobtrusive personality detection in the workplace.
Sepsis‐induced human lymphocyte apoptosis and cytokine production in “humanized” mice

Sepsis is the leading cause of death in critically ill patients in the United States with over 210,000 deaths annually. One stumbling block to an effective therapy of sepsis has been the lack of a clinically relevant animal model. There are important distinctions in the mouse versus human immune system regarding the host response to invading pathogens. These differences may explain the disappointing results in many sepsis clinical trials despite the clear efficacy of these agents in mouse models of sepsis. The purpose of the present study was to develop a “humanized” mouse model of sepsis and to determine if the model recapitulated the major findings of lymphocyte apoptosis and cytokine response that exist in patients with sepsis. Two‐day‐old NOD‐scid IL2rγnull mice received an adoptive transfer of hCD34+ hematopoietic cord blood stem cells. These mice acquired a functional human innate and adaptive immune system, as evidenced by the development of all lineages of human immune cells as well as by mounting a DTH response. Eight weeks post‐transfer, mice were made septic using the highly clinical relevant CLP model of sepsis, and sepsis induced marked elevations in human pro‐ and anti‐inflammatory cytokines as well as a dramatic increase in human T and B cell apoptosis. Collectively, these results show that the humanized mouse model recapitulates many of the classic findings in patients with sepsis. Therefore, it represents an advanced, clinically relevant model for mechanistic studies of sepsis and testing of novel therapies.
Fronto-striatal network activation leads to less fatigue in multiple sclerosis

Background: The fronto-striatal network has been implicated in both fatigue, a common multiple sclerosis (MS) symptom, and goal attainment, which has been shown to reduce fatigue in healthy individuals. Objectives: To investigate whether stimulation of the fronto-striatal network through goal attainment (potential monetary gain) leads to fatigue reduction in MS and healthy control (HC) participants. Methods: In all, 14 healthy and 19 MS participants performed a gambling task during functional magnetic resonance imaging (fMRI). Participants were presented with an opportunity to receive monetary reward during the outcome condition of the task but not during the no outcome condition. Self-reported fatigue measures were obtained after each condition and outside of the scanner. Structural alterations were also examined. Results: A significant decrease in fatigue was observed after the outcome condition compared to the no outcome condition in both groups. Significantly greater activation was observed in the ventral striatum in association with the outcome condition compared to the no outcome condition in both groups. Ventromedial prefrontal cortex showed significantly greater activation during the no outcome condition compared to the outcome condition with greater difference between conditions in the HC group. Conclusion: This is the first functional neuroimaging study showing that stimulation of the fronto-striatal network through goal attainment leads to decreased on-task fatigue in MS and healthy participants.
Cell Cycle Arrest by Transforming Growth Factor β1 near G1/S Is Mediated by Acute Abrogation of Prereplication Complex Activation Involving an Rb-MCM Interaction

ABSTRACT Understanding inhibitory mechanisms of transforming growth factor β1 (TGF-β1) has provided insight into cell cycle regulation and how TGF-β1 sensitivity is lost during tumorigenesis. We show here that TGF-β1 utilizes a previously unknown mechanism targeting the function of prereplication complexes (pre-RCs) to acutely block S-phase entry when added to cells in late G1, after most G1 events have occurred. TGF-β1 treatment in early G1 suppresses Myc and CycE-Cdk2 and blocks pre-RC assembly. However, TGF-β1 treatment in late G1 acutely blocks S-phase entry by inhibiting activation of fully assembled pre-RCs, with arrest occurring prior to the helicase unwinding step at G1/S. This acute block by TGF-β1 requires the function of Rb in late G1 but does not involve Myc/CycE-Cdk2 suppression or transcriptional control. Instead, Rb mediates TGF-β1 late-G1 arrest by targeting the MCM helicase. Rb binds the MCM complex during late G1 via a direct interaction with Mcm7, and TGF-β1 blocks their dissociation at G1/S. Loss of Rb or overexpression of Mcm7 or its Rb-binding domain alone abrogates late-G1 arrest by TGF-β1. These results demonstrate that TGF-β1 acutely blocks entry into S phase by inhibiting pre-RC activation and suggest a novel role for Rb in mediating this effect of TGF-β1 through direct interaction with and control of the MCM helicase.
Review Paper: Considerations for Determining if a Natural Product Is an Effective Wound-Healing Agent

Many research groups are examining natural products for their vulnerary activity. On the basis of the countless years of folklore and clinical observations, these preparations are being fractionated and applied to wounds in a variety of animal species. Despite this work, the number of agents that have documented ability to enhance wound healing is minimal. This article provides a brief review of some of the major historical milestones in wound care and provides a framework for studying plant products so as to achieve statistically significant results in biologically important models of healing. Only with the use of reference standards, data from appropriate animal models, and human trials can we begin to mine the great potential of these natural products.
FFT-based test of a yield monitor circuit

In this paper, we present a simple way to improve an existing electrical defect identification and location method. We show that replacing the DC voltage analysis by an FFT voltage analysis allows to reduce by a factor of about two the number of test vectors required to locate and identify defects on a yield monitor chip. Moreover, we suggest a minor modification to the monitor, doubling the reduction factor. Simulations results show that the location and identification potential is preserved.
Nostophycin Biosynthesis Is Directed by a Hybrid Polyketide Synthase-Nonribosomal Peptide Synthetase in the Toxic Cyanobacterium Nostoc sp. Strain 152

ABSTRACT Cyanobacteria are a rich source of natural products with interesting pharmaceutical properties. Here, we report the identification, sequencing, annotation, and biochemical analysis of the nostophycin (npn) biosynthetic gene cluster. The npn gene cluster spans 45.1 kb and consists of three open reading frames encoding a polyketide synthase, a mixed polyketide nonribosomal peptide synthetase, and a nonribosomal peptide synthetase. The genetic architecture and catalytic domain organization of the proteins are colinear in arrangement, with the putative order of the biosynthetic assembly of the cyclic heptapeptide. NpnB contains an embedded monooxygenase domain linking nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) catalytic domains and predicted here to hydroxylate the nostophycin during assembly. Expression of the adenylation domains and subsequent substrate specificity assays support the involvement of this cluster in nostophycin biosynthesis. Biochemical analyses suggest that the loading substrate of NpnA is likely to be a phenylpropanoic acid necessitating deletion of a carbon atom to explain the biosynthesis of nostophycin. Biosyntheses of nostophycin and microcystin resemble each other, but the phylogenetic analyses suggest that they are distantly related to one another.
Carer and clinician perceptions of the use of emergency medical services by people with dementia: a qualitative study

Abstract A growing number of older people are accessing emergency medical services (EMS), and many calls to EMS are made by, or on behalf of, people with dementia. Their needs are frequently complex; however, EMS staff are often given minimal guidance on ensuring patient safety, accurate diagnosis, and timely transfer to the most appropriate care. This study aimed to qualitatively explore the EMS experiences of carers for people with dementia and assess the views of EMS staff on the management of dementia, using focus groups and interviews. Themes were focussed on the circumstances surrounding EMS calls to people with dementia. These can prove frustrating due to a lack of information sharing, limited alternatives to hospital attendance and the amount of time that it can take to meet the complex needs of a person with dementia.
Regression-Based Summarization of Email Conversations

In this paper we present a regression-based machine learning approach to email thread summarization. The regression model is able to take advantage of multiple gold-standard annotations for training purposes, in contrast to most work with binary classifiers. We also investigate the usefulness of novel features such as speech acts. This paper also introduces a newly created and publicly available email corpus for summarization research. We show that regression-based classifiers perform better than binary classifiers because they preserve more information about annotator judgements. In our comparison between different regression-based classifiers, we found that Bagging and Gaussian Processes have the highest weighted recall.
Multiword expressions: linguistic precision and reusability

This paper discusses the approach to multiword expressions being adopted in the LinGO English Resource Grammar (http://lingo.stanford.edu), a broad-scale bidirectional grammar of English in the HPSG framework. We discuss how the lexicon of multiword expressions is encoded in a database and describe the implications for building a reusable lexical resource.
Predicting Glaucoma Severity Levels in Human Eyes: Integrating CNN and Random Forest for accurate classification

This study prompts intervention and effective treatment, it is crucial to accurately categorize the various levels of glaucoma severity in human eyes. To accurately classify glaucoma sickness, this research introduces a novel methodology that combines Convolutional Neural Networks, also known as CNNs, and Random Forests synergistically. Our model displays amazing accuracy in differentiating between intensity classes by taking advantage of CNNs' skill at extracting complex information from medical pictures and Random Forests' talent for ensemble learning. The method includes careful data augmentation, preprocessing, and aggregation, which strengthens the model's durability. The effectiveness of the model over a range of glaucoma levels of severity may be understood by a thorough performance evaluation that takes variables like accuracy, recollection, and F1 score into account. The results highlight its potential as a tool for diagnosis for healthcare practitioners, enabling early detection. This study provides a transformational approach to improving glaucoma diagnosis and demonstrates the possibility of multidisciplinary collaboration between machine learning and medical field expertise. The results highlight the wider significance of technology-driven medical improvements in tackling complicated medical issues, with potential applications to other eye illnesses and beyond.
Bidirectional Decoding for Statistical Machine Translation

This paper describes the right-to-left decoding method, which translates an input string by generating in right-to-left direction. In addition, presented is the bidirectional decoding method, that can take both of the advantages of left-to-right and right-to-left decoding method by generating output in both ways and by merging hypothesized partial outputs of two directions. The experimental results on Japanese and English translation showed that the right-to-left was better for Englith-to-Japanese translation, while the left-to-right was suitable for Japanese-to-English translation. It was also observed that the bidirectional method was better for English-to-Japanese translation.