Miracle-dz/UniFAR-FTUs huggingface dataset:
On Exact and ∞-Rényi Common Informations

Recently, two extensions of Wyner’s common information—exact and Rényi common informations—were introduced respectively by Kumar, Li, and El Gamal (KLE), and the present authors. The class of common information problems refers to determining the minimum rate of the common input to two independent processors needed to generate an exact or approximate joint distribution. For the exact common information problem, an exact generation of the target distribution is required, while for Wyner’s and α-Rényi common informations, the relative entropy and Rényi divergence with order α were respectively used to quantify the discrepancy between the synthesized and target distributions. The exact common information is larger than or equal to Wyner’s common information. However, it was hitherto unknown whether the former is strictly larger than the latter. In this paper, we first establish the equivalence between the exact and ∞-Rényi common informations, and then provide single-letter upper and lower bounds for these two quantities. For doubly symmetric binary sources, we show that the upper and lower bounds coincide, which implies that for such sources, the exact and ∞-Rényi common informations are completely characterized. Interestingly, we observe that for such sources, these two common informations are strictly larger than Wyner’s. This answers an open problem posed by KLE.
Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification

Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.
Porcine transmissible gastroenteritis virus nonstructural protein 2 contributes to inflammation via NF-κB activation

ABSTRACT Transmissible gastroenteritis virus (TGEV) infection causes acute enteritis in swine of all ages, and especially in suckling piglets. Small intestinal inflammation is considered a central event in the pathogenesis of TGEV infections, and nuclear factor-kappa B (NF-κB) is a key transcription factor in the inflammatory response. However, it is unclear whether NF-κB is crucial for inducing inflammation during a TGEV infection. Our results show that NF-κB was activated in swine testicular (ST) cells and intestinal epithelial cell lines J2 (IPEC-J2) cells infected with TGEV, which is consistent with the up-regulation of pro-inflammatory cytokines. Treatment of TGEV-infected ST cells and IPEC-J2 cells with the NF-κB-specific inhibitor caused the down-regulation of pro-inflammatory cytokine expression, but did not significantly affect TGEV replication. Individual TGEV protein screening results demonstrated that Nsp2 exhibited a high potential for activating NF-κB and enhancing the expression of pro-inflammatory cytokines. Functional domain analyzes indicated that the first 120 amino acid residues of Nsp2 were essential for NF-κB activation. Taken together, these data suggested that NF-κB activation was a major contributor to TGEV infection-induced inflammation, and that Nsp2 was the key viral protein involved in the regulation of inflammation, with amino acids 1–120 playing a critical role in activating NF-κB. Abbreviations: TCID50: 50% tissue culture infectious dose; DMEM: Dulbecco’s Modified Eagle Medium; eNOS: Endothelial nitric oxide synthase; FBS: fetal bovine serum; IFA: Indirect immunofluorescence; IκB: inhibitor of nuclear factor kappa-B; IL: interleukin; IPEC-J2: intestinal epithelial cell lines J2; IKK: IκB kinase; Luc: luciferase reporter gene; mAbs: monoclonal antibodies; MOI: multiple of infection; Nsp: nonstructural protein; NF-κB: nuclear factor-kappa ; ORFs: open reading frames; PBS: phosphate-buffered saline; p65 p-p65: phosphorylated; RT-PCR: reverse transcription PC; SeV: Sendai virus; ST: swine testicular; TGEV: Transmissible gastroenteritis virus; TNF-α: tumor necrosis factor α; UV-TGEV: Ultraviolet light-inactivated TGEV; ZnF: zinc finger
Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach

In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this research. Using transfer learning approach, we reached F1-score of 98.90\% and 98.01\% on the legal small and large test dataset.
Depression‐like symptoms of withdrawal in a genetic mouse model of binge methamphetamine intake

Binge methamphetamine (MA) users have higher MA consumption, relapse rates and depression‐like symptoms during early periods of withdrawal, compared with non‐binge users. The impact of varying durations of MA abstinence on depression‐like symptoms and on subsequent MA intake was examined in mice genetically prone to binge‐level MA consumption. Binge‐level MA intake was induced using a multiple‐bottle choice procedure in which mice were offered one water drinking tube and three tubes containing increasing concentrations of MA in water, or four water tubes (control group). In two studies, depression‐like symptoms were measured using a tail‐suspension test and a subsequent forced‐swim test, after forced abstinence of 6 and 30 hours from a 28‐day course of chronic MA intake. An additional study measured the same depression‐like symptoms, as well as MA intake, after prolonged abstinence of 1 and 2 weeks. MA high drinking mice and one of their progenitor strains DBA/2J escalated their MA intake with increasing MA concentration; however, MA high drinking mice consumed almost twice as much MA as DBA/2J mice. Depression‐like symptoms were significantly higher early after MA access was withdrawn, compared to levels in drug‐naïve controls, with more robust effects of MA withdrawal observed in MA high drinking than DBA/2J mice. When depression‐like symptoms were examined after 1 or 2 weeks of forced abstinence in MA high drinking mice, depression‐like symptoms dissipated, and subsequent MA intake was high. The MA high drinking genetic mouse model has strong face validity for human binge MA use and behavioral sequelae associated with abstinence.
Regeneration of a Bioengineered Thyroid Using Decellularized Thyroid Matrix.

BACKGROUND Hypothyroidism is a common hormone deficiency condition. Regenerative medicine approaches, such as a bioengineered thyroid, have been proposed as potential therapeutic alternatives for patients with hypothyroidism. This study demonstrates a novel approach to generate thyroid grafts using decellularized rat thyroid matrix. METHODS Isolated rat thyroid glands were perfused with 1% sodium dodecyl sulfate to generate a decellularized thyroid scaffold. The rat thyroid scaffold was then recellularized with rat thyroid cell line to reconstruct the thyroid by perfusion seeding technique. As a pilot study, the decellularized rat thyroid scaffold was perfused with human-derived thyrocytes and parathyroid cells. RESULTS The decellularization process retained the intricate three-dimensional microarchitecture with a perfusable vascular network and native extracellular matrix components, allowing efficient reseeding of the thyroid matrix with the FRTL-5 rat thyroid cell line generating three-dimensional follicular structures in vitro. In addition, the recellularized thyroid showed successful cellular engraftment and thyroid-specific function, including synthesis of thyroglobulin and thyroid peroxidase. Moreover, the decellularized rat thyroid scaffold could further be recellularized with human-derived thyroid cells and parathyroid cells to reconstruct a humanized bioartificial endocrine organ, which maintained expression of critical genes such as thyroglobulin, thyroid peroxidase, and parathyroid hormone. CONCLUSION These findings demonstrate the utility of a decellularized thyroid extracellular matrix scaffold system for the development of functional, bioengineered thyroid tissue, which could potentially be used to treat hypothyroidism.
The influence of racial discrimination on smoking among young black men: A prospective analysis

Abstract Racial discrimination is a documented risk factor for smoking among Black men; mechanisms of effect and protective processes remain to be investigated. This study examined the mediating effect of anger/hostility on the association between racial discrimination and smoking and the influence of protective social ties in buffering the effects of racial discrimination. Hypotheses were tested using longitudinal data from a study of 505 Black men ages 19–22. Data collected for the study occurred at three timepoints, each 12 months apart. Racial discrimination is associated with increases in cigarette smoking between baseline and follow-up. Anger/hostility partially mediated this association. Moderational analyses indicated that discrimination had little or no impact on men’s smoking in the presence of protective social ties. Study findings suggest that supportive networks make crucial contributions to the reduction of cigarette smoking and feelings of anger within this population. Intervention developers should consider targeting relationship dynamics with supportive networks to understand coping mechanisms and to reduce substance use among young black men.
Oleandrin and Its Derivative Odoroside A, Both Cardiac Glycosides, Exhibit Anticancer Effects by Inhibiting Invasion via Suppressing the STAT-3 Signaling Pathway

The cardiac glycosides oleandrin and odoroside A, polyphenolic monomer compounds extracted from Nerium oleander, have been found to have antitumor effects on various tumors at low doses. However, the mechanisms of anticancer effects of oleandrin and odoroside A are not well known. Therefore, in this study, we aimed to investigate the anticancer effects of oleandrin and odoroside A and their associated mechanisms in highly metastatic MDA-MB-231 breast cancer cells and radiotherapy-resistant (RT-R) MDA-MB-231 cells. Our results showed that oleandrin and odoroside A dose-dependently decreased the colony formation and the invasion of both cell lines at nanomolar ranges. Furthermore, oleandrin (50 nM) and odoroside A (100 nM) reduced octamer-binding transcription factor 3/4 (OCT3/4) and β-catenin levels and matrix metalloproteinase-9 (MMP-9) activity. Finally, we found that phospho-STAT-3 levels were increased in MDA-MB-231 and RT-R-MDA-MB-231, but not in endothelial cells (ECs), and that the levels were significantly decreased by oleandrin (50 nM) and odoroside A (100 nM). Inhibition of phospho-signal transducer and activator of transcription (STAT)-3 significantly reduced OCT3/4 and β-catenin levels and MMP-9 activity, ultimately resulting in reduced invasion. These results suggest that the anticancer effects of oleandrin and odoroside A might be due to the inhibition of invasion through of phospho-STAT-3-mediated pathways that are involved in the regulation of invasion-related molecules.
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions

Majority of the current dimensionality reduction or retrieval techniques rely on embedding the learned feature representations onto a computable metric space. Once the learned features are mapped, a distance metric aids the bridging of gaps between similar instances. Since the scaled projection is not exploited in these methods, discriminative embedding onto a hyperspace becomes a challenge. In this paper, we propose to inwardly scale feature representations in proportional to projecting them onto a hypersphere manifold for discriminative analysis. We further propose a novel, yet simpler, convolutional neural network based architecture and extensively evaluate the proposed methodology in the context of classification and retrieval tasks obtaining results comparable to state-of-the-art techniques.
Advancing PICO element detection in biomedical text via deep neural networks

MOTIVATION In evidence-based medicine (EBM), defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical text that belong to the four components typically reported in clinical trials: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). In this work, we propose a novel deep learning model for recognizing PICO elements in biomedical abstracts. Based on the previous state-of-the-art bidirectional long-short term memory (biLSTM) plus conditional random field (CRF) architecture, we add another layer of biLSTM upon the sentence representation vectors so that the contextual information from surrounding sentences can be gathered to help infer the interpretation of the current one. In addition, we propose two methods to further generalize and improve the model: adversarial training and unsupervised pre-training over large corpora. RESULTS We tested our proposed approach over two benchmark datasets. One is the PubMed-PICO dataset, where our best results outperform the previous best by 5.5%, 7.9%, and 5.8% for P, I, and O elements in terms of F1 score, respectively. And for the other dataset named NICTA-PIBOSO, the improvements for P/I/O elements are 3.9%, 15.6%, and 1.3% in F1 score, respectively. Overall, our proposed deep learning model can obtain unprecedented PICO element detection accuracy while avoiding the need for any manual feature selection. AVAILABILITY AND IMPLEMENTATION Code is available at https://github.com/jind11/Deep-PICO-Detection. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
The Influence of Sexual Orientation on Attributions of Blame Toward Victims of Sexual Assault

ABSTRACT Previous research has indicated that the sexual orientation of a sexual assault victim influences perceptions of blame. Although researchers have studied perceptions of blame toward straight and lesbian sexual assault victims, none have yet assessed perceptions of blame toward bisexual victims. The purpose of the current study is to examine perceptions of blame toward a female sexual assault victim and to determine whether the victim’s sexual orientation impacts the level of attributed blame. Despite no previous research examining the impact of bisexuality on victim blaming, misconceptions that bisexual females are highly sexual, promiscuous, and untrustworthy make it likely that this population would be subject to greater levels of blame than either straight or lesbian victims. After random assignment to a vignette description of a sexual assault and completion of a victim-blaming questionnaire, results failed to support our hypotheses; participants did not blame the bisexual victim of sexual assault more than either the straight or lesbian victims. Further contrary to expected findings, males did not hold more blaming attitudes than females across conditions. Nonetheless, this study is important given the finding that attributions of blame influence whether sexual assault victims choose to disclose their assault, the failure of which can lead to negative mental health outcomes. Ultimately, the current study was a first step in understanding whether bisexual assault victims are evaluated differently than their straight and lesbian counterparts.
Valproic Acid Inhibits Proliferation and Reduces Invasiveness in Glioma Stem Cells Through Wnt/β Catenin Signalling Activation

Glioblastoma is the most common malignant brain tumour in adults. The failure of current therapies can be ascribed to glioma stem cells (GSCs), which can rapidly repopulate the tumour following the initial treatment. The study of histone deacetylase inhibitors, such as valproic acid (VPA), is becoming an attractive field in cancer research. However, the exact mechanisms underlying its anti-cancer effect remain to be elucidated due to its pleiotropic effects on several cell-signalling pathways. Ingenuity Pathway Analysis (IPA) bioinformatics analysis was performed on genome-wide data regarding GSCs methylome to identify the signalling pathways mainly affected by methylation changes induced by VPA. Real time PCR and luciferase reporter assay were used to better investigate VPA effects on Wnt/β-catenin signalling pathway. VPA effect on GSC proliferation was evaluated by 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) and Trypan blue assays. Finally, VPA impact on GSC motility was demonstrated by Boyden chamber assay and further confirmed evaluating the expression levels or localisation, through western blot or immunofluorescence, of Twist1, Snail1, E-Cadherin and N-Cadherin. The bioinformatics analyses performed on GSCs methylome highlighted that Wnt/β-catenin signalling was affected by the methylation changes induced by VPA, which could influence its activation status. In particular, we pointed out a general activation of this pathway after VPA exposure, which was accompanied by an inhibitory potential on GSCs proliferation. Finally, we also proved VPA’s ability to inhibit GSCs invasion through Snail1 and Twist1 downregulation and E-Cadherin relocalisation. VPA treatment may represent a new, interesting therapeutic approach to affect GSC proliferation and motility, but further investigations are certainly needed.
Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.
Prospects of Using Poultry by-Products in the Technology of Chopped Semi-Finished Products

In this paper the technology of preparation the protein-fat emulsion and its effect to sensory, physicochemical and microbial characteristics of meat cutlets are presented. The protein-fat emulsion consists of 75% of the chicken crests, 15% of the vegetable oil and 10% water. The protein, fat and ash content of emulsion are 9.53%, 6.38% and 0.95%, respectively. The amino acid composition of the protein-fat emulsion includes a complete set of essential amino acids - 36.2%. Adding of protein-fat emulsion as an ingredient in meat cutlets increase the total protein content, improves the sensory parameters and consistency of meat cutlets.
Switchable control over in vivo CAR T expansion, B cell depletion, and induction of memory

Significance Chimeric antigen receptor (CAR) T cell therapy represents a powerful strategy in immuno-oncology. Nevertheless, associated life-threatening toxicities and chronic B cell aplasia have underscored the need to control engineered T cells in the patient. To address these challenges, we have previously developed a switchable CAR (sCAR) T cell platform that allows dose-titratable control over CAR T cell activity by using antibody-based switches. Here, we demonstrate in a syngeneic murine model that the switchable platform can impart antitumor efficacy while dissociating long-term persistence from chronic B cell aplasia. Further, the functional reversibility of the switchable platform can be leveraged to incorporate “rest” phases through cyclical dosing of the switch to enable the induction of a robust central memory population for in vivo, on-demand expansion of sCAR T cells. Chimeric antigen receptor (CAR) T cells with a long-lived memory phenotype are correlated with durable, complete remissions in patients with leukemia. However, not all CAR T cell products form robust memory populations, and those that do can induce chronic B cell aplasia in patients. To address these challenges, we previously developed a switchable CAR (sCAR) T cell system that allows fully tunable, on/off control over engineered cellular activity. To further evaluate the platform, we generated and assessed different murine sCAR constructs to determine the factors that afford efficacy, persistence, and expansion of sCAR T cells in a competent immune system. We find that sCAR T cells undergo significant in vivo expansion, which is correlated with potent antitumor efficacy. Most importantly, we show that the switch dosing regimen not only allows control over B cell populations through iterative depletion and repopulation, but that the “rest” period between dosing cycles is the key for induction of memory and expansion of sCAR T cells. These findings introduce rest as a paradigm in enhancing memory and improving the efficacy and persistence of engineered T cell products.