Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Approach for Prediction of Essential Protein from PPI Network

Essential proteins play a vital role in survival as well as the reproduction of an organism. It can be identified by the proteinprotein interaction (PPI) network. By the characteristics of the interaction network, we can differentiate essential protein from non-essential ones. It is necessary to understand the PPI for getting knowledge of protein functions and the information they carry. With the knowledge of essential proteins, it is possible to identify disease, lethal proteins, and design drugs like antibiotics. As experimental methods are time-consuming, error-prone and laborious, the computational approach becomes popular to predict essential proteins. Many kinds of researches have been done to identify essential proteins. Among them, machine learning techniques are found more promising. In this paper, we have proposed a method to identify essential protein using eight features calculated from the PPI network. We prepare dataset and classify it with three classifiers named XGBoost (eXtreme Gradient Boosting) Tree, C 5.0 and an ensemble method that includes two classifiers (XGBoost Tree and C 5.0). After evaluating results, we found that C 5.0 outperforms the results of XGBoost Tree and ensemble method. We also compare the results with other existing methods and get a very good improvement over the others.
A Boolean network inference from time-series gene expression data using a statistical method

Accurate gene regulatory network inference from time-series data is a challenging problem in computational biology. Recently, various methods have been developed to infer a gene regulatory network from time series data. Most of which are not scalable, because they limited the regulatory genes by 1 to 3. To resolve the problem, we propose an efficient statistical method called chi-square test for inferring gene regulatory network. A significant threshold p-value is considered to test the independence between target gene and regulatory gene in the chi-square test. Moreover, the p-value is corrected by Bonferroni Step-down (Holm) to reduce the false positives rate. A Boolean network is employed to model gene regulatory network because it is simple to implement, and it can capture network dynamics. To evaluate the performance of our method, we conducted an extensive simulation based on artificial datasets and compare the performance of our propose method with Relevance network and Pearson correlation methods, the experimental result shows that the proposed method significantly outperforms them with respect to structural accuracies. We further tested our method in two biological networks: SOS response of E.coli regulatory network and C.elegans cell cycle network. The simulation results demonstrated that our proposed method achieves better structural performance than relevance network and Pearson correlation methods also in the real dataset.
Influence of adiposity, insulin resistance and intrahepatic triglyceride content on insulin kinetics.

BACKGROUND Insulin is a key regulator of metabolic function. The effects of excess adiposity, insulin resistance and hepatic steatosis on the complex integration of insulin secretion and hepatic and extrahepatic tissue extraction are not clear. METHODS A hyperinsulinemic-euglycemic clamp and a 3-hour oral glucose tolerance test were used to evaluate insulin sensitivity and insulin kinetics after glucose ingestion in three groups: i) lean with normal intrahepatic triglyceride (IHTG) and glucose tolerance (Lean-NL; n=14); ii) obese with normal IHTG and glucose tolerance (Obese-NL; n=24); and iii) obese with hepatic steatosis and prediabetes (Obese-NAFLD; n=22). RESULTS Insulin sensitivity progressively decreased and insulin secretion progressively increased from Lean-NL to Obese-NL to Obese-NAFLD. Fractional hepatic insulin extraction progressively decreased from Lean-NL to Obese-NL to Obese-NAFLD, whereas total hepatic insulin extraction (molar amount removed) was greater in Obese-NL and Obese-NAFLD than Lean-NL. Insulin appearance in the systemic circulation and extrahepatic insulin extraction progressively increased from Lean-NL to Obese-NL to Obese-NAFLD. Total hepatic insulin extraction plateaued at high rates of insulin delivery, whereas the relationship between systemic insulin appearance and total extrahepatic extraction was linear. CONCLUSION Hyperinsulinemia after glucose ingestion in Obese-NL and Obese-NAFLD is due to an increase in insulin secretion, without a decrease in total hepatic or extrahepatic insulin extraction. However, the liver's maximum capacity to remove insulin is limited because of a saturable extraction process. The increase in insulin delivery to the liver and extrahepatic tissues in Obese-NAFLD is unable to compensate for the increase in insulin resistance, resulting in impaired glucose homeostasis.
Acupuncture as adjuvant therapy for diabetic foot

Abstract Background: Diabetic foot (DF) problems are common throughout the world, about one-fourth of them develop a foot ulcer and serious cases would suffer from amputation, which seriously affects the patient's work and life. Previous studies indicated that acupuncture as adjuvant therapy would be effective in treating DF. However, these studies have no consistent results. Therefore, the aim of our study was to explore the efficacy and safety of acupuncture as adjuvant therapy for DF. Methods: The randomized controlled trials associated with acupuncture therapy (or as adjuvant therapy) for DF will be included. We will search 6 electronic databases relevant to health sciences, including PubMed, Embase, the Cochrane Library, the Chinese databases Sino-Med, CNKI, and WANFANG database. All searches were from databases inception to March 30, 2019. The primary outcomes are the total curative effective rate, and the hemodynamic parameter and adverse events will be deemed as secondary outcomes. The Stata15.1 software and Review Manager (RevMan 5.3; Cochrane Collaboration, Copenhagen, Denmark) will be used for analysis, to assess the bias risk, subgroup analysis, and data synthesis. Results: In this systematic review and meta-analysis, we will synthesize the studies to assess the safety and efficacy of acupuncture as adjuvant therapy for DF. Conclusion: The summary of our study will clarify whether acupuncture as adjuvant therapy could be an efficient method for DF.
An ex-situ surface profile measurement scheme using a zonal wavefront sensor with simultaneous presence of reference and test wavefronts

If a zonal wavefront sensor such as the Shack-Hartmann wavefront sensor is used to measure the surface profile, the sensing scheme apart from the test wavefront requires a reference wavefront. In order to switch between the two there is a need to replace the test surface by a reference surface such as a mirror. This often introduces inaccuracies in the measurement. In this paper, we introduce an experimental arrangement comprising wave plates and polarizing beam splitters where both the reference and the test wavefronts can be simultaneously present or one can easily switch from reference wavefront to test wavefront.
Seasonal changes in physical capacities of basketball players according to competitive levels and individual responses

Purpose The aim of this study was to quantify changes in physical capacities of thirty-eight basketball players selected from different teams, as well as from varying competitive levels (i.e. Division I, Division II and Division III) during the preparation and in-season periods. Methods Pre (T1) and post (T2) preparation period and during regular season (T3), the players completed a Yo-Yo Intermittent Recovery test—level 1. Following a 3 to 8 days-break, players performed a 6-min continuous running test (Mognoni’s test), a counter-movement jump test and a 5-min high-intensity intermittent running test. Results Blood lactate concentration measured after the Mognoni’s test was significantly reduced from T1 to T2, and from T2 to T3 (P<0.001, ƞ2 = 0.424). The distance covered during the Yo-Yo Intermittent Recovery test was significantly increased only from T1 to T2 in Division II and III (P<0.001, ƞ2 = 0.789). Similarly, the physiological responses to high-intensity intermittent running test were improved only from T1 to T2 (all P<0.001, ƞ2 = 0.495 to 0.652). Despite significant changes observed in running tests from T1 to T2, at individual level 35–55% of players did not show a very likely improvement. Relative peak power produced during vertical jumps at T3 by Division I players was increased compared to T1 (ANOVA interaction, P = 0.037, ƞ2 = 0.134). Conclusions The main improvements in physical capacities occurred during the preparation period, when the aerobic fitness and the ability to sustain high-intensity intermittent efforts were moderately-to-largely improved. However, it appears that the preparation period does not consistently impact on vertical jump variables. Aerobic fitness and force/power production during vertical jumps appear to improve across the competitive season (slightly-to-moderately). Physical tests should be used to identify weaknesses in physical performance of players and to monitor their fatigue status, with the aim to develop individualized training programs.
Detection and Analysis of Heartbeats in Seismocardiogram Signals

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space).
Optimization of the optical path of the methane telemetry system based on TDLAS technology

Spectroscopic measurement of methane gas concentrations using absorption lines in the near infra-red (the 1.67 μm region) has been demonstrated by several research teams. In this paper, a methane telemetry system based on tunable diode laser absorption spectroscopy (TDLAS) is proposed and demonstrated. The center wavelength of the laser is locked to the methane absorption peak and the second harmonic detection technology is used to realize the real time monitoring of the methane gas concentration. To receive more signal light and enhance the signal to noise ratio, a lens and its focal length, the spot size of the reflecting surface and a filter plate are used to optimizing the optical path. The experimental results show that after optimizing methane telemetry system the minimum detection limit can be enhance from 20000 ppm to 5000 ppm in the distance of 20 meters.
Brix percentage estimation using artificial intelligence approaches

In this paper, a measurement system using absorption spectroscopy was designed and realized. Using alternative measurement method, the system could perform with minimum calibration requirement. Features of sucrose solutions with Brix percentage from 12 oBrix to 16 oBrix were measured and extracted. Moreover, three artificial intelligent methods, namely artificial neural networks, artificial neural networks with principal component analysis, and genetic algorithm, were applied to predict unknown concentrations. The key difference between methods was the assumption on residual error’s statistical characteristics. Experimental results showed that the correlation and collinearity in structure of measured data were essential for higher prediction accuracy in artificial intelligent methods. In our experiments, genetic algorithm provided the most accurate predictions.
Enzyme immobilization offers a robust tool to scale up the production of longer, diverse, natural glycosaminoglycan oligosaccharides.

Although structurally diverse, longer glycosaminoglycan (GAG) oligosaccharides are critical to understand human biology, few are available. The major bottleneck has been the predominant production of oligosaccharides, primarily disaccharides, upon enzymatic depolymerization of GAGs. In this work, we employ enzyme immobilization to prepare hexasaccharide and longer sequences of chondroitin sulfate in good yields with reasonable homogeneity. Immobilized chondroitinase ABC displayed good efficiency, robust operational pH range, broad thermal stability, high recycle-ability, and excellent distribution of products in comparison to the free enzyme. Diverse sequences could be chromatographically resolved into well-defined peaks and characterized using LC-MS. Enzyme immobilization technology could enable easier access to diverse longer GAG sequences.
Proliferation-, estrogen-, and T-cell-related metagenes to predict outcome after adjuvant/neoadjuvant chemotherapy for operable breast cancer in the ECTO trial.

1014 Background: Predicting recurrence in operable breast cancer (BC) despite optimal chemotherapy would be relevant to new drug development and tailored treatments. Methods: A large series (n=3,154) of public Affymetrix gene-expression profiles (GEP) was used to define prognostic/predictive metagenes in different BC subtypes. In ER+/HER2- a proliferation and an ER-related metagene were combined to predict low, intermediate and high risk of recurrence. In TN and in HER2+ a T cell metagene was used to predict low, intermediate and high risk (higher expression associated with lower risk). The metagenes were validated in patients enrolled in the phase III ECTO trial (Gianni L. JCO 2009) and treated with the same taxane-anthracycline-CMF regimen as neoadjuvant or adjuvant therapy before endocrine therapy if indicated. The outcome was distant event free survival (DEFS). Results: 283 good quality GEPs were obtained (neoadjuvant n=121; adjuvant n=162) from 464 retrospectively collected samples. Median follow-up .
Dose-Response Study on Surfactant Nebulization Therapy During Nasal Continuous Positive Airway Pressure Ventilation in Spontaneously Breathing Surfactant-Deficient Newborn Piglets*

Supplemental Digital Content is available in the text. Objectives: The current clinical treatment of neonates with respiratory distress syndrome includes endotracheal intubation and intratracheal instillation of exogenous surfactant. Nebulization of surfactant offers an attractive alternative. The aims of this study were to test nebulization as a noninvasive method of administering surfactant and determine the optimal dose for the treatment of respiratory distress syndrome–associated pathophysiology of the neonatal lungs. Design: Prospective, randomized, animal model study. Setting: An experimental laboratory. Subjects: Thirty-six newborn piglets. Interventions: Different doses (100, 200, 400, and 600 mg/kg) of poractant alfa were administered via a vibrating membrane nebulizer (eFlow-Neos; Pari Pharma GmbH, Starnberg, Germany) or a bolus administration using the intubation–surfactant–extubation (Insure) technique (200 mg/kg) to spontaneously breathing newborn piglets (n = 6/group) with bronchoalveolar lavage–induced respiratory distress syndrome during nasal continuous positive airway pressure (180 min). Measurements and Main Results: Pulmonary, hemodynamic, and cerebral effects were assessed. Histologic analysis of lung and brain tissue was also performed. After repeated bronchoalveolar lavage, newborn piglets developed severe respiratory distress syndrome. Rapid improvement in pulmonary status was observed in the Insure group, whereas a dose-response effect was observed in nebulized surfactant groups. Nebulized poractant alfa was more effective at doses higher than 100 mg/kg and was associated with similar pulmonary, hemodynamic, and cerebral behavior to that in the Insure group, but improved lung injury scores. Conclusions: In newborn piglets with severe bronchoalveolar lavage–induced respiratory distress syndrome, our results demonstrate that the administration of nebulized poractant alfa using an investigational customized eFlow-Neos nebulizer is an effective and safe noninvasive surfactant administration technique.
Diagnostic value of Anthropometric Measurements for Familial Partial Lipodystrophy, Dunnigan variety.

CONTEXT Familial partial lipodystrophy, Dunnigan variety (FPLD2) is a rare autosomal dominant disorder due to LMNA causal variants, which is characterized by loss of subcutaneous fat from the extremities and predisposition to metabolic complications. The diagnostic value of various anthropometric measurements for FPLD2 remains unknown. OBJECTIVE To determine specificity and sensitivity of anthropometric measurements for the diagnosis of FPLD2. METHODS We measured skinfold thickness and regional body fat by dual energy X-ray absorptiometry (DXA) in 50 adult females and 6 males with FPLD2 at UT Southwestern and compared their data with the sex- and age-matched controls from the National Health and Nutrition Examination Survey (NHANES) 1999-2010. We further compared data from 1,652 unaffected females from the Dallas Heart Study and 23 females with FPLD2 from the National Institutes of Health with NHANES data. RESULTS The DXA-derived lower limb fat (%) had the best specificity (0.995) and sensitivity (1.0) compared to the upper limb fat, truncal fat, the ratio of lower limb to truncal fat, and triceps skinfold thickness for adult females with FPLD2. The lower limb fat below 1st percentile of NHANES females had a false positive rate of 0.0054 and a false negative rate of 0. The diagnostic value of anthropometric parameters could not be determined for males due to small sample size. CONCLUSIONS The lower limb fat (%) is the best objective anthropometric measure for diagnosing FPLD2 in females. Women with < 1st percentile lower limb fat should undergo genetic testing for FPLD2, especially if they have metabolic complications.
Oncogene-independent BCR-like signaling adaptation confers drug resistance in Ph-like ALL.

Children and adults with Philadelphia chromosome-like B cell acute lymphoblastic leukemia (Ph-like B-ALL) experience high relapse rates despite best-available conventional chemotherapy. Ph-like ALL is driven by genetic alterations that activate constitutive cytokine receptor and kinase signaling, and early-phase trials are investigating the potential of tyrosine kinase inhibitor (TKI) addition to chemotherapy to improve clinical outcomes. However, preclinical studies have shown that JAK or PI3K pathway inhibition is insufficient to eradicate the most common cytokine receptor-like factor 2 (CRLF2)-rearranged Ph-like ALL subset. We thus sought to define additional essential signaling pathways required in Ph-like leukemogenesis for improved therapeutic targeting. Herein, we describe a novel adaptive signaling plasticity of CRLF2-rearranged Ph-like ALL following selective TKI pressure, which occurs in the absence of genetic mutations. Interestingly, we observed that Ph-like ALL cells have activated SRC, ERK and PI3K signaling consistent with activated B-cell receptor (BCR) signaling, although they do not express cell surface mu heavy chain (uHC). Combinatorial targeting of JAK/STAT, PI3K, and 'BCR-like' signaling with multiple TKIs and/or dexamethasone prevented this signaling plasticity and induced complete cell death, demonstrating a more optimal and clinically pragmatic therapeutic strategy for CRLF2-rearranged Ph-like ALL.
Facial Paralysis and Communicative Participation: The Importance of Facial Symmetry at Rest

Objective: There is a paucity of research devoted to understanding the communication restrictions encountered by facial paralysis patients. We aim to explore the relationship between patient-reported restrictions in communicative participation and objective facial paralysis severity using validated scales of facial movement. Methods: We performed a pilot retrospective study using a consecutive series of adult patients with a diagnosis of unilateral facial paralysis. In addition to baseline demographics, subjects were evaluated using the Communicative Item Participation Bank Short Form (CPIB), Electronic Facial Assessment by Computer Evaluation (eFACE), and Sunnybrook Facial Grading System (SFGS). Results: Twenty patients were included, 10 (50%) of whom were female with a mean age of 61 ± 13 years and mean duration of facial paralysis of 53 ± 82 months. The mean CPIB score was 14.6 ± 10.0 (range 0-29) and was comparable to scores of patients with conditions known to cause significant communicative disability. The mean eFACE scores were 67.4 ± 29.2, 44.2 ± 30.1, and 73.8 ± 30.0 for the static, dynamic, and synkinesis domains, respectively, with a composite smile score of 58.5 ± 16.9. After adjusting for age, gender, and duration of facial paralysis, significant moderate correlations were observed between the CPIB and the static eFACE domain (r = –0.51, P = .03) and smile composite score (r = 0.48, P = 0.0049), in addition to between the CPIB and SFGS synkinesis domain (r = 0.48, P = 0.04). Conclusions: Patients with unilateral facial paralysis experience significant limitations in communicative participation. These restrictions demonstrate moderate to strong correlations with objective assessments of facial paralysis and quality of life measures. Communicative participation may be a helpful means of tracking response to treatment. Level of Evidence: IV
Imaging epigenetics study of schizophrenia with structure-enforced collaborative regression

In the study of complex mental disorders like schizophrenia (SZ), while imaging genetics has achieved great success, imaging epigenetics is attracting increasing attention as it considers the impact of environmental factors on gene expression and resulting phenotypic changes. In this study, we aimed to fill the gap by jointly analyzing imaging and epigenetics data to study SZ. More specifically, we proposed a novel structure-enforced collaborative regression model (SCoRe) to extract co-expressed discriminative features related to SZ from fMRI and DNA methylation data. SCoRe can utilize phenotypical information while enforce an agreement between multiple data views. Moreover, it also considers the group structure within each view of data. The brain network based on fMRI data can be divided into 116 regions of interests (ROIs) based on anatomical structures of the brain and the DNA methylation data can be grouped based on pathway information, which are used as prior knowledge to be incorporated into the learning model. After validation through simulation test, we applied the model to SZ study with data collected by MIND Clinical Imaging Consortium (MCIC). Through integrating fMRI and DNA methylation data of 184 participants (104 SZ and 80 healthy subjects), we succeeded in identifying 8 important brain regions and 3 genes associated with SZ. This study can shed light on the understanding of SZ from both brain imaging and epigenomics, complementary to imaging genomics.