Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence and Impacts of Inflammatory Bowel Diseases among Kidney Transplant Recipients: A Meta-Analysis

Background: The incidence of inflammatory bowel diseases (IBD) and its significance in kidney transplant recipients is not well established. We conducted this systematic review and meta-analysis to assess the incidence of and complications from IBD in adult kidney transplant recipients. Methods: Eligible articles were searched through Ovid MEDLINE, EMBASE, and the Cochrane Library from inception through April 2020. The inclusion criteria were adult kidney transplant patients with reported IBD. Effect estimates from the individual studies were extracted and combined using the fixed-effects model when I2 ≤ 50% and random-effects model when I2 > 50%. Results: of 641 citations, a total of seven studies (n = 212) were included in the systematic review. The mean age was 46.2 +/− 6.9 years and up to 51.1% were male. The mean duration of follow-up was 57.8 +/− 16.8 months. The pooled incidence of recurrent IBD was 27.6% (95% CI, 17.7–40.5%; I2 0%) while the pooled incidence of de novo IBD was 18.8% (95% CI, 10.7–31.0%; I2 61.3%). The pooled incidence of post-transplant IBD was similar across subgroup analyses. Meta-regression analyses showed no association between the incidence of IBD and age, male sex, and follow-up duration. For post-transplant complications, the pooled incidence of post-transplant infection was 4.7% (95% CI, 0.5–33.3%; I2 73.7%). The pooled incidence of graft rejection and re-transplantation in IBD patients was 31.4% (95% CI, 14.1–56.1%; I2 76.9%) and 30.4% (95% CI, 22.6–39.5%; I2 0%). Conclusion: Recurrent and de novo IBD is common among kidney transplant recipients and may result in adverse outcomes.
Using Chou’s 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods

Abstract N 6-methyladenine (m6A), a type of modification mostly affecting the downstream biological functions and determining the levels of gene expression, is mediated by the methylation of adenine in nucleic acids. It is also a key factor for influencing biological processes and has attracted attention as a target for treating diseases. Here, an ensemble predictor named as TL-Methy, was developed to identify m6A sites across the genome. TL-Methy is a 2-level machine learning method developed by combining the support vector machine model and multiple features extraction methods, including nucleic acid composition, di-nucleotide composition, tri-nucleotide composition, position-specific trinucleotide propensity, Bi-profile Bayes, binary encoding, and accumulated nucleotide frequency. For Homo sapiens, TL-Methy method reached the accuracy of 91.68% on jackknife test and of 92.23% on 10-fold cross validation test; For Mus musculus, TL-Methy method achieved the accuracy of 93.66% on jackknife test and of 97.07% on 10-fold cross validation test; For Saccharomyces cerevisiae, TL-Methy method obtained the accuracy of 81.57% on jackknife test and of 82.54% on 10-fold cross validation test; For rice genome, TL-Methy method achieved the accuracy of 91.87% on jackknife test and of 93.04% on 10-fold cross validation test. The results via these two test approaches demonstrated the robustness and practicality of our TL-Methy model. The TL-Methy model may be as a potential method for m6A site identification. Communicated by Ramaswamy H. Sarma
TATA Binding Protein (TBP) Promoter Drives Ubiquitous Expression of Marker Transgene in the Adult Sea Anemone Nematostella vectensis

Nematostella vectensis has emerged as one as the most established models of the phylum Cnidaria (sea anemones, corals, hydroids and jellyfish) for studying animal evolution. The availability of a reference genome and the relative ease of culturing and genetically manipulating this organism make it an attractive model for addressing questions regarding the evolution of venom, development, regeneration and other interesting understudied questions. We and others have previously reported the use of tissue-specific promoters for investigating the function of a tissue or a cell type of interest in vivo. However, to our knowledge, genetic regulators at the whole organism level have not been reported yet. Here we report the identification and utilization of a ubiquitous promoter to drive a wide and robust expression of the fluorescent protein mCherry. We generated animals containing a TATA binding protein (TBP) promoter upstream of the mCherry gene. Flow cytometry and fluorescent microscopy revealed expression of mCherry in diverse cell types, accounting for more than 90% of adult animal cells. Furthermore, we detected a stable mCherry expression at different life stages and throughout generations. This tool will expand the existing experimental toolbox to facilitate genetic engineering and functional studies at the whole organism level.
Differential Viewpoints for Ground Terrain Material Recognition

Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based representations based on internet-mined single-view images captured in the scene. We take a middle-ground approach for material recognition that takes advantage of both rich radiometric cues and flexible image capture. A key concept is differential angular imaging, where small angular variations in image capture enables angular-gradient features for an enhanced appearance representation that improves recognition. We build a large-scale material database, Ground Terrain in Outdoor Scenes (GTOS) database, to support ground terrain recognition for applications such as autonomous driving and robot navigation. The database consists of over 30,000 images covering 40 classes of outdoor ground terrain under varying weather and lighting conditions. We develop a novel approach for material recognition called texture-encoded angular network (TEAN) that combines deep encoding pooling of RGB information and differential angular images for angular-gradient features to fully leverage this large dataset. With this novel network architecture, we extract characteristics of materials encoded in the angular and spatial gradients of their appearance. Our results show that TEAN achieves recognition performance that surpasses single view performance and standard (non-differential/large-angle sampling) multiview performance.
Perceptions of Secondary School Athletic Trainers in the Diagnosis of Exertional Heat Stroke.

CONTEXT Health care providers, including athletic trainers (ATs), may not be using the best practices for diagnosing exertional heat stroke (EHS), including rectal thermometry. Therefore, patients continue to be susceptible to death from EHS. OBJECTIVE To examine the health belief model and its association with using rectal thermometry as the best practice for diagnosing EHS. DESIGN Cross-sectional study. SETTING Web-based survey. PATIENTS OR OTHER PARTICIPANTS A total of 208 secondary school ATs completed an online survey, and the data of 159 were included in the analysis. MAIN OUTCOME MEASURE(S) The survey contained 2 primary sections: AT characteristics and health belief model structured questions assessing perceptions and techniques used to diagnose EHS. Answers to the latter questions were rated on a 5-point Likert scale. We performed binary logistic regression to ascertain the effects of the health belief model constants (eg, perceived susceptibility, barriers), age, sex, and the type of school at which the AT worked on the likelihood that participants would use best practice for diagnosing patients with EHS. RESULTS Only 33.3% (n = 53) of the participating ATs reported they used best practice, including rectal thermometers to obtain core body temperature. The binary logistic regression was different for the 5 constructs: perceived susceptibility (\chi _6^2 = 22.30, P = .001), perceived benefits (\chi _6^2 = 71.79, P < .001), perceived barriers (\chi _6^2 = 111.22, P < .001), perceived severity (\chi _6^2 = 56.27, P < .001), and self-efficacy (\chi _6^2 = 64.84, P < .001). Analysis of these data showed that older ATs were at greater odds (P ≤ .02) of performing best practice. CONCLUSIONS These data suggested that the health belief model constructs were associated with the performance of best practice, including using rectal thermometry to diagnose EHS. Researchers should aim to create tailored interventions based on health behavior to improve the adoption of best practice.
Large-Scale Nonlinear AUC Maximization via Triply Stochastic Gradients

Learning to improve AUC performance for imbalanced data is an important machine learning research problem. Most methods of AUC maximization assume that the model function is linear in the original feature space. However, this assumption is not suitable for nonlinear separable problems. Although there have been some nonlinear methods of AUC maximization, scaling up nonlinear AUC maximization is still an open question. To address this challenging problem, in this paper, we propose a novel large-scale nonlinear AUC maximization method (named as TSAM) based on the triply stochastic gradient descents. Specifically, we first use the random Fourier feature to approximate the kernel function. After that, we use the triply stochastic gradients <italic>w.r. t. </italic> the pairwise loss and random feature to iteratively update the solution. Finally, we prove that TSAM converges to the optimal solution with the rate of <inline-formula><tex-math notation="LaTeX">$ \mathcal {O}(1/t)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="huang-ieq1-3024987.gif"/></alternatives></inline-formula> after <inline-formula><tex-math notation="LaTeX">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="huang-ieq2-3024987.gif"/></alternatives></inline-formula> iterations. Experimental results on a variety of benchmark datasets not only confirm the scalability of TSAM, but also show a significant reduction of computational time compared with existing batch learning algorithms, while retaining the similar generalization performance.
Learning probabilistic neural representations with randomly connected circuits

Significance We present a theory of neural circuits’ design and function, inspired by the random connectivity of real neural circuits and the mathematical power of random projections. Specifically, we introduce a family of statistical models for large neural population codes, a straightforward neural circuit architecture that would implement these models, and a biologically plausible learning rule for such circuits. The resulting neural architecture suggests a design principle for neural circuit—namely, that they learn to compute the mathematical surprise of their inputs, given past inputs, without an explicit teaching signal. We applied these models to recordings from large neural populations in monkeys’ visual and prefrontal cortices and show them to be highly accurate, efficient, and scalable. The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.
Inter-Relationships between Test Weight, Thousand Kernel Weight, Kernel Size Distribution and Their Effects on Durum Wheat Milling, Semolina Composition and Pasta Processing Quality

Although most of the durum wheat produced in the Canadian prairies in 2017 and 2018 met the test weight (TW) requirements for the top grades of Canada Western Amber Durum (CWAD), some samples of top grades were inferior in milling quality. To understand the abnormality, this study was conducted to investigate TW, thousand kernel weight (TKW) and kernel size distribution (KSD) in relation to durum milling potential, semolina composition and pasta quality. With reduction of kernel size, semolina and total milling yields decreased progressively, and kernels passing through no.6 slotted sieve had detrimental impact on milling. The overall relationship between TW and milling yields appeared to be genotype dependent. At similar TW, variety showed lower milling yields had greater proportion of smaller kernels. By account for the difference in KSD, greater relationships (R2 > 0.91, p < 0.001) were found for TKW and proportion of kernels passing No.6 slotted sieve with milling yields than TW (R2 = 0.75, p < 0.001). This infers potential use of small kernels (passing No.6 slotted sieve) as a new objective grading factor for rapid prediction of milling quality of CWAD. Although small kernels exhibited much higher yellow pigment than the larger ones, pasta made from small kernels was duller, redder and less yellow, likely due to the higher semolina ash and protein contents, which adversely affected pasta color.
Anxious arousal alters prefrontal cortical control of stopping

Anxiety heightens vigilance and stimulus‐driven attention to the environment, which may in turn disrupt cognitive control processes such as response inhibition. How this unfolds at the neural level is unclear. Previous evidence implicates the right inferior frontal gyrus (IFG) as an important cortical node in both stimulus‐driven attention and inhibitory control. Here we used magnetoencephalography (MEG) to investigate the neural mechanisms involved in the relationship between threat‐induced anxiety and stopping during a stop‐signal task, where a visual go signal was occasionally followed by an auditory stop signal. Healthy individuals (N = 18) performed the task during the threat of unpredictable shocks and safety to modulate anxious arousal. Behaviorally, we observed that stopping was impaired during threat (i.e. slower estimated stop‐signal reaction times), indicating that anxious arousal weakens inhibitory control. MEG source analyses revealed that bilateral IFG and right dorsal prefrontal cortex showed increased beta‐band activity (14–30 Hz) to the stop signal that varied as a function of successful stopping during nonanxious (safe) conditions only. Moreover, peak beta‐band responses from right IFG were inversely correlated with stopping efficiency during nonanxious conditions. These findings support theoretical claims that beta oscillations function to maintain the current sensorimotor state, and that the lack of differential beta‐band activity in prefrontal cortices underlies anxiety‐related deficits in inhibitory control. We specifically argue that altered right IFG functioning might directly link impaired cognitive control to heightened stimulus‐driven responding in anxiety states.
Antimicrobial Peptides with Enhanced Salt Resistance and Antiendotoxin Properties

A strategy was described to design antimicrobial peptides (AMPs) with enhanced salt resistance and antiendotoxin activities by linking two helical AMPs with the Ala-Gly-Pro (AGP) hinge. Among the designed peptides, KR12AGPWR6 demonstrated the best antimicrobial activities even in high salt conditions (NaCl ~300 mM) and possessed the strongest antiendotoxin activities. These activities may be related to hydrophobicity, membrane-permeability, and α-helical content of the peptide. Amino acids of the C-terminal helices were found to affect the peptide-induced permeabilization of LUVs, the α-helicity of the designed peptides under various LUVs, and the LPS aggregation and size alternation. A possible model was proposed to explain the mechanism of LPS neutralization by the designed peptides. These findings could provide a new approach for designing AMPs with enhanced salt resistance and antiendotoxin activities for potential therapeutic applications.
Machine learning surrogate models for prediction of point defect vibrational entropy

The temperature variation of the defect densities in a crystal depends on vibrational entropy. This contribution to the system thermodynamics remains computationally challenging as it requires a diagonalization of the system's Hessian which scales as $O({N}^{3})$ for a crystal made of $N$ atoms. Here, to circumvent such a heavy computational task and make it feasible even for systems containing millions of atoms, the harmonic vibrational entropy of point defects is estimated directly from the relaxed atomic positions through a linear-in-descriptor machine learning approach of order $O(N)$. With a size-independent descriptor dimension and fixed model parameters, an excellent predictive power is demonstrated on a wide range of defect configurations, supercell sizes, and external deformations well outside the training database. In particular, formation entropies in a range of $250{k}_{B}$ are predicted with less than $1.6{k}_{B}$ error from a training database whose formation entropies span only $25{k}_{B}$ (training error less than $1.0{k}_{B}$). This exceptional transferability is found to hold even when the training is limited to a low-energy superbasin in the phase space while the tests are performed for a different liquid-like superbasin at higher energies.
Modeling hydrologic processes and potential responses to climate change in an agro-silvo-pastoral watershed in the Mediterranean area

Abstract. Precipitation changes and water use patterns are two factors affecting the water quantity; obviously, hydrologic processes are always linked to many elements in the watershed scale, so to understand water management issues it is fundamental to analyze the different elements of hydrologic processes occurring in the watershed. In this study, the “SWAT” model (Soil and Water Assessment Tool) has been used to simulate the water balance for the present climate conditions on a semi arid watershed located in the central North of Morocco (R'dom). The study watershed covers an area of 1993 km 2 , and is hosting farming, pasture and forestry related activities. The water stress situation in the R'dom watershed can be summarized as limited resource facing increasing water demand. SWAT model was first run and calibrated under current climate; and was driven with downscaled climate simulations to generate future hydrological projections for R'dom watershed in the 2031 to 2050 horizon under two Representative Concentration Pathways (RCPs): 4.5 and 8.5. The results of the study showed that the water balance in R'dom watershed is dominated by evapotranspiration and the water resources distribution within the watershed is uneven and follows a decreasing gradient matching the flow direction. The main results of climate change scenarios showed that R'dom watershed will undergo significant decrease of water resources availability with more economic impact under the scenario RCP8.5 as all areas hosting the economical activities will be affected and the highest changes of water yield should be under this scenario.
Likelihood ratio test for the nonnegative polynomial cone hypotheses and associated confidence bands

h = 1, . , N , where ψ(t) = ψn(t) = (1, t, . . . , tn)> and c = (c0, c1, . . . , cn) are column vectors in Rn+1. The errors εh are independently distributed as the normal distribution N(0, σ2). T ⊆ R is the region of the explanatory variable t where the model (1) is defined. Typically, T is a bounded interval in R. We assume that sufficient statistics of the model (1), that is, the ordinal least square estimator ĉ of c, and when σ2 is unknown, the unbiased variance estimator σ̂2 of σ2 distributed independently of ĉ are available. In this paper, we deal with the hypothesis of positivity, or the hypothesis of superiority:
DESIGN AND SIMULATION ANALYSIS OF RESONANT LLC CONVERTER FOR AUTOMOTIVE APPLICATIONS

1PG student, EEE Dept., RV College of Engineering, Bengaluru, Karnataka, India 2Associate Professor, EEE Dept., RV College of Engineering, Bengaluru, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract In this present scenario of the electric vehicles, there are lot of researches going on the various applications of the electric vehicle like the on board chargers, charge stations and other automotive applications in the vehicle which requires various voltage of supply for their application. One of those applications is where we use the resonant LLC converter. Inductor Inductor Capacitor(LLC) resonant converter has gained attention of researchers and engineers these days because of its advantages such as low switching losses due to ZVS turn on, possibility of high frequency operation, high power density over other existing resonant converters. In this paper LLC resonant converter is designed for a constant output voltage with the Ziegler-Nichols’ PID tuning method. MATLAB/Simulink model of the DC/DC resonant LLC converter is built for the automotive application in which superiority of the outcome validated.