Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatially Explicit Modeling of Schistosomiasis Risk in Eastern China Based on a Synthesis of Epidemiological, Environmental and Intermediate Host Genetic Data

Schistosomiasis japonica is a major parasitic disease threatening millions of people in China. Though overall prevalence was greatly reduced during the second half of the past century, continued persistence in some areas and cases of re-emergence in others remain major concerns. As many regions in China are approaching disease elimination, obtaining quantitative data on Schistosoma japonicum parasites is increasingly difficult. This study examines the distribution of schistosomiasis in eastern China, taking advantage of the fact that the single intermediate host serves as a major transmission bottleneck. Epidemiological, population-genetic and high-resolution ecological data are combined to construct a predictive model capable of estimating the probability that schistosomiasis occurs in a target area (“spatially explicit schistosomiasis risk”). Results show that intermediate host genetic parameters are correlated with the distribution of endemic disease areas, and that five explanatory variables—altitude, minimum temperature, annual precipitation, genetic distance, and haplotype diversity—discriminate between endemic and non-endemic zones. Model predictions are correlated with human infection rates observed at the county level. Visualization of the model indicates that the highest risks of disease occur in the Dongting and Poyang lake regions, as expected, as well as in some floodplain areas of the Yangtze River. High risk areas are interconnected, suggesting the complex hydrological interplay of Dongting and Poyang lakes with the Yangtze River may be important for maintaining schistosomiasis in eastern China. Results demonstrate the value of genetic parameters for risk modeling, and particularly for reducing model prediction error. The findings have important consequences both for understanding the determinants of the current distribution of S. japonicum infections, and for designing future schistosomiasis surveillance and control strategies. The results also highlight how genetic information on taxa that constitute bottlenecks to disease transmission can be of value for risk modeling.
Cardiomyocyte diffusible redox mediators control Trypanosoma cruzi infection: role of parasite mitochondrial iron superoxide dismutase.

Chagas disease (CD), caused by the protozoa Trypanosoma cruzi, is a chronic illness in which parasites persist in the host-infected tissues for years. T. cruzi invasion in cardiomyocytes elicits the production of pro-inflammatory mediators [TNF-α, IL-1β, IFN-γ; nitric oxide (·NO)], leading to mitochondrial dysfunction with increased superoxide radical (O2·-), hydrogen peroxide (H2O2) and peroxynitrite generation. We hypothesize that these redox mediators may control parasite proliferation through the induction of intracellular amastigote programmed cell death (PCD). In this work, we show that T. cruzi (CL-Brener strain) infection in primary cardiomyocytes produced an early (24 h post infection) mitochondrial dysfunction with H2O2 generation and the establishment of an oxidative stress evidenced by FoxO3 activation and target host mitochondrial protein expression (MnSOD and peroxiredoxin 3). TNF-α/IL-1β-stimulated cardiomyocytes were able to control intracellular amastigote proliferation compared with unstimulated cardiomyocytes. In this condition leading to oxidant formation, an enhanced number of intracellular apoptotic amastigotes were detected. The ability of H2O2 to induce T. cruzi PCD was further confirmed in the epimastigote stage of the parasite. H2O2 treatment induced parasite mitochondrial dysfunction together with intra-mitochondrial O2·- generation. Importantly, parasites genetically engineered to overexpress mitochondrial Fe-superoxide dismutase (Fe-SODA) were more infective to TNF-α/IL-1β-stimulated cardiomyocytes with less apoptotic amastigotes; this result underscores the role of this enzyme in parasite survival. Our results indicate that cardiomyocyte-derived diffusible mediators are able to control intracellular amastigote proliferation by triggering T. cruzi PCD and that parasite Fe-SODA tilts the process toward survival as part of an antioxidant-based immune evasion mechanism.
PLC access control: a security analysis

A Programmable Logic Controller (PLC) is a very common industrial control system device used to control output devices based on data received (and processed) from input devices. Given the central role that PLCs play in deployed industrial control systems, it has been a preferred target of ICS attackers. A quick search in the ICS-CERT repository reveals that out of a total of 589 advisories, more than 80 target PLCs. Stuxnet attack, considered the most famous reported incident on ICS, targeted mainly PLCs. Most of the PLC reported incidents are rooted in the fact that the PLC being accessed in an unauthorized way. In this paper, we investigate the PLC access control problem. We discuss several access control models but we focus mainly on the commonly adopted password-based access control. We show how such passwordbased mechanism can be compromised in a realistic scenario as well as the list the attacks that can be derived as a consequence. This paper details a set of vulnerabilities targeting recent versions of PLCs (2016) which have not been reported in the literature.
Finite Element Model Updating Using Frequency Response Function of Incomplete Strain Data

A method is presented to detect changes in stiffness and mass parameters of a structure using strain data in the frequency domain. Sensitivity of the strain-based frequency response function is characterized as a function of the changes in stiffness, mass, and damping at the element level. The transfer function of a damaged structure is approximated using the measured natural frequencies of the damaged structure and the analytical mode shapes of the intact structure. These approximations result in a set of quasi-linear sensitivity equations. A sensitivity-based algorithm is proposed for finite element model updating. These sensitivities are used to select the measured frequency points for model updating and to investigate the weighting of sensitivity equations. The proposed method was successfully applied to a plane truss and a plane frame structure using numerically simulated, error-contaminated strain data.
Curvature and transport inequalities for Markov chains in discrete spaces

We study various transport-information inequalities under three different notions of Ricci curvature in the discrete setting: the curvature-dimension condition of Bakry and \'Emery, the exponential curvature-dimension condition of Bauer \textit{et al.} and the coarse Ricci curvature of Ollivier. We prove that under a curvature-dimension condition or coarse Ricci curvature condition, an $L_1$ transport-information inequality holds; while under an exponential curvature-dimension condition, some weak-transport information inequalities hold. As an application, we establish a Bonnet-Meyer's theorem under the curvature-dimension condition CD$(\kappa,\infty)$ of Bakry and \'Emery.
Electrolyte disturbances in elderly population of coastal Karnataka

Introduction: Various anatomical and physiological changes have been reported in renal system with advancing age. These changes might lead to electrolyte disturbances in elderly even in the absence of kidney diseases, which are responsible for high morbidity and mortality . Electrolyte disorders in elderly are least explored in India. So we aim to conduct a retrospective comparison of serum electrolytes in elderly and young adults as well as to study the correlation between age and serum electrolytes. Methodology: A retrospective study was conducted in patients attending teaching hospital attached to Karwar Institute of Medical Sciences. Total 227 patients’ data was studied after excluding conditions affecting electrlytes, out of which 70 were elderly and 157 were young adults.Serum electrolytes were measured in biochemistry laboratory using an electrolyte analyzer which works on the principle of ion selective electrodes. Results: We observed significantly low sodium levels ( P=0.0385) and high potassium levels (P=0.0009) in elderly patients compared to controls. Linear regression analysis showed an extremely significant negative correlation (P=0.0002) between age and sodium, correlation coefficient r= -0.2449, R 2 = 0.060. Conclusion: We didn’t find derangement of electrolytes in elderly, but we observed lowered sodium and high potassium in geriatric population as compared to young adults. This demands better care and awareness of electrolyte imbalances which might lead to high morbidity and mortality if neglected. It necessitates immediate treatment options to avoid further complications in elderly.
Effect of Esmolol and Diltiazem Infusions on Hemodynamic Response to Pneumoperitoneum on Laparoscopic Simple Nephrectomy: A Randomized Controlled Trial

Background: Several studies have been conducted in search of appropriate drugs to attenuate hemodynamic responses in laparoscopic cholecystectomy, but till date, no such study has been conducted on patients undergoing laparoscopic nephrectomy. Aim: The study was designed to assess the effect of esmolol and diltiazem infusions on hemodynamic changes during routine laparoscopic transperitoneal simple nephrectomy. Settings and Design: A prospective double-blinded randomized control trial. Subjects and Methods: A total of 120 nonhypertensive patients of the American Society of Anesthesiologists physical Status I and II aged 20–60 years about to undergo laparoscopic nephrectomy were enrolled for the study after obtaining Institute Ethical committee approval. The patients were randomly allocated to one of the three groups. Group I (control group) received 10 ml normal saline intravenously (i.v.) before induction of anesthesia, followed by continuous infusion 10–20 ml/h. Group II (esmolol group) received i.v. esmolol 1 mg/kg diluted in 10 ml similarly, followed by continuous infusion of 10–20 ml/h (5–10 μg/kg/min). Group III (diltiazem group) received i.v. diltiazem 0.2 mg/kg diluted in 10 ml before induction of anesthesia, followed by continuous infusion at 10–20 ml/h (0.08–1.25) μg/kg/min. After completion of surgery, all the infusions were stopped. Data recording were done for changes in hemodynamics throughout the surgery. Statistical Analysis: was done using Chi-square test for categorical data and one-way ANOVA for continuous data. Tukey (“honestly significant difference”) post hoc test was applied for intra- and inter-group comparison in cases where ANOVA was significant. P < 0.05 is considered statistically significant. Results: All hemodynamic data: Heart rate (HR), systolic, diastolic, and mean arterial pressure were observed to be significantly higher (P < 0.001) from baseline in control group at the time of creation of pneumoperitoneum, till 20 min thereafter. The values were comparable to baseline in both esmolol and diltiazem group. On intergroup comparison, the patients in esmolol group had significantly lower HR and blood pressures than in diltiazem group at the creation of pneumoperitoneum and at extubation (P < 0.05). Mean dose of esmolol and diltiazem used were 7.25 ± 1.33 μg/kg/min and 1.14 ± 0.77 μg/kg/min, respectively. Conclusion: Both esmolol and diltiazem infusion provide stable intraoperative hemodynamics and protection against stress response triggered by pneumoperitoneum in patients undergoing laparoscopic transperitoneal nephrectomy.
Plastid translation is essential for lateral root stem cell patterning in Arabidopsis thaliana

ABSTRACT The plastid evolved from a symbiotic cyanobacterial ancestor and is an essential organelle for plant life, but its developmental roles in roots have been largely overlooked. Here, we show that plastid translation is connected to the stem cell patterning in lateral root primordia. The RFC3 gene encodes a plastid-localized protein that is a conserved bacterial ribosomal protein S6 of β/γ proteobacterial origin. The rfc3 mutant developed lateral roots with disrupted stem cell patterning and associated with decreased leaf photosynthetic activity, reduced accumulation of plastid rRNAs in roots, altered root plastid gene expression, and changes in expression of several root stem cell regulators. These results suggest that deficiencies in plastid function affect lateral root stem cells. Treatment with the plastid translation inhibitor spectinomycin phenocopied the defective stem cell patterning in lateral roots and altered plastid gene expression observed in the rfc3 mutant. Additionally, when prps17 defective in a plastid ribosomal protein was treated with low concentrations of spectinomycin, it also phenocopied the lateral root phenotypes of rfc3. The spectinomycin treatment and rfc3 mutation also negatively affected symplasmic connectivity between primary root and lateral root primordia. This study highlights previously unrecognized functions of plastid translation in the stem cell patterning in lateral roots. Summary: Successful plastid gene expression is required for stem cell patterning in lateral root primordia as revealed by genetic and pharmacological impairment of plastid translation.
A Rule-Based Prognostic Model for Type 1 Diabetes by Identifying and Synthesizing Baseline Profile Patterns

Objective To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction. Research Design and Methods RuleFit is used to identify the risk-predictive baseline profile patterns of demographic, immunologic, and metabolic markers, using 356 subjects who were randomized into the control arm of the prospective Diabetes Prevention Trial-Type 1 (DPT-1) study. A novel latent trait model is developed to synthesize these baseline profile patterns for disease risk prediction. The primary outcome was Type 1 Diabetes (T1D) onset. Results We identified ten baseline profile patterns that were significantly predictive to the disease onset. Using these ten baseline profile patterns, a risk prediction model was built based on the latent trait model, which produced superior prediction performance over existing risk score models for T1D. Conclusion Our results demonstrated that the underlying disease progression process of T1D can be detected through some risk-predictive patterns of demographic, immunologic, and metabolic markers. A synthesis of these patterns provided accurate prediction of disease onset, leading to more cost-effective design of prevention trials of T1D in the future.
Social Synchrony on Complex Networks

Social synchrony (SS) is an emergent phenomenon in human society. People often mimic others which, over time, can result in large groups behaving similarly. Drawing from prior empirical studies of SS in online communities, here we propose a discrete network model of SS based on four attributes: 1) depth of action; 2) breadth of impact, i.e., a large number of actions are performed with a large group of people involved; 3) heterogeneity of role, i.e., people of higher degree play more important roles; and 4) lastly, emergence of phenomenon, i.e., it is far from random. We analyze our model both analytically and with simulations, and find good agreement between the two. We find this model can well explain the four characters of SS, and thus hope it can help researchers better understand human collective behavior.
Estimation of atrial fibrillation using arbitrary normal ECG segments based on convolutional neural networks

In this study, we proposed a novel method to estimate atrial fibrillation (AF) based on convolutional neural networks (CNNs). The CNNs model consisted of five layers including convolutional and max-pooling layer. Dropout and batch-normalization were performed to prevent overfitting. For performance evaluation, arbitrary normal ECG segments were used from MIT-BIH Normal Sinus Rhythm Database (nsrdb) and Atrial Fibrillation Database (afdb). The ECG signal was preprocessed by moving average filter (2nd order) and discrete wavelet transform (DWT) was used to remove the baseline wander and spike noise. The result showed the accuracy of 99.1%, sensitivity of 99.8%, and specificity of 98.5%, respectively, for the test set.
AptRank: an adaptive PageRank model for protein function prediction on bi‐relational graphs

Motivation: Diffusion‐based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood‐based and module‐based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function‐function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two‐layer network model. Results: We first construct a Bi‐relational graph (Birg) model comprised of both protein‐protein association and function‐function hierarchical networks. We then propose two diffusion‐based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two‐layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. Availability and Implementation: The MATLAB code is available at https://github.rcac. purdue.edu/mgribsko/aptrank. Contact: gribskov@purdue.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Automated Partitioning of Concurrent Discrete-Event Systems for Distributed Behavioral Identification

The aim of behavioral identification of discrete-event systems is to build, from a sequence of observed inputs/outputs events, an understandable model that exhibits both the direct relations between inputs and outputs events (i.e., the observable behavior of the system) and the internal state evolutions (i.e., the unobservable behavior). Since parallelism hinders the construction of monolithic models, distributed identification builds instead the models of subsystems. This paper proposes an automated partitioning of the system and optimal regarding the readability of the identified distributed models, thus fitting reverse-engineering purposes. To solve the optimization problem, a first solution is extracted from the observable behavior; then additional solutions are computed by agglomerative clustering. The approach is applied to a benchmark, resulting in an adequate functional partition.Note to Practitioners—Identification is an approach to obtain the models of an existing closed-loop discrete-event system from an observed I/O sequence, discovering both sequential and concurrent processes within the same system. The result is an understandable and compact model that approximates the observed behavior and can be used for reverse engineering. To get better insight on the behavior of the system, splitting it into subsystems and studying distributed models might be easier. Besides, identifying smaller subsystems reduces the computational cost. The main contribution of this paper is therefore a partitioning approach for distributed DES identification. Only the knowledge of the I/Os and the observed I/O sequence are required to provide a partitioning adapted to reverse engineering and compact distributed models, in reasonable computation times.
Systematic Review of the Non-Specific Effects of Bacillus Calmette-Guérin Vaccine on Child Mortality

Background: Until a different vaccine is given, BCG provides non-specific protection against diseases other than tuberculosis, mainly respiratory infections and sepsis. Methods: A systematic review was made using the databases Medline, Lilacs, Cochrane Library, Scopus and the WHO review of BCG, using the terms BCG, nonspecific effects, heterologous immunity, and child mortality. The objective was to quantify the effect of BCG on all-cause mortality until a different vaccine was given in children up to 5 years of age in low-income countries. Randomized trials and observational studies performed in low-income populations where all-cause mortality was reported were selected. Results: Fifty-nine articles were found. Nine studies had a low to moderate risk of bias; they consisted of two randomized trials, six cohort studies and one case-control study; they were performed in Guinea-Bissau, India, Benin, Malawi and Senegal. The effect estimates were homogeneous, with I2=0.0% (p=0.71). Meta-analysis of all nine studies using a random effects model yielded an effect estimate of 0.56 (95% CI 0.46-0.69). The combined estimate for the two randomized trials of BCG-Denmark that had a low risk of bias was 0. 52 (95% CI 0.33- 0.82). Conclusions: The two randomized trials and the seven observational studies suggests that, until a different vaccine is given, administration of the strains of BCG used in these studies approximately halved all-cause mortality in children under five years of age in these low-income countries.
Step by Step Design Procedure of a Distribution Static Synchronous Compensator ( DSTATCOM ) Ahmet TEKE

DSTATCOM is one of the power conditioning devices that is used to mitigate power quality problems in distribution systems. The overall performance of the DSTATCOM is strictly related with the proper selection of power circuit configuration and controller algorithm. The power circuit of DSTATCOM consists of dc link capacitor, inverter and passive filter. The control circuit of DSTATCOM consists of reference signal extraction, DC link voltage control, AC voltage control and switching signal generation. Compensating current reference signal is generally derived from the measured quantities by the use of the Instantaneous Symmetrical Component Theory (ISCT) and dq theory based method. A proportional–integral (PI) controller is generally used to maintain a constant voltage at the dc-link of a Voltage-Source Inverter (VSI). Furthermore, by connecting a delta connected inductor-capacitor-inductor (LCL) passive filter at inverter output, the high order harmonics generated by the DSTATCOM can be easily and effectively eliminated. This study presents the design procedures for power and control circuits of 300 kVA DSTATCOM in detail.
A 11 µW 0°C–160°C temperature sensor in 90 nm CMOS for adaptive thermal monitoring of VLSI circuits

This paper reports design, efficiency and measurement results of the temperature sensor based on substrate bipolar transistors and a PTAT multiplier for adaptive thermal monitoring of deep-submicron VLSI circuits. The prototype temperature sensor with un-calibrated 3σ accuracy of 0.9°C within a 0°C-160°C temperature range has been fabricated in standard single poly, six metal 90nm CMOS, consumes only 11μW at 1V power supply and measures 0.05mm2.