Miracle-dz/UniFAR-FTUs huggingface dataset:
GROUND MOTION MODELING FOR RISK AND RELIABILITY ASSESSMENT OF SAN FRANCISCO INFRASTRUCTURE SYSTEMS

Infrastructure network resilience to major hazards, such as earthquakes, is a critical attribute that impacts the magnitude and extent of direct and indirect losses as well as post-disaster recovery. However, network resilience estimation is a nontrivial issue due to the uncertainty of hazards and the complexity of the response of large networks to perturbation. This research details a methodology to evaluate the resilience of networks subjected to seismic hazards by combining the most recent hazard and risk estimation techniques. This paper focuses on the ground motion intensity simulation aspect of the research, which utilizes current rupture forecasts and ground motion prediction equations in conjunction with a ground motion spatial correlation model. Recent research reports that ground motion estimation using solely median values without consideration of spatially correlated residuals may be inadequate in characterizing seismic events within a region; thus, this research rigorously pursues accurate spatial correlation representation through the proposed ground motion model. This model is used to generate realistic ground motion intensity maps considering a wide range of earthquake scenarios to supplement existing maps in the San Francisco area to enhance the rigor and merit of subsequent network reliability analysis. This research lays the groundwork for future assessments of critical infrastructure networks, which aims to provide insight regarding efficient retrofit of networks to improve their resilience to seismic hazards and expedite post-disaster recovery.
Secondary Hemophagocytic Lymphohistiocytosis

Introduction: Hemophagocytic lymphohistiocytosis (HLH) is a rare hematological disorder which may be fatal due to uncontrolled activation of immune system. It is characterized by hemophagocytosis in bone marrow, liver or lymph nodes along with presence of fever, pancytopenia, splenomegaly. It has been associated with bacterial, viral, fungal and parasitic infection. The study aims to explore the utility of bone marrow aspiration in patients with pancytopenia and bicytopenia which supports the diagnosis in a scenario where there is clinical suspicion of HLH. Material and Methods: prospective study carried out in department of pathology from August 2018 to July 2019. We studied 88 cases of pancytopenia and bicytopenia. All relevant clinical history and investigations were collected. Bone marrow aspiration done and smears were studied in all the cases. Result: Out of 88 cases studied 28 patients were having bicytopenia and 60 were having pancytopenia with male to female ratio 13 :9.Out of these 88 patients we reported 3 patients as having HLH who fulfill the criteria according to Revised Diagnostic Guidelines for hemophagocytic lymphohistiocytosis (HLH). Conclusion: An early diagnosis and treatment is the key to decrease the mortality caused by this dreadful disease and if left undiagnosed and undertreated, Hemophagocytic lymphohistiocytosis can be rapidly progressive and potentially fatal.
Statistical Applications in Genetics and Molecular Biology A New Explained-Variance Based Genetic Risk Score for Predictive Modeling of Disease Risk

The goal of association mapping is to identify genetic variants that predict disease, and as the field of human genetics matures, the number of successful association studies is increasing. Many such studies have shown that for many diseases, risk is explained by a reasonably large number of variants that each explains a very small amount of disease risk. This is prompting the use of genetic risk scores in building predictive models, where information across several variants is combined for predictive modeling. In the current study, we compare the performance of four previously proposed genetic risk score methods and present a new method for constructing genetic risk score that incorporates explained variance information. The methods compared include: a simple count Genetic Risk Score, an odds ratio weighted Genetic Risk Score, a direct logistic regression Genetic Risk Score, a polygenic Genetic Risk Score, and the new explained variance weighted Genetic Risk Score. We compare the methods using a wide range of simulations in two steps, with a range of the number of deleterious single nucleotide polymorphisms (SNPs) explaining disease risk, genetic modes, baseline penetrances, sample sizes, relative risks (RR) and minor allele frequencies (MAF). Several measures of model performance were compared including overall power, C-statistic and Akaike’s Information Criterion. Our results show the relative performance of methods differs significantly, with the new explained variance weighted GRS (EV-GRS) generally performing favorably to the other methods.
Statistical Applications in Genetics and Molecular Biology Regression-Based Multi-Trait QTL Mapping Using a Structural Equation Model

Quantitative trait loci (QTL) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for the correlation among multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. Structural equation modeling (SEM) allows researchers to explicitly characterize the causal structure among the variables and to decompose effects into direct, indirect, and total effects. In this paper, we developed a multi-trait SEM method of QTL mapping that takes into account the causal relationships among traits related to grain yield. Performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait analysis and the multi-trait least-squares analysis, our multi-trait SEM improves statistical power of QTL detection and provides important insight into how QTLs regulate traits by investigating the direct, indirect, and total QTL effects. The approach also helps build biological models that more realistically reflect the complex relationships among QTL and traits and is more precise and efficient in QTL mapping than single trait analysis.
Statistical Applications in Genetics and Molecular Biology Improving Divergence Time Estimation in Phylogenetics : More Taxa vs . Longer Sequences

Maximum Likelihood (ML) is used as a standard method for estimating divergence times in phylogenetic trees. The method is consistent and hence the precision can be improved by analyzing longer sequences. In this paper we show that the precision can be improved also by including more taxa to the existing tree. It is a theoretical study, complemented with simulations, showing that the gain in precision is faster with increasing sequence length than with increasing number of taxa. We further compare the results of estimating divergence times using Maximum Likelihood with the much faster and less complex estimation method of Mean Path Length (MPL), which works with the evolution model of Jukes-Cantor (1969). It is shown that MPL is as good as ML in estimating divergence times of nodes that are located near the root in the tree, but ML is better in estimating the divergence times of nodes lower down.
Hardy Cotoneasters for North Dakota

Hardy cotoneasters include a number of shrubs valued chiefly for their attractive foliage and reasonably neat habit of growth. They could be classified as being 'filler' shrubs. Many of the cotoneasters that can be grown under North Dakota conditions are susceptible to fire blight, a bacterial disease. For general use, home plantings would probably be restricted to the commonly available Peking cotoneaster and the red-berried European cotoneaster, both of which are highly acceptable shrubs whether used in informal borders, foundation plantings or as hedges.
Statistical Applications in Genetics and Molecular Biology MC-Normalization : A Novel Method for Dye-Normalization of Two-Channel Microarray Data

Pre-processing plays a vital role in two-color microarray data analysis. An analysis is characterized by its ability to identify differentially expressed genes (its sensitivity) and its ability to provide unbiased estimators of the true regulation (its bias). It has been shown that microarray experiments regularly underestimate the true regulation of differentially expressed genes. We introduce the MC-normalization, where C stands for channel-wise normalization, with considerably lower bias than the commonly used standard methods. The idea behind the MC-normalization is that the channels' individual intensities determine the correction, rather than the average intensity which is the case for the widely used MAnormalization. The two methods were evaluated using spike-in data from an in-house produced cDNA-experiment and a publicly available Agilent-experiment. The methods were applied on background corrected and non-background corrected data. For the cDNA-experiment the methods were either applied separately on data from each of the print-tips or applied on the complete array data. Altogether 24 analyses were evaluated. For each analysis the sensitivity, the bias and two variance measures were estimated. We prove that the MC-normalization has lower bias than the MA-normalization. The spike-in data confirmed the theoretical result and suggest that the difference is significant. Furthermore, the empirical data suggest that the MC-and MA-normalization have similar sensitivity. A striking result is that print-tip normalizations did have considerably higher sensitivity than analyses using the complete array data.
Statistical Applications in Genetics and Molecular Biology Sub-Modular Resolution Analysis by Network Mixture Models

Inferring the structure of networks usually involves the attempt of retrieving their modular organization and knowing its possible interpretation, while quantifying the involved computational complexity through the methods and algorithms to be used. In protein interactomics, it is assumed that even the most recently created interactomes are known only up to a certain degree of coverage and accuracy, due to both experimental and computational limitations. Therefore, we need to infer from the measured interactomes about real interactomes as much as we infer from samples relative to a reference population. In order to exploit additional information sources, it is common to integrate multiple omic data and pursue method fusion. Particularly after the advent of highthroughput technologies, the increased complexity of data-intensive applications has determined an important role for network inference. Consequently, advances in spectral clustering, community detection algorithms and modularity optimization methods have been proposed, according to both deterministic and probabilistic solutions. We have considered the two kinds of approaches, and applied some of the available methods to two human interactomes obtained from high-throughput small-scale experiments and mass spectrometry measurements. The main motivation of this study is refining the resolution spectrum at which protein modularity maps can be studied. First, we started by a coarse-grained interactome decomposition through core and community structures, and by applying sub-sampling to the interactome adjacency matrix. Then, we switched to stochastic methods to uncover fine-grained interactome components, and applied both variational and mixture statistical models. Lastly, we integrated our analysis with the biological validation of the retrieved modules. Overall, the proposed approach shows potential for calibrating modularity detection in protein interactomes at different resolutions.
Generalized water resources assessment based on watershed hydrologic cycle model I.Assessment approach

The concept of generalized water resources is discussed and the necessity and feasibility of assessment of generalized water resources are investigated.On the basis of watershed distributed hydrologic cycle models an approach for assessment of generalized water resoureces is established and corresponding computation formula is proposed.The procedure for calculating various components of generalized water resources by using the WEP-L model is given as an example for demonstration.A method for distinguishing the high efficiency utilized water and low efficiency utilized water according to the assessment of their effectiveness to be utilized is proposed.
ICTV Virus Taxonomy Profile: Aoguangviridae 2023

The family Aoguangviridae includes dsDNA viruses that have been associated with marine archaea. Currently, members of this virus family are known through metagenomics. Virions are predicted to consist of an icosahedral capsid and a helical tail, characteristic of members in the class Caudoviricetes. Aoguangviruses have some of the largest genomes among archaeal viruses and possess most of the components of the DNA replication machinery as well as auxiliary functions. The family Aoguangviridae includes the species Aobingvirus yangshanense. Many unclassified relatives of this virus group, referred to as ‘magroviruses’, have been discovered by metagenomics in globally distributed marine samples. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the family Aoguangviridae, which is available at ictv.global/report/aoguangviridae.
A Robust H∞ State Feedback Controller Enabling a Medium Voltage Five-Level Grid-Connected Inverter for Grid Code Compliance

A novel robust ${H}_\infty $ state-feedback control system providing optimal stability as well as robust performance for a three-phase medium voltage five-level grid-connected inverter is proposed in this paper. The proposed one Degree of Freedom (DoF) control system provides high-quality sinusoidal grid current and guarantees optimal robust performance for the grid-connected five-level inverter in case of grid voltage disturbances, grid-side inductor parametric uncertainty, and ensures optimal reference tracking with optimal transient response in case of different grid fault scenarios following different grid standards such as the North American Electricity Reliability Corporation (NERC) and IEEE Std 2800-2022. To that end, the parametric uncertainty of the system is modeled as a polytopic-type uncertainty and a Linear Matrix Inequality (LMI)-based state-feedback controller based on convex optimization is implemented on the five-level grid-connected inverter. The system modelling as well as the design of the proposed controller will be discussed in this paper. The performance of the designed controller with the five-level grid-connected inverter is tested under different scenarios. Results are provided in PSCAD on a 5 MW, 34.5 kV system to validate the effectiveness of the proposed robust ${H}_\infty $ control system. Moreover, scaled-down hardware test results are provided to further validate the performance of the controller.
Frequency of Occurrence of Leukocytosis in Patients with Acute Exacerbation of Copd at a Tertiary Centre

COPD is a major cause of chronic morbidity and mortality; it is currently the fourth highest cause of death in the world, and is predicted to be the third leading cause of mortality worldwide by the year 2020 [1-2]. COPD is identified mainly according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines; [post bronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity ratio less than 0.7; severity determined by FEV1 alone] combined with a history of exposure to risk factors [2]. Exacerbations of respiratory symptoms in COPD are of major importance because of their profound and long lasting adverse effects on patients [3]. Frequent episodes accelerate loss of lung function, affect the quality of life of patients, and are associated with poor survival. There are some specific blood biomarkers for predicting lung function decline, acute exacerbation, or even mortality from big longitudinal cohort studies. However, their performance is depressing [4] . Peripheral blood leukocytosis has already been well investigated in the aspect of inflammatory biomarkers for COPD. The level of leukocytes is a key parameter in the clinical assessment of patients with COPD exacerbation, with and without fever. The presentation of high WBC count makes it important to distinguish the effect of the glucocorticosteroid from the effect of the possible infection [5-7].
FPGA-based Accelerator for Convolutional Neural Network Application in Mobile Robotics

This paper presents the development of a Convolutional Neural Network accelerator based on a Field Programmable Gate Array (FPGA) for the steering control application in a mobile robot. The FPGA-based implementation was benchmarked against an equivalent implementation using a general-purpose processor. The FPGA outperforms the general-purpose processor by significantly reducing processing time. However, the quantization process introduces increased estimation error, and the concurrent execution of the processor for data preprocessing in the accelerator implementation leads to higher power consumption. While the proposed solution is specific to the robot steering control problem, the general insights can be extrapolated to other applications that require high computing performance within power constraints.
Special Section on Drug Delivery Technologies — Minireview Nanotechnology and Immunotherapy in Ovarian Cancer: Tracing New Landscapes

Ovarian cancer (OC) is the seventh most common cancer in women worldwide. Standard therapeutic treatments involve debulking surgery combined with platinum-based chemother- apies. Of the patients with advanced-stage cancer who initially respond to current treatments, 50% – 75% relapse. Immunotherapy-based approaches aimed at boosting antitumor immunity have recently emerged as promising tools to challenge tumor progression. Treatments with inhibitors of immune checkpoint molecules have shown impressive results in other types of tumors. However, only 15% of checkpoint inhibitors evaluated have proven successful in OC due to the immunosuppressive environment of the tumor and the transport barriers. This limits the efficacy of the existing immunotherapies. Nanotechnology-based delivery systems hold the potential to overcome such limitations. Various nanoformulations including polymeric, li- posomes, and lipid – polymer hybrid nanoparticles have already been proposed to improve the biodistribution and targeting capabilities of drugs against tumor-associated immune cells, including dendritic cells and macrophages. In this review, we examine the impact of immunotherapeutic approaches that are currently under consideration for the treatment of OC. In this review, we also provide a comprehensive analysis of the existing nanoparticle-based synthetic strategies and their limitations and advantages over standard treatments. Furthermore, we discuss how the strength of the combination of nanotechnology with immunotherapy may help to overcome the current therapeutic limitations associated with their individual application and unravel a new paradigm in the treatment of this malignancy.
Special Section on Drug Delivery Technologies — Minireview Protein Chimerization: A New Frontier for Engineering Protein Therapeutics with Improved Pharmacokinetics

With the advancement of medicine, the utility of protein thera- peutics is increasing exponentially. However, a significant number of protein therapeutics suffer from grave limitations, which include their subpar pharmacokinetics. In this study, we have reviewed the emerging field of protein chimerization for improving the short circulatory half-life of protein therapeutics. We have discussed various aspects of protein therapeutics aiming at their mechanism of clearance and various approaches used to increase their short circulatory half-life with principal focus on the concept of chimerization. Furthermore, we have comprehensively reviewed various components of chimera, such as half-life extension partners and linkers, their shortcom- ings, and prospective work to be undertaken for developing effective chimeric protein therapeutics.
Identification of a PANoptosis-related Gene Signature for Predicting the Prognosis, Tumor Microenvironment and Therapy Response in Breast Cancer

Breast cancer (BC) is the most prevalent malignancy among women worldwide. Mounting evidence suggests that PANoptosis participates in cancer development and therapy. However, the role of PANoptosis in BC remains unclear. In this study, we identified ten PANoptosis-related genes using Cox regression analysis, random forest (RF) algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. A PANoptosis-related score (PRS) was calculated based on the coefficient of LASSO. Notably, we divided the patients into high- and low-risk groups according to the PRS and revealed a negative correlation between PRS and overall survival. Next, a nomogram model was constructed and validated to improve the clinical application of PRS. Functional enrichment analyses and the Bayesian network demonstrated that differentially expressed genes between high- and low-risk groups were mainly enriched in immune-related pathways. Besides, we found significant differences in tumor mutation burden and tumor immune microenvironment between patients in these two groups using bulk-RNA and single-cell RNA sequencing data. Furthermore, charged multivesicular body protein 2B (CHMP2B) was identified as the hub gene by combining LASSO, weighted gene co-expression network analysis, RF and eXtreme Gradient Boosting. Importantly, using immunohistochemistry analysis based on our tissue microarray, we found that CHMP2B was highly expressed in tumor tissue, and CD4 and CD8 were more likely to be positive in the CHMP2B-negative group. Survival analyses revealed that CHMP2B adversely impacted the survival of BC patients. In conclusion, we not only constructed a highly accurate predictive model based on PRS, but also revealed the importance of PANoptosis-related gene signature in the modulation of the tumor microenvironment and drug sensitivity in BC.
Analysis on spatial distribution of soil available heavy metals and its influential factors in a lead-zinc mining area of Guangxi,China

By means of geostatistics,a study was carried out to investigate spatial distribution of available heavy metals (Cu,Zn,Pb,Cd) in agricultural soils in a Lead-zinc mining area of Guangxi Zhuang Autonomous Region,China. At the same time,the factors which influence the distribution of available heavy metals were analyzed. Results showed that the soil was polluted by Cd,Zn,Pb,Cu in different degrees. And the contamination in paddy soil is more serious than that in arid land soil. Contrast to the background value of heavy metals in soil at Guangxi,the contaminated degree of Cd in soil is most serious that exceeding standard rate of Cd is 100% and the mean exceeding standard are 312.94 and 33.67 times of Cd in the paddy soils and arid land soils,respectively. The exceeding standard rates of Zn are 100% and 34% in the paddy soils and arid land soils,respectively. The contaminated degree of Cu is slight and the exceeding standard rate of Cu is only 7%. The ratios of nugget to sill of available Cd,Zn,Pb,Cu are 2.7%,0.2%,6.5%,0.13%,respectively,indicating the spatial distribution of available Cd,Zn,Pb,Cu are similar and auto-correlated. The contents of available Cd,Zn,Pb,Cu showed obvious directional characteristics from west northwest to east southeast,and the contents of available Cd,Zn,Pb,Cu reduced gradually along the irrigation ditches from WNW to ESE. The contents of available Cd,Zn,Pb,Cu of arid land soils in west were lower. The correlation analysis showed that spatial distribution of available Cd,Zn,Pb,Cu had been effected significantly by the soil basic physical chemistry character. It is positive correlation between available Cd,Zn,Pb,Cu with organic matter,while that is significantly negative correlation with pH,cation exchange capacity and clay.