Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable selection with group LASSO approach: Application to Cox regression with frailty model

Abstract In analysis of survival outcomes supplemented with both clinical information and high-dimensional gene expression data, use of the traditional Cox proportional hazards model fails to meet some emerging needs in biomedical research. First, the number of covariates is generally much larger the sample size. Secondly, predicting an outcome based on individual gene expression is inadequate because multiple biological processes and functional pathways regulate phenotypic expression. Another challenge is that the Cox model assumes that populations are homogenous, implying that all individuals have the same risk of death, which is rarely true due to unmeasured risk factors among populations. In this paper we propose group LASSO with gamma-distributed frailty for variable selection in Cox regression by extending previous scholarship to account for heterogeneity among group structures related to exposure and susceptibility. The consistency property of the proposed method is established. This method is appropriate for addressing a wide variety of research questions from genetics to air pollution. Simulated and real world data analysis shows promising performance by group LASSO compared with other methods, including group SCAD and group MCP. Future research directions include expanding the use of frailty with adaptive group LASSO and sparse group LASSO methods.
Cystic Fibrosis Related Bone Disease

22 Context: Young adults with cystic fibrosis (CF) are at risk for low bone density and fractures, but the 23 underlying alterations in bone microarchitecture that may contribute to their increased fracture risk are 24 currently unknown. 25 Objective: The main goal of this study was to utilize high resolution peripheral quantitative computed 26 tomography (HR-pQCT) to characterize the bone micro-architecture, volumetric bone mineral density 27 (vBMD), and estimated strength of the radius and tibia in young adults with CF compared to healthy 28
Social and economic factors influencing under-five mortality in Zimbabwe during 1996-2005

This study addressed important issues on infant and child mortality in Zimbabwe. The broad objective of the study is to establish levels and trends of under-5 mortality and to determine the impact of maternal, socioeconomic and environmental contamination variables on infant and child mortality. Data from four DHS surveys conducted in Zimbabwe were used. It was found that mortality at all ages below 5 years old remained more or less constant from the period 1990-1994 to the period 1995-1999 and declined from the period 1995-1999 to 2001-2005. Mortality below 5 years old declined from 102 deaths per 1,000 live births during 1995-1999 to 82 deaths in 2001-2005. This decline was unexpected and it is argued that this decline is probably not genuine. Various types of evidence are provided to support the view that this decline in mortality probably did not take place. Analysis of ZDHS 200506 showed that births of order 6+ and short preceding interval (intervals of less than or equal to 18 months) had the highest infant mortality risk. Infants with these characteristics were significantly more likely (2.75 times) to die in infancy relative to births of order 2-5 and long preceding birth interval (p<0.001). The infant mortality risk associated with multiple births was 2.08 times more relative to singleton births (p<0.001). The results indicated that socioeconomic variables did not have a distinct impact on infant mortality. Determinants of child mortality were different in relative importance from those of infant mortality. The effect of maternal education, though not significant, implied a decline in child mortality with increasing maternal schooling. The provision of piped drinking water and flush toilets to the households had a stronger impact on child mortality than infant mortality. Including HIV prevalence in the models elevated the odds of dying in infancy and childhood stages by 10 percent and 63 percent, respectively. This suggests that HIV/AIDS directly and/or indirectly influences the current levels of under-5 mortality
Sensorless Commutation Method for Low-Voltage BLDC Motors Based on Unfiltered Line Voltage

This study presents a filterless and sensorless commutation method for low-voltage brushless DC motors. The proposed method utilizes controlled DC-link inverter instead of the Pulse-Width Modulation (PWM) scheme. Therefore, motor voltages and currents become free from the high-frequency noise of PWM switching, thereby decreasing motor losses. Consequently, the method does not require any Low-Pass Filter (LPF) and it does not involve speed-dependent phase delay caused by the LPF. However, current commutation deteriorates waveform of line voltages. Thus, specific functions are defined to compensate for the current commutation spikes and remove false zero-crossing points of line voltages. Furthermore, the use of unfiltered line voltages eliminates the requirement of any phase shifter. Hence, the main superiority of the proposed method over preceding sensorless commutation methods is the simultaneous elimination of the phase shifter and LPF, which makes the method simple and cost-effective. The simulation and experimental results show the effectiveness and validity of the method.
Tartarus: A honeypot based malware tracking and mitigation framework

—On a daily basis many of the hosts connected to the Internet experience continuous probing and attack from malicious entities. Detection and defence from these malicious entities has primarily been the concern of Intrusion Detection Systems, Intrusion Prevention Systems and Anti-Virus software. These systems rely heavily on known signatures to detect nefar-ious trafﬁc. Due to the reliance on known malicious signatures, these systems have been at a serious disadvantage when it comes to detecting new, never before seen malware. This paper will introduce Tartarus which is a malware tracking and mitigation framework that makes use of honeypot technology in order to detect malicious trafﬁc. Tartarus implements a dynamic quarantine technique to mitigate the spread of self propagating malware on a production network. In order to better understand the spread and impact of internet worms Tartarus is used to construct a detailed demographic of potentially malicious hosts on the internet. This host demographic is in turn used as a blacklist for ﬁrewall rule creation. The sources of malicious trafﬁc is then illustrated through the use of a geolocation based visualisation.
The Radiobiological Characterization of Human and Porcine Lens Cells Suggests the Importance of the ATM Kinase in Radiation-Induced Cataractogenesis

Studies about radiation-induced human cataractogenesis are generally limited by (1) the poor number of epithelial lens cell lines available (likely because of the difficulties of cell sampling and amplification) and (2) the lack of reliable biomarkers of the radiation-induced aging process. We have developed a mechanistic model of the individual response to radiation based on the nucleoshuttling of the ATM protein (RIANS). Recently, in the frame of the RIANS model, we have shown that, to respond to permanent endo- and exogenous stress, the ATM protein progressively agglutinates around the nucleus attracted by overexpressed perinuclear ATM-substrate protein. As a result, perinuclear ATM crowns appear to be an interesting biomarker of aging. The radiobiological characterization of the two human epithelial lens cell lines available and the four porcine epithelial lens cell lines that we have established showed delayed RIANS. The BFSP2 protein, found specifically overexpressed around the lens cell nucleus and interacting with ATM, may be a specific ATM-substrate protein facilitating the formation of perinuclear ATM crowns in lens cells. The perinuclear ATM crowns were observed inasmuch as the number of culture passages is high. Interestingly, 2 Gy X-rays lead to the transient disappearance of the perinuclear ATM crowns. Altogether, our findings suggest a strong influence of the ATM protein in radiation-induced cataractogenesis.
Comparing Morse Complexes Using Optimal Transport: An Experimental Study Supplementary Materials

We use an example in Fig. 1 to explain the intuition behind the Wasserstein distance, GW distance, and FGW distance. We consider two scalar fields shown on the left, with corresponding Morse graphs shown on the right. To compute OT-type distances, we use the geometric locations of nodes as attributes, and the shortest geodesics between nodes to describe the network structures; see Sec. 4 for details.
Autonomous Robust Skill Generation Using Reinforcement Learning with Plant Variation

This paper discusses an autonomous space robot for a truss structure assembly using some reinforcement learning. It is difficult for a space robot to complete contact tasks within a real environment, for example, a peg-in-hole task, because of error between the real environment and the controller model. In order to solve problems, we propose an autonomous space robot able to obtain proficient and robust skills by overcoming error to complete a task. The proposed approach develops skills by reinforcement learning that considers plant variation, that is, modeling error. Numerical simulations and experiments show the proposed method is useful in real environments.
Understanding the Coupled Surface-Groundwater System from Event to Decadal Scale using an Un-calibrated Hydrologic Model and Data Assimilation

Understanding the Coupled Surface-Groundwater System from Event to Decadal Scale using an Un-calibrated Hydrologic Model and Data Assimilation by Jing Tao Department of Civil and Environmental Engineering Duke University Date:_______________________ Approved: ___________________________ Ana P. Barros, Supervisor ___________________________ John E. Dolbow ___________________________ Marco Marani ___________________________ Brian McGlynn ___________________________ Wade Crow An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Civil and Environmental Engineering in the Graduate School of Duke University 2015 Copyright by Jing Tao 2015
Forensic Assertive Community Treatment in a Continuum of Care for

Non-forensic or regular assertive community treatment (ACT) is a well-researched method of caring for persons with severe mental illness in the community (Stein and Test 1980). Regular ACT has positive effects on nonforensic outcomes, such as number of hospital admissions or length of stay (Burns and Firn 2002; Crawford et al. 2004; Coldwell and Bender 2007; Marshall and Lockwood 2011). Burns (2011) identifies six aspects of regular ACT that are essential for these positive outcomes: the presence of embedded psychiatrists; coverage 24 h a day, 7 days a week; a relatively low caseload; an integrated substance use treatment offer; integrated vocational and social support; and a high rate of home visits (Burns 2011). However, regular ACT has poor effects on forensic outcome measures, such as new arrests or incarcerations (Beach et al. 2013; Bond et al. 2001). This suggests a need for forensic adaptations of regular ACT (ForACT) that result in positive effects on both forensic and non-forensic outcome measures (Lamberti et al. 2004). One way of adapting ACT for patients that are referred for judicial actions (forensic patients) is to combine the main aspects of current forensic rehabilitation theories with the six elements described by Burns (2011). Currently, the main Abstract Non-forensic or regular assertive community treatment (ACT) has positive effects on non-forensic outcomes but has poor effects on forensic outcome measures. In this study, we examined non-forensic and forensic outcome measures of a forensic adaptation of ACT (ForACT) within a continuum of care for internees. Data were collected retrospectively from files of 70 participants in the ForACT group who had been released from a forensic hospital. The control group comprised internees who had left prison and entered community-based care (n = 56). The ForACT group demonstrated significantly better outcomes on forensic measures, such as arrests and incarcerations,
Numerical simulation of Faraday waves

Laboratoire de Physique et Mécanique des Milieux Hétérogènes (PMMH) Ecole Supérieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI) Centre National de la Recherche Scientifique (CNRS) UMR 7636 Université Paris 6 et Paris 7 10, rue Vauquelin 75231 Paris Cedex 5 France Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur (LIMSI) Centre National de la Recherche Scientifique (CNRS) UPR 3251 BP133 91403 Orsay Cedex France
Genetic Diversity of Common Wild Wheat Species in Armenia Using ISSR Markers

Corresponding Author: Tatevik Aloyan Scientific Center of Agrobiotechnology, ANAU Armenian National Agrarian University, Teryan 74, Yerevan, Armenia Email: tatevaloyan22@gmail.com Abstract: Armenia is one of the primary genealogical centers of numerous crops, the wild relatives of many of which are still found in Armenia today. Population limits of three out of 4 species of wild relatives of endemic wheat in Armenia (T. araraticum Jakubz, T. urartu Tumanian ex Gandilyan, T. boeoticum Boiss.) have been reduced in recent decades as a result of various ecological and economic functions, they are facing the danger of extinction and are registered in the Red Book of Plants of the Republic of Armenia. In order to identify these species as selection raw material, genetic characterization of populations was carried out according to ISSR-PCR markers, as well as decoding genetic formulas and barcodes. As a result, according to the coefficients of the genetic pattern, the studied wild types of wheat are characterized as basic or typical gene pools with the least quantity and frequency of rare alleles. The populations are stable and in conditions of efficient number of isolates, the gene pool is capable of self-reproduction. The obtained results as genetic markers will provide an opportunity to further clarify the origin of the species, evolution, patterns of inheritance of useful economic traits, cell genetic characteristics of the species, natural resistance to adverse environmental factors and diseases, etc.
Timing of antibiotic administration and outcomes for Medicare patients hospitalized with community-acquired pneumonia.

BACKGROUND Pneumonia accounts for more than 600 000 Medicare hospitalizations yearly. Guidelines have recommended antibiotic treatment within 8 hours of arrival at the hospital. METHODS We performed a retrospective study using medical records from a national random sample of 18 209 Medicare patients older than 65 years who were hospitalized with community-acquired pneumonia from July 1998 through March 1999. Outcomes were severity-adjusted mortality, readmission within 30 days of discharge, and length of stay (LOS). RESULTS Among 13 771 (75.6%) patients who had not received outpatient antibiotic agents, antibiotic administration within 4 hours of arrival at the hospital was associated with reduced in-hospital mortality (6.8% vs 7.4%; adjusted odds ratio [AOR], 0.85; 95% confidence interval [CI], 0.74-0.98), mortality within 30 days of admission (11.6% vs 12.7%; AOR, 0.85; 95% CI, 0.76-0.95), and LOS exceeding the 5-day median (42.1% vs 45.1%; AOR, 0.90; 95% CI, 0.83-0.96). Mean LOS was 0.4 days shorter with antibiotic administration within 4 hours than with later administration. Timing was not associated with readmission. Antibiotic administration within 4 hours of arrival was documented for 60.9% of all patients and for more than 50% of patients regardless of hospital characteristics. CONCLUSIONS Antibiotic administration within 4 hours of arrival was associated with decreased mortality and LOS among a random sample of older inpatients with community-acquired pneumonia who had not received antibiotics as outpatients. Administration within 4 hours can prevent deaths in the Medicare population, offers cost savings for hospitals, and is feasible for most inpatients.
Personalized Machine Learning using Passive Sensing and Ecological Momentary Assessments for Meth Users in Hawaii: A Research Protocol

Background: Artificial intelligence (AI)-powered digital therapies which detect meth cravings delivered on consumer devices have the potential to reduce these disparities by providing remote and accessible care solutions to Native Hawaiians, Filipinos, and Pacific Islanders (NHFPI) communities with limited care solutions. However, NHFPI are fully understudied with respect to digital therapeutics and AI health sensing despite using technology at the same rates as other races. Objective: We seek to fulfill two research aims: (1) Understand the feasibility of continuous remote digital monitoring and ecological momentary assessments (EMAs) in NHFPI in Hawaii by curating a novel dataset of longitudinal FitBit biosignals with corresponding craving and substance use labels. (2) Develop personalized AI models which predict meth craving events in real time using wearable sensor data. Methods: We will develop personalized AI/ML (artificial intelligence/machine learning) models for meth use and craving prediction in 40 NHFPI individuals by curating a novel dataset of real-time FitBit biosensor readings and corresponding participant annotations (i.e., raw self-reported substance use data) of their meth use and cravings. In the process of collecting this dataset, we will glean insights about cultural and other human factors which can challenge the proper acquisition of precise annotations. With the resulting dataset, we will employ self-supervised learning (SSL) AI approaches, which are a new family of ML methods that allow a neural network to be trained without labels by being optimized to make predictions about the data itself. The inputs to the proposed AI models are FitBit biosensor readings and the outputs are predictions of meth use or craving. This paradigm is gaining increased attention in AI for healthcare. Conclusions: We expect to develop models which significantly outperform traditional supervised methods by fine-tuning to an individual subject's data. Such methods will enable AI solutions which work with the limited data available from NHFPI populations and which are inherently unbiased due to their personalized nature. Such models can support future AI-powered digital therapeutics for substance abuse.
Endogenous development and Southern Europe

Endogenous development and South Europe - an introduction. Part 1 Local and regional development - a new approach: territory and economic development - the example of diffuse industrialization local development and flexible accumulation - learning from history and policy industrial districts - structure and transformation environment and development in the backward regions of South Europe. Part 2 Endogenous patterns of development - the national cases: diffuse industrialization and small firms - the Italian pattern in the 1970s local development and the regional state in Spain development and the local productive spaces - study on the Ave Valle (Portugal) intermediate regions and forms of social reproduction - three Greek cases. Part 3 Local development policies and strategies: local industrial policies in favour of small and medium-sized firms - the French case local development initiatives under incipient regional autonomy - the Spanish experience in the 80s autonomous development as a strategy for regional policy - a balance-sheet of Spanish experience with special reference to Andalusia industrilization and endogenous development in Portugal - problems and perspectives local structures and spatial policies in Greece.
Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models

Objective: To propose a novel approach for enhancing clinical prediction models by combining structured and unstructured data with multimodal data fusion. Methods: We presented a comprehensive framework that integrated multimodal data sources, including textual clinical notes, structured electronic health records (EHRs), and relevant clinical data from National Electronic Injury Surveillance System (NEISS) datasets. We proposed a novel hybrid fusion method, which incorporated state-of-the-art pre-trained language model, to integrate unstructured clinical text with structured EHR data and other multimodal sources, thereby capturing a more comprehensive representation of patient information. Results: The experimental results demonstrated that the hybrid fusion approach significantly improved the performance of clinical prediction models compared to traditional fusion frameworks and unimodal models that rely solely on structured data or text information alone. The proposed hybrid fusion system with RoBERTa language encoder achieved the best prediction of the Top 1 injury with an accuracy of 75.00% and Top 3 injuries with an accuracy of 93.54%. Conclusion: Our study highlights the potential of integrating natural language processing (NLP) techniques with multimodal data fusion for enhancing clinical prediction models' performances. By leveraging the rich information present in clinical text and combining it with structured EHR data, the proposed approach can improve the accuracy and robustness of predictive models. The approach has the potential to advance clinical decision support systems, enable personalized medicine, and facilitate evidence-based health care practices. Future research can further explore the application of this hybrid fusion approach in real-world clinical settings and investigate its impact on improving patient outcomes.
Rates of SARS-CoV-2 transmission between and into California state prisons

Correctional institutions are a crucial hotspot amplifying SARS-CoV-2 spread and disease disparity in the U.S. In the California state prison system, multiple massive outbreaks have been caused by transmission between prisons. Correctional staff are a likely vector for transmission into the prison system from surrounding communities. We used publicly available data to estimate the magnitude of flows to and between California state prisons, estimating rates of transmission from communities to prison staff and residents, among and between residents and staff within facilities, and between staff and residents of distinct facilities in the state's 34 prisons through March 22, 2021. We use a mechanistic model, the Hawkes process, reflecting the dynamics of SARS-CoV-2 transmission, for joint estimation of transmission rates. Using nested models for hypothesis testing, we compared the results to simplified models (i) without transmission between prisons, and (ii) with no distinction between prison staff and residents. We estimated that transmission between different facilities' staff is a significant cause of disease spread, and that staff are a vector of transmission between resident populations and outside communities. While increased screening and vaccination of correctional staff may help reduce introductions, large-scale decarceration remains crucially needed as more limited measures are not likely to prevent large-scale disease spread.