Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating models of marital functioning to understand the mental health consequences of the Great Recession

It is well established that negative financial events during macroeconomic crises have a significant impact on individuals’ mental health. Much less is known about how and for whom economic crises impact mental health. Using data from the Midlife in the United States study, we examine the mental health impact of the Great Recession in the U.S. Drawing on predictions from the Vulnerability-Stress-Adaptation Model of Marriage and the Family Stress Model, we examined whether increases in marital disagreements mediated the link between recession adversities (e.g., unemployment, increased debt, loss of a home) and mental health following the recession (2013–2014), controlling for prerecession marital disagreements and mental health (2004–2006). We found that those who experienced a greater number of recession adversities showed increased marital disagreements following the Great Recession, which were in turn associated with poorer mental health (negative affect and affective disorder). These associations held after controlling for prerecession levels of gender, age, race, and education. Furthermore, those who had lower income before the recession experienced greater increases in negative affect following the recession. These findings highlight the importance of marital processes in how the Great Recession is linked to mental health.
Considerations on Trajectory Planning Models in Automated Driving

: The SPARC concept for fully-automated driving was introduced in 2012 as a way to overcome the highly discretized maneuver planning approaches that had previously dominated the DARPA Grand and Urban Challenges. The aim was to deﬁne a single planner that is responsible for all forward driving tasks, including intersections, highways and pre-crash situations; that is based on a sound statistical framework including prediction and sensing uncertainties; and that can trade off between goals like safety, comfort and efﬁciency in a uniﬁed and transparent way. Since then, several similar approaches have been presented, most notably the Bertha Benz drive of 2013, in which the 100 km distance between Mannheim and Pforzheim was covered autonomously in a Mercedes–Benz S-500 Intelligent Drive. During the same time, the SPARC approach has been reﬁned in several respects, including its vehicle models and optimization techniques. This technical report provides an overview of the avenues explored to motivate the current form of the SPARC concept, based on frequently-asked questions that arose during the various technical presentations of the concept.
Training Workflows for Faster Self-Driving Results Joint AI Solutions for Autonomous Vehicle Development from NetApp and NVIDIA

The vision of autonomous vehicles on our roads is likely to become a reality in the near future. However, many challenges need to be explored and resolved before self-driving cars are feasible. NetApp, a leading company in the storage industry, is working with NVIDIA, a leading AI computational company, to build the first industrial reference architecture for the automotive industry (Arnette
Collaborative provenance for workflow-driven science and engineering

Met de groei van wetenschappelijke kennis en de stijging van het aantal studies dat gebruikmaakt van informatie van verschillende wetenschappelijke disciplines wordt de complexiteit van systematisch wetenschappelijk onderzoek aanzienlijk vergroot. Om de ‘grote’ wetenschappelijke vragen te beantwoorden gebruiken wetenschappers computergebaseerde methodieken die bijna dagelijks worden aangepast. Veranderingen in de computerwetenschap en technologie hebben geleid tot een verzameling van gereedschappen ontworpen om het wetenschappelijke proces meer efficient en sneller te maken. Deze gereedschappen zijn in staat om de creatie en uitvoering van computergestuurde taken te vereenvoudigen en zijn bekend onder de naam ‘wetenschappelijke workflows’. Ilkay Altintas presenteert vier belangrijke bijdragen op het gebied van samenwerkende workflows, genaamd collaborative provenance. De voornaamste bijdragen in het vastleggen van samenwerkende oorspronginformatie in de studie leiden tot de ontwikkeling van computersystemen die interoperatief samenwerken en het hergebruik van workflowresultaten vergroten.
Fast Radio Wave Propagation Prediction by Heterogeneous Parallel Architectures with Incoherent Memory

—The present correspondence deals with radio wave propagation for urban scenarios on the cell broadband engine. Binary space partitioning trees are used to split the building data into manageable size for the parallel units of the cell. By choosing the size of the tree leafs both data transfer between units and runtime of the algorithm is signiﬁcantly improved. The implementation of these techniques allowing for reﬂection demonstrates promising results for speeding up radio wave propagation.
Small catalytic RNA: Structure, function and application

We have utilized a combination of photochemical cross-linking techniques and site-directed mutagenesis to obtain secondary and tertiary structure information for the self-cleaving, self-ligating subsequence of RNA from the negative strand of Satellite Tobacco Ringspot Virus. We have found that the helical regions fold about a hinge to promoting four different possible tertiary interactions, creating a molecular of similar shape to a paperclip. A model suggesting that the ``paperclip`` and ``hammerhead`` RNAs share a similar three dimensional structure is proposed. We have used a self-cleaving RNA molecule related to a subsequence of plant viroids, a ``hammerhead,`` to study the length-dependent folding of RNA produced during transcription by RNA polymerase. We have used this method to determine the length of RNA sequestered within elongating E. coli and T7 RNA polymerase complexes. The data show that for E. coli RNA polymerase 12{plus_minus}1 nucleotides are sequestered within the ternary complex, which is consistent with the presence of an RNA-DNA hybrid within the transcription bubble, as proposed by others. The result for T7 RNA polymerase differs from E. coli RNA polymerase, with only 10{plus_minus}1 nucleotides sequestered within the ternary complex, setting a new upper limit for the minimum RNA-DNA required for a stable elongating complex.more » Comparisons between E. coli and T7 RNA polymerase are made. The relevance of the results to models or transcription termination, abortive initiation, and initiation to elongation mode transitions are discussed. « less
A Comparison of the Diagnostic Accuracy of in-situ and Digital Image-Based Assessments of Coral Health and Disease

The prevalence of coral disease is steadily increasing throughout the global ocean, and there is a growing need for efficient methods for detecting and monitoring coral health. At present, coral health assessments are primarily conducted using in-situ surveys, which record visual observations of disease in the field. Recent technological advancements allow researchers to instead collect high-resolution imagery of benthic habitats, and these images can be used in conjunction with digital tools to assess the health of coral colonies at a later time. However, little is known about the relative efficacy or diagnostic accuracy of these two approaches. This study contrasts the diagnostic accuracy of in-situ and digital methodologies for detecting diseases and adverse health conditions affecting corals. Multiple 1 m2 plots are surveyed on coral reefs located on both the windward and leeward side of Hawaii Island. For each plot, an in-situ visual analysis of coral health is conducted by a diver and images are collected and rendered into a high-resolution orthomosaic for subsequent digital analysis. Both methods assess the same coral colonies, resulting in paired health diagnoses for multiple health conditions. Lacking a gold-standard diagnosis of health conditions, a latent class model is used to estimate the sensitivity (true positive rate) and specificity (true negative rate) of both methods. We find that in-situ assessments of coral health have a higher sensitivity and lower specificity in detecting health conditions when compared to digital analyses based on orthomosaics. However, the effect size is relatively modest, indicating that while the in-situ method provides a more sensitive diagnostic approach, the techniques are of comparable accuracy, and should both be considered viable methods of characterizing and monitoring coral health.
Fine Mapping of the Wheat Leaf Rust Resistance Gene LrLC10 (Lr13) and Validation of Its Co-segregation Markers

Wheat leaf rust, caused by the fungus Puccinia triticina Eriks. (Pt), is a destructive disease found throughout common wheat production areas worldwide. At its adult stage, wheat cultivar Liaochun10 is resistant to leaf rust and the gene for that resistance has been mapped on chromosome 2BS. It was designated LrLC10 and is the same gene as cataloged gene Lr13 by pedigree analysis and allelism test. We fine-mapped it using recessive class analysis (RCA) of the homozygous susceptible F2 plants derived from crosses using Liaochun10 as the resistant, male parent. Taking advantage of the re-sequencing data of Liaochun10 and its counterpart susceptible parent, we converted nucleotide polymorphisms in the LrLC10 interval between the resistant and susceptible parents into molecular markers to saturate the LrLC10 genetic linkage map. Four indel markers were added in the 1.65 cM map of LrLC10 flanked by markers CAUT163 and Lseq22. Thirty-two recombinants were identified by those two markers from the 984 F2 homozygous susceptible plants and were further genotyped with additional ten markers. LrLC10 was finally placed in a 314.3 kb region on the Chinese Spring reference sequence (RefSeq v1.0) that contains three high confidence genes: TraesCS2B01G182800, TraesCS2B01G182900, and TraesCS2B01G183000. Sequence analysis showed several variations in TraesCS2B01G182800 and TraesCS2B01G183000 between resistant and susceptible parents. One KASP marker and an indel marker were designed based on the differences in those two genes, respectively, and were validated to be diagnostic co-segregating markers for LrLC10. Our results both improve marker-assisted selection and help with the map-based cloning of LrLC10.
A Participatory Investigation of Bovine Health and Production Issues in Pakistan

Systems to record the frequency of animal health events in Pakistan are limited. A participatory approach was used to address gaps in farmers' knowledge and understanding of bovine health and production issues in five agroecological zones (AEZs) of Pakistan. Participatory tools, including simple ranking, pairwise ranking, constraint impact scoring, and constraint profiling were used in group discussions with farmers and animal health professionals (AHPs) in six districts of two provinces, Punjab and Sindh. The results of the ranking activities showed that foot-and-mouth disease (FMD), clinical mastitis, ticks, hemorrhagic septicemia, reproductive disorders, blackleg, and endoparasites were the most important bovine health and production constraints for small-scale dairy farmers. Constraint impact scoring showed that the participants perceived that: (1) milk production was severely affected by FMD and mastitis; (2) blackleg and parasitism led to poor growth rates and reduced meat production; (3) reproductive disorders and mastitis caused major economic losses (due to the high cost of treatment); and (4) blackleg and hemorrhagic septicemia were the leading causes of mortality in cattle and buffaloes. Although there was strong agreement in responses and constraint impact scores between farmers and AHPs, farmers were more concerned about health issues that cause high mortalities, whereas AHPs emphasized the importance of disorders with a high economic impact. Despite socioeconomic differences among AEZs, farmers' knowledge about bovine health and production constraints was similar. The findings from this study revealed that farmers had limited understanding of the risk factors and routes of transmission of various infectious diseases of bovines, which emphasizes the need to develop and implement tailored extension programs in Pakistan to control contagious diseases of animals and to improve the profitability of small-scale dairy farmers.
Investigating the Causal Effect of Brain Expression of CCL2, NFKB1, MAPK14, TNFRSF1A, CXCL10 Genes on Multiple Sclerosis: A Two-Sample Mendelian Randomization Approach

Multiple Sclerosis (MS) exhibits considerable heterogeneity in phenotypic expression, course, prognosis and response to therapy. This suggests this disease involves multiple, as yet poorly understood, causal mechanisms. In this work we assessed the possible causal link between gene expression level of five selected genes related to the pro-inflammatory NF-κB signaling pathway (i.e., CCL2, NFKB1, MAPK14, TNFRSF1A, CXCL10) in ten different brain tissues (i.e., cerebellum, frontal cortex, hippocampus, medulla, occipital cortex, putamen, substantia nigra, thalamus, temporal cortex and intralobular white matter) and MS. We adopted a two-stage Mendelian Randomization (MR) approach for the estimation of the causal effects of interest, based on summary-level data from 20 multiplex Sardinian families and data provided by the United Kingdom Brain Expression Consortium (UKBEC). Through Radial-MR and Cochrane’s Q statistics we identified and removed genetic variants which are most likely to be invalid instruments. To estimate the total causal effect, univariable MR was carried out separately for each gene and brain region. We used Inverse-Variance Weighted estimator (IVW) as main analysis and MR-Egger Regression estimator (MR-ER) and Weighted Median Estimator (WME) as sensitivity analysis. As these genes belong to the same pathway and thus they can be closely related, we also estimated their direct causal effects by applying IVW and MR-ER within a multivariable MR (MVMR) approach using set of genetic instruments specific and common (composite) to each multiple exposures represented by the expression of the candidate genes. Univariate MR analysis showed a significant positive total causal effect for CCL2 and NFKB1 respectively in medulla and cerebellum. MVMR showed a direct positive causal effect for NFKB1 and TNFRSF1A, and a direct negative causal effect for CCL2 in cerebellum; while in medulla we observed a direct positive causal effect for CCL2. Since in general we observed a different magnitude for the gene specific causal effect we hypothesize that in cerebellum and medulla the effect of each gene expression is direct but also mediated by the others. These results confirm the importance of the involvement of NF-κB signaling pathway in brain tissue for the development of the disease and improve our understanding in the pathogenesis of MS.
Over the Horizon: The Present and Future of Endovascular Neural Recording and Stimulation

The past decade has witnessed an explosion in applications for neural recording and stimulation in the treatment of clinical disorders. Neuromodulatory approaches are now a mainstay of care for essential tremor and Parkinson’s disease, and are expanding rapidly into a wide range of other neurological and psychiatric diseases. In parallel, advancements in endovascular approaches to cerebrovascular diseases have resulted in minimally invasive techniques that deliver devices to neural tissue in the central and peripheral nervous systems, with significantly improved safety and efficacy. In this review, we discuss the history of endovascular neural recording and stimulation, its current progress, and applications for neurological disease.
Nucleotide sequence of human elongation factor-1β cDNA

Elongation factor-i, which is composed of four subunits: ae, (, -y and 6, participates in protein synthesis by binding aminoacyl-tRNAs to 80S ribosomes under hydrolysis of GTP. The complex is composed of two complementary factors with a distinct function: EF-lx, binding GTP and aminoacyl-tRNA and EF-1(3'y, serving as a GDP/GTP exchange enhancer; the two factors are functionally equivalent to prokaryotic EF-Tu and EF-Ts. The actual GDP/GTP exchange activity in EF-1(3'y resides in the EF-13 subunit. The functions of EF-17 and the more recently discovered EF-I6 are less well defined. Since our goal is to have at our disposal a human in vivo system for functional studies on Elongation Factors, we started, after having isolated human EF-la cDNAs (1), to look for similar EF-l1fy and -6 cDNA sequences. We decided to get protein sequence information first from pig EF-1,B to which the Artemia purification procedure was applied (2). As a result, a mixed oligonucleotide corresponding to the peptide sequence KPWDDE, which is completely conserved between Artemia EF-1(3 and EF-lb, Xenopus EF-1,B and EF-16 (3) and pig EF-113 was synthesised and used to screen a human lambda gtlO cDNA library. A 400 bp partial EF-1(3 cDNA clone was isolated and then used as a probe to rescreen the library yielding a lambda gtlO phage with a 1 kb insert. All techniques were carried out according to standard procedures as described in (4). Direct sequencing was performed on the PCR-amplified
Temporal stability of lipid-shelled microbubbles during acoustically-mediated blood-brain barrier opening

Non-invasive blood-brain barrier (BBB) opening using focused ultrasound (FUS) is being tested as a means to locally deliver drugs into the brain. Such FUS therapies require injection of pre-formed microbubbles, currently used as contrast agents in ultrasound imaging. Although their behavior during exposure to imaging sequences has been well-described, our understanding of microbubble stability within a therapeutic field is still not complete. Here, we study the temporal stability of lipid-shelled microbubbles during therapeutic FUS exposure in two timescales: the short timescale (i.e., μs of low-frequency ultrasound exposure) and the long timescale (i.e., days post-activation). We first simulated the microbubble response to low-frequency sonication, and found a strong correlation between viscosity and fragmentation pressure. Activated microbubbles had a concentration decay constant of 0.02 d−1 but maintained a quasi-stable size distribution for up to 3 weeks (<10% variation). Microbubbles flowing through a 4-mm vessel within a tissue-mimicking phantom (5% gelatin) were exposed to therapeutic pulses (fc: 0.5 MHz, peak-negative pressure: 300 kPa, pulse length: 1 ms, pulse repetition frequency: 1 Hz, n = 10). We recorded and analyzed their acoustic emissions, focusing on emitted energy and its temporal evolution, alongside the frequency content. Measurements were repeated with concentration-matched samples (107 microbubbles/ml) on day 0, 7, 14, and 21 after activation. Temporal stability decreased while inertial cavitation response increased with storage time both in vitro and in vivo, possibly due to changes in the shell lipid content. Using the same parameters and timepoints, we performed BBB opening in a mice (n = 3). BBB opening volume measured through T1-weighted contrast-enhanced MRI was equal to 19.1 ± 7.1 mm3, 21.8 ± 14 mm3, 29.3 ± 2.5 mm3, and 38 ± 20.1 mm3 on day 0, 7, 14, and 21, respectively, showing no significant difference over time (p-value: 0.49). Contrast enhancement was 24.9 ± 1.7%, 23.7 ± 11.7%, 28.9 ± 5.3%, and 35 ± 13.4%, respectively (p-value: 0.63). In conclusion, the in-house made microbubbles studied here maintain their capacity to produce similar therapeutic effects over a period of 3 weeks after activation, as long as the natural concentration decay is accounted for. Future work should focus on stability of commercially available microbubbles and tailoring microbubble shell properties toward therapeutic applications.
Cancer is associated with the severity and mortality of patients with COVID-19: a systematic review and meta-analysis

Background: Cancer patients are considered a highly vulnerable population in the COVID-19 epidemic, but the relationship between cancer and the severity and mortality of patients with COVID-19 remains unclear. This study aimed to explore the prevalence of cancer in patients with COVID-19 and to examine whether cancer patients with COVID-19 may be at an increased risk of severe illness and mortality. Methods: A comprehensive electronic search in seven databases was performed, to identified studies reporting the prevalence of cancer in COVID-19 patients, or providing data of cancer between patients with severe or non-severe illness or between non-survivors and survivors. Meta-analyses were performed to estimate the pooled prevalence and odds risk (OR) using the inverse variance method with the random-effects model. Results: Thirty-four studies with 8080 patients were included. The pooled prevalence of cancer in patients with COVID-19 was 2.0% (95% CI: 2.0% to 3.0%). The prevalence in Italy (5.0%), France (6.0%), and Korea (4.0%) were higher than that in China (2.0%). Cancer was associated with a 2.84-fold significantly increased risk of severe illness (OR = 2.84, 95%CI: 1.75 to 4.62, P < 0.001) and a 2.60-fold increased risk of death (OR = 2.60, 95%CI: 1.28 to 5.26, P = 0.008) in patients with COVID-19. Sensitivity analyses showed that the results were stable after excluding studies with a sample size of less than 100. Conclusions: Cancer patients have an increased risk of COVID-19 and cancer was associated with a significantly increased risk of severity and mortality of patients with COVID-19.
Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome

The study of disease-relevant gene modules is one of the main methods to discover disease pathway and potential drug targets. Recent studies have found that most disease proteins tend to form many separate connected components and scatter across the protein-protein interaction network. However, most of the research on discovering disease modules are biased toward well-studied seed genes, which tend to extend seed genes into a single connected subnetwork. In this paper, we propose N2V-HC, an algorithm framework aiming to unbiasedly discover the scattered disease modules based on deep representation learning of integrated multi-layer biological networks. Our method first predicts disease associated genes based on summary data of Genome-wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) studies, and generates an integrated network on the basis of human interactome. The features of nodes in the network are then extracted by deep representation learning. Hierarchical clustering with dynamic tree cut methods are applied to discover the modules that are enriched with disease associated genes. The evaluation on real networks and simulated networks show that N2V-HC performs better than existing methods in network module discovery. Case studies on Parkinson's disease and Alzheimer's disease, show that N2V-HC can be used to discover biological meaningful modules related to the pathways underlying complex diseases.