Miracle-dz/UniFAR-FTUs huggingface dataset:
The etiologies of Kawasaki disease

Kawasaki disease (KD) is a systemic vasculitis that affects young children and can result in coronary artery aneurysms. The etiology is currently unknown, but new clues from the epidemiology of KD in Japan, the country of highest incidence, are beginning to shed light on what may trigger this acute inflammatory condition. Additional clues from the global changes in KD incidence during the COVID-19 pandemic, coupled with a new birth cohort study from Japan, point to the potential role of person-to-person transmission of an infectious agent. However, the rising incidence of KD in Japan, with coherent waves across the entire country, points to an increasing intensity of exposure that cannot be explained by person-to-person spread. This Review discusses new and historical observations that guide us toward a better understanding of KD etiology and explores hypotheses and interpretations that can provide direction for future investigations. Once the etiology of KD is determined, accurate diagnostic tests will become available, and new, less expensive, and more effective targeted therapies will likely be possible. Clearly, solving the mystery of the etiologies of KD remains a priority for pediatric research.
Temporal and Spatial Patterns of Ship Traffic in the Canadian Arctic from 1990 to 2015 + Supplementary Appendix 1: Figs. S1–S7 (See Article Tools)

The limited availability of consistent, longitudinal data sources for marine traffic in Arctic Canada has presented significant challenges for researchers, policy makers, and planners. Temporally and spatially accurate shipping data that reveal historical and current traffic trends are vital to plan safe shipping corridors, develop infrastructure, plan and manage protected areas, and understand the potential environmental and cultural impacts of change, as well as for sovereignty and safety considerations. This study uses a recently developed geospatial database of ship traffic to provide the first synthesized overview of the spatial and temporal variability of different vessel types in Arctic Canada during the 26-year period from 1990 to 2015. This examination shows that, overall, the distance traveled by ships in Arctic Canada nearly tripled (from 364 179 km in 1990 to 918 266 km in 2015), that the largest proportion of ship traffic in the region is from general cargo vessels and government icebreakers (including research ships), and that the fastest growing vessel type by far is pleasure craft (private yachts). Spatial shifts in vessel activity over the last quarter century have favoured areas with active mine sites, as well as the southern route of the Northwest Passage. As a result, some communities, including Baker Lake, Chesterfield Inlet, Pond Inlet, and Cambridge Bay, are experiencing greater increases in ship traffic.
Genetic risk score for insulin resistance based on gene variants associated to amino acid metabolism in young adults

Circulating concentration of arginine, alanine, aspartate, isoleucine, leucine, phenylalanine, proline, tyrosine, taurine and valine are increased in subjects with insulin resistance, which could in part be attributed to the presence of single nucleotide polymorphisms (SNPs) within genes associated with amino acid metabolism. Thus, the aim of this work was to develop a Genetic Risk Score (GRS) for insulin resistance in young adults based on SNPs present in genes related to amino acid metabolism. We performed a cross-sectional study that included 452 subjects over 18 years of age. Anthropometric, clinical, and biochemical parameters were assessed including measurement of serum amino acids by high performance liquid chromatography. Eighteen SNPs were genotyped by allelic discrimination. Of these, ten were found to be in Hardy-Weinberg equilibrium, and only four were used to construct the GRS through multiple linear regression modeling. The GRS was calculated using the number of risk alleles of the SNPs in HGD, PRODH, DLD and SLC7A9 genes. Subjects with high GRS (≥ 0.836) had higher levels of glucose, insulin, homeostatic model assessment- insulin resistance (HOMA-IR), total cholesterol and triglycerides, and lower levels of arginine than subjects with low GRS (p < 0.05). The application of a GRS based on variants within genes associated to amino acid metabolism may be useful for the early identification of subjects at increased risk of insulin resistance.
The Effect of Vitamin D Supplementation to Parameter of Sarcopenia in Elderly People: a Systematic Review and Meta-Analysis

Background Vitamin D plays an essential role in promoting skeletal muscle metabolism. Several studies show that vitamin D may help the elderly prevent sarcopenia. Nevertheless, the outcome remains debatable. Our meta-analysis aimed to summarize the effect of vitamin D supplementation on sarcopenia-related parameters. Methods We searched PubMed, Cochrane, Springer, SAGE Journals, and Scopus abstracts on 10th December 2021 for relevant studies. We included articles that studied the effect of vitamin D on muscle mass, muscle strength, and physical performance. The aim was to measure the muscle mass, muscle strength, and physical performance both at baseline and at the end of the intervention. Results A total of 6,628 participants from 35 studies were included. Most of the studies used oral vitamin D, whereas only one study used intramuscular injection. The effect of vitamin D supplementation showed no effect on appendicular skeletal muscle mass (SMD = .05 [95% CI, .33 – .44], p = .79). Regarding muscle strength, vitamin D supplementation did not have a significant effect on muscle strength which is handgrip strength (p = .26). Respecting physical performance, vitamin D supplementation did not affect TUG (Timed Up and Go) (p = .45). Conclusions Vitamin D supplementation had minimal effect on sarcopenia-related parameters. Further research into understanding the role of Vitamin D in preventing the progressivity of sarcopenia still needs to be explored.
NETs unleashed: neutrophil extracellular traps boost chemotherapy against colorectal cancer

Chemotherapy, which primarily acts on cancer cells, can influence the tumor microenvironment and the recruitment and behavior of stromal cells. In this issue of the JCI, Li et al. explored the potent anticancer effect of the combination of a glutaminase inhibitor (CB-839) and 5-FU against PIK3CA-mutant colorectal cancer tumors. This chemotherapy treatment strongly induced the recruitment of neutrophils that formed neutrophil extracellular traps in cancer, which actively killed cancer cells by inducing apoptosis. This study substantially advances our understanding of the multifaceted role of neutrophils and NETs in the outcome of anticancer treatment.
Can using a pre-trained deep learning model as the feature extractor in the bag-of-deep-visual-words model always improve image classification accuracy?

This article investigates whether higher classification accuracy can always be achieved by utilizing a pre-trained deep learning model as the feature extractor in the Bag-of-Deep-Visual-Words (BoDVW) classification model, as opposed to directly using the new classification layer of the pre-trained model for classification. Considering the multiple factors related to the feature extractor -such as model architecture, fine-tuning strategy, number of training samples, feature extraction method, and feature encoding method—we investigate these factors through experiments and then provide detailed answers to the question. In our experiments, we use five feature encoding methods: hard-voting, soft-voting, locally constrained linear coding, super vector coding, and fisher vector (FV). We also employ two popular feature extraction methods: one (denoted as Ext-DFs(CP)) uses a convolutional or non-global pooling layer, and another (denoted as Ext-DFs(FC)) uses a fully-connected or global pooling layer. Three pre-trained models—VGGNet-16, ResNext-50(32×4d), and Swin-B—are utilized as feature extractors. Experimental results on six datasets (15-Scenes, TF-Flowers, MIT Indoor-67, COVID-19 CXR, NWPU-RESISC45, and Caltech-101) reveal that compared to using the pre-trained model with only the new classification layer re-trained for classification, employing it as the feature extractor in the BoDVW model improves the accuracy in 35 out of 36 experiments when using FV. With Ext-DFs(CP), the accuracy increases by 0.13% to 8.43% (averaged at 3.11%), and with Ext-DFs(FC), it increases by 1.06% to 14.63% (averaged at 5.66%). Furthermore, when all layers of the pre-trained model are fine-tuned and used as the feature extractor, the results vary depending on the methods used. If FV and Ext-DFs(FC) are used, the accuracy increases by 0.21% to 5.65% (averaged at 1.58%) in 14 out of 18 experiments. Our results suggest that while using a pre-trained deep learning model as the feature extractor does not always improve classification accuracy, it holds great potential as an accuracy improvement technique.
A role for TMEM63 in the lung

Surfactants are essential for breathing. Although major progress has been made in the past half century toward an understanding of surfactant secretion mechanisms, the identity of the mechanosensor that couples breathing to surfactant secretion has remained elusive. In this issue of the JCI, Chen, Li, and colleagues provide evidence that the mechanosensor is the transmembrane 63 (TMEM63) ion channel. These findings open new avenues for future research into lung mechanobiology.
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) allows studying the development of cells in unprecedented detail. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data are expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree structure in two dimensions is highly desirable for biological interpretation and exploratory analysis. Results Our two contributions are an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data, and a visualization method respecting the tree structure. We extract the tree structure by means of a density-based maximum spanning tree on a vector quantization of the data and show that it captures biological information well. We then introduce density-tree biased autoencoder (DTAE), a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space. We compare to other dimension reduction methods and demonstrate the success of our method both qualitatively and quantitatively on real and toy data. Availability and implementation Our implementation relying on PyTorch and Higra is available at github.com/hci-unihd/DTAE. Supplementary information Supplementary data are available at Bioinformatics online.
Devolatilization characteristics of cellulose, hemicellulose, lignin and the selected biomass during thermochemical gasification: Experiment and modeling studies

There is much interest in using switchgrass as a potential feedstock to produce energy and fuels. Thermochemical conversions, such as gasification and pyrolysis, are efficient ways of converting switchgrass into energy and fuels. The goal of this study was to investigate reaction kinetics and the nature of volatiles evolved during the thermochemical conversions of switchgrass and the contributions of its cellulose, hemicellulose and lignin to the decomposition. To accomplish this, a thermogravimetric analyzer (TGA) coupled with a Fourier Transform Infrared Spectrometer (FTIR) was used. Weight loss kinetics and a gas evolution profile of switchgrass and its components were analyzed under inert and oxidizing conditions. Significant weight loss of switchgrass occurred in the temperature range of 220 to 420 °C in nitrogen atmosphere and 220 to 390 °C in air atmosphere depending on heating rate.
Role of Plant Genetic Resources in Food Security

Within the last 13 000 yearsmany crop species were domesticated and spread to a range of agri-ecological environments, varying by species (Hancock 2012a). There was manual selection for both food and agronomic characteristics, and natural selection for adaptation to new agro-ecological environments. Such selection was affected by available gene pools, continuing sources of genetic diversity from wild relatives andmutations, natural selection pathways fromstabilising to directional, and both allo- and auto-polyploidy, to result in unique gene pool patterns for each crop (Hancock, 2012b; Cortes et al., 2013). Thedistribution of wheatwas expanded greatlywith the addition of the Aegilops tauschii D genome to tetraploid durum wheat, thereby enabling hexaploid wheat to adapt to a much wider agroecological range from the subtropics to high latitudes, and to provide a wider diversity of food uses (Hancock, 2012c).
Landmark guided forwarding

Wireless mobile ad hoc network routing presents some extremely challenging research problems. While primarily trying to provide connectivity, algorithms may also be designed to minimise resource consumption such as power, or to trade off global optimisation against the routing protocol overheads. In this thesis, we focus on the problems of maintaining network connectivity in the presence of node mobility whilst providing a balance between global efficiency and robustness. The common design goal among existing wireless ad hoc routing solutions is to search for an optimal topological path between a source and a destination for some shortest path metric. We argue that the goal of establishing an end to end globally optimal path is unsustainable as the network diameter, traffic volume, number of nodes all increase in the presence of moderate node mobility. Some researchers have proposed using geographic position-based forwarding, rather than a topological-based approach. In position-based forwarding, besides knowing about its own geographic location, every node also acquires the geographic position of its surrounding neighbours. Packet delivery in general is achieved by first learning the destination position from a location service. This is followed by addressing the packet with the destination position before forwarding the packet on to a neighbour that, amongst all other neighbours, is geographically nearest to the destination. It is clear that in the ad hoc scenario, forwarding only by geodesic position could result in situations that prevent the packet from advancing further. To resolve this, some researchers propose improving delivery guarantees by routing the packet along a planar graph constructed from a Gabriel (GG) or a Relative Neighbour Graph (RNG). This approach however has been shown to fail frequently when position information is inherently inaccurate, or neighbourhood state is stale, such as is the case in many plausible deployment scenarios, e.g. due to relative mobility rates being higher than location service update frequency. We propose Landmark Guided Forwarding (LGF), an algorithm that harnesses the strengths of both topological and geographical routing algorithms. LGF is a hybrid scheme that leverages the scaling property of the geographic approach while using local topology knowledge to mitigate location uncertainty. We demonstrate through extensive simulations that LGF is suited both to situations where there are high mobility rates, and deployment when there is inherently less accurate position data. Our results show that Landmark Guided Forwarding converges faster, scales better and is more flexible in a range of plausible mobility scenarios than representative protocols from the leading classes of existing solutions, namely GPSR, AODV and DSDV.
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification

Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppose we have a testing example, whose label can be correctly predicted by a DNN classifier. An attacker can add a small carefully crafted noise to the testing example such that the DNN classifier predicts an incorrect label, where the crafted testing example is called adversarial example . Such attacks are called evasion attacks . Evasion attacks are one of the biggest challenges for deploying DNNs in safety and security critical applications such as self-driving cars. In this work, we develop new methods to defend against evasion attacks. Our key observation is that adversarial examples are close to the classification boundary. Therefore, we propose region-based classification to be robust to adversarial examples. For a benign/adversarial testing example, we ensemble information in a hypercube centered at the example to predict its label. Specifically, we sample some data points from the hypercube centered at the testing example in the input space; we use an existing DNN to predict the label for each sampled data point; and we take a majority vote among the labels of the sampled data points as the label for the testing example. In contrast, traditional classifiers are point-based classification , i.e., given a testing example, the classifier predicts its label based on the testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets demonstrate that our region-based classification can significantly mitigate evasion attacks without sacrificing classification accuracy on benign examples. Specifically, our region-based classification achieves the same classification accuracy on testing benign examples as point-based classification, but our region-based
Parametric validity index of clustering for microarray gene expression data

An important area of genomic signal processing is microarray gene expression data analysis, which employs clustering algorithms to group individual genes or samples in a population. Due to the non-unique nature of clustering, the cluster validation is necessary for evaluating the results of clustering algorithms. In this paper, we propose a parametric validity index (PVI) which employs two tunable parameter α and β to control the proportions of objects being taken into account to calculate the dissimilarities. There are two advantages of the proposed PVI: on one hand, its computational complexity is low, and on the other hand, it has flexibility of tuning the parameters to meet different datasets, especially the microarray datasets. The PVI can be averaged over a range of values of α and β. We investigate the new PVI for assessing five clustering algorithms in four microarray datasets. The experimental results appear to suggest that the proposed PVI has relatively robust performance and provides fairly accurate judgements.
Fuelling the Fires: The Contribution of Wood Charcoal Analysis to a Landscape Scale Project at and Around Pre-Conquest Iron Age Silchester and a Reflection on Its Wider Implications

ABSTRACT The contribution of targeted wood charcoal analysis (anthracology) to understanding of the 1st millennium BC Pre-Conquest Late Iron Age oppidum and transition to Early Roman town life at Silchester and nearby late prehistoric hinterland sites investigated by the Silchester Environs project is considered. Attention is given to whether substantive differences in charcoal assemblages of varying size and origin are discernible through time and space, and to their value in elucidating landscape, environment, woodland structure, taphonomy, site function and lifestyles. This paper aims to take stock of the work so far and reflect on what lessons can be learned within and beyond the project. Site-level data are summarised and contrasted for the reader, while full context-level interpretation is published elsewhere [Barnett Forthcoming a. “The Early Roman Wood Charcoal and Waterlogged Wood at Silchester. ” In Silchester Insula IX: The Claudio-Neronian occupation of the Iron Age Oppidum. Britannia Monograph Series, edited by M. G. Fulford, A. Clarke, E. Durham, and N. Pankhurst. London: The Society for the Promotion of Roman Studies; and Barnett Forthcoming c. “Overview of the Archaeobotanical Evidence. ” In Silchester Environs: The Landscape Context of Iron Age Calleva, edited by C. Barnett, and M. G. Fulford. Oxford: Oxbow Books monograph].
Stature estimation from forearm lengths in North Karnataka population; India

Stature is an important parameter of personal identification of an individual there are many studies conducted along with others parameters such as age, sex, race, etc. The present study is an attempt to examine the relationship between the stature and right forearm length of 100 males and 100 females of north Karnataka, population in age ranging from 21 to 35 years. Linear and multiple regression equations formula for stature estimation were calculated. The co-relation co-efficient between stature and both right and left forearm length were found to be positive and statistically significant (P Keywords: Human anatomy, Anthropology, Stature, North Karnataka.
Measurements of the Directional Spectrum across the Equilibrium Saturation Ranges of Wind-Generated Surface Waves

AbstractIt is now well accepted that to better understand the coupling between the atmosphere and the ocean, and improve coupled ocean–atmosphere models, surface wave processes need to be taken into account. Here, properties of the directional distributions of the surface wave field across the equilibrium and saturation ranges are investigated from airborne lidar data collected during the ONR Southern California 2013 (SoCal2013) experiment, conducted off the coast of Southern California in November 2013. During the field effort, detailed characterization of the marine atmospheric boundary layer was performed from Research Platform (R/P) Floating Instrument Platform (FLIP), moored at the center of the aircraft operational domain. The wind speed ranged from approximately 1–2 to up to 11 m s−1, while the significant wave height varied from 0.8 to 2.5 m during the 10 days of data collection considered in the analysis. The directional wavenumber spectrum exhibits a clear, bimodal distribution that extends well.
Universal Relationship Between Viscous and Inertial Permeability of Geologic Porous Media

Fluid flow through geologic porous media is represented by Darcy's law and its inertial and nonlinear extension, the Forchheimer equation. These relationships equate the product of the driving potential gradient and phenomenological coefficients representing momentum resistance and dissipation to flux. From decades of research, the coefficient of viscous permeability (kv) in Darcy's law is largely predictable, but this is not the case for the coefficient of inertial permeability (ki) in the Forchheimer equation. Synthesizing results from thousands of laboratory and field flow tests and pore‐scale flow model results, we show that ki can be predicted from kv via the equation ki = 1010kv3/2 across 12 and 20 orders of magnitude in kv and ki, respectively. Since it is related with ki, kv is thus sufficient for predicting flow across viscous to inertial regimes for most geologic porous media.