Miracle-dz/UniFAR-FTUs huggingface dataset:
A system for full Stokes vector measurement for low concentration glucose sensing

A high performance system for full Stokes vector measurements was developed. The proposed system comprised a polarization scanning generator (PSG) and a high accuracy polarization state analyzer (PSA) was proposed. The PSG generated full state of polarization of light by using voltage driven electro-optics modulator without using any mechanical moving parts. The PSA was employed to record the intensity of output polarized lights in a high speed manner. The accuracy of proposed system was 10-4 for all Stokes vector (S0, S1, S2, S3) measurements in the full state of polarization of lights. An application of proposed system for low concentration glucose in aqueous solution sensing with/without scattering effects was demonstrated. The sensitivity of the optical rotation angle of CB property to changes in the concentration of glucose sample was examined over the range from 0 to 0.5g/dl. The results confirm that the proposed system is able to detect glucose at fine concentration of 0.02g/dl. The linear variation of the optical rotation angle and different glucose concentration at different scattering effects was obtained. In general, the new measurement system proposed in this study provided a fast and reliable method to measure all Stokes vectors and its potential applications in biological sensing.
Whyis 2: An Open Source Framework for Knowledge Graph Development and Research

Whyis is the first open source framework for creating custom provenance-driven knowledge graph applications, or KGApps , supporting three principal tasks: knowledge curation, inference, and interaction. It has been used in knowledge graph projects in materials science, health informatics, and radio spectrum policy. All knowledge in Whyis graphs are encapsulated in nanopublications, which simplifies and standardizes the production of qualified knowledge in knowledge graphs. The architecture of Whyis enables what we consider to be essential requirements for knowledge graph construction, maintenance, and use. These requirements include support for automated and manual curation of knowledge from diverse sources, provenance traces of all knowledge, domain-specific user interaction, and generalized distributed knowledge inference. We coin the term “Nano-scale knowledge graph” to refer to nanopublication-driven knowledge graphs. Knowledge graph developers can use Whyis to configure custom sets of knowledge curation pipelines using custom data importers and semantic extract, transform, and load scripts. The flexible, nanopublication-based architecture of Whyis lets knowledge graph developers integrate, extend, and publish knowledge from heterogeneous sources on the web. Whyis KGApps and are easily developed locally, managed using source control, and deployable via continuous integration, server deployment scripts, and as docker containers.
Characterization of severely burned patients and evaluation of factors associated to mortality. An observational retrospective cohort study in San Vicente Fundación Hospital

Introduction Burns are common in developing countries and place a large burden on the medical and social care systems. However, information about management and outcomes from such countries is scarce. The purpose of this study was to analyze the epidemiology and main factors related to the mortality in severely burned patients at the Hospital Universitario San Vicente Fundación in Medellín, Colombia. Methods An observational retrospective cohort study was conducted. To establish prognostic factors associated with mortality, we analyzed variables such as age, sex, burned surface, and degree of burn, among others. Demographic, clinic, and management features as well as complications and factors associated with mortality were analyzed using logistic regression. Results 4516 clinical histories were reviewed, 225 were included in the study. 76.9% were men, with a median age of 35 years; 64.9% were fire burns. The median burned body surface area was 42%. There were inhalation injuries in 135 patients and ocular in 106 patients. The main complication was infection followed by rhabdomyolysis. The overall hospital stay was 27 days, and the median length of stay at the intensive care unit was 7 days with in-hospital mortality of 30.7%. The variables associated with mortality were age, burned body surface area, degree of burn, and kidney injury. Surgical intervention was protective. Conclusions Severely burned patients in our hospital have similar outcomes and, in some cases, better outcomes than those reported in the literature in countries with similar characteristics, and we have seen that in the last years, there has been a better experience in the management of these patients. Elderly, extension, and depth of burnt tissue are markers of poor outcomes. Early surgery and intubation have shown better outcomes, probably due to infection control and removal of necrotic tissue, airway management, and ventilatory support for metabolic and hemodynamic derangement.
Finger vein image denoising based on compressive sensing

We present a Gradient Projection for Square Reconstruction(GPSR) algorithm for solving bound constrained quadratic programming problem to reduce the noise in synthetic vein images,which are blurred by various noises.This algorithm is based on the compressive sensing theory.The edge of the vein was extracted by Canny operator to verify the GPSR method.Experiment results show that,compared with the total variation denoising algorithms developed by Rudin,Osher and Fatemi(also called ROF),using the proposed GPSR algorithm can obtain finger vein image with higher Signal to Noise Ratio(SNR),and clearer edge of the vein.So this algorithm can provide more accurate information for vein recognition and extraction.
Community phylogenetic structure shapes pathogen outbreak potential in an epidemiological multi-host model

Biodiversity loss and anthropogenic alterations to species communities are impacting disease emergence events. These trends may be related through mechanisms in which biodiversity either increases (amplification) or decreases (dilution) diseases among hosts. Biodiversity effects can be direct, when contacts with competent hosts are replaced by sink hosts, or indirect through regulation of abundances and depend on the disease-competence of the added host. Here, we introduce a multi-host compartmental disease model, weighting host competence by their evolutionary history. We explore two scenarios: (1) where the probability of transmission depends on the evolutionary distance between the transmitting and recipient hosts, and (2) where transmission depends on the evolutionary distance from the receiving host to the primary host of the pathogen. Using simulations, and estimating the host community outbreak potential (R0), we show how differences in phylogenetic structure can switch host communities from diluting to amplifying a disease, even when species richness is unchanged. Common ecophylogenetic metrics of community structure that capture variation in the pairwise distances among hosts, are able to explain >90% of the variation in R0 across simulations. Our study provides a simple illustration of how host evolutionary histories can drive disease dynamics.
Proteomic characterization of aging-driven changes in the mouse brain by co-expression network analysis

Brain aging causes a progressive decline in functional capacity and is a strong risk factor for dementias such as Alzheimer’s disease. To characterize age-related proteomic changes in the brain, we used quantitative proteomics to examine brain tissues, cortex and hippocampus, of mice at three age points (3, 15, and 24 months old), and quantified more than 7000 proteins in total with high reproducibility. We found that many of the proteins upregulated with age were extracellular proteins, such as extracellular matrix proteins and secreted proteins, associated with glial cells. On the other hand, many of the significantly downregulated proteins were associated with synapses, particularly postsynaptic density, specifically in the cortex but not in the hippocampus. Our datasets will be helpful as resources for understanding the molecular basis of brain aging.
xRead: a coverage-guided approach for scalable construction of read overlapping graph

The development of long-read sequencing is promising to high-quality and comprehensive de novo assembly for various species around the world. However, it is still challenging for genome assemblers to well-handle thousands of genomes, tens of gigabase level genome sizes and terabase level datasets simultaneously and efficiently, which is a bottleneck to large de novo sequencing studies. A major cause is the read overlapping graph construction that state-of-the-art tools usually have to cost terabyte-level RAM space and tens of days for that of large genomes. Such lower performance and scalability are not suited to handle the numerous samples to be sequenced. Herein, we propose xRead, an iterative overlapping graph approach that achieves high performance, scalability and yield simultaneously. Under the guidance of its novel read coverage-based model, xRead uses heuristic alignment skeleton approach to implement incremental graph construction with highly controllable RAM space and faster speed. For example, it enables to process the 1.28 Tb A. mexicanum dataset with less than 64GB RAM and obviously lower time-cost. Moreover, the benchmarks on the datasets from various-sized genomes suggest that it achieves higher accuracy in overlap detection without loss of sensitivity which also guarantees the quality of the produced graphs. Overall, xRead is suited to handle numbers of datasets from large genomes, especially with limited computational resources, which may play important roles in many de novo sequencing studies.
Characterizing glucokinase variant mechanisms using a multiplexed abundance assay

Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms of human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. We assayed the abundance of 95% of GCK missense and nonsense variants, and found that 43% of hypoactive variants have a decreased cellular abundance. By combining our abundance scores with predictions of protein thermodynamic stability, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.
Reintroduction of resistant frogs facilitates landscape-scale recovery in the presence of a lethal fungal disease

Vast alteration of the biosphere by humans is causing a sixth mass extinction, driven in part by an increase in emerging infectious diseases. The emergence of the lethal fungal pathogen (Batrachochytrium dendrobatidis; “Bd”) has devastated global amphibian biodiversity, with hundreds of species experiencing declines or extinctions. With no broadly applicable methods available to reverse these impacts in the wild, the future of many amphibians appears grim. The once-common mountain yellow-legged (MYL) frog is emblematic of amphibians threatened by Bd. Although most MYL frog populations are extirpated following disease outbreaks, some persist and eventually recover. Frogs in these recovering populations have increased resistance against Bd infection, consistent with evolution of resistant genotypes and/or acquired immunity. We conducted a 15-year landscape-scale reintroduction study and show that frogs collected from recovering populations and reintroduced to vacant habitats can reestablish populations despite the presence of Bd. In addition, results from viability modeling suggest that many reintroduced populations have a low probability of extinction over 50 years. To better understand the role of evolution in frog resistance, we compared the genomes of MYL frogs from Bd-naive and recovering populations. We found substantial differences between these categories, including changes in immune function loci that may confer increased resistance, consistent with evolutionary changes in response to Bd exposure. These results provide a rare example of how reintroduction of resistant individuals can allow the landscape-scale recovery of disease-impacted species. This example has broad implications for the many taxa worldwide that are threatened with extinction by novel pathogens. Significance Statement Understanding how species persist despite accelerating global change is critical for the conservation of biodiversity. Emerging infectious diseases can have particularly devastating impacts, and few options exist to reverse these effects. We used large-scale reintroductions of disease-resistant individuals in an effort to recover a once-common frog species driven to near-extinction by a disease that has decimated amphibian biodiversity. Introduction of resistant frogs allowed reestablishment of viable populations in the presence of disease. In addition, resistance may be at least partially the result of natural selection at specific immune function genes, which show evidence for selection in recovering populations. The evolution of resistance and reintroduction of resistant individuals could play an important role in biodiversity conservation in our rapidly changing world.
Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative Studies

In neuroimaging research, the utilization of multi-centric analyses is crucial for obtaining sufficient sample sizes and representative clinical populations. Data harmonization techniques are typically part of the pipeline in multi-centric studies to address systematic biases and ensure the comparability of the data. However, most multi-centric studies require centralized data, which may result in exposing individual patient information. This poses a significant challenge in data governance, leading to the implementation of regulations such as the GDPR and the CCPA, which attempt to address these concerns but also hinder data access for researchers. Federated learning offers a privacy-preserving alternative approach in machine learning, enabling models to be collaboratively trained on decentralized data without the need for data centralization or sharing. In this paper, we present Fed-ComBat, a federated framework for batch effect harmonization on decentralized data. Fed-ComBat extends existing centralized linear methods, such as ComBat and distributed as d-ComBat, and nonlinear approaches like ComBat-GAM in accounting for potentially nonlinear and multivariate covariate effects. By doing so, Fed-ComBat enables the preservation of nonlinear covariate effects without requiring centralization of data and without prior knowledge of which variables should be considered nonlinear or their interactions, differentiating it from ComBat-GAM. We assessed Fed-ComBat and existing approaches on simulated data and multiple cohorts comprising healthy controls (CN) and subjects with various disorders such as Parkinson’s disease (PD), Alzheimer’s disease (AD), and autism spectrum disorder (ASD). Results indicate that Fed-ComBat outperforms centralized ComBat in the presence of nonlinear effects and is comparable to centralized methods such as ComBat-GAM. Using synthetic data, Fed-ComBat is able to better reconstruct the target unbiased function by 35% (RMSE = 0.5952) with respect to d-ComBat (RMSE = 0.9162) and 12% with respect to our proposal to federate ComBat-GAM, d-ComBat-GAM (RMSE= 0.6751) and exhibits comparable results on MRI-derived phenotypes to centralized methods as ComBat-GAM without the need of prior knowledge on potential nonlinearities.
Oxic microbial electrosynthesis can be more energy efficient for biomass production than knallgas or photosynthesis based processes

Data on the efficiency and development of continuous processes are scarce in the emerging field of oxic microbial electrosynthesis (OMES). Therefore, the recently isolated knallgas bacterium Kyrpidia spormannii was observed in a bioelectrochemical flow cell setup to study biomass formation and energy efficiency of cathode dependent growth. The study revealed that a potential of -500 mV vs. the standard hydrogen electrode (SHE) caused differences in the structure of the biofilm developing on the cathode, but had almost no impact on biomass growth behavior compared to -375 mV vs. SHE. No growth was observed at 0 mV vs SHE. Coulombic efficiency (CE) was calculated for the cultivation at -500 mV vs. SHE. The process can be conducted with the same electron efficiency as traditional gas fermentation. The solar energy demand with 67.89 kWh kg-1 dry biomass is highly competitive to alternative and already established processes for converting (solar) energy to biomass. Additionally, with suggestions for a biomass harvesting method and subsequent recultivation, proof of principle for a continuously operable process was provided. The results pave the way for a new concept in carbon dioxide-based biotechnology. Significance statement To mitigate global climate change, it is imperative to transition the human economy to a different resource foundation, moving away from fossil fuels and reducing atmospheric carbon dioxide levels. Biotechnological production based on CO2 necessitates a supply of energy and electrons. This study reveals that an oxic process, wherein bacteria are directly cultivated as a biofilm on the cathode surface of a bioelectrochemical system, can exhibit higher energy efficiency than plant-based systems or systems reliant on hydrogen generated through water electrolysis. This technology could be instrumental in establishing carbon dioxide and renewable energy as the foundation for feed, food, and platform chemical production.
Transformer Based Semantic Relation Typing for Knowledge Graph Integration

More and more knowledge graphs (KGs) are generated in various domains. Applications using more than one KG require an integrated view of those KGs, which, in the first place, requires a common schema or ontology. Merging schemas requires not only equivalence mappings between classes but also other semantic relations, like sub-class, superclass, etc. In this paper, we introduce TaSeR , a T r a nsformer based model for Se mantic R elation Typing, which is able to decide which type of relation holds between two given classes. The approach can differentiate between equivalent class, sub-/superclass, part of/has part, cohyponym, and no relation at all. With the latter outcome, it is not only possible to refine given class alignments, but also filter incorrect correspondences. The models are trained based on examples from general knowledge graphs as well as fine-tuned on the test case at hand. The former models can be directly used to predict a relation without further training. We show that those models are able to outperform other approaches which solve a similar task. For the evaluation, a new measure is introduced which credits for proximal matches.
A search for fast-moving features in the optical remnant of 3C 58

Red-sensitive plates of the optical remnant of 3C 58 (G130.7 + 3.1), obtained at the prime-focus of the Hale 5 m telescope in 1977 and 1989, have been used to search for rapidly expanding knots of nebular emission. No such nebulosity was found. If the optical nebula represent the expanding shell of a supernova, then its age must be a few thousand years. A more likely alternative is that the nebulosity that is presently visible (which is significantly enriched in nitrogen) represents quasi-stationary circumstellar material that was excited and compressed by passage of the fast-moving shell of a supernova that exploded about 800 years ago. 7 refs.
Prediction for Slope Displacement Based on Unequal Interval GM(1,1) Model

Prediction for slope displacement can prevent the occurrence of slope disasters effectively.The original grey GM(1,1) model is usually used in simulation and prediction of equidistant monitoring data sequentce,but the actual situation is the monitoring data which obtained appear unequal interval phenomenon because of some reason.So,unequal interval grey GM(1,1) model is established based on analyzing the establishing principal of original equal interval GM(1,1) model,and the method for calculating the grey parameter is discussed.This improved model is applied for analyzing the B3 monitoring data of a slope of the YU-QIAN highway,by comparing the grey parameter calculation of improved method with those of the conventional method,it is proved that the modified GM(1,1) model is of high accuracy and prediction results agrees well with the actual behavior of the slope.
System Design and Performance Analysis of Indoor Real-time Localization using UWB Infrastructure

Indoor localization and tracking poses a uniquely challenging aspect for asset tracking. Unreliability of traditional positioning systems, such as GNSS, in indoor applications calls for specialized systems to attain similar levels of accuracy and precision. To this end, Real Time Location System (RTLS) based on deployable anchors and tags are typically employed for these applications. The precision and accuracy of such RTLS systems is limited by the chosen anchors and tags. In this paper, the performance of DecaWave’s Ultra-wide-band (UWB) MDEK1001 is evaluated and tested under ideal Line-of-Sight (LOS) and non-LOS (NLOS) scenarios. The effect of obstacles and anchor bias on accuracy is also evaluated. Different anchor deployment configurations are tested to determine the impact of anchor quantity and position on tag position accuracy. Additionally, this paper provides an overview of EMSLab-RTLS client-server generated for real-time location applications.
Continuous Action Learning Automata Optimizer for training Artificial Neural Networks

This paper introduces a Continuous-Action Learning Automata (CALA) game optimizer that provides a generalized way to use a game of CALA agents to train Artificial Neural Networks (ANNs) and Deep ANNs. This method uses both game theory and learning automata, which makes it a computationally efficient method when compared against other non-gradient and non-back propagation methods. Since the CALA game optimizer does not use gradients or back propagation, issues such as the vanishing gradient problem do not manifest, which allows for the use of multiple activation functions such as sigmoid or tanh even in a Deep ANN.
Piecewise Rapidly-Exploring Random Tree Star

In this paper, we propose the piecewise technique of Rapidly-exploring Random Tree-Star (P-RRT*) algorithm used in low or medium specification agent(s) (rovers) in the two- dimensional (2-D) workspace. The traditional RRT, RRT*, and other path planning algorithms however efficient they have become; all treat a given environment as a whole and attempt to find a feasible path. This may result in higher memory utilization and a significant increase in processing time.We utilize the RRT* algorithm as the base and integrate it with the piecewise approach. Through P-RRT* technique, given an environment with no obstacles, we attempt to minimize the three vital elements used in the RRT* path planning algorithm (memory, power consumption, and time). A 2D simulation is utilized for demonstration purposes. Given a large workspace, we simulate over subregional workspaces where the number of nodes and step size are adjusted properly to minimize the cost. The simulation results show that dividing the entire simulation workspace into subregions and treating each subregion as a new workspace not only reduces memory utilization and processing time but also the power consumption as a result.The simulation results are shown versus the traditional RRT* algorithm; similar constraints are set for both the piecewise RRT* technique and the traditional RRT* algorithm; meaning that the number of nodes and step size is the same for both methods.