Miracle-dz/UniFAR-FTUs huggingface dataset:
Opposing effects of calcium stress in human muscle: defective glucose utilization leading to diabetes vs. stress-adaptive effects of a junctophilin fragment

Contraction of striated muscle, which is controlled by movements of calcium ions between cellular stores (SR) and cytosol, is the function consuming the most energy in mammals, which also makes muscle the main user of circulating glucose and regulator of its level. Accordingly, inadequate removal of glucose by muscle is the main proximate cause of hyperglycemia, leading to insulin resistance and diabetes. Multiple diseases of muscle course with chronically elevated cytosolic calcium. Here we show in a sample of malignant hyperthermia-susceptible (MHS) patients, a significant decrease in content of junctophilin1, an essential protein of the couplon —the supramolecular device that effects calcium release from the SR— accompanied by an increase in a 44 kDa C-terminal junctophilin fragment, JPh44, and the activating cleavage of GSK3 β , a specialized kinase that inhibits glycogen synthase. We trace these changes to activation, by the excess calcium, of calpain1, a serine/threonine kinase. By high-resolution imaging of antibody markers or fluorescent probes expressed in patients’ and murine muscle and patient-derived and C2C12 myotubes, we demonstrate detailed colocalization of junctophilin1 with the calcium channel RyR1, marking the couplon, and of calpain1 with both. We also show that the JPh44 fragment moves away from the couplon and into nuclei, a movement greatly increased in MHS. Cultured myotubes expressing a JPh44-like construct had altered gene transcription in multiple pathways, including changes that promote glucose utilization. Together with less transcription, the expression of GSK3 β was reduced, which predicts increased glucose utilization. In summary, the changes observed in these patients add to the wide-ranging alterations in metabolism that eventually lead to hyperglycemia and diabetes, assign the changes to proteolysis by calcium-activated calpain1, and reveal the production and beneficial, stress-adaptive function of a novel regulator of transcription, JPh44. Similar stress-adaptive roles have been described for a junctophilin2 fragment in heart failure.
Genetic mapping of multiple metabolic traits identifies novel genes for adiposity, lipids and insulin secretory capacity in outbred rats

Despite the successes of human genome-wide association studies, the causal genes underlying most metabolic traits remain unclear. We used outbred heterogeneous stock (HS) rats, coupled with expression data and mediation analysis, to identify quantitative trait loci (QTLs) and candidate gene mediators for adiposity, glucose tolerance, serum lipids and other metabolic traits. Physiological traits were measured in 1519 male HS rats, with liver and adipose transcriptomes measured in over 410 rats. Genotypes were imputed from low coverage whole genome sequence. Linear mixed models were used to detect physiological and expression QTLs (pQTLs and eQTLs, respectively), employing both SNP- and haplotype-based models for pQTL mapping. Genes with cis-eQTLs that overlapped pQTLs were assessed as causal candidates through mediation analysis. We identified 15 SNP-based pQTLs and 19 haplotype-based pQTLs, of which 11 were in common. Using mediation, we identified the following genes as candidate mediators of pQTLs: Grk5 for a fat pad weight pQTL on Chr1, Krtcap3 for fat pad weight and serum lipids pQTLs on Chr6, Ilrun for a fat pad weight pQTL on Chr20 and Rfx6 for a whole pancreatic insulin content pQTL on Chr20. Furthermore, we verified Grk5 and Ktrcap3 using gene knock-down/out models, thereby shedding light on novel regulators of obesity.
Defining coarse-grainability in a model of structured microbial ecosystems

Despite their complexity, microbial ecosystems appear to be at least partially “coarse-grainable” in that some properties of interest can be adequately described by effective models of dimension much smaller than the number of interacting lineages (frequently in the dozens or hundreds). This is especially puzzling since recent studies demonstrate that a surprising amount of functionally relevant diversity is present at all levels of resolution, down to strains differing by 100 nucleotides or fewer. Rigorously defining coarse-grainability and understanding the conditions for its emergence is of critical importance for understanding microbial ecosystems. To begin addressing these questions, we propose a minimal model for investigating hierarchically structured ecosystems within the framework of resource competition. We use our model to operationally define coarse-graining quality based on reproducibility of the outcomes of a specified experiment and show that a coarse-graining can be operationally valid despite grouping together functionally diverse strains. We further show that, at least within our model, a high diversity of strains (while nominally more complex) may in fact facilitate coarse-grainability. However, this only applies if the pool of interacting strains is sampled from the appropriate “native” environment, as we discuss.
Loan Default Prediction Using Spark Machine Learning Algorithms

Loan lending has been an important business activity for both individuals and ﬁnancial institutions. Proﬁt and loss of ﬁnancial lenders to an extent depend on loan repayment. Though loan lending is beneﬁcial for both lenders and borrowers, it does carry a great risk of the inability of the loan receiver to repay back the loan. This inability is termed as loan default. Loan default prediction is a crucial process that should be carried out by ﬁnancial lenders to help them ﬁnd out if a loan can default or not. Successful loan default prediction can help ﬁnancial institutions to decrease the number of bad loan issues and eventually increase proﬁt. The aim of this paper is to use data mining techniques to bring out insight from data then build a loan prediction model us-ing machine learning algorithms on the Sparks Big Data platform. Six supervised machine learning classiﬁcation algorithms are applied to predict loan default: Logistic Regression, Decision Tree, Random Forest, Gradient Boosted Tree (GBTs), Factorization Machines (FM) and Linear Support Vector Machine (LSVM). Accuracy, precision, recall, ROC curve and F measure are used to evaluate the models and the results compared. We achieve the highest accuracy of 99.62% using the Decision Tree and Random Forest Models.
The Influence of Suspension Insulator Head Structure to Its Electrical Performance

The puncture performance is one of the most important reference parameter to evaluate the general performance of the insulator,and improving the puncture value has great significance for insulator internal insulation quality and its operating stability.Optimizing the product design structure reasonable has become the effective way to improve the product performance,when the ability of the dielectric materials could not be improved.Through the finite-element analysis software ANSYS,the affection of different head structure to its field strength is analyzed,and the reasonable field strength distribution,is justified,the ideal result can be obtained when the round radius between 8.2~15 mm,if the radius is above 15 mm,the carrying surface of the product is too small,has great influence on mechanical property;if the radius is below 8.2 mm,then the electric field strength is much larger,puncture performance of product is greatly influenced.
Adaptation of Compositional Data Analysis in Deep Learning to Predict Pasture Biomass Proportions

Dry biomass weight measurements from a quadrat in a paddock for grass, clover and weeds when expressed as percentages of total dry herbage mass are compositional in nature. Unlike real valued regression problems, prediction of compositional data is handled differently in statistics because of its closure property where the components of the composition are positive data adding up to a constant sum and is therefore constrained in the simplex space, in our case 100%. Our motivation in this paper was to study whether the adaptation of compositional data analysis (CoDa) techniques in deep learning improves the prediction results over the best performing deep learning model we used in our earlier paper [Narayanan et al., 2021]. Although the log ratio transformation of targets is an appropriate adaptation of CoDa and is interesting for Biomass prediction, our study indicates that the CoDa adaptation does not improve the prediction errors over our earlier method.
Distributed ℋ2-Matrices for Boundary Element Methods

Standard discretization techniques for boundary integral equations, e.g., the Galerkin boundary element method, lead to large densely populated matrices that require fast and efficient compression techniques like the fast multipole method or hierarchical matrices. If the underlying mesh is very large, running the corresponding algorithms on a distributed computer is attractive, e.g., since distributed computers frequently are cost-effective and offer a high accumulated memory bandwidth. Compared to the closely related particle methods, for which distributed algorithms are well-established, the Galerkin discretization poses a challenge, since the supports of the basis functions influence the block structure of the matrix and therefore the flow of data in the corresponding algorithms. This article introduces distributed ℋ2-matrices, a class of hierarchical matrices that is closely related to fast multipole methods and particularly well-suited for distributed computing. While earlier efforts required the global tree structure of the ℋ2-matrix to be stored in every node of the distributed system, the new approach needs only local multilevel information that can be obtained via a simple distributed algorithm, allowing us to scale to significantly larger systems. Experiments show that this approach can handle very large meshes with more than 130 million triangles efficiently.
Fingerphoto Recognition Using Cross-Reference-Matching Multi-Layer Features

A fingerphoto-recognition method-which efficiently combines multi-layer biometric features for accurate personal identification even with low-quality images-is proposed. The proposed method extracts both knuckleprint patterns and vein patterns that do not require high-resolution cameras for observation. The distance between one of these patterns and a registered template is calculated in reference to the location-shifting information of the matching using the other pattern. This calculation is performed mutually, and the pair of obtained distance are fused. Since knuckleprint patterns and vein patterns have a low correlation, the positions in these patterns where the enrolled template matches differ when verifying an impostor. Only genuine attempts yield the distance between the best aligned patterns. In experiments to compare the performance of the proposed method with conventional methods, finger images of 230 subjects were collected by using built-in cameras of two laptop computers. Each finger was represented by two templates each with size of 32 × 64, and a maximum of four fingers per person was used for matching. The experimental results show that the proposed method yields an equal error rate (EER) of 1.093%, compared to 4.114% by the conventional method using knuckleprint patterns.
An Implementation Study of Network Data Analytic Function in 5G

Network automation and intelligence are evolutionary directions in 5G, and network data analytic function (NWDAF) plays a key role to realize this vision. In this work, we present an implementation result of NWDAF in free5GC that is an open software for 3GPP mobile core networks. The implemented NWDAF module consists of 1) model training logical function (MTLF) to train the model and 2) analytics logic function (AnLF) to provide analytic results based on the trained model. We have verified the operability of NWDAF and released it through Github. Extensive experimental study will be conducted in our future work.
Methods in Constrained Community Detection: An Integer Optimization Model and Heuristic Approach for Cohort Creation

As a result of the COVID-19 pandemic, many organizations and schools have switched to a virtual environ-ment. Recently, as vaccines have become more readily available, organizations and educational institutions have started shifting from virtual environments to physical office spaces and schools. For the highest level of safety and caution with respect to the containment of COVID-19, the shift to in-person interaction requires a thoughtful approach. With the help of an Integer Programming (IP) Optimization model, it is possible to formulate the objective function and constraints to determine a safe way of returning to the office through cohort development. In addition to our IP formulation, we developed a heuristic approximation method. Starting with an initial contact matrix, these methods aim to reduce additional contacts introduced by subgraphs representing the cohorts. These formulations can be generalized to other applications that benefit from constrained community detection.
Fuzzy Controlled LED Lighting Compensation for Aeroponics System

Solar is one of the primary sources of light energy for plants, and it is significant to its energy conversion process – photosynthesis. Due to some unpreventable factors and restricted locations, plants do not constantly receive their needed energy, affecting the crops. With technological advancements, artificial lightings are integrated into agricultural systems to compensate for this lack of supply. The given aeroponics system uses two different spectra of light-emitting diodes (LED) – white and an equal combination of red and blue – as lighting compensation. These artificial lights are controlled by fuzzy logic, which treats the lux value as the input and the brightness as the output. On this setup, having a p-value of 0.0006 compared to 0.1592, the blue and red-light compensation creates a significant improvement. The same is for the number of leaves, with the blue and red having a value of 0.0398 and white with 0.1828. Lastly, the identical light spectra created the most prominent gap with the weight reading 6.25g. The combination of red and blue had proven a better effect than white and regular sunlight concerning the plants’ height, weight, and the number of leaves.
Deep Reinforcement Learning based Resource Allocation in Dense Sliced LoRaWAN Networks

Long-Range Wide Area Network (LoRaWAN) is a rapidly expanding communication system for Low Power Wide Area Network (LPWAN) in the Internet of Things (IoTs) deployments. It employs an Adaptive Data Rate (ADR) scheme that optimizes data rate, airtime, and energy consumption. Recently, the use of Network Slicing (NS) in LoRa Wannetworks is being widely studied and a hot topic for the latest research in the literature. Network resources must be efficiently assigned to IoT devices in an isolated manner in order to handle and support specific Quality of Service (QoS) requirements for each slice. However, in dense LoRaWAN networks, the ADR scheme is insufficient for efficient resource allocation to meet the QoS requirements of each slice. In this article, we propose a DRL-based approach for intra-slicing resource allocation in dense LoRa Wannetworks. In each slice, we implemented multi-agent DRL that allocates Spreading Factor (SF) and Transmission Power (TP) to IoT devices to meet QoS requirements, i.e. we replaced the conventional ADR scheme with multi-agent DQN with different reward function design for each slice according to QoS requirements. Experimental results realized in real conditions show that our approach outperforms the existing ADR scheme for all the slices.
Microbiological studies of ion exchange and combined water treatment plants

Filter materials biofouling in baromembrane water treatment plants at thermal power plants is an urgent problem. Biofouling causes ion - exchange resins “aging”, membranes destruction and leads to an increase in the content of organic impurities in purified water. The article discusses the operating water treatment plant of the branch of JSC “Tatenergo” Kazan CHPP-2. The article presents the results of a microbiological study of water samples from all stages of a water treatment plant according to the method of Droycon Bioconcepts Inc. To determine the types and the amount of bacteria, special BART-tests were used - Hab-BART, Slim-BART and Srb-BART. The research results allow us to assess the biocorrosion and water treatment equipment contamination risks, which makes it possible to predict unscheduled biocidal treatment of microfiltration and reverse osmosis units. Continuous monitoring of microorganisms at the water treatment plant allows maintaining high quality water purification and ensuring reliable filters operation.
Accurate Identification of Potholes on the Road using Federated Learning

In developing countries like Bangladesh maintaining roads properly is a challenging task. Potholes on the road can lead to various accidents putting the lives and property of drivers, passengers, and pedestrians at risk. Due to increasing the number of potholes on the roads, the number of accidents is climbing day by day. Therefore, continuous collection and up-gradation of road condition data with the most recent information are highly necessary. Identification of potholes can abet drivers to select the right path to avoid accidents or vehicle damage as well as assist the concerned Government department to take immediate measures to fill up the potholes for the benefit of the commuters. Concerning this problem, existing solutions in the literature are highly error-prone and time-consuming. On the other hand, no one gives concentration to renew the information on potholes in a little period. In this paper, we develop an immensely proper pothole identification system by using federated learning. The method recognizes potholes and produces a notification for drivers. Whenever a vehicle faces a pothole a response notification is transmitted to the central server, which then aggregates whole data and gets updated road conditions in every phase. The proposed system outclasses the latest works with which it is compared in a matter of accuracy.
SpamFender: A Semi-supervised Incremental Spam Classification System across Social Networks

Social network users receive a large amount of social data every day. These data may contain malicious unwanted social spams, even though each social network has its social spam filtering mechanism. Moreover, spammers may send spam to multiple social networks concurrently, and the spam on the same topic from different social networks has similarities. Therefore, it is crucial to building a universal spam detection system across different social networks that can effectively fend off spam continuously. In this paper, we designed and implemented a tool Spam-Fender to facilitate spam detection across social networks. In order to utilize the raw social data obtained from multiple social networks, we utilized a semi-supervised learning method to convert unlabelled data into usable data for training the model. Moreover, we developed an incremental learning method to enable the model to learn new data continuously. Performance evaluations demonstrate that our proposed system can effectively detect social spam with satisfactory accuracy levels. In addition, we conducted a case study on the COVID-19 dataset to evaluate our system.
Hardware-oriented Groupwise Topology for CNN Accelerator

For edge AI inference applications, one of the major challenges is to accomplish huge computation load using limited hardware resources and lowest power consumption. A groupwise architecture is proposed in this paper for accelerating neural network workload via hardware group parallelism and optimized processing element utilization. To achieve better efficiency and higher accuracy, the hardware is optimized by using customized groupwise networks and configurable processing element. The experiment results show that it could improve the accuracy by 2%, using CIFAR-10 dataset and MobileNet as the baseline model, at the expense of little growth in the number of parameters. Moreover, the groupwise topology also enhances hardware inference performance, in terms of frames per second, without additional hardware resource for running YOLO-Face network.
Transconductance Boosting Technique for Bandwidth Extension in Low-Voltage and Low-Noise Optical TIAs

This brief presents a TIA topology with an active feedback path and a current bleeding circuit to relax the tradeoffs among gain, bandwidth and headroom limitations in a low voltage design. Using a two-stage common-source amplifier in the feedback path creates adequate degrees-of-freedom in the design for lowering the input resistance of the TIA and improving the bandwidth without degrading the amplifier gain. Analysis of the TIA transfer function and noise provide design guidelines for a well-behaved time response and low input-referred current noise. To demonstrate efficiency of the proposed technique, two designs in $0.18~\mu \text{m}$ CMOS technology with different values of the input capacitor are presented. The comparison of the designs with two categories of TIAs reported in the literature validates effectiveness of the presented transconductance boosting technique and demonstrates the flexibility of the proposed topology for different applications with a vast variety of the capacitance values at the TIA input.
Temporally resolved light emission and optical emission spectroscopy of surface flashover in vacuum

Early light emission provides information about the dominant mechanisms culminating in vacuum surface flashover (anode-initiated vs. cathode-initiated) for particular geometries. From experimental evidence gathered elsewhere, for the case of an insulator oriented at 45° with respect to the anode, anode-initiated flashover is believed to dominate since the field at the anode triple point is roughly three times that of the cathode. Similar to previous work performed on cathode-initiated flashover, light emission from the voltage rise through the impedance collapse is collected into two optical fibers focused on light emanating from the insulator in regions near the anode and cathode. The optical fibers are either connected to PMTs for spectrally integrated localized light intensity information or to a spectrograph used in conjunction with an ICCD camera. Challenges associated with localizing the flashover for optical diagnostics and incorporating the optical diagnostics into the high-field environment are discussed. Initial results for cross-linked polystyrene (Rexolite 1422) support the premise that flashover is initiated from the anode for these geometries, as early light from the anode leads cathode light up to photocathode saturation. Early spectroscopy results show promise for future characterization of the spatio-temporal development of emission from desorbed gas species across the insulator surface and identification of bulk insulator involvement if it occurs.