Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontogeny and Trophic Factor Sensitivity of Gastrointestinal Projecting Vagal Sensory Cell Types

Abstract Vagal sensory neurons (VSNs) located in the nodose ganglion provide information, such as stomach stretch or the presence of ingested nutrients, to the caudal medulla via specialized cell types expressing unique marker genes. Here, we leverage VSN marker genes identified in adult mice to determine when specialized vagal subtypes arise developmentally and the trophic factors that shape their growth. Experiments to screen for trophic factor sensitivity revealed that brain-derived neurotrophic factor (BDNF) and glial cell-derived neurotrophic factor (GDNF) robustly stimulate neurite outgrowth from VSNs in vitro. Perinatally, BDNF was expressed by neurons of the nodose ganglion itself, while GDNF was expressed by intestinal smooth muscle cells. Thus, BDNF may support VSNs locally, whereas GDNF may act as a target-derived trophic factor supporting the growth of processes at distal innervation sites in the gut. Consistent with this, expression of the GDNF receptor was enriched in VSN cell types that project to the gastrointestinal tract. Last, the mapping of genetic markers in the nodose ganglion demonstrates that defined vagal cell types begin to emerge as early as embryonic day 13, even as VSNs continue to grow to reach gastrointestinal targets. Despite the early onset of expression for some marker genes, the expression patterns of many cell type markers appear immature in prenatal life and mature considerably by the end of the first postnatal week. Together, the data support location-specific roles for BDNF and GDNF in stimulating VSN growth, and a prolonged perinatal timeline for VSN maturation in male and female mice.
Evaluation of affinity-purification coupled to mass spectrometry approaches for capture of short linear motif-based interactions

Low affinity and transient protein-protein interactions, such as short linear motif (SLiM)-based interactions, require dedicated experimental tools for discovery and validation. Here, we evaluated and compared biotinylated peptide pulldown and protein interaction screen on peptide matrix (PRISMA) coupled to mass-spectrometry (MS) using a set of peptides containing interaction motifs. Eight different peptide sequences that engage in interactions with three distinct protein domains (KEAP1 Kelch, MDM2 SWIB, and TSG101 UEV) with a wide range of affinities were tested. We found that peptide pulldown can be an effective approach for SLiM validation, however, parameters such as protein abundance and competitive interactions can prevent the capture of known interactors. The use of tandem peptide repeats improved the capture and preservation of some interactions. When testing PRISMA, it failed to provide comparable results for a model peptide that successfully pulled down a known interactor using biotinylated peptide pulldown. Overall, in our hands, we find that albeit more laborious, biotin-peptide pulldown was more successful in terms of validation of known interactions. Our results highlight that the tested affinity-capture MS-based methods for validation of SLiM-based interactions from cell lysates are suboptimal, and we identified parameters for consideration for method development.
Patient-derived extracellular matrix demonstrates role of COL3A1 in blood vessel mechanics

Vascular Ehlers-Danlos Syndrome (vEDS) is a rare autosomal dominant disease caused by mutations in the COL3A1 gene, which renders patients susceptible to aneurysm and arterial dissection and rupture. To determine the role of COL3A1 variants in the biochemical and biophysical properties of human arterial ECM, we developed a method for synthesizing ECM directly from vEDS donor fibroblasts. We found that the protein content of the ECM generated from vEDS donor fibroblasts differed significantly from ECM from healthy donors, including upregulation of collagen subtypes and other proteins related to ECM structural integrity. We further found that ECM generated from a donor with a glycine substitution mutation was characterized by increased glycosaminoglycan content and unique viscoelastic mechanical properties, including increased time constant for stress relaxation, resulting in a decrease in migratory speed of human aortic endothelial cells when seeded on the ECM. Collectively, these results demonstrate that causal COL3A1 mutations lead to the synthesis of ECM that differs in composition, structure, and mechanical properties from healthy donors. These results further suggest that ECM mechanical properties could serve as a prognostic indicator for patients with vEDS, and the insights provided by the approach demonstrate the broader utility of cell-derived ECM in disease modeling.
Machine learning based diagnostics of schizophrenia patients

Schizophrenia is a major psychiatric disorder which significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. Because of that, reliable diagnosis of schizophrenia is of big interest. In this paper, machine learning based diagnostics of schizophrenia is designed. Classification models are applied to event-related potentials (ERPs) calculated from electroencephalo-gram (EEG) records of patients and healthy subjects performing modification of the visual cued Go-NoGo task. The sample consisted of 200 adult individuals, with an age ranging between 18 and 50 years. In order to apply machine learning models various features are extracted from ERPs. Process of feature extraction is parametrized through a special procedure and parameters of this procedure are selected through a grid-search technique along with model hyperparameters. Feature extraction is followed by Sequential Feature Selection transformation in order to prevent overtitting and reduce computational complexity. Support vector machines and Random Forest models are trained on the resulting feature set. Sensitivity and specificity of the best model are 91% and 91.7% respectively.
Gradient Tree Boosting for HR Talent Management Application

Talent Management is a stage process of a company to find, sort, improve, and retain the best talent of employees for the needs of the company or agency. A company recommends or requires Talent Management to go through an open recruitment process by taking a final test and selection, namely an interview or a recommendation. However, the weakness of the current system is still dominant using a recommendation system that contains elements of the subjectivity of related parties which is not by the results determined from the capacity test, employee performance, and psychological tests. From these weaknesses, in this study, a machine learning approach with the Gradient Tree Boosting (GTB) method was developed to recommend the position of structural officials. In this study, we use a talent management dataset from a university in Indonesia. The experimental result shows the promising performance of our proposed method.
Application of numerical Lie group integrators to parabolic PDEs

This paper presents a way of solving a parabolic PDE with Rung e-Kutta Methods on manifolds as described in papers [6], [1]. These methods a re designed to solve ODEs on manifolds using Lie-group theory. An essential step of Runge-Kutta Methods on manifolds is a Lie algebra action which in this case corres ponds to solving a simple parabolic PDE. Both Spectral Methods and Finite Difference Methods were tested to obtain approximations to Lie algebra actions. The goal was t o develop methods which are more stable than classical Runge-Kutta Methods applied to a parabolic PDE. AMS Subject Classification:58J35.
Understanding Artificial Intelligence Adoption Predictors: Empirical Insights from A Large-Scale Survey

Scholars, policymakers and managers have already agreed on the critical role that artificial intelligence (AI) technologies will play on both businesses competitiveness and country's economic growth. However, our knowledge of factor influencing AI adoption by organization is still limited. Responding to calls for more empirical research on AI adoption at firm-level, we used data collected in 2019 from a European-Wide survey of a representative sample of 9272 firms on the adoption or non-adoption of 10 different types of AI technologies. We investigated predictors of adoption, intention to adopt and non-adoption of AI using Multivariate logistic regression. Consistent with previous research, our results confirm that firm size and perceived obstacles matter for AI adoption.
AdaBoost Based C4.5 Accuracy Improvement on Credit Customer Classification

Credit has become commonplace in today's society. Many people choose to take credit to support their economy, both for business capital and other activities. In order to produce the right decision, credit recipient customers can be classified according to their possible payment performance. The research was conducted to improve the accuracy of the Decision Tree C.45 algorithm by using Adaboosting in classifying credit customers, to get the most optimal accuracy in terms of credit customer classification. With AdaBoost improvement, the accuracy of the c4.5 algorithm was significantly improved from 45.38% to 100% and has a much higher accuracy rate when compared to naive bayes which has been improved as well as a comparison.
Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study

As part of the energy sector transformation, a substantial deployment of large-scale energy storage systems (ESS) is expected to support the integration of variable renewable energy sources (VRES). Understanding the value of this technology is of high relevance for investors and policy markets to assess their potential role in future energy systems. The present paper develops a model aiming to assess how large-scale ESSs participating in the day-ahead electricity market affect the market social economic welfare (SEW). This model considers two alternative bidding strategies by the ESSs owners participating in the market (i.e., a price-taker and a price-maker approach). The presented analysis considers a large case study representing the Belgian wholesale day-ahead electricity market, in which, in addition to existing ESSs, additional storage capacity is simulated for varying shares of VRES. Results suggest there is an added societal value driven by the participation of large-scale ESS in the electricity market in Belgium.
Primary and High School Question Classification based on Bloom's Taxonomy

One of the student's abilities that must be measured in the learning process is cognitive ability. This measurement is essential to establish the level of students' understanding of the material that has been taught. Giving questions with the appropriate difficulty level is an attempt to measure these cognitive abilities. According to Bloom's taxonomy, there are six levels of cognitive abilities, remembering, understanding, applying, analyzing, evaluating, and creating. An automatic question classifier is helpful for teachers to develop a high-quality question and comply with different cognitive levels. This paper proposed automation question classification using the classical classification method (Support Vector Machine and Multinomial Naïve Bayes) and the pre-trained language model, IndoBERT. We conducted an experiment based on 670 mathematics and Indonesian language questions in primary and high school. The evaluation results show that the best classification accuracy was achieved by the IndoBERT method, which is 82% for classifying mathematics questions and 63% for classifying Indonesian Language questions. We analyzed that the small number of datasets and unbalanced at each taxonomy level affected the classification results.
Coupling control in the few-electron regime of quantum dot arrays using 2-metal gate levels in CMOS technology

Scalability is one of the biggest advantages of silicon spin qubits over other platforms, making them very promising candidates in the quest for quantum computing. In this work we approach the regime of interest for large-scale qubit integration, showing that we can deliver high electrostatic coupling control and individual tunability over an array of quantum dots (QDs). To do this we use FDSOI devices fabricated with 2-metal gate levels in an industry-compatible CMOS process. We operate them at 100mK, and in a dot-configuration where large control on tunnel barriers is leveraged. In the many-electron regime, we observe the transition of quantum dot array from single- to triple-dot configurations. Moreover, in the few-electron regime, we demonstrate the effective and in-situ modulation of the tunnel coupling between two adjacent QDs.
Intertwining of Communities, Core and Periphery in Complex Networks

Meso-scale structures, like communities and core-periphery, have been extensively studied in complex networks. However, their coexistence and collective dynamics are not well understood. Communities represent dense connections between a set of nodes. On the other hand, the core of a network encompasses the influential nodes (superspreaders). It is not well understood whether a community comprises equally influential nodes or nodes from a spectrum of influential powers. We propose various metrics to quantify the influence composition of communities in a network. Shell number (aka coreness: maximal core number of a node), calculated using K-shell decomposition algorithm, has been used as an indicator of influence. We also quantify the influential power of a community as a whole. Most of the communities, specifically influential communities, are observed to possess varying levels of influence. k-shell decomposition has also been applied to the induced subgraph of communities to unravel local cores. Thereafter, the overlap between local and global cores is studied. One of the significant findings is the dense presence of core nodes in only a few communities. Further, it is seen that globally influential nodes are mostly locally influential as well. However, the reverse may or may not be true.
GLCM Feature Extraction and PCA for Tuberculosis Detection with Neural Network

Automatic recognition system for medical images is quite a challenging job in the medical image processing field. X-rays, CT, and MRI all provide medical pictures and other modalities which are utilized for diagnostic purposes. As in medical sector, detecting tuberculosis (TB) is a very important stage before further treatment is carried out. Human interpretation of a vast array of X-ray pictures can result in detection mistakes, so an automatic recognition system is needed that can detect TB disease. In this study, we use a dataset with two classes and extract GLCM-based texture features from each class, and apply them to a two-layer feed-forward neural network, which gives a classification rate of 99%.
The Impact of Influencer Marketing on Online Advertising Click and Buying Behaviour during COVID-19

We Are Social reports the development of Social Media year by year. It means social media is very influential for every person. Recently, marketing used influencers as part of its marketing strategy. However, several previous studies found the negative impact of using influencers. Therefore, this study, which uses a qualitative method with the SEM-PLS technique, will look at the factors that influence influencers and the impact on whether customers are interested in buying the product or not. The qualitative study facilitates by Google Form and using the snowball sampling technique to collect data due to the limitation of social access in COVID-19. Eleven factors use as the research model. The study used 227 respondents, but only 64.7% of people were interested in buying products after seeing influencers. Furthermore, 35.3% of people do not buy products for various reasons. The results of this study are beneficial for developing the use of influencers in marketing in the future.
Small Area Estimation

Inference techniques for unbalanced and balanced two-factor experiments under an inverse Gaussian model can be used in the area of estimation for small regions. Chaubey et al., (1996) consider the analysis of variance methodology for the inverse Gaussian law and adapt it for estimation of small area parameters in finite populations. We now turn to the details of this development as outlined by Chaubey et al.
Advanced Clearing Model in Prosumer Centric Local Flexibility Market

The local flexibility market models have emerged as a market-based solution to respond to the challenges that the increase in distributed energy resources caused in the power and energy systems. Using Smart Grid enabling technologies, consumers and prosumers are prepared to respond to any possible demand-side flexibility event. In this scope, this work presents an advanced bidding model for the prosumers/consumers’ participation in a local flexibility market to solve existing issues in the local grid. The proposed advanced model consists of a single-sided auction-based clearing method where prosumer offers are ranked and chosen according to the price and other characteristics, such as their location and distance to the problem to be solved. The aim is to prioritize and select the offers that have a more positive impact on the situation to solve at the lowest possible cost.
NEKI ASPEKTI SIMBOLIQKOG RAQUNA { PRIMENA MAPLE-A U NASTAVI MATEMATIKE

A system for automatically tracking and locking to an applied scanning input signal the frequency of which may vary over a range of frequencies includes a PLL comprising a VCO and phase detector as well as a digital frequency counting section which forms a closed loop with the VCO. The VCO is operated preferably at 32 times the frequency of the applied input signal and provides a clock signal to the digital section which also receives the input signal. The digital section includes a counter and decoder-latch wherein the clock signal that is supplied to the counter is used to measure the frequency of the applied input signal. Counting and decoding result on alternating periods of the applied input signal such that the decoder-latch decides if the count is too low, too high or within the range of the PLL and controls the operating frequency of the VCO to find and maintain phase lock.
Source localization during tactile stimulation and imagery of the upper limbs

In this study we present the results of mu range event-related desynchronization/synchronization (ERD/S) source estimations. ERD was provoked by tactile stimulation (TS) or tactile imagery (TI), the electroencephalography (EEG) data were acquired with the high-density EEG (hdEEG), and the inverse problem was solved by using the sLORETA method. The results of source estimation have shown that both TS and TI lead to significant mu range ERD with its sources localized in sensorimotor cortex, which points to that tactile imagery shares the neuronal substrate of tactile perception.
Possibilities and Limitations of Memristor Crossbars for Neuromorphic Computing

Extensive development of new neuromorphic element base – non-volatile memory elements based on new physical principles (ReRAM, FRAM etc.) is conducted. These memory elements are used to implement programmable synaptic weights in crossbar architecture, and enable neural network to conduct in-memory computations. However, the sneak currents and leakage currents are a serious limitation on the achievable dimensionality of rows and columns of the crossbar. The features of the implementation of neural networks on memristor crossbars are considered.
A Pipeline ADC with Negative C-assisted SC Amplifier Canceling Gain Error and Nonlinearity

A 12-b pipeline ADC based on a new negative $\boldsymbol{C}$ - assisted MDAC is presented. Proposed negative $\boldsymbol{C}$ scheme aids to cancel gain error and nonlinearity by generating negative charges at the summing node during the amplification phase. A design strategy for ensuring stability even when the negative $\boldsymbol{C}$ coexists is also introduced in this work. In addition, a highly $\boldsymbol{G}_{\mathbf{m}}$-linearized low-gain amplifier is proposed for negative $\boldsymbol{C}$ implementation. The prototype ADC fabricated in a 28nm CMOS achieves 54.7dB SNDR and 71.8dB SFDR without any calibration at 320MS/s, demonstrating that the proposed scheme improves SFDR by +32dB across the Nyquist band.
Semantic-SRF: Sparse Multi-view Indoor Semantic Segmentation with Stereo Neural Radiance Fields

We study the effect of prior training of neural implicit networks on the task of semantic segmentation of room-scale scenes. For this, we train Stereo Radiance Field (SRF) on color and density information obtained from dense views of training scenes. In return, SRF learns general scene radiance, geometry and appearance features. We further extend SRF to Semantic-SRF that enables the decoder of SRF to predict semantics. Later we use sparse semantic views to fine-tune the implicit representations on novel-scenes. This allows us to extract scene-level semantics using only a few semantically labelled images. We show the efficacy of this approach on a photorealistic indoor room dataset, namely Replica. We compare the approach with another implicit network that does not use prior training, namely Semantic-NeRF. We demonstrate that as the number of sparse views decreases, the scene-level features enable the network to persist its performance.
Optimizing privacy preservation in Wireless VANETS

The growth in technology and modern vehicles is increasing, so it is vital to ensure safety and reliability for the Internet of Vehicles (IOV). The war between threat actors and security researchers is inevitable. Radio communication technologies are fragile and open to different attacks due to their open-access environment nature which causes many issues such as road safety, security, and privacy. Over time, different privacy-preserving and security schemes have been proposed to avoid the lousy actor attacks, computations, and communication overheads that arise as the limitations of the many proposed schemes. Several schemes have been proposed by cyber security researchers, authors, and practitioners to overcome such privacy and security challenges. The proposed scheme includes: (i) An architecture based on Fog Cloud Vehicular Adhoc Networks. (ii) The solution based on the public key cryptography to protect the privacy using an efficient privacy protection scheme. (iii) Identify, Investigate and analyze the 4 different dynamic routing protocol among using the simulation and record the obtained results for each protocol, performed the graphical analysis of the obtained result over the simulation duration. The identified best routing protocol is used on the model in the VANET communication along with the implementation of ECC to analyze the performance impact. Lastly, the performance is evaluated after the implementation of the proposed solution.
A graph convolutional network for classification of resting-state fMRI data

Machine learning methods have been widely applied in neurophysiological data studies for classification and clusterization purposes. However, analysis of connectivity data requires consideration it’s graph-like structure to utilize it’s structural and functional information. To address this issue, we propose a machine learning model based on graph convolutional network for classification of connectivity matrices obtained from fMRI data. We show that out relatively simple model has a good generalization ability and is able to achieve high performance on moderate amount of data.