Miracle-dz/UniFAR-FTUs huggingface dataset:
Unlocking potential: The impact of AI on education technology

According to several international journals, artificial intelligence in education (AIET) is a more recent field in the educational sector. Even though it has been there for close to 30 years, educators are still confused about how to utilize it pedagogically on a larger scale and how it may have a substantial impact on teaching and learning as per SDG-4 Indicator 4.4. 1, which tracks the proportion of educators and academia with the necessary information technology skills, putting them on the road to better employment and understanding Education 4.0. This article postulates a review of the impacts of AI in education and briefs the number of published studies in the area of AI in education, which has expanded as a result of the growing usage of artificial intelligence (AI) technology in education. However, extensive evaluations have been conducted to fully study the numerous facets of this topic. This study seeks to address this gap by utilizing PRISMA to detect trends and issues relevant to AI applications in education (AIET) based on publications from 2000 to 2022. The review's findings show that the academic community is becoming more interested in applying AI to education. The primary research questions covered in this study are those related to the origin: Rise in AI, Importance, and Impact of AI on Education Technology, as well as related areas such as intelligent tutoring systems for education AI challenges in the education sector, and future scope of AI and ChatGPT-3 in higher education.
Ultralight, strong, and self-reprogrammable mechanical metamaterials

Versatile programmable materials have long been envisioned that can reconfigure themselves to adapt to changing use cases in adaptive infrastructure, space exploration, disaster response, and more. We introduce a robotic structural system as an implementation of programmable matter, with mechanical performance and scale on par with conventional high-performance materials and truss systems. Fiber-reinforced composite truss-like building blocks form strong, stiff, and lightweight lattice structures as mechanical metamaterials. Two types of mobile robots operate over the exterior surface and through the interior of the system, performing transport, placement, and reversible fastening using the intrinsic lattice periodicity for indexing and metrology. Leveraging programmable matter algorithms to achieve scalability in size and complexity, this system design enables robust collective automated assembly and reconfiguration of large structures with simple robots. We describe the system design and experimental results from a 256–unit cell assembly demonstration and lattice mechanical testing, as well as a demonstration of disassembly and reconfiguration. The assembled structural lattice material exhibits ultralight mass density (0.0103 grams per cubic centimeter) with high strength and stiffness for its weight ( 11.38 kilopascals and 1.1129 megapascals, respectively), a material performance realm appropriate for applications like space structures. With simple robots and structure, high mass-specific structural performance, and competitive throughput, this system demonstrates the potential for self-reconfiguring autonomous metamaterials for diverse applications. Programmable matter can reconfigure and adapt autonomously, extending to high-performance mechanical materials at scale.
Decarboxylase activity of the non-starter lactic acid bacterium Loigolactobacillus rennini gives crack defects in Gouda cheese through the production of γ-aminobutyric acid

ABSTRACT Ten Gouda cheese wheels with an age of 31 weeks from six different batch productions were affected by a crack defect and displayed an unpleasant off-flavor. To unravel the causes of these defects, the concentrations of free amino acids, other organic acids, volatile organic compounds, and biogenic amines were quantified in zones around the cracks and in zones without cracks, and compared with those of similar Gouda cheeses without crack defect. The Gouda cheeses with cracks had a significantly different metabolome. The production of the non-proteinogenic amino acid γ-aminobutyric acid (GABA) could be unraveled as the key mechanism leading to crack formation, although the production of the biogenic amines cadaverine and putrescine contributed as well. High-throughput amplicon sequencing of the full-length 16S rRNA gene based on whole-community DNA revealed the presence of Loigolactobacillus rennini and Tetragenococcus halophilus as most abundant non-starter lactic acid bacteria in the zones with cracks. Shotgun metagenomic sequencing allowed to obtain a metagenome-assembled genome of both Loil. rennini and T. halophilus. However, only Loil. rennini contained genes necessary for the production of GABA, cadaverine, and putrescine. Metagenetics further revealed the brine and the rennet used during cheese manufacturing as the most plausible inoculation sources of both Loil. rennini and T. halophilus. IMPORTANCE Crack defects in Gouda cheeses are still poorly understood, although they can lead to major economic losses in cheese companies. In this study, the bacterial cause of a crack defect in Gouda cheeses was identified, and the pathways involved in the crack formation were unraveled. Moreover, possible contamination sources were identified. The brine bath might be a major source of bacteria with the potential to deteriorate cheese quality, which suggests that cheese producers should regularly investigate the quality and microbial composition of their brines. This study illustrated how a multiphasic approach can understand and mitigate problems in a cheese company. Crack defects in Gouda cheeses are still poorly understood, although they can lead to major economic losses in cheese companies. In this study, the bacterial cause of a crack defect in Gouda cheeses was identified, and the pathways involved in the crack formation were unraveled. Moreover, possible contamination sources were identified. The brine bath might be a major source of bacteria with the potential to deteriorate cheese quality, which suggests that cheese producers should regularly investigate the quality and microbial composition of their brines. This study illustrated how a multiphasic approach can understand and mitigate problems in a cheese company.
Research on dynamic matching technology of Class EF high frequency inverter in wireless charging system

Class EF high frequency inverters have attracted much attention in the field of wireless charging because of their simple structure and high frequency efficiency. However, in practical applications, the load sensitivity of Class EF inverters affects the performance of the system, especially in the wireless charging process, the gap and position offset of the coil can cause resonance imbalance. To solve the problem of resonance state imbalance caused by coil position change, this paper proposes a wireless charging system that adopts switch state detection and dynamic matching technology. ZVS detection circuit is used to detect switch tube state, peak current is used to detect the matching state of the system, and the output impedance of the inverter is matched with the inductance and capacitor turn-on T-type impedance compression network. Realize the dynamic tuning of inverter. Finally, a radio energy transmission system based on Class EF high frequency inverter is built, and the feasibility of the detection method and dynamic matching technology is verified by simulation.
Joint sparse recovery condition via orthogonal matching pursuit

In order to solve multiple measurement vector (MMV) sparse reconstruction problem which recovering the K row sparse matrix X by using Y=AX, the article studies the accurate reconstruction condition of orthogonal matching algorithm (OMP) with respect to (MMV) sparse reconstruction problem. Restricted isometry property (RIP) condition is used to evaluate whether the proposed algorithm can be reconstructed accurately. The article proposes a sufficient condition based on RIP. There has been proven that the OMP algorithm can be used to ensure an accurate recovery process when the K+1th order RIP constant of matrix A satisfying δK+1<1/√K+1. In particular, for a K-row sparse matrix X, when the K+1th order RIP constant of the coefficient matrix A is equal to 1/√k+1, the OMP algorithm may fail. In other words, the terms of the proposal are very strict.
Internal Regional Migration Analysis and Modeling of Population Concentration for Ulaanbaatar, Mongolia

Rapid urbanization of Ulaanbaatar (UB) has caused serious urban issues such as the expansion of its urban extents, owing to the increased migration of Mongolians from rural areas. This study analyzes the population concentration of UB from 2005–2011 using the migration preference index (MPI) and multiple regression models. The MPI results show constant and excessive outflows from rural to UB. The multiple regression models indicate that these flows are related to high wage, small numbers of unemployed people, and proximity to UB. Large livestock losses were also an important factor during the period in which the huge natural hazard, called the dzud, occurred.
Diabetes Advances Cardiomyocyte Senescence through miR-103a-3p/ Rnd3/STAT3 Signaling

Rnd3, a small Rho-GTPase, is involved in various cardiovascular diseases, but its role in diabetes-induced cardiomyocyte senescence is unclear. This study aims to investigate the role of Rnd3 in cardiomyocyte senescence in diabetes cardiomyopathy (DCM). Rnd3 mRNA levels were reduced in peripheral blood mononuclear cells in diabetic patients, which was negatively correlated to patients age but positively correlated to cardiac function. Older mice exhibited compromised cardiac function, increased SA-{beta}-gal-positive cells, and Senescence-associated secretory phenotype (SASP) marker levels versus younger mice. H9C2 and AC16 cardiomyocytes were exposed to high glucose ([HG] =35 mmol/L D-glucose), and an inhibited Rnd3 expression but enhanced cellular senescence were detected. Rnd3 expression in H9C2 cells was disrupted using CRISPR/Cas9 technology, and a greater cellular senescence was observed in HG-insulted cardiomyocytes than that in normal glucose ([NG] =5.5 mmol/L D-glucose) control. MicroRNA sequencing was conducted on AC16 cells to identify differentially expressed microRNAs regulated by HG, and miR-103a-3p was identified as a key HG-regulated microRNA that binds to Rnd3 3'UTR. AAV9 carrying miR-103a-3p sponges and Rnd3-overexpressing plasmids mitigated cellular senescence in diabetic rats. Interestingly, miR-103a-3p levels positively correlated with blood glucose concentrations. HG stimulation amplified phosphorylated STAT3(Tyr705) in H9C2 cells, which was intensified by Rnd3 knockout. Rnd3 binds with STAT3 in cytoplasm and promote proteasome-induced ubiquitination of STAT3. S3I-201 mitigated HG-activated STAT3 and cardiomyocyte senescence. Taken together, diabetes causes cardiomyocyte senescence through miR-103a-3p/Rnd3/STAT3 signaling, suggesting a potential treatment strategy for DCM.
Venturicidin A affects the mitochondrial membrane potential and induces kDNA loss in Trypanosoma brucei

Neglected tropical diseases caused by trypanosomatid parasites have devastating health and economic consequences, especially in tropical areas. New drugs or new combination therapies to fight these parasites are urgently needed. Venturicidin A, a macrolide extracted from Streptomyces, inhibits the ATP synthase complex of fungi and bacteria. However, its effect on trypanosomatids is not fully understood. In this study, we tested venturicidin A on a panel of trypanosomatid parasites using Alamar Blue assays and found it to be highly active against Trypanosoma brucei and Leishmania donovani, but much less so against Trypanosoma evansi. Using fluorescence microscopy we observed a rapid loss of the mitochondrial membrane potential in T. brucei bloodstream forms upon venturicidin A treatment. Additionally, we report the loss of the mitochondrial DNA in approximately 40 to 50% of the treated parasites. We conclude that venturicidin A targets the ATP synthase of T. brucei, and we suggest that this macrolide could be a candidate for antitrypanosomatid drug repurposing, drug combinations, or medicinal chemistry programs.
Development of new low transformation temperature welding consumable to prevent cold cracking in high strength steel welds

A low transformation temperature (LTT) welding consumable has been developed to prevent cold cracking in high strength steel welded joints without preheating. In the LTT welded joint, the residual tensile stress is reduced by martensitic expansion of weld metal formed by the LTT consumable. In the weld cracking tests, cold cracking in the LTT weld metal is successfully prevented under high restraint conditions, but cold cracking occurs at very low joint restraint strength in case the weld metal is fully martensitic. Chemical compositions of the consumable are designed to retain austenite in martensite in the newly developed weld metal to absorb the diffusible hydrogen into the austenite to prevent cold cracking. In the newly developed LTT weld metal, cold cracking is almost fully suppressed without preheating under every joint restraint condition.
Inferring the selective history of CNVs using a maximum likelihood model

Copy number variations (CNVs) – structural variations generated by deletion and/or duplication that result in change in DNA dosage – are prevalent in nature. CNVs can drastically affect the phenotype of an organism and have been shown to be both involved in genetic disorders and be used as raw material in adaptive evolution. Unlike single-nucleotide variations, the often large and varied effects of CNVs on phenotype hinders our ability to infer their selective advantage based on the population genetics data. Here, we present a likelihood-based approach, dubbed PoMoCNV, that estimates the evolutionary parameters of CNVs based on population genetics data. As a case study, we analyze the genomics data of 40 strains of Caenorhabditis elegans, representing four different populations. We take advantage of the data on chromatin accessibility to interpret the evolutionary parameters of CNVs inferred by PoMoCNV. We further test the reliability of PoMoCNV by estimating the evolutionary parameters of CNVs for mutation-accumulation experiments in C. elegans with varying levels of genetic drift. Significance Inferring the evolutionary parameters of copy number variations (CNVs) based on population genetics data is crucial to understand their role in evolution. However, given the diversity in the size and effects of CNVs, such inference poses a challenge. We developed a likelihood-based approach called PoMoCNV to address this issue.
Joint-specific rheumatoid arthritis fibroblast-like synoviocyte regulation identified by integration of chromatin access and transcriptional activity

The mechanisms responsible for the distribution and severity of joint involvement in rheumatoid arthritis (RA) are not known. To explore whether site-specific FLS biology might be associated with location-specific synovitis and explain the predilection for hand (wrist/metacarpal phalangeal joints) involvement in RA, we generated transcriptomic and chromatin accessibility data from FLS to identify the transcription factors (TFs) and pathways. Networks were constructed by integration of chromatin accessibility and gene expression data. Analysis revealed joint-specific patterns of FLS phenotype, with proliferative, migratory, proinflammatory, and matrix-degrading characteristics observed in resting FLS derived from the hand joints compared with hip or knee. TNF-stimulation amplified these differences, with greater enrichment of proinflammatory and proliferative genes in hand FLS compared with hip and knee FLS. Hand FLS also had the greatest expression of markers associated with an ‘activated’ state relative to the ‘resting’ state, with the greatest cytokine and MMP expression in TNF-stimulated hand FLS. Predicted differences in proliferation and migration were biologically validated with hand FLS exhibiting greater migration and cell growth than hip or knee FLS. Distinctive joint-specific FLS biology associated with a more aggressive inflammatory response might contribute to the distribution and severity of joint involvement in RA.
PREDICTING GENE DISEASE ASSOCIATIONS WITH KNOWLEDGE GRAPH EMBEDDINGS FOR DISEASES WITH CURTAILED INFORMATION

Knowledge graph embeddings (KGE) are a powerful technique used in the biological domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and in particular the limitations for diseases with reduced information on gene-disease associations. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGEs by implementing state-of-the-art methods, and two novel algorithms: DLemb and BioKG2Vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that our novel approaches outperform existing algorithms in both scenarios. Our results indicate that data preprocessing and integration influence the quality of the predictions and that the embeddings efficiently encodes biological information when compared to a null model. Finally, we employed KGE to predict genes associated with Intervertebral disc degeneration (IDD) and showed that functions relevant to the disease are enriched in the genes prioritized from the model GRAPHICAL ABSTRACT
Genomic database furnishes a spontaneous example of a functional Class II glycyl-tRNA synthetase urzyme

The chief barrier to studies of how genetic coding emerged is the lack of experimental models for ancestral aminoacyl-tRNA synthetases (AARS). We hypothesized that conserved core catalytic sites could represent such ancestors. That hypothesis enabled engineering functional “urzymes” from TrpRS, LeuRS, and HisRS. We describe here a fourth urzyme, GlyCA, detected in an open reading frame from the genomic record of the arctic fox, Vulpes lagopus. GlyCA is homologous to a bacterial heterotetrameric Class II GlyRS-B. Alphafold2 predicted that the N-terminal 81 amino acids would adopt a 3D structure nearly identical to the HisRS urzyme (HisCA1). We expressed and purified that N-terminal segment. Enzymatic characterization revealed a robust single-turnover burst size and a catalytic rate for ATP consumption well in excess of that previously published for HisCA1. Time-dependent aminoacylation of tRNAGly proceeds at a rate consistent with that observed for amino acid activation. In fact, GlyCA is actually 35 times more active in glycine activation by ATP than the full-length GlyRS-B α-subunit dimer. ATP-dependent activation of the 20 canonical amino acids favors Class II amino acids that complement those favored by HisCA and LeuAC. These properties reinforce the notion that urzymes represent the requisite ancestral catalytic activities to implement a reduced genetic coding alphabet.
A multi-scale feature fusion model based on biological knowledge graph and transformer-encoder for drug-drug interaction prediction

Drug-Drug Interaction (DDI) refers to the combined effects that occur when a patient takes multiple medications simultaneously or within the same period. This interaction can either enhance the therapeutic effects of the drugs or inhibit their efficacy, and in severe cases, it can even lead to adverse drug reactions (ADRs). Thus, it is crucial to identify potential DDIs, as this information is significant for both biological research and clinical medicine. However, most existing works only consider the information of individual drugs or focus on the local correlation between a few medical entities, thus overlooking the global performance of the entire human medical system and the potential synergistic effects of multi-scale information. Consequently, these limitations hinder the predictive ability of models. In this paper, we propose an innovative multi-scale feature fusion model called ALG-DDI, which can comprehensively incorporate attribute information, local biological information, and global semantic information. To achieve this, we first employ the Attribute Masking method to obtain the embedding vector of the molecular graph. Next, ALG-DDI leverages heterogeneous graphs to capture the local biological information between drugs and several highly related biological entities. The global semantic information is also learned from the medicine-oriented large knowledge graphs. Finally, we employ a transformer encoder to fuse the multi-scale drug representations and feed the resulting drug pair vector into a fully connected neural network for prediction. Experimental evaluations on datasets of varying sizes and different classification tasks demonstrate that ALG-DDI outperforms other state-of-the-art models.
De novo-designed minibinders expand the synthetic biology sensing repertoire

Synthetic and chimeric receptors capable of recognizing and responding to user-defined antigens have enabled “smart” therapeutics based on engineered cells. These cell engineering tools depend on antigen sensors which are most often derived from antibodies. Advances in the de novo design of proteins have enabled the design of protein binders with the potential to target epitopes with unique properties and faster production timelines compared to antibodies. Building upon our previous work combining a de novo-designed minibinder of the Spike protein of SARS-CoV-2 with the synthetic receptor synNotch (SARSNotch), we investigated whether minibinders can be readily adapted to a diversity of cell engineering tools. We show that the Spike minibinder LCB1 easily generalizes to a next-generation proteolytic receptor SNIPR that performs similarly to our previously reported SARSNotch. LCB1-SNIPR successfully enables the detection of live SARS-CoV-2, an improvement over SARSNotch which can only detect cell-expressed Spike. To test the generalizability of minibinders to diverse applications, we tested LCB1 as an antigen sensor for a chimeric antigen receptor (CAR). LCB1-CAR enabled CD8+ T cells to cytotoxically target Spike-expressing cells. Our findings suggest that minibinders represent a novel class of antigen sensors that have the potential to dramatically expand the sensing repertoire of cell engineering tools.