Miracle-dz/UniFAR-FTUs huggingface dataset:
Controllable interface-tailored strategy to reduce the nanotribological properties of Ti3C2T x by depositing MoS2 using atomic layer deposition

Ti3C2T x MXene has attracted widespread attention in lubrication owing to its unique structure and surface properties. However, the inferior nanotribological properties of Ti3C2T x still limit its applications in nano lubricants. Herein, we propose a controllable interface-tailored strategy to reduce the nanotribological properties of Ti3C2Tx by depositing MoS2 nano-sheet on its surface using atomic layer deposition (ALD). The nanotribological properties of the MoS2/Ti3C2T x nanocomposites synthesized by ALD are studied by atomic force microscope for the first time. At the optimal 20 ALD MoS2 cycles, the nanofriction of MoS2/Ti3C2T x has been reduced by 57%, 46%, and 44% (at 5, 10, and 15 nN load, respectively), while the adhesion has been reduced by 59%, compared to the original Ti3C2T x . The results can contribute to understanding of the nanotribological mechanisms of Ti3C2T x composites and provide the potential prospects for Ti3C2T x as a nanoscale adjustable lubricant.
Prevalence, Symptoms, and Triggering Factors of Panic Attacks among Dental Students in Riyadh Saudi Arabia—A Cross Sectional Survey

Panic disorder by definition is an anxiety disorder of unexpected and repeated episodes of intense fear. Panic attacks are usually diagnosed by four or more of a set of symptoms that include palpitations, sweating, trembling, shortness of breath, chest pain, nausea, dizziness, and hot flushes. They usually interfere with daily life situations and also interfere with education. Hence, the aim of this study was to assess the prevalence of panic attacks, their symptoms, and triggering factors among dental students in Riyadh, Saudi Arabia. Data were collected from 394 students using a structured and validated questionnaire. The prevalence of panic attacks among dental students in Riyadh, Saudi Arabia, was 42.9%. Most of the participants who experienced higher episodes of panic attacks were females (53.4%) when compared to males (24.5%). Third year students displayed greater (58.3%) episodes of panic attacks compared to their respective counterparts. The most reported symptom of panic attacks was rapid or pounding heartbeat followed by breathlessness, chest pain, and shaking or trembling. It was also noted that most of the participants (63.31%) encountered a panic attack for the first time after joining dental school. The situations where dental students frequently experienced panic attacks were during exams, clinic procedures, giving presentations, and especially while under a lot of stress. The high occurrence of panic attacks among dental students highlights the importance of providing support programs and implementing preventive measures to help students, particularly those who are most susceptible to higher levels of these psychological conditions. Dental students experiencing panic attacks should be provided with necessary counseling sessions or psychiatric consultation in order to overcome such scenarios. Dental schools should consider these findings when planning the dental curriculum. Hence, the role of the faculty members is essential in these situations to provide support for the affected students.
Effects of Proton Irradiation on GaN Vacuum Electron Nanodiodes

Gallium nitride (GaN)-based nanoscale vacuum electron devices, which offer advantages of both traditional vacuum tube operation and modern solid-state technology, are attractive for radiation-hard applications due to the inherent radiation hardness of vacuum electron devices and the high radiation tolerance of GaN. Here, we investigate the radiation hardness of top-down fabricated n-GaN nanoscale vacuum electron diodes (NVEDs) irradiated with 2.5-MeV protons (p) at various doses. We observe a slight decrease in forward current and a slight increase in reverse leakage current as a function of cumulative protons fluence due to a dopant compensation effect. The NVEDs overall show excellent radiation hardness with no major change in electrical characteristics up to a cumulative fluence of 5E14 p/cm2, which is significantly higher than the existing state-of-the-art radiation-hardened devices to our knowledge. The results show promise for a new class of GaN-based nanoscale vacuum electron devices for use in harsh radiation environments and space applications.
Intertidal Grow-Out Technique, Not Eelgrass (Zostera marina), Influences Performance of Pacific Oysters (Magallana gigas)

Colocation of farmed shellfish in seagrass, although not permitted in some regions of the USA, has been proposed as a means to reach cobenefits of habitat and food production. Seagrass could benefit bivalves through protection from predation or abiotic stressors but conversely can reduce water flow and food delivery. At 10 farm sites in Washington state (USA), we tested the performance of Pacific oysters (Magallana (Crassostrea) gigas) grown in ground culture or off-bottom culture across a range of seagrass densities. Oysters averaged 32 m−2 and 30% shell cover on ground culture beds. Oysters in off-bottom culture showed a negative relationship between size and density across beds, consistent with growth and mortality through the crop cycle, and the largest oysters were at densities similar to ground culture. Within each farm site, ground and off-bottom beds were selected with each of three categories of seagrass (none, sparse, dense). Dense seagrass on aquaculture beds was half the density of nearby unfarmed seagrass beds. For outplanted oysters over 3–9 months, many aspects of oyster performance improved when oysters were elevated above the sediment. Relative to ground culture, off-bottom oysters had better survival (85% vs. 69%), 7% larger size, and 48% higher condition after 3 months in summer, and the survival advantages persisted over winter. Oyster survival on-bottom was especially impaired in finer sediment. No oyster performance differences were associated with seagrass, except for 9-month results, available for five of 10 farms. After 9 months at these five farms, oyster survival showed a small negative effect of dense seagrass, and shell size showed a small positive effect of sparse seagrass. Consequently, seagrass may not provide a boost to colocated intertidal shellfish, but we found little evidence of trade-offs in which maintaining seagrass would reduce yield of farmed oysters. Moving oysters out of the boundary layer and away from soft sediment improves both survival and tissue growth aspects of yield.
DDBJ update in 2023: the MetaboBank for metabolomics data and associated metadata

Abstract The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) provides database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), DDBJ accepts and distributes nucleotide sequence data as well as their study and sample information along with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute (EBI). Besides INSDC databases, the DDBJ Center provides databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank) and human genetic and phenotypic data (JGA: Japanese Genotype-phenotype Archive). These database systems have been built on the National Institute of Genetics (NIG) supercomputer, which is also open for domestic life science researchers to analyze large-scale sequence data. This paper reports recent updates on the archival databases and the services of the DDBJ Center, highlighting the newly redesigned MetaboBank. MetaboBank uses BioProject and BioSample in its metadata description making it suitable for multi-omics large studies. Its collaboration with MetaboLights at EBI brings synergy in locating and reusing public data.
Obesity Cut-Off Points Using Prepregnancy Body Mass Index according to Cardiometabolic Conditions in Pregnancy

Aim To suggest cut-off points for body mass index (BMI) using gestational hypertension, preeclampsia, and gestational diabetes mellitus (GDM) as cardiometabolic conditions in pregnancy. Methods In this prospective study, singleton pregnant women from the fetal medicine service of the Brazilian Unified Health System were included. The pregnancy, perinatal, and newborn data were obtained from the clinical medical records. Maternal anthropometry included an assessment of weight and height and the prepregnancy BMI evaluation categorized according to the World Health Organization cut-off points. The area under the curve and confidence interval values from receiver operator curves were generated to identify the optimal cut-off points using prepregnancy BMI with better sensitivity and specificity. Results Data on 218 pregnancies were analyzed, with 57.9% (n = 124) being classified as overweight/obese, 11% (n = 24) with GDM, 6.9% (n = 15) with preeclampsia, and 11.0% (n = 24) with gestational hypertension. The BMI cut-off points for predicting cardiometabolic conditions were 27.52 kg/m2 (S: 66.7%; E: 63.8%) for women with GDM; 27.40 kg/m2 (S: 73.3%; E: 62.4%; S: 79.2%; E: 64.9%; S: 70.3%; E: 66.3%) for women with preeclampsia, gestational hypertension, and gestational hypertension plus preeclampsia, respectively; and 27.96 kg/m2 (S: 69.6%; E: 65.6%) for women with preeclampsia plus GDM. Conclusion The findings suggest that the optimal prepregnancy BMI cut-off point is around 27 kg/m2 for pregnant women with maternal cardiometabolic conditions.
Advanced Mathematics Exercise Recommendation Based on Automatic Knowledge Extraction and Multilayer Knowledge Graph

Higher education is rapidly growing in the online learning landscape. However, current personalized recommendation techniques struggle with the precise extraction of complex mathematical semantics, hindering accurate perception of learners' cognitive states and relevance of recommendations. This article proposes a framework for extracting complex mathematical semantics and providing personalized exercise recommendations. We design a tree-based position encoding method to enhance the accuracy of positional representation for mathematical expressions in the pretrained model, aiming to improve the performance of downstream tasks. We propose an automatic method for extracting knowledge attributes based on expert annotations, enabling interpretable cognitive diagnosis. Furthermore, we employ sequential pattern mining to discover the knowledge usage patterns in exercises, generate learning paths using a multilayer knowledge graph, and leverage cognitive diagnostic results to enhance the relevance of recommendations. Experimental results show a 2.0% improvement in mathematical symbol embedding on mathematical formula retrieval tasks and knowledge attribute extraction accuracy ranging from 66.5% to 81.7%. Learners' posttest scores significantly improve during group testing with good consistency between online cognitive diagnosis and self-diagnosis.
Guiding principles and proposed classification system for the responsible adoption of artificial intelligence in scientific writing in medicine

The integration of large language models (LLMs) and artificial intelligence (AI) into scientific writing, especially in medical literature, presents both unprecedented opportunities and inherent challenges. This manuscript evaluates the transformative potential of LLMs for the synthesis of information, linguistic enhancements, and global knowledge dissemination. At the same time, it raises concerns about unintentional plagiarism, the risk of misinformation, data biases, and an over-reliance on AI. To address these, we propose governing principles for AI adoption that ensure integrity, transparency, validity, and accountability. Additionally, guidelines for reporting AI involvement in manuscript development are delineated, and a classification system to specify the level of AI assistance is introduced. This approach uniquely addresses the challenges of AI in scientific writing, emphasizing transparency in authorship, qualification of AI involvement, and ethical considerations. Concerns regarding access equity, potential biases in AI-generated content, authorship dynamics, and accountability are also explored, emphasizing the human author’s continued responsibility. Recommendations are made for fostering collaboration between AI developers, researchers, and journal editors and for emphasizing the importance of AI’s responsible use in academic writing. Regular evaluations of AI’s impact on the quality and biases of medical manuscripts are also advocated. As we navigate the expanding realm of AI in scientific discourse, it is crucial to maintain the human element of creativity, ethics, and oversight, ensuring that the integrity of scientific literature remains uncompromised.
Distributed Fault Estimation and Fault-Tolerant Control of Interconnected Systems With Plug-and-Play Features

This paper is concerned with distributed fault estimation and fault-tolerant control for interconnected systems. Based on the local and interconnected information relevant to neighbors, a distributed fault estimation observer (DFEO) is constructed in a fully distributed fashion for each subsystem. The generated estimation errors are decoupled with external disturbances such that the DFEO has an asymptotical estimation feature. Then, using the estimated state and fault, a fault compensation based distributed fault-tolerant control (DFTC) law is designed for each subsystem. Sufficient conditions in the form of linear matrix inequalities (LMIs) are presented to guarantee the closed-loop system’s properties of stability and disturbance attenuation. Moreover, the proposed DFEOs and DFTC laws allow subsystems to be added or removed in a plug-and-play (PnP) fashion, which enhances the scalability of interconnected systems. Finally, the effectiveness and performance of the proposed method are demonstrated through simulations conducted on the power network system (PNS).
Limit cycles in Norwegian lemmings: tensions between phase–dependence and density-dependence

Ever since Elton, the 3–5 year density cycles in lemmings (and other microtines) in Fennoscandia have troubled scientists. Explanations have involved intrinsic regulation and trophic interactions. We have analysed yearly changes in fall abundances for lemmings over 25 years from two local mountain sites in South Norway. These time series appear to have an underlying nonlinear structure of order two. Fitting a piece–wise linear threshold model of maximum order two, the most parsimonious model was, however, of first order for both series. The resulting dynamics from this model is a limit cycle. Reformulating the model in terms of abundances yields a model which combines (delayed) density–dependent effects and the influence of the cyclic phase. The delayed density–dependence of one part of the model is consistent with an effect of specialist predators during the peak and crash phases of the cycle, although other trophic interactions cannot be excluded.
Degradation Kinetic Studies of BSA@ZIF-8 Nanoparticles with Various Zinc Precursors, Metal-to-Ligand Ratios, and pH Conditions

Nanosized zeolitic imidazolate framework particles (ZIF-8 nanoparticles [NPs]) have strong potential as effective carriers for both in vivo and in vitro protein drug delivery. Synthesis of ZIF-8 and stability of protein encapsulation within ZIF-8 are affected by several factors, notably the metal ion source and molar ratio. To systematically investigate these factors, we investigated such effects using BSA as a model test protein to synthesize BSA@ZIF-8 NPs at various metal-to-ligand (M:L) ratios. SEM, FTIR, XRD, and DLS were applied to characterize the morphology and structure of BSA@ZIF-8 NPs and their effects on protein loading capacity. Degradation kinetics and protein release behavior of BSA@ZIF-8 NPs were evaluated at pH 5.0 (to simulate the tumor environment) and pH 7.4 (to mimic the blood environment). Our objective was to define optimal combinations of the high protein loading rate and rapid release under varying pH conditions, and we found that (i) the yield of BSA@ZIF-8 NPs decreased as the M:L ratio increased, but the protein content increased. This highlights the need to strike a balance between cost-effectiveness and practicality when selecting ZIF-8 NPs with different molar ratios for protein-based drug formulation. (ii) BSA@ZIF-8 NPs exhibited cruciate flower-like shapes when synthesized using Zn(NO3)2 as the zinc precursor at M:L ratios of 1:16 or 1:20. In all other cases, the NPs displayed a regular rhombic dodecahedral structure. Notably, the release behavior of the NPs did not differ significantly between these morphologies. (iii) When Zn(OAc)2 was used as the zinc precursor, the synthesized ZIF-8 NPs exhibited a smaller size compared to the Zn(NO3)2-derived ZIF-8 NPs. (iv) The release rate and amount of BSA protein were higher at pH 5.0 compared to pH 7.4. (v) Among the different formulations, BSA@ZIF-8 with an M:L ratio of 1:16 at pH 5.0 was observed to have a shorter time to reach a plateau (0.5 h) and higher protein release, making it suitable for locally rapid administration in a tumor environment. BSA@ZIF-8 prepared from Zn(OAc)2 at an M:L ratio of 1:140 showed the slower release of BSA protein over a 24-h period, indicating its suitability for sustained release delivery. In conclusion, our findings provide a useful basis for the practical application of ZIF-8 NPs in protein-based drug delivery systems.
Exploration of the truncated cytosolic Hsp70 in plants - unveiling the diverse T1 lineage and the conserved T2 lineage

The 70-kDa heat shock proteins (Hsp70s) are chaperone proteins involved in protein folding processes. Truncated Hsp70 (Hsp70T) refers to the variant lacking a conserved C-terminal motif, which is crucial for co-chaperone interactions or protein retention. Despite their significance, the characteristics of Hsp70Ts in plants remain largely unexplored. In this study, we performed a comprehensive genome-wide analysis of 192 sequenced plant and green algae genomes to investigate the distribution and features of Hsp70Ts. Our findings unveil the widespread occurrence of Hsp70Ts across all four Hsp70 forms, including cytosolic, endoplasmic reticulum, mitochondrial, and chloroplast Hsp70s, with cytosolic Hsp70T being the most prevalent and abundant subtype. Cytosolic Hsp70T is characterized by two distinct lineages, referred to as T1 and T2. Among the investigated plant and green algae species, T1 genes were identified in approximately 60% of cases, showcasing a variable gene count ranging from one to several dozens. In contrast, T2 genes were prevalent across the majority of plant genomes, usually occurring in fewer than five gene copies per species. Sequence analysis highlights that the putative T1 proteins exhibit higher similarity to full-length cytosolic Hsp70s in comparison to T2 proteins. Intriguingly, the T2 lineage demonstrates a higher level of conservation within their protein sequences, whereas the T1 lineage presents a diverse range in the C-terminal and SBDα region, leading to categorization into four distinct subtypes. Furthermore, we have observed that T1-rich species characterized by the possession of 15 or more T1 genes exhibit an expansion of T1 genes into tandem gene clusters. The T1 gene clusters identified within the Laurales order display synteny with clusters found in a species of the Chloranthales order and another species within basal angiosperms, suggesting a conserved evolutionary relationship of T1 gene clusters among these plants. Additionally, T2 genes demonstrate distinct expression patterns in seeds and under heat stress, implying their potential roles in seed development and stress response.
Sheeted vein Sn-W mineralization and greisenization associated with economic kaolinization, Goonbarrow china clay pit, St. Austell, Cornwall, England; geologic relationships and geochronology

Westphalian granite. Fifty-four new K/Ar age determinations for micas from granitic rocks, greisens, metafelsite dikes, and kaolinized granite are statistically identical. Sheeted vein mineralization, greisenization, and kaolinization were simultaneous processes which occurred in the time interval between granite crystallization at 301 + or - 12 m.y. and cooling below a mica closure temperature approximately 250 degrees C at 273.6 + or - 1.1 m.y. Kaolinization was a single-stage, hydrothermal process related to formation of the sheeted quartz-tourmaline + or - cassiterite + or - wolframite veins. --Modified journal abstract.
Artificial Intelligence-Based Chatbots to Combat COVID-19 Pandemic: A Scoping Review

Context: Artificial intelligence (AI) Chatbots are computer programs that simulate human conversation and use artificial intelligence, including machine learning and natural language processing, to interact with users via natural language. With the outbreak of the COVID-19 pandemic, the use of digital health technologies such as chatbots has accelerated. Objectives: This study aims to investigate the application of AI chatbots in combating the COVID-19 pandemic and explore their features. Methods: We reviewed the literature on health chatbots during the COVID-19 pandemic. PubMed, Scopus, Web of Science, and Google Scholar were searched using relevant keywords such as “chatbot”, “conversational agent,” and “artificial intelligence”. To select the relevant articles, we conducted title, abstract, and full-text screening based on inclusion and exclusion criteria. Chatbots, their applications, and design features were extracted from the selected articles. Results: Out of 673 articles initially identified, 17 articles were eligible for inclusion. We categorized the selected AI chatbots based on their roles, applications, and design characteristics. Around 70% of chatbots were designed to play a preventive role. Our review identified 8 key applications of the AI chatbots during the COVID-19 pandemic, which include (1) information dissemination and education, (2) self-assessment and screening, (3) connecting to health centers, (4) combating misinformation and fake news, (5) patient tracking and service delivery (6) mental health (7) monitoring exposure (8) vaccine information and scheduling. AI chatbots were deployed on various platforms, including mobile apps, web, and social media. Mobile-based chatbots were the most frequent. All chatbots used Natural Language Understanding (NLU) methods to understand natural language input and act on the user’s request. More than 50% of AI chatbots used NLU platforms, including Google Dialogflow, Rasa framework, and IBM Watson. Conclusions: AI chatbots can play an effective role in combating the COVID-19 pandemic. Increasing people’s awareness, optimizing the use of health resources, and reducing unnecessary encounters are some of the advantages of using AI chatbots during the COVID-19 outbreak. Using NLU platforms can be a suitable solution for developing AI chatbots in the healthcare domain. With advancements in the field of artificial intelligence, it seems that AI chatbots will have a promising future in healthcare, particularly in public health, chronic disease management, and mental health.