Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Effects of Power‐Frequency Magnetic Field Exposure on B‐Cell Differentiation From Human Hematopoietic Stem/Progenitor Cells

The causal relationship between exposure to power‐frequency magnetic fields (MFs) and childhood leukemia has long been controversial. The most common type of childhood leukemia is acute B‐lymphoblastic leukemia caused by abnormal proliferation of B cells in the early differentiation process. Here, we focused on B‐cell early differentiation and aimed to evaluate the effects of exposing cells to power‐frequency MF. First, we optimized an in vitro differentiation protocol of human hematopoietic stem/progenitor cells (HSPCs) to B‐cell lineages. Following validation of the responsiveness of the protocol to additional stimulations and the uniformity of the experimental conditions, human HSPCs were continuously exposed to 300 mT of 50 Hz MF for 35 days of the differentiation process. These experiments were performed in a blinded manner. The percentages of myeloid or lymphoid cells and their degree of differentiation from pro‐B to immature‐B cells in the MF‐exposed group showed no significant changes compared with those in the control group. Furthermore, the expression levels of recombination‐activating gene (RAG)1 and RAG2 in the B cells were also similar to those in the control group. These results indicate that exposure to 50 Hz MF at 300 mT does not affect the human B‐cell early differentiation from HSPCs. © 2023 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.
The engineered CRISPR‐Mb2Cas12a variant enables sensitive and fast nucleic acid‐based pathogens diagnostics in the field

Summary Existing CRISPR/Cas12a‐based diagnostic platforms offer accurate and vigorous monitoring of nucleic acid targets, but have the potential to be further optimized for more efficient detection. Here, we profiled 16 Cas12a orthologs, focusing on their trans‐cleavage activity and their potential as diagnostic enzymes. We observed the Mb2Cas12a has more robust trans‐cleavage activity than other orthologs, especially at lower temperatures. An engineered Mb2Cas12a‐RRVRR variant presented robust trans‐cleavage activity and looser PAM constraints. Moreover, we found the existing one‐pot assay, which simultaneously performed Recombinase Polymerase Amplification (RPA) and Cas12a reaction in one system, resulted in the loss of single‐base discrimination during diagnosis. Therefore, we designed a reaction vessel that physically separated the RPA and Cas12a steps while maintaining a closed system. This isolated but closed system made diagnostics more sensitive and specific and effectively prevented contamination. This shelved Mb2Cas12a‐RRVRR variant‐mediated assay detected various targets in less than 15 min and exhibited equal or greater sensitivity than qPCR when detecting bacterial pathogens, plant RNA viruses and genetically modified crops. Overall, our findings further improved the efficiency of the current CRISPR‐based diagnostic system and undoubtedly have great potential for highly sensitive and specific detection of multiple sample types.
Aerodynamic Performance of the NACA 2412 Airfoil at Low Reynolds Number

This paper shows a project by three honors students in an undergraduate engineering program. Students used a 3D printer to fabricate a wing section of the NACA 2412 airfoil. The section has a chord length of 230 mm and a total assembled width of 305 mm. The wing was printed in three separate, hollow sections including a 25mm wide inner section and two 140mm wide outer sections assembled on each side of the inner section. The inner section contained 36 surface holes that were attached by copper tubes and Tygon tubing connected to a multi-manometer. The experiments were compared with CFD simulations using ANSYS Fluent software. Detailed descriptions of the experimental design, fabrication, set-up, learning process and cost are included in the paper together with links to tutorials to repeat the experimental setup as well as a tutorial of the mesh generation and settings for the CFD simulations. Finally, the paper will provide a description of the outcomes for the project, student involvement and response, and an assessment of the student learning procedure.
N-of-1 Trials in Cancer Drug Development

Summary: The current approaches for cancer drug development lag behind an accelerated need in the field for a fast and efficient method for evaluating drugs in the personalized medicine era. In that regard, N-of-1 studies emerge as a potential addition to the drug development arsenal, although there are several considerations before its broad application becomes feasible. In essence, N-of-1 trials are a departure from the traditional “drug-centric” model to a “patient-centric” model. Herein, we review the concept of N-of-1 trials and provide real-world examples of their use in the developmental therapeutics field. N-of-1 trials offer an exceptional opportunity for fast-tracking of cancer drug development in the precision oncology era.
Causality Network of Infectious Disease Revealed With Causal Decomposition

Causal inference in the field of infectious disease attempts to gain insight into the potential causal nature of an association between risk factors and diseases. Simulated causality inference experiments have shown preliminary promise in improving understanding of the transmission of infectious diseases but still lack sufficient quantitative causal inference studies based on real-world data. Here, we investigate the causal interactions between three different infectious diseases and related factors, using causal decomposition analysis, to characterize the nature of infectious disease transmission. We show that the complex interactions between infectious disease and human behavior have a quantifiable impact on transmission efficiency of infectious diseases. Our findings, by shedding light on the underlying transmission mechanism of infectious diseases, suggest that causal inference analysis is a promising approach to determine epidemiological interventions.
Evaluation of Electromagnetic Interference between Critical Medical Devices and New Generation Cellular Phones

Abstract The electromagnetic emissions from cellular phones and other wireless devices can interfere with the functioning of medical devices when placed in proximity to the devices. Hence, the aim of this study is to evaluate the electromagnetic interference (EMI) between critical medical equipment and second-generation (2G) and third-generation (3G) cellular phones. A total of 10 medical devices were tested, including electrocardiogram (ECG) monitor, ventilator, and two models of defibrillators. The in situ tests were conducted in accordance with the procedures laid out in the American National Standard Institute (ANSI) standard C63.18 for open-site and ad-hoc testing of medical devices. A distortion was observed in the recording of the ECG, treadmill test, and pulse oximeter if either the 2G or 3G cellular phone was kept in proximity during the ringing or conversation modes. Similarly, a noise was generated in the speaker of ultrasonic fetal heart detector whenever 2G or 3G cellular phones were brought closer to it during both modes of operation. However, infusion pump, ventilators, phototherapy unit, and defibrillators were not susceptible to EMI from both the cellular phones.
The proliferation of human mucosal-associated invariant T cells requires a MYC-SLC7A5-glycolysis metabolic axis

Mucosal-associated invariant T (MAIT) cells are an abundant population of innate T cells that recognize bacterial ligands and play a key role in host protection against bacterial and viral pathogens. Upon activation, MAIT cells undergo proliferative expansion and increase their production of effector molecules such as cytokines. In this study, we found that both mRNA and protein abundance of the key metabolism regulator and transcription factor MYC was increased in stimulated MAIT cells. Using quantitative mass spectrometry, we identified the activation of two MYC-controlled metabolic pathways, amino acid transport and glycolysis, both of which were necessary for MAIT cell proliferation. Last, we showed that MAIT cells isolated from people with obesity showed decreased MYC mRNA abundance upon activation, which was associated with defective MAIT cell proliferation and functional responses. Collectively, our data uncover the importance of MYC-regulated metabolism for MAIT cell proliferation and provide additional insight into the molecular basis for the functional defects of MAIT cells in obesity. Description Innate-like T cell metabolism is controlled by MYC and impaired during obesity. The master regulator of innate T cell metabolism Mucosal associated invariant T (MAIT) cells are a type of innate-like T cells that are enriched in adipose, gut, and liver tissue and that recognize bacterial metabolites. Upon activation, they proliferate and produce cytokines to promote host defense. Kedia-Mehta et al. showed that MAIT proliferation depended on MYC-associated pathways involving amino acid transport and glycolysis. Furthermore, MAIT cells from patients with obesity showed disrupted function and engagement of these pathways. These findings demonstrate that the canonical metabolic pathways found in conventional T cells are active in MAIT cells and may be relevant to the development of MAIT cell-based therapies and the study of obesity. —AEB
The Motion Equation of a Spring-Magnet-Mass System Placed in Nonlinear Magnetic Field. An Analytical Solution of Elliptic Sine form Functions

In this work, w e are studying about a special oscillator system, which consists of one spring and a magnet-mass. The system is placed in nonlinear magnetic field, produced by two other permanent magnets, which are oriented for attraction, where can appear different types of oscillations. The magnetbody is simultaneously the subject of the linear field of spring and also of the nonlinear magnetic field of permanent magnets which has inverse quadratic dependence on distance. We are studying the ideal case, without friction, where the oscillations are produced wi th energy conservation, the oscillator system is started by applying the initial impulse and we consider the hypothesis that magnetic field produced by the permanent magnets is conservative and there is no loss of en ergy in the magnetic interactions. We are going to find the law of motion for the general case of study and a typically numerical application will be done.
The human placenta: new perspectives on its formation and function during early pregnancy

The placenta has evolved to support the development of the embryo and fetus during the different intrauterine periods of life. By necessity, its development must precede that of the embryo. There is now evidence that during embryogenesis and organogenesis, the development of the human placenta is supported by histotrophic nutrition secreted from endometrial glands rather than maternal blood. These secretions provide a plentiful supply of glucose, lipids, glycoproteins and growth factors that stimulate rapid proliferation and differentiation of the villous trophoblast. Furthermore, evidence from endometrial gland organoids indicates that expression and secretion of these products are upregulated following sequential exposure to oestrogen, progesterone and trophoblastic and decidual hormones, in particular prolactin. Hence, a feed-forward signalling dialogue is proposed among the trophoblast, decidua and glands that enables the placenta to stimulate its own development, independent of that of the embryo. Many common complications of pregnancy represent a spectrum of disorders associated with deficient trophoblast proliferation. Increasing evidence suggests that this spectrum is mirrored by one of impaired decidualization, potentially compromising histotroph secretion through diminished prolactin secretion and reduced gland function. Optimizing endometrial wellbeing prior to conception may therefore help to prevent common pregnancy complications, such as miscarriage, growth restriction and pre-eclampsia.
Point-like defect on Schrödinger particles under flux field with harmonic oscillator plus Mie-type potential: application to molecular potentials

In this analysis, we study the quantum motions of a non-relativistic particle confined by the Aharonov–Bohm (AB) flux field with harmonic oscillator plus Mie-type potential in a point-like defect. We determine the eigenvalue solution of the particles analytically and discuss the effects of the topological defect and flux field with this potential. This eigenvalue solution is then used in some diatomic molecular potential models (harmonic oscillator plus Kratzer, modified Kratzer and attractive Coulomb potentials) and presented as the eigenvalue solutions. Afterwards, we consider a general potential form (superposition of pseudoharmonic plus Cornell-type potential) in the quantum system and analyse the effects of various factors on the eigenvalue solution. It is shown that the eigenvalue solutions are modified by the topological defect of a point-like global monopole and flux field compared to the results obtained in the flat space.
Variation in how cognitive control modulates sentence processing

Prior research suggests that cognitive control can assist the comprehension of sentences that create conflict between interpretations, at least under some circumstances. However, the mixed pattern of results suggests that cognitive control may not always be necessary for accurate comprehension. We tested whether cognitive control recruitment for language processing is systematically variable, depending on the type of sentential ambiguity or conflict, individual differences in cognitive control, and task demands. Participants completed two sessions in a web-based experiment. The first session tested conflict modulation using interleaved Stroop and sentence comprehension trials. Critical sentences contained syntax-semantics or phrase-attachment conflict. In the second session, participants completed three cognitive control and three working memory tasks. Exploratory factor analysis was used to index individual differences in a cognitive control factor and a working memory factor. At the group level, there were no significant conflict modulation effects for either syntax-semantics or phrase-attachment conflict. At the individual differences level, the cognitive control factor correlated with offline comprehension accuracy but not online processing measures for both types of conflict. Together, the results suggest that the role of cognitive control in sentence processing may vary according to task demands. When overt decisions are required, individual differences in cognitive control may matter such that better cognitive control results in better language comprehension performance. The results add to the mixed evidence on conflict modulation and raise questions about the situations under which cognitive control influences online processing.
Self-consistent Sierpinski iteration of toughening mechanism in elastomer undergoing scaled segment-chain-network

Understanding working principles and toughening mechanisms of soft elastomers has been a huge challenge due to their significant scaling effects from molecules to bulk polymer. In this study, by combining Sierpinski fractal and scaling theory, an extended Kelvin model is developed to investigate the mechanical behaviour of the soft elastomer undergoing scaled segment-chain-network. According to the scaling theory, a radial distribution function was initially introduced to explain the diffusion and relaxation behaviours of molecular segments with the Sierpinski fractal features. The self-consistent iteration of the Sierpinski fractal is then used to describe the scaling effects of segments and networks. Rubber elasticity of the polymer network is further formulated based on the self-consistent iteration equation and scaling theory. A constitutive stress–strain relationship is also derived to explore the toughness mechanism and working principle in the polymer elastomer. Finally, the effectiveness of the proposed model is verified using finite-element analysis and experimental results reported in the literature, to explore a scaling insight into toughening mechanisms of elastomers governed by the self-consistent Sierpinski iteration.
Genetic correlation between aggressive signals and fighting

Theoretical analyses indicate that aggressive signals should positively correlate with the signallers' willingness and abilities to fight. Few experimental studies, however, have tested this prediction. In two experiments employing distinct, ecologically realistic protocols, we quantified the association between aggressive signals and fighting in fruit fly genotypes and found high positive genetic correlations between threat and fighting (rG = 0.80 and 0.74). Our results add to the growing body of experimental work indicating that aggressive signals have relatively high informational value.
Morphology and niche evolution influence hummingbird speciation rates

How traits affect speciation is a long-standing question in evolution. We investigate whether speciation rates are affected by the traits themselves or by the rates of their evolution, in hummingbirds, a clade with great variation in speciation rates, morphology and ecological niches. Further, we test two opposing hypotheses, postulating that speciation rates are promoted by trait conservatism or, alternatively, by trait divergence. To address these questions, we analyse morphological (body mass and bill length) and niche traits (temperature and precipitation position and breadth, and mid-elevation), using a variety of methods to estimate speciation rates and correlate them with traits and their evolutionary rates. When it comes to the traits, we find faster speciation in smaller hummingbirds with shorter bills, living at higher elevations and experiencing greater temperature ranges. As for the trait evolutionary rates, we find that speciation increases with rates of divergence in the niche traits, but not in the morphological traits. Together, these results reveal the interplay of mechanisms through which different traits and their evolutionary rates (conservatism or divergence) influence the origination of hummingbird diversity.
Transmission dynamics of lyssavirus in Myotis myotis: mechanistic modelling study based on longitudinal seroprevalence data

We investigated the transmission dynamics of lyssavirus in Myotis myotis and Myotis blythii, using serological, virological, demographic and ecological data collected between 2015 and 2022 from two maternity colonies in northern Italian churches. Despite no lyssavirus detection in 556 bats sampled over 11 events by reverse transcription-polymerase chain reaction (RT-PCR), 36.3% of 837 bats sampled over 27 events showed neutralizing antibodies to European bat lyssavirus 1, with a significant increase in summers. By fitting sets of mechanistic models to seroprevalence data, we investigated factors that influenced lyssavirus transmission within and between years. Five models were selected as a group of final models: in one model, a proportion of exposed bats (median model estimate: 5.8%) became infectious and died while the other exposed bats recovered with immunity without becoming infectious; in the other four models, all exposed bats became infectious and recovered with immunity. The final models supported that the two colonies experienced seasonal outbreaks driven by: (i) immunity loss particularly during hibernation, (ii) density-dependent transmission, and (iii) a high transmission rate after synchronous birthing. These findings highlight the importance of understanding ecological factors, including colony size and synchronous birthing timing, and potential infection heterogeneities to enable more robust assessments of lyssavirus spillover risk.
Security Threat Mitigation for Smart Contracts: A Comprehensive Survey

The blockchain technology, initially created for cryptocurrency, has been re-purposed for recording state transitions of smart contracts—decentralized applications that can be invoked through external transactions. Smart contracts gained popularity and accrued hundreds of billions of dollars in market capitalization in recent years. Unfortunately, like all other computer programs, smart contracts are prone to security vulnerabilities that have incurred multibillion-dollar damages over the past decade. As a result, many automated threat mitigation solutions have been proposed to counter the security issues of smart contracts. These threat mitigation solutions include various tools and methods that are challenging to compare. This survey develops a comprehensive classification taxonomy of smart contract threat mitigation solutions within five orthogonal dimensions: defense modality, core method, targeted contracts, input-output data mapping, and threat model. We classify 133 existing threat mitigation solutions using our taxonomy and confirm that the proposed five dimensions allow us to concisely and accurately describe any smart contract threat mitigation solution. In addition to learning what the threat mitigation solutions do, we also show how these solutions work by synthesizing their actual designs into a set of uniform workflows corresponding to the eight existing defense core methods. We further create an integrated coverage map for the known smart contract vulnerabilities by the existing threat mitigation solutions. Finally, we perform the evidence-based evolutionary analysis, in which we identify trends and future perspectives of threat mitigation in smart contracts and pinpoint major weaknesses of the existing methodologies. For the convenience of smart contract security developers, auditors, users, and researchers, we deploy and maintain a regularly updated comprehensive open-source online registry of threat mitigation solutions, called Security Threat Mitigation (STM) Registry at https://seit.egr. msu.edu/research/stmregistry/.
Comparative study of DNN accelerators on FPGA

As an important technology and research direction to achieve AI, deep learning has been widely applied in the fields of computer vision, speech recognition, and natural language processing. How to effectively accelerate the computing power of deep learning has always been the focus of scientific research. Among various acceleration technologies, FPGA has the advantages of reconfiguration, high performance, small size, and low latency. As more and more FPGA-based neural network accelerators are developed, we notice there is a lack of a complete and detailed overview. In this paper, we give a comparative study of DNN accelerators on FPGA from the aspects of hardware structures, design ideas and optimization strategies. We further compare the performance of different acceleration technologies in different models and present the prospects of the FPGA accelerators for deep learning.