Miracle-dz/UniFAR-FTUs huggingface dataset:
Nephropathy in a Child with Severe Recessive Dystrophic Epidermolysis Bullosa Treated with Cyclophosphamide: A Case Report

Long-term inflammation and recurrent skin infection in recessive dystrophic epidermolysis bullosa (RDEB) are associated with the presence of immunoglobulin A (IgA)-containing immune complexes in the glomerulus. Only eight pediatric RDEB cases with IgA nephropathy (IgAN) have been documented in English-language literature. Most RDEB patients with IgAN progress to kidney failure within 5 years of diagnosis, indicating that these patients may require more intensive early treatment compared to those with primary IgAN. However, diagnosing IgAN in RDEB cases with severe cutaneous manifestations can be challenging. Herein, we report a rare case of nephropathy in an 11-year-old boy with severe RDEB and a frameshift mutation on the COL7A1 gene, which may manifest as kidney disorders. He presented with persistent hematuria and progressing proteinuria. A presumptive IgAN diagnosis was based on clinical features and increased IgA serum levels, as kidney biopsy was refused by his parents. Nephrotic-range proteinuria persisted despite initial steroid and lisinopril treatment. Monthly intravenous cyclophosphamide (IV CPA; 500 mg/m2) led to proteinuria remission and preservation of kidney function for 2 years posttreatment. We conclude that COL7A1 mutations may result in extracutaneous manifestations, including kidney disorders. The association between IgA-containing immune complex deposits in the glomerulus and recurrent skin infection in RDEB may indicate IgAN, particularly when kidney biopsy is infeasible due to severe skin manifestations. In our case, positive results with IV CPA suggest further investigation is needed to explore its potential role in non-rapidly progressing IgAN in children with RDEB.
Mass Spectrometry Detects Sphingolipid Metabolites for Discovery of New Strategy for Cancer Therapy from the Aspect of Programmed Cell Death

Sphingolipids, a type of bioactive lipid, play crucial roles within cells, serving as integral components of membranes and exhibiting strong signaling properties that have potential therapeutic implications in anti-cancer treatments. However, due to the diverse group of lipids and intricate mechanisms, sphingolipids still face challenges in enhancing the efficacy of different therapy approaches. In recent decades, mass spectrometry has made significant advancements in uncovering sphingolipid biomarkers and elucidating their impact on cancer development, progression, and resistance. Primary sphingolipids, such as ceramide and sphingosine-1-phosphate, exhibit contrasting roles in regulating cancer cell death and survival. The evasion of cell death is a characteristic hallmark of cancer cells, leading to treatment failure and a poor prognosis. The escape initiates with long-established apoptosis and extends to other programmed cell death (PCD) forms when patients experience chemotherapy, radiotherapy, and/or immunotherapy. Gradually, supportive evidence has uncovered the fundamental molecular mechanisms underlying various forms of PCD leading to the development of innovative molecular, genetic, and pharmacological tools that specifically target sphingolipid signaling nodes. In this study, we provide a comprehensive overview of the sphingolipid biomarkers revealed through mass spectrometry in recent decades, as well as an in-depth analysis of the six main forms of PCD (apoptosis, autophagy, pyroptosis, necroptosis, ferroptosis, and cuproptosis) in aspects of tumorigenesis, metastasis, and tumor response to treatments. We review the corresponding small-molecule compounds associated with these processes and their potential implications in cancer therapy.
Generalized logistic equation on Networks

In this paper, we consider a general single species model in a heterogeneous environment of n patches ( n ≥ 2), where each patch follows a generalized logistic law. First, we prove the global stability of the model.Second,inthecaseofperfectmixing,i.e.whenthemigrationratetendstoinfinity,thetotalpopulation follows a generalized logistic law with a carrying capacity which in general is di ff erent from the sum of the n carrying capacities. Next, we give some properties of the total equilibrium population and we compute its derivative at no dispersal. In some particular cases, we determine the conditions under which fragmentation and migration can lead to a total equilibrium population which might be greater or smaller than the sum of the n carrying capacities. Finally, we study an example of two-patch model where the first patch follows a logistic law and the second a Richard’s law, we give a complete classification of the model parameter space as to whether dispersal is beneficial or detrimental to the sum of two carrying capacities.
Late stages of high rate tension of aluminum melt: Molecular dynamic simulation

With the help of molecular dynamic simulation, we investigate late stages of aluminum melt tension up to the deformation degree of about 10, including a stage of bubble liquid, a foamed melt, and a fragmentation with formation of droplets. Complete fracture of melt is a complex process, which includes nucleation of pores, growth and coalescence of neighboring pores, thinning and breaking of walls between them with the formation of a system of jets, and, finally, breaking of jets into droplets. The transition from the foamed melt to the system of jets and the subsequent fragmentation into droplets occur at the volume fraction of condensed matter considerably smaller than 0.1. The number of pores at the volume fraction of condensed matter about 0.5 and the number of droplets at the final stage of fragmentation are not directly connected with each other. At the same time, both numbers are increased together with the increase in the strain rate and have the same order of magnitude. At the stage of melt with pores, the growth and coalescence of pores are controlled by surface tension, which allows us to construct an analytical estimation for time dependence of the pore average radius. Also, we propose analytical estimations for the mean pressure of melt with pores, which remain negative, and for the work of tension. A few times larger work is spent on the tension of melt with pores if compared with the initial stage of tension near the dynamic strength threshold. The last fact is favorable for the production of the foamed aluminum by means of the high-rate tension of its melt.
Feasibility, Perceived Impact, and Acceptability of a Socially Assistive Robot to Support Emotion Regulation With Highly Anxious University Students: Mixed Methods Open Trial

Background Mental health difficulties among university students have been rising rapidly over the last decade, and the demand for university mental health services commonly far exceeds available resources. Digital interventions are seen as one potential solution to these challenges. However, as in other mental health contexts, digital programs often face low engagement and uptake, and the field lacks usable, engaging, evidence-supported mental health interventions that may be used flexibly when students need them most. Objective The aim of this study is to investigate the feasibility and acceptability of a new, in situ intervention tool (Purrble) among university students experiencing anxiety. As an intervention, Purrble was designed to provide in situ support for emotion regulation (ER)—a well-known transdiagnostic construct—directly in the moments when individuals are facing emotionally challenging situations. A secondary aim is to consider the perceived impact of Purrble on youth mental health, as reported by students over a 7-week deployment. Methods A mixed methods open trial was conducted with 78 under- and postgraduate students at Oxford University. Participants were recruited based on moderate to high levels of anxiety measured by Generalized Anxiety Disorder-7 at baseline (mean 16.09, SD 3.03). All participants had access to Purrble for 7 weeks during the spring term with data on their perceived anxiety, emotion dysregulation, ER self-efficacy, and engagement with the intervention collected at baseline (pre), week 4 (mid), and week 8 (postintervention). Qualitative responses were also collected at the mid- and postintervention points. Results The findings demonstrated a sustained engagement with Purrble over the 7-week period, with the acceptability further supported by the qualitative data indicating that students accepted Purrble and that Purrble was well-integrated into their daily routines. Exploratory quantitative data analysis indicated that Purrble was associated with reductions in student anxiety (dz=0.96, 95% CI 0.62-1.29) and emotion dysregulation (dz=0.69, 95% CI 0.38-0.99), and with an increase in ER self-efficacy (dz=–0.56, 95% CI –0.86 to –0.26). Conclusions This is the first trial of a simple physical intervention that aims to provide ongoing ER support to university students. Both quantitative and qualitative data suggest that Purrble is an acceptable and feasible intervention among students, the engagement with which can be sustained at a stable level across a 7-week period while retaining a perceived benefit for those who use it (n=32, 61% of our sample). The consistency of use is particularly promising given that there was no clinician engagement or further support provided beyond Purrble being delivered to the students. These results show promise for an innovative intervention model, which could be complementary to the existing interventions.
Single-ended quality measurement of a music content via convolutional recurrent neural networks

The paper examines the usage of Convolutional Bidirectional Recurrent Neural Network (CBRNN) for a problem of quality measurement in a music content. The key contribution in this approach, compared to the existing research, is that the examined model is evaluated in terms of detecting acoustic anomalies without the requirement to provide a reference (clean) signal. Since real music content may include some modes of instrumental sounds, speech and singing voice or diﬀerent audio eﬀects, it is more complex to analyze than clean speech or artiﬁcial signals, especially without a comparison to the known reference content. The presented results might be treated as a proof of concept, since some speciﬁc types of artefacts are covered in this paper (examples of quantization defect, missing sound, distortion of gain characteristics, extra noise sound). However, the described model can be easily expanded to detect other impairments or used as a pre-trained model for other transfer learning processes. To examine the model eﬃciency several experiments have been performed and reported in the paper. The raw audio samples were transformed into Mel-scaled spectrograms and transferred as input to the model, ﬁrst independently, then along with additional features (Zero Crossing Rate, Spectral Contrast). According to the obtained results, there is a signiﬁcant increase in overall accuracy (by 10.1%), if Spectral Contrast information is provided together with Mel-scaled spectrograms. The paper examines also the inﬂuence of recursive layers on eﬀectiveness of the artefact classiﬁcation
Sensitivity analysis in linear regression

The Prediction Matrix Role of Variables in a Regression Equation Effects of an Observation on a Regression Equation Assessing the Influence of Multiple Observations Joint Impact of a Variable and an Observation Assessing the Effect of Errors of Measurements A Study of Model Sensitivity by the Generalized Linear Model Approach Computational Considerations. Appendix: Summary of Vector and Matrix Norms, Proofs of Three Theorems.
Electrostatic Tuning of Bilayer Graphene Edge Modes

We study the effect of a local potential shift induced by a side electrode on the edge modes at the boundary between gapped and ungapped bilayer graphene. A potential shift close to the gapped-ungapped boundary causes the emergence of unprotected edge modes, propagating in both directions along the boundary. These counterpropagating edge modes allow edge backscattering, as opposed to the case of valley-momentum-locked edge modes. We then calculate the conductance of a bilayer graphene wire in presence of finger-gate electrodes, finding strong asymmetries with energy inversion and deviations from conductance quantization that can be understood with the gate-induced unprotected edge modes.
Doctoral Virtual Writing Group: For Us, By Us

This Writers’ Forum piece addresses the importance and impact of community within the graduate experience through inception and evolution of a virtual writing group by six doctoral students attending geographically distant universities. By acknowledging relevant academic literature and sharing our experiences as best practices, we propose that virtual writing groups serve a practical application for academic success and scholarship. This forum piece will assist graduate students as they engage in academic writing and the faculty who manage and support student endeavors. HRD practitioners and academic programs can leverage this forum piece to implement the application of virtual writing spaces to enhance other adopted communities of practice
A novel residual learning-based deep learning model integrated with attention mechanism and SVM for identifying tea plant diseases

Tea is one of the most valuable crops in many tea-producing countries. However, tea plants are vulnerable to various diseases, which reduce tea production. Early diagnosis of diseases is crucial to averting their detrimental effects on the growth and quality of tea. Conventional disease identification methods depend on the manual analysis of disease features by experts, which is time-consuming and resource-intensive. Moreover, published approaches based on computer vision left a broad scope for improving accuracy and reducing computational costs. This work attempts to design an automated learning-based model by leveraging the power of deep learning methods with reduced computational costs for accurately identifying tea diseases. The proposed work uses a Convolutional Neural Network architecture based on depthwise separable convolutions and residual networks integrated with a Support Vector Machine. Additionally, an attention module is added to the model for precise extraction of disease features. An image dataset is constructed comprising the images of healthy and diseased tea leaves infected with blister blight, grey blight, and red rust. The performance of the proposed model is evaluated on the self-generated tea dataset and compared with eight other state-of-the-art deep-learning models to establish its significance. The model achieves an overall accuracy of 99.28%.
Track Fastener Defect Detection Model Based on Improved YOLOv5s

Defect detection of track fasteners is a prerequisite for safe and reliable railroad operation. The traditional manual visual inspection method has been unable to meet the growing demand for railroad network inspection in China. To achieve the need for accurate, fast, and intelligent detection of rail fasteners, this paper proposes a rail fastener defect detection model based on improved YOLOv5s. Firstly, the convolutional block attention module (CBAM) is added to the Neck network of the YOLOv5s model to enhance the extraction of essential features by the model and suppress the information of minor features. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to realize the multi-scale feature fusion of the model. Finally, the K-means++ algorithm is used to re-cluster the dataset to obtain the anchor box suitable for the fastener dataset and improve the positioning ability of the model. The experimental results show that the improved model achieves an average mean precision (mAP) of 97.4%, a detection speed of 27.3 FPS, and a model memory occupancy of 15.5 M. Compared with the existing target detection model, the improved model has the advantages of high detection accuracy, fast detection speed, and small model memory occupation, which can provide technical support for edge deployment of rail fastener defect detection.
Impact of the non-distinctness and non-ignorability on the inference by multiple imputation in multivariate multilevel data: a simulation assessment

ABSTRACT Multiple imputation (MI) is an increasingly popular method for analysing incomplete multivariate data sets. One of the most crucial assumptions of this method relates to mechanism leading to missing data. Distinctness is typically assumed, which indicates a complete independence of mechanisms underlying missingness and data generation. In addition, missing at random or missing completely at random is assumed, which explicitly states under which conditions missingness is independent of observed data. Despite common use of MI under these assumptions, plausibility and sensitivity to these fundamental assumptions have not been well-investigated. In this work, we investigate the impact of non-distinctness and non-ignorability. In particular, non-ignorability is due to unobservable cluster-specific effects (e.g. random-effects). Through a comprehensive simulation study, we show that MI inferences suggest that nonignoriability due to non-distinctness do not immediately imply dismal performance while non-ignorability due to missing not at random leads to quite subpar performance.
FINITE CAPACITY ENERGY PACKET NETWORKS

This paper surveys research on mathematical models that predict the performance of digital devices that operate with intermittent energy sources. The approach taken in this work is based on the “Energy Packet Network” paradigm where the arrival of data to be processed or transmitted, and the energy to operate the system, are modeled as discrete random processes. Our assumption is that these devices will capture energy from intermittent ambient sources such as vibrations, heat or light, and capture it onto electrical energy that may be stored in batteries or capacitors. The devices consume this energy intermittently for processing and for wired or wireless transmission. Thus, both the arrival of energy to the device, and the devices workload, are modeled as random processes. Based on these assumptions, we discuss probability models based on Markov chains that can be used to predict the effective rates at which such devices operate. We also survey related work that models networks of such systems.
The New Ice Age of Musculoskeletal Intervention: Role of Percutaneous Cryoablation in Bone and Soft Tissue Tumors

In the rapidly evolving field of interventional oncology, minimally invasive methods, including CT-guided cryoablation, play an increasingly important role in tumor treatment, notably in bone and soft tissue cancers. Cryoablation works using compressed gas-filled probes to freeze tumor cells to temperatures below −20 °C, exploiting the Joule–Thompson effect. This cooling causes cell destruction by forming intracellular ice crystals and disrupting blood flow through endothelial cell damage, leading to local ischemia and devascularization. Coupling this with CT technology enables precise tumor targeting, preserving healthy surrounding tissues and decreasing postoperative complications. This review reports the most important literature on CT-guided cryoablation’s application in musculoskeletal oncology, including sarcoma, bone metastases, and bone and soft tissue benign primary tumors, reporting on the success rate, recurrence rate, complications, and technical aspects to maximize success for cryoablation in the musculoskeletal system.
Bio-Inspired Design of Superconducting Spiking Neuron and Synapse

The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump. The dynamic processes in such a system are characterised by a duration of picoseconds and an energy level of attojoules. In this work, two superconducting models of a biological neuron are studied. New modes of their operation are identified, including the so-called bursting mode, which plays an important role in biological neural networks. The possibility of switching between different modes in situ is shown, providing the possibility of dynamic control of the system. A synaptic connection that mimics the short-term potentiation of a biological synapse is developed and demonstrated. Finally, the simplest two-neuron chain comprising the proposed bio-inspired components is simulated, and the prospects of superconducting hardware biosimilars are briefly discussed.
Does Maslow’s Hierarchy of Needs Explain Volunteer Time Allocations? An Exploration of Motivational Time Allowances Using the American Time Use Survey

This study examines whether there is an optimal set point along Maslow’s (1943) hierarchy of needs associated with disaggregated forms of volunteerism. Taniguchi’s (2012) study examined major life domains (e.g., work, education, and religion) for associations with formal and informal volunteerism. An alternative approach is to include life domains with variables measuring motivational concerns to better identify time allocation patterns for disaggregated volunteerism. This study analyzed the results of 9,435 time diaries recorded on the 2019 American Time Use Survey (ATUS). The time allocated to formal and informal volunteerism associates with intermediate belongingness concerns. There is no association between time spent on self-esteem and self-actualization concerns and informal volunteering. Tertiary education as a baseline measure for self-actualization shares a weaker association than belonging with formal volunteering. The data suggest that research into maximizing formal volunteerism may be searching at the wrong point at self-actualization. Implications are discussed for motivating volunteerism.
Foodborne Diseases Due to Underestimated Hazard of Joint Mycotoxin Exposure at Low Levels and Possible Risk Assessment

The subject of this review paper is to evaluate the underestimated hazard of multiple mycotoxin exposure of animals/humans for the appearance of foodborne ailments and diseases. The significance of joint mycotoxin interaction in the development of foodborne diseases is discussed, and appropriate conclusions are made. The importance of low feed/food levels of some target mycotoxins co-contaminations in food and feedstuffs for induction of target foodborne mycotoxicoses is also studied in the available literature. The appropriate hygiene control and the necessary risk assessment in regard to possible hazards for animals and humans are also discussed, and appropriate suggestions are made. Some internationally recognized prophylactic measures, management of the risk, and the necessity of elaboration of new international regulations in regard to the maximum permitted levels are also carefully discussed and analysed in the cases of multiple mycotoxin contaminations. The necessity of harmonization of mycotoxin regulations and control measures at international levels is also discussed in order to facilitate food trade between the countries and to ensure global food safety.