Miracle-dz/UniFAR-FTUs huggingface dataset:
Cumulative Deleterious Effects of Tetrahydrocannabinoid (THC) and Ethanol on Mitochondrial Respiration and Reactive Oxygen Species Production Are Enhanced in Old Isolated Cardiac Mitochondria

Delta 9 tetrahydrocannabinol (THC), the main component of cannabis, has adverse effects on the cardiovascular system, but whether concomitant ethanol (EtOH) and aging modulate its toxicity is unknown. We investigated dose responses of THC and its vehicle, EtOH, on mitochondrial respiration and reactive oxygen production in both young and old rat cardiac mitochondria (12 and 90 weeks). THC dose-dependently impaired mitochondrial respiration in both groups, and such impairment was enhanced in aged rats (−97.5 ± 1.4% vs. −75.6 ± 4.0% at 2 × 10−5 M, and IC50: 0.7 ± 0.05 vs. 1.3 ± 0.1 × 10−5 M, p < 0.01, for old and young rats, respectively). The EtOH-induced decrease in mitochondrial respiration was greater in old rats (−50.1 ± 2.4% vs. −19.8 ± 4.4% at 0.9 × 10−5 M, p < 0.0001). Further, mitochondrial hydrogen peroxide (H2O2) production was enhanced in old rats after THC injection (+46.6 ± 5.3 vs. + 17.9 ± 7.8%, p < 0.01, at 2 × 10−5 M). In conclusion, the deleterious cardiac effects of THC were enhanced with concomitant EtOH, particularly in old cardiac mitochondria, showing greater mitochondrial respiration impairment and ROS production. These data improve our knowledge of the mechanisms potentially involved in cannabis toxicity, and likely support additional caution when THC is used by elderly people who consume alcohol.
Interpretable Subgraph Feature Extraction for Hyperlink Prediction

Hyperlink prediction aims to predict interactions among multiple entries, constituting a practical yet challenging problem in the literature. While a handful of solutions have been proposed, they generally operate on the entire hypergraph. A practical subgraph-based solution not only enables better identification of localized characteristics of the central hyperedge but also alleviates scalability concerns. In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features. The rationale behind SSF is that hyperedges and non-hyperedges exhibit distinct local patterns, which can be unveiled through the assimilation of subgraph structural features. To this end, we utilize well-established structural heuristics such as walks and loops as the fundamental building blocks. We commence by extracting a subgraph encompassing each focal hyperedge, subsequently integrating an edge weakening scheme to facilitate feature extraction from the initial subgraph and its variations. The extracted feature vector is interpretable, and the designed edge weakening scheme empowers SSF with an adaptive capability to handle hypergraphs with varying densities. Lastly, a multilayer perceptron classifier is trained for prediction. Experiment results on ten real-world hypergraph networks demonstrate the effectiveness of the proposed approach. The source code of SSF is available at this URL1.1https://github.com/KXDY233/SSF
Construction of Short-Term Traffic Flow Prediction Model Based on IoT and Deep Learning Algorithms

— On a global scale, traffic problems are an essential factor affecting urban operations, particularly challenging the frequent occurrence of traffic congestion and accidents. The solution to the problem requires real-time and accurate prediction of traffic flow. This article mainly explores the application of the Internet of Things and deep learning in traffic flow prediction, aiming to solve the problem where existing methods cannot meet the requirements of real-time and accuracy. IoT devices, such as road sensors and in-vehicle GPS devices, which provides rich information for traffic flow prediction. With the ability of deep learning, it can not only learn and abstract a large amount of complex traffic data but also handle traffic flow prediction tasks in various complex situations. During the model construction process, the complexity of the road network was fully considered, practical algorithms were designed to fuse multi-source data, and the structure of the model was optimized to meet the needs of real-time prediction. The experimental results show that the absolute error of the test results is generally less than 6km/h, which can better reflect the traffic speed of the road section in the future.
On the role of models and modularity in the design and development of Digital Humanities tools

This article emphasizes the importance of defining models and building modular software for digital humanities tools. In particular, we address the availability of modular models for tools as a necessity to cope with the rapidly changing technological landscape that has produced a large set of short-lived tools. We propose an approach that aims to mitigate the effects of software obsolescence that leverages modular models.
Momentum is All You Need for Data-Driven Adaptive Optimization

Adaptive gradient methods, e.g., ADAM, have achieved tremendous success in data-driven machine learning, especially deep learning. Employing adaptive learning rates according to the gradients, such methods are able to attain rapid training of modern deep neural networks. Nevertheless, they are observed to suffer from compromised generalization capacity compared with stochastic gradient descent (SGD) and tend to be trapped in local minima at an early stage during the training process. Intriguingly, we discover that the issue can be resolved by substituting the gradient in the second raw moment estimate term with its exponential moving average version in ADAM. The intuition is that the gradient with momentum contains more accurate directional information, and therefore its second-moment estimation is a more preferable option for learning rate scaling than that of the raw gradient. Thereby we propose ADAM$^{3}$ as a new optimizer reaching the goal of training quickly while generalizing much better. Extensive experiments on a variety of tasks and models demonstrate that ADAM$^{3}$ exhibits state-of-the-art performance and superior training stability consistently. Considering the simplicity and effectiveness of ADAM$^{3}$, we believe it has the potential to become a new standard method in deep learning. Code is provided at https://github.com/wyzjack/AdaM3.
Navigating the shift in Bangladeshi host community’s perceptions towards the Rohingya refugees: a declining sympathy

Generosity and selflessness from the host community in Cox’s Bazar were deemed to be instrumental in supporting Rohingyas who sought refuge in Bangladesh in 2017. Thousands of Rohingyas had to flee from their own country to save lives due to state-supported military violence. Initially, Bangladeshi media and civil society were largely supportive of the Rohingyas. However, the initial sympathy later withered away and may have turned into frustration and hostility. Based on 39 in-depth interviews with hot community members and humanitarian professionals, this paper argues that protraction of the crisis, inability to access natural resources due to the refugee camps, some Rohingyas’ involvement in various unlawful activities, a perceived sense of neglect from the international community, and disruption in local labour market/trade affecting cost of living conditions for low-income people seem to have played important roles in creating widespread tensions between the host community and Rohingya refugees. We contend that findings of this study will add to the critical scholarship of humanitarian development in deepening the understanding of host and refugee communities’ relationships. This paper will also have a positive impact on future policies toward harmonious coexistence between host communities and displaced refugees and potential sustainable solutions to the crisis.
Silvicultural systems and cutting methods for old-growth lodgepole pine forests in the Central Rocky Mountains /

Guidelines are provided to help forest managers and silviculturists develop evenand uneven-aged cutting practices needed to convert pure and mixed old-growth lodgepole pine forests into managed stands. Guidelines consider stand conditions, succession, windfall risk, and insect and disease susceptibility. Cutting practices— clearcutting, shelterwood, and selection—are designed to integrate timber production with increased water yield, maintained water quality, improved wildlife habitat, and enhanced opportunities for recreation and scenic beauty. Cover Photo. — 1Old-growth lodgepole pine on the Fraser Experimental Forest, Colorado. USDA Forest Service General Technical Report RM-127 April 1986 Silvicultural Systems and Cutting Methods for Old-Growth Lodgepole Pine Forests in the Central Rocky Mountains Robert R. Alexander, Chief Silviculturist Rocky Mountain Forest and Range Experiment Station 1 ^Headquarters is in Fort Collins, in cooperation with Colorado State University.
Unfairness in Distributed Graph Frameworks

In the era of big data, distributed graph processing frameworks have become important in processing large-scale graph datasets. Such distributed frameworks exhibit major advantages with respect to scalability, and provide various ways to speed up sequential graph algorithms. However, the literature lacks an analysis on the fairness properties of such distributed algorithms. In this work, we analyze several important distributed frameworks and graph analysis algorithms with respect to their fairness properties. Across numerous real-world network datasets, we demonstrate that distributed algorithms often exhibit worse fairness performance as compared to their sequential counterparts. Moreover, we observe that this phenomenon is often strongly connected to the homophily of the graph dataset– the tendency of nodes to connect to other nodes of the same class.
Effect of Guanylate Cyclase-22-like on Ovarian Development of Orius nagaii (Hemiptera: Anthocoridae)

Simple Summary In this study, the effect of the targeted silencing of OnGCY on the reproduction of the pirate bug Orius nagaii was evaluated. OnGCY silencing significantly inhibited ovarian development and egg production and significantly reduced the expression of the gene encoding the vitellogenin (Vg) gene. These data provide insights into the function of OnGCY in insect reproduction, which could guide research for the mass rearing of natural enemies for the biological control of insect pests. Abstract This study identified and characterized the gene encoding recep tor-type guanylate cyclase-22-like (GCY-22; OnGCY) from the pirate bug Orius nagaii, an important biological control agent. The full-length cDNA of the GCY of O. nagaii was obtained by rapid amplification of cDNA ends (RACE); it had a total length of 4888 base pairs (bp), of which the open reading frame (ORF) was 3750 bp, encoding a polypeptide of 1249 amino acid residues. The physicochemical properties of OnGCY were predicted and analyzed by using relevant ExPASy software, revealing a molecular formula of C6502H10122N1698O1869S57, molecular weight of ~143,811.57 kDa, isoelectric point of 6.55, and fat index of 90.04. The resulting protein was also shown to have a signal peptide, two transmembrane regions, and a conserved tyrosine kinase (tyrkc). Silencing OnGCY by RNA interference significantly inhibited ovarian development and decreased fertility in female O. nagaii in the treated versus the control group. Additionally, OnGCY silencing significantly decreased the expression levels of other GCY and Vg genes. Thus, these results clarify the structure and biological function of OnGCY, which has an important role in insect fecundity. The results also provide a reference for agricultural pest control and future large-scale breeding of biological control agents.
TGFβ level in healthy and children with Marfan syndrome—effective reduction under sartan therapy

Introduction Transforming growth factor β (TGFβ) metabolism plays an important role in the pathogenesis of Marfan syndrome (MFS). Accordingly, drug therapy uses TGFβ receptor blockade to slow down the cardiovascular manifestations, above all aortic root dilatation. Angiotensin II type 1 receptor blockers (ARBs) have been shown to reduce TGFβ levels in adults. Data on childhood are lacking and are now being investigated in the TiGer For Kids study presented here. Methods We examined 125 children without chronic disease and 31 pediatric Marfan patients with a proven FBN1 variant with regard to TGFβ levels. In addition, we measured TGFβ levels during the initiation of ARB therapy in pediatric Marfan patients. Results In children without chronic disease, TGFβ levels were found to decrease from childhood to adolescence (p < 0.0125). We could not measure a relevantly increased TGFβ level in pediatric Marfan patients. However, we showed a significant suppression of the TGFβ level after treatment with ARBs (p < 0.0125) and a renewed increase shortly before the next dose. Discussion The TGFβ level in childhood changes in an age-dependent manner and decreases with age. The TGFβ level drops significantly after taking ARBs. Based on our experience and data, a TGFβ receptor blockade in childhood seems reasonable. So far, TGFβ level cannot be used as an MFS screening biomarker.
Simulation of the cumulative hydrological response to green infrastructure

In this study, we evaluated the cumulative hydrologic performance of green infrastructure in a residential area of the city of Parma, Ohio, draining to a tributary of the Cuyahoga River. Green infrastructure included the following spatially distributed devices: 16 street‐side bioretention cells, 7 rain gardens, and 37 rain barrels. Data consisted of rainfall and outfall flow records for a wide range of storm events, including pretreatment and treatment periods. The Stormwater Management Model was calibrated and validated to predict the hydrologic response of green infrastructure. The calibrated model was used to quantify annual water budget alterations and discharge frequency over a 6 year simulation period. For the study catchment, we observed a treatment effect with increases of 1.4% in evaporation, 7.6% in infiltration, and a 9.0% reduction in surface runoff. The hydrologic performance of green infrastructure was evaluated by comparing the flow duration curve for pretreatment and treatment outfall flow scenarios. The flow duration curve shifted downward for the green infrastructure scenario. Discharges with a 0.5, 1, 2, and 5 year return period were reduced by an average of 29%. Parameter and predictive uncertainties were inspected by implementing a Bayesian statistical approach.
Mission-Driven Inverse Design of Blended Wing Body Aircraft with Machine Learning

The intent of this work was to investigate the feasibility of developing machine learning models for calculating values of airplane configuration design variables when provided time-series, mission-informed performance data. Shallow artificial neural networks were developed, trained, and tested using data pertaining to the blended wing body (BWB) class of aerospace vehicles. Configuration design parameters were varied using a Latin-hypercube sampling scheme. These data were used by a parametric-based BWB configuration generator to create unique BWBs. Performance for each configuration was obtained via a performance estimation tool. Training and testing of neural networks was conducted using a K-fold cross-validation scheme. A random forest approach was used to determine the values of predicted configuration design variables when evaluating neural network accuracy across a blended wing body vehicle survey. The results demonstrated the viability of leveraging neural networks in mission-dependent, inverse design of blended wing bodies. In particular, feed-forward, shallow neural network architectures yielded significantly better predictive accuracy than cascade-forward architectures. Furthermore, for both architectures, increasing the number of neurons in the hidden layer increased the prediction accuracy of configuration design variables by at least 80%.
Species diversity and distribution patterns of Noctuidae (Lepidoptera) in China

A total of 3 751 species noctuids belonging to 845 genera of 20 subfamilies from China were inventoried into a database, and their distribution patterns were analyzed. The results indicated that most of the Chinese noctuids could be assigned into four faunal elements: Eurytopic, Palaearctic, Oriental and East Asian. Among them East Asian element predominated over the other elements, making up 51.35 percent of the total species,then Oriental element occupying 25.51%,Palaearctic 18.45% and Eurytopic only 2.26%. The distribution of the family in the seven zoogeographical regions of China was characterized by the compounds of various elements in various taxa. Eurytopic element (85 species) was fairly well-distributed in the seven regions. Oriental (957 species) and Palaearctic (692 species) elements were distributed in stairs with latitude by distinct orientation. For East Asian element, South China pattern (1 363 species) and North China pattern (290 species) were of isolated-distribution; Central China pattern (273 species) were of multi-region and cross-boundary distribution,but mainly distributed in North-China Region,Central-China Region,Qinghai-Xizang Region and South-West Region. Parsimony analysis of endemicity (PAE) was made to identify areas of endemism. The regions and distribution of these areas of endemism were mostly in congruence with the zoogeographical region division of China, but Taiwan was close to the Central-China Region in endemism. Based on the results of key fauna analysis, the priority regions for conservation were proposed in the sequence of Taiwan, Yunan, Xinjiang, Xizang, Sichuan, Hainan, Heilongjiang and so on.
Learning Structural Similarities from Handwriting on Papyri - An Application to Scribe Characterization

Computational analysis of historical manuscripts has remained an interesting as well as a challenging research problem for the pattern recognition community. The recent advancements in different areas of machine learning and the successful completion of joint projects between computer scientists and paleographers have resulted in increased research interest in ‘digital paleography’. Among various tasks the rich collections of historical documents offer, this study focuses on characterizing of writer from handwriting on papyri. We formulate the problem in a similarity learning framework and exploit Siamese neural networks to compare an input pair of writing samples. The highly degraded images of handwriting on papyri are binarized using a deep learning-based binarization technique. Patches of handwriting are subsequently extracted by sampling the complete image and pairs of samples from the same and different writers are produced. These positive and negative pairs are employed to fine-tune a Siamese neural network with different popular architectures as the backbone model. An experimental study on the samples in the GRK-Papyri dataset with 97 writing samples reports a test accuracy of 62% on DenseNet-121 and 69% with omniglot-trained CNN. The preliminary findings are quite promising and can be employed in writer identification, verification, and retrieval frameworks.
Ionic Liquid and PVDF Composite Films for Triboelectric Nanogenerator

The output of a triboelectric nanogenerator using a composite film consisting of a small amount of ionic liquid (IL) and polyvinylidene fluoride (PVDF) was evaluated. The IL concentration ranged from 0 to 50 ppm. The output reached a maximum at the IL concentration of 20 ppm, and then decreased when concentration exceeded 20 ppm. The charge density depth distribution caused by the contact separation of the PVDF composite film was measured by the pulsed electroacoustic method. The IL addition is increased the charge density, and thus the power output.
Renewal theory provides a universal quantitative framework to characterise the continuous regeneration of rotational events in cardiac fibrillation

Background Cardiac fibrillation is thought to be maintained by rotational activity, with pivoting regions called phase singularities (PSs). Despite a century of research, no clear quantitative framework exists to model the fundamental processes responsible for the continuous formation and destruction of rotors in fibrillation. Objective We conducted a multi-modality, multi-species study of AF/VF under the hypothesis that PS formation/destruction in fibrillation can be modelled as self-regenerating renewal processes, producing exponential distributions of inter-event times governed by constant rate-parameters defined by the prevailing properties of the system. Methods PS formation/destruction was studied and cross-validated in 5 models, using basket recordings and optical mapping from: i) human persistent AF (n = 20), ii) tachypaced sheep AF (n = 5), iii) rat AF (n = 4), iv) rat VF (n = 11) and v) computer simulated AF (SIM). Hilbert phase maps were constructed. PS lifetime data were fitted by exponential probability distribution functions (PDFs) computed using maximum entropy theory, and the rate parameter (λ) determined. A systematic review was conducted to cross-validate with source data from literature. Results PS destruction/formation distributions showed good fits to an exponential in all systems (R2 ≥ 0.90). In humans, λ = 4.6%/ms (95%CI,4.3,4.9)), sheep 4.4%/ms (95%CI,4.1,4.7)), rat AF 38%/ms (95%CI,22,55), rat VF 46%/ms (95%CI,31.2,60.2) and SIM 5.4%/ms (95%CI,4.1,6.7). All PS distributions identified through systematic review were exponential with λ comparable to experimental data. Conclusion These results provide a universal quantitative framework to explain rotor formation and destruction in AF/VF, and a platform for therapeutic advances in cardiac fibrillation.
Bibliometric and scientometric analysis on biomarkers and molecular mechanisms for physical frailty and sarcopenia

Introduction The influence of physical frailty and sarcopenia (PFS) on the well-being of older people and continuous pressure on the healthcare systems has prompted a research on the pathophysiology and molecular mechanisms of these conditions. Nonetheless some biomarkers have been suggested as potential markers for PFS none of them have been shown to highlight the complex nature of PFS, which reveals that there is a need for an understanding of the possible biomarker candidates. The aim of this study was to identify the current research hotspots, status, and trends in the field of biomarkers and molecular mechanisms for PFS. Methods The bibliometric and scientometric analyses were performed using VOSviewer (version 1.6.18) and open source software platform Cytoscape v.3. 9 (for visualizing and constructing a network of keywords). Data of publications (from 1997 to 2023) related to biomarkers and molecular mechanisms of PFS were obtained (in May 2023) from the database of Science Citation Index Expanded of Web of Science, Scopus, and PubMed. The keywords obtained from the Scopus database were used to perform a meaningful keyword analysis. A network of keyword relationships was build using Cytoscape. Results In this study, we present biomarker keywords for PFS in relation to other keywords potentially designating processes and mechanisms and reveal the biomarker identities and current contexts in which these biomarker identities are discussed. Conclusions Over recent years, scientific interest in the field of PFS has increased and focused on the inflammatory process and probably will be concentrated on myokines (such as cytokines and small proteins) that are synthetized and released by skeletal muscles in response to physical activity. Moreover, proteomic and genetic markers are deeply involved in PFS.