bluuebunny/crossref_metadata_2025 huggingface dataset:
Accuracy of self-diagnosis in conditions commonly managed in primary care: diagnostic accuracy systematic review and meta-analysis
Authors: McLellan Julie , Heneghan Carl , Roberts Nia , Pluddemann Annette
Published: January 2023

Objectives

To assess the diagnostic accuracy of self-diagnosis compared with a clinical diagnosis for common conditions in primary care.

Design

Systematic review. Meta-analysis.

Data sources

Medline, Embase, Cochrane CENTRAL, Cochrane Database of Systematic Reviews and CINAHL from inception to 25 January 2021.

Study selection

Eligible studies were prospective or retrospective studies comparing the results of self-diagnosis of common conditions in primary care to a relevant clinical diagnosis or laboratory reference standard test performed by a healthcare service provider. Studies that considered self-testing only were excluded.

Data extraction

Two authors independently extracted data using a predefined data extraction form and assessed risk of bias using Quality Assessment of Diagnostic Accuracy Studies-2.

Methods and results

5047 records identified 18 studies for inclusion covering the self-diagnosis of three common conditions: vaginal infection (five studies), common skin conditions (four studies) and HIV (nine studies). No studies were found for any other condition. For self-diagnosis of vaginal infection and common skin conditions, meta-analysis was not appropriate and data were reported narratively. Nine studies, using point-of-care oral fluid tests, reported on the accuracy of self-diagnosis of HIV and data were pooled using bivariate meta-analysis methods. For these nine studies, the pooled sensitivity was 92.8% (95% CI, 86% to 96.5%) and specificity was 99.8% (95% CI, 99.1% to 99.9%). Post hoc, the robustness of the pooled findings was tested in a sensitivity analysis only including four studies using laboratory testing as the reference standard. The pooled sensitivity reduced to 87.7% (95% CI, 81.4% to 92.2%) and the specificity remained the same. The quality of all 18 included studies was assessed as mixed and overall study methodology was not always well described.

Conclusions and implications of key findings

Overall, there was a paucity of evidence. The current evidence does not support routine self-diagnosis for vaginal infections, common skin conditions and HIV in primary care.

PROSPERO registration number

CRD42018110288.
Robotic-guided direct transtemporal embolization of an indirect carotid cavernous fistula
Authors: Karas Patrick J , Lee Jae Eun , Frank Thomas S , Morden Frances Tiffany , Shaltoni Hashem , Kan Peter
Published: January 2023

A middle-aged patient presented with right-sided chemosis, exophthalmos, and progressive visual loss. Digital subtraction angiography revealed a type D carotid-cavernous fistula (CCF). Transarterial embolization through the internal maxillary artery was unsuccessful, and there was no venous access to the CCF. A robotic-guided direct transtemporal embolization of the CCF with Onyx was performed, resulting in successful fistula obliteration and symptom resolution. This is the first reported case of a robotic-guided direct transcranial CCF embolization. We include a technical video that demonstrates this procedure (Supplemental File 1)
Physical and mechanical performance of an HDPE geomembrane in 10 mining solutions with different pHs
Authors: Abdelaal Fady B. , Rowe R. Kerry
Published: January 2023

The degradation in physical and mechanical properties of a 1.5 mm thick HDPE geomembrane immersed in seven different low pH and three high pH simulated mining solutions is examined at temperatures between 95 and 40 °C for 3 years. The solutions with pH between 0.5 and 13.5 encompass solutions found in copper, nickel, uranium, gold, and silver heap leach pads. The geomembrane did not exhibit any chemical degradation during the three years of incubation in all the low-pH solutions. However, in the solutions with pH 9.5, 11.5, and 13.5, some of the geomembrane’s physical and mechanical properties are shown to reach nominal failure at 95 and 85 °C. While the geomembrane examined shows superior performance in the acidic environments than in the basic solutions examined, its performance in such extremely basic environments is still better than in neutral reduced municipal solid waste leachate. Using Arrhenius’ modelling, the predicted times for the antioxidant depletion stage of the geomembrane examined in composite liner configuration range between 31 years in pH 13.5 to 51 years in pH 0.5 for pad liners at 50 °C, exceeding a typical leaching period of the ore of around 20 years in different heap leaching operations.
Connecting habitat to species abundance: the role of light and temperature on the abundance of walleye in lakes
Authors: Mahlum Shad , Vitense Kelsey , Corson-Dosch Hayley , Platt Lindsay , Read Jordan S. , Schmalz Patrick J. , Treml Melissa , Hansen Gretchen J. A.
Published: January 2023

Walleye ( Sander vitreus) are an ecologically important species managed for recreational, tribal, and commercial harvest. Walleye prefer cool water and low light conditions, and therefore changing water temperature and clarity potentially impacts walleye habitat and populations across the landscape. Using survey data collected from 1993 to 2018 from 312 lakes in Minnesota, we evaluated the relationship between thermal-optical habitat and the relative abundance of small (0–300 mm), medium (300–450 mm), and large (450 + mm) walleye. Thermal-optical habitat was positively correlated with the relative abundance of small and medium walleye but not large walleye. Walleye were more abundant in larger, naturally reproducing lakes opposed to smaller, stocked lakes. Thermal-optical habitat changed in 59% of lakes since 1980 (26% increasing and 33% decreasing) and appears to be driven primarily by changes in water clarity and thus optical habitat area. Our study provides important insights into local and regional drivers that influence walleye populations that can be used to assist fisheries managers in setting population goals and managing harvest.
NER in Cyber Threat Intelligence Domain Using Transformer with TSGL
Authors: Huang Yuhuang , su Mang , Xu Yuting , Liu Tian
Published: January 2023

In response to the continuous sophistication of cyber threat actors, it is imperative to make the best use of cyber threat intelligence converted from structured or semi-structured data and Named Entity Recognition (NER) techniques that contribute to extracting critical cyber threat intelligence. To promote the NER research in Cyber Threat Intelligence (CTI) domain, we provide a Large Dataset for NER in Cyber Threat Intelligence (LDNCTI). On the LDNCTI corpus, we investigated the feasibility of mainstream transformer-based models in CTI domain. To settle the problem of unbalanced label distribution, we introduce a transformer-based model with a Triplet Loss based on metric learning and Sorted Gradient harmonizing mechanism (TSGL). Our experimental results show that the LDNCTI well represents critical threat intelligence and that our transformer-based model with the new loss function outperforms previous schemes on the Dataset for NER in Threat Intelligence (DNRTI) and the dataset for NER in Advanced Persistent Threats (APTNER).
Learning Spatiotemporal-Selected Representations in Videos for Action Recognition
Authors: Zhang Jiachao , Tong Ying , Jiao Liangbao
Published: January 2023

Action recognition is a challenging task of modeling both spatial and temporal context. Numerous works focus on architectures modality and successfully make worthy progress on this task. While due to the redundancy in time and the limit of computation resources, several works focus on the efficiency study like frame sampling, some for untrimmed videos, and some for trimmed videos. With the intent of improving the effectiveness of action recognition, we propose a novel Computational Spatiotemporal Selector (CSS) to refine and reinforce the key frames with discriminative information in video. Specifically, CSS includes two modules: Temporal Adaptive Sampling (TAS) module and Spatial Frame Resolution (SFR) module. The former can refine the key frames in the temporal space for capturing the key motion information, while the latter can further zoom out some refined frames in the spatial space for eliminating the discrimination-irrelevant structural information. The proposed CSS is flexible to be embedded into most representative action recognition models. Experiments on two challenging action recognition benchmarks, i.e., ActivityNet1.3 and UCF101, show that the proposed CSS improves the performance over most existing models, not only on trimmed videos but also untrimmed videos.
Modeling, Simulation and Digital Control of Full-Bridge LLC Resonant Converter
Authors: Cao Xuanwei , Wu Kaiyuan , Wang Yifei , Yin Tong , Zhang Tao
Published: January 2023

In order to demonstrate the performance of an LLC resonant converter with digital control, in this paper, we analyze the operating principles of the full-bridge LLC resonant converter. According to the operating characteristics of the full-bridge LLC resonant converter at different frequency bands, a simulation model was established using MATLAB Simulink software, and simulations were carried out. The digital control system of full-bridge LLC resonant converter was then designed based on the STM32 microcontroller. The simulation and experimental results demonstrated that the LLC resonant main circuit was always operating within the zero voltage switching (ZVS) state of the primary MOSFET and the zero current switching (ZCS) state of the secondary rectifier diode, while soft-switching was achieved in the full load range. The digital control of the full-bridge LLC resonant converter was ideally realized based on STM32. The input disturbance load test result validated the closed-loop concept of the proposed LLC resonant converter. The step response results in both simulation and load tests show that the power supply system has the desired dynamic response characteristics with low overshoot, short rise time and settling time, while the load step response results in both tests showed that the system has a strong anti-interference ability and robustness.
New CMOS Linear Transconductors and their Applications
Authors: Rai Manish , Senani Raj , Singh Abdhesh Kumar
Published: January 2023

The object of this paper is to introduce a new CMOS differential input single output (DISO) transconductor (realizable from eight MOSFETs operating in saturation) and a dual input dual output (DIDO) transconductor (realizable with 16 MOSFETs operating in saturation) and their applications in realizing grounded/ floating, positive/ negative, electronically controllable resistors and inductors. The workability of all the prepositions has been demostrated by SPICE simulations using TSMC 0.18[Formula: see text] m CMOS technology parameters. The paper, thus, adds a number of useful electronically-controllable circuits to the existing repertoire of CMOS analog circuits for signal processing.
Dynamics of a predator–prey system with sublethal effects of pesticides on pests and natural enemies
Authors: Dai Xiangjun , Jiao Jianjun , Quan Qi , Zhou Airen
Published: January 2023

Considering the influence of sublethal concentration of pesticides on pests and natural enemies, we propose a pest-management model with impulsive effect on chemical control and biological control strategies–periodic spraying pesticide and releasing predatory natural enemies. By using the Floquet theory and the comparison theorem of impulsive differential equations, a sufficient condition for the global asymptotic stability of the pest-eradication periodic solution is obtained. The persistence of the system is further studied, and a sufficient condition for the persistence of the system is obtained. Finally, some numerical simulations are shown to verify our theoretical works. Our works indicate that the sublethal effects of insecticides and the release of predatory natural enemies play significant roles in pest control in agricultural production.
Bifurcation analysis of a diffusive predator–prey model with hyperbolic mortality and prey-taxis
Authors: Li Yan , Lv Zhiyi , Zhang Fengrong , Hao Hui
Published: January 2023

In this paper, we study a diffusive predator–prey model with hyperbolic mortality and prey-taxis under homogeneous Neumann boundary condition. We first analyze the influence of prey-taxis on the local stability of constant equilibria. It turns out that prey-taxis has influence on the stability of the unique positive constant equilibrium, but has no influence on the stability of the trivial equilibrium and the semi-trivial equilibrium. We then derive Hopf bifurcation and steady state bifurcation related to prey-taxis, which imply that the prey-taxis plays an important role in the dynamics.
Preparation and characterization of Si-doped LaAlO3 solid electrolyte for IT-SOFCs
Authors: Yang Jie , Zhang Qing , Dong Li , Li Hongming , Tian Changan
Published: January 2023

In this paper, the nonmetallic Si[Formula: see text]-doped modified lanthanum aluminate (LaAlO3) solid electrolyte for the first time, which has particular scientific significance, and LaAl[Formula: see text][Formula: see text]Si[Formula: see text]O[Formula: see text][Formula: see text] material was successfully prepared by the sol–gel method. LaAl[Formula: see text]Si[Formula: see text]O[Formula: see text] is a ceramic material sintered at 1100[Formula: see text]C for 2 h in an air atmosphere. The surface microstructure and electrochemical properties of the samples were tested by multiple characterization methods. The results show that the LaAl[Formula: see text]Si[Formula: see text]O[Formula: see text] has good sintering activity, high crystallinity, and a grain radius of approximately 25 nm. At 800[Formula: see text]C, the ionic conductivity is above 1.05E− 04 S⋅cm[Formula: see text] and the activation energy is lower than 1.19 eV.
Communication to Reduce Dependency and Enhance Empowerment Using ‘New’ Media: Evidence from Practice in UK Flood Risk Areas
Authors: Ball Tom , Nash-Williams Grace
Published: January 2023

Sharing of risk knowledge for extreme events is taking place against a backdrop of changing societal communication patterns, in which the flow of information is increasingly multi-directional, within and between individuals, wider communities and a variety of authorities using online media. We present qualitative findings from the CASCADE knowledge exchange project and a case study, from a flood risk area, on the role of social networks using such ‘new’ media as engagement tools in building resilience to flooding. The data emerged from a workshop held in 2018, together with a study into changing communication practice in the Thames Valley near Windsor, UK. It was found that engagement is occurring both during events, as an emergency management tool, and between events, often linked to strategic management such as flood defense and related planning. The qualitative findings were analyzed to investigate whether knowledge and information sharing in emergencies may lead to co-operative sharing between emergencies. According to evidence from workshop discussions across the seminars, and empirical evidence from the flood risk zone, social networks formed and/or enhanced using new media can help promote consensus but also have the potential to accentuate distrust and divide managers and the community at risk. Relevant factors were the nature of the risk faced, nature of event-related protection activity, whether extreme weather events were occurring or had occurred in the recent past, and sociocultural aspects such as the degree of general engagement of civil society, linked to location. There is a possibility that new media may thus reinforce existing power structures, including acknowledged paternalistic attitudes by management authorities and pre-conceived ideas from at-risk communities. In terms of the contribution that social media can make toward the goal of social learning for resilience, the specific role of online social media as a communication tool continues to evolve. It was noted from the workshop that there is a potential for producers of information to act also as consumers (the ‘prosumer effect’), but gaining benefit from this trend requires some changes to existing interaction patterns within and between risk management authorities and communities. More investment may be required in forms of engagement that build relationships of trust, using ‘traditional’ (face-to-face) approaches.
Arabic Span Extraction-based Reading Comprehension Benchmark (ASER) and Neural Baseline Models
Authors: Biltawi Mariam M. , Awajan Arafat , Tedmori Sara
Published: January 2023

Machine reading comprehension (MRC) requires machines to read and answer questions about a given text. This can be achieved through either predicting answers or extracting them. Extracting answers from text involves predicting the first and last index of the answer span within the paragraph. Training machines to answer questions requires datasets that are created for such a purpose. The lack of availability of benchmarking datasets for the Arabic language has hindered research into machine reading comprehension from Arabic text. The aim of this article is to propose an Arabic Span-Extraction-based Reading Comprehension Benchmark (ASER) and complement it with neural baseline models for performance evaluations. Detailed steps are depicted for building and evaluating ASER, which is an Arabic dataset created manually for the task of machine reading comprehension. It contains 10,000 records from different domains and is divided into training and testing sets. The results of ASER evaluation led to the conclusion that it is a challenging benchmark since the answers have varying lengths and human performance resulted in an exact match of 42%. On the other hand, two main baseline models were the focus of ASER experimentation: the sequence-to-sequence (Seq2Seq) model with different neural networks and the bidirectional attention flow (BIDAF) model. These experiments were implemented using different embeddings, and the results showed an exact match with lower values than human performance.
Multi-Stage Machine Learning Model for Hierarchical Tie Valence Prediction
Authors: Singh Karandeep , Lee Seungeon , Labianca Giuseppe (Joe) , Fagan Jesse Michael , Cha Meeyoung
Published: January 2023

Individuals interacting in organizational settings involving varying levels of formal hierarchy naturally form a complex network of social ties having different tie valences (e.g., positive and negative connections). Social ties critically affect employees’ satisfaction, behaviors, cognition, and outcomes—yet identifying them solely through survey data is challenging because of the large size of some organizations or the often hidden nature of these ties and their valences. We present a novel deep learning model encompassing NLP and graph neural network techniques that identifies positive and negative ties in a hierarchical network. The proposed model uses human resource attributes as node information and web-logged work conversation data as link information. Our findings suggest that the presence of conversation data improves the tie valence classification by 8.91% compared to employing user attributes alone. This gain came from accurately distinguishing positive ties, particularly for male, non-minority, and older employee groups. We also show a substantial difference in conversation patterns for positive and negative ties with positive ties being associated with more messages exchanged on weekends, and lower use of words related to anger and sadness. These findings have broad implications for facilitating collaboration and managing conflict within organizational and other social networks.
LSR: Energy-Efficient Multi-Modulation Communication for Inhomogeneous Wireless IoT Networks
Authors: Trüb Roman , Da Forno Reto , Biri Andreas , Beutel Jan , Thiele Lothar
Published: January 2023

In many real-world wireless IoT networks, the application dictates the location of the nodes and therefore the link characteristics are inhomogeneous. Furthermore, nodes may in many scenarios only communicate with the Internet-attached gateway via multiple hops. If an energy-efficient short-range modulation scheme is used, nodes that are reachable only via high-path-loss links cannot communicate. Using a more energy-demanding long-range modulation allows connecting more nodes but would be inefficient for nodes that are easily reachable via low-path-loss links. Combining multiple modulations is challenging, as low-power radios usually only support the use of a single modulation at a time. In this article, we present the Long-Short-Range (LSR) protocol which supports low-power multi-hop communication using multiple modulations and is suited for networks with inhomogeneous link characteristics. To reduce the inherent redundancy of long-range modulations, we present a method to determine the connectivity graph of the network during regular data communication without adding significant overhead. In simulations, we show that LSR allows for reducing power consumption significantly for many scenarios when compared to a state-of-the-art multi-hop communication protocol using a single long-range modulation. We demonstrate the applicability of LSR with an implementation on real hardware and a testbed with long-range links.
Coding From Binding? Molecular Interactions at the Heart of Translation
Authors: Zagrovic Bojan , Adlhart Marlene , Kapral Thomas H.
Published: January 2023

The mechanism and the evolution of DNA replication and transcription, the key elements of the central dogma of biology, are fundamentally well explained by the physicochemical complementarity between strands of nucleic acids. However, the determinants that have shaped the third part of the dogma—the process of biological translation and the universal genetic code—remain unclear. We review and seek parallels between different proposals that view the evolution of translation through the prism of weak, noncovalent interactions between biological macromolecules. In particular, we focus on a recent proposal that there exists a hitherto unrecognized complementarity at the heart of biology, that between messenger RNA coding regions and the proteins that they encode, especially if the two are unstructured. Reflecting the idea that the genetic code evolved from intrinsic binding propensities between nucleotides and amino acids, this proposal promises to forge a link between the distant past and the present of biological systems.
Ball-and-Chain Inactivation in Potassium Channels
Authors: Sukomon Nattakan , Fan Chen , Nimigean Crina M.
Published: January 2023

Carefully orchestrated opening and closing of ion channels control the diffusion of ions across cell membranes, generating the electrical signals required for fast transmission of information throughout the nervous system. Inactivation is a parsimonious means for channels to restrict ion conduction without the need to remove the activating stimulus. Voltage-gated channel inactivation plays crucial physiological roles, such as controlling action potential duration and firing frequency in neurons. The ball-and-chain moniker applies to a type of inactivation proposed first for sodium channels and later shown to be a universal mechanism. Still, structural evidence for this mechanism remained elusive until recently. We review the ball-and-chain inactivation research starting from its introduction as a crucial component of sodium conductance during electrical signaling in the classical Hodgkin and Huxley studies, through the discovery of its simple intuitive mechanism in potassium channels during the molecular cloning era, to the eventual elucidation of a potassium channel structure in a ball-and-chain inactivated state.
Instructive Surprises in the Hydrological Functioning of Landscapes
Authors: Kirchner James W. , Benettin Paolo , van Meerveld Ilja
Published: January 2023

Landscapes receive water from precipitation and then transport, store, mix, and release it, both downward to streams and upward to vegetation. How they do this shapes floods, droughts, biogeochemical cycles, contaminant transport, and the health of terrestrial and aquatic ecosystems. Because many of the key processes occur invisibly in the subsurface, our conceptualization of them has often relied heavily on physical intuition. In recent decades, however, much of this intuition has been overthrown by field observations and emerging measurement methods, particularly involving isotopic tracers. Here we summarize key surprises that have transformed our understanding of hydrological processes at the scale of hillslopes and drainage basins. These surprises have forced a shift in perspective from process conceptualizations that are relatively static, homogeneous, linear, and stationary to ones that are predominantly dynamic, heterogeneous, nonlinear, and nonstationary.
▪Surprising observations and novel measurements are transforming our understanding of the hydrological functioning of landscapes.▪Even during storm peaks, streamflow is composed mostly of water that has been stored in the landscape for weeks, months, or years.▪Streamflow and tree water uptake often originate from different subsurface storages and from different seasons’ precipitation.▪Stream networks dynamically extend and retract as the landscape wets and dries, and many stream reaches lose flow into underlying aquifers.
Who’s talking to whom: microbiome-enteric nervous system interactions in early life
Authors: Ganz Julia , Ratcliffe Elyanne M.
Published: January 2023

The enteric nervous system (ENS) is the intrinsic nervous system of the gastrointestinal tract (GI) and regulates important GI functions, including motility, nutrient uptake, and immune response. The development of the ENS begins during early organogenesis and continues to develop once feeding begins, with ongoing plasticity into adulthood. There has been increasing recognition that the intestinal microbiota and ENS interact during critical periods, with implications for normal development and potential disease pathogenesis. In this review, we focus on insights from mouse and zebrafish model systems to compare and contrast how each model can serve in elucidating the bidirectional communication between the ENS and the microbiome. At the end of this review, we further outline implications for human disease and highlight research innovations that can lead the field forward.
IGFBP3 Enhances Treatment Outcome and Predicts Favorable Prognosis in ABC-DLBCL
Authors: Li Han-Bing , Wang Di , Zhang Yue , Shen Di , Che Yi-Qun
Published: January 2023

Chemoresistance is a key obstacle in the clinical treatment and management of activated B cell-like diffuse large B-cell lymphoma (ABC-DLBCL), which leads to the poor prognosis of patients. Exploring novel biomarkers to early warn drug resistance and ameliorate the patients’ outcome in ABC-DLBCL is urgent and crucial. Previously, we found that insulin-like growth factor-binding protein 3 (IGFBP3) was remarkably associated with immunochemotherapy treatment response through microarray screening. Based on a retrospective cohort (n = 160) and a GEO cohort (n = 292), here we determined the positive expression rate of IGFBP3 and analyzed the role of IGFBP3 in treatment response and prognostics in ABC-DLBCL. The results demonstrated that the complete response (CR) rate of R-CHOP treatment was higher in ABC-DLBCL with IGFBP3 positive expression than those with IGFBP3 negative expression (42.0% vs 26.4%), and IGFBP3 positive expression in ABC-DLBCL was significantly correlated with enhanced therapeutic response (P = 0.037). High level of IGFBP3 was negatively correlated with tumorigenesis and development and predicted favorable survival time in ABC-DLBCL. In conclusion, IGFBP3 may be utilized as a promising biomarker for prognosis evaluation and a potential therapy target in ABC-DLBCL patients.
Conditional TransGAN‐Based Data Augmentation for PCB Electronic Component Inspection
Authors: Wang Chenglong , Huang Guanghan , Huang Zhiyuan , He Weiming
Published: January 2023

Automatic recognition and positioning of electronic components on PCBs can enhance quality inspection efficiency for electronic products during manufacturing. Efficient PCB inspection requires identification and classification of PCB components as well as defects for better quality assurance. The small size of the electronic component and PCB defect targets means that there are fewer feature areas for the neural network to detect, and the complex grain backgrounds of both datasets can cause significant interference, making the target detection task challenging. Meanwhile, the detection performance of deep learning models is significantly impacted due to the lack of samples. In this paper, we propose conditional TransGAN (cTransGAN), a generative model for data augmentation, which enhances the quantity and diversity of the original training set and further improves the accuracy of PCB electronic component recognition. The design of cTransGAN brings together the merits of both conditional GAN and TransGAN, allowing a trained model to generate high‐quality synthetic images conditioned on the class embeddings. To validate the proposed method, we conduct extensive experiments on two datasets, including a self‐developed dataset for PCB component detection and an existing dataset for PCB defect detection. Also, we have evaluated three existing object detection algorithms, including Faster R‐CNN ResNet101, YOLO V3 DarkNet‐53, and SCNet ResNet101, and each is validated under four experimental settings to form an ablation study. Results demonstrate that the proposed cTransGAN can effectively enhance the quality and diversity of the training set, leading to superior performance on both tasks. We have open‐sourced the project to facilitate further studies.
Alleviating Students’ Naive Theory on Newton’s Laws of Motion through Problem Optimization and Scaffolding Discussion
Authors: Suwasono Purbo , Sutopo Sutopo , Handayanto Supriyono Koes , Mufti Nandang , Sunaryono Sunaryono , Taufiq Ahmad
Published: January 2023

The purpose of this study is to find out what types of problems and scaffolding should be set in problem-based learning to alleviate students’ naive theories. This study employed a sample of three classes with a problem-based learning process using three variations of problems and scaffolding. The findings suggested that there were significant differences among the three groups of students. The highest reduction in students’ naive theories occurred in classes that used open problems equipped with discussion scaffolding. Based on the interpretation of the video results of student discussions and the average posttest class using open problems equipped with discussion scaffolding, students’ naive theories were mostly reduced. Problems in problem-based learning are open-ended, which encourage students to use their naive theories to solve problems. With the scaffolding that has been designed, students’ character grows as scientists. Based on the interview results, the class that used closed problems equipped with scaffolding tended to use their notes while in high school, which he was not clear about, except for doing exam questions. However, in a classroom that uses open problems equipped with discussion scaffolding and in classes that use open problems without discussion scaffolding, it is stated that students like to solve problems in their real lives. Thus, open problems in problem-based learning are the key to successful learning, whereas discussion scaffolding strengthens the implementation of these open problems.
Optimal Operation of Energy Hub Systems under Resiliency Response Options
Authors: Khodadadi Ali , Abedinzadeh Taher , Alipour Hasan , Pouladi Jaber
Published: January 2023

The economic and resilient operation of power systems has always been one of the main priorities of energy systems. In spite of improvements in various fields of energy systems, especially power systems, the issue of resilience has become more important. For this purpose, this paper proposes a multiobjective optimization model to improve the economic performance of energy hub systems and improve the resilience of electrical consumers. Also, consumer welfare, which is a function of the energy not supplied index, is maximized over a 24-hour period by considering extreme weather conditions. The ε-constraint method is applied to solve the proposed model by transforming the multiobjective optimization problem into several single-objective optimization problems. The max-min fuzzy method is also used to select the optimal solution among the Pareto solutions. A sample hub system is made up of electrical, thermal, and gas loads, electrical and thermal energy sources, and storage systems employed as a test system. A group of actions is applied to improve the resilience of the system, which may be affected by outages caused by storms under the resilience response program (RRP). The results proved the efficiency of the proposed RRP in improving economics and resilience.
Resilience among Parents and Children with Autism Spectrum Disorder
Authors: Ghanouni Parisa , Eves Laura
Published: January 2023

Resilience plays a pivotal role to offset stress among families of children with autism spectrum disorder (ASD). Although the majority of previous studies investigated resilience in parents, it is unclear what factors contribute to resilience in children. Thus, we aimed to explore resilience experienced by parents of children with ASD and how it affects children’s resilience. We invited 50 parents of a child with ASD, 13 years old or younger, across various Canadian provinces in an online survey. Parental resilience was positively associated with household income and negatively associated with parental stress. Resilience in children with ASD was positively associated with their social participation at home and community. Findings indicate a relationship between resilience in children with ASD and their participation, suggesting new ways to increase resilience in children with ASD by enhancing their participation.
Elman Neural Network‐Based Direct Lift Automatic Carrier Landing Nonsingular Terminal Sliding Mode Fault‐Tolerant Control System Design
Authors: Wu Qilong , Zhu Qidan , Han Shuai
Published: January 2023

The purpose of this paper is to develop the control system using the Elman neural network (ENN) and nonsingular terminal sliding mode control (NTSMC) to improve the automatic landing capability of carrier‐based aircraft based on direct lift control (DLC) when subjected to carrier air‐wake disturbance and actuator failure. First, the carrier‐based aircraft landing model is derived. Then, the NTSMC is proposed to ensure the system’s robustness and achieve accurate trajectory tracking performance in a finite time. Due to the inclusion of nonsingularity in NTSMC, the steady‐state response of the control system can be effectively improved. In addition, the ENN is derived using an adaptive learning algorithm to approximate the actuator faults and system uncertainties. To further ensure the accurate tracking of the ideal glide path by the carrier‐based aircraft, the NTSMC system using an ENN estimator is proposed. Finally, this method is tested by adding different types of actuator failures. The simulation results show that the designed longitudinal fault‐tolerant carrier landing system has strong robustness and fault‐tolerant ability and improves the accuracy of carrier‐based aircraft landing trajectory tracking.
Research on Risk Identification of Coal and Gas Outburst Based on PSO‐CSA
Authors: Peng Ji , Shiliang Shi , Yi Lu , He Li
Published: January 2023

Aiming at the identification of coal and gas outburst risk, using the advantages of the clone selection algorithm (CSA), such as self‐adaptation and robustness, and the characteristics of fast convergence of particle swarm optimization (PSO) algorithm, the complex decoding problem, and mutation process brought by CSA binary coding are used. It is difficult to control the problem. Using PSO optimization, the problem of abnormal detection and identification in coal and gas outburst monitoring is developed and studied, and a CSA coal and gas outburst risk anomaly detection and identification model based on PSO optimization variation is established. The model uses the coal and gas outburst index data as a collection of antigen‐stimulated antibodies to achieve abnormal detection and identification of measured data. With the help of the measured data, the verification results show that the model can effectively detect and identify the risk of coal and gas outburst, and the identification results are consistent with the risk of coal and gas outburst in the field. It can be used as an effective risk identification model to guide coal mining work.
Investigation of the Active Compounds and Important Pathways of Huaiqihuang Granule for the Treatment of Immune Thrombocytopenia Using Network Pharmacology and Molecular Docking
Authors: Chen Wenwen , Kan Hongtao , Qin Min , Yang Jia , Tao Wanjun , XiaoYang
Published: January 2023

Aim and Objective. This study was designed to explore the active compounds and significant pathways of Huaiqihuang Granule (HQHG) for treating immune thrombocytopenia (ITP) using LC‐MS/MS analysis, molecular docking, and network pharmacology. Materials and Methods. Compounds of HQHG were scanned by LC‐MS/MS, and the target profiles of compounds were identified based on SwissTarget Prediction. ITP target proteins were collected from various databases. Then, KEGG pathway and GO enrichment analyses were performed to explore the signaling pathways related to HQHG for ITP. The PPI and drug‐herbs‐compounds‐targets‐pathways network were constructed using Cytoscape 3.7.2. Finally, Discovery studio software was used to confirm the key targets and active compounds from HQHG. Results. A total of 187 interacting targets of 19 potentially active compounds in HQHG and 3837 ITP‐related targets were collected. Then, 187 intersection targets were obtained. A total of 20 key targets including EGFR, CASP3, TNF, STAT3, and ERBB2 were identified through PPI network analysis. These targets were mainly focused on the biological processes of positive regulation of protein phosphorylation, cellular response to organonitrogen compound, and cellular response to nitrogen compound. 20 possible pathways of HQHG in the treatment of ITP were identified through KEGG enrichment. EGFR, CASP3, TNF, and STAT3 are the four most important target proteins, while adenosine, caffeic acid, ferulic acid, quercetin‐3β‐D‐glucoside, rutin, scopoletin, and tianshic acid are the most important active compounds, which were validated by molecular docking simulation. Conclusion. This study demonstrated that HQHG produced relief effects against ITP by regulating multitargets and multipathways with multicompounds. And the combined data provide novel insight of drug developing for ITP.
Rhinacanthin C Ameliorates Insulin Resistance and Lipid Accumulation in NAFLD Mice via the AMPK/SIRT1 and SREBP‐1c/FAS/ACC Signaling Pathways
Authors: Gong Zhiqiang , Han Sha , Li Chenlin , Meng Tianxiu , Huo Yu , Liu Xianfu , Huang Yanhong , Yang Lifang
Published: January 2023

Rhinacanthin C (RC) is a naphthoquinone ester with an anti‐inflammatory activity extracted from Rhinacanthus nasutus (L.) Kurz (Rn). It has been proven to improve hyperglycemia and hyperlipidemia, but the prevention and mechanism of RC in nonalcoholic fatty liver disease (NAFLD) are not clear. In the current study, we first extracted RC from Rn using ethyl acetate and identified it by HPLC, MS, and NMR. At the same time, molecular docking analysis of RC with AMPK and SREBP‐1c was performed using AutoDock software. In addition, the mouse model of NAFLD was induced by a high‐fat diet in vivo, and low, medium, and high concentrations of RC were used for intervention. The results showed that RC significantly reduced the body mass and liver body coefficient of NAFLD mice, inhibited liver inflammation and fat accumulation, and improved insulin resistance. Further studies showed that RC significantly reduced the levels of serum leptin and resistin, upregulated the expression levels of adiponectin and adiponectin receptor in the liver, and inhibited the expression levels of MCP‐1, TNF‐α, and IL‐6. In terms of mechanism, RC upregulates the expression of p‐AMPK and SIRT1 and downregulates the expression of p‐p65, SREBP‐1c, Fas, Acc‐α, PPAR‐γ, and SCD1. These studies suggest that RC improves insulin resistance and lipid accumulation in NAFLD by activating the AMPK/SIRT1 and SREBP‐1c/Fas/ACC pathways, respectively.
Osteoblast-Specific Overexpression of Nucleolar Protein NO66/RIOX1 in Mouse Embryos Leads to Osteoporosis in Adult Mice
Authors: Chen Qin , Sinha Krishna M. , de Crombrugghe Benoit , Krahe Ralf
Published: January 2023

In previous study, we showed that nucleolar protein 66 (NO66) is a chromatin modifier and negatively regulates Osterix activity as well as mesenchymal progenitor differentiation. Genetic ablation of the NO66 (RIOX1) gene in cells of the Prx1-expressing mesenchymal lineage leads to acceleration of osteochondrogenic differentiation and a larger skeleton in adult mice, whereas mesenchyme-specific overexpression of NO66 inhibits osteochondrogenesis resulting in dwarfism and osteopenia. However, the impact of NO66 overexpression in cells of the osteoblast lineage in vivo remains largely undefined. Here, we generated osteoblast-specific transgenic mice overexpressing a FLAG-tagged NO66 transgene driven by the 2.3 kB alpha-1type I collagen (Col1a1) promoter. We found that overexpression of NO66 in cells of the osteoblast lineage did not cause overt defects in developmental bones but led to osteoporosis in the long bones of adult mice. This includes decreased bone volume (BV), bone volume density (bone volume/total volume, BV/TV), and bone mineral density (BMD) in cancellous compartment of long bones, along with the accumulation of fatty droplets in bone marrow. Ex vivo culture of the bone marrow mesenchymal stem/stromal cells (BMSCs) from adult Col1a1-NO66 transgenic mice showed an increase in adipogenesis and a decrease in osteogenesis. Taken together, these data demonstrate a crucial role for NO66 in adult bone formation and homeostasis. Our Col1a1-NO66 transgenic mice provide a novel animal model for the mechanistic and therapeutic study of NO66 in osteoporosis.
Undaria pinnatifida (Wakame) Intake Ameliorates High-Fat Diet-Induced Glucose Intolerance via Promoting GLUT4 Expression and Membrane Translocation in Muscle
Authors: Imai Motoki , Kawakami Fumitaka , Chiba Mutsumi , Kanzaki Makoto , Maruyama Hiroko
Published: January 2023

Type 2 diabetes mellitus (T2DM), a lifestyle-related disease, is developed due to eating habits and decreased physical activity. Diabetes also increases the risk of cancer and major neurodegenerative diseases; controlling the onset of diabetes helps prevent various illnesses. Eating seaweed, such as Undaria pinnatifida (wakame), is a part of the Asian food culture. Therefore, we analyzed the antidiabetic effect of wakame intake using the high-fat diet-induced diabetes mouse model. Furthermore, we analyzed the effect of wakame extract on the cell membrane translocation of glucose transporter-4 (GLUT4) and activation of insulin signal molecules, such as AKT and AMPK, in insulin-sensitive tissues. Differentiated C2C12 cells were incubated with wakame components. The membrane translocation of GLUT4 and phosphorylation of AKT and AMPK were investigated with immunofluorescence staining and Western blotting, respectively. Also, male C57BL/6J mice were fed the normal diet (ND), high-fat diet (HFD), ND with 1% wakame powder (ND + W), or HFD with 1% wakame powder (HFD + W). We evaluated the effect of wakame intake on high-fat diet-induced glucose intolerance using an oral glucose tolerance test. Moreover, we analyzed insulin signaling molecules, such as GLUT4, AKT, and AMPK, in muscle using Western blotting. GLUT4 membrane translocation was promoted by wakame components. Also, GLUT4 levels and AKT and AMPK phosphorylation were significantly elevated by wakame components in C2C12 cells. In addition, the area under the curve (AUC) of the HFD + W group was significantly smaller than that of the HFD group. Furthermore, the level of GLUT4 in the muscle was increased in the wakame intake group. This study revealed that various wakame components exerted antidiabetic effects on the mice on a high-fat diet by promoting glucose uptake in the skeletal muscle, enhancing GLUT4 levels, and activating AKT and AMPK.
Case Studies for Overcoming Challenges in Using Big Data in Cancer
Authors: Sweeney Shawn M. , Hamadeh Hisham K. , Abrams Natalie , Adam Stacey J. , Brenner Sara , Connors Dana E. , Davis Gerard J. , Fiore Louis D. , Gawel Susan H. , Grossman Robert L. , Hanlon Sean E. , Hsu Karl , Kelloff Gary J. , Kirsch Ilan R. , Louv Bill , McGraw Deven , Meng Frank , Milgram Daniel , Miller Robert S. , Morgan Emily , Mukundan Lata , O'Brien Thomas , Robbins Paul , Rubin Eric H. , Rubinstein Wendy S. , Salmi Liz , Schaller Teilo H. , Shi George , Sigman Caroline C. , Srivastava Sudhir
Published: January 2023

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.