bluuebunny/crossref_metadata_2025_split huggingface dataset:
Species-specific microRNA discovery and target prediction in the soybean cyst nematode
Authors: Ajila Victoria , Colley Laura , Ste-Croix Dave T. , Nissan Nour , Cober Elroy R. , Mimee Benjamin , Samanfar Bahram , Green James R. 
Published: October 2023

AbstractThe soybean cyst nematode (SCN) is a devastating pathogen for economic and food security considerations. Although the SCN genome has recently been sequenced, the presence of any miRNA has not been systematically explored and reported. This paper describes the development of a species-specific SCN miRNA discovery pipeline and its application to the SCN genome. Experiments on well-documented model nematodes (Caenorhabditis elegans and Pristionchus pacificus) are used to tune the pipeline’s hyperparameters and confirm its recall and precision. Application to the SCN genome identifies 3342 high-confidence putative SCN miRNA. Prediction specificity within SCN is confirmed by applying the pipeline to RNA hairpins from known exonic regions of the SCN genome (i.e., sequences known to not be miRNA). Prediction recall is confirmed by building a positive control set of SCN miRNA, based on a limited deep sequencing experiment. Interestingly, a number of novel miRNA are predicted to be encoded within the intronic regions of effector genes, known to be involved in SCN parasitism, suggesting that these miRNA may also be involved in the infection process or virulence. Beyond miRNA discovery, gene targets within SCN are predicted for all high-confidence novel miRNA using a miRNA:mRNA target prediction system. Lastly, cross-kingdom miRNA targeting is investigated, where putative soybean mRNA targets are identified for novel SCN miRNA. All predicted miRNA and gene targets are made available in appendix and through a Borealis DataVerse open repository (https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/30DEXA).
A novel fractional-order dead-time compensating controller for the wireless networks
Authors: Devan P. Arun Mozhi , Ibrahim Rosdiazli , Omar Madiah , Bingi Kishore , Nagarajapandian M. , Abdulrab Hakim 
Published: October 2023

AbstractWireless technology is becoming increasingly critical in industrial environments in recent years, and the popular wireless standards are WirelessHART, ZigBee, WLAN and ISA100.11a, commonly used in closed-loop systems. However, wireless networks in closed-loop control experience packet loss or drops, system delay and data threats, leading to process instability and catastrophic system failure. To prevent such issues, it is necessary to implement dead-time compensation control. Traditional techniques like model predictive and predictive PI controllers are frequently employed. However, these methods’ performance is sluggish in wireless networks, with processes having long dead times and set-point variations, potentially affecting network and process performance. Therefore, this paper proposes a fractional calculus-based predictive PI compensator for wired and wireless networks in the process control industries. The proposed technique has been simulated and evaluated on industrial process models, including pressure, flow, and temperature, where measurement and control are carried out wirelessly. The wireless network’s performance has been evaluated based on packet loss, reduced throughput, and increased system latency. The proposed compensator outperformed traditional methods, demonstrating superior set-point tracking, disturbance rejection, and delay compensation characteristics in the performance evaluations of the first, second, and third-order systems. Overall, the findings indicate that the proposed compensator enhances wireless networks’ performance in the process control industry and improves system stability and reliability by reducing almost half of the overshoot and settling an average of 8.3927% faster than the conventional techniques in most of the systems.
Human perception of spatial frequency varies with stimulus orientation and location in the visual field
Authors: Kirsch Wladimir , Kunde Wilfried 
Published: October 2023

AbstractNeuroanatomical variations across the visual field of human observers go along with corresponding variations of the perceived coarseness of visual stimuli. Here we show that horizontal gratings are perceived as having lower spatial frequency than vertical gratings when occurring along the horizontal meridian of the visual field, whereas gratings occurring along the vertical meridian show the exact opposite effect. This finding indicates a new peculiarity of processes operating along the cardinal axes of the visual field.
Evidence of elevated heavy metals concentrations in wild and farmed sugar kelp (Saccharina latissima) in New England
Authors: Shaughnessy Brianna K. , Jackson Brian P. , Byrnes Jarrett E. K. 
Published: October 2023

AbstractSeaweed farming in the United States is gaining significant financial and political support due to prospects to sustainably expand domestic economies with environmentally friendly products. Several networks are seeking appropriate synthesis of available science to both inform policy and substantiate the sector’s sustainability claims. Significant knowledge gaps remain regarding seaweed-specific food hazards and their mitigation; a resource-intensive challenge that can inhibit sustainable policies. This is particularly concerning for rapidly expanding Saccharina latissima (sugar kelp) crops, a brown seaweed that is known to accumulate heavy metals linked to food hazards. Here, we present baseline information about concentrations of arsenic, cadmium, lead, and mercury, in both wild and farmed sugar kelp from the New England region. We interpret our findings based on proximity to potential sources of contamination, location on blade, and available heavy metals standards. Contrary to our expectations, high concentrations were widespread in both wild and farmed populations, regardless of proximity to contamination. We find, like others, that cadmium and arsenic consistently reach levels of regulatory concern, and that dried seaweeds could harbor higher concentrations compared to raw products. We also share unique findings that suggest some toxins concentrate at the base of kelp blades. Our results are one step towards aggregating vital data for the region to expand its seaweed farming footprint.
Females exhibit smaller volumes of brain activation and lower inter-subject variability during motor tasks
Authors: Andrushko Justin W. , Rinat Shie , Kirby Eric D. , Dahlby Julia , Ekstrand Chelsea , Boyd Lara A. 
Published: October 2023

AbstractPast work has shown that brain structure and function differ between females and males. Males have larger cortical and sub-cortical volume and surface area (both total and subregional), while females have greater cortical thickness in most brain regions. Functional differences are also reported in the literature, yet to date little work has systematically considered whether patterns of brain activity indexed with functional magnetic resonance imaging (fMRI) differ between females and males. The current study sought to remediate this issue by employing task-based whole brain motor mapping analyses using an openly available dataset. We tested differences in patterns of functional brain activity associated with 12 voluntary movement patterns in females versus males. Results suggest that females exhibited smaller volumes of brain activation across all 12 movement tasks, and lower patterns of variability in 10 of the 12 movements. We also observed that females had greater cortical thickness, which is in alignment with previous analyses of structural differences. Overall, these findings provide a basis for considering biological sex in future fMRI research and provide a foundation of understanding differences in how neurological pathologies present in females vs males.
Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest
Authors: Cannet Arnaud , Simon-Chane Camille , Histace Aymeric , Akhoundi Mohammad , Romain Olivier , Souchaud Marc , Jacob Pierre , Sereno Darian , Gouagna Louis-Clément , Bousses Philippe , Mathieu-Daude Françoise , Sereno Denis 
Published: October 2023

AbstractHematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field application are required to strengthen efforts in the entomological survey of arthropods with medical interest. Large scales and proactive entomological surveys of Aedes mosquitoes need skilled technicians and/or costly technical equipment, further puzzled by the vast amount of named species. In this study, we developed an automatic classification system of Aedes species by taking advantage of the species-specific marker displayed by Wing Interferential Patterns. A database holding 494 photomicrographs of 24 Aedes spp. from which those documented with more than ten pictures have undergone a deep learning methodology to train a convolutional neural network and test its accuracy to classify samples at the genus, subgenus, and species taxonomic levels. We recorded an accuracy of 95% at the genus level and > 85% for two (Ochlerotatus and Stegomyia) out of three subgenera tested. Lastly, eight were accurately classified among the 10 Aedes sp. that have undergone a training process with an overall accuracy of > 70%. Altogether, these results demonstrate the potential of this methodology for Aedes species identification and will represent a tool for the future implementation of large-scale entomological surveys.
MCM10, a potential diagnostic, immunological, and prognostic biomarker in pan-cancer
Authors: Chen Dengwang , Zhong Na , Guo Zhanwen , Ji Qinglu , Dong Zixuan , Zheng Jishan , Ma Yunyan , Zhang Jidong , He Yuqi , Song Tao 
Published: October 2023

AbstractMicrochromosome maintenance (MCM) proteins are a number of nuclear proteins with significant roles in the development of cancer by influencing the process of cellular DNA replication. Of the MCM protein family, MCM10 is a crucial member that maintains the stability and extension of DNA replication forks during DNA replication and is significantly overexpressed in a variety of cancer tissues, regulating the biological behaviour of cancer cells. But little is understood about MCM10’s functional role and regulatory mechanisms in a range of malignancies. We investigate the impact of MCM10 in human cancers by analyzing data from databases like the Gene Expression Profiling Interaction Analysis (GEPIA2), Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), among others. Possible relationships between MCM10 and clinical staging, diagnosis, prognosis, Mutation burden (TMB), microsatellite instability (MSI), immunological checkpoints, DNA methylation, and tumor stemness were identified. The findings demonstrated that MCM10 expression was elevated in the majority of cancer types and was connected to tumor dryness, immunocytic infiltration, immunological checkpoints, TMB and MSI. Functional enrichment analysis in multiple tumors also identified possible pathways of MCM10 involvement in tumorigenesis. We also discovered promising MCM10-targeting chemotherapeutic drugs. In conclusion, MCM10 may be a desirable pan-cancer biomarker and offer fresh perspectives on cancer therapy.
Effects of combined training or moderate intensity continuous training during a 3-week multidisciplinary body weight reduction program on cardiorespiratory fitness, body composition, and substrate oxidation rate in adolescents with obesity
Authors: D’Alleva Mattia , Lazzer Stefano , Tringali Gabriella , De Micheli Roberta , Bondesan Adele , Abbruzzese Laura , Sartorio Alessandro 
Published: October 2023

AbstractThis study aimed to investigate the effects of combined training (COMB, a combination of moderate-intensity continuous training-MICT and high-intensity interval training-HIIT) vs. continuous MICT administered during a 3-week in-hospital body weight reduction program (BWRP) on body composition, physical capacities, and substrate oxidation in adolescents with obesity. The 3-week in-hospital BWRP entailed moderate energy restriction, nutritional education, psychological counseling, and two different protocols of physical exercise. Twenty-one male adolescents with obesity (mean age: 16.1 ± 1.5 years; mean body mass index [BMI] 37.8 ± 4.5 kg m−2) participated in this randomized control trial study (n:10 for COMB, n:11 MICT), attending ~ 30 training sessions. The COMB group performed 3 repetitions of 2 min at 95% of peak oxygen uptake (V′O2 peak) (e.g., HIIT ≤ 20%), followed by 30 min at 60% of V′O2 peak (e.g., MICT ≥ 80%). Body composition, V′O2 peak, basal metabolic rate (BMR), energy expenditure, and substrate oxidation rate were measured during the first week (W0) and at the end of three weeks of training (W3). The two training programs were equivalent in caloric expenditure. At W3, body mass (BM) and fat mass (FM) decreased significantly in both groups, although the decrease in BM was significantly greater in the MICT group than in the COMB group (BM: − 5.0 ± 1.2 vs. − 8.4 ± 1.5, P < 0.05; FM: − 4.3 ± 3.0 vs. − 4.2 ± 1.9 kg, P < 0.05). V′O2 peak increased only in the COMB by a mean of 0.28 ± 0.22 L min−1 (P < 0.05). The maximal fat oxidation rate (MFO) increased only in the COMB group by 0.04 ± 0.03 g min−1 (P < 0.05). COMB training represents a viable alternative to MICT for improving anthropometric characteristics, physical capacities, and MFO in adolescents with obesity during a 3-week in-hospital BWRP.
Microbiome variations among age classes and diets of captive Asian elephants (Elephas maximus) in Thailand using full-length 16S rRNA nanopore sequencing
Authors: Klinsawat Worata , Uthaipaisanwong Pichahpuk , Jenjaroenpun Piroon , Sripiboon Supaphen , Wongsurawat Thidathip , Kusonmano Kanthida 
Published: October 2023

AbstractAsian elephant (Elephas maximus) is the national symbol of Thailand and linked to Thai history and culture for centuries. The elephant welfare improvement is one of the major components to achieve sustainable captive management. Microbiome inhabiting digestive tracts have been shown with symbiotic relations to host health. This work provided high-resolution microbiome profiles of 32 captive elephants at a species level by utilizing full-length 16S rRNA gene nanopore sequencing. Eleven common uncultured bacterial species were found across elephants fed with solid food including uncultured bacterium Rikenellaceae RC9 gut group, Kiritimatiellae WCHB1-41, Phascolarctobacterium, Oscillospiraceae NK4A214 group, Christensenellaceae R-7 group, Oribacterium, Oscillospirales UCG-010, Lachnospiraceae, Bacteroidales F082, uncultured rumen Rikenellaceae RC9 gut group, and Lachnospiraceae AC2044 group. We observed microbiome shifts along the age classes of baby (0–2 years), juvenile (2–10 years), and adult (> 10 years). Interestingly, we found distinct microbiome profiles among adult elephants fed with a local palm, Caryota urens, as a supplement. Potential beneficial microbes have been revealed according to the age classes and feed diets. The retrieved microbiome data could be provided as good baseline microbial profiles for monitoring elephant health, suggesting further studies towards dietary selection suitable for each age class and the use of local supplementary diets.
Developing a risk prediction tool for lung cancer in Kent and Medway, England: cohort study using linked data
Authors: Howell David , Buttery Ross , Badrinath Padmanabhan , George Abraham , Hariprasad Rithvik , Vousden Ian , George Tina , Finnis Cathy 
Published: October 2023

Abstract
Background
Lung cancer has the poorest survival due to late diagnosis and there is no universal screening. Hence, early detection is crucial. Our objective was to develop a lung cancer risk prediction tool at a population level.

Methods
We used a large place-based linked data set from a local health system in southeast England which contained extensive information covering demographic, socioeconomic, lifestyle, health, and care service utilisation. We exploited the power of Machine Learning to derive risk scores using linear regression modelling. Tens of thousands of model runs were undertaken to identify attributes which predicted the risk of lung cancer.

Results
Initially, 16 attributes were identified. A final combination of seven attributes was chosen based on the number of cancers detected which formed the Kent & Medway lung cancer risk prediction tool. This was then compared with the criteria used in the wider Targeted Lung Health Checks programme. The prediction tool outperformed by detecting 822 cases compared to 581 by the lung check programme currently in operation.

Conclusion
We have demonstrated the useful application of Machine Learning in developing a risk score for lung cancer and discuss its clinical applicability.
Developing HSS iteration schemes for solving the quadratic matrix equation AX2+BX+C=0$AX^{2}+BX+C=0$
Authors: Erfanifar Raziyeh , Hajarian Masoud 
Published: October 2023

AbstractThe quadratic matrix equation (QME)
occurs in the branches such as the quadratic eigenvalue problems and quasi‐birth‐death processes. Also, the numerical solution of QMEs is an essential step in many computational methods for linear‐quadratic and robust control, filtering, controller order reduction, inner‐outer factorization, spectral factorization, and other applications. In this study, schemes are presented to solve the QME based on the Hermitian and skew‐Hermitian splitting (HSS). It is shown that the proposed schemes converge to the solutions of the QME. Finally, some examples are solved to discover the application of the schemes in comparison with Newton's method.
IoUNet++: Spatial cross‐layer interaction‐based bounding box regression for visual tracking
Authors: Wang Shilei , Han Yamin , Sun Baozhen , Ning Jifeng 
Published: October 2023

AbstractAccurate target prediction, especially bounding box estimation, is a key problem in visual tracking. Many recently proposed trackers adopt the refinement module called IoU predictor by designing a high‐level modulation vector to achieve bounding box estimation. However, due to the lack of spatial information that is important for precise box estimation, this simple one‐dimensional modulation vector has limited refinement representation capability. In this study, a novel IoU predictor (IoUNet++) is designed to achieve more accurate bounding box estimation by investigating spatial matching with a spatial cross‐layer interaction model. Rather than using a one‐dimensional modulation vector to generate representations of the candidate bounding box for overlap prediction, this paper first extracts and fuses multi‐level features of the target to generate template kernel with spatial description capability. Then, when aggregating the features of the template and the search region, the depthwise separable convolution correlation is adopted to preserve the spatial matching between the target feature and candidate feature, which makes their IoUNet++ network have better template representation and better fusion than the original network. The proposed IoUNet++ method with a plug‐and‐play style is applied to a series of strengthened trackers including DiMP++, SuperDiMP++ and SuperDIMP_AR++, which achieve consistent performance gain. Finally, experiments conducted on six popular tracking benchmarks show that their trackers outperformed the state‐of‐the‐art trackers with significantly fewer training epochs.
Adaptively optimal energy management for integrated hydrogen energy systems
Authors: Li Hengyi , Qin Boyu , Zhao Yuhang , Li Fan , Wu Xiaoman , Ding Tao 
Published: October 2023

AbstractIntegrated hydrogen energy systems (IHESs) have become attractive alternatives to cope with the depletion of fossil fuels and increasingly severe climate change problems. This paper proposes an adaptively optimal energy scheduling method based on deep deterministic policy gradient (DDPG) to improve the operational efficiency of IHES. The optimal scheduling problem is formulated as a Markov decision process problem with action space, environmental states, and action‐value function. The DDPG‐based optimal energy management algorithm with actor‐critic structure is proposed based on policy gradients and neural networks. Through actor‐critic network training and policy iteration, the energy management scheme can be adaptively optimized according to the dynamic responses of IHES. The benefits of the proposed algorithm are analysed through time‐domain simulations, and the scheduling robustness under different uncertain conditions is verified.
A risk‐averse framework for techno‐economic analysis and size optimization of an off‐grid hybrid energy system
Authors: Aramoon Azim , Askarzadeh Alireza , Ghaffari Abolfazl 
Published: October 2023

AbstractIn the power system, optimal sizing of hybrid energy systems (HESs) is a vital and challenging issue. Optimal sizing leads to designing a cost‐effective and reliable power generation system. In such a problem, modelling the load uncertainty helps the planner to make appropriate decisions for optimizing the performance of HESs against possible changes of the electrical load. In this paper, techno‐economic and optimal sizing of an off‐grid photovoltaic‐diesel generator‐fuel cell (PV‐diesel‐FC) HES is investigated in two frameworks, risk‐neutral strategy and risk‐averse strategy, where the load uncertainty is modelled by information gap decision theory. To size the HES, objective function is defined as the total net present cost and with respect to a desirable loss of power supply probability, size of the system components is optimally determined. In the risk‐averse framework, the sizing problem is solved with different critical tolerance levels of the objective function and the results are evaluated. Over the case study, simulation results show that at risk‐averse framework, optimal combination of PV, diesel generator and FC leads to a cost‐effective and reliable HES.
Update zur Glukosemessung
Authors: Thomas Andreas 
Published: October 2023

ZUSAMMENFASSUNGDie Glukosemessung hat sich zu einer Säule der Diabetesbehandlung entwickelt. Speziell die kontinuierliche Glukosemessung (Continuous Glucose Monitoring; CGM). zählt zu den größten Innovationen in den letzten Jahrzehnten mit einem deutlich größeren Einfluss auf das Diabetesmanagement als beispielsweise die Entwicklung der Insulinanaloga. Vergleichbar ist dies vielleicht noch mit der Entwicklung der automatisierten Insulinabgabe. Allerdings ist CGM bei den AID-Systemen (AID – automated insulin delivery; automatische Insulindosierung) immanenter Bestandteil. Auf zuverlässige Systeme für die nicht-invasive Glukosemessung muss noch gewartet werden. Auch diese würden zur weiteren Entlastung der Diabetespatienten beitragen.
Update zur Phytotherapie bei Rückenschmerzen
Authors: Vlachojannis Julia , Chrubasik-Hausmann Sigrun 
Published: October 2023

SummaryMehr als 20 % der Bevölkerung leiden an chronischen unspezifischen Rückenschmerzen. Bei einer gemeinsamen Auswertung von Guidelines aus 15 Ländern ergab sich eine Therapieempfehlung für nichtsteroidale Antirheumatika, Antidepressiva zusätzlich zu Gymnastik und psychosozialer Therapie. Doch wegen möglicher Nebenwirkungen unter Einnahme der Synthetika sollten zuvor pflanzliche Medikamente versucht werden. Als basale pflanzliche Entzündungshemmer stehen das Hagebuttenpulver aus Dänemark und ein wässriger Teufelskrallenwurzelextrakt mit > 50 mg Harpagosid in der Tagesdosis zur Verfügung, mit oder ohne Einnahme eines hypericinreichen Johanniskrautextrakts oder eines wirkstoffreichen Safranextrakts. Bei akuten Exazerbationen chronischer Rückenschmerzen kann zusätzlich ein auf Salicin standardisierter Weidenrindenextrakt hilfreich sein.