bluuebunny/crossref_metadata_2025_split huggingface dataset:
Population genomic signatures of the oriental fruit moth related to the Pleistocene climates
Authors: Cao Li-Jun , Song Wei , Chen Jin-Cui , Fan Xu-Lei , Hoffmann Ary Anthony , Wei Shu-Jun 
Published: February 2022

AbstractThe Quaternary climatic oscillations are expected to have had strong impacts on the evolution of species. Although legacies of the Quaternary climates on population processes have been widely identified in diverse groups of species, adaptive genetic changes shaped during the Quaternary have been harder to decipher. Here, we assembled a chromosome-level genome of the oriental fruit moth and compared genomic variation among refugial and colonized populations of this species that diverged in the Pleistocene. High genomic diversity was maintained in refugial populations. Demographic analysis showed that the effective population size of refugial populations declined during the penultimate glacial maximum (PGM) but remained stable during the last glacial maximum (LGM), indicating a strong impact of the PGM rather than the LGM on this pest species. Genome scans identified one chromosomal inversion and a mutation of the circadian gene Clk on the neo-Z chromosome potentially related to the endemicity of a refugial population. In the colonized populations, genes in pathways of energy metabolism and wing development showed signatures of selection. These different genomic signatures of refugial and colonized populations point to multiple impacts of Quaternary climates on adaptation in an extant species.
Spectroscopic characterization of two peroxyl radicals during the O2-oxidation of the methylthio radical
Authors: Wu Zhuang , Shao Xin , Zhu Bifeng , Wang Lina , Lu Bo , Trabelsi Tarek , Francisco Joseph S. , Zeng Xiaoqing 
Published: February 2022

AbstractThe atmospheric oxidation of dimethyl sulfide (DMS) yields sulfuric acid and methane sulfonic acid (MSA), which are key precursors to new particles formed via homogeneous nucleation and further cluster growth in air masses. Comprehensive experimental and theoretical studies have suggested that the oxidation of DMS involves the formation of the methylthio radical (CH3S•), followed by its O2-oxidation reaction via the intermediacy of free radicals CH3SOx• (x = 1–4). Therefore, capturing these transient radicals and disclosing their reactivity are of vital importance in understanding the complex mechanism. Here, we report an optimized method for efficient gas-phase generation of CH3S• through flash pyrolysis ofS-nitrosothiol CH3SNO, enabling us to study the O2-oxidation of CH3S• by combining matrix-isolation spectroscopy (IR and UV–vis) with quantum chemical computations at the CCSD(T)/aug-cc-pV(X + d)Z (X = D and T) level of theory. As the key intermediate for the initial oxidation of CH3S•, the peroxyl radical CH3SOO• forms by reacting with O2. Upon irradiation at 830 nm, CH3SOO• undergoes isomerization to the sulfonyl radical CH3SO2• in cryogenic matrixes (Ar, Ne, and N2), and the latter can further combine with O2to yield another peroxyl radical CH3S(O)2OO• upon further irradiation at 440 nm. Subsequent UV-light irradiation (266 nm) causes dissociation of CH3S(O)2OO• to CH3SO2•, CH2O, SO2, and SO3. The IR spectroscopic identification of the two peroxyl radicals CH3SOO• and CH3S(O)2OO• is also supported by18O- and13C-isotope labeling experiments.
Representative community divisions of networks
Authors: Kirkley Alec , Newman M. E. J. 
Published: February 2022

AbstractMethods for detecting community structure in networks typically aim to identify a single best partition of network nodes into communities, often by optimizing some objective function, but in real-world applications there may be many competitive partitions with objective scores close to the global optimum and one can obtain a more informative picture of the community structure by examining a representative set of such high-scoring partitions than by looking at just the single optimum. However, such a set can be difficult to interpret since its size can easily run to hundreds or thousands of partitions. In this paper we present a method for analyzing large partition sets by dividing them into groups of similar partitions and then identifying an archetypal partition as a representative of each group. The resulting set of archetypal partitions provides a succinct, interpretable summary of the form and variety of community structure in any network. We demonstrate the method on a range of example networks.
Community-based rangeland management in Namibia improves resource governance but not environmental and economic outcomes
Authors: Coppock D. Layne , Crowley Luke , Durham Susan L. , Groves Dylan , Jamison Julian C. , Karlan Dean , Norton Brien E. , Ramsey R. Douglas 
Published: February 2022

AbstractClassic theories suggest that common pool resources are subject to overexploitation. Community-based resource management approaches may ameliorate tragedy of the commons effects. Here we use a randomized evaluation in Namibia’s communal rangelands to study a comprehensive four-year program to support community-based rangeland and cattle management. We find that the program led to persistent and large improvements for eight of thirteen indices of social and behavioral outcomes. Effects on rangeland health, cattle productivity and household economics, however, were either negative or nil. Positive impacts on community resource management may have been offset by communities’ inability to control grazing by non-participating herds and inhibited by an unresponsive rangeland sub-system. This juxtaposition, in which measurable improvements in community resource management did not translate into better outcomes for households or rangeland health, demonstrates the fragility of the causal pathway from program implementation to intended socioeconomic and environmental outcomes. It also points to challenges for improving climate change–adaptation strategies.
Aerobic exercise improves episodic memory in late adulthood: a systematic review and meta-analysis
Authors: Aghjayan Sarah L. , Bournias Themistokles , Kang Chaeryon , Zhou Xueping , Stillman Chelsea M. , Donofry Shannon D. , Kamarck Thomas W. , Marsland Anna L. , Voss Michelle W. , Fraundorf Scott H. , Erickson Kirk I. 
Published: February 2022

AbstractBackgroundAerobic exercise remains one of the most promising approaches for enhancing cognitive function in late adulthood, yet its potential positive effects on episodic memory remain poorly understood and a matter of intense debate. Prior meta-analyses have reported minimal improvements in episodic memory following aerobic exercise but have been limited by restrictive inclusion criteria and infrequent examination of exercise parameters.MethodsWe conducted a meta-analysis of randomized controlled trials to determine if aerobic exercise influences episodic memory in late adulthood (M = 70.82 years) and examine possible moderators. Thirty-six studies met inclusion criteria, representing data from 2750 participants.ResultsHere we show that aerobic exercise interventions are effective at improving episodic memory (Hedges’g = 0.28;p = 0.002). Subgroup analyses revealed a moderating effect of age (p = 0.027), with a significant effect for studies with a mean age between 55–68 but not 69–85. Mixed-effects analyses demonstrated a positive effect on episodic memory among studies with a high percentage of females (65–100%), participants with normal cognition, studies reporting intensity, studies with a no-contact or nonaerobic physical activity control group, and studies prescribing >3900 total minutes of activity (range 540–8190 min).ConclusionsAerobic exercise positively influences episodic memory among adults ≥55 years without dementia, with larger effects observed among various sample and intervention characteristics—the clearest moderator being age. These results could have far-reaching clinical and public health relevance, highlighting aerobic exercise as an accessible, non-pharmaceutical intervention to improve episodic memory in late adulthood.
Bipolarithizole A, an antifungal phenylthiazole-sativene merosesquiterpenoid from the potato endophytic fungus Bipolaris eleusines
Authors: Ai Hong-Lian , Shi Bao-Bao , Li Wei , He Juan , Li Zheng-Hui , Feng Tao , Liu Ji-Kai 
Published: February 2022

Bipolarithizole A (1) is a phenylthiazole-sativene sesquiterpenoid hybrid isolated from the fungus Bipolaris eleusines. It shows anti-pathogenic fungi activity against Rhizoctonia solani with an MIC value of 16 μg mL−1.
Membrane phosphatidylserine allosterically regulates the cytosolic phospholipase A2 activityviaan electrostatic-switch mechanism
Authors: Geng Feng , Zhang Guangxu , Wang Yadi , Lü Junhong 
Published: February 2022

Experimental and theoretical approaches suggest an electrostatic-switch allosteric mechanism for cytosolic phospholipase A2 activation on the cell membrane interface under the inflammatory state.
LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities
Authors: Singh Dushyant Kumar , Sobti Rajeev , Jain Anuj , Malik Praveen Kumar , Le Dac‐Nhuong 
Published: February 2022

AbstractUrbanization is expected to hold about 50% of the world population by 2050 and there will be stress on available resources including food and freshwater. Further, inefficient utilization of irrigation water possesses additional challenges and it is predicted that by 2050, India will face shortage of freshwater resources. Urban agriculture and Precision Agriculture are the possible solutions in smart cities for the predicted challenges providing food security and safety, sustainable life in cities. Internet of Things (IoT) and Machine Learning (ML) are the possible solutions to smart cities' farming. Agricultural activities such as irrigation of plants, rain and drought conditions monitoring supported by IoT and ML succour farmers to righteous decision on agriculture activities. A detailed study is carried out on the role, opportunities, and different aspects of smart cities, urban farming, communication technologies, IoT and ML with respect to agriculture. The article presents the design of intelligent irrigation system based on soil and weather conditions. The soil and weather parameters are selected through various research articles in Agriculture 4.0 and ML. The article also juxtapositions the designed weather station with the various patents developed. The system developed in this article provides a cost‐effective and state‐of‐the‐art solution to local weather monitoring.
Superpixel segmentation algorithm based on local network modularity increment
Authors: Liu Tianli , Dai Fang , Guo Wenyan , Zhao Fengqun , Wang Junfeng , Wang Xiaoxia 
Published: February 2022

AbstractSuperpixel segmentation is a kind of image preprocessing technology and a popular research direction in image processing. The purpose of superpixel segmentation is to reduce the complexity of image processing. The most widely applied Simple Linear Iterative Clustering (SLIC) superpixel segmentation algorithm has high operating efficiency. However, under‐segmentation is prone to occur when the number of given superpixel regions is too small. In order to improve the segmentation accuracy, the superpixel segmentation algorithm based on local network modularity increment (LocalNet) from the perspective of network community detection is proposed here. The adjacency network is constructed according to the colour similarity of image pixels, the local community centre is found by the degree of network nodes, and the local network structure of the community is constructed. The modularity increment is employed as the boundary constraint to improve the segmentation accuracy of superpixel segmentation. Through the experimental comparison with the SLIC algorithm, its improved algorithm, and the algorithm proposed in recent years, the results show that our LocalNet algorithm significantly improves in segmentation accuracy Furthermore, the segmentation effect has obvious advantages under the premise that the segmentation speed is not much different from that of the other five algorithms.
Mathematical model for forwarding packets in communication network
Authors: Hussein Samaa A. I. , Zaki Fayez W. , Ashour Mohammed M. 
Published: February 2022

AbstractThe software‐defined networking (SDN) represents the future backbone of the Internet. In general, to understand the performance and the limitation of SDN is a prerequisite for deploying these networks and making the most of their resources. For achieving that goal, this paper discusses the difference between traditional networks and SDN over wide area network (WAN) where an accurate queuing model of packet forwarding is proposed to measure the performance of many factors affecting the system, such as response time, service rate, utilization, throughput and delay through a vEdge router. Additionally, it focusses for studying algebraic estimates of the probability distribution of the system states. The Matrix‐Geometric solution procedure of a phase‐type distribution queue with first‐in, first‐out (FIFO) discipline is used. The analysis results indicate that the proposed queuing model gives a more accurate approximation for software‐defined WAN (SD‐WAN) controller system performance than the present, where this solution shows that the most important results are benefits of centralized management of SD‐WAN, an increase in bandwidth through separating the control plan from the data plan, thus reducing volume operations in the data plan, a delay and an increase in packet transfer speed. Additionally, it improves business performance and diminishes budget.
Parameter identification of direct‐drive permanent magnet synchronous generator based on EDMPSO‐EKF
Authors: Xiao Qianghui , Liao Kaixian , Shi Chuandong , Zhang Yang 
Published: February 2022

AbstractAiming at the problem that extended Kalman filter (EKF) is difficult to determine the appropriate system and measurement noise matrices in parameter identification of direct‐drive permanent magnet synchronous generator (D‐PMSG), a parameter identification method of D‐PMSG based on mean particle swarm optimization with extreme disturbance (EDMPSO)‐EKF is proposed. Firstly, by analysing the principle of EKF, a dual‐thread identification model is established, then the particle swarm optimization (PSO) algorithm is improved to jump out of the local optimum by adding extremum interference and taking average extremum, and a suitable fitness function is designed. Finally, the improved PSO algorithm is applied to the adaptive optimization of EKF system noise matrix and measurement noise matrix, and the system performs the parameter identification after obtaining the optimal noise matrix. The simulation and experiment results show that the proposed method has better convergence speed and can more accurately identify the parameters of stator resistance, inductance and flux linkage, and it has better identification accuracy and generalization ability than the traditional method.
Emission from HCN and CH3OH in comets
Authors: Bergman P. , Lerner M. S. , Olofsson A. O. H. , Wirström E. , Black J. H. , Bjerkeli P. , Parra R. , Torstensson K. 
Published: February 2022

Aims. The aim of this work is to characterise HCN and CH3OH emission from recent comets.
Methods. We used the Onsala 20-m telescope to search for millimetre transitions of HCN towards a sample of 11 recent and mostly bright comets in the period from December 2016 to November 2019. Also, CH3OH was searched for in two comets. The HCN sample includes the interstellar comet 2I/Borisov. For the short-period comet 46P/Wirtanen, we were able to monitor the variation of HCN emission over a time-span of about one month. We performed radiative transfer modelling for the observed molecular emission by also including time-dependent effects due to the outgassing of molecules.
Results. HCN was detected in six comets. Two of these are short-period comets and four are long-period. Six methanol transitions were detected in 46P/Wirtanen, enabling us to determine the gas kinetic temperature. From the observations, we determined the molecular production rates using time-dependent radiative transfer modelling. For five comets, we were able to determine that the HCN mixing ratios lie near 0.1% using contemporary water production rates, ${Q_{{{\rm{H}}_2}{\rm{O}}}}$, taken from other studies. This HCN mixing ratio was also found to be typical in our monitoring observations of 46P/Wirtanen but here we notice deviations of up to 0.2% on a daily timescale which could indicate short-time changes in outgassing activity. From our radiative transfer modelling of cometary comae, we find that time-dependent effects on the HCN level populations are of the order of 5–15% when ${Q_{{{\rm{H}}_2}{\rm{O}}}}$ is around 2 × 1028 mol s−1. The effects may be stronger for comets with lower ${Q_{{{\rm{H}}_2}{\rm{O}}}}$. The exact details of the time-dependent effects depend on the amount of neutral and electron collisions, radiative pumping, and molecular parameters such as the spontaneous rate coefficient.
Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network
Authors: Mascarenhas Miguel , Ribeiro Tiago , Afonso João , Ferreira João P.S. , Cardoso Hélder , Andrade Patrícia , Parente Marco P.L. , Jorge Renato N. , Mascarenhas Saraiva Miguel , Macedo Guilherme 
Published: February 2022

Abstract
Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images.
Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation.
Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second.
Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.
Auto-Antikörper-Diagnostik in der Diabetologie – Aktueller Stand der Analytik und klinische Anwendung in Deutschland
Authors: Thaler Markus , Roos Marcel , Petersmann Astrid , Seissler Jochen , Peter Andreas , Landgraf Rüdiger , Müller Ulrich A. , Müller-Wieland Dirk , Nauck Matthias , Heinemann Lutz , Schleicher Erwin , Luppa Peter 
Published: February 2022

ZusammenfassungDie Messung von spezifischen Autoantikörpern gegen beta-Zellproteine (beta-AAK) hat in den letzten Jahren das diagnostische Repertoire in der Diabetologie erweitert. Das Vorliegen von beta-AAK kann als erstes Stadium in der Entwicklung eines Typ-1-Diabetes mellitus (DM) gewertet werden, ohne dass Symptome bzw. metabolische Veränderungen vorliegen. Da sich diese oft Jahre vor der klinischen Manifestation in Personen mit hohem Erkrankungsrisiko nachweisen lassen, stellen sie wichtige prädiktive und frühdiagnostische Marker dar. Weiterhin kann die Bestimmung von beta-AAK zur Unterscheidung von Patienten mit einem Typ-1-DM auf der einen und Typ-2-DM und Maturity-Onset Diabetes of the Young (MODY) auf der anderen Seite indiziert sein. Auch für die Differenzialdiagnostik von Patienten mit Insulinmangel aufgrund einer autoimmunen Betazelldestruktion und von Patienten mit klinisch sehr ähnlichem „severe-insulin-deficient“-Diabetes, die aber beide eine unterschiedliche Prognose haben, ist die Antikörperdiagnostik zielführend. Die Abschätzung des Risikos für die Entwicklung eines Typ-1-DM bei Patienten, die an autoimmunen Endokrinopathien leiden, stellt einen weiteren Einsatzbereich für beta-AAK dar.Analytisch sind die beta-AAK mit recht unterschiedlichen Methoden messbar; häufig aber weichen die erhaltenen Messergebnisse bei verschiedenen Testmethoden beträchtlich voneinander ab. Es müssen daher eigene Cut-off Werte vom beauftragten Labor definiert werden, um die erhaltenen Ergebnisse klinisch interpretieren zu können. Zur besseren Vergleichbarkeit der Messergebnisse gibt es derzeit international abgestimmte Harmonisierungsbestrebungen. Für teilnehmende Laboratorien angebotene Ringversuche für die Bestimmungen der Autoantikörper gegen Insulin (IAA), Insulinoma-Antigen 2 (IA-2), Zink Transporter-8 (ZnT8) und Glutamatdecarboxylase (GAD65) können die analytische Qualität ebenfalls verbessern.
Effect of Surgical Treatment for Deep Infiltrating Endometriosis on Pelvic Floor Disorders: A Systematic Review with Meta-analysis
Authors: Fraga Mirian Vieira , Benetti-Pinto Cristina Laguna , Yela Daniela Angerame , Mira Ticiana Alves de , Brito Luiz Gustavo Oliveira 
Published: February 2022

Abstract
Objectives To evaluate the impact of surgical treatment of deep infiltrative endometriosis (DIE) on pelvic floor dysfunction (urinary incontinence [UI], pelvic organ prolapse [POP], fecal incontinence [FI)] or constipation, and sexual function [dyspareunia]).
Data Source The present systematic review was performed in the PubMed database. For the selection of studies, articles should be published by January 5, 2021, without language restriction.
Study Selection Six randomized controlled studies that evaluated surgical treatment for DIE and the comparison of different surgical techniques were included.
Data Collection The studies were selected independently by title and abstract by two authors. Disagreements were resolved by a third author. All included studies were also evaluated according to the Cochrane risk of bias tool and the quality of the evidence was analyzed using the GRADE criteria. Subgroup analysis by different treatments and follow-up periods was also performed.
Results Six studies were included in the quantitative analysis. The risk of bias between studies showed an uncertain risk of bias for most studies, with concealment of allocation being the least reported category. The quality of the evidence was considered low. High heterogeneity was found between the studies. No study has evaluated UI or POP comparatively before and after surgery.
Conclusion Dyspareunia and FI have improved after the surgical procedure, but it was not possible to demonstrate which surgical technique was related to these outcomes as there was surgical heterogeneity. This diversity was found across data, with the recommendation of future prospective studies addressing pelvic floor disorders with DIE.