bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acting pre-emptively reduces the long-term costs of managing herbicide resistance
Authors: Varah Alexa , Ahodo Kwadjo , Childs Dylan Z. , Comont David , Crook Laura , Freckleton Robert P. , Goodsell Rob , Hicks Helen L. , Hull Richard , Neve Paul , Norris Ken 
Published: March 2024

AbstractGlobally, pesticides improve crop yields but at great environmental cost, and their overuse has caused resistance. This incurs large financial and production losses but, despite this, very diversified farm management that might delay or prevent resistance is uncommon in intensive farming. We asked farmers to design more diversified cropping strategies aimed at controlling herbicide resistance, and estimated resulting weed densities, profits, and yields compared to prevailing practice. Where resistance is low, it is financially viable to diversify pre-emptively; however, once resistance is high, there are financial and production disincentives to adopting diverse rotations. It is therefore as important to manage resistance before it becomes widespread as it is to control it once present. The diverse rotations targeting high resistance used increased herbicide application frequency and volume, contributing to these rotations’ lack of financial viability, and raising concerns about glyphosate resistance. Governments should encourage adoption of diverse rotations in areas without resistance. Where resistance is present, governments may wish to incentivise crop diversification despite the drop in wheat production as it is likely to bring environmental co-benefits. Our research suggests we need long-term, proactive, food security planning and more integrated policy-making across farming, environment, and health arenas.
A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques
Authors: Ali Aamir , Hassan Ali , Keerio M. U. , Mugheri Noor H. , Abbas Ghulam , Hatatah Mohammed , Touti Ezzeddine , Yousef Amr 
Published: March 2024

AbstractOptimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.
Benchmarking long-read aligners and SV callers for structural variation detection in Oxford nanopore sequencing data
Authors: Helal Asmaa A. , Saad Bishoy T. , Saad Mina T. , Mosaad Gamal S. , Aboshanab Khaled M. 
Published: March 2024

AbstractStructural variants (SVs) are one of the significant types of DNA mutations and are typically defined as larger-than-50-bp genomic alterations that include insertions, deletions, duplications, inversions, and translocations. These modifications can profoundly impact the phenotypic characteristics and contribute to disorders like cancer, response to treatment, and infections. Four long-read aligners and five SV callers have been evaluated using three Oxford Nanopore NGS human genome datasets in terms of precision, recall, and F1-score statistical metrics, depth of coverage, and speed of analysis. The best SV caller regarding recall, precision, and F1-score when matched with different aligners at different coverage levels tend to vary depending on the dataset and the specific SV types being analyzed. However, based on our findings, Sniffles and CuteSV tend to perform well across different aligners and coverage levels, followed by SVIM, PBSV, and SVDSS in the last place. The CuteSV caller has the highest average F1-score (82.51%) and recall (78.50%), and Sniffles has the highest average precision value (94.33%). Minimap2 as an aligner and Sniffles as an SV caller act as a strong base for the pipeline of SV calling because of their high speed and reasonable accomplishment. PBSV has a lower average F1-score, precision, and recall and may generate more false positives and overlook some actual SVs. Our results are valuable in the comprehensive evaluation of popular SV callers and aligners as they provide insight into the performance of several long-read aligners and SV callers and serve as a reference for researchers in selecting the most suitable tools for SV detection.
Geologically-constrained GANomaly network for mineral prospectivity mapping through frequency domain training data
Authors: Sabbaghi Hamid , Tabatabaei Seyed Hassan , Fathianpour Nader 
Published: March 2024

AbstractGenerative adversarial networks (GAN) and various deep autoencoders have been frequently executed to recognize multi-element geochemical anomalies linked to different ore resources in recent decade. Efficient recognition of multi-element geochemical anomaly patterns is a significant issue in mineral exploration targeting. Traditional procedures have not sufficient capability to perform efficient pattern recognition. While, deep learning algorithms as influential subset of machine learning algorithms can present magnificent conclusions in classification and pattern recognition. Because those have robust ability in extracting high-level features of complex inputs. Although, many deep learning algorithms were used to recognize geochemical anomalies but the GANs have demonstrated specific dignity in recognizing multi-element geochemical anomaly patterns. But, these frameworks should be constrained to learn geological knowledge and yield reasonable potential maps. In this regard, a novel geologically-constrained GANomaly was trained with frequency domain training data to recognize multi-element geochemical anomalies. Application of the geologically-constrained GANomaly network with considering mineral system parameters of the Au–Cu mineralization in the Feyzabad district, NE Iran was eventuated to suitable results. The success-rate curves demonstrated that produced map of frequency domain geochemical data has traced 86.68% Au–Cu occurrences via 30% corresponded area while produced map of spatial domain geochemical data has traced 80.13% Au–Cu occurrences via 30% corresponded area.
Prognostic model revealing pyroptosis-related signatures in oral squamous cell carcinoma based on bioinformatics analysis
Authors: Qi Lu , Tang Zhangui 
Published: March 2024

AbstractOne of the most common oral carcinomas is oral squamous cell carcinoma (OSCC), bringing a heavy burden to global health. Although progresses have been made in the intervention of OSCC, 5 years survival of patients suffering from OSCC is poor like before regarding to the high invasiveness of OSCC, which causes metastasis and recurrence of the tumor. The relationship between pyroptosis and OSCC remains to be further investigated as pyroptosis in carcinomas has gained much attention. Herein, the key pyroptosis-related genes were identified according to The Cancer Genome Atlas (TCGA) dataset. Additionally, a prognostic model was constructed based upon three key genes (CTLA4, CD5, and IL12RB2) through least absolute shrinkage and selection operator (LASSO) analyses, as well as univariate and multivariate COX regression in OSCC. It was discovered that the high expression of these three genes was associated with the low-risk group. We also identified LAIR2 as a hub gene, whose expression negatively correlated with the risk score and the different immune cell infiltration. Finally, we proved that these three genes were independent prognostic factors linked to overall survival (OS), and reliable consequences could be predicted by this model. Our study revealed the relationship between pyroptosis and OSCC, providing insights into new treatment targets for preventing and treating OSCC.
A 17-level quadruple boost switched-capacitor inverter with reduced devices and limited charge current
Authors: Hosseinpour Majid , Derakhshandeh Masoumeh , Seifi Ali , Shahparasti Mahdi 
Published: March 2024

AbstractIn this paper, a quadruple boost switched-capacitor multi-level inverter is proposed. The proposed structure utilizes a DC source, 11 switches, and a diode to achieve 17-level output voltage levels. This structure consists of three capacitors with the ability for self-balancing voltages. The capacitors achieve automatic voltage balancing through a series/parallel connection with the input voltage source. To control the switching pulses of the switches, level-shifted pulse width modulation (LS-PWM) strategy has been employed. A comparative evaluation has been performed between the proposed structure and structures presented in recent articles, considering various parameters such as voltage gain, number of DC sources, number of semiconductor devices, maximum blocking voltage (MBV), and total standing voltage (TSV). Considering this comparison, the lower number of semiconductor devices for generating a 17-level output with suitable voltage gain, and especially the cost-effectiveness of the structure, are the main advantages of the proposed configuration. In addition, a soft charging method has been employed to limit the inrush current of capacitors. Moreover, the power losses of the proposed structure have been investigated, indicating its acceptable efficiency. Finally, for the analysis and validation of the proposed structure's performance, an experimental prototype has been implemented and evaluated under various conditions. The results indicate satisfactory performance of the proposed structure under various stable and dynamic operating conditions.
A fast-flexible strategy based approach to solving employee scheduling problem considering soft work time
Authors: Ma Kangxin , Yang Changchun , Xu Haojie , Lv Hexin , Hong Feng 
Published: March 2024

AbstractEmployee scheduling aims to assign employees to shifts to satisfy daily workload and constraints. Some employee scheduling problems and their variants have been proven NP-hard, and a series of works have been done. However, the existing algorithms consider the fixed work time, which may cause plenty of overstaffing and understaffing phenomenons. Hence, this paper proposes a fast-flexible strategy based approach (FFS) to solve it. FFS introduces the idea of soft work time, which allows the work time of employees can be adjusted in a range. Based on this, we set the flextime strategy to decide the specific work time of each employee every day. Besides, FFS adopts a pairwise-allocated strategy and proficiency average matrix to boost its efficiency and effectiveness. Finally, the extensive experimental evaluation shows that FFS is more effective and efficient than the baselines for solving the employee scheduling problem considering soft work time.
Changing of mechanical property and bearing capacity of strongly chlorine saline soil under freeze-thaw cycles
Authors: Ding Shijun , Li Shaomin , Kong Sen , Li Qiuyang , Yang Taohui , Nie Zhibao , Zhao Gaowen 
Published: March 2024

AbstractFreeze-thaw cycles and compactness are two critical factors that significantly affect the engineering properties and safety of building foundations, especially in seasonally frozen regions. This paper investigated the effects of freeze-thaw cycles on the shear strength of naturally strongly chlorine saline soil with the compactness of 85%, 90% and 95%. Three soil samples with different compactness were made. Size and mass changes were measured and recorded during freeze-thaw cycles. Shear strength under different vertical pressures was determined by direct shear tests, and the cohesion and friction angle were measured and discussed. Microstructure characteristic changes of saline soil samples were observed using scanning electron microscopy under different freeze-thaw cycles. Furthermore, numerical software was used to calculate the subsoil-bearing capacity and settlement of the electric tower foundation in the Qarhan Salt Lake region under different freeze-thaw cycles. Results show that the low-density soil shows thaw settlement deformation, but the high-density soil shows frost-heaving deformation with the increase in freeze-thaw cycles. The shear strength of the soil samples first increases and then decreases with the increase in freeze-thaw cycles. After 30 freeze-thaw cycles, the friction angle of soil samples is 28.3%, 29.2% and 29.6% lower than the soil samples without freeze-thaw cycle, the cohesion of soil samples is 71.4%, 60.1% and 54.4% lower than the samples without freeze-thaw cycle, and the cohesion and friction angle of soil samples with different compactness are close to each other. Microstructural changes indicate that the freeze-thaw cycle leads to the breakage of coarse particles and the aggregation of fine particles. Correspondingly, the structure type of soil changes from a granular stacked structure to a cemented-aggregated system. Besides, the quality loss of soil samples is at about 2% during the freeze-thaw cycles. Results suggest that there may be an optimal compactness between 90 and 95%, on the premise of meeting the design requirements and economic benefits. This study can provide theoretical guidance for foundation engineering constructions in seasonally frozen regions.
Skillful prediction of length of day one year ahead in multiple decadal prediction systems
Authors: Yoon Hyunsuk , Choi Jung , Son Seok-Woo , Scaife Adam A. 
Published: March 2024

AbstractDespite a small amplitude, Length of Day (LOD) change, which varies from one year to another due to changes in Atmospheric Angular Momentum (AAM), determines the accuracy of Global Positioning System (GPS) time calculation. In this study, we examine the prediction skill of LOD and AAM in nine decadal prediction systems archived for the Decadal Climate Prediction Project. A persistence and rebound in LOD prediction skill at one year or longer lead time is found in most models. A poleward propagation of AAM anomaly via wave-mean flow interaction is also qualitatively well reproduced. This long-lead prediction of LOD and AAM is attributed to reliable predictions of the El Niño–Southern Oscillation (ENSO) and the Quasi-Biennial Oscillation (QBO), the former being more systematically related than the latter. This result indicates that the improved ENSO prediction and atmospheric wave-mean flow interaction may lead to better prediction of LOD, AAM and related extratropical climate in the decadal prediction systems.
Informing immunotherapy with multi-omics driven machine learning
Authors: Li Yawei , Wu Xin , Fang Deyu , Luo Yuan 
Published: March 2024

AbstractProgress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
An optrode array for spatiotemporally-precise large-scale optogenetic stimulation of deep cortical layers in non-human primates
Authors: Clark Andrew M. , Ingold Alexander , Reiche Christopher F. , Cundy Donald , Balsor Justin L. , Federer Frederick , McAlinden Niall , Cheng Yunzhou , Rolston John D. , Rieth Loren , Dawson Martin D. , Mathieson Keith , Blair Steve , Angelucci Alessandra 
Published: March 2024

AbstractOptogenetics has transformed studies of neural circuit function, but remains challenging to apply to non-human primates (NHPs). A major challenge is delivering intense, spatiotemporally-precise, patterned photostimulation across large volumes in deep tissue. Such stimulation is critical, for example, to modulate selectively deep-layer corticocortical feedback circuits. To address this need, we have developed the Utah Optrode Array (UOA), a 10×10 glass needle waveguide array fabricated atop a novel opaque optical interposer, and bonded to an electrically addressable µLED array. In vivo experiments with the UOA demonstrated large-scale, spatiotemporally precise, activation of deep circuits in NHP cortex. Specifically, the UOA permitted both focal (confined to single layers/columns), and widespread (multiple layers/columns) optogenetic activation of deep layer neurons, as assessed with multi-channel laminar electrode arrays, simply by varying the number of activated µLEDs and/or the irradiance. Thus, the UOA represents a powerful optoelectronic device for targeted manipulation of deep-layer circuits in NHP models.
A neural network-based model framework for cell-fate decisions and development
Authors: Paczkó Mátyás , Vörös Dániel , Szabó Péter , Jékely Gáspár , Szathmáry Eörs , Szilágyi András 
Published: March 2024

AbstractGene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.
Glutamine suppresses senescence and promotes autophagy through glycolysis inhibition-mediated AMPKα lactylation in intervertebral disc degeneration
Authors: Zhang Yangyang , Huang Zhengqi , Han Weitao , Wu Jiajun , Li Shuangxing , Qin Tianyu , Zhang Chao , Shi Ming , Han Shun , Gao Bo , Jin Song , Xiao Yin , Xu Kang , Ye Wei 
Published: March 2024

AbstractRegulating metabolic disorders has become a promising focus in treating intervertebral disc degeneration (IDD). A few drugs regulating metabolism, such as atorvastatin, metformin, and melatonin, show positive effects in treating IDD. Glutamine participates in multiple metabolic processes, including glutaminolysis and glycolysis; however, its impact on IDD is unclear. The current study reveals that glutamine levels are decreased in severely degenerated human nucleus pulposus (NP) tissues and aging Sprague-Dawley (SD) rat nucleus pulposus tissues, while lactate accumulation and lactylation are increased. Supplementary glutamine suppresses glycolysis and reduces lactate production, which downregulates adenosine-5’-monophosphate-activated protein kinase α (AMPKα) lactylation and upregulates AMPKα phosphorylation. Moreover, glutamine treatment reduces NP cell senescence and enhances autophagy and matrix synthesis via inhibition of glycolysis and AMPK lactylation, and glycolysis inhibition suppresses lactylation. Our results indicate that glutamine could prevent IDD by glycolysis inhibition-decreased AMPKα lactylation, which promotes autophagy and suppresses NP cell senescence.
The future is local? Contextualizing municipal agendas on climate change in Chile
Authors: Lehmann Rosa , Irigoyen Rios Alejandra 
Published: March 2024

AbstractSocial science literature on the political strategies used to mitigate and adapt to climate change has discussed the possibilities of local administrations and the multifaceted obstacles in their path. This case study looks at Chile, where debates about local climate agendas and policies have gained political relevance in recent years. It considers municipal administrations, with a focus on their agendas and the (potential) challenges they face. Building on the evaluation of literature, policy documents, and semi-structured interviews, the study concludes that local administrations have gained leeway for action due to changes in national regulation and integration into wider networks, but tight budgets for dedicated climate policies persist. The governance structures in which local climate agendas are embedded, as well as contextual constraints, reflect Chile’s institutional and neoliberal politeconomic arrangements.