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Background A common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited. Results To address this gap, we have developed the Intervene tool, which provides an easy and automated interface for the effective intersection and visualization of genomic region or list sets, thus facilitating their analysis and interpretation. Intervene contains three modules: venn to generate Venn diagrams of up to six sets, upset to generate UpSet plots of multiple sets, and pairwise to compute and visualize intersections of multiple sets as clustered heat maps. Intervene, and its interactive web ShinyApp companion, generate publication-quality figures for the interpretation of genomic region and list sets. Conclusions Intervene and its web application companion provide an easy command line and an interactive web interface to compute intersections of multiple genomic and list sets. They have the capacity to plot intersections using easy-to-interpret visual approaches. Intervene is developed and designed to meet the needs of both computer scientists and biologists. The source code is freely available at https://bitbucket.org/CBGR/intervene , with the web application available at https://asntech.shinyapps.io/intervene .

Since the Black Lives Matter movement rose to mainstream prominence, the academic enterprise has started recognizing the systematic racism present in science. However, there have been relatively few efforts to make sure that the language used to communicate science is inclusive. Here, I quantify the number of research articles published between 2000 and 2020 that contained non-inclusive terms with racial connotations, such as “blacklist” and “whitelist”, or “master” and “slave”. This reveals that non-inclusive language is being increasingly used in the life sciences literature, and I urge the global academic community to expunge these archaic terms to make science inclusive for everyone.

Decision-making in uncertain and imprecise environments often requires robust mathematical frameworks capable of effectively representing and aggregating conflicting information. Circular Intuitionistic Fuzzy Sets (C-IFSs) are powerful tools for modeling such complexities by combining intuitionistic and cubic fuzzy set properties. In this study, we extend the applicability of C-IFSs by integrating them with the Hamacher operational framework, offering a more flexible and adaptive approach to aggregation. To this end, we propose six novel aggregation operators: Circular Intuitionistic Fuzzy Hamacher Weighted Average (CIFHWA), Circular Intuitionistic Fuzzy Hamacher Ordered Weighted Average (CIFHOWA), Circular Intuitionistic Fuzzy Hamacher Hybrid Weighted Average (CIFHHWA), Circular Intuitionistic Fuzzy Hamacher Weighted Geometric (CIFHWG), Circular Intuitionistic Fuzzy Hamacher Ordered Weighted Geometric (CIFHOWG), and Circular Intuitionistic Fuzzy Hamacher Hybrid Weighted Geometric (CIFHHWG). These operators are designed to address multi-criteria decision-making challenges with improved precision. We further develop score and accuracy functions to rank C-IFSs and propose a neural-based scheme employing cubic correlation coefficients to enhance computational efficiency. A detailed numerical example validates the framework’s effectiveness, illustrating its practical utility. Additionally, comparative analyses with existing techniques and sensitivity and robustness examinations demonstrate its superiority in handling complex decision-making scenarios. The findings underscore the advantages of using Hamacher-based operations with C-IFSs, offering a novel contribution to fuzzy decision-making and opening avenues for future research in uncertain data analysis.

This study introduces operational laws for Aczél-Alsina aggregation within the framework of generalized bipolar neutrosophic sets (GBNS), tailored for group decision-making scenarios. Novel aggregation operators, including the Generalized Bipolar Neutrosophic Aczél-Alsina Weighted Average (GBNAAWA), Generalized Bipolar Neutrosophic Aczél-Alsina Ordered Weighted Average (GBNAAOWA), Generalized Bipolar Neutrosophic Aczél-Alsina Hybrid Weighted Average (GBNAAHWA), Generalized Bipolar Neutrosophic Aczél-Alsina Weighted Geometric (GBNAAWG), Generalized Bipolar Neutrosophic Aczél-Alsina Ordered Weighted Geometric (GBNAAOWG), and Generalized Bipolar Neutrosophic Aczél-Alsina Hybrid Weighted Geometric (GBNAAHWG), are proposed to address complex decision-making processes under uncertainty. The methodology is demonstrated through a case study and an illustrative example to validate its practical applicability. Comparative and sensitivity analyses highlight the robustness and adaptability of the proposed operators in various decision contexts. Key findings, discussions, and limitations are presented to provide insights into the method’s effectiveness and areas for future research. This work contributes to advancing decision-making models by integrating Aczél-Alsina aggregation with bipolar neutrosophic theory, offering a novel approach to handling ambiguity and conflicting information.

This paper deals with the mathematical analysis of Tuberculosis by using fractal fractional operator. Mycobacterium TB is the bacteria that causes tuberculosis. This airborne illness mostly impacts the lungs but may extend to other body organs. When the infected individual coughs, sneezes or speaks, the bacterium gets released into the air and travels from one person to another. Five classes have been formulated to study the dynamics of this disease: susceptible class, infected of DS, infected of MDR, isolated class, and recovered class. To study the suggested fractal fractional model’s wellposedness associated with existence results, and boundedness of solutions. Further, the invariant region of the considered model, positive solutions, equilibrium point, and reproduction number. One would typically employ a fractional calculus approach to obtain numerical solutions for the fractional order Tuberculosis model using the Adams-Bashforth-Moulton method. The fractional order derivatives in the model can be approximated using appropriate numerical schemes designed for fractional order differential equations.

Excessive reliance on chemical fertilizer (CF) in conventional farming is a serious concern owing to its negative effects on soil health, the environment, and crop productivity. Organic manure is an alternative source of fertilizer to reduce the amount of CF usage in agriculture, decrease environmental pollution, and ensure sustainable crop production. This study assessed the integrated effect of poultry manure (PM) and cattle manure (CM) with CF on soil properties, plant physiology, and rice grain yield. Additionally, the difference in pre-and post-anthesis dry matter (DM) and nitrogen (N) accumulation and their relationship with grain yield was also determined. Pot experiments were performed in the early and late growing season at the experimental station of Guangxi University, China, in 2018. A total of six treatments, i.e., T.sub.1 -CF.sub.0 ; T.sub.2 -100% CF; T.sub.3 -60% CM + 40% CF; T.sub.4 -30% CM + 70% CF; T.sub.5 -60% PM + 40% CF, and T.sub.6 -30% PM + 70% CF were used in this pot experiment. Results showed that T.sub.6 enhanced leaf photosynthetic efficiency by 11% and 16%, chlorophyll content by 8% and 11%, panicle number by 12% and 16%, and grain yield by 11% and 15% in the early and late seasons, respectively, compared to T.sub.2 . Similarly1, post-anthesis N and DM accumulation, N uptake, and soil properties (i.e., soil organic carbon, total N, and bulk density) were improved with integrated CF and manure treatments over the sole CF treatments. Interestingly, increases in post-anthesis N uptake and DM production were further supported by enhanced N-metabolizing enzyme activities (i.e., nitrate reductase, glutamine synthetase, and glutamate oxoglutarate aminotransferase during the grain-filling period in combined treatments. In-addition, the linear regression analysis showed that post-anthesis DM (R.sup.2 = 0.95) and N (R.sup.2 = 0.96) accumulation were highly associated with grain yield of rice. Thus, the combination of 30% N from PM or CM with 70% N from CF (i.e., urea) is a promising option for improvement of soil quality and rice grain yield. Furthermore, our study provides a sustainable nutrient management plan to increase rice yield with high N use efficiency.

Background Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. It is critical to locate these TF-DNA interactions to understand transcriptional regulation. Efforts to predict bona fide TFBSs benefit from the availability of experimental data mapping DNA binding regions of TFs (chromatin immunoprecipitation followed by sequencing - ChIP-seq). Results In this study, we processed ~ 10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~ 56 million TFBSs with experimental and computational support for direct TF-DNA interactions for 644 TFs in > 1000 cell lines and tissues. These TFBSs were used to predict > 197,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface ( https://unibind.uio.no/ ), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions. Conclusions UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.

With the global food deficit increasing and rising climate change issues, there is a need to find green solutions to improve soil fertility and productivity while enhancing soil biochemical quality and reducing the ecological impact of agriculture. Biochar is a potentially cost-effective, carbonaceous resource with many agricultural and environmental applications. As a soil amendment, it improves soil physical and biochemical properties and increases soil fertility and productivity—particularly over the long-term—increasing soil aggregation, water retention, pH, and microbial activities, thus, improving overall soil quality, potentially helping to reduce chemical fertilizer needs over time. The extent of biochar’s impact on soil physiochemical properties varies depending on biochar source, type, size, inherent soil characteristics, cropping system, etc. Moreover, biochar has significant potential in soil and water remediation, especially through its unique adsorption and chemical properties capable to capture and immobilize pollutants such as metal(loid)s, organic pollutants, and hazardous emerging contaminants such as microplastics. Further, biochar has also emerged as a key strategic, cost-effective material to tackle global issues such as climate change mitigation, reducing the net greenhouse gas emission to minimize global warming potential. However, a knowledge gap remains as to understanding the long-term persistence of biochar on agroecosystem, optimal biochar application rate for the diversity of biochar-soil-crop-environmental conditions, interaction of biochar with inherent soil carbon stock, specific mechanisms of biochar’s effect on soil biotic properties, quantification of carbon sequestration, greenhouse gas emissions, synergy or potential antagonistic effects with other carbon sources such as compost, manure, residues, etc., its modification for environmental applications and associated environmental and human risks over long-term. Further research is needed to evaluate the long-term impacts of types and sizes of biochar on overall soil quality to recommend suitable application practices based on soil management and cropping system. Also, its environmental applications need to be finetuned for wider and target specific applications to tackle pressing environmental issues such as soil and water pollution.

Increased levels of greenhouse gases in the atmosphere and associated climatic variability is primarily responsible for inducing heat waves, flooding and drought stress. Among these, water scarcity is a major limitation to crop productivity. Water stress can severely reduce crop yield and both the severity and duration of the stress are critical. Water availability is a key driver for sustainable cotton production and its limitations can adversely affect physiological and biochemical processes of plants, leading towards lint yield reduction. Adaptation of crop husbandry techniques suitable for cotton crop requires a sound understanding of environmental factors, influencing cotton lint yield and fiber quality. Various defense mechanisms e.g. maintenance of membrane stability, carbon fixation rate, hormone regulation, generation of antioxidants and induction of stress proteins have been found play a vital role in plant survival under moisture stress. Plant molecular breeding plays a functional role to ascertain superior genes for important traits and can offer breeder ready markers for developing ideotypes. This review highlights drought-induced damage to cotton plants at structural, physiological and molecular levels. It also discusses the opportunities for increasing drought tolerance in cotton either through modern gene editing technology like clustered regularly interspaced short palindromic repeat (CRISPR/Cas9), zinc finger nuclease, molecular breeding as well as through crop management, such as use of appropriate fertilization, growth regulator application and soil amendments.

The combined use of organic manure and chemical fertilizer (CF) is considered to be a good method for sustaining high crop yields and improving soil quality. We performed a field experiment in 2019 at the research station of Guanxi University, to investigate the effects of cattle manure (CM) and poultry manure (PM) combined with CF on soil physical and biochemical properties, rice dry matter (DM) and nitrogen (N) accumulation and grain yield. We also evaluated differences in pre-and post-anthesis DM and N accumulation and their contributions to grain yield. The experiment consisted of six treatments: no N fertilizer (T 1 ), 100% CF (T 2 ), 60% CM + 40% CF (T 3 ), 30% CM + 70% CF (T 4 ), 60% PM + 40% CF (T 5 ), and 30% PM + 70% CF (T 6 ). All CF and organic manure treatments provided a total N of 150 kg ha −1 . Results showed that the treatment T 6 increased leaf net photosynthetic rate ( Pn ) by 11% and 13%, chlorophyll content by 13% and 15%, total biomass by 9% and 11% and grain yield by 11% and 17% in the early and late season, respectively, compared with T 2 . Similarly, the integrated manure and CF treatments improved post-antheis DM accumulation and soil properties, such as bulk density, organic carbon, total N, microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) relative to the CF-only treatments. Interestingly, increases in post-anthesis DM and N accumulation were further supported by enhanced leaf Pn and activity of N-metabolizing enzyme during the grain-filling period. Improvement in Pn and N-metabolizing enzyme activity were due to mainly improved soil quality in the combined manure and synthetic fertilizer treatments. Redundancy analysis (RDA) showed a strong relationship between grain yield and soil properties, and a stronger relationship was noted with soil MBC and MBN. Conclusively, a combination of 30% N from PM or CM with 70% N from CF is a promising option for improving soil quality and rice yield.