Explore the vast range of titles available.

Artificial intelligence (AI) has become a significantly growing field in the environmental sector due to its ability to solve problems, make decisions, and recognize patterns. The significance of AI in wildlife acoustic monitoring is particularly important because of the vast amounts of data that are available in this field, which can be leveraged for computer vision and interpretation. Despite the increasing use of AI in wildlife ecology, its future in acoustic wildlife monitoring remains uncertain. To assess its potential and identify future needs, a scientific literature review was conducted on 54 works published between 2015 and March 2022. The results of the review showed a significant rise in the utilization of AI techniques in wildlife acoustic monitoring over this period, with birds (N = 26) gaining the most popularity, followed by mammals (N = 12). The most commonly used AI algorithm in this field was Convolutional Neural Network, which was found to be more accurate and beneficial than previous categorization methods in acoustic wildlife monitoring. This highlights the potential for AI to play a crucial role in advancing our understanding of wildlife populations and ecosystems. However, the results also show that there are still gaps in our understanding of the use of AI in wildlife acoustic monitoring. Further examination of previously used AI algorithms in bioacoustics research can help researchers better understand patterns and identify areas for improvement in autonomous wildlife monitoring. In conclusion, the use of AI in wildlife acoustic monitoring is a rapidly growing field with a lot of potential. While significant progress has been made in recent years, there is still much to be done to fully realize the potential of AI in this field. Further research is needed to better understand the limitations and opportunities of AI in wildlife acoustic monitoring, and to develop new algorithms that can improve the accuracy and usefulness of this technology.

The novel coronavirus SARS-CoV-2 was first isolated in late 2019; it has spread to all continents, infected over 700 million people, and caused over 7 million deaths worldwide to date. The high transmissibility of the virus and the emergence of novel strains with altered pathogenicity and potential resistance to therapeutics and vaccines are major challenges in the study and treatment of the virus. Ongoing screening efforts aim to identify new cases to monitor the spread of the virus and help determine the danger connected to the emergence of new variants. Given its sensitivity and specificity, nucleic acid amplification tests (NAATs) such as RT-qPCR are the gold standard for SARS-CoV-2 detection. However, due to high costs, complexity, and unavailability in low-resource and point-of-care (POC) settings, the available RT-qPCR assays cannot match global testing demands. An alternative NAAT, RT-LAMP-based SARS-CoV-2 detection offers scalable, low-cost, and rapid testing capabilities. We have developed an automated RT-LAMP-based microfluidic chip that combines the RNA isolation, purification, and amplification steps on the same device and enables the visual detection of SARS-CoV-2 within 40 min from saliva and nasopharyngeal samples. The entire assay is executed inside a uniquely designed, inexpensive disposable microfluidic chip, where assay components and reagents have been optimized to provide precise and qualitative results and can be effectively deployed in POC settings. Furthermore, this technology could be easily adapted for other novel emerging viruses.

Hepatitis C virus (HCV) infections occur in approximately 3% of the world population. The development of an enhanced and extensive-scale screening is required to accomplish the World Health Organization’s (WHO) goal of eliminating HCV as a public health problem by 2030. However, standard testing methods are time-consuming, expensive, and challenging to deploy in remote and underdeveloped areas. Therefore, a cost-effective, rapid, and accurate point-of-care (POC) diagnostic test is needed to properly manage the disease and reduce the economic burden caused by high case numbers. Herein, we present a fully automated reverse-transcription loop-mediated isothermal amplification (RT-LAMP)-based molecular diagnostic set-up for rapid HCV detection. The set-up consists of an automated disposable microfluidic chip, a small surface heater, and a reusable magnetic actuation platform. The microfluidic chip contains multiple chambers in which the plasma sample is processed. The system utilizes SYBR green dye to detect the amplification product with the naked eye. The efficiency of the microfluidic chip was tested with human plasma samples spiked with HCV virions, and the limit of detection observed was 500 virions/mL within 45 min. The entire virus detection process was executed inside a uniquely designed, inexpensive, disposable, and self-driven microfluidic chip with high sensitivity and specificity.

Cluster bean ( Cyamopsis tetragonoloba (L.) Taub 2n = 14, is commonly known as Guar. Apart from being a vegetable crop, it is an abundant source of a natural hetero-polysaccharide called guar gum or galactomannan. Here, we are reporting a chromosome-scale reference genome assembly of a popular cluster bean cultivar RGC-936, by combining sequencing data from Illumina, 10X Genomics, Oxford Nanopore technologies. An initial assembly of 1580 scaffolds with an N50 value of 7.12 Mb was generated and these scaffolds were anchored to a high density SNP linkage map. Finally, a genome assembly of 550.31 Mb (94% of the estimated genome size of ~ 580 Mb (through flow cytometry) with 58 scaffolds was obtained, including 7 super scaffolds with a very high N50 value of 78.27 Mb. Phylogenetic analysis using single copy orthologs among 12 angiosperms showed that cluster bean shared a common ancestor with other legumes 80.6 MYA. No evidence of recent whole genome duplication event in cluster bean was found in our analysis. Further comparative transcriptomics analyses revealed pod-specific up-regulation of genes encoding enzymes involved in galactomannan biosynthesis. The high-quality chromosome-scale cluster bean genome assembly will facilitate understanding of the molecular basis of galactomannan biosynthesis and aid in genomics-assisted improvement of cluster bean.

Skeletal muscle fibers rely upon either oxidative phosphorylation or the glycolytic pathway with much less reliance on oxidative phosphorylation to achieve muscular contractions that power mechanical movements. Species with energy-intensive adaptive traits that require sudden bursts of energy have a greater dependency on glycolytic fibers. Glycolytic fibers have decreased reliance on OXPHOS and lower mitochondrial content compared to oxidative fibers. Hence, we hypothesized that gene loss might have occurred within the OXPHOS pathway in lineages that largely depend on glycolytic fibers. The protein encoded by the COA1/MITRAC15 gene with conserved orthologs found in budding yeast to humans promotes mitochondrial translation. We show that gene disrupting mutations have accumulated within the COA1 gene in the cheetah, several species of galliform birds, and rodents. The genomic region containing COA1 is a well-established evolutionary breakpoint region in mammals. Careful inspection of genome assemblies of closely related species of rodents and marsupials suggests two independent COA1 gene loss events co-occurring with chromosomal rearrangements. Besides recurrent gene loss events, we document changes in COA1 exon structure in primates and felids. The detailed evolutionary history presented in this study reveals the intricate link between skeletal muscle fiber composition and the occasional dispensability of the chaperone-like role of the COA1 gene.

Volatile low complexity regions (LCRs) are a novel source of adaptive variation, functional diversification and evolutionary novelty. An interplay of selection and mutation governs the composition and length of low complexity regions. High %GC and mutations provide length variability because of mechanisms like replication slippage. Owing to the complex dynamics between selection and mutation, we need a better understanding of their coexistence. Our findings underscore that positively selected sites (PSS) and low complexity regions prefer the terminal regions of genes, co-occurring in most Tetrapoda clades. We observed that positively selected sites within a gene have position-specific roles. Central-positively selected site genes primarily participate in defence responses, whereas terminal-positively selected site genes exhibit non-specific functions. Low complexity region-containing genes in the Tetrapoda clade exhibit a significantly higher %GC and lower ω (dN/dS: non-synonymous substitution rate/synonymous substitution rate) compared with genes without low complexity regions. This lower ω implies that despite providing rapid functional diversity, low complexity region-containing genes are subjected to intense purifying selection. Furthermore, we observe that low complexity regions consistently display ubiquitous prevalence at lower purity levels, but exhibit a preference for specific positions within a gene as the purity of the low complexity region stretch increases, implying a composition-dependent evolutionary role. Our findings collectively contribute to the understanding of how genetic diversity and adaptation are shaped by the interplay of selection and low complexity regions in the Tetrapoda clade.

Globally, shrimp aquaculture is a vital source of food, but disease outbreaks present serious economic challenges. Traditional diagnostic techniques, such as visual inspection and polymerase chain reaction (PCR), are limited in their ability to detect diseases in real-time because they are either resource-intensive or prone to human error. In order to overcome these obstacles, we propose FeatherNetX, a lightweight deep learning framework, designed for automated shrimp diseases classification and deployment in resource-constrained settings. Black Gill (BG), White Spot Syndrome Virus (WSSV), Yellow Head Virus, and Healthy classes were among the publicly accessible shrimp disease image datasets used to train the model using 5-fold cross-validation approach. FeatherNetX outperforms other models with an average accuracy of 93% ± 0.059, a small model size (0.739 M parameters, 2.82 MB) and low computational cost (0.48 GFLOPs) while outperforming traditional architectures in terms of efficiency. In order to improve interpretability, disease-relevant regions were visualized using Grad-CAM++, which demonstrated a high degree of correspondence between activated regions and ground-truth disease areas. Additionally, the model was incorporated into a desktop application that could classify images in real-time and offline into specific classes with confidence reporting. It achieved 94% accuracy on test images that were unseen and took an average of less than 0.2 seconds per image to classify. This study bridges the gap between deep learning research and practical aquaculture practice by offering a strong framework for automated classification of shrimp disease through the combination of lightweight architecture, model interpretability, and practical deployment.

Galactomannan is a polymer of high economic importance and is extracted from the seed endosperm of clusterbean ( C. tetragonoloba ). In the present study, we worked to reveal the stage-specific galactomannan biosynthesis and its regulation in clusterbean. Combined electron microscopy and biochemical analysis revealed high protein and gum content in RGC-936, while high oil bodies and low gum content in M-83. A comparative transcriptome study was performed between RGC-936 (high gum) and M-83 (low gum) varieties at three developmental stages viz. 25, 39, and 50 days after flowering (DAF). Total 209,525, 375,595 and 255,401 unigenes were found at 25, 39 and 50 DAF respectively. Differentially expressed genes (DEGs) analysis indicated a total of 5147 shared unigenes between the two genotypes. Overall expression levels of transcripts at 39DAF were higher than 50DAF and 25DAF. Besides, 691 (RGC-936) and 188 (M-83) candidate unigenes that encode for enzymes involved in the biosynthesis of galactomannan were identified and analyzed, and 15 key enzyme genes were experimentally validated by quantitative Real-Time PCR. Transcription factor (TF) WRKY was observed to be co-expressed with key genes of galactomannan biosynthesis at 39DAF. We conclude that WRKY might be a potential biotechnological target (subject to functional validation) for developing high gum content varieties.

Waterlogging is a constant threat to crop productivity and ecological biodiversity. Plants face multiple challenges during waterlogging stress like metabolic reprogramming, hypoxia, nutritional depletion, reduction in gaseous exchange, pH modifications, microbiome alterations and disease promotion all of which threaten plants survival. Due to global warming and climatic change, the occurrence, frequency and severity of flooding has dramatically increased posing a severe threat to food security. Thus, developing innovative crop management technologies is critical for ensuring food security under changing climatic conditions. At present, the top priority among scientists is to find nature-based solutions to tackle abiotic or biotic stressors in sustainable agriculture in order to reduce climate change hazards to the environment. In this regard, utilizing plant beneficial microbiome is one of the viable nature based remedial tool for mitigating abiotic stressors like waterlogging. Beneficial microbiota provides plants multifaceted benefits which improves their growth and stress resilience. Plants recruit unique microbial communities to shield themselves against the deleterious effects of biotic and abiotic stress. In comparison to other stressors, there has been limited studies on how waterlogging stress affects plant microbiome structure and their functional traits. Therefore, it is important to understand and explore how waterlogging alters plant microbiome structure and its implications on plant survival. Here, we discussed the effect of waterlogging stress in plants and its microbiome. We also highlighted how waterlogging stress promotes pathogen occurrence and disease development in plants. Finally, we highlight the knowledge gaps and areas for future research directions on unwiring how waterlogging affects plant microbiome and its functional traits. This will pave the way for identifying resilient microbiota that can be engineered to promote their positive interactions with plants during waterlogging stress.

Plants are very often confronted by different heavy metal (HM) stressors that adversely impair their growth and productivity. Among HMs, chromium (Cr) is one of the most prevalent toxic trace metals found in agricultural soils because of anthropogenic activities, lack of efficient treatment, and unregulated disposal. It has a huge detrimental impact on the physiological, biochemical, and molecular traits of crops, in addition to being carcinogenic to humans. In soil, Cr exists in different forms, including Cr (III) “trivalent” and Cr (VI) “hexavalent”, but the most pervasive and severely hazardous form to the biota is Cr (VI). Despite extensive research on the effects of Cr stress, the exact molecular mechanisms of Cr sensing, uptake, translocation, phytotoxicity, transcript processing, translation, post-translational protein modifications, as well as plant defensive responses are still largely unknown. Even though plants lack a Cr transporter system, it is efficiently accumulated and transported by other essential ion transporters, hence posing a serious challenge to the development of Cr-tolerant cultivars. In this review, we discuss Cr toxicity in plants, signaling perception, and transduction. Further, we highlight various mitigation processes for Cr toxicity in plants, such as microbial, chemical, and nano-based priming. We also discuss the biotechnological advancements in mitigating Cr toxicity in plants using plant and microbiome engineering approaches. Additionally, we also highlight the role of molecular breeding in mitigating Cr toxicity in sustainable agriculture. Finally, some conclusions are drawn along with potential directions for future research in order to better comprehend Cr signaling pathways and its mitigation in sustainable agriculture.