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Early and reliable diagnostic tools are critical for slowing the progression of Alzheimer’s disease (AD), a neurodegenerative disorder characterized by memory loss and cognitive decline. This study introduces, ViTTL, lightweight deep learning framework for assessing the severity of AD using MRI data. ViTTL integrates Vision Transformers (ViT) with pre-trained convolutional neural networks utilized in transfer learning mode, to extract informative features from 2D MRI slices. Among the evaluated combinations, the ViT-DenseNet201 model integrated with an artificial neural network (ANN) classifier achieved the highest classification accuracy (99.89%) on the OASIS dataset. To ensure interpretability, we incorporated LIME and GRAD-CAM method, which consistently focus on cortical and hippocampal regions known to be associated with Alzheimer’s pathology. The average Dice similarity coefficient across runs was 0.85 with a standard deviation of 0.03, indicating high consistency in the model’s focus regions against ground truth annotations by expert radiologists. ViTTL also achieved a substantial reduction in model size from 83.0 MB to 6.47 MB enabling deployment in resource-limited environments without compromising performance. Validation on an independent dataset (Kaggle) and comparative performance analysis against state-of-the-art methods further support the robustness and generalizability. These findings demonstrate that ViTTL is a promising tool for accurate, interpretable, and resource-efficient AD diagnosis, with strong potential for clinical translation and patient outcome improvement. The related codes are available at https://github.com/RuhikaSharma/enhanced-alzheimer-risk-assessment .

Virtual screening (VS) aids in prioritizing unknown bio-interactions between compounds and protein targets for empirical drug discovery. In standard VS exercise, roughly 10% of top-ranked molecules exhibit activity when examined in biochemical assays, which accounts for many false positive hits, making it an arduous task. Attempts for conquering false-hit rates were developed through either ligand-based or structure-based VS separately; however, nonetheless performed remarkably well. Here, we present an advanced VS framework—automated hit identification and optimization tool (A-HIOT)—comprises chemical space-driven stacked ensemble for identification and protein space-driven deep learning architectures for optimization of an array of specific hits for fixed protein receptors. A-HIOT implements numerous open-source algorithms intending to integrate chemical and protein space leading to a high-quality prediction. The optimized hits are the selective molecules which we retrieve after extreme refinement implying chemical space and protein space modules of A-HIOT. Using CXC chemokine receptor 4, we demonstrated the superior performance of A-HIOT for hit molecule identification and optimization with tenfold cross-validation accuracies of 94.8% and 81.9%, respectively. In comparison with other machine learning algorithms, A-HIOT achieved higher accuracies of 96.2% for hit identification and 89.9% for hit optimization on independent benchmark datasets for CXCR4 and 86.8% for hit identification and 90.2% for hit optimization on independent test dataset for androgen receptor (AR), thus, shows its generalizability and robustness. In conclusion, advantageous features impeded in A-HIOT is making a reliable approach for bridging the long-standing gap between ligand-based and structure-based VS in finding the optimized hits for the desired receptor. The complete resource (framework) code is available at https://gitlab.com/neeraj-24/A-HIOT . Graphical Abstract

The MADS-box transcription factors play essential roles in various processes of plant growth and development. In the present study, phylogenetic analysis of 142 apple MADS-box proteins with that of other dicotyledonous species identified six putative Dormancy-Associated MADS-box (DAM) and four putative Flowering Locus C-like (FLC-like) proteins. In order to study the expression of apple MADS-box genes, RNA-seq analysis of 3 apical and 5 spur bud stages during dormancy, 6 flower stages and 7 fruit development stages was performed. The dramatic reduction in expression of two MdDAMs , MdMADS063 and MdMADS125 and two MdFLC -like genes, MdMADS135 and MdMADS136 during dormancy release suggests their role as flowering-repressors in apple. Apple orthologs of Arabidopsis genes, FLOWERING LOCUS T , FRIGIDA , SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 and LEAFY exhibit similar expression patterns as reported in Arabidopsis, suggesting functional conservation in floral signal integration and meristem determination pathways. Gene ontology enrichment analysis of predicted targets of DAM revealed their involvement in regulation of reproductive processes and meristematic activities, indicating functional conservation of SVP orthologs (DAM) in apple. This study provides valuable insights into the functions of MADS-box proteins during apple phenology, which may help in devising strategies to improve important traits in apple.

Nucleotide binding site leucine-rich repeats (NBS-LRR) disease resistance proteins play an important role in plant defense against pathogen attack. A number of recent studies have been carried out to identify and characterize NBS-LRR gene families in many important plant species. In this study, we identified NBS-LRR gene family comprising of 1015 NBS-LRRs using highly stringent computational methods. These NBS-LRRs were characterized on the basis of conserved protein motifs, gene duplication events, chromosomal locations, phylogenetic relationships and digital gene expression analysis. Surprisingly, equal distribution of Toll/interleukin-1 receptor (TIR) and coiled coil (CC) (1 ∶ 1) was detected in apple while the unequal distribution was reported in majority of all other known plant genome studies. Prediction of gene duplication events intriguingly revealed that not only tandem duplication but also segmental duplication may equally be responsible for the expansion of the apple NBS-LRR gene family. Gene expression profiling using expressed sequence tags database of apple and quantitative real-time PCR (qRT-PCR) revealed the expression of these genes in wide range of tissues and disease conditions, respectively. Taken together, this study will provide a blueprint for future efforts towards improvement of disease resistance in apple.

Background Adventitious root formation is considered a major developmental step during the propagation of difficult to root plants, especially in horticultural crops. Recently, adventitious roots induced through plant tissue culture methods have also been used for production of phytochemicals such as flavonoids, anthocyanins and anthraquinones. It is rather well understood which horticultural species will easily form adventitious roots, but the factors affecting this process at molecular level or regulating the induction process in in vitro conditions are far less known. The present study was conducted to identify transcripts involved in in vitro induction and formation of adventitious roots using Arnebia euchroma leaves at different time points (intact leaf (control), 3 h, 12 h, 24 h, 3 d, 7 d, 10 d and 15 d). A. euchroma is an endangered medicinal Himalayan herb whose root contains red naphthoquinone pigments. These phytoconstituents are widely used as an herbal ingredient in Asian traditional medicine as well as natural colouring agent in food and cosmetics. Results A total of 137.93 to 293.76 million raw reads were generated and assembled to 54,587 transcripts with average length of 1512.27 bps and N50 of 2193 bps, respectively. In addition, 50,107 differentially expressed genes were identified and found to be involved in plant hormone signal transduction, cell wall modification and wound induced mitogen activated protein kinase signalling. The data exhibited dominance of auxin responsive (AUXIN RESPONSE FACTOR8, IAA13 , GRETCHEN HAGEN3.1) and sucrose translocation (BETA-31 FRUCTOFURANOSIDASE and MONOSACCHARIDE-SENSING protein1) genes during induction phase. In the initiation phase, the expression of LATERAL ORGAN BOUNDARIES DOMAIN16, EXPANSIN-B15, ENDOGLUCANASE25 and LEUCINE-rich repeat EXTENSION-like proteins was increased. During the expression phase, the same transcripts, with exception of LATERAL ORGAN BOUNDARIES DOMAIN16 were identified. Overall, the transcriptomic analysis revealed a similar patterns of genes, however, their expression level varied in subsequent phases of in vitro adventitious root formation in A. euchroma. Conclusion The results presented here will be helpful in understanding key regulators of in vitro adventitious root development in Arnebia species, which may be deployed in the future for phytochemical production at a commercial scale.

NACHT NTPases and AP-ATPases belongs to STAND (signal transduction ATPases with numerous domain) P-loop NTPase class, which are known to be involved in defense signaling pathways and apoptosis regulation. The AP-ATPases (also known as NB-ARC) and NACHT NTPases are widely spread throughout all kingdoms of life except in plants, where only AP-ATPases have been extensively studied in the scenario of plant defense response against pathogen invasion and in hypersensitive response (HR). In the present study, we have employed a genome-wide survey (using stringent computational analysis) of 67 diverse organisms viz., archaebacteria, cyanobacteria, fungi, animalia and plantae to revisit the evolutionary history of these two STAND P-loop NTPases. This analysis divulged the presence of NACHT NTPases in the early green plants (green algae and the lycophyte) which had not been previously reported. These NACHT NTPases were known to be involved in diverse functional activities such as transcription regulation in addition to the defense signaling cascades depending on the domain association. In Chalmydomonas reinhardtii, a green algae, WD40 repeats found to be at the carboxyl-terminus of NACHT NTPases suggest probable role in apoptosis regulation. Moreover, the genome of Selaginella moellendorffii, an extant lycophyte, intriguingly shows the considerable number of both AP-ATPases and NACHT NTPases in contrast to a large repertoire of AP-ATPases in plants and emerge as an important node in the evolutionary tree of life. The large complement of AP-ATPases overtakes the function of NACHT NTPases and plausible reason behind the absence of the later in the plant lineages. The presence of NACHT NTPases in the early green plants and phyletic patterns results from this study raises a quandary for the distribution of this STAND P-loop NTPase with the apparent horizontal gene transfer from cyanobacteria.

Nitrate is the main source of inorganic nitrogen for plants, which also act as signaling molecule. Present study was aimed to understand nitrate regulatory mechanism in Brassica juncea cultivars, with contrasting nitrogen-use-efficiency (NUE) viz . Pusa Bold (PB, high-NUE) and Pusa Jai Kisan (PJK, low-NUE), employing RNA-seq approach. A total of 4031, 3874 and 3667 genes in PB and 2982, 2481 and 2843 genes in PJK were differentially expressed in response to early, low (0.25 mM KNO 3 ), medium (2 mM KNO 3 ) and high (4 mM KNO 3 ) nitrate treatments, respectively, as compared to control (0 mM KNO 3 ). Genes of N-uptake ( NRT1 . 1 , NRT1 . 8 , and NRT2 . 1 ), assimilation ( NR1 , NR2 , NiR , GS1 . 3 , and Fd-GOGAT ) and remobilization ( GDH2 , ASN2–3 and ALaT ) were highly-upregulated in PB than in PJK in response to early nitrate treatments. We have also identified transcription factors and protein kinases that were rapidly induced in response to nitrate, suggesting their involvement in nitrate-mediated signaling. Co-expression network analysis revealed four nitrate specific modules in PB, enriched with GO terms like, “Phenylpropanoid pathway”, “Nitrogen compound metabolic process” and “Carbohydrate metabolism”. The network analysis also identified HUB transcription factors like mTERF, FHA, Orphan, bZip and FAR1, which may be the key regulators of nitrate-mediated response in B . juncea .

The spice Ferula assa-foetida L., also known as asafoetida, is widely recognized for its medicinal and culinary applications. The non-native status of the plant and the prolonged dormancy of its seeds pose significant challenges for large-scale cultivation in India. In vitro organogenesis offers an effective solution to these obstacles. Establishing reliable in vitro regeneration protocols requires standardized statistical methods to evaluate univariate and multivariate data for optimizing specific traits. However, these methods have limitations when handling complex, nonlinear inputs, often producing large prediction errors that reduce the reliability of trait optimization. This study developed an in vitro regeneration system for F. assa-foetida L. and identified optimal PGRs for somatic embryogenesis and shoot organogenesis through image-based morphological analysis. Predictive models were created using DL and ML algorithms. Calli induced from leaf explants was cultured on the Murashige and Skoog medium supplemented with various combinations and concentrations of thidiazuron (TDZ), 6-benzylaminopurine (BAP), and α-naphthaleneacetic acid (NAA), as experimental variables. Seven ML approaches, namely random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), extreme gradient boosting (XG Boost), naïve bayes, and logistic regression, alongside five DL models—convolutional neural network (CNN), MobileNet, region-based convolutional neural network (RCNN), residual neural network (ResNet), and visual geometry group (VGG19)—were employed to predict the best PGRs for somatic embryogenesis and shoot organogenesis. Among them, the convolutional neural network (CNN) achieved the highest accuracy (87%), outperforming baseline ML models such as logistic regression and decision tree (82%). This pioneering study in F. assa-foetida L. presents an AI-driven, image-based framework for predicting optimal PGRs, offering a scalable approach to enhance micropropagation in endangered medicinal plants.

Bacterial lipases with activity spanning over a broad temperature and substrate range have several industrial applications. An efficient enzyme-producing bacterium Chryseobacterium polytrichastri ERMR1:04, previously reported from Sikkim Himalaya, was explored for purification and characterization of cold-adapted lipase. Optimum lipase production was observed in 1% (v/v) rice bran oil, pH 7 at 20°C. Size exclusion and hydrophobic interaction chromatography purified the enzyme up to 21.3-fold predicting it to be a hexameric protein of 250 kDa, with 39.8 kDa monomeric unit. MALDI-TOF-MS analysis of the purified lipase showed maximum similarity with alpha/beta hydrolase (lipase superfamily). Biochemical characterization of the purified enzyme revealed optimum pH (8.0), temperature (37°C) and activity over a temperature range of 5–65°C. The tested metals (except Cu2+ and Fe2+) enhanced the enzyme activity and it was tolerant to 5% (v/v) methanol and isopropanol. The Km and Vmax values were determined as 0.104 mM and 3.58 U/mg, respectively for p -nitrophenyl palmitate. Bioinformatics analysis also supported in vitro findings by predicting enzyme's broad temperature and substrate specificity. The compatibility of the purified lipase with regular commercial detergents, coupled with its versatile temperature and substrate range, renders the given enzyme a promising biocatalyst for potential detergent formulations.

This article studies the generation of entropy disturbances by laminar premixed flames. The total entropy generation equals the integrated ratio of the local heat release rate and the local temperature, namely, ∫ ( q ˙ / T ) d V . Due to this path dependency, evaluating this integral requires an understanding of how the heat release is distributed in the temperature space. Several studies evaluate the local entropy generation as ( ∫ q ˙ d V ) / T b , where T b refers to the burned gas temperature, implicitly assuming all the heat release occurs at T b . Such an approximation is motivated by the high activation energy nature of combustion chemistry. This work evaluates this assumption by comparing it to results from one-dimensional premixed flame calculations for hydrogen, methane, and propane-air flames over a range of pressures, equivalence ratios, and preheat temperatures, quantified via the ratio κ . We show that this assumption is quite reasonable for methane and propane-air flames (with errors ranging from 5% to 25%) but deviates significantly from the exact results for hydrogen flames (where errors can be as high as 50%). In general, the peak heat release moves to lower temperatures as preheat temperature is increased. Noting that the temperature sensitivity of heat release is directly related to the activation energy, we use Law's approach to extract global activation energies and show that the deviations of κ from unity can be approximately correlated with β e f f . Finally, we show that significant improvements in entropy generation calculations can be obtained by estimating ∫ ( q ˙ / T ) d V using ( ∫ q ˙ d V ) / T p e a k , where T p e a k is the temperature at which the reaction rate peaks. This estimation leads to predictions of ∼5% within the exact value for the hydrocarbon cases but can still be in significant error for hydrogen at certain conditions.