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Background: Two natural steroids derived from cholesterol pathways are testosterone and progesterone, androgen and antiandrogen receptor binding. Steroid androgen antagonists can be prescribed to treat an array of diseases and disorders such as gender dysphoria. In men, androgen antagonists are frequently used to treat prostate cancer and hyperplasia. Sex hormones regulate the expression of the viral receptors in COVID-19 progression, and these hormones may act as a metabolic signal-mediating response to changes in glucose and Reactive Oxygen Species (ROS). The objective of the present study is to use artificial intelligence (AI) applications in healthcare to predict the targets and to assess biological assays of novel steroid derivatives prepared in house from the commercially available 16-dehydropregnenolone acetate (DPA^®) aimed at achieving the metabolic stability of glucose and steroid brain homeostasis. This suggests the introduction of aromatic or aliphatic structures in the steroid B-ring and D-ring. This is important since the roles of 5α-reductase and ROS in brain control of glucose and novel steroids homeostasis remain unclear. Methods: A tool prediction was used as a tuned algorithm, with the novel steroid derivatives data in web interface to carry out their pharmacological evaluation. The new steroidal derivatives were determined with neuroprotection effect using the select biomarkers of oxidative stress on induced hypoglycemic male rat brain and liver. The enzyme kinetics was established by the inhibition of the 5α-reductase enzyme on the brain myelin. Results: We used novel chemical structures to order the information of a Swiss data bank that allow target predictions. Biological assays suggest that steroid derivatives with an electrophilic center can interact more efficiently with the 5α-reductase enzyme, and by this way, induce neuroprotection in hypoglycemia model. All compounds were synthesized with a yield of 30–80% and evaluated with tool target prediction to understand the molecular mechanisms underlying a given phenotype or bioactivity and to rationalize possible favorable or unfavorable side effects, as well as to predict off-targets of known molecules and to clear the way for drug repurposing. Apart, they turned out to be good inhibitors for the 5α-reductase enzyme. Conclusions: The probed efficacy of these novel steroids with respect to spironolactone control appears to be a promising compound for future hormonal therapy with neuroprotection activity in glucose disorder status. However, further research with clinically meaningful endpoints is needed to optimize the use of androgen antagonists in these hormonal therapies in COVID-19 progression. Full article

Precise distinguishing of maize blends and the evaluation of kernel losses enhances the accurate measurement of harvest loss. To address the low accuracy and poor anti-interference ability of traditional maize kernel detection methods under complex conditions, this paper proposes a multi-channel kernel impact detection algorithm based on discrete wavelet transform (DWT). The algorithm extracts feature band energies of kernel impacts through DWT multi-resolution analysis and counts kernels based on the duration of the energy signal. Therefore, weak signals are able to be effectively detected, thus correcting the missed errors that traditional monitoring systems produce for weak kernel signals. The monitoring system’s efficacy was assessed across various operational conditions. Test findings reveal that within the operating ranges of kernel flow rate of 20–40 kernels/s, sensor mounting angle of 30–60°, and mounting height of 300–500 mm, the system’s average detection accuracy reaches 94.4% and maintains good stability under different conditions. Compared with traditional detection systems, the system designed in this research exhibits superior sensitivity to weak kernel signals and higher monitoring accuracy. Finally, it was verified via practical field experiments that the designed sensor basically achieved the expected performance, and the recognition accuracy of the kernels in the mixture reaches 94%. Full article

The gearbox is a critical component in modern industrial systems, directly determining the operational reliability of machinery. Therefore, effective fault diagnosis is essential to ensure its proper functioning. Modern diagnostic approaches often employ accelerometers to monitor vibration signals and apply data-driven techniques for fault identification, achieving considerable success. However, deep learning-based methods still face limitations due to their “black-box” nature and lack of interpretability. To address these issues, this paper proposes a knowledge-guided selective wavelet kernel fusion neural network. By integrating diagnostic domain knowledge into data-driven modeling, the proposed method enhances both the interpretability and diagnostic performance of intelligent fault diagnosis systems. First, a multi-kernel convolutional module is designed based on domain knowledge and embedded into a Modern Temporal Convolutional Network. Then, an attention-based selective wavelet kernel fusion strategy is introduced to adaptively fuse kernels according to the distribution of different datasets. Finally, the effectiveness of the proposed method is validated on two public datasets. Experimental results demonstrate that the approach not only provides prior interpretability, which overcoming the black-box limitation of deep learning, but also further improves diagnostic accuracy. Full article

While there is a consensus that the cytochrome b₆f complex (cytb₆f) in algae and plants is involved in the regulatory mechanism of oxygenic photosynthesis known as light-induced state transitions (STs), no such consensus exists for cyanobacteria. Here, we provide the first direct functional evidence for cytb₆f using single-point mutation data. We introduced a PetD-Phe124Ala substitution in the cyanobacterium Synechocystis sp. PCC 6803 to test the key predictions of the hydrophobic-mismatch (HMM) model for cytb₆f-driven STs in all oxygenic photosynthetic species. These predictions concern the role of the Phe/Tyr124^fg-loop-PetD and the extent and kinetic characteristics of STs. The effects of PetD-F124A mutation on STs were monitored using 77K and Pulse-Amplitude-Modulated (PAM) fluorescence. For comparison, we employed a phycobilisome (PBS)-less Synechocystis mutant and wild-type (WT) strain, as well as the stn7 mutant and WT of Arabidopsis plant. The PetD-F124A mutation reduced the extent of STs and selectively affected the two-exponential kinetics components of the transitions. Under State 1 conditions, the mutant exhibited ~60% less energetic decoupling of PBS from photosystem I (PSI) compared to the WT. It is explainable by the HMM model with the inability of the PetD-F124A mutant, during the induction phase of the State 2→State 1 transition to adopt the cytb₆f conformation with minimal hydrophobic thickness. PAM-derived parameters indicated that PSII electron transport function is not inhibited, and no detectable effect on cyclic electron transport around PSI was observed under low-light conditions. Circular dichroism and differential scanning calorimetry confirmed that both the PSI trimer/monomer ratio and the structural integrity of the PBSs are preserved in the mutant. The compensatory response to the mutation includes decreased PSI content and an increase in PBS rod size. In conclusion, (1) cytb₆f is involved in cyanobacterial STs; (2) evidence is provided supporting the HMM model; (3) the electron transfer and signal transduction functions of cytb₆f are separated into distinct domains; and (4) the signaling pathway regulating STs and pigment-protein composition in Synechocystis involves PetD-Phe124. Full article

Pridopidine is a highly selective sigma-1 receptor (S1R) agonist in clinical development for Huntington’s disease (HD) and amyotrophic lateral sclerosis (ALS). The S1R is a ubiquitous chaperone protein enriched in the central nervous system and regulates multiple pathways critical for neuronal cell function and survival, including cellular stress responses, mitochondrial function, calcium signaling, protein folding, and autophagy. S1R has a crucial role in the ER mitochondria-associated membrane (MAM), whose dysfunction is implicated in several neurodegenerative diseases. By activating the S1R, pridopidine corrects multiple cellular pathways necessary to the cell’s ability to respond to stress, which are disrupted in neurodegenerative diseases. Pridopidine restores MAM integrity; rescues Ca²⁺ homeostasis and autophagy; mitigates ER stress, mitochondrial dysfunction, and oxidative damage; and enhances brain-derived neurotrophic factor (BDNF) axonal transport and secretion, synaptic plasticity, and dendritic spine density. Pridopidine demonstrates neuroprotective effects in in vivo models of neurodegenerative diseases (NDDs). Importantly, pridopidine demonstrates the biphasic dose response characteristic of S1R agonists. In clinical trials in HD and ALS, pridopidine has shown benefits across multiple endpoints. Pridopidine’s mechanism of action, modulating core cellular survival pathways, positions it as a promising candidate for disease modification for different nervous system disorders. Its broad therapeutic potential includes neurodevelopmental disorders, and rare diseases including Wolfram syndrome, Rett syndrome, and Vanishing White Matter Disease. Here, we review the experimental data demonstrating pridopidine’s S1R-mediated neuroprotective effects. These findings underscore the therapeutic relevance of S1R activation and support further investigation of pridopidine for the treatment of different neurodegenerative diseases including ALS and HD. Full article

Building high-quality multimodal instruction datasets is often time-consuming and costly. Recent studies have shown that a small amount of carefully selected high-quality data can be more effective for improving LVLM performance than large volumes of low-quality data. Based on these observations, we propose an error-guided multimodal sample selection framework with hallucination suppression for LVLM fine-tuning. First, semantic embeddings of queries are clustered to form balanced subsets that preserve task diversity. A visual contrastive decoding module is then used to reduce hallucinations and expose genuinely difficult examples. For closed-ended tasks, such as object detection, we estimate sample value using prediction accuracy; for open-ended question answering, we use the perplexity of generated responses as a difficulty signal. Within each cluster, high-error or high-perplexity samples are preferentially selected to construct a compact yet informative training set. Experiments on the InsPLAD detection benchmark and the PowerQA visual question answering dataset show that our method consistently outperforms random sampling under the same data budget, achieving higher F1, cosine similarity, BLEU (Bilingual Evaluation Understudy), and GPT-4o-based evaluation scores. This demonstrates that hallucination-aware, uncertainty-driven data selection can improve LVLM robustness and data efficiency. Full article

The complex environment of metal mines causes significant noise interference in microseismic signals. This leads to low accuracy and high false alarm rates when using the conventional Short-Term Average/Long-Term Average (STA/LTA) method for first-arrival picking. To address these issues, this paper proposes an improved approach that combines Variational Mode Decomposition (VMD) with STA/LTA(V-STA/LTA). The proposed method selects effective mode components through multimodal decomposition. Subsequently, an energy-weighted fusion is achieved based on energy distribution characteristics to improve the accuracy of arrival time-picking. First, the microseismic signal is decomposed by VMD. The center frequencies of the Intrinsic Mode Functions (IMFs) are then calculated through Fast Fourier Transform (FFT). This helps identify and retain the effective mode components, reducing noise interference. Next, the STA/LTA method is applied to each selected mode component for first-arrival picking. Finally, the results from the different components are fused based on their energy weights for improving picking precision. In low signal-to-noise ratio (SNR) conditions, the effectiveness of the V-STA/LTA method was verified through simulation experiments and field data tests. In theoretical simulations, according to test results from multiple sets of different signal-to-noise ratios, the root mean square error (RMSE) (0.0005) and mean absolute error (MAE) (0.00055) of V-STA/LTA are significantly lower than those of STA/LTA and AIC. In actual data, the average accuracy (99.77%) is nearly 1 percentage point higher than that of the traditional STA/LTA (98.93%), improving the accuracy of microseismic signal arrival time-picking. Full article

Abiotic stresses such as drought and heat severely constrain the growth and productivity of Solanaceae crops, including potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum L.), yet the conserved regulatory mechanisms underlying their stress adaptation remain incompletely understood. Here, we performed an integrative meta-analysis of publicly available transcriptomic datasets, complemented by comparative and evolutionary analyses across the Solanum genus. Functional annotation revealed coordinated transcriptional reprogramming characterized by induction of protective processes, including molecular chaperone activity, oxidative stress responses, and immune signaling, accompanied by repression of photosynthetic and primary metabolic pathways, reflecting energy reallocation under stress conditions. Promoter motif and transcription factor enrichment analyses implicated the bZIP, bHLH, DOF, and BBR/BPC families as central regulators of drought- and heat-induced transcriptional programs. Orthogroup inference and $K_a/K_s$ analysis across representative Solanum species demonstrated a predominance of purifying selection, indicating evolutionary conservation of regulatory network architecture. Integration of motif occurrence, co-expression profiles, and protein–protein interaction data enabled reconstruction of regulatory networks and identification of conserved hub transcription factors coordinating stress responses. Comparative analysis revealed distinct but conserved transcriptional signatures for heat and drought shared between potato and tomato, indicative of conserved abiotic stress strategies across Solanaceae. Full article

Goat milk can be a vehicle for beneficial microorganisms, such as probiotic lactic acid bacteria (LAB). During lactic fermentation, the hydrolysis of milk proteins can improve their nutritional properties and sensory attributes and even have beneficial health effects. The objective of this study was to evaluate the caseinolytic activity of LAB strains with probiotic potential and to monitor the changes induced by fermentation and during storage in milk components using Fourier transform infrared (FTIR) spectroscopy. First, the proteolytic activity of 36 LAB strains isolated from dairy products was qualitatively assessed. Then, 17 strains with probiotic potential and moderate to high proteolytic activity were selected for further analysis. Casein proteolysis was found to be strain-dependent, with a decrease in total protein concentration ranging from 28% to 87% and an increase in amino acids ranging from 29% to 88%. Furthermore, a notable difference was observed in the amide bands in the FTIR spectra between the beginning and end of incubation, showing a decrease in the intensities of the bands attributed to proteins. In fermented goat milk, LAB growth resulted in a final count between 0.62 and 2.6 log CFU/mL, a 0.29 to 2.0 drop in pH, and lactic acid production between 0.20 and 1 g/L. FTIR spectra revealed time-dependent modifications in amide I and II bands accompanied by a marked reduction in carbohydrate content and an increase in lactic acid signal. After 21 days of storage, the viability of the strains, pH, and lactic acid in the fermented milks were not substantially modified. These results highlight the potential of lactic fermentation with strains selected for their probiotic potential as an approach to producing value-added goat milk products, as well as the usefulness of FTIR spectroscopy for characterizing complex systems such as goat milk. Full article

Background: Industrial power time-series exhibit strong daily/weekly periodicities and nonstationary behaviors that challenge generic deep autoencoders. Methods: We take first differences of the signal, compute the FFT spectrum, and map top spectral peaks to a small set of modeling window sizes. For each window, a GELU-activated CNN–GRU autoencoder is trained under the Unsupervised Anomaly Detection (USAD) paradigm (one encoder, two decoders with an adversarial phase). Reconstruction errors are measured with Dynamic Time Warping (DTW) to mitigate phase jitter, and final anomaly decisions are obtained by fitting an Isolation Forest to the error distribution. On a three-year, single-site dataset (15 min sampling), the approach detects abrupt spikes/drops and slow drifts across sub-daily to daily rhythms; FFT-selected windows of 11, 16, 24, 32, and 96 time steps (15 min units) cover the dominant cycles. Conclusions: FFT-guided multi-window training and inference, combined with a USAD-based model, DTW-aware scoring, and Isolation Forest, yields a practical unsupervised detector for smart-factory monitoring and near-real-time deployment. Full article

Teleost social behavior plays an important role in foraging, reproduction, and aquaculture management, yet its physiological basis remains poorly understood. This study investigated individual differences in sociability in the large yellow croaker (Larimichthys crocea) using behavioral assays and metabolomic profiling in the brain–intestine axis. Behavioral tests revealed that high-sociability (HS) fish spent significantly more time near conspecifics than low-sociability (LS) fish, indicating clear behavioral divergence between groups. Targeted metabolomics of brain tissue showed distinct neurotransmitter signatures between HS and LS individuals, including significant differences in acetylcholine, DOPAC, xanthurenic acid, and glutamine. Untargeted intestinal metabolomics identified 65 differential metabolites between groups. Intestinal metabolites such as LEA and CEA exhibited significant group-specific variation and were functionally associated with CB1 and CB2 cannabinoid receptors, suggesting a potential endocannabinoid-mediated contribution to sociability differences. Differential metabolites enriched in amino–sugar and nucleotide–sugar metabolic pathways. Integration of behavioral and metabolomic data suggests that neurotransmitter regulation and gut–brain metabolic signaling jointly contribute to sociability differences in large yellow croaker. These findings provide mechanistic insights into social behavior and offer potential biomarkers for welfare assessment and selective breeding in aquaculture. Full article

Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl a). Although chl a is a crucial indicator of phytoplankton biomass and nutrient overloading, it reflects the outcome of eutrophication rather than its cause. Nutrients, the primary “drivers” of eutrophication, are essential indicators for predicting the potential phytoplankton growth in water bodies, allowing adoption of effective preventive measures. Long-term monitoring of nutrients combined with multiple water quality indicators using remotely sensed data could lead to a more precise assessment of the trophic state. Retrieving non-optically active constituents, such as nutrients and DO, remains challenging due to their weak optical characteristics and low signal-to-noise ratios. This work is an attempt to review the current progress in the retrieval of un-ionized ammonia (NH₃), ammonium (NH₄⁺), ammoniacal nitrogen (AN), nitrite (NO₂⁻), nitrate (NO₃⁻), dissolved inorganic nitrogen (DIN), phosphate (PO₄³⁻), dissolved inorganic phosphorus (DIP), silicate (SiO₂) and dissolved oxygen (DO) using remotely sensed data. Most studies refer to Case II highly nutrient-enriched water bodies. The commonly used spaceborne and airborne sensors, along with the selected spectral bands and band indices, per study area, are presented. There are two main model categories for predicting nutrient and DO concentration: empirical and artificial intelligence (AI). Comparative studies conducted in the same study area have shown that ML and NNs achieve higher prediction accuracy than empirical models under the same sample size. ML models often outperform NNs when training data are limited, as they are less prone to overfitting under small-sample conditions. The incorporation of a wider range of conditions (e.g., different trophic state, seasonality) into model training needs to be tested for model transferability. Full article

Early age of onset is a major predictor of poor disease course in Bipolar Disorder (BD) and Schizophrenia (SCZ), often associated with greater symptom severity, cognitive decline, and worse outcomes. However, the biological mechanisms that shape age- and sex-specific vulnerability remain unclear, limiting progress toward early identification and intervention. To address this gap, we conducted an integrative transcriptomic study of 369 postmortem dorsolateral prefrontal cortex samples from the CommonMind Consortium. Differential gene expression, Weighted Gene Co-Expression Network Analysis, and gene set enrichment analysis were applied to identify pathways associated with age of onset, complemented by sex-stratified models and cellular deconvolution. To assess predictive signals, we applied a rigorous two-stage machine-learning framework using nested cross-validation, with Lasso feature selection followed by L2-regularized logistic classification. Performance was evaluated solely on held-out test folds. Genes and modules linked to earlier onset showed consistent enrichment for calcium signaling, with downregulation of CACNA1C and multiple adenylate-cyclase-related transcripts, while female-specific analyses revealed selective dysregulation of cyclase-associated pathways. Network analysis identified a calcium-enriched module associated with onset and sex, and diagnosis-specific modeling highlighted MAP2K7 in early-onset BD. The predictive model achieved an AUC of 0.63, and the top 50 machine-learning features were significantly enriched in calcium signaling pathway. These findings converge on calcium–cAMP signaling networks as key drivers of early psychiatric vulnerability and suggest biomarkers for precision-targeted interventions. Full article

Non-contact heart rate estimation technology based on frequency-modulated continuous wave (FMCW) radar has garnered extensive attention in single-target scenarios, yet it remains underexplored in multi-target environments. Accurate discrimination of multiple targets and precise estimation of their heart rates constitute key challenges in the multi-target domain. To address these issues, we propose a novel scheme for multi-target heart rate estimation. First, a high-precision distance-bin selection (HDBS) method is proposed for target localization in the range domain. Next, multiple-input multiple-output (MIMO) array processing is combined with the Root-multiple signal classification (Root-MUSIC) algorithm for angular domain estimation, enabling accurate discrimination of multiple targets. Subsequently, we propose an efficient method for interference suppression and vital sign extraction that cascades variational mode decomposition (VMD), local mean decomposition (LMD), and wavelet thresholding (WT) termed as VLW, which enables high-quality heartbeat signal extraction. Finally, to achieve high-precision and super-resolution heart rate estimation with low computational burden, an improved fast iterative interpolated beamforming (FIIB) algorithm is proposed. Specifically, by leveraging the conjugate symmetry of real-valued signals, the improved FIIB algorithm reduces the execution time by approximately 60% compared to the standard version. In addition, the proposed scheme provides sufficient signal-to-noise ratio (SNR) gain through low-complexity accumulation in both distance and angle estimation. Six experimental scenarios are designed, incorporating densely arranged targets and front-back occlusion, and extensive experiments are conducted. Results show this scheme effectively discriminates multiple targets in all tested scenarios with a mean absolute error (MAE) below 2.6 beats per minute (bpm), demonstrating its viability as a robust multi-target heart rate estimation scheme in various engineering fields. Full article

Alopecia is a multifactorial disorder in which immune, endocrine, metabolic, and microbial systems converge within the follicular microenvironment. In alopecia areata (AA), loss of immune privilege, together with interferon-γ- and interleukin-15-driven activation of the JAK/STAT cascade, promotes cytotoxic infiltration, whereas selective inhibitors, including baricitinib, ritlecitinib, and durvalumab, restore immune balance and permit anagen reentry. In androgenetic alopecia (AGA), excess dihydrotestosterone and androgen receptor signaling increase DKK1 and prostaglandin D2, suppress Wnt and β-catenin activity, and drive follicular miniaturization. Combination approaches utilizing low-dose oral minoxidil, platelet-rich plasma, exosome formulations, and low-level light therapy enhance vascularization, improve mitochondrial function, and reactivate metabolism, collectively supporting sustained regrowth. Elucidation of intracellular axes such as JAK/STAT, Wnt/BMP, AMPK/mTOR, and mitochondrial redox regulation provides a mechanistic basis for rational, multimodal intervention. Advances in stem cell organoids, biomaterial scaffolds, and exosome-based therapeutics extend treatment from suppression toward structural follicle reconstruction. Recognition of microbiome and mitochondria crosstalk underscores the need to maintain microbial homeostasis and redox stability for durable regeneration. This review synthesizes molecular and preclinical advances in AA and AGA, outlining intersecting signaling networks and regenerative interfaces that define a framework for precision and sustained follicular regeneration. Full article