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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning

Role of diode laser (810 nm) and/or fluoride varnish for the treatment of gamma-irradiated hypersensitive human dentin (An in vitro study)

Abstract Head and neck cancer is the sixth most frequent cancer worldwide. Dentin hypersensitivity (DH) is a common complication in patients undergoing radiotherapy. Gamma radiation significantly affects dental hard tissues, particularly dentin. Management of DH can be achieved using different agents such as fluoride and laser. This study aimed to evaluate the effectiveness of diode laser (810 nm), fluoride varnish, and their combination in managing DH in gamma-irradiated hypersensitive human dentin. Forty dentin specimens were assigned into four groups, G; gamma-irradiation only, GF; gamma-irradiation then fluoride varnish, GL; gamma-irradiation then laser, GFL; gamma-irradiation then fluoride then laser. Specimens were qualitatively assessed before and after citric acid challenge using environmental scanning electron microscope (ESEM). Image analysis for ESEM photomicrographs was performed to quantitatively evaluate the percentage of open dentinal tubules (DTs). The percentage of open DTs of group G before citric acid challenge showed the highest value that insignificantly increased after acid challenge. The percentage of open DTs of GF and GL groups significantly increased after citric acid challenge. The percentage of open DTs of group GFL insignificantly increased after citric acid challenge. Groups can be arranged according to the percentage of open DTs descendingly before citric acid challenge: G > GL > GFL > GF and after citric acid challenge: G > GF > GL > GFL. The combination of diode laser and fluoride varnish proves most effective in maintaining DTs occlusion following citric acid, offering a promising strategy for managing DH in patients undergoing radiotherapy.

Discovery of FoTO1 and Taxol genes enables biosynthesis of baccatin III

Abstract Plants make complex and potent therapeutic molecules1,2, but sourcing these molecules from natural producers or through chemical synthesis is difficult, which limits their use in the clinic. A prominent example is the anti-cancer therapeutic paclitaxel (sold under the brand name Taxol), which is derived from yew trees (Taxus species)3. Identifying the full paclitaxel biosynthetic pathway would enable heterologous production of the drug, but this has yet to be achieved despite half a century of research4. Within Taxus’ large, enzyme-rich genome5, we suspected that the paclitaxel pathway would be difficult to resolve using conventional RNA-sequencing and co-expression analyses. Here, to improve the resolution of transcriptional analysis for pathway identification, we developed a strategy we term multiplexed perturbation × single nuclei (mpXsn) to transcriptionally profile cell states spanning tissues, cell types, developmental stages and elicitation conditions. Our data show that paclitaxel biosynthetic genes segregate into distinct expression modules that suggest consecutive subpathways. These modules resolved seven new genes, allowing a de novo 17-gene biosynthesis and isolation of baccatin III, the industrial precursor to Taxol, in Nicotiana benthamiana leaves, at levels comparable with the natural abundance in Taxus needles. Notably, we found that a nuclear transport factor 2 (NTF2)-like protein, FoTO1, is crucial for promoting the formation of the desired product during the first oxidation, resolving a long-standing bottleneck in paclitaxel pathway reconstitution. Together with a new β-phenylalanine-CoA ligase, the eight genes discovered here enable the de novo biosynthesis of 3’-N-debenzoyl-2’-deoxypaclitaxel. More broadly, we establish a generalizable approach to efficiently scale the power of co-expression analysis to match the complexity of large, uncharacterized genomes, facilitating the discovery of high-value gene sets.

Deformable detection transformers for domain adaptable ultrasound localization microscopy with robustness to point spread function variations

A novel meta-heuristic algorithm based on candidate cooperation and competition

Metabolic adaptations direct cell fate during tissue regeneration

Abstract Although cell-fate specification is generally attributed to transcriptional regulation, emerging data also indicate a role for molecules linked with intermediary metabolism. For example, α-ketoglutarate (αKG), which fuels energy production and biosynthetic pathways in the tricarboxylic acid (TCA) cycle, is also a co-factor for chromatin-modifying enzymes1–3. Nevertheless, whether TCA-cycle metabolites regulate cell fate during tissue homeostasis and regeneration remains unclear. Here we show that TCA-cycle enzymes are expressed in the intestine in a heterogeneous manner, with components of the αKG dehydrogenase complex4–6 upregulated in the absorptive lineage and downregulated in the secretory lineage. Using genetically modified mouse models and organoids, we reveal that 2-oxoglutarate dehydrogenase (OGDH), the enzymatic subunit of the αKG dehydrogenase complex, has a dual, lineage-specific role. In the absorptive lineage, OGDH is upregulated by HNF4 transcription factors to maintain the bioenergetic and biosynthetic needs of enterocytes. In the secretory lineage, OGDH is downregulated through a process that, when modelled, increases the levels of αKG and stimulates the differentiation of secretory cells. Consistent with this, in mouse models of colitis with impaired differentiation and maturation of secretory cells, inhibition of OGDH or supplementation with αKG reversed these impairments and promoted tissue healing. Hence, OGDH dependency is lineage-specific, and its regulation helps to direct cell fate, offering insights for targeted therapies in regenerative medicine.

An unmanned intelligent inspection technology based on improved reinforcement learning algorithm for power large-area multi-scene inspection

Ancient DNA helps trace stinky Roman fish sauce to its source

Plastic water bottle detection model using computer vision in aquatic environments

Abstract Watershed macrotrash contamination is difficult to measure and requires tedious and labor-intensive processes. This work proposes an automated approach to waste counting, focusing on using computer vision, deep learning, and object tracking algorithms to acquire accurate counts of plastic bottles as they advect down rivers and streams. By using a combination of several publicly available labeled trash and plastic bottle image datasets, the model was trained to achieve high performance with the YOLOv8 object detection model. This was paired with the Norfair object tracking library and a novel post-processing algorithm to filter out false positives. The model performed extremely accurately over the test scenarios with just one false positive and recalls in excess of 0.947.

A novel fault diagnosis method for gearbox based on RVMD and TELM with composite chaotic grey wolf optimizer

Small molecule compound ZYZ-384 targets SMYD3 to alleviate aging

Distal locking mechanism influences surgical and radiological outcomes in proximal femoral nailing using distal wedge versus distal screw designs

Abstract This study aims to evaluate the radiological outcomes of two proximal femoral nails that share similar proximal geometry and sizes, but differ in their distal locking mechanisms. This retrospective study included 244 patients with AO 31-A1/A3 intertrochanteric fractures treated with either Wedge-wing proximal femoral nail (Ww-PFN) (n = 158) or Distally Wedge proximal femoral nail (Dw-PFN) (n = 86). Radiological parameters such as fracture reduction quality, tip-apex distance (TAD), neck-shaft angle (NSA) and time to fracture healing were compared. The Dw-PFN group demonstrated significantly shorter fracture healing time (12.45 ± 9.7 vs. 15.0 ± 3.4 months, p < 0.001) and better fracture reduction quality (p < 0.001) compared to the Ww-PFN group. NSA decreased in both groups postoperatively, with a greater mean decrease observed in the Dw-PFN group; this difference was not statistically significant (p = 0.175). However, complication rates were not different. (p = 0.342). The expandable talon mechanism of the Dw-PFN was associated with significantly faster fracture healing and shorter surgical duration compared to the Ww-PFN system. The mean surgical time was 34.1 ± 6.5 min for the Dw-PFN group and 50.6 ± 10.4 min for the Ww-PFN group (p = 0.001). These findings suggest that the talon-type distal fixation may be a favorable alternative in clinical practice. Notably, despite the lower rate of good fracture reduction in the Dw-PFN group (28.5% vs. 71.3%), complication and failure rates were comparable, further supporting the safety and clinical feasibility of this locking mechanism.

Fabrication of a low-kink-radius bilayer vascular scaffold incorporating a TPU stent fabricated via melt electrowriting and an electrospun PCL/PU/gelatin layer

Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations

Abstract Over 100 co-crystal structures of KRAS switch-II pocket (SII-P) targeting inhibitors are currently available in the RCSB Protein Data Bank. These publicly available structures are invaluable tools that have led to a more in-depth understanding of KRAS SII-P pocket, which is crucial for targeted drug design efforts. The binding modes (co-crystal structures) of two KRAS(G12C) inhibitors that have advanced to the clinical trials, GDC-6036 (divarasib) and LY3537982 (olomorasib), are currently unavailable. Here, we reveal the putative binding modes of these two G12C inhibitors by utilizing the available structural data of analogous compounds and molecular dynamics (MD) simulations (total simulation time of 200 micros). Our biochemical assays confirm high affinity of both inhibitors for KRAS(G12C), with differential susceptibility to typical resistance associated co-mutations. A co-mutation at Tyr96 reduces the affinity of both inhibitors, whereas a mutation at His95 negatively impacts only GDC-6036. While both inhibitors display low activity towards KRAS(WT), LY3537982 maintains high activity against other RAS isoforms with G12C mutation. As the simulation predictions align with the experimental results, our findings indicate that microsecond timescale simulations can be a valuable tool for predicting binding modes.

Hydro-chemical profiling and contaminant source identification in agricultural canals using data driven clustering approaches

Abstract Canal networks are vital for irrigated agriculture in semi-arid regions, yet their water quality is increasingly endangered by diffuse agro-chemical runoff and unregulated effluent discharges. Despite this growing risk, long-term, high-resolution assessments that simultaneously capture spatial patterns and seasonal dynamics remain scarce—leaving practitioners with limited evidence for targeted interventions. Addressing this gap, the study sampled ten canal sites monthly for 11 months across Charkhi Dadri District (Haryana, India) and analysed sixteen physicochemical parameters, including heavy metals and irrigation-relevant ions. A suite of multivariate techniques—R- and Q-mode hierarchical clustering, principal-component analysis (PCA), correlation matrices and one-way ANOVA—was employed to disentangle pollution drivers, while the Irrigation Water Quality Index (IWQI) translated complex chemistry into management-ready scores. Two principal components explained 72.6% of variance, with aluminium, iron and copper emerging as dominant contributors; ANOVA revealed significant seasonal shifts (p < 0.05) in these metals. Cluster analysis pinpointed contamination hotspots, and IWQI values of 67.3–85.5 classified canal water as “good” to “very good” for irrigation. By integrating granular spatiotemporal monitoring with advanced multivariate statistics, the study delivers a scalable framework for managing irrigation canals in data-limited, semi-arid landscapes.

How fast are you ageing? Ordinary brain scans reveal the pace

3D printed bone nails loaded with ceftriaxone sodium for localized drug delivery

Knowledge, attitudes, and willingness of patients with thyroid diseases toward thyroid thermal ablation techniques

A comparative investigation of ultrasonication and magnetic stirring methods for green synthesis of zinc oxide nanoparticles using Punica granatum peels

Aqua-MC as a simple open access code for uncountable runs of AquaCrop

Abstract Understanding uncertainty in crop modeling is essential for improving prediction accuracy and decision-making in agricultural management. Monte Carlo simulations are widely used for uncertainty and sensitivity analysis, but their application to closed-source models like AquaCrop presents significant challenges due to the lack of direct access to source code. This study introduces Aqua-MC, an automated framework designed to facilitate Monte Carlo simulations in AquaCrop by integrating probabilistic parameter selection, iterative execution, and uncertainty quantification within a structured workflow. To demonstrate its effectiveness, Aqua-MC was applied to wheat yield modeling in Qazvin, Iran, where parameter uncertainty was assessed using 3000 Monte Carlo simulations. The DYNIA (Dynamic Identifiability Analysis) method was employed to evaluate the time-dependent sensitivity of 47 model parameters, providing insights into the temporal evolution of parameter influence. The results revealed that soil evaporation and yield predictions exhibited the highest uncertainty, while transpiration and biomass outputs were more stable. The study also highlighted that many parameters had low impact, suggesting that reducing the number of free parameters could enhance model efficiency. Despite its advantages, Aqua-MC has some limitations, including its computational intensity and reliance on the GLUE method, which may overestimate uncertainty bounds. To improve applicability, future research should focus on parallel computing, cloud-based execution, integration with machine learning techniques, and expanding Aqua-MC to multi-crop studies. By overcoming the limitations of closed-source models, Aqua-MC provides a scalable and efficient solution for performing large-scale uncertainty analysis in crop modeling.