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Quantifying golden-ratio deviations in the tree drawing test to identify patients with Alzheimer’s disease
Observations on some bio-ecological attributes of cryptic one-piece wood-feeding termites (Blattodea, Isoptera) in Himachal Pradesh (India)
Nitrogen fixation rates increase with diazotroph richness in the global ocean
Abstract Marine nitrogen fixation is a key process to support and maintain the ocean’s primary production, yet our knowledge of the distribution and diversity of the diazotrophic microbes that are capable of fixing nitrogen is very limited. Here, integrating microscopic and metagenomic data, we determine the biogeography and richness of the main diazotrophic taxa across the global ocean. Analyzing 22,000 records and 15 species, we deduce a latitudinal gradient in diazotroph richness, with higher richness to the tropics driven by temperature and nutrient levels. Cyanobacteria dominate in nutrient-poor gyres, while non-cyanobacterial diazotrophs thrive in nutrient-rich zones. Across the global ocean, diazotroph richness is found to correlate positively with nitrogen fixation rates, suggesting a positive biodiversity-ecosystem function relationship. While this relationship is robust to spatial autocorrelation and confounding environmental drivers, spatial dependence in the global datasets and potential unmeasured covariates may influence local-scale inferences. The findings suggest that positive biodiversity–ecosystem functioning relationships with implications for global biogeochemical cycling exist in marine plankton.
Development of non-destructive durian fruit maturity detection tool based on multi-variable sensor for harvest quality optimisation
Abstract Durian maturity critically affects consumer acceptance, market value, and postharvest losses, yet its assessment remains largely subjective. This study presents a non-destructive multimodal sensing system integrating gas/VOC sensing, thermal imaging, and acoustic measurement for durian maturity classification. A dataset of 90 fruits representing three ripeness stages, namely unmature, partially mature, and mature, was evaluated using supervised machine learning with a fruit-level grouped validation procedure to reduce potential data leakage from repeated measurements. Gas/VOC sensors captured maturity-related changes in alcohol, VOC-related response, CO₂, and O₂, while thermal imaging provided surface temperature information and acoustic measurement provided tapping-based dB response. These sensor responses were consistent with destructive validation trends, including decreasing flesh firmness and increasing soluble solids and alcohol content. Among the evaluated models, the neural network achieved the highest performance, with 96.91% accuracy and an AUC of 0.98. Ablation analysis showed that gas/VOC features were the main contributors to classification performance, thermal features provided complementary information, and scalar acoustic dB alone had limited discriminatory ability. These findings demonstrate the potential of multimodal sensor fusion and machine learning for non-destructive durian maturity assessment.
Multi-criteria decision-making under comparison: benchmarking and optimal model selection for flood susceptibility mapping
X-ray histology on rapidly fixed fresh tumor tissue samples for fast resection margin assessment
Abstract Accurate assessment of the resection margin is of key importance for patient outcome after tumor surgery. Intraoperative assessment is preferable but present methods like fresh-frozen sectioning risk sampling errors and provide only 2D information. Classical 2D histology has better quality but is too slow for intraoperative feedback. In the present proof-of-concept study we show that laboratory x-ray histology based on propagation-based phase-contrast microtomography has potential for 3D intraoperative resection margin assessment. Our system includes a liquid–metal-jet high-brightness microfocus source, precision stages and a high-resolution detector, providing fast, high-contrast, near-cellular-resolution 3D imaging. The tissue is prepared by rapid acetone fixation before the 3D x-ray imaging. We demonstrate the method on several tumor types in liver and pancreas, as well as on different sarcomas. After the x-ray histology, classical histology was performed on the same samples for comparison. The two methods compare excellently on the liver and pancreas samples and reasonably on the morphologically more difficult sarcomas. The imaging currently takes 4–5 h with our prototype instrument, and we outline how to go below 1 h for the full procedure. We conclude that 3D x-ray histology can be developed to provide intraoperative resection margin assessment.
Detection of early-stage lung adenocarcinoma using a novel extracellular vesicle and particle-based blood test
Structural equation modeling of the effect of childhood trauma on premenstrual syndrome: the mediating role of rumination in adolescent girls
Atherogenic index of plasma and stroke risk across blood pressure strata in Chinese adults
Abstract This study investigated the association between the atherogenic index of plasma (AIP) and stroke risk across blood pressure (BP) strata, with exploratory assessments of the bidirectional mediating roles of body mass index (BMI) and AIP. We included 8,695 participants from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). AIP was calculated as log₁₀(TG/HDL-C). Time-dependent Cox models were used to estimate the hazard ratios (HRs). Restricted cubic spline (RCS) and mediation analyses were performed. Higher AIP was independently associated with increased stroke risk (HR: 1.60, 95% CI 1.22‑2.09).This association was observed in both the high-normal BP (HR: 2.08, 95% CI 1.21‑3.59) and hypertension strata (HR: 1.47, 95% CI 1.03‑2.11), with no significant interaction by BP strata ( P = 0.660). RCS analysis showed a dose-response relationship between baseline AIP levels and stroke risk across all participants, high-normal BP, and hypertension strata. Exploratory mediation analysis suggested that BMI mediated 20.59% of AIP’s effect on stroke, while AIP mediated 20.52% of BMI’s effect. AIP is an independent risk factor for stroke, with no significant effect modification by BP strata. Exploratory mediation analyses suggest that integrating AIP screening with BMI management could be beneficial for stroke prevention, a finding that requires prospective validation.
Assessing collagenase-a and type-II mediated degradation of human type-I collagen by photoacoustic spectroscopy toward clinical translation in cancer progression
Abstract The current study reports the design and development of a compact photoacoustic (PA) sensor for label‑free detection of collagen degradation, a key hallmark of tumor-associated matrix remodeling. The performance of the sensor was evaluated using human type I collagen subjected to controlled enzymatic digestion upon collagenase‑A and collagenase type‑II treatment, to mimic different stages of degraded collagen (intact, mild, and extensive degradation states), and recording corresponding PA spectra. The analyses of the PA spectra revealed treatment-dependent spectral differences with progressively increased signal strengths from control to collagenase‑A and collagenase type‑II treated samples. Intact collagen exhibited comparatively weaker PA responses, whereas collagenase-treated samples demonstrated altered signal amplitudes consistent with enzymatic disruption of collagen structure. A Light Gradient Boosting Machine-based analysis of the PA spectra showed a classification accuracy of ~ 98% within the experimental conditions evaluated in this study. Further, UV-Visible, fluorescence spectroscopy, dynamic light scattering, and scanning electron microscopy experiments demonstrated a change in absorption intensity, shift in emission maxima, gradual decrease in hydrodynamic size, and surface morphology, respectively, supporting progressive collagen degradation. To obtain an initial indication of probe performance in complex biological environments, preliminary PA measurements were also performed in freshly excised murine colon, pancreas, and spleen, revealing tissue-dependent spectral variations and partial clustering through multivariate analysis. Collectively, these findings support the potential applicability of the PA sensor as a label-free analytical platform for monitoring collagen remodeling and related extracellular-matrix alterations.
Clinically grounded retinal representation learning from minimal supervision
The relationship of eating attitudes and food insecurity with health outcomes in adults with celiac disease
A dynamic reward framework for scalable and efficient IoT-WSN routing using deep reinforcement learning
LipoLoad:RB: a nanocarrier comprising bacterial lipoprotein LipoMetQ and Rose Bengal
Abstract While conventional nanocarriers like liposomes are effective for small-molecule delivery, their fabrication often involves complex, multi-step processes. This work provides a proof-of-concept demonstrating a bacterial lipoprotein as a viable, genetically encoded, and self-assembling nanocarrier. We show specifically that the detergent-solubilized lipoprotein LipoMetQ from Neisseria meningitidis spontaneously forms micelle-like nanoparticles (termed LipoLoad), which entrap the small molecule Rose Bengal (RB) using a simple procedure of mixing and centrifugal ultrafiltration. The resulting LipoLoad: RB formulation was analyzed by dynamic light scattering and negative-stain TEM. Entrapment was found to decrease RB aggregation and enable a sustained release profile in vitro relative to the free drug. Furthermore, MTT assays performed on a subset of cancer cell lines revealed that LipoLoad: RB increased the intrinsic cytotoxic activity of RB. These results establish LipoLoad as a novel, biologically encoded nanocarrier. The facile production method, which does not require specialized equipment, and the formulation’s stability underscore the broad potential of bacterial lipoproteins as a modular platform for nanotechnology.
Changes in trauma symptoms of discrimination after MDMA-assisted psychotherapy for posttraumatic stress disorder
Altitude driven mechanisms and machine learning prediction of the evaporation paradox reversal in the Nyainqentanglha Mountains
Experimental and computational evaluation of surface geometry effects on underwater acoustic beam shaping with piezoelectric transducers
Abstract This paper examines how radiating surface structure can influence the properties of beam-shaping of the piezoelectric piston type underwater acoustic transducers. The study is done using a broad theoretical, numerical and experimental method. This study presents a comprehensive analysis of the far-field radiation characteristics of circular, square, hexagonal, and octagonal piston-type ultrasonic transducers for underwater applications. The models were validated with three-dimensional finite element simulations and experimental measurements using a wafer Tonpilz transducer prototype. This analysis demonstrates that piston geometry has no significant effect on radiation characteristics for small apertures whose Equivalent Circular Diameter (ECD) is less than half the wavelength. The circular piston is found to exhibit superior performance at increased apertures. Result shows that circular pistons provide superior beam uniformity, narrow main lobes, and low side-lobe levels, making them highly efficient for focused energy transmission, sonar, and underwater communication systems. Experimental validation is provided only for the circular case, while theoretical and numerical results are presented for all pistons. This control of the beam makes the circular piston the solution to accurate acoustic control. Finite element analysis and experimental measurements on a circular wafer transducer proved the validity of the theoretical models. This high level of agreement validates that the findings can directly applied to designing advanced underwater acoustic arrays.
A low-cost "plant-scanner" platform for automated detection of Ustilago maydis infection in maize using deep learning
Abstract Ustilago maydis is a biotrophic fungus that causes smut disease in maize, leading to tumor formation on aerial parts of the plant. While U. maydis has been a model for plant-fungal interaction studies, no tool has existed to automatically quantify infection symptoms under laboratory conditions for deep learning analysis. To address this, we developed a rotating camera system that captures videos of plants under customized lighting and shutter settings. These videos were used to train machine learning models to distinguish between healthy and infected plants. Two detection approaches have been presented. In the first approach, by employing a naive masking technique and combining classical machine learning classifiers utilizing handcrafted features, the model achieved a reasonable performance, with an Area Under the Curve (AUC) of maximum 0.90 on the Receiver Operating Characteristic in one of the classifiers, showing relatively high sensitivity and specificity. The second approach utilizes pre-trained YOLO11 model for object detection and further classification. The YOLO11-based approach outperforms traditional methods, achieving near-perfect validation accuracy (AUC: 0.99–1.00), demonstrating its superiority for real-time, scalable applications. Our toolset, featuring a cost-efficient and customizable scanning platform with open building-blocks design, provides a valuable resource as a proof-of-concept for unbiased disease symptom detection and scoring, with potential applications in other plant pathology studies. This point enables easy replication and adaptation by other research laboratories which makes the platform robust, scalable and practical beyond our specific application.
Association between insulin resistance metabolic score (METS‐IR) and gestational diabetes mellitus: a prospective cohort study
Abstract The metabolic score for insulin resistance (METS-IR) is a non-insulin-based surrogate marker of insulin resistance. However, its utility for identifying gestational diabetes mellitus (GDM) risk in early pregnancy remains unclear. This study included 585 singleton pregnant women from a prospective cohort study in South Korea. METS-IR was assessed at 10–14 weeks of gestation. Multivariable logistic regression was used to examine the association between METS-IR and subsequent GDM. Subgroup and sensitivity analyses were performed to evaluate the robustness of the findings, and receiver operating characteristic curve analysis was used to assess the predictive performance of METS-IR. Among 585 participants, 36 women developed GDM. In the fully adjusted model, METS-IR remained positively associated with GDM when analyzed as a continuous variable (OR = 1.18, 95% CI: 1.10–1.26, P < 0.001). Compared with women in the lowest METS-IR tertile, those in the highest tertile had a higher risk of GDM (OR = 5.50, 95% CI: 1.43–21.06, P = 0.013). The association was consistent in subgroup and sensitivity analyses. METS-IR showed good discriminative ability for GDM, with an area under the curve of 0.808 (95% CI: 0.726–0.890). The optimal cutoff value was 34.5, with a sensitivity of 66.7% and specificity of 86.9%. Higher METS-IR at 10–14 weeks of gestation was independently associated with an increased risk of GDM. METS-IR may serve as a simple early-pregnancy marker to help identify women at high risk for GDM.