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Isolation and characterization of heavy metal tolerant microalgae from old mining areas of Saxony
Abstract Heavy metal contamination poses a major threat to ecosystems and human health, particularly in mining-impacted areas. Algal biosorption offers a promising, low-cost, and sustainable approach to mitigate this problem. In this study, five microalgal strains were bioprospected and isolated from abandoned mining sites and evaluated for their tolerance and ability to remove Cu, Cd, and Cr(VI) from aqueous solutions. Laboratory experiments were performed to assess heavy metal tolerance and biosorption efficiency under controlled conditions. The results demonstrated that isolates Chlorella vulgaris RG1-4 and Tetradesmus obliquus Ehr33-9 exhibited the highest tolerance and biosorption capacity for Cu, with removal efficiencies of 100.00 mg/g and 89.73 mg/g, respectively. The isolate Lobochlamys segnis Ehr31-1 showed the highest tolerance and biosorption capacity for Cd, reaching 93.17 mg/g. These findings highlight the potential of locally adapted microalgal strains as effective biosorbents for remediating heavy metal-contaminated water sources.
Lean tissue mass is associated with adverse outcomes across different stages of chronic kidney disease: a systematic review and meta-analysis
Abstract In chronic kidney disease it is hypothesised that the association between loss of lean tissue mass (LLTM) and mortality is purely a function of multimorbidity. We conducted a systematic review in CKD patients to quantify the strength of association between LLTM and mortality or frailty surrogates, including hospitalisation and quality of life (QoL). Muscle mass was estimated using different whole-body bioimpedance methods (BI-MM). Searches of electronic databases identified 132 studies for inclusion (147542 dialysis patients; 15378 CKD G3−5 patients; 356 kidney transplant recipients [KTR], with 14429 deaths). From 67 studies reporting unadjusted analyses, 52 (78%) demonstrated associations between LLTM and mortality. In 80 studies reporting analyses adjusting for age, sex, and multimorbidity, 59 (74% overall: 74, 67 and 100% in dialysis, CKD, and KTR studies respectively) reported an association. Meta-analysis of dialysis studies reporting adjusted survival analyses found each degree decrease in phase angle or a lean tissue index < 10th percentile was associated with a 92 and 49% higher hazard of mortality respectively. In studies reporting hospitalisation and QoL measures, 63 and 76% reported associations with BI-MM respectively. In conclusion, having accounted for multimorbidity, LLTM remained associated with mortality and frailty surrogates in CKD, irrespective of the BI-MM method used.
Phenolic content and biological activities of Lenzites betulina extracts obtained by ultrasonic-assisted optimization approaches
Generative super-resolution of turbulent flows via stochastic interpolants
Abstract Capturing the intricate multiscale features of turbulent flows remains a fundamental challenge due to the limited resolution of experimental data and the computational cost of high-fidelity simulations. In many practical scenarios only coarse representations of the flows are feasible, leaving crucial fine-scale dynamics unresolved. This study addresses that limitation by leveraging generative models to perform super-resolution of velocity fields and reconstruct the unresolved scales from low-resolution conditionals. In particular, the recently formalized stochastic interpolants are employed to super-resolve a case study of two-dimensional turbulence. Key to our approach is the iterative application of stochastic interpolants over local patches of the flow field, that enables efficient reconstruction without the need to process the full domain simultaneously. The patch-wise strategy is shown to yield physically consistent super-resolved flow snapshots, and key statistical quantities – such as the kinetic energy spectrum – are accurately recovered. Moreover, the patch-wise approach is observed to produce super-resolutions of a quality comparable to those produced using a full field approach, and, in general, stochastic interpolants are observed to outperform contesting generative models across a range of metrics. Although only demonstrated for a 2D case study, these results highlight the potential of using stochastic interpolants to super-resolve turbulent flows.
Relationships between voice features and fat/lean body mass in Polish males and females
Brain-infiltrating CD8 T cells retain functional activity to protect against acute Zika virus infection
Abstract Zika virus (ZIKV) infection can cause severe neurological complications, yet the role of CD8 + T cells in controlling viral pathogenesis in the brain remains unclear. Using Ifnar1 − / − mice, which lack type I interferon signaling, we demonstrate that ZIKV infection triggers significant infiltration of CD8 + T cells into the brain, accompanied by neurological defects. ZIKV-experienced CD8 + T cells exhibit enhanced cytotoxic potential, and adoptive transfer of these cells improves survival. In contrast, blocking their infiltration exacerbates brain inflammatory and injury-associated signatures, highlighting their protective contribution. Furthermore, PD-1 blockade worsens ZIKV pathology, suggesting that PD-1 expression reflects an activated rather than exhausted state. These findings underscore an important role of infiltrating CD8 + T cells in reducing ZIKV-induced CNS inflammation and suggest that modulating their response could serve as a potential therapeutic strategy for ZIKV-associated neurological disease.
Thapsigargin enhanced chemotherapeutic sensitivity of irinotecan in the inflammation-induced colorectal cancer model in mice
Practical sparse data-driven constitutive modeling via transfer learning in physics-encoded neural networks
Abstract Data-driven constitutive models, owing to their inherent flexibility, can outperform traditional plasticity-based models in certain aspects. When calibrating these models, ensuring adherence to fundamental mechanical principles allows the calibrated models, referred to as physics-encoded neural networks (PeNNs), to be effectively integrated into finite element method (FEM) software for boundary value problem simulations. However, calibration challenges arise when only limited data are available. Addressing this issue, this study employs transfer learning. Synthetic labeled data, derived from traditional constitutive models were used to pre-train PeNNs. Subsequently, these pre-trained PeNNs are fine-tuned using implicitly labeled data from high-fidelity experimental records. The fine-tuned models are integrated into FEM software as user materials to conduct extensive drained and undrained triaxial test simulations. An analysis of the simulation results highlights the impact of the available volume of experimental data, the quantity of synthetic data, and key configurations in the fine-tuning process, such as the architecture of the fine-tuning model, frozen parameters, and batch size. Results indicate that through robust PeNN models and meticulous modeling, transfer learning can establish a data-driven constitutive model with limited experimental records, achieving superior simulation performance compared to the synthetic model alone. This underscores the potential of combining cost-effective synthetic and experimental data to advance constitutive modeling.
Pressure and coping strategies of caregivers of children with autism spectrum disorder in rural areas: a qualitative study
Individual and institutional factors influencing dentists’ practice in underserved areas
Abstract Access to dental care is a key determinant of oral health, yet disparities in provider distribution across the United States contribute to inequitable access, particularly in underserved areas. To predict dentists’ likelihood of practicing in underserved settings and identify factors influencing practice location decisions, we developed explainable machine learning models using national data from 56,175 dentists who graduated between 2000 and 2022. We examined 76 predictors including individual- and dental school-level characteristics and defined outcomes as practicing in Federally Qualified Health Centers, dental shortage areas, or rural dental shortage areas. Our models demonstrated strong predictive performance, and the results revealed that general practice specialty, fewer years of experience, non-owner practice status, and demographic factors such as gender and race were strongly associated with practicing in underserved areas. Institutional characteristics, including dental school location and diversity index, also played a significant role. The relationship between educational debt, experience, and practice outcomes varied by practice type and race/ethnicity. These findings highlight the value of explainable machine learning in informing targeted workforce policies that address individual, institutional, and demographic drivers of dentist distribution to improve access to dental care in underserved communities.
Synthesis of silica nanoparticles from rice husk to determine insecticidal properties against Glyphodes pyloalis walker (Lepidoptera: Crambidae)
Existence and uniqueness of solutions for fuzzy fractional integro-differential equations with boundary conditions
Correction: Association of attenuated leptin signaling pathways with impaired cardiac function under prolonged high-altitude hypoxia
Quantitative assessment of ecological security and its influencing factors in the Danjiangkou Reservoir based on a health–risk–service framework
Statistics and law analysis of personal safety accidents of power grid enterprises in China from 2014 to 2024
Associations of Sexual Desire with Demographic and Relationship Variables
Altitudinal variation in leaf morphology and functional traits of sea-buckthorn (Hippophae rhamnoides) in Gilgit region, Pakistan
Abstract Sea-buckthorn is a multi-purpose plant that provides food, feed, fuel, and medicine. Climate conditions affect its adaptability more than the terrain characteristics. Limited research has focused on its leaf functional traits important for climate adaptation. This study characterized seventy sea-buckthorn accessions from five locations in Gilgit region of northern Pakistan (2444–3172 m.a.s.l) for leaf-functional traits (leaf angle, hairs, rolling, groove, and wettability – summarized as leaf traits and physiological traits including stomatal conductance (gs), leaf relative water content (RWC), transpiration (E), water use efficiency (WUE) and photosynthesis (A)). Leaf surface structures were observed under a scanning electron microscope. Majority of accessions at Misgar (higher altitude) showing 0–20% inward leaf rolling, medium groove, and semi-erect leaf angle had higher E, gs, and lower WUE, RWC, moderate A, and hydrophilic leaf surface. Comparatively, most of accessions at middle altitudes (Passu) indicated adaptive leaf characters i.e., semi-droopy leaves, light leaf groove, 20–40% leaf rolling, moderate trichome density with hydrophobicity (> 90°) and high drop rolling efficiency (< 15°), and had higher A (8.9 ± 1.4 µmol CO 2 m −2 s −1 ), WUE (4.8 ± 0.4 mmol CO 2 mol −1 H 2 O), and fruiting (~ 67%). Moreover, the positive association of WUE with fruit yield indicated high photosynthetic productivity of such accessions. Umbrella-shaped peltate leaf trichomes were 151–175 μm long, with higher densities on abaxial surface, appressed to the epidermis and two layers with overlapping rays-shields. Stomatal density was higher on the abaxial surface, mostly covered by the trichomes. This study provides theoretical backgrounds of an ideotype suited for climate resilience supporting sea buckthorn germplasm conservation.