Browse Articles

Towards a quantum synapse for quantum sensing

Modelling solute transport and deformation of clay soil under the chemo-hydro-mechanical coupling actions based on fractal theory

RETRACTED ARTICLE: BMP2 expression in oral squamous cell carcinoma and its effects on SCC9 cell biological behavior

Abstract This study examined bone morphogenetic protein 2 ( BMP2 ) expression in oral squamous cell carcinoma (OSCC) and its effects on the biological behavior of OSCC cells, along with potential underlying mechanisms. BMP2 expression in OSCC was analyzed using mRNA data from The Cancer Genome Atlas and Genomics Expression Omnibus Database (GEO). SCC9 cells were transfected in vitro with small interfering RNA targeting BMP2 (si-BMP2), a negative control sequence (si-NC), BMP2 plasmid, or empty plasmid (vector). After transfection, Cell Counting Kit-8 assays, colony formation, scratch wound healing, Transwell, flow cytometry, quantitative reverse transcription polymerase chain reaction, and Western blot analyses were conducted to assess changes in SCC9 cell behavior in response to altered BMP2 expression and to explore relevant signaling pathways. BMP2 upregulation promoted SCC9 cell proliferation, migration, and invasion; inhibited apoptosis; and activated the Smad1/5 and p38 signaling pathways. Conversely, BMP2 downregulation inhibited SCC9 cell proliferation, migration, and invasion; promoted apoptosis; and suppressed the Smad1/5 and p38 pathways. BMP2 is highly expressed in OSCC and may drive its progression through the BMP/Smad and p38 mitogen-activated protein kinase signaling pathways, indicating potential prognostic value and promise as a therapeutic target for small-molecule OSCC treatments.

Refined modeling and coupled vibration analysis of the sleeper lifting device in long span bridges under temperature and train dynamics

On the origin of accretion flow photon index–quasi-periodic oscillation frequency (Γ–vQPO) relation

An improved polar lights optimization algorithm for global optimization and engineering applications

Numerical simulation and physical modeling of staged control support for roadway stability in water rich weakly cemented sandstone

Effect of levothyroxine treatment on serum lipids and pregnancy outcomes in pregnant women with isolated hypothyroxinemia

International PhD students make emergency plans in fear of US immigration raids

Associations between perceived stress and cortisol biomarkers in predominantly Latino adolescents

Tariffs hit science labs: Trump’s levies raise cost of supplies

Climate seasonality and predictability during the middle stone age and implications for technological diversification in early Homo sapiens

Abstract Regionalisation is considered to be a hallmark of the Middle Stone Age (MSA) compared to the Early Stone Age. Yet what drove diversification around a shared technological substrate that persisted across Africa for hundreds of thousands of years remains debated. Non-mutually exclusive hypotheses include region-specific styles in manufacture, social signalling, cultural drift between geographically isolated populations, and diverse environmental adaptations, as well as the impacts of unequal research histories and intensities. We explore the potential ecological bases of behavioural diversity during the MSA between two well-studied and diverse areas: eastern and northwestern Africa. We utilise a set of standardised bioclimatic simulations, as well as a time series decomposition algorithm, to determine the nature and extent of regional differences in terms of environmental productivity, seasonality and predictability at MSA sites through time. Our results highlight that, compared to human occupations of eastern Africa, northwestern African MSA occupations are associated with colder, drier and less productive environments, albeit colder, but wetter and more productive compared to surrounding areas, with higher temperature seasonality and more predictable climates across millennia. We then theoretically consider the implications of our results for technological diversification between these two regions during the Middle to Late Pleistocene, such as for the investment in specific risk mitigation strategies for dealing with seasonally mobile resources in northern localities, and the diversification of MSA toolkits in tropical eastern Africa.

Optimal design of agro-residue filled poly(lactic acid) biocomposites using an integrated CRITIC-CoCoSo multi-criteria decision-making approach

Abstract In recent years, there has been a rise in environmental awareness, leading to increased efforts to develop eco-friendly materials as alternatives to petroleum-based polymers. This study examined the performance optimization of poly(lactic acid) (PLA) biocomposites filled with agricultural byproducts at concentrations ranging from 0 to 20% by weight, highlighting their potential as substitutes for commodity plastics. The agro-residues used as fillers were flax seed meal and rapeseed straw. A hybrid decision-making algorithm was proposed, utilizing the “criteria importance through inter-criteria correlation” (CRITIC) alongside the “combined compromise solution” (CoCoSo), aimed at identifying the optimal alternative among the evaluated samples. The algorithm considered several attributes, including mechanical traits evaluated via tensile, flexural, and impact tests, hardness, water absorption, biodegradation, and production cost. The findings revealed that the strength properties, including tensile, flexural, impact, and water absorption, were most advantageous for neat PLA. In contrast, the highest modulus values were recorded for the biocomposite filled with 20 wt% rapeseed straw. The biocomposites exhibit increased hardness as agro-waste content rose, with the highest hardness observed in the biocomposite filled with 20 wt% flax seed meal. The study on biodegradation indicates that a higher content of agro-waste promotes disintegration, with flax seed meal emerging as the most effective additive in this context. The findings show that adding various agricultural byproducts in varying amounts affects the evaluated properties differently. Hence, the hybrid CRITIC-CoCoSo optimization approach is utilized to choose the optimal biocomposite. The findings show that the biocomposite with 20 wt% rapeseed straw demonstrated optimal physico-mechanical and biodegradation properties, making it a promising eco-friendly alternative for future applications.

Unexpected microbial diversity in new Caledonia’s ultramafic ecosystems with conservation implications in a biodiversity hotspot

Study on the degradation mechanism of mechanical properties of red sandstone under static and dynamic loading after different high temperatures

Trump team removes senior NIH chiefs in shock move

Spherical multigrid neural operator for improving autoregressive global weather forecasting

Abstract Data-driven approaches for global weather forecasting have shown great potential. However, conventional architectures of these models struggle with spherical distortions, leading to unstable autoregressive forecasts. Although methods such as spherical Fourier neural operator (SFNO) based on spherical harmonic convolution can alleviate these problems, they face the challenge of high computational cost. Here, we introduce a spherical multigrid neural operator (SMgNO) that integrates spherical harmonic convolution and low resolution SFNO in the multigrid framework, effectively alleviating data distortions while requiring few computational resources. Experiments for spherical shallow water equations and medium-range global weather forecasting demonstrate the effectiveness and robustness of SMgNO. For 500 hPa geopotential height with a 7 days lead time, SMgNO achieves a 9.31% and 6.83% improvement in anomaly correlation coefficient over IFS T42 and SFNO, respectively. Furthermore, SMgNO requires only 10% floating-point operations of SFNO for forward propagation and 30.90% less GPU memory than SFNO during training.

Daily briefing: This brain structure filters which thoughts we become aware of

Long-term gamma-aminobutyric acid (GABA) treatment fails to regain beta-cell function in longstanding type 1 diabetes in a randomized trial

Abstract Gamma-amino butyric acid (GABA) has in experimental studies been found to promote beta-cell proliferation, enhance insulin secretion and reduce inflammation, positioning it as a candidate drug for type 1 diabetes (T1D) therapy. This phase I/II randomized controlled trial assessed the safety and efficacy of long-term treatment with Remygen ® (Diamyd Medical), a controlled-release oral GABA formulation, as a potential beta-cell regenerative therapy in adults with long-standing T1D. Thirty-five male subjects with T1D (≥ 5 years) were randomized into three arms receiving the study drug(s) once daily for 6 months: GABA 200 mg (Arm 1), GABA 600 mg (Arm 2) and GABA 600 mg + alprazolam 0.5 mg for 3 months followed by GABA 600 mg alone for 3 months (Arm 3). Safety measures, hormonal counter-regulation during hypoglycemic clamps, fasting- and stimulated C-peptide levels, were assessed at multiple timepoints. Safety concerns included elevated aspartate aminotransferase (AST) in nine subjects, leading to the withdrawal of two subjects. Most elevations were, however, transient with no dose-differences. No effects were observed on fasting- or stimulated C-peptide levels, CGM metrics or HbA1c. Hypoglycemic hormonal counter-regulation was unaltered. To conclude, we found no clinical evidence of a beta-cell regenerative effect of GABA, but side effects were commonly observed.

Advancing plant leaf disease detection integrating machine learning and deep learning

Abstract Conventional techniques for identifying plant leaf diseases can be labor-intensive and complicated. This research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy to overcome the difficulty of the conventional methods. Our proposed method uses deep learning (DL) to extract features from photos of plant leaves and machine learning (ML) for further processing. To capture complex illness patterns, convolutional neural networks (CNNs) such as VGG19 and Inception v3 are utilized. Four distinct datasets—Banana Leaf, Custard Apple Leaf and Fruit, Fig Leaf, and Potato Leaf—were used in this investigation. The experimental results we received are as follows: for the Banana Leaf dataset, the combination of Inception v3 with SVM proved good with an Accuracy of 91.9%, Precision of 92.2%, Recall of 91.9%, F1 score of 91.6%, AUC of 99.6% and MCC of 90.4%, FFor the Custard Apple Leaf and Fruit dataset, the combination of VGG19 with kNN with an Accuracy of 99.1%, Precision of 99.1%, Recall of 99.1%, F1 score of 99.1%, AUC of 99.1%, and MCC of 99%, and for the Fig Leaf dataset with Accuracy of 86.5%, Precision of 86.5%, Recall of 86.5%, F1 score of 86.5%, AUC of 93.3%, and MCC of 72.2%. The Potato Leaf dataset displayed the best performance with Inception v3 + SVM by an Accuracy of 62.6%, Precision of 63%, Recall of 62.6%, F1 score of 62.1%, AUC of 89%, and MCC of 54.2%. Our findings explored the versatility of the amalgamation of ML and DL techniques while providing valuable references for practitioners seeking tailored solutions for specific plant diseases.