Search Results (89)

Background/Objectives. Type 2 diabetes mellitus (T2DM) has been linked to an increased risk of several cancers. However, the influence of metformin and dipeptidyl peptidase-4 inhibitors (DPP4is) on the risk of cancers remains unclear. We investigated the association between using DPP4is and/or metformin and cancer risk compared with other glucose-lowering drugs (GLDs). Methods. This retrospective multicenter cohort study was performed using 11 hospital databases standardized to the OMOP Common Data Model (CDM) within the Observational Health Data Sciences and Informatics (OHDSI) network. T2DM patients using only DPP4is and/or metformin (DPP4is/Met group) were compared with those using other GLDs (other GLD group). From 413,344 eligible patients, propensity score (PS) 1:1 matching yielded 6674 patients in each group. Cox proportional hazards models were used to analyze cancer risk, and a random-effects meta-analysis was performed to calculate hazard ratios (HRs). Results. The DPP4is/Met group exhibited a significantly lower risk of incident cancer than the other GLD group (HR, 0.54; 95% CI, 0.41–0.69). This association was consistent across all hospitals. Regarding cancer-specific distributions, the DPP4is/Met group showed lower proportions of breast and prostate cancers, whereas the other GLD group showed higher proportions of lower gastrointestinal cancers. Conclusions. In this large multicenter study, using DPP4is and metformin showed a substantial association with a lower risk of cancer in T2DM patients relative to other GLDs. These findings suggest a potential protective effect of metformin and support the neutral-to-beneficial effect on cancer of DPP4is. Full article

Grapevine leafroll disease (GLD), caused by a complex of grapevine leafroll-associated viruses (GLRaVs), is among the most widespread and economically damaging viral diseases of grapevine. While its physiological and yield impacts are well recognized, the broader ecological implications for vineyard ecosystems remain poorly understood. This review integrates traditional literature analysis with bibliometric approaches to synthesize current knowledge on GLRaV occurrence, diversity, host responses, epidemiology, diagnostics, and management. Data from 729 peer-reviewed articles were categorized into six research clusters: global occurrence and first reports, viral diversity and characterization, host–pathogen interactions, epidemiology and vector dynamics, effects on vine physiology and fruit composition, and diagnostic and management strategies. Our findings highlight GLRaVs as dynamic pathogens shaped by genetic variability, human-mediated plant trade, and ecological interactions with vectors and vineyard biodiversity. Knowledge gaps persist regarding mixed infections, underexplored viticultural regions, ecological impacts, and sustainable management. Future work should prioritize high-resolution genomics, multi-omics approaches, improved diagnostics, ecological studies, and innovative management tools. By framing GLD not only as an agronomic but also as an ecological challenge, this review provides a foundation for more holistic strategies to safeguard vineyard health and productivity. Full article

The use of lactose-utilizing microalgae offers a sustainable and cost-effective approach for the bioconversion of dairy industry side-streams and the reduction in microalgae production costs. This work aims to improve the biomass productivity of the lactose-utilizing microalgal strain Graesiella emersonii MSCL 1718 in concentrated cheese whey permeate. It was demonstrated that the mixotrophic growth of the axenic G. emersonii culture resulted in a significantly higher biomass productivity in 20% permeate medium compared to the heterotrophic cultivation. Furthermore, supplementation of the permeate medium with iron, zinc, cobalt, and molybdenum resulted in 12.8%, 12.9%, 9.3%, and 28.9% significant increases (p < 0.05) in biomass synthesis, respectively, compared to the control permeate group. In the subsequent experiment, G. emersonii cultivated in molybdenum-supplemented permeate resulted in 0.34 ± 0.02 g/(L·d) biomass productivity and twofold higher lipid content (30.21 ± 1.29%) compared to the photoautotrophic control in defined synthetic medium. Analysis of the fatty acid composition revealed a twofold increase in saturated fatty acids, reaching 62.16% under mixotrophic cultivation in permeate, compared with the photoautotrophic control. Overall, concentrated cheese permeate proved to be a suitable medium for G. emersonii biomass production, supporting both enhanced growth and increased lipid accumulation. Full article

Green and low-carbon development (GLD) is central to facilitating the high-quality transitional development of economic and social sectors, as well as to the achievement of China’s “dual carbon” goals. The digital economy (DE), a burgeoning economic paradigm, serves as a potent driver for GLD by leveraging its intrinsic strengths in innovation-led growth and cross-sectoral industrial integration. Drawing on the TOE (Technology-Organization-Environment) framework, this study employs dynamic Qualitative Comparative Analysis (QCA) and regression analysis to examine panel data (2014–2023) of 44 core coastal cities in the Yangtze River Economic Belt, aiming to identify the driving paths of GLD. The research results indicate that a single dimension in the DE cannot constitute the necessary condition for regional GLD. Specifically, there are 6 configurational paths for high-level GLD (categorized into “organization-led” and “technology-organization-environment multi-driven” models) and 3 paths for low-level GLD (summarized as “three-dimensional constraint” and “technology-organization deficiency” models). In terms of the driving effect, the technology-organization-environment multi-driven configurational path exerts the strongest promotional effect on regional GLD. This study yields a valuable theoretical foundation for understanding the synergistic role of multidimensional DE elements in driving GLD, while also delivering actionable insights for local governments to identify contextually tailored GLD trajectories. Full article

Grapevineleafroll disease (GLD) is one of the more severe and persistent diseases in grapevines worldwide and is caused by several species of grape leafroll-associated viruses (GLRaVs). GLRaVs enter vines mainly by infected plant material or insect vectors. Mealybugs (Hemiptera: Pseudococcidae) are important vectors of GLRaVs and, among them, Pseudococcus viburni is the primary key vector in many regions. To reduce GLRaV spread, acquiring vines from virus-free certified nurseries, removing infected vines, and controlling insect vectors are crucial control tools. Sustainable mealybug control relies on eco-friendly products, cultural practices that limit mealybug population growth, and biological control by natural enemies. For P. viburni, biological control is primarily based on the action of predators and parasitoids, such as Cryptolaemus montrouzieri Mulsant and Acerophagus flavidulus Brethes, respectively, which will obviously have a different mode of action than chemical insecticides. However, the long-term effect of biological control on GLRaV spread within vineyards has rarely been studied. With the aim of better predicting the impact of biological control on insect vectors, such as mealybugs, we developed a mathematical model to predict the GLRaV spread. The results highlight the importance of establishing vineyards with virus-free material and having a pest management program that reduces the vector population to reduce the economic loss from GLRaVs. Full article

To enhance small object detection in UAV aerial imagery suffering from low resolution and complex backgrounds, this paper proposes GLDS-YOLO, an improved lightweight detection model. The model integrates four core modules: Group Shuffle Attention (GSA) to strengthen small-scale feature perception, Large Separable Kernel Attention (LSKA) to capture global semantic context, DCNv4 to enhance feature adaptability with reduced parameters, and further proposes a novel Small-object-enhanced Multi-scale and Structure Detail Enhancement (SMSDE) module, which enhances edge-detail representation of small objects while maintaining lightweight efficiency. Experiments on VisDrone2019 and DOTA1.0 demonstrate that GLDS-YOLO achieves superior detection performance. On VisDrone2019, it improves mAP@0.5 and mAP@0.5:0.95 by 12.1% and 7%, respectively, compared with YOLOv11n, while maintaining competitive results on DOTA. These results confirm the model’s effectiveness, robustness, and adaptability for complex small object detection tasks in UAV scenarios. Full article

Urban wastewater is composed of nutrients such as nitrogen and phosphorus, organic matter, heavy metals, pathogens, and micropollutants. If untreated, these contribute to eutrophication and environmental degradation. Microalgae-based bioremediation offers a sustainable solution, showing promise for pollutant removal and high-value bioproduct generation. This study evaluates the efficacy of Galdieria sulphuraria ACUF 427 in treating urban wastewater, with a focus on nutrient removal and phycocyanin production at different optical densities (OD 2, OD 4, and OD 6). Nutrient removal rates (RRs) were analysed for ammonium nitrogen (N-NH₄⁺), ammonia nitrogen (N-NH₃), phosphate phosphorus (P-PO₄³⁻), and chemical oxygen demand (COD). The RR for N-NH₄⁺ increased with optical density, reaching 7.49 mg/L/d at an optical density of 6. Similar trends were observed for N-NH₃ and P-PO₄³⁻, with peak removal at OD 6. COD removal remained high across all ODs, though differences between OD 4 and OD 6 were not statistically significant. Significant variations (p < 0.05) in nutrient removal were noted across the ODs, except for COD between OD 4 and OD 6. Biomass growth and phycocyanin production were significantly higher in the wastewater compared to the control (Allen Medium), with the most effective performance observed at an optical density (OD) of 6. Maximum growth rates were 0.241 g/L/d at OD 6, 0.178 g/L/d at OD 4, and 0.120 g/L/d at OD 2. These results highlight the potential of G. sulphuraria as an agent for wastewater bioremediation and the production of high-value compounds, particularly at elevated cell densities, where we achieved superior nutrient removal and biomass production. Full article

e-Games have become increasingly important in supporting the development of children with language delays. However, most existing educational games were not designed using usability guidelines tailored to the specific needs of this group. While various general and game-specific guidelines exist, they often have limitations. Some are too broad, others only address limited features of e-Games, and many fail to consider needs relevant to children with speech and language challenges. Therefore, this paper introduced a new collection of usability guidelines, called eGLD (e-Game for Language Delay), specifically designed for evaluating and improving educational games for children with language delays. The guidelines were created based on Quinones et al.’s methodology, which involves seven stages from the exploratory phase to the refining phase. eGLD consists of 19 guidelines and 131 checklist items that are user-friendly and applicable, addressing diverse features of e-Games for treating language delay in children. To conduct the first validation of eGLD, an experiment was carried out on two popular e-Games, “MITA” and “Speech Blubs”, by comparing the usability issues identified using eGLD with those identified by Nielsen and GUESS (Game User Experience Satisfaction Scale) guidelines. The experiment revealed that eGLD detected a greater number of usability issues, including critical ones, demonstrating its potential effectiveness in assessing and enhancing the usability of e-Games for children with language delay. Based on this validation, the guidelines were refined, and a second round of validation is planned to further ensure their reliability and applicability. Full article

This study, based on the vessel position data of pump-suction beam trawlers and the integrated species distribution model (ISDM), deeply analyzes the spatio-temporal distribution characteristics of the habitat of Antarctic krill and the contributions of key environmental factors. The Convolutional Neural Network–attention model (CNN–attention model) was used to identify the fishing status of the vessel position data of Norwegian pump-suction beam trawlers for Antarctic krill during the fishing seasons from 2021 to 2023. Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. The model results show that the Area Under the Curve (AUC) and True Skill Statistic (TSS) indices for all months exceed 0.9, with an average AUC of 0.997 and a TSS of 0.973, indicating extremely high accuracy of the model in habitat prediction. Further analysis of environmental factors reveals that Geographical Offshore Linear Distance (GLD) and chlorophyll concentration (CHL) are the main factors affecting habitat suitability, contributing 34.9% and 25.2%, respectively, and their combined contribution exceeds 60%. In addition, factors such as sea surface height (SSH), sea surface temperature (SST), sea ice concentration (SIC), and sea surface salinity (SSS) have impacts on the habitat distribution to varying degrees, and each factor exhibits different suitability response characteristics in different seasons and sub-regions. There is no significant correlation between the habitat area of Antarctic krill and catch (p > 0.05), while there is a significant positive correlation between the fishing duration and the catch (p < 0.001), indicating that a longer fishing duration can effectively increase the Antarctic krill catch. Full article

Aroma is an important processing and consumption quality trait of fruits and vegetables, and terpenes produced from the terpenoid metabolic pathway are a critical component of chili fruit flavor. This pathway involves the participation of at least eighteen enzymes, such as AACT, HMGS, HMGR, MVK, PMK, MVD, FPPS, GGPPS, DXS, DXR, MCT, CMK, MECPS, HDS, HDR, GPPS, IDI, and TPS. In this study, the genome wide information, expression characteristics, and relationship with terpenoids of terpenoid pathway genes are analyzed in C. annuum. The results showed that C. annuum has sixty-seven genes related to terpene metabolic pathways. Non-targeted metabolomics studies found that the content of aromatic terpenoids α-calacorene, α-cubene, and cis-β-farnesene increased with fruit development in HDL fruits, while linalool and nerolidol were much higher in GLD608. Correlation analyses between qRT-PCR and metabolome data showed that the expression levels of CaHMGS-3, CaMVD-1, CaCMK-1, and CaGGPPS-2 were positively correlated with the content of linalool, a flavor monoterpene alcohol. CaMECPS-1 was positively correlated with cis-β-farnesene, and there was also a significant positive regulatory relationship between CaTPS-5 and nerolidol relationship. In conclusion, the present study provides genetic resources for further studies on the gene regulatory mechanisms of flavor synthesis and terpenoid metabolic pathways in chili. Full article

Specific emitter identification (SEI), as an emerging physical-layer security authentication method, is crucial for maintaining information security in the Internet of Things. However, existing deep learning-based SEI methods require extensive labeled data for training, which are often unavailable in untrusted scenarios. Furthermore, due to the subtle nature of radio-frequency fingerprints, unsupervised SEI struggles to achieve high accuracy in identification without the guidance of labels. In this paper, we propose an unsupervised SEI method based on group label-driven contrastive learning (GLD-CL). We propose a novel method for constructing the dataset: all input samples derived from the same received signal segment are grouped together and assigned a unique identifier, termed the group label. Based on this, we improve the loss function of self-supervised contrastive learning. With the assistance of group labels, the feature vectors of the same class in the feature space become more closely clustered, enhancing the accuracy of unsupervised SEI. Extensive experimental results based on real-world datasets demonstrate that the normalized mutual information of GLD-CL achieves 96.4% accuracy, representing an improvement of 5.68% or more compared to the baseline algorithms. Furthermore, GLD-CL exhibits robust performance, achieving good identification accuracy across various signal-to-noise ratio scenarios. Full article

With the effect of light on the conidial formation of Aspergillus oryzae now being known, the molecular mechanism of its light response has become a research hotspot. However, the light-regulated genes investigated in earlier studies do not clearly explain the light response patterns of related genes at the transcriptional level. This study employed RNA sequencing technology to preliminarily identify the light-regulated genes among the genes related to conidia production and photoreception in A. oryzae GDMCC 3.31. Subsequently, the effects of light dose on the light-regulated genes were analyzed by qRT-PCR. We identified a total of six genes (tcsA, catA, gld1, Aowc-1, abaA, and AofphA) as light-regulated genes. The expression pattern of abaA was dependent on the light spectrum and light dose. When the light dose was maintained at a high level, the abaA gene served as a red–green light-regulated gene. Otherwise, the abaA gene showed no response to light. The phytochrome-like gene AofphA was regulated by red and blue light with a biphasic response under varying light doses, suggesting the existence of a light dose threshold. These findings provide new targets for the photoresponse molecular mechanisms in A. oryzae. Full article

The genus Acidithiobacillus has been widely used in bioleaching, and novel strains in this genus, such as A. ferriphilus, have also been confirmed to possess bioleaching capabilities. In this study, an Acidithiobacillus ferriphilus strain, QBS3, was isolated from zinc-rich sulfide mine drainage using the gradient dilution method. QBS3 is a Gram-negative, 1.3 µm rod-shaped bacterium with small red colonies. It showed a high iron oxidation efficiency of 0.361 g/(L·h) and a sulfur oxidation efficiency of 0.206 g/(L·d). QBS3 has sphalerite bioleaching ability; using QBS3 for pure sphalerite bioleaching, 18.8% of zinc was extracted in 14 days at 1% pulp density. Whole genome sequencing was performed on QBS3. Functional prediction showed that 9.13% of the genes were involved in replication, recombination, and repair. Bioleaching-related genes were analyzed, including iron and sulfur oxidation genes, and carbon and nitrogen fixation genes. For iron oxidation, the Cyc2→RusA pathway and Iro→RusB pathway were found in QBS3. In terms of sulfur oxidation, QBS3 has an incomplete SOX system and lacks the SDO gene, but Rho and Trx may complement the SOX system, enabling QBS3 to oxidize sulfur. QBS3 has multiple sets of carbon fixation genes, and nitrogen fixation genes were also identified. A hypothetical sphalerite bioleaching model is proposed; this study provides a theoretical basis for the zinc sulfide ore bioleaching industry. Full article

Methamphetamine (METH) use disorder (MUD) is a public health catastrophe. Herein, we used a METH self-administration model to assess behavioral responses to the dopamine receptor D1 (DRD1) antagonist, SCH23390. Differential gene expression was measured in the dorsal striatum after a 30-day withdrawal from METH. SCH23390 administration reduced METH taking in all animals. Shock Resistant (SR) rats showed greater incubation of METH seeking, which was correlated with increased Creb1, Cbp, and JunD mRNA expression. Cytoplasmic polyadenylation element binding protein 4 (Cpeb4) mRNA levels were increased in shock-sensitive (SS) rats. SS rats also showed increased protein levels for cleavage and polyadenylation specificity factor (CPSF) and germ line development 2 (GLD2) that are CPEB4-interacting proteins. Interestingly, GLD2-regulated GLUN2A mRNA and its protein showed increased expression in the shock-sensitive rats. Taken together, these observations identified CPEB4-regulated molecular mechanisms acting via NMDA GLUN2A receptors as potential targets for the treatment of METH use disorder. Full article

In recent years, the agriculture sector has undergone a significant digital transformation, integrating artificial intelligence (AI) technologies to harness and analyze the growing volume of data from diverse sources. Machine learning (ML), a powerful branch of AI, has emerged as an essential tool for developing knowledge-based agricultural systems. Grapevine red blotch disease (GRBD) and grapevine leafroll disease (GLD) are viral infections that severely impact grapevine productivity and longevity, leading to considerable economic losses worldwide. Conventional diagnostic methods for these diseases are costly and time-consuming. To address this, ML-based technologies have been increasingly adopted by researchers for early detection by analyzing the foliar symptoms linked to viral infections. This study focused on detecting GRBD and GLD symptoms using Convolutional Neural Networks (CNNs) in computer vision. YOLOv5 outperformed the other deep learning (DL) models tested, such as YOLOv3, YOLOv8, and ResNet-50, where it achieved 95.36% Precision, 95.77% Recall, and an F1-score of 95.56%. These metrics underscore the model’s effectiveness at accurately classifying grapevine leaves with and without GRBD and/or GLD symptoms. Furthermore, benchmarking was performed with two edge computer devices, where Jetson NANO obtained the best cost–benefit performance. The findings support YOLOv5 as a reliable tool for early diagnosis, offering potential economic benefits for large-scale agricultural monitoring. Full article