Browse Articles

Metabarcoding assessment of the diet of an introduced continental lizard to an oceanic island reveals dietary niche conservatism

Abstract Invasive species can have devastating effects when introduced into remote island ecosystems, and a fundamental aspect of this concerns the diet of these exotic taxa. Here, we employed a DNA metabarcoding approach to determine the diet of the lizard Agama picticauda on Réunion Island, where it was introduced in 1995. Two separate markers were used to identify dietary components: COI for animals and trnL for plants. The arthropod aspect was notably conservative, with the agama continuing to predominantly consume ants, as they do in their native range. A variety of other invertebrates were also preyed upon, the vast majority being introduced species. For plants, again a wide variety was detected, and while most could not be identified fully, it seems that agamas are deliberately consuming many species, rather than accidentally ingesting them along with targeted invertebrates. Agamas may play a role in seed dispersal of invasive plant species. We also detected some nematode groups, although with limited comparative sequences, these could not be identified to the species level. Several invertebrate records appear to be new for Réunion Island, highlighting how reptiles can be considered as excellent biodiversity samplers, with barcoding diet studies providing novel data on poorly known invertebrate groups. The minimal identification of endemic prey items may reflect the fact that agamas are still predominantly occupying anthropogenically disturbed parts of the island. Our study therefore provides baseline data that can be used to determine the impact of this introduced lizard as it spreads through the ecosystem.

Interphasial Catalytic Anion Reduction for Stable Anode-Free Sodium-Metal Batteries

Plasma-driven formation of vertically aligned silver–phosphorus core–shell nanostructures

Direct Access to Chiral Tertiary Alcohols via Copper-Catalyzed Enantioconvergent <i>O</i> -Alkylation of Water with Racemic α-Tertiary Haloamides

Automated body composition quantification from non-contrast CT for urolithiasis classification and exploratory incident risk assessment

Distributed MAC scheduling in IEEE 802.15.7-oriented VLC networks via federated deep reinforcement learning

Abstract Visible light communication (VLC) networks modeled through a scheduling-level IEEE 802.15.7-oriented PHY/MAC abstraction are sensitive to line-of-sight blockage, receiver orientation, ambient-light noise, heterogeneous traffic loads, and inter-luminaire optical interference, which limits the effectiveness of fixed medium access control (MAC) policies in dense deployments. This study presents a federated deep reinforcement learning framework for distributed MAC scheduling in multi-luminaire IEEE 802.15.7-oriented VLC networks. Each luminaire is modeled as a local scheduling agent that selects the served receiver, transmission slot, optical power level, and physical-layer (PHY) mode from local queue, channel, blockage, interference, and illumination states. Instead of sharing raw observations, luminaires periodically exchange model parameters with a federated aggregation server to coordinate policy updates while preserving data locality. The proposed method is evaluated in a custom discrete-time simulator for a $$5\times 5\times 3~\textrm{m}^3$$ indoor VLC scenario with four ceiling luminaires, 8–32 receivers, stochastic traffic arrivals, receiver mobility, ambient-light noise, and line-of-sight blockage. Results averaged over 30 independent runs show that, at $$K=32$$ receivers, the proposed scheduler reduces average packet latency from 45 ms to 31 ms and 95th-percentile latency from 92 ms to 66 ms relative to the implemented resource-constrained centralized deep Q-network (DQN) baseline. Under a blockage probability of 0.3, the packet delivery ratio increases from 0.858 to 0.902, while under high ambient-light noise the packet error rate decreases from 0.064 to 0.049. The method also achieves a Jain fairness index of 0.96, reduces average synchronization overhead from $$18.5\%$$ to $$4.7\%$$ at a synchronization interval of 10 episodes, and shortens convergence time from 940 to 670 episodes at $$N=9$$ luminaires. Illumination and flicker diagnostics show that executed actions satisfy the normalized feasibility mask after filtering. These results indicate that, within the adopted IEEE 802.15.7-oriented simulation abstraction, periodic federated parameter sharing improves the scheduling trade-off by reducing delay and coordination cost while preserving mask-enforced lighting feasibility, improving empirical reliability and fairness, and showing favorable multi-luminaire scalability trends.

Timestep-conditioned Attention and Multi-dimensional Evidence framework for efficient multimodal chest X-ray anomaly detection

Cellular and subcellular localization of the copper transporter CTR1 in human postmortem hippocampus and striatum

Vision expert guided inspection for industrial anomaly detection

Industrial anomaly detection (IAD), aiming at automatically identifying abnormal patterns that deviate from the normal manufacturing process, plays a critical role in ensuring product quality and equipment safety for intelligent manufacturing systems. In this work, we delve into exploring the generalized and subtle-pattern awarded defect detection. We also propose a visual expert-guided multi-scale anomaly detection method. As the extracted regions often exhibit subtle and vague features that hamper the precise and reliable detection, we leverage the established super-resolution technique to enhance the spatial resolution and recover fine-grained details. It facilitates more discriminative defect representation and improves the model’s capacity at localize anomalies at finer scales. The multi-scale fusion module is constructed by the graph attention network. It aggregates the suspicious regions across different scales by modeling their inter-scale dependencies and contextual relationships. As it dynamically weights and localities those features, it preserves both the micro irregularities and macro structural deviations, hence offering comprehensive anomaly information. Extensive experiments under zero-shot and few-shot settings were conducted on several public datasets. The results demonstrate that the proposed method consistently outperforms existing mainstream approaches in both image-level and pixel-level anomaly detection, achieving pixel-level values of 98.6% and 98.1% under the 4-shot setting on two major benchmarks, and 94.6% under the zero-shot setting, with particularly strong capability in detecting subtle defects on fine-grained textures. It also exhibits enhanced robustness and generalization in cross-domain transfer scenarios.

Deprescribing in Patients With Cardiovascular Disease Experiencing Polypharmacy: A Scientific Statement From the American Heart Association

Polypharmacy in patients with cardiovascular disease occurs frequently across the age spectrum and can lead to inappropriate prescribing and adverse outcomes. Despite polypharmacy being a known problem for decades, there is limited guidance describing how to manage polypharmacy in patients with cardiovascular disease, including when and how to initiate deprescribing strategies. Although polypharmacy can occur at any age, studies often focus on older adults. The prevalence and consequences of polypharmacy, coupled with current gaps in the deprescribing literature, highlight the need for a scientific statement focused on deprescribing in all patients with cardiovascular disease. Deprescribing can improve outcomes and can apply to both cardiovascular and noncardiovascular drugs because both contribute to polypharmacy and poor outcomes associated with inappropriate prescribing in patients with cardiovascular disease. This scientific statement reviews the consequences of polypharmacy in patients with cardiovascular disease and provides tailored deprescribing strategies across the life span, with an emphasis on the unique considerations in pediatric, adult, and older adult populations. These strategies include observing clinical cues and triggers, using validated deprescribing tools, engaging in shared decision-making, and leveraging the roles of all members of the health care team to address barriers to deprescribing. Last, this scientific statement highlights current challenges related to polypharmacy and deprescribing and suggests some strategies to address them.

Retraction Note: Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images

C═C/N═O Metathesis Enables Oxidative Decarboxylation

A novel design using a virtual control group to evaluate non-inferiority of nevirapine and lamivudine dual maintenance in HIV therapy

Purpose Reducing the number of drugs in combined antiretroviral therapy (cART) likely reduces toxicity. We hypothesized that dual therapy (DT) with nevirapine (NVP) and lamivudine (3TC) would be non-inferior to a virtual control without treatment failure. Methods This multicenter study enrolled patients on cART with HIV plasma viral load (pVL) &lt;50 cp/ml for ≥2 years and on NVP for ≥6 months. Patients were compared to a simulated virtual control group with an assumed failure rate of zero. Those with prior Non-Nucleoside Reverse Transcriptase Inhibitor failure or 3TC resistance were excluded. Treatment was simplified to DT with NVP/3TC for 48 weeks, with quarterly pVL-measurements. The primary endpoint was confirmed virologic failure (pVL ≥ 200 cp/mL). A 4% non-inferiority margin and sample size of 201 were set, with a stopping rule if three virologic failures occurred. Results From April 2019 to January 2023, 201 patients from five centers in Switzerland and Germany started DT, which 194 participants completed. Two patients (1.03%, 95% CI: –0.92% to 3.68%) reached the primary endpoint for failure due to adherence issues. No additional failures were observed during a 12-month post-study follow-up of 184 participants. Conclusions Simplification to NVP and 3TC was as effective as the ideal virtual control. However, the results of NVP and 3TC maintenance therapy are only applicable to people living with HIV who meet the study’s inclusion and exclusion criteria. Virtual controls could improve research efficiency and warrant further evaluation.

Cardiovascular Health Equity: Time for a New Era of Science, Sociology, and Interventions

Retraction Note: Improving the power production efficiency of microbial fuel cell by using biosynthesized polyanaline coated Fe3O4 as pencil graphite anode modifier

Inferring plant-bee-microbe associations: Foragers, hive workers, and honey tell complementary stories

Pollinators, both wild and managed, form diverse associations with plants and microbes which affect the wellbeing of the plants and the pollinators. The method by which these associations are sampled impacts our understanding of the system. The common ways to understand pollinator-plant or pollinator-plant-microbe associations are to observe flower visits of insects, or to collect foraging individuals and identify the pollen and microbes they carry. Honey bees offer a test case for methods of sampling these associations. Hives of managed honey bees host thousands of pollinator individuals together with jointly-collected nectar which is turned into honey. Previous studies have used DNA preserved in honey to infer honey bee associations with plants and microbes. Here, we sampled honey, individual bees while they were foraging, and groups of bees from inside the hive. We identified plants and microbes on the surface of the bees or in the honey using DNA metabarcoding – expecting that bees sampled singly or in groups would reveal a subset of the associations recorded in the communal honey deposits. However, we found that each sample type revealed different aspects of the richness and community composition of plants and microbes encountered by bees. Both honey samples and hive bees had more plant and microbial taxa per sample than samples of individual bees. Though individual bees are subsets of the larger colony, pollen and microbe associations recovered from individual bees did not represent a subsample of associations recovered from groups of hive bees or from honey. Thus, while each sampling technique provides information about honey bee ecology, they are not equivalent. DNA in honey represents time-integrated associations between bees and the surrounding ecosystem; the hive bees provide a snapshot of current colony-level associations, and individual foraging bees capture intraspecific variation in foraging preferences and microbe exposure.

Pregnancy after idiopathic granulomatous mastitis does not increase the rate of disease recurrence: a retrospective cohort study on 54 pregnancies

Effect of Cosmos Caudatus supplementation and aerobic exercise on selected neurobehaviour, biochemical profile and histology in rats with mild cognitive impairment (MCI) induced by AlCl3: Study Protocol

Background Cosmos caudatus (C. caudatus) or ‘ulam raja’ is a local plant with antioxidant properties and has the potential to act against oxidative-related conditions found such as in neurodegenerative diseases. Similarly, physical exercise is a consolidated strategy on the prevention of cognitive deficits. Based on the systematic review conducted by Joseph et al. (2023), a study protocol was developed to ensure the combined effect of C. caudatus supplementation and exercise provided improvement against cognitive impairment. There are limitations on studies looking at combined effect of flavonoid and exercise where either one of the interventions provided improvement to the behavioural tests and biomarkers assessed but not when given in combination. Moreover, to our understanding, in the last five years there has been limited research done on the combined effect of flavonoid and exercise against cognitive impairment (based on Pubmed search on 10 June 24; ScienceDirect search on 10 June 24). Therefore, we elucidated a study protocol that looks at the combined effect of C. caudatus supplementation and exercise against AlCl 3 -induced cognitive impairment in rats and the possible mechanisms involved in its neuroprotective effects in male rats. Method Male Wistar rats will be divided into different groups: control, physical exercise (treadmill running), supplemented with C. caudatus or in combination. Consequently, neurobehavioural tests (novel object recognition test, open field test &amp; Y-maze), biochemical tests and histology assessment will be determined to unravel the possible neuroprotective capability against AlCl 3 -induced neurotoxicity. The duration of exercise training is four weeks while C. caudatus is supplemented for 21 days. Outcome The primary outcomes will be neurobehaviour changes at baseline, after 21 days of AlCl 3 -induced rats and at the end of intervention. While the secondary outcomes will be biochemical profile (Oxidative stress markers, inflammatory markers) and brain histology of AlCl 3 -induced rats. Discussion and conclusion Combining exercise training with C. caudatus supplementation will produce synergistic effects, leading to significant improvements in spatial memory impairment and oxidative stress. This combined approach is expected to be more effective than using either intervention alone, potentially restoring spatial memory and antioxidant levels to normal. Consequently, the findings of this study could hold significant value for aging adults, providing safe and cost-effective strategies for managing neurodegenerative disorders.

Scalable hierarchical federated graph-transformer architecture for efficient multi-modal intrusion detection in 6G UAV-assisted vehicular IoT

Abstract The increasing growth of 6G-empowered UAV-assisted vehicular IoT systems brings forth unprecedented scalability issues for distributed intrusion detection, especially in the context of non-IID data distributions and heterogeneous edge environments. Centralized and flat federated systems do not efficiently coordinate large-scale, latency-sensitive and resource-constrained nodes. In this research, we present a scalable hierarchical federated Graph-Transformer architecture for effective multi-modal intrusion detection spanning UAV-edge-cloud tiers. The platform employs hierarchical aggregation among cars, UAVs, and regional edge servers for reducing communication overhead (CO) and speeding up convergence in non-IID scenarios. Meanwhile, a hybrid Graph Neural Network (GNN) and Transformer backbone is adopted to model spatial topology and temporal dynamics, and a lightweight multi-modal fusion is employed to integrate network traffic, telemetry and channel condition information. To improve the efficiency of the system, we propose adaptive aggregation scheduling and communication compression algorithms that considerably reduce bandwidth consumption and training latency. The experimental results on the CIC-IoT-2023, ToN-IoT and Edge-IIoTset datasets exhibit enhanced scalability with over 98.20% detection accuracy and up to 38.00% transmission cost reduction compared to the flat federation baselines. The work presents a scalable and system-efficient approach for next generation distributed intrusion detection in large-scale 6G vehicle ecosystems.

Air quality index prediction using machine learning regression models: A comparative analysis

Air plays a vital role in human life, and poor air quality can lead to respiratory infections. Given the significant impact of air quality on people’s health, monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), and sulfur dioxide (SO 2 ), which are prevalent in highly polluted areas and contribute to a wide range of illnesses. Particulate matter, such as PM2.5 (particles with an aerodynamic diameter of less than 2.5 µm) and PM10, poses additional health risks. To address these concerns, this study focuses on predicting AQI values for major cities in Pakistan, specifically Karachi and Peshawar, using four prominent ML algorithms: Random Forest (RF), Gradient Boosting (GB), Linear Regression (LR), and Ridge Regression (RR). The results indicate that the models effectively predicted AQI using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). This research’s novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization. Additionally, the study compares evaluation metric results across different cities, highlighting the importance of using standard scalers to achieve optimal model performance. This research underscores the value of advanced ML techniques for accurate AQI prediction and analysis.