Browse Articles

Metagenomic analysis of the denture-associated oral microbiome in patients with denture stomatitis

MACML: Marrying attention and convolution-based meta-learning method for few-shot IoT intrusion detection

The widespread deployment of Internet of Things (IoT) devices has made them prime targets for cyberattacks. Existing intrusion detection systems (IDSs) heavily rely on large-scale labeled datasets, which limits their effectiveness in detecting novel attacks under few-shot scenarios. To address this challenge, we propose a meta-learning-based intrusion detection method called MACML (Marrying Attention and Convolution-based Meta-Learning). It integrates a self-attention mechanism to capture global dependencies and a convolutional neural network to extract local features, thereby enhancing the model’s overall perception of traffic characteristics. MACML adopts an optimization-based meta-learning framework that enables rapid adaptation to new tasks using only a small number of training samples, improving detection performance and generalization capability. We evaluate MACML on the CICIDS2018 and CICIoT2023 datasets. Experimental results show that, with only 10 training samples, MACML achieves an average accuracy of 98.75% and a detection rate of 99.17% on the CICIDS2018 dataset. On the CICIoT2023 dataset, it reaches 94.47% accuracy and a 95.32% detection rate, outperforming existing state-of-the-art methods.

Photo-homogenization assisted segregation easing technique (PHASET) for highly efficient and stable wide-bandgap perovskite solar cells

Multi-scale effects of runoff time series and its improved prediction methods

A multi-strategy enhanced secretary bird optimization algorithm for high-precision inverse kinematics in robotic arms

The assembly of pyrotechnic grain demands high precision and stability in robotic arm motion control due to the small shell apertures and stringent assembly accuracy requirements. Inverse kinematics is a core technology in robotic arm motion control. This paper constructs a robotic arm inverse kinematics model by reformulating the inverse kinematics problem as a constrained optimization problem and employs a multi-strategy improved Secretary Bird Optimization Algorithm (ISBOA) to achieve high-precision solutions. Aiming at the problems of restricted solution set exploration, easy to fall into local optimization and slow convergence when solving the inverse kinematics of multi-DOF robotic arm by SBOA, this paper introduces the oppositional variational perturbation, golden sine development and evolutionary strategy to optimize the formation of ISBOA, and verifies its effectiveness through numerical experiments. Simulation experiments using 4, 6, and 7-DOF robotic arms are conducted, with inverse solution results analyzed via PCA dimensionality reduction and K-means clustering, demonstrating the superiority of ISBOA in inverse solution diversity. Finally, a MATLAB-CoppeliaSim-UR16e experimental platform is developed to compare ISBOA with traditional analytical and Newton iterative method. Results are analyzed in terms of assembly accuracy, singular position handling, grasping success rate, and assembly success rate, confirming ISBOA’s advantages in pyrotechnic grain assembly and its potential for engineering applications.

NEXN protects against vascular calcification by promoting SERCA2 SUMOylation and stabilization

Assessment of university students’ earthquake coping strategies using artificial intelligence methods

Abstract Earthquakes are one of the most destructive natural disasters that pose a serious threat to human life and infrastructure worldwide. The aim of this study is to evaluate the coping strategies of adult individuals in Turkey regarding earthquake stress using artificial intelligence-based methods. The data was collected from 858 university students living in Turkey during January, February, and March 2024. A dataset was created using the ‘Coping Scale for Earthquake Stress.’ Prediction models were established using artificial intelligence algorithms such as Logistic Regression (LR), Bagging, and Random Forest (RF) based on information from 24 variables. The cross-validation method was applied during model training. The Logistic Regression algorithm achieved the highest accuracy rate of 98.60%, while the Bagging algorithm demonstrated the lowest performance with an accuracy rate of 79.95%. The Random Forest algorithm showed moderate performance with an accuracy rate of 85.89%. The findings provide important insights into the coping strategies of the community regarding earthquake stress. This study is expected to contribute significantly to areas such as disaster management, psychology, public health, and community resilience.

The speed of change in climate and land cover is associated with the speed of biodiversity changes in avian assemblages of the United States

Changes in climate and land use land cover are a widely recognized threat to the stability of natural species assemblages’ composition and biodiversity. Species-specific responses to these changes can result in a rearrangement of the species composition of assemblages, altering the stability, resilience, and functioning of the ecosystems of which these assemblages are a part. We assessed the relationship between the rate of change in avian species richness and assemblage dissimilarity and the rate of change in climatic and land use land cover variables across 30 years in five ecoregions of the United States. Areas where effects of changing land use land cover and/or climate were most strongly felt were high elevations and latitudes. Rates of species replacement and loss were associated with changing environmental factors in opposite directions. Rates of change in biodiversity were more strongly predicted by rates of change in land use land cover than by rates of change in climate. For the species assemblages studied here, rapidly changing climate and/ or land use land cover was more strongly affecting total assemblage dissimilarity patterns than species richness even though species richness has received much more research attention. Trends in multiple biodiversity indices capture multiple levels of action (richness vs. assemblage dissimilarity). A study that integrates these allows us to observe the complex and changing interrelationships between biodiversity and the environment (climate and land cover), and thus, plan effectively for preservation of processes that generate patterns of biodiversity.

Ten thousand hour stable zinc air batteries via Fe and W dual atom sites

Long chain fatty acid transport via SLC27A1 enhances DAG-3-P synthesis and accelerates colorectal cancer metastasis

Besse relaxation difference scheme for a nonlinear integro-differential equation

In this paper, Besse relaxation difference and compact difference scheme for a nonlinear integro-differential equation, which is crucial in modeling complex systems with memory and nonlocal effects, are proposed. A Besse relaxation difference scheme is developed by combining Besse relaxation time discretization with second-order spatial discretization, in which the Besse relaxation technique enhanced the accuracy and stability of dealing with nonlinear terms. To further improve spatial accuracy, a fourth-order compact finite difference approximation is used to construct the Besse relaxation compact difference scheme. To verify the effectiveness of the proposed Besse relaxation difference schemes, we have established the unconditional stability and optimal convergence of both methods in the discrete L2 norms. Numerical experiments demonstrate that these relaxation schemes attain the predicted convergence rates and high accuracy for smooth solutions, singular solutions, as well as featuring unbounded derivatives solutions.

Drought recovery in plants triggers a cell-state-specific immune activation

Abstract All organisms experience stress as an inevitable part of life, from single-celled microorganisms to complex multicellular beings. The ability to recover from stress is a fundamental trait that determines the overall resilience of an organism, yet stress recovery is understudied. To investigate how plants recover from drought, we examine a fine-scale time series of RNA sequencing starting 15 min after rehydration following moderate drought. We reveal that drought recovery is a rapid process involving the activation of thousands of recovery-specific genes. To capture these rapid recovery responses in different Arabidopsis thaliana ( A. thaliana ) leaf cell types, we perform a single-nucleus transcriptome analysis at the onset of drought recovery, identifying a cell type-specific transcriptional state developing independently across cell types. To further validate the cell-type specific transcriptional changes observed during drought recovery, we employ spatial transcriptomics using multiplexed error-robust fluorescence in situ hybridization (MERFISH), revealing anatomical localization of recovery-induced gene expression programs across Arabidopsis leaf tissues. Furthermore, we reveal a recovery-induced activation of the immune system that occurs autonomously, and which enhances pathogen resistance in vivo in A. thaliana , wild tomato ( Solanum pennellii) and domesticated tomato ( Solanum lycopersicum cv. M82). Since rehydration promotes microbial proliferation and thereby increases the risk of infection, the activation of drought recovery-induced immunity may be crucial for plant survival in natural environments. These findings indicate that drought recovery coincides with a preventive defense response, unraveling the complex regulatory mechanisms that facilitate stress recovery in different plant cell types.

Exploring movement entrainment in an ecologically valid concert setting

Abstract Moving to the beat is one of the most natural human reactions while listening to music and can be defined as movement entrainment. Listeners’ reactions to music have been discussed as the result of the music, the context, and the listener. However, as music is a social phenomenon typically performed in live settings, movement entrainment and its contributing factors have never been investigated in ecologically valid concerts. In a preliminary study (n = 42) during a rock/pop concert, we confirmed the validity of our method of measuring acceleration data of the audience with portable devices on the arm and torso. In our main study (n = 69), we measured audience data at two big band concerts with different conditions: One was seated, the other unseated. With several consecutive Hierarchical Linear Models, we identified factors predicting movement entrainment: location of measurement (arm vs. torso), metrical level (BPM vs. half- and double-time level), musical factors (the different pieces), concert condition (seated vs. unseated), and the personal characteristic of urge to dance. Our study shows that there are multiple factors contributing to movement entrainment and can be seen as a first step towards studying this phenomenon in ecologically valid concert settings.

A water solubility prediction algorithm based on the StackBoost model

Aqueous solubility, an essential physical property of compounds, has significant applications across various fields. However, verifying the solubility of compounds through experimental methods often requires substantial human and material resources. To address this issue, this study introduces the StackBoost model for predicting the solubility of organic compounds and systematically compares it with five well-known ensemble learning algorithms: Adaptive Boosting (AdaBoost), Gradient Boosted Regression Trees (GBRT), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination (R2) of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. Furthermore, this study further conducted high-throughput screening on large-scale datasets and successfully identified compounds with high potential for water solubility. Additionally, the model’s generalization ability is verified through transfer learning. Although the performance of the StackBoost model decreases when applied to different datasets, it still shows considerable transferability, making it a more generalizable prediction model for aqueous solubility.

A spatial long-read approach at near-single-cell resolution reveals developmental regulation of splicing and polyadenylation sites in distinct cortical layers and cell types

Abstract Genome-wide spatial long-read approaches often lack single-cell resolution and yield limited read lengths. Here, we introduce spatial ISOform sequencing (Spl-ISO-Seq), which reveals exons and polyadenylation sites with near-single-cell resolution. Spl-ISO-Seq selects long cDNAs and doubles to triples read lengths compared to standard preparations. Adding a highly specific software tool (Spl-ISOquant) and comparing human post-mortem pre-puberty (8–11 years) to post-puberty (16–19 years) visual cortex samples, we find that cortex harbors stronger splicing and poly(A)-site regulation than white matter. However, oligodendrocyte regulation is stronger in white matter. Among cortical layers, layer 4 has the most developmentally-regulated splicing changes in excitatory neurons and in poly(A) sites. We also find repeat elements downstream of developmentally-regulated layer 4 exons. Overall, alternative splicing changes are linked to post-synaptic structure and function. These results root developmental splicing changes during puberty in specific layers and cell types. More generally, our technologies enable exciting observations for any complex tissue.

Nanofat promotes wound healing in skin following exposure to ionizing radiation

Abstract Radiotherapy, while effective in cancer treatment, can lead to side effects, such as radiodermatitis with potential long-term consequences including telangiectasias, ulceration and fibrosis of the skin, eventually resulting in impaired wound healing. In this study, we analyzed whether the healing of such challenging wounds can be improved by nanofat (NF). NF is generated by mechanical emulsification and filtration of fat samples and, thus, is a random mixture of adipose-derived stem cells, microvascular fragments, extracellular matrix components and growth factors. Two months after localized ionizing radiation of the skin with a total dose of 20 Gy, full-thickness wounds were created in dorsal skinfold chambers of mice, which were filled with platelet-rich plasma (PRP; control, n = 8) or NF fixed in PRP (PRP + NF, n = 8). The healing process was assessed by means of stereomicroscopy, intravital fluorescence microscopy, histology and immunohistochemistry over 14 days. The closure of PRP + NF-treated wounds was accelerated, as indicated by significantly smaller wound areas on day 14 when compared to controls. This was associated with a higher density of blood-perfused microvessels inside the wounds. Moreover, PRP + NF-treated wounds showed a tendency towards an improved granulation tissue formation, lymphatic drainage and M2/M1 macrophage ratio. Taken together, these findings suggest that the application of NF represents a promising therapeutic strategy for the management of complex wounds in irradiated skin.

“After reducing alcohol, things now work well at home”: Perceived impacts of the Mlambe intervention on alcohol use, relationship dynamics, household economics, and HIV treatment adherence in Malawian couples

Unhealthy alcohol use is prevalent in sub-Saharan Africa and can worsen poverty, couple relationships, and HIV treatment outcomes. In response, we assessed participant experiences with Mlambe, a pilot study of an economic and relationship-strengthening intervention for couples living with unhealthy alcohol use and HIV. Exit interviews were conducted with a subset of 20 couples who participated in a pilot trial of Mlambe in Zomba, Malawi. A 10-month intervention consisted of financial literacy and couples counseling sessions and incentivized savings accounts. Eligible couples were married, ≥ 18, with at least one partner with unhealthy alcohol use (according to AUDIT-C screen), and currently on antiretroviral therapy (ART). Interviews were recorded, transcribed, translated, and analyzed using dyadic framework analysis. Intervention couples reported that alcohol use caused many problems and that reducing or quitting use brought improvements to their physical and emotional health, and the well-being of their marriages and households. Before Mlambe, couples reported that quarrels about alcohol use were common. After the intervention, couples reported improvements to their relationships and less alcohol use, which led to more open and respectful couple communication and other marital improvements including increased sexual satisfaction and trust. Women and men described that post-intervention, men reduced spending on alcohol which improved availability of money for household needs such as food and clothing, and that feeling more economically secure reduced stress and led to a more “peaceful family.” After Mlambe, male drinkers described more motivation to pursue income-generating activities, and that reduced alcohol use led to greater medication adherence as they no longer forgot to take ART when drinking. Mlambe may contribute to positive change for couple relationships and health behaviors through mechanisms including reduced conflict and poverty related to reduced alcohol use. The model appears promising for couples in resource-poor settings where HIV, poverty, and alcohol use are mutually reinforcing.

All-optically controlled phased-array for ultrasonics

Abstract The ability to dynamically shape ultrasound fields is critical for emerging applications in therapeutic ultrasound, particle manipulation and tissue engineering. While existing phased arrays provide beam steering for imaging, these newer applications require higher intensities. This complicates the electrical driving and ultimately limits the array size and spatial complexity of the field. Here, we introduce a scalable architecture for driving phased arrays using a single power source and light-responsive analog phase shifters. Compared to conventional arrays, which drive each channel independently, our device only needs one amplifier. Moreover, the phase shift can be continuously varied between  ±π based on light intensity. Using our phase shifter, we demonstrate dynamic, multi-focal ultrasound beams, fast beam steering, and spatially-complex beams including acoustic vortices. Because of its simple, analog design, optical addressing, and superior phase control, this architecture paves the way for very large transducer arrays and the generation of high-intensity, spatially-complex ultrasound fields.

Quantum geometry in hexagonal circuit QED lattice with triple leg stripline resonators

Investigation of carbapenemase-encoding genes in Burkholderia cepacia and Aeromonas sobria isolates from nosocomial infections in Iraqi patients

Burkholderia cepacia and Aeromonas sobria are difficult to eradicate due to their innate resistance to a variety of medications, and cause various diseases. The aim of this study was to investigate the occurrence of carbapenemase genes and patterns of antibiotic resistance in isolates of B. cepacia and A. sobria. Randomly, 120 clinical specimens have been collected in patients with nosocomial infections. Selective media were used to culture ear swabs, urine, burns, wounds and cerebrospinal fluids. According to biochemical tests and the VITEK-2 system, 75 of these demonstrated positive growth with B. cepacia and A. sobria. Metallo-β-lactamase (MBL) synthesis was phenotypically screened using the meropenem-EDTA disc test. The recA gene in B. cepacia and the genes encoding carbapenemase in both species were found using PCR tests. Among the 75 isolates assessed 20 (26.6%) were A. sobria and 55 (73.3%) were B. cepacia. Piperacillin, cefepime, and ceftriaxone showed antimicrobial resistance of 100%, followed by ceftazidime (97.3%), cefazolin (96%), and piperacillin/ tazobactam (94.6%). Intermediate resistance was reported with aztreonam (61.3%), meropenem (49.3%), trimethoprim-sulfamethoxazole (49.3%), gentamicin (46.6%), levofloxacin (44%), and ciprofloxacin (44%). It is important to note minocycline (40%), amikacin (40%) imipenem (36%) and tigecycline (34.6%), had the lowest resistance rates, hence their relatively higher efficacy against the tested isolates. In this investigation, the B. cepacia was confirmed to be found via the recA gene. The overall prevalence of carbapenemase genes was 92.8% (52/56) with blaKPC accounting for 80.8% (42/52) and blaGES for 19.2% (10/52) of the total. Specifically, 38 (90.51%) of the 42 (76.36%) B. cepacia isolates that were positive in carbapenem resistance carried blaKPC gene, 2 (4.81%) isolates carried blaGES, and 2 (4.81%) had no detectable carbapenemase gene. In the case of the 14 A. sobria carbapenem-resistant isolates, there were 4 isolates (28.6%) that had blaKPC, 8 isolates (57.1%) that had blaGES and there were 2 isolates (14.3%) that did not have any carbapenemase genes. None of isolates studied tested positive for the blaIMP gene. The recent study concluded that recA gene identification was more sensitive and specific technique for detection B. cepacia complex isolates. Since the prevalence of carbapenemase producers is high, careful infection control measures, rapid diagnostics, and antimicrobial stewardship must be implemented by clinicians. It is necessary that combination therapy be guided and early detectable to ensure better outcomes and restrict resistance.