Improving the Long-Term Stability of PbTe-Based Thermoelectric Modules: From Nanostructures to Packaged Module Architecture.

Nanostructured lead telluride PbTe is among the best-performing thermoelectric materials, for both p- and n-types, for intermediate temperature applications. However, the fabrication of power-generating modules based on nanostructured PbTe still faces challenges related to the stability of the materials, especially nanoprecipitates, and the bonding of electric contacts. In this study, in situ high-temperature transmission electron microscopy observation confirmed the stability of nanoprecipitates in p-type Pb0.973Na0.02Ge0.007Te up to at least ∼786 K. Then, a new architecture for a packaged module was developed for improving durability, preventing unwanted interaction between thermoelectric materials and electrodes, and for reducing thermal stress-induced crack formation. Finite element method simulations of thermal stresses and power generation characteristics were utilized to optimize the new module architecture. Legs of nanostructured p-type Pb0.973Na0.02Ge0.007Te (maximum zT ∼ 2.2 at 795 K) and nanostructured n-type Pb0.98Ga0.02Te (maximum zT ∼ 1.5 at 748 K) were stacked with flexible Fe-foil diffusion barrier layers and Ag-foil-interconnecting electrodes forming stable interfaces between electrodes and PbTe in the packaged module. For the bare module, a maximum conversion efficiency of ∼6.8% was obtained for a temperature difference of ∼480 K. Only ∼3% reduction in output power and efficiency was found after long-term operation of the bare module for ∼740 h (∼31 days) at a hot-side temperature of ∼673 K, demonstrating good long-term stability.

Comparing Learning Outcomes of Machine-Guided Virtual Reality-Based Training With Educator-Guided Training in a Metaverse Environment: Randomized Controlled Trial.

BACKGROUND: Virtual reality (VR) modules are commonly used for health care training, such as adult advanced cardiac life support (ACLS), due to immersion and engagement. The metaverse differs from current VR serious gaming by enabling shared social connections, while current VR modules focus on computer-based content without social interaction. Educators in the metaverse can foster communication and collaboration during training sessions. OBJECTIVE: This study aimed to compare learning outcomes of VR-based, machine-guided training with educator-guided, VR-based training in the metaverse environment. METHODS: A total of 62 volunteered students from Acibadem Mehmet Ali Aydinlar University Vocational School for Anesthesiology were randomly divided into 2 groups of 31 participants each: one group received VR-based training with machine guidance (MG), and the other received VR-based training with educator guidance (EG) in the metaverse. The members of both groups undertook VR-based basic training for ACLS. Afterward, the MG group was trained with a VR-based advanced training module, which provides training with full MG, whereas the EG group attended the VR-based, educator-guided training in the metaverse. The primary outcome of the study was determined by the exam score of the VR-based training module. Descriptive statistics defined continuous variables such as VR exam scores and time spent on machine- or educator-guided training. The correlation between training time and VR exam scores was assessed with the Spearman rank correlation, and nonnormally distributed variables were compared using the Mann-Whitney U test. Statistical significance was set at P<.05, with analyses executed by MedCalc Statistical Software (version 12.7.7). RESULTS: Comparing the VR test scores between the MG and EG groups revealed no statistically significant difference. The VR test scores for the EG group had a median of 86 (range 11-100). In contrast, the MG group scores had a median of 66 (range 13-100; P=.08). Regarding the correlation between the duration of machine-guided or educator-guided training and VR-based exam scores, for the MG group, =0.569 and P=.005 were obtained. For the EG group, this correlation was found to be =0.298 and P=.10. While this correlation is statistically significant for the MG group, it is not significant for the EG group. The post hoc power analysis (80%), considering the correlation between the time spent on training and exam scores, supported this finding. CONCLUSIONS: The results of this study suggest that a well-designed, VR-based serious gaming module with MG could provide comparable learning outcomes to VR training in the metaverse with EG for adult ACLS training. Future research with a larger sample size could explore whether social interaction with educators in a metaverse environment offers added benefits for learners. TRIAL REGISTRATION: ClinicalTrials.gov NCT06288087; https://clinicaltrials.gov/study/NCT06288087.

CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture.

Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or activities. The driver's distractions or activities convey meaningful information to the ADS, enhancing the driver/ vehicle safety in real-time vehicle driving. The classification of driver distraction or activity is challenging due to the unpredictable nature of human driving. This paper proposes a convolutional block attention module embedded in Visual Geometry Group (CBAM VGG16) deep learning architecture to improve the classification performance of driver distractions. The proposed CBAM VGG16 architecture is the hybrid network of the CBAM layer with conventional VGG16 network layers. Adding a CBAM layer into a traditional VGG16 architecture enhances the model's feature extraction capacity and improves the driver distraction classification results. To validate the significant performance of our proposed CBAM VGG16 architecture, we tested our model on the American University in Cairo (AUC) distracted driver dataset version 2 (AUCD2) for cameras 1 and 2 images. Our experiment results show that the proposed CBAM VGG16 architecture achieved 98.65% classification accuracy for camera 1 and 97.85% for camera 2 AUCD2 datasets. The CBAM VGG16 architecture also compared the driver distraction classification performance with DenseNet121, Xception, MoblieNetV2, InceptionV3, and VGG16 architectures based on the proposed model's accuracy, loss, precision, F1 score, recall, and confusion matrix. The drivers' distraction classification results indicate that the proposed CBAM VGG16 has 3.7% classification improvements for AUCD2 camera 1 images and 5% for camera 2 images compared to the conventional VGG16 deep learning classification model. We also tested our proposed architecture with different hyperparameter values and estimated the optimal values for best driver distraction classification. The significance of data augmentation techniques for the data diversity performance of the CBAM VGG16 model is also validated in terms of overfitting scenarios. The Grad-CAM visualization of our proposed CBAM VGG16 architecture is also considered in our study, and the results show that VGG16 architecture without CBAM layers is less attentive to the essential parts of the driver distraction images. Furthermore, we tested the effective classification performance of our proposed CBAM VGG16 architecture with the number of model parameters, model size, various input image resolutions, cross-validation, Bayesian search optimization and different CBAM layers. The results indicate that CBAM layers in our proposed architecture enhance the classification performance of conventional VGG16 architecture and outperform the state-of-the-art deep learning architectures.

SARC-UNet: A coronary artery segmentation method based on spatial attention and residual convolution.

BACKGROUND AND OBJECTIVE: Coronary artery segmentation is a pivotal field that has received increasing attention in recent years. However, this task remains challenging because of the inhomogeneous distributions of the contrast agent and dim light, resulting in noise, vascular breakages and small vessel losses in the obtained segmentation results. METHODS: To acquire better automatic blood vessel segmentation results for coronary angiography images, a UNet-based segmentation network (SARC-UNet) is constructed for coronary artery segmentation; this approach is based on residual convolution and spatial attention. First, we use the low-light image enhancement (LIME) approach to increase the contrast and clarity levels of coronary angiography images. Then, we design two residual convolution fusion modules (RCFM1 and RCFM2) that can successfully fuse the local and global information of coronary images while also capturing the characteristics of finer-grained blood vessels, hence preventing the loss of tiny blood vessels in the segmentation findings. Finally, using a cascaded waterfall structure, we create a new location-enhanced spatial attention (LESA) mechanism that can efficiently improve the long-distance dependencies between coronary vascular pixel features, eradicating vascular ruptures and noise in the segmentation results. RESULTS: This article subjectively and objectively evaluates the experimental results. This method has performed well on five general indicators. Furthermore, it outperforms the connectivity indicators proposed in this article. This method can effectively segment blood vessels and obtain higher accuracy results. CONCLUSIONS: Numerous experiments have shown that the suggested method outperforms the state-of-the-art approaches, particularly in terms of vessel connectivity and small blood vessel segmentation.

Heterogeneous biomedical entity representation learning for gene-disease association prediction.

Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial role in biological function. Identifying associations between genes and diseases is critical for diagnosis, prevention, prognosis, and drug development. Genes that encode proteins with similar sequences are often implicated in related diseases, as proteins causing identical or similar diseases tend to show limited variation in their sequences. Predicting gene-disease association (GDA) requires time-consuming and expensive experiments on a large number of potential candidate genes. Although methods have been proposed to predict associations between genes and diseases using traditional machine learning algorithms and graph neural networks, these approaches struggle to capture the deep semantic information within the genes and diseases and are dependent on training data. To alleviate this issue, we propose a novel GDA prediction model named FusionGDA, which utilizes a pre-training phase with a fusion module to enrich the gene and disease semantic representations encoded by pre-trained language models. Multi-modal representations are generated by the fusion module, which includes rich semantic information about two heterogeneous biomedical entities: protein sequences and disease descriptions. Subsequently, the pooling aggregation strategy is adopted to compress the dimensions of the multi-modal representation. In addition, FusionGDA employs a pre-training phase leveraging a contrastive learning loss to extract potential gene and disease features by training on a large public GDA dataset. To rigorously evaluate the effectiveness of the FusionGDA model, we conduct comprehensive experiments on five datasets and compare our proposed model with five competitive baseline models on the DisGeNet-Eval dataset. Notably, our case study further demonstrates the ability of FusionGDA to discover hidden associations effectively. The complete code and datasets of our experiments are available at https://github.com/ZhaohanM/FusionGDA.

A twin analysis to estimate genetic and environmental factors contributing to variation in weighted gene co-expression network module eigengenes.

Multivariate network-based analytic methods such as weighted gene co-expression network analysis are frequently applied to human and animal gene-expression data to estimate the first principal component of a module, or module eigengene (ME). MEs are interpreted as multivariate summaries of correlated gene-expression patterns and network connectivity across genes within a module. As such, they have the potential to elucidate the mechanisms by which molecular genomic variation contributes to individual differences in complex traits. Although increasingly used to test for associations between modules and complex traits, the genetic and environmental etiology of MEs has not been empirically established. It is unclear if, and to what degree, individual differences in blood-derived MEs reflect random variation versus familial aggregation arising from heritable or shared environmental influences. We used biometrical genetic analyses to estimate the contribution of genetic and environmental influences on MEs derived from blood lymphocytes collected on a sample of N = 661 older male twins from the Vietnam Era Twin Study of Aging (VETSA) whose mean age at assessment was 67.7 years (SD = 2.6 years, range = 62-74 years). Of the 26 detected MEs, 14 (56%) had statistically significant additive genetic variation with an average heritability of 44% (SD = 0.08, range = 35%-64%). Despite the relatively small sample size, this demonstration of significant family aggregation including estimates of heritability in 14 of the 26 MEs suggests that blood-based MEs are reliable and merit further exploration in terms of their associations with complex traits and diseases.

ResNeXt-CC: a novel network based on cross-layer deep-feature fusion for white blood cell classification.

Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved competitive performance in medical image classification. In this paper, we propose a white blood cell classification network called ResNeXt-CC for cytopathological images. First, we transform cytopathological images from the RGB color space to the HSV color space so as to precisely extract the texture features, color changes and other details of white blood cells. Second, since cell classification primarily relies on distinguishing local characteristics, we design a cross-layer deep-feature fusion module to enhance our ability to extract discriminative information. Third, the efficient attention mechanism based on the ECANet module is used to promote the feature extraction capability of cell details. Finally, we combine the modified softmax loss function and the central loss function to train the network, thereby effectively addressing the problem of class imbalance and improving the network performance. The experimental results on the C-NMC 2019 dataset show that our proposed method manifests obvious advantages over the existing classification methods, including ResNet-50, Inception-V3, Densenet121, VGG16, Cross ViT, Token-to-Token ViT, Deep ViT, and simple ViT about 5.5-20.43% accuracy, 3.6-23.56% F1-score, 3.5-25.71% AUROC and 8.1-36.98% specificity, respectively.

Assessing the Environmental Benefits of Extending the Service Lifetime of Solar Photovoltaic Modules.

Requiring no fuel for generation and negligible material/energy for operation and maintenance, photovoltaic (PV) systems have environmental impacts mostly due to the production of modules and the commissioning of power plants. Thus, extending the service lifetime of these systems from 30 to 40 years through an enhanced lamination process for module production potentially reduces environmental impacts per unit energy generated. Life cycle assessment is employed to evaluate the environmental impacts under scenarios for resource utilizations for the new lamination process, operation and maintenance requirements in the extended service lifetime, and degradation rates of the devised modules. Extending the service lifetime significantly reduces environmental impacts across categories, with a 21-27% reduction in global warming potential on the pessimistic and optimistic ends. At least 20% impact reduction is achieved in most impact categories, even under a pessimistic scenario. Considering uncertainty models in the life cycle inventories, samples are generated for scenarios via Monte Carlo simulation, and with significant improvements with large effects in most environmental impact categories, deterministic impact comparisons are supported by ANOVA and Tukey tests. Production strategies for more durable and reliable PV modules have a significant potential in contributing to global sustainability efforts.

Enhancement of the degradation capacity of IsPETase by acidic amino acids insertion and carbohydrate-binding module fusion.

The biocatalytic degradation of poly(ethylene terephthalate) (PET) through enzymatic methods has garnered considerable attention due to its environmentally friendly and non-polluting nature, as well as its high specificity. While previous efforts in enhancing IsPETase performance have focused on amino acid substitutions in protein engineering, we introduced an amino acid insertion strategy in this work. By inserting a negatively charged acidic amino acid, Glu, at the right-angle bend of IsPETase, the binding capability between the enzyme's active pocket and PET was improved. The resulted mutant IsPETase9394insE exhibited enhanced hydrolytic activity towards PET at various temperatures ranging from 30 to 45 ℃ compared with the wild-type IsPETase. Notably, a 10.04-fold increase was observed at 45 ℃. To further enhance PET hydrolysis, different carbohydrate-binding modules (CBMs) were incorporated at the C-terminus of IsPETase9394insE. Among these, the fusion of CBM from Verrucosispora sioxanthis exhibited the highest enhancement, resulting in a 1.82-fold increase in PET hydrolytic activity at 37 ℃ compared with the IsPETase9394insE. Finally, the engineered variant was successfully employed for the degradation of polyester filter cloth, demonstrating its promising hydrolytic capacity. In conclusion, this research presents an alternative enzyme engineering strategy for modifying PETases and enriches the pool of potential candidates for industrial PET degradation.

Low-light image enhancement using generative adversarial networks.

In low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images.

Development of a High-Sensitivity Millimeter-Wave Radar Imaging System for Non-Destructive Testing.

There is an urgent need to develop non-destructive testing (NDT) methods for infrastructure facilities and residences, etc., where human lives are at stake, to prevent collapse due to aging or natural disasters such as earthquakes before they occur. In such inspections, it is desirable to develop a remote, non-contact, non-destructive inspection method that can inspect cracks as small as 0.1 mm on the surface of a structure and damage inside and on the surface of the structure that cannot be seen by the human eye with high sensitivity, while ensuring the safety of the engineers inspecting the structure. Based on this perspective, we developed a radar module (absolute gain of the transmitting antenna: 13.5 dB; absolute gain of the receiving antenna: 14.5 dB) with very high directivity and minimal loss in the signal transmission path between the radar chip and the array antenna, using our previously developed technology. A single-input, multiple-output (SIMO) synthetic aperture radar (SAR) imaging system was developed using this module. As a result of various performance evaluations using this system, we were able to demonstrate that this system has a performance that fully satisfies the abovementioned indices. First, the SNR in millimeter-wave (MM-wave) imaging was improved by 5.4 dB compared to the previously constructed imaging system using the IWR1443BOOST EVM, even though the measured distance was 2.66 times longer. As a specific example of the results of measurements on infrastructure facilities, the system successfully observed cracks as small as 0.1 mm in concrete materials hidden under glass fiber-reinforced tape and black acrylic paint. In this case, measurements were also made from a distance of about 3 m to meet the remote observation requirements, but the radar module with its high-directivity and high-gain antenna proved to be more sensitive in detecting crack structures than measurements made from a distance of 780 mm. In order to estimate the penetration length of MM waves into concrete, an experiment was conducted to measure the penetration of MM waves through a thin concrete slab with a thickness of 3.7 mm. As a result, Λexp = 6.0 mm was obtained as the attenuation distance of MM waves in the concrete slab used. In addition, transmission measurement experiments using a composite material consisting of ceramic tiles and fireproof board, which is a component of a house, and experiments using composite plywood, which is used as a general housing construction material in Japan, succeeded in making perspective observations of defects in the internal structure, etc., which are invisible to the human eye.

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial-temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 µVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.

Development and Validation of the Needlestick Injury Prevention (N-SIP) Module.

INTRODUCTION: Needlestick injuries (NSIs) pose a significant occupational hazard to healthcare workers (HCWs), with potential risks of exposure to bloodborne pathogens. The development of effective training modules is crucial to addressing NSI prevention and enhancing HCWs' knowledge and risk perception. This study aims to develop and validate the Needlestick Injury Prevention Module (N-SIP) using the ADDIE model (Florida State University, FL), which stands for Analysis, Design, Development, Implementation, and Evaluation, to improve NSI-related knowledge and risk perception among House Officers (HOs) in healthcare settings. METHODS: The study utilized approaches comprising literature review, module development using the ADDIE model, content validation by experts, and face validation among HOs. The N-SIP module addressed various aspects of NSI prevention, including background information, bloodborne viral infections, infection prevention practices, and occupational safety. The evaluation involved content validation by expert panels and face validation by HOs. RESULTS: The content validity of the N-SIP module was rigorously evaluated through expert review and validation by subject matter experts and HOs. The experts' feedback ensured the quality, relevance, and comprehensiveness of the module's instructional materials. Furthermore, face validity was assessed among HOs to ensure the module's clarity, appropriateness, and perceived effectiveness in addressing NSI prevention. The positive response from HOs indicated favorable perceptions of the module's content and instructional design, affirming its potential to effectively enhance perceptions related to NSI prevention among HCWs. CONCLUSION: The development and evaluation of the N-SIP represent a significant advancement in addressing NSIs among HCWs. Through a structured approach informed by the ADDIE model, the N-SIP module offers a comprehensive and tailored learning experience aimed at enhancing NSI-related knowledge and risk perception among HOs. The study findings underscore the importance of effective training interventions in promoting a culture of safety and reducing occupational hazards in healthcare settings.

Perceptions of Attitude, Ethics, and Communication (AETCOM) Modules Among Indian Medical Graduates in Their First Professional Year: An Educational Observational Study.

Background The National Medical Commission (NMC) of India has redesigned the graduate medical education program to equip Indian medical graduates with essential information, skills, attitudes, values, and responsiveness as physicians in their initial interactions with the community. Central to this initiative is the Attitude, Ethics, and Communication (AETCOM) module, designed as a guide for educators and institutions to implement a comprehensive, long-term program. This aims to ensure that students develop competency as clinicians, leaders, team players, communicators, lifelong learners, and professionals. Objectives The aim of this study is to evaluate students' perceptions of the AETCOM modules during their first year. Methodology This cross-sectional study was conducted at BLDE (Deemed to be University), Vijayapura, India, utilizing self-administered, semi-structured questionnaires for data collection. The study included second- and third-year medical students, with all respondents who submitted their responses being included in the study. The total sample size comprised 123 students. Results Ninety-eight percent of the students agreed that the NMC had taken excellent initiative with the AETCOM module and found its duration sufficient. They suggested that the teaching-learning techniques should include more interactive sessions. Conclusions Feedback from the AETCOM module is crucial for enhancing its effectiveness, and it should be gathered from all medical colleges to propose necessary improvements.

Identification of chitinase from Bacillus velezensis strain S161 and its antifungal activity against Penicillium digitatum.

Previous studies have demonstrated the presence of chitinase in Bacillus velezensis through extensive genomic sequencing and experimental analyses. However, the detailed structure, functional roles, and antifungal activity of these chitinases remain poorly characterized. In this study, genomic screening identified three genes-chiA, chiB, and lpmo10-associated with chitinase degradation in B. velezensis S161. These genes encode chitinases ChiA and ChiB, and lytic polysaccharide monooxygenase LPMO10. Both ChiA and ChiB contain two CBM50 binding domains and one catalytic domain, whereas LPMO10 includes a signal peptide and a single catalytic domain. The chitinases ChiA, its truncated variant ChiA2, and ChiB were heterologously expressed in Escherichia coli. The purified enzymes efficiently degraded colloidal chitin and inhibited the spore germination of Penicillium digitatum. Notably, even after losing one CBM50 domain, the resultant enzyme, consisting of the remaining CBM50 domain and the catalytic domain, maintained its colloidal chitin hydrolysis and antifungal activity, indicating commendable stability. These results underscore the role of B. velezensis chitinases in suppressing plant pathogenic fungi and provide a solid foundation for developing and applying chitinase-based biocontrol strategies.

All-back-contact neutral-colored transparent crystalline silicon solar cells enabling seamless modularization.

Transparent solar cells (TSCs) hold substantial potential as continuous energy generators, enabling their use in situations where conventional devices may not be feasible. However, research aimed at modularizing TSCs for the purpose of regulating the overall voltage and current they produce, a critical step toward practical application, is still in its nascent stages. In this study, we explored a custom-designed, all-back-contact (ABC) configuration, which situates all electrical contacts on the rear side, to create glass-like transparent crystalline silicon (c-Si) solar cells and seamless modules. The ABC design not only demonstrates high power conversion efficiency (PCE) in solar cells but also ensures unobstructed visibility through transparent solar modules. Notably, ABC-transparent c-Si solar cells achieved a peak PCE of 15.8% while maintaining an average visible transmittance of 20%. Through seamlessly interconnecting the unit cells, the output voltage and power were systematically tuned from 0.64 V and 15.8 mW (for a 1 cm2-sized unit cell) to 10.0 V and 235 mW (for a 16 cm2-sized module). Furthermore, we successfully demonstrated the photocharging of a smartphone using a transparent ABC solar module.

Ultrastable Carboxyl-Functionalized Pore-Space-Partitioned Metal-Organic Frameworks for Gas Separation.

Isoreticular chemistry, which enables property optimization by changing compositions without changing topology, is a powerful synthetic strategy. One of the biggest challenges facing isoreticular chemistry is to extend it to ligands with strongly coordinating substituent groups such as unbound -COOH, because competitive interactions between such groups and metal ions can derail isoreticular chemistry. It is even more challenging to have an isoreticular series of carboxyl-functionalized MOFs capable of encompassing chemically disparate metal ions. Here, with the simultaneous introduction of carboxyl functionalization and pore space partition, a family of carboxyl-functionalized materials is developed in diverse compositions from homometallic Cr3+ and Ni2+ to heterometallic Co2+/V3+, Ni2+/V3+, Co2+/In3+, Co2+/Ni2+. Cr-MOFs remain highly crystalline in boiling water. Unprecedentedly, one Cr-MOF can withstand the treatment cycle with 10m NaOH and 12m HCl, allowing reversible inter-conversion between unbound -COOH acid form and -COO- base form. These materials exhibit excellent sorption properties such as high uptake capacity for CO2 (100.2 cm3 g-1) and hydrocarbon gases (e.g., 142.1 cm3 g-1 for C2H2, 110.5 cm3 g-1 for C2H4) at 1 bar and 298K, high benzene/cyclohexane selectivity (up to ≈40), and promising separation performance for gas mixtures such as C2H2/CO2 and C2H2/C2H4.

An Approach to the Design and Development of an Accredited Continuing Professional Development e-Learning Module on Virtual Care.

Unlabelled: Virtual care appointments expanded rapidly during COVID-19 out of necessity and to enable access and continuity of care for many patients. While previous work has explored health care providers' experiences with telehealth usage on small-scale projects, the broad-level adoption of virtual care during the pandemic has expounded opportunities for a better understanding of how to enhance the integration of telehealth as a regular mode of health care services delivery. Training and education for health care providers on the effective use of virtual care technologies are factors that can help facilitate improved adoption and use. We describe our approach to designing and developing an accredited continuing professional development (CPD) program using e-learning technologies to foster better knowledge and comfort among health care providers with the use of virtual care technologies. First, we discuss our approach to undertaking a systematic needs assessment study using a survey questionnaire of providers, key informant interviews, and a patient focus group. Next, we describe our steps in consulting with key stakeholder groups in the health system and arranging committees to inform the design of the program and address accreditation requirements. The instructional design features and aspects of the e-learning module are then described in depth, and our plan for evaluating the program is shared as well. As a CPD modality, e-learning offers the opportunity to enhance access to timely continuing professional education for health care providers who may be geographically dispersed across rural and remote communities.

MAPK3-MYB36-ARF1 module regulates the tanshinone formation in Salvia miltiorrhiza.

Salvia miltiorrhiza, known as Danshen, is a traditional Chinese medicinal plant with significant cardiovascular benefits, attributed to its secondary metabolites, particularly tanshinones. Despite their medicinal value, tanshinones occur in low natural abundance, necessitating research to increase their content. This study explores the role of the ARF transcription factor (SmARF1) in tanshinone accumulation in Danshen. Overexpressing SmARF1 in hairy roots significantly increased tanshinone levels. EMSA and Dual-LUC assays revealed that SmMYB36, a transcription factor interacting with SmMAPK3, binds to and regulates the SmARF1 promoter. SmMYB36 alone inhibited the expression of SmARF1 gene, while its interaction with SmMAPK3 enhanced SmARF1 promoter activity. This MAPK3-MYB36-ARF1 module elucidates a complex regulatory mechanism for tanshinone biosynthesis, offering insights for targeted enhancement of tanshinone content through advanced biotechnological approaches.

How carbohydrate-binding module affects the catalytic properties of endoglucanase.

The important role of Carbohydrate-binding module (CBM) in the cellulases catalytic activity has been widely studied. CBM3 showed highest affinity for cellulose substrate with 84.69 % adsorption rate among CBM1, CBM2, CBM3, and CBM4 in this study. How CBM affect the catalytic properties of GH5 endoglucanase III from Trichoderma viride (TvEG3) was systematically explored from two perspectives: the deletion of its own CBM(TvEG3dc) and the replacement of high substrate affinity CBM3 (TvEG3dcCBM3). Compared with TvEG3, TvEG3dc lost its binding ability on Avicel and filter paper, but its catalytic activity did not change significantly. The binding ability and catalytic activity of TvEG3dcCBM3 to Avicel increased 348.3 % and 372.51 % than that of TvEG3, respectively. The binding ability and catalytic activity of TvEG3dcCBM3 to filter paper decreased 51.7 % and 33.33 % than that of TvEG3, respectively. Further structural analysis of TvEG3, TvEG3dc, and TvEG3dcCBM3 revealed no changes in the positions and secondary structures of the key amino acids. These results demonstrated that its own CBM1 of TvEG3 did not affect its catalytic activity center, so it had no effect on its catalytic activity. But CBM3 changed the adsorption affinity for different substrates, which resulted in a change in the catalytic activity of the substrate.