Analyzing urinary hippuric acid as a metabolic health biomarker through a supramolecular architecture.

The prevalence of metabolic disorders has been found to increase concomitantly with alternations in habitual diet and lifestyle, indicating the importance of metabolic health monitoring for early warning of high-risk status and suggesting effective intervention strategies. Hippuric acid (HA), as one of the most abundant metabolites from the gut microbiota, holds potential as a regulator of metabolic health. Accordingly, it is imperative to establish an efficient, sensitive, and affordable method for large-scale population monitoring, revealing the association between HA level and metabolic disorders. Upon systematic screening of macrocycle•dye reporter pair, a supramolecular architecture (guanidinomethyl-modified calix[5]arene, GMC5A) was employed to sense urinary HA by employing fluorescein (Fl), whose complexation behavior was demonstrated by theoretical calculations, accomplishing quantification of HA in urine from 249 volunteers in the range of 0.10 mM and 10.93 mM. Excitedly, by restricted cubic spline, urinary HA concentration was found to have a significantly negative correlation with the risk of metabolic disorders when it exceeded 0.76 mM, suggesting the importance of dietary habits, especially the consumption of fruits, coffee, and tea, which was unveiled from a simple questionnaire survey. In this study, we accomplished a high throughput and sensitive detection of urinary HA based on supramolecular sensing with the GMC5A•Fl reporter pair, which sheds light on the rapid quantification of urinary HA as an indicator of metabolic health status and early intervention by balancing the daily diet.

1D p-type molecular-based coordination polymer semiconductor with ultrahigh mobility.

One-dimensional (1D) semiconductor nanostructures exhibit exceptional performance in mitigating short-channel effects and ensuring low power consumption. However, the scarcity of high-mobility p-type 1D materials impedes further advancement. Molecular-based materials offer high designability in structure and properties, making them a promising candidate for 1D p-type semiconductor materials. A molecular-based 1D p-type material was developed under the guidance of coordination chemistry. Cu-HT (HT is the abbreviation of p-hydroxy thiophenol) combines the merits of highly orbital overlap between Cu and S, fully covered surface modification with phenol functional groups, and unique cuprophilic (Cu-Cu) interactions. As such, Cu-HT has a remarkable hole mobility of 27.2 cm2 V-1 s-1, which is one of the highest reported values for 1D molecular-based materials to date and even surpass those of commonly used amorphous silicon as well as the majority of 1D inorganic materials. This achievement underscores the significant potential of coordination polymers in optimizing carrier transport and represents a major advancement in the synthesis of high-performance, 1D p-type semiconductor materials.

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases.

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

Identification of T2W hypointense ring as a novel noninvasive indicator for glioma grade and IDH genotype.

BACKGROUND: This study aimed to evaluate the T2W hypointense ring and T2-FLAIR mismatch signs in gliomas and use these signs to construct prediction models for glioma grading and isocitrate dehydrogenase (IDH) mutation status. METHODS: Two independent radiologists retrospectively evaluated 207 glioma patients to assess the presence of T2W hypointense ring and T2-FLAIR mismatch signs. The inter-rater reliability was calculated using the Cohen's kappa statistic. Two logistic regression models were constructed to differentiate glioma grade and predict IDH genotype noninvasively, respectively. Receiver operating characteristic (ROC) analysis was used to evaluate the developed models. RESULTS: Of the 207 patients enrolled (119 males and 88 females, mean age 51.6 ± 14.8 years), 45 cases were low-grade gliomas (LGGs), 162 were high-grade gliomas (HGGs), 55 patients had IDH mutations, and 116 were IDH wild-type. The number of T2W hypointense ring signs was higher in HGGs compared to LGGs (p < 0.001) and higher in the IDH wild-type group than in the IDH mutant group (p < 0.001). There were also significant differences in T2-FLAIR mismatch signs between HGGs and LGGs, as well as between IDH mutant and wild-type groups (p < 0.001). Two predictive models incorporating T2W hypointense ring, absence of T2-FLAIR mismatch, and age were constructed. The area under the ROC curve (AUROC) was 0.940 for predicting HGGs (95% CI = 0.907-0.972) and 0.830 for differentiating IDH wild-type (95% CI = 0.757-0.904). CONCLUSIONS: The combination of T2W hypointense ring, absence of T2-FLAIR mismatch, and age demonstrate good predictive capability for HGGs and IDH wild-type. These findings suggest that MRI can be used noninvasively to predict glioma grading and IDH mutation status, which may have important implications for patient management and treatment planning.

Enhanced multi view 3D reconstruction with improved MVSNet.

Although 3D reconstruction has been widely used in many fields as a key component of environment perception, existing technologies still have the potential for further improvement in 3D scene reconstruction. We propose an improved reconstruction algorithm based on the MVSNet network architecture. To glean richer pixel details from images, we suggest deploying a DE module integrated with a residual framework, which supplants the prevailing feature extraction mechanism. The DE module uses ECA-Net and dilated convolution to expand the receptive field range, performing feature splicing and fusion through the residual structure to retain the global information of the original image. Moreover, harnessing attention mechanisms refines the 3D cost volume's regularization process, bolstering the integration of information across multi-scale feature volumes, consequently enhancing depth estimation precision. When assessed our model using the DTU dataset, findings highlight the network's 3D reconstruction scoring a completeness (comp) of 0.411 mm and an overall quality of 0.418 mm. This performance is higher than that of traditional methods and other deep learning-based methods. Additionally, the visual representation of the point cloud model exhibits marked advancements. Trials on the Blended MVS dataset signify that our network exhibits commendable generalization prowess.

Tolerance threshold of a pelagic species in China to total dissolved gas supersaturation: from the perspective of survival characteristics and swimming ability.

Total dissolved gas (TDG) supersaturation downstream of dams can occur in the Yangtze River basin and is known to cause stress and even death in fish. Consequently, it is important to establish tolerance thresholds of endemic fish to protect local aquatic resources. We conducted experiments to assess survival characteristics and swimming ability of bighead carp, an important commercial fish dwelling in the Yangtze River, to evaluate its tolerance threshold to TDG supersaturation. The typical external symptoms of gas bubble trauma (GBT) were observed and the time when the fish lost equilibrium and died were recorded. The results showed that the mortality occurred when TDG level exceeded 125%, with obvious symptoms such as exophthalmos and bubbles on the head. The interval between loss of equilibrium and mortality decreased with an increase in TDG level. Neither exposure time nor TDG level significantly affected the critical swimming speed (Ucrit) of fish exposed to non-lethal exposure (110%, 120% and 125% TDG) over a 7 day period. Significant reductions in Ucrit were found under 130% and 135% TDG conditions when the exposure lasted 52.0 h and 42.9 h, respectively. The Ucrit also significantly decreased after exposure of 1.6 h under 140% TDG condition. Moreover, after exposure to 140% TDG for 39.2 h, 135% TDG for 56.5 h and 130% TDG for 95.9 h, bighead carp were transferred into air saturated water to recover for 24 h or 48 h; however, swimming performance remained impaired. The results of this study indicate that 125% TDG was the highest TDG level where limited mortality was observed and the swimming ability was not impaired, showing that 125% TDG can be set as the tolerance threshold of this species to guide the operation of dams in the Yangtze River Basin.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder.

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter, V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

TDG prediction model improvement by analysis and validation of experiments on a dam model.

The combination of aerated flows and a high-pressure environment in a stilling basin can result in the supersaturation of total dissolved gas (TDG) downstream of hydraulic projects, posing an ecological risk to aquatic populations by inducing gas bubble disease (GBD) or other negative effects. There is limited literature reporting TDG mass transfer experiments on a complete physical dam model; most existing research is based on measurements in prototype tailwaters. In this study, TDG mass transfer experiments were conducted on a physical model of an under-constructed dam, with TDG-supersaturated water as the inflow, and TDG concentrations were meticulously monitored within the stilling basin. The measurements indicate that the TDG saturation at the outlet of the stilling basin decreased by 13.7% and 10.6% compared to the inlet for the two cases, respectively. Subsequently, an improved TDG prediction model was developed by incorporating a sub-grid air entrainment model and a phase-constrained scalar model. The numerical simulation results were compared with experimental data, indicating a maximum error in TDG saturation at all measured points of less than ± 3%. Moreover, the TDG saturation showed an error of only ± 0.3% at the outlet of the stilling basin. This model has broad applicability to various flow types for obtaining TDG mass transfer results and evaluating mitigation measures of TDG supersaturation to reduce the harmful effects on aquatic ecosystems.

A design of a nature-like fishway to solve the fractured river connectivity caused by small hydropower based on hydrodynamics and fish behaviors.

Achieving a harmonious alignment between the biological characteristics of fish and hydrodynamics patterns is crucial for ensuring the efficacy of fish passage facilities. In this study, based on the hydrodynamic characteristics of the river and the biological characteristics of fish, we evaluated the internal flow field in the nature-like fishway of Congen II hydropower station located along the Chabao river and explored methods to improve the operation efficiency. Based on comprehensive considerations of the flow field, turbulent kinetic energy, and the migration pathways of fish, it is found that the implementation of a continuous oblique bottom slope represents a more cost-effective and operationally convenient solution. The influence of different permutation of bulkheads in the nature-like fishway on operational efficiency was further examined. Our investigation revealed that the nature-like fishway with the continuous slope of 2% and the arrangement of three bulkheads in each row (model 3) exhibited a relatively simple velocity distribution and linear flow line, which poses challenges for fish in locating resting areas. In addition, the distribution of low turbulence kinetic energy area in the mainstream made it less favorable for fish to transition from the mainstream to the rest area within the fishway. The nature-like fishway with the continuous slope of 2% and the arrangement of two or three bulkheads in staggered rows (model 4) demonstrated better performance. Several potential fish migration routes for both model 3 and model 4 were proposed based on the numerical simulation results. In model 3, fish exhibited a continuous sprint through the concentrated high-speed area, which was less favorable for fish to rest and forage. In contrast, model 4 exhibited a diversified flow velocity distribution, enabling fish to make timely changes in their direction during migration. This feather proved to be advantageous in enhancing fish migration within the passage. The design of nature-like fishway in this study provides an important reference and technical support for the construction and optimization of the nature-like fishway for low dams, and is of great significance for restoring river connectivity destroyed by small hydropower construction and improving fish migration.

Estimating the water quality index based on interpretable machine learning models.

The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and machine learning models exhibit significant advantages in terms of timeliness and nonlinear data fitting. We used a machine learning model with optimized parameters to predict the WQI, and the light gradient boosting machine achieved good predictive performance. The machine learning model trained based on the entire Dianchi Lake water quality data achieved coefficient of determination (R2), mean square error, and mean absolute error values of 0.989, 0.228, and 0.298, respectively. In addition, we used the Shapley additive explanations (SHAP) method to interpret and analyse the machine learning model and identified the main water quality parameter that affects the WQI of Dianchi Lake as NH4+-N. Within the entire range of Dianchi Lake, the SHAP values of NH4+-N varied from -9 to 3. Thus, in future water environmental governance, it is necessary to focus on NH4+-N changes. These results can provide a reference for the treatment of lake water environments.

Neuroimaging features in a patient with non-ketotic hyperglycaemic seizures: A case report.

BACKGROUND: Non-ketotic hyperglycaemic (NKH) seizures are a rare neurological complication of diabetes caused by hyperglycaemia in non-ketotic and non-hyperosmotic states. The clinical characteristics of NKH seizures are atypical and lack unified diagnostic criteria, leading to potential misdiagnoses in the early stages of the disease. CASE SUMMARY: This report presents a rare case of NKH seizures in a 52-year-old male patient with a history of type 2 diabetes mellitus. We performed comprehensive magnetic resonance imaging (MRI) studies at admission, 12 d post-admission, and 20 d post-discharge. The imaging techniques included contrast-enhanced head MRI, T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging, susceptibility-weighted imaging, magnetic resonance spectroscopy (MRS), and magnetic resonance venography. At the time of admission, T2WI and FLAIR of the cranial MRI showed that the left parieto-occipital cortex had gyrus-like swelling and high signal, and subcortical stripes had low signal. MRS showed a reduced N-acetylaspartate peak and increased creatine and choline peaks in the affected areas. A follow-up MRI 20 d later showed that the swelling and high signal of the left parieto-occipital cortex had disappeared, and the low signal of the subcortex had disappeared. CONCLUSION: This case study provides valuable insights into the potential pathogenesis, diagnosis, and treatment of NKH seizures. The comprehensive MRI findings highlight the potential utility of various MRI sequences in diagnosing and characterizing NKH seizures.

TDG generation in a ski-jump spillway with a fully or partial-flip bucket.

Ski-jump spillways are frequently used as discharge structures for high dams during floods with high energy heads. The selection of bucket types at the end of spillways has a pronounced effect on the hydraulics of jet characteristics, such as trajectories and entrained air features. However, there is no literature reporting how changes in the bucket types influence TDG generation. This study compares the hydraulic characteristics and TDG mass transfer properties of a hydraulic project under construction using both the traditional fully-flip bucket and the partial-flip bucket configurations. The results indicate that, the use of the partial-flip bucket at the end of the spillway significantly disperses the water flow and yields better energy dissipation effects. At low flow rates (lower than 400 m3/s for the dam in this study), there is little difference in the downstream TDG saturation between the traditional fully-flip bucket and the partial-flip bucket, the average difference is 1.6 % in three cases with a low flow rate. However, at high flow rates (higher than 400 m3/s), the partial-flip bucket generates more TDG compared to the traditional fully-flip bucket, reaching up to 6.2 % at the maximum flow rate. This phenomenon stems from significant changes in the hydrodynamics of the stilling basin at high flow rates due to variations in the flip bucket type. When strict control of TDG generation is necessary downstream of dams, the use of the partial-flip bucket should be carefully considered. This is because, at high flow rates, the partial-flip bucket might result in higher TDG saturation than the fully-flip bucket.

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks.

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

Lightweight Vehicle Detection Based on Improved YOLOv5s.

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Conflict between urbanization and water environmental protection: Lessons from the Xiangjiang River Basin in China.

China, the largest developing country, has experienced rapid urbanization since its reform and opening-up. However, the increasing pollution load from urban areas has deteriorated urban river water quality, contradicting the concept of sustainable and green development promoted by the Chinese government. This situation elucidates governmental shortcomings in systematic environmental protection. Our study revealed that the current wastewater treatment plant (WWTP) discharge standards in urban areas are insufficient for attaining the desired urban river water quality and thus intensify the conflict between urbanization and water environmental protection. As urbanization continues, the urban population will grow, further exacerbating pollution and conflict. Our focus was the Xiangjiang River basin in Zunyi, a typical urbanized city in China. Using a validated one-dimensional mathematical model, we compared the water quality in the Xiangjiang River between current and upgraded WWTP discharge standards. The results showed that the water quality in the Xiangjiang River falls short of the standards, with more than 60 % of the river exceeding limits. However, upgrading WWTP discharge standards significantly reduces the proportion of river sections exceeding limits, with only 0.4 % exceeding standards during specific periods. This enhancement greatly improved the Xiangjiang River's water quality, aided in restoring the entire water environment in the basin, and supported water environmental protection goals. Our research findings offer crucial support for local governments in shaping comprehensive water environmental protection policies and insights for addressing similar environmental challenges caused by rapid urbanization in other developing regions.

The Prevalence, Epidemiological, and Molecular Characterization of Methicillin-Resistant Staphylococcus aureus (MRSA) in Macau (2017-2022).

Macau, recognized as a global tourism hub and the world's most densely populated region, provides a unique environment conducive to methicillin-resistant Staphylococcus aureus (MRSA) transmission in healthcare and community settings, posing a significant public health concern both locally and globally. The epidemiology and molecular characteristics of MRSA in the distinct city of Macau remain largely unelucidated. This five-year longitudinal study (2017-2022) examined the local prevalence and molecular typing of MRSA in Macau, with future MRSA type distribution predicted through ARIMA modeling. We subsequently analyzed the epidemiological characteristics of MRSA, including specimen source, clinical department, collection year, season, patient age, sex, and the annual number of tourists. Comprehensive antibiotic resistance profiles of the strains were also assessed. Of 504 clinically isolated S. aureus strains, 183 (36.3%) were identified as MRSA by the cefoxitin disk diffusion method and validated through multi-locus sequence typing (MLST). The MRSA detection rate showed an upward trend, increasing from 30.1% in 2017 to 45.7% in 2022. SCCmec type IV was predominant (28.9%), followed by types II (25.4%), III (22.1%), and V (22.1%). The primary sources of MRSA isolates were sputum (39.2%) and secretions (25.6%). Older age emerged as a risk factor for MRSA infection, whereas no significant associations were found with seasonal variations, gender, or the annual number of tourists. Despite displaying universal resistance to cefoxitin, oxacillin, and benzylpenicillin, MRSA isolates in Macau remained fully sensitive to vancomycin, tigecycline, quinupristin, nitrofurantoin, and linezolid. Continuous surveillance and analysis of MRSA distribution in Macau could provide invaluable insights for the effective management of MRSA prevention and control measures within healthcare settings.

Determination of chloropropanol esters and glycidyl esters in instant noodles based on solid-phase microextraction with chitosan-ß-cyclodextrin coated fiber.

We developed a method for the determination of chloropropanol esters and glycidyl esters (GE) in instant noodles using solid-phase microextraction with chitosan-ß-cyclodextrin (CS-ß-CD) coated fiber coupled with gas chromatography-tandem mass spectrometry. The developed low-cost fiber coating can improve the sensitivity of the method. Immobilized enzymes can improve operational stability and reusability compared to free enzymes, thereby reducing costs. The adsorption isotherm was modeled using the Langmuir model, while the adsorption kinetics followed the pseudo second-order model. The limit of detection was 0.3 ng/L. The method exhibited satisfactory recoveries for the analytes, ranging from 80.2 % to 105.3 %, with relative standard deviations < 9.9 %. Furthermore, the results of the exposure assessment showed that chloropropanol esters do not pose unacceptable risks to different age groups. However, the margin of exposure for GE suggested a potential health risk for populations between the ages of 3 and 12 years old.

Sports game intervention aids executive function enhancement in children with autism - An fNIRS study.

Executive dysfunction is a prevalent issue in children diagnosed with autism spectrum disorder (ASD). While the efficacy of physical exercise in enhancing cognitive abilities in these children is well-documented, research exploring the relationship between physical exercise and brain function remains limited. This study aimed to investigate the impact of cognitively stimulating exercise on executive functions (EF) in children with ASD. The study enrolled thirty children with ASD who were randomly allocated into two groups: a sports game learning group (n = 15) and a control group (n = 15). Functional near-infrared spectroscopy was utilized to monitor cerebral function alterations pre- and post- an eight-week intervention program. The study focused on three core components of executive function: working memory, inhibitory control (IC), and cognitive flexibility (CF). Results revealed a significant improvement in the EF in the intervention group. After eight weeks of intervention, neural activity, along with improved EF performance, was enhanced significantly in the prefrontal cortex (PFC). During post-intervention, EF tasks were also significantly activated in the dorsolateral PFC, orbitofrontal cortex, and frontal pole area. Furthermore, an increase in short-distance functional connectivity within the PFC was observed during resting states. These results imply that engagement in sports game training can significantly improve EF information processing, augmenting task-related cortical activations and the efficiency of brain function networks in children with ASD.

Climate change: approach to intervention using expression vector for carbonic anhydrase via glycosylphosphatidylinositol.

Climate change is a change in the usual weather found in a place. The climate change has a major impact not only on natural disasters of the Earth but also on human health. The climate crisis is then no longer a future concern. It includes both the global warming driven by human emissions of greenhouse gases (GHG), and the resulting large-scale shifts in weather patterns. Global warming can occur from a variety of causes, both natural and human induced. The primary GHG in Earth's atmosphere, listed in decreasing order of average global mole fraction, are: water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3). Today, scientists around the world continue to try and solve the puzzle of climate change. It is clear that to address climate change, the amount of CO2 released into the atmosphere by industrial process has to be reduced because once it is added to the atmosphere, it can continue to affect climate for thousands of years. For such a purpose, an approach to intervention using expression vectors for any protein targeting to the cell plasma membrane via the glycosylphosphatidylinositol, GPI, anchor is suggested. The resulting GPI-anchored proteins would be useful for studying intermolecular interactions, especially gene-environment interactions, in investigating the potential impact of any chemical compounds on any genes of interest and could be used for carbonic anhydrase (CA)-based CO2-capture (environmental application). This approach would be crucial not only for capturing CO2 via GPI and CA but also for the production of CA enzyme as well as its stabilization and therefore useful for combating the global warming of climate change.