Antifouling Double-Skinned Forward Osmosis Membranes by Constructing Zwitterionic Brush-Decorated MWCNT Ultrathin Films

Pressure retarded osmosis (PRO) process is hindered by severe fouling occurring within the porous support of the forward osmosis (FO) membranes. We designed a novel double-skinned FO membrane containing a polyamide salt-rejecting layer and a zwitterionic brush-decorated, multiwalled carbon nanotube (MWCNT/PSBMA) foulant-resisting layer on the back side. Our results demonstrated that the coating of the MWCNT/PSBMA layer on the porous polyketone (PK) support imparted enhanced hydrophilicity and smaller membrane pore size, thereby providing excellent resistance toward both protein adhesion and bacterial adsorption. We also further evaluated this resultant double-skinned membrane (i.e., TFC-MWCNT/PSBMA) in dynamic PRO fouling experiments using protein and alginate as model organic foulants. Compared to the pristine TFC-PK and hydrophobic TFC-MWCNT membranes, the TFC-MWCNT/PSBMA membrane exhibited not only the lowest water flux decline but also the highest water flux recovery after simple physical flushing. These results shed light on fabrication of antifouling PRO membranes for water purification purposes

BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data

Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are commonly missing, and an appropriate data imputation method is necessary in meteorological data applications. In this paper, we focus on filling long gaps in meteorological observation data at field sites. A deep learning-based model, BiLSTM-I, is proposed to impute missing half-hourly temperature observations with high accuracy by considering temperature observations obtained manually at a low frequency. An encoder-decoder structure is adopted by BiLSTM-I, which is conducive to fully learning the potential distribution pattern of data. In addition, the BiLSTM-I model error function incorporates the difference between the final estimates and true observations. Therefore, the error function evaluates the imputation results more directly, and the model convergence error and the imputation accuracy are directly related, thus ensuring that the imputation error can be minimized at the time the model converges. The experimental analysis results show that the BiLSTM-I model designed in this paper is superior to other methods. For a test set with a time interval gap of 30 days, or a time interval gap of 60 days, the root mean square errors (RMSEs) remain stable, indicating the model’s excellent generalization ability for different missing value gaps. Although the model is only applied to temperature data imputation in this study, it also has the potential to be applied to other meteorological dataset-filling scenarios

Data_Sheet_2_The prevalence and characteristics of frailty in cirrhosis patients: a meta-analysis and systematic review.DOCX

ObjectivesThis study aimed to assess the prevalence of frailty in cirrhosis patients and the distribution of age, sex, and body mass index (BMI) in cirrhotic patients with frailty.MethodsWe performed a thorough literature search using PubMed, Embase, Web of Science, and the Cochrane Library from inception to 29 February 2024. The estimated prevalence with a 95% confidence interval (CI) was calculated with a random effect model. Subgroup analysis and sensitivity analysis were performed to assess the heterogeneity and characterize the distribution of age, sex, and body mass index (BMI) in cirrhotic patients. Publication bias was assessed by the funnel plot, Begg's test, and Egger's test.ResultsThe 16 included studies, which were all observational, reported a prevalence of frailty in 8,406 cirrhosis patients ranging from 9 to 65%, and the overall estimated prevalence was 27% (95% CI: 21–33%; I2 = 97.7%, P ConclusionThe current study suggests that cirrhosis patients have a high prevalence of frailty. Compared with the non-frail cohort, the frail patients tend to be male, older, and have a lower BMI with worse liver function.</p

Data_Sheet_1_The prevalence and characteristics of frailty in cirrhosis patients: a meta-analysis and systematic review.DOCX

ObjectivesThis study aimed to assess the prevalence of frailty in cirrhosis patients and the distribution of age, sex, and body mass index (BMI) in cirrhotic patients with frailty.MethodsWe performed a thorough literature search using PubMed, Embase, Web of Science, and the Cochrane Library from inception to 29 February 2024. The estimated prevalence with a 95% confidence interval (CI) was calculated with a random effect model. Subgroup analysis and sensitivity analysis were performed to assess the heterogeneity and characterize the distribution of age, sex, and body mass index (BMI) in cirrhotic patients. Publication bias was assessed by the funnel plot, Begg's test, and Egger's test.ResultsThe 16 included studies, which were all observational, reported a prevalence of frailty in 8,406 cirrhosis patients ranging from 9 to 65%, and the overall estimated prevalence was 27% (95% CI: 21–33%; I2 = 97.7%, P ConclusionThe current study suggests that cirrhosis patients have a high prevalence of frailty. Compared with the non-frail cohort, the frail patients tend to be male, older, and have a lower BMI with worse liver function.</p

Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods

Enriched Environment Promotes Cognitive Function Recovery following Cerebral Ischemic Injury via Upregulating GABAergic and Glutamatergic Systems in the Contralateral Hippocampus

Poststroke cognitive impairment severely affects the long-term recovery of patients. However, it remains unknown whether an enriched environment can remodel contralateral hippocampal function and promote cognitive function recovery after cerebral ischemic injury. To further explore, 36 C57BL/6 mice that underwent permanent middle cerebral artery occlusion (pMCAO) were randomly assigned to three groups: enriched environment (EE), standard condition (SC), and sham surgery (Sham). After 21 days of intervention, the Morris water maze and step-through test was utilized for testing the cognitive function of the mice, cresyl violet staining for measuring the degree of atrophy in the hippocampal tissues, and western blotting for quantitating the expression levels of GA1B, GAD67, and NR2B, and immunohistochemistry for levels of NR2B in the CA1 region of the contralateral hippocampus. The results showed that cognitive function-related behavioral performance decreased in the SC group, and performance was better in the EE group than that in the SC group (p  0.05); levels of GA1B, GAD67, and NR2B in the contralateral hippocampus were significantly higher in the EE group than those in the SC group (p < 0.01); and the level of NR2B in the CA1 region of the contralateral hippocampus significantly increased in the EE group compared to the SC group (p < 0.01). We believe that contralateral hippocampal function is inhibited after cerebral ischemic injury, further affecting cognitive function. However, enriched environment can upregulate GABAergic and glutamatergic systems in the contralateral hippocampus to promote cognitive function recovery after cerebral ischemic injury