The Masquelet induced membrane technique with PRP-FG-nHA/PA66 scaffold can heal a rat large femoral bone defect.

BACKGROUND: Masquelet membrane induction technology is one of the treatment strategies for large bone defect (LBD). However, the angiogenesis ability of induced membrane decreases with time and autologous bone grafting is associated with donor site morbidity. This study investigates if the PRP-FG-nHA/PA66 scaffold can be used as a spacer instead of PMMA to improve the angiogenesis ability of induced membrane and reduce the amount of autologous bone graft. METHODS: Platelet rich plasma (PRP) was prepared and PRP-FG-nHA/PA66 scaffold was synthesized and observed. The sustained release of VEGFA and porosity of the scaffold were analyzed. We established a femur LBD model in male SD rats. 55 rats were randomly divided into four groups depending on the spacer filled in the defect area. "Defect only" group (n = 10), "PMMA" group (n = 15), "PRP-nHA/PA66" group (n = 15) and "PRP-FG-nHA/PA66" group (n = 15 ). At 6 weeks, the spacers were removed and the defects were grafted. The induced membrane and bone were collected and stained. The bone formation was detected by micro-CT and the callus union was scored on a three point system. RESULTS: The PRP-FG-nHA/PA66 scaffold was porosity and could maintain a high concentration of VEGFA after 30 days of preparation. The induced membrane in PRP-FG-nHA/PA66 group was thinner than PMMA, but the vessel density was higher.The weight of autogenous bone grafted in PRP-FG-nHA/PA66 group was significantly smaller than that of PMMA group. In PRP-FG-nHA/PA66 group, the bone defect was morphologically repaired. CONCLUSION: The study showed that PRP-FG-nHA/PA66 scaffold can significantly reduce the amount of autologous bone graft, and can achieve similar bone defect repair effect as PMMA. Our findings provide some reference and theoretical support for the treatment of large segmental bone defects in humans.

Responses of vegetation cover to hydro-climatic variations in Bosten Lake Watershed, NW China.

Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surface water and vegetation in the Bosten Lake Watershed across multiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9-16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake's level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T≥ 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake's water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions.

Impact of meteorological variability on diurnal and seasonal net ecosystem productivity in a desert riparian forest ecosystem.

The desert riparian forests are susceptible to meteorological changes and contribute significantly to the net ecosystem productivity (NEP) variations of arid ecosystems. However, the responsive patterns of their NEP variations to the meteorological variabilities remain inadequately comprehended. To address this gap, we utilized seven years of eddy covariance flux measurements in a representative desert riparian forest to investigate the NEP variations and its response to changing meteorological factors across diverse temporal scales. The results revealed significant periodic variations in half-hourly NEP, with dominant cycles spanning from five hours to one year, with a principal oscillation period of one day. Key meteorological factors including global solar radiation (Rg), relative humidity (RH), air temperature (Ta), soil temperature (Ts), and vapor pressure deficit (VPD) exhibited synchronization with NEP on daily scales. This synchronization, coupled with the observed one-day periodic NEP variations, provides robust evidence supporting the existence of a circadian rhythm in the ecosystem carbon exchange of desert riparian forest regulated by meteorological conditions. Seasonal patterns were significant in the impact of Rg phase, Ta diurnal amplitude, and VPD diurnal amplitude on NEP diurnal amplitude and phase. The NEP diurnal amplitude significantly, directly, and positively affected daily NEP in both the dormant and growing seasons, whereas its phase yielded significant negative effects (P< 0.05). The averages, amplitudes, and phases of diurnal meteorological conditions controlled the daily NEP by regulating NEP diurnal amplitude and phase. These findings provide evidence that the variability in circadian rhythms, caused by the increase in diurnal Ta and VPD, significantly impact the daily NEP at an ecosystem scale. This study enriches our comprehension of the meteorological mechanisms governing diurnal and seasonal carbon uptake dynamics within desert riparian forests, providing fresh insights into the direct and indirect roles of climate change in shaping patterns of ecosystem carbon exchange.

Reconfigurable memlogic long wave infrared sensing with superconductors.

Optical sensors with in-cell logic and memory capabilities offer new horizons in realizing machine vision beyond von Neumann architectures and have been attempted with two-dimensional materials, memristive oxides, phase-changing materials etc. Noting the unparalleled performance of superconductors with both quantum-limited optical sensitivities and ultra-wide spectrum coverage, here we report a superconducting memlogic long-wave infrared sensor based on the bistability in hysteretic superconductor-normal phase transition. Driven cooperatively by electrical and optical pulses, the device offers deterministic in-sensor switching between resistive and superconducting (hence dissipationless) states with persistence > 105 s. This results in a resilient reconfigurable memlogic system applicable for, e.g., encrypted communications. Besides, a high infrared sensitivity at 12.2 µm is achieved through its in-situ metamaterial perfect absorber design. Our work opens the avenue to realize all-in-one superconducting memlogic sensors, surpassing biological retina capabilities in both sensitivity and wavelength, and presents a groundbreaking opportunity to integrate visional perception capabilities into superconductor-based intelligent quantum machines.

Modeling of Metal Powder Densification under Hot Isostatic Pressing.

The consolidation of metal powders is a complex thermomechanical process, and the temperature has a significant effect on the density distribution in the compact. The consolidation process of metal powders with an average particle size of 10 µm, 25 µm, and 50 µm under hot isostatic pressure was simulated by finite element modeling. The distribution and evolution of the relative density after being hot isostatic pressing (HIP) under 1050 °C/130 MPa/4 h, 1150 °C/130 MPa/4 h, and 1250 °C/130 MPa/4 h conditions were simulated, respectively. The experimental data of HIP at 1050 °C/130 MPa/4 h were used to verify the modeling results via the geometric change in the container. The relative density difference between the simulated results and the experimental results at different positions was less than 2%. This methodology called "modeling prediction, experimental validation" can accelerate experimental discovery in an economic manner.

The influence of lifestyle habits on levels of depression among rural middle school students in Northeastern China.

Background: Depression rates among adolescents have risen dramatically over the past decade. Therefore, preventing depression among adolescents is particularly important. Differences in lifestyle habits may play a role in depression. Purpose: This study aimed to explore the influence of living habits on depression levels among rural middle school students in Northeast China and to provide a theoretical basis for developing interventions to reduce depression levels in middle school students. Methods: A total of 296 middle school students aged 13-15 years from Benxi City, Northeast China completed the anthropometric measurements, Physical Activity Scale-3 (PARS-3), and the Self-Rating Depression Scale (SDS). Their average screen time in the most recent week, parents' education level, and monthly family income were collected through a questionnaire. Results: Females had higher depression scores than males (41.0 ± 6.9 vs. 37.9 ± 8.0). Physical activity (ß = -0.38, t = -7.06, P < 0.01), family income (ß = -0.20, t = -4.07, P < 0.01), screen time (ß = 0.16, t = 3.34, P < 0.01), age (ß = 0.15, t = 3.16, P < 0.01), sex (ß = -0.13, t = -2.74, P < 0.01), and sleep quality (ß = -0.08, t = -1.87, P < 0.01) are important factors related to depression levels. Conclusion: The preliminary analysis results showed that among middle school students in rural Northeast China, the depression level of females was significantly higher than that of males. Poor quality sleep, low levels of physical activity, low household income, and long screen time were positively associated with depression. Therefore, strengthening physical activity, improving sleep quality, and reducing screen time are of clinical relevance in preventing and reducing depression.

Prediction of Grain Size in a High Cobalt Nickel-Based Superalloy.

With the advancement in computational approaches and experimental, simulation, and modeling tools in recent decades, a trial-and-validation method is attracting more attention in the materials community. The development of powder metallurgy Ni-based superalloys is a vivid example that relies on simulation and experiments to produce desired microstructure and properties in a tightly controlled manner. In this research, we show an integrated approach to predicting the grain size of industrial forgings starting from lab-scale cylindrical compression by employing modeling and experimental validation. (a) Cylindrical compression tests to obtain accurate flow stress data and the hot working processing window; (b) double-cone tests of laboratory scale validation; (c) sub-scale forgings for further validation under production conditions; and (d) application and validation on full-scale industrial forgings. The procedure uses modeling and simulation to predict metal flow, strain, strain rate, temperature, and the resulting grain size as a function of thermo-mechanical processing conditions. The models are calibrated with experimental data until the accuracy of the modeling predictions is at an acceptable level, which is defined as the accuracy at which the results can be used to design and evaluate industrial forgings.

Cytoplasm Hydrogelation-Mediated Cardiomyocyte Sponge Alleviated Coxsackievirus B3 Infection.

Viral myocarditis (VMC), commonly caused by coxsackievirus B3 (CVB3) infection, lacks specific treatments and leads to serious heart conditions. Current treatments, such as IFNα and ribavirin, show limited effectiveness. Herein, rather than inhibiting virus replication, this study introduces a novel cardiomyocyte sponge, intracellular gelated cardiomyocytes (GCs), to trap and neutralize CVB3 via a receptor-ligand interaction, such as CAR and CD55. By maintaining cellular morphology, GCs serve as sponges for CVB3, inhibiting infection. In vitro results revealed that GCs could inhibit CVB3 infection on HeLa cells. In vivo, GCs exhibited a strong immune escape ability and effectively inhibited CVB3-induced viral myocarditis with a high safety profile. The most significant implication of this study is to develop a universal antivirus infection strategy via intracellular gelation of the host cell, which can be employed not only for treating defined pathogenic viruses but also for a rapid response to infection outbreaks caused by mutable and unknown viruses.

Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models.

This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 color fundus photographs across eight disease categories, which was later augmented to 17,766 images. Five well-known convolutional neural networks (CNNs)-efficientnetb0, mobilenetv2, shufflenet, resnet50, and resnet101-and a custom-built CNN were integrated and trained on this dataset. Image sizes were standardized, and model performance was evaluated via accuracy, Kappa coefficient, and precision metrics. Shufflenet and efficientnetb0demonstrated strong performances, while our custom 17-layer CNN outperformed all with an accuracy of 0.930 and a Kappa coefficient of 0.920. Furthermore, we found that the fusion of image features with classical machine learning classifiers increased the performance, with Logistic Regression showcasing the best results. Our study highlights the potential of AI and deep learning models in accurately classifying eye diseases and demonstrates the efficacy of custom-built models and the fusion of deep learning and classical methods. Future work should focus on validating these methods across larger datasets and assessing their real-world applicability.

Peptide aptamer-based time-resolved fluoroimmunoassay for CHIKV diagnosis.

BACKGROUND: Chikungunya virus (CHIKV) and Dengue virus (DENV) have similar clinical symptoms, which often induce misdiagnoses. Therefore, an antigen detection diagnostic system that can clearly identify these two viruses is desirable. METHODS: In this study, we developed a novel peptide with high affinity and specificity to CHIKV, and further constructed peptide aptamer-based TRFIA assay to efficiently detect CHIKV. Peptide aptamer B2 (ITPQSSTTEAEL) and B3 (DTQGSNWI) were obtained through computer-aided design and selected as CHIKV-specific peptide aptamers based on their high binding affinity, strong hydrogen bonding, and RMSD of molecular docking. Then, a sandwich-Time-Resolved Fluoroimmunoassay (TRFIA) was successfully constructed for the detection of the interaction between peptide aptamers and viruses. RESULTS: When using B2 as the detection element, highly specific detection of CHIKV E2 was achieved with detection limits of 8.5 ng/ml in PBS solution. Variation coefficient between inter-assay showed the disturbances received from the detection of clinical fluid specimens (including serum and urine), were also within acceptable limits. The detection limits for 10-fold dilution serum and urine were 57.8 ng/mL and 147.3 ng/mL, respectively. The fluorescent signal intensity exhibited a good linear correlation with E2 protein concentration in the range of 0-1000 ng/mL, indicating the potential for quantitative detection of E2 protein. CONCLUSIONS: These results demonstrate that the construction of peptide aptamers with high affinity and specificity provides an excellent method for rapid diagnostic element screening, and the developed peptide aptamer B2 contributed to better detection of CHIKV viral particles compared to traditional antibodies.

A method of constructing a dynamic chart depth model for coastal areas.

The depth is important for vessel navigation at sea. Currently, most vessels use electronic navigation charts to navigate at sea. In coastal areas, especially close to shallow water areas, the dynamic change of the water level is very important to safe navigation. Ships calculate the change of water level by using up-to-date tide tables, to obtain the dynamic water depth in the channels. However, the depth caused by the tide and non-tidal components may reach several meters in some seas, causing the dynamic depth below the safety depth, which can easily lead to grounding of vessels stranding accidents. The channel is regularly dredged to achieve navigational depth. Without regular dredging, the offshore non-channel area becomes the common area of ship grounding. The dynamic chart depth model studied in this article can provide real-time depth, which serves the ships navigation in the non-channel. The model incorporates the chart depth and the dynamic water levels on the same reference datum. The chart depth is from the electronic navigational chart depth. The dynamic water levels are constructed by the simulated tidal levels and continuous series of nontidal residual. We then designed a deviation correction method to reduce the discrepancy of the simulated tidal level with the actual water level, including datum offset correction and residual water level correction. Finally, by merging the revised dynamic water levels with the electronic navigational chart depth, we obtained the dynamic chart depth model of the study region.

Intracellular hydrogelation of macrophage conjugated probiotics for hitchhiking delivery and combined treatment of colitis.

Immune cell membrane coated nanomedicine was developed to neutralize cytokines via receptor-ligand interaction, which showed potential for the treatment of inflammatory bowel disease (IBD). However, cell membrane isolation and re-assembly process involved protein loss and spatial disorder, which reduced the sequestration efficiency towards cytokines. In addition, oral administration of probiotics was accepted for IBD treatment via gut microbiota modulation, but most probiotics showed weak adhesion to intestine mucosa and were quickly expelled from gastrointestinal tract. Herein, an intracellular hydrogelation technology was proposed to construct gelated peritoneal macrophage (GPM) with intact membrane structure, resulting from the avoidance of membrane isolation and re-assembly process. GPM efficiently neutralized multiple cytokines in vitro and in vivo to ameliorate inflammatory Caco-2 â€‹cells and colitis rats by regulating oxidative stress, inflammation level and intestinal barrier repair. Moreover, the probiotics (Nissle1917, EcN) were easily attached on GPM surface through specific recognition, to construct GPM-EcN conjugate for GPM hitchhiking delivery to colitis tissue. Conjugation process of GPM and EcN showed no damage on bacterial physiological function. Due to the chemical attachment on inflammatory cells, GPM carried the attached EcN hand-in-hand to accumulate in the colitis tissue of IBD rat, and enhanced intestine retention time of EcN in comparison to free EcN, which improved bacterial diversity, and shifted the microbiota community and acid metabolites to an anti-inflammatory phenotype. This study transferred the hydrogel synthesis from in vitro to intracellular cytoplasm, and came to a new insight of conjugating strategy of GPM and probiotics for hitchhiking delivery and combined anti-IBD treatment.

Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors.

BACKGROUND: To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. METHODS: We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. RESULTS:  Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. CONCLUSIONS:  We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.

Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms.

The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04 µg·cm-2). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R2 = 0.88), mean absolute error (MAE = 3.40 µg·cm-2), root mean square error (RMSE = 4.23 µg·cm-2), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite.

Clinical use of color Doppler ultrasonography to predict and evaluate the collateral development of two common revascularizations in patients with moyamoya disease.

Objective: To explore the value of color Doppler ultrasonography (CDU) to predict preoperatively and evaluate postoperatively the collateral development of two common revascularizations in patients with moyamoya disease (MMD). Methods: We prospectively enrolled 49 patients with MMD who underwent unilateral superficial temporal artery (STA) -middle cerebral artery (MCA) anastomosis or encephalo-duro-arterio-synangiosis (EDAS). The parameters of the extracranial arteries, including STA, internal carotid artery (ICA), external carotid artery (ECA), and vertebral artery (VA), were performed before and at 3-6 months after surgery. DSA results were used to assess surgical collateral development. Results: To predict good collateral development before STA-MCA anastomosis, the preoperative D > 1.75 mm in the STA had the highest area under the Receiver Operating Characteristic curve (AUC). To predict good collateral development before EDAS, the preoperative EDV > 12.00 cm/s in the STA had the highest AUC. To evaluate the good collateral development after STA-MCA anastomosis, the postoperative EDV > 16.50 cm/s in the STA had the highest AUC. To evaluate the good collateral development after EDAS, an increase of D of 0.15 mm in the STA had the highest AUC. Logistic regression analysis showed that the preoperative RI and EDV in the STA were highly correlated with collateral development. Besides, the preoperative RI was an independent risk factor for collateral development. Conclusion: CDU could predict preoperatively and evaluate postoperatively the collateral development of STA-MCA anastomosis and EDAS surgery postoperatively by detecting ultrasound parameters of the STA.

The Combination of Platelet Rich Plasma Gel, Human Umbilical Mesenchymal Stem Cells and Nanohydroxyapatite/polyamide 66 Promotes Angiogenesis and Bone Regeneration in Large Bone Defect.

BACKGROUND: In the present study, a novel tissue engineering bone graft including platelet rich plasma gel (PRP gel), human umbilical mesenchymal stem cells (HUMSCs) and nanohydroxyapatite/polyamide 66 (nHA-PA66) was constructed. We explored whether the composite scaffolds could enhance the angiogenesis and bone repair capacity in rat femoral large bone defect (LBD). This study aimed to provide evidence for the clinical application of the composite scaffold in LBD treatment. METHODS: PRP was prepared, the platelets and growth factors were measured. HUMSCs were isolated and identified. the osteogenic capacity of PRP in vitro was measured. Then HUMSCs-PRP-gel/nHA-PA66 composite scaffolds were synthesized and observed. The proliferation and osteogenesis differentiation of HUMSCs on the composite scaffold was measured. The angiogenic capacity of PRP in vitro was measured by capillary-like tube formation assay. Finally, the angiogenesis and bone repair capacity of the composite scaffolds was measured in rat LBD. RESULTS: PRP contained high level of platelets and growth factors after activation, and promoted osteogenic and angiogenic differentiation in vitro. The HUMSCs-PRP-gel/nHA-PA66 composite scaffold was porosity and promoted the proliferation and osteogenesis differentiation of HUMSCs. At 12th weeks, more micro-vessels and new bone were formed around the composite scaffolds compared with other groups, the defect was almost repaired. CONCLUSION: Our study for the first time identified that the combination of PRP gel, HUMSCs and nHA-PA66 scaffold could significantly promote angiogenesis and bone regeneration in rat LBD, which may have implications for its further application in clinical LBD treatment.

Multidimensional soil salinity data mining and evaluation from different satellites.

Soil salinization, a common land degradation mode, restricts the ecological environment and is a global issue due to climate change. Accurately, quickly and effectively monitoring soil salinity is critical for governmental institutions that develop hazard prevention and mitigation strategies. Remote sensing (RS) technology provides a viable alternative to traditional field work due to its large area coverage, abundant spectral information and nearly constant observations. Key issues in RS-based soil salinity monitoring include the lack of both data-mining techniques for obtaining spectral band information and comprehensive considerations of synergies among different spectra. The main objective of this study was to provide in-depth explorations of data mining and integration algorithms from different satellites to multidimensionally evaluate soil salinity models. The Ebinur Lake Wetland Reserve (Xinjiang Province, China) was selected as a case study. First, ground-measured visible and near infrared (VIS-NIR) spectral data were combined with the RS band to simulate Landsat 8 (L8) and Sentinel 2 (S2) and 3 (S3) data. Second, one-dimensional RS bands and 15 soil salinity and vegetation indices were selected, and 15 spectral data transformations (reciprocal, differential, absorbance, etc.) were obtained. Two- and three-dimensional spectral indices were constructed, and the response relationships between different spectral indices and soil electrical conductivity (EC) were comprehensively explored. Finally, an integrated multidimensional algorithm was used to estimate soil salinity in high-performance models for the three satellites. The results showed that all data-mining-based model combinations performed well for all satellites (R2 > 0.80). However, with multidimensional model combinations, S3 presented the highest predictive capability (R2 = 0.89, RMSE = 2.57 mS·cm-1, RPD = 2.05), followed by S2 (R2 = 0.86, RMSE = 2.71 mS·cm-1, RPD = 1.90) and L8 (R2 = 0.85, RMSE = 2.84 mS·cm-1, RPD = 1.87). Therefore, data mining with integration algorithms in model combinations performs significantly better than previous models and could be considered a promising method for obtaining improved results from soil salinity susceptibility models in similar cases.

A global meta-analysis of heavy metal(loid)s pollution in soils near copper mines: Evaluation of pollution level and probabilistic health risks.

With the rapid development of the mining industry, the pollution of heavy metal(loid)s in soils near copper (Cu) mining sites is a significant concern worldwide. However, the pollution status and probabilistic health risks of heavy metal(loid)s of soils associated with Cu mines, have rarely been studied on a global scale. In this study, eight heavy metal(loid) concentrations in soil samples taken near 102 Cu mining sites worldwide were obtained through a literature review. Based on this database, the heavy metal(loid) pollution and ecological risk in soils near Cu mines were evaluated. Most of the study sites exceeded the moderately to heavily polluted levels of Cu and Cd; compared to other regions, higher pollution levels were observed at sites in Oman, China, Australia, and the United Kingdom. Soil pollution by Cd, Pb, and Zn at agricultural sites was higher than that in non-agricultural sites. In addition, these heavy metal(loid)s produced a high ecological risk to soils around Cu mining sites in which the contribution of Cd, Cu, and As reached up to 46.5%, 21.7%, and 18.4%, respectively. The mean hazard indices of the eight heavy metal(loid)s were 0.209 and 0.979 for adults and children, respectively. The Monte Carlo simulation further predicted that 1.40% and 29.9% of non-carcinogenic risk values for adults and children, respectively, exceeded the safe level of 1.0. Moreover, 84.5% and 91.0% of the total cancer risk values for adults and children, respectively, exceeded the threshold of 1E-04. Arsenic was the main contributor to non-carcinogenic risk, while Cu had the highest exceedance of carcinogenic risk. Our findings indicate that the control of Cu, Cd, and As should be prioritized because of their high incidence and significant risks in soils near Cu mines. These results provide valuable inputs for policymakers in designing effective strategies for reducing the exposure of heavy metal(loid)s in this area worldwide.

Assessing the effect of fine particulate matter on adverse birth outcomes in Huai River Basin, Henan, China, 2013-2018.

Previous studies have indicated that maternal exposure to particles with aerodynamic diameter <2.5 µm (PM2.5) is associated with adverse birth outcomes. However, the critical exposure windows remain inconsistent. A retrospective cohort study was conducted in Huai River Basin, Henan, China during 2013-2018. Daily PM2.5 concentration was collected using Chinese Air Quality Reanalysis datasets. We calculated exposures for each participant based on the residential address during pregnancy. Binary logistic regression was used to examine the trimester-specific association of PM2.5 exposure with preterm birth (PTB), low birth weight (LBW) and term LBW (tLBW), and we further estimated monthly and weekly association using distributed lag models. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each 10 µg/m3 increase in PM2.5 exposure. Stratified analyses were performed by maternal age, infant gender, parity, and socioeconomic status (SES). In total, 196,780 eligible births were identified, including 4257 (2.2%) PTBs, 3483 (1.8%) LBWs and 1770 (0.9%) tLBWs. Maternal PM2.5 exposure during the second trimester were associated with the risk of PTB and LBW. At the monthly level, the PTB and LBW risks were associated with PM2.5 exposure mainly in the 4th -6th month. By estimating the weekly-specific association, we observed that critical exposure windows of PM2.5 exposure and PTB were in the 18th- 27th gestational weeks. Stronger associations were found in younger, multiparous mothers and those with a female baby and in low SES. In conclusion, the results indicate that maternal PM2.5 exposure during the second trimester was associated with PTB and LBW. Younger, multiparous mothers and those with female babies and in low SES were susceptible.

Dinuclear Nickel-Oxygen Cluster-Based Metal-Organic Frameworks with Octahedral Cages for Efficient Xe/Kr Separation.

Xe/Kr separation is industrially important but remains a daunting issue in chemical separations. Herein, a fluorinated metal-organic framework (MOF), [Ni2(µ2-O)(TFBPDC)(tpt)2]n (named JXNU-13-F), built from 3,3',5,5'-tetrakis(fluoro)biphenyl-4,4'-dicarboxylic (TFBPDC2-) and 2,4,6-tri(4-pyridinyl)-1,3,5-triazine (tpt) ligands is provided. JXNU-13-F displays a three-dimensional (3D) framework constructed from distorted octahedral cages and an impressive Xe capacity of 144 cm3 g-1 at 273 K and 1 bar, ranking among top MOFs. The high Xe uptake and moderate Xe/Kr adsorption selectivity endow JXNU-13-F with efficient Xe/Kr separation demonstrated by experimental column breakthrough tests. The comparative studies of gas adsorption between isostructural JXNU-13-F and JXNU-13 (the nonfluorinated analogue ([Ni2(µ2-O)(BPDC))(tpt)2]n with biphenyl-4,4'-dicarboxylic (BPDC2-)) revealed that the F groups serve as the innocent groups during the Xe and Kr adsorption in JXNU-13-F. Thus, a combination of highly hydrophobic and π-electron-rich pore surfaces made of aromatic rings with strong interactions with the Xe atom possessing large polarizability and appropriate pore sizes that match well Xe having a large atom diameter has resulted in high Xe uptake and effective Xe/Kr separation characteristics of JXNU-13-F.