Layered Hydride LiH₄ with a Pressure-Insensitive Superconductivity

For hydride superconductors, each significant advance is built upon the discovery of novel H-based structural units, which in turn push the understanding of the superconducting mechanism to new heights. Based on first-principles calculations, we propose a metastable LiH4 with a wavy H layer composed of the edge-sharing pea-like H18 rings at high pressures. Unexpectedly, it exhibits pressure-insensitive superconductivity manifested by an extremely small pressure coefficient (dTc/dP) of 0.04 K/GPa. This feature is attributed to the slightly weakened electron–phonon coupling with pressure, caused by the reduced charge transfer from Li atoms to wavy H layers, significantly suppressing the substantial increase in the contribution of phonons to Tc. Its superconductivity originates from the strong coupling between the H 1s electrons and the high-frequency phonons associated with the H layer. Our study extends the list of H-based structural units and enhances the in-depth understanding of pressure-related superconductivity

Bioclim data

Bioclim variables (Bio1-Bio19) for the 43 sites used in landscape genetic analysis

Genotype data

Microsatellite genotype of 869 Myriophyllum spicatum inviduals from 58 populations

Data_Sheet_1_Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes.PDF

BackgroundObservational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR).MethodsWe conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis.ResultsOur study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1–1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87–0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721–0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872–0.950], p = 1.52 × 10–5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963–0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778–0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008–1.206], p = 0.033).ConclusionIn summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.</p

Data_Sheet_4_Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes.PDF

BackgroundObservational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR).MethodsWe conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis.ResultsOur study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1–1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87–0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721–0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872–0.950], p = 1.52 × 10–5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963–0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778–0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008–1.206], p = 0.033).ConclusionIn summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.</p

Data_Sheet_3_Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes.PDF

BackgroundObservational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR).MethodsWe conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis.ResultsOur study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1–1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87–0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721–0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872–0.950], p = 1.52 × 10–5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963–0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778–0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008–1.206], p = 0.033).ConclusionIn summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.</p

Data_Sheet_2_Mendelian randomization analysis demonstrates the causal effects of IGF family members in diabetes.PDF

BackgroundObservational studies have consistently shown significant associations between the IGF family and metabolic diseases, including diabetes. However, these associations can be influenced by confounding factors and reverse causation. This study aimed to assess the causal relationship between the IGF family and diabetes using Mendelian randomization (MR).MethodsWe conducted a two-sample MR analysis to investigate the causal effects of the IGF family on diabetes. Instrumental variables for the IGF family and diabetes were derived from summary-level statistics obtained from genome-wide association studies. Horizontal pleiotropy was assessed using MR-Egger regression and the weighted median method. We applied the inverse-variance weighted method as part of the conventional MR analysis to evaluate the causal impact of the IGF family on diabetes risk. To test the robustness of the results, we also employed MR-Egger regression, the weighted median method, and a leave-one-out analysis.ResultsOur study revealed that IGF-1 causally increases the risk of Type 2 Diabetes (T2D), while IGFBP-6, adiponectin and INSR decreases the risk (IGF-1, OR 1.02 [95% CI 1–1.03], p = 0.01; IGFBP-6, OR 0.92 [95% CI 0.87–0.98], p = 0.01; Adiponectin, OR 0.837 [95% CI 0.721–0.970], p = 0.018; INSR, OR 0.910 [95% CI 0.872–0.950], p = 1.52 × 10–5). Additionally, genetically lower levels of IGF-1 and IGFBP-5, along with higher levels of IGFBP-7, were associated with an increased risk of Type 1 Diabetes (T1D) (IGF-1, OR 0.981 [95% CI 0.963–0.999], p = 0.037; IGFBP-5, OR 0.882 [95% CI 0.778–0.999], p = 0.049; IGFBP-7, OR 1.103 [95% CI 1.008–1.206], p = 0.033).ConclusionIn summary, our investigation has unveiled causal relationships between specific IGF family members and T1D and T2D through MR analysis. Generally, the IGF family appears to reduce the risk of T1D, but it presents a more complex and controversial role in the context of T2D. These findings provide compelling evidence that T2D is intricately linked with developmental impairment. Our study results offer fresh insights into the pathogenesis and the significance of serum IGF family member concentrations in assessing diabetes risk.</p

Popcorn-Derived Porous Carbon for Energy Storage and CO₂ Capture

Porous carbon materials have drawn tremendous attention due to its applications in energy storage, gas/water purification, catalyst support, and other important fields. However, producing high-performance carbons via a facile and efficient route is still a big challenge. Here we report the synthesis of microporous carbon materials by employing a steam-explosion method with subsequent potassium activation and carbonization of the obtained popcorn. The obtained carbon features a large specific surface area, high porosity, and doped nitrogen atoms. Using as an electrode material in supercapacitor, it displays a high specific capacitance of 245 F g^–1 at 0.5 A g^–1 and a remarkable stability of 97.8% retention after 5000 cycles at 5 A g^–1. The product also exhibits a high CO₂ adsorption capacity of 4.60 mmol g^–1 under 1066 mbar and 25 °C. Both areal specific capacitance and specific CO₂ uptake are directly proportional to the surface nitrogen content. This approach could thus enlighten the batch production of porous nitrogen-doped carbons for a wide range of energy and environmental applications

Mechanism and Kinetic Model for Autocatalysis in Liquid–Liquid System: Oxidation of Dibutyl Sulfide with Aqueous Hydrogen Peroxide

The oxidation of dibutyl sulfide with aqueous hydrogen peroxide as a liquid–liquid reaction was investigated. The autocatalysis, solubility of H₂O₂ in organic phase, and effects of temperature, stirring speed, initial organic DBSO concentration, and initial aqueous H₂O₂ concentration were studied. Solvent effect of dibutyl sulfoxide was proposed for liquid–liquid autocatalysis. The intrinsic reaction was considered as the determining step, and all the other steps were considered as equilibrium processes. Considering interfacial reaction and dynamic equilibrium of hydrogen peroxide between the two phases, the reaction was divided into exterior and interior stages. Exterior and interior mechanisms were proposed for the corresponding stages, and kinetic models were established. The parameters of kinetic model were estimated with the experimental data, and the activation energies of exterior and interior reaction were 30.62 and 73.50 kJ/mol. The validity of the kinetic models with estimated parameters was studied, and good agreements were observed between the experimental results and the model results

Elimination of interference from environmental policies.

Elimination of interference from environmental policies.</p