Diaqua­bis­(2,2′-bi-1H-imidazole)­man­ganese(II) benzene-1,4-di­carboxyl­ate

The asymmetric unit of the title compound, [Mn(C6H6N4)2(H2O)2](C8H4O4), contains one-half each of the centrosymmetric cation and anion. The MnII atom is coordinated by four N atoms [Mn—N = 2.2168 (14) and 2.2407 (14) Å] from two 2,2′-biimidazole ligands and two water mol­ecules [Mn—O = 2.2521 (14) Å] in a distorted octa­hedral geometry. Inter­molecular N—H⋯O and O—H⋯O hydrogen bonds consol­idate the crystal packing, which also exhibits π–π inter­actions between five-membered rings, with a centroid–centroid distance of 3.409 (2) Å

Improvement of Electron Probe Microanalysis of Boron Concentration in Silicate Glasses

The determination of low boron concentrations in silicate glasses by electron probe microanalysis (EPMA) remains a significant challenge. The internal interferences from the diffraction crystal, i.e. the Mo-B4C large d-spacing layered synthetic microstructure crystal, can be thoroughly diminished by using an optimized differential mode of pulse height analysis (PHA). Although potential high-order spectral interferences from Ca, Fe, and Mn on the BKα peak can be significantly reduced by using an optimized differential mode of PHA, a quantitative calibration of the interferences is required to obtain accurate boron concentrations in silicate glasses that contain these elements. Furthermore, the first-order spectral interference from ClL-lines is so strong that they hinder reliable EPMA of boron concentrations in Cl-bearing silicate glasses. Our tests also indicate that, due to the strongly curved background shape on the high-energy side of BKα, an exponential regression is better than linear regression for estimating the on-peak background intensity based on measured off-peak background intensities. We propose that an optimal analytical setting for low boron concentrations in silicate glasses (≥0.2 wt% B2O3) would best involve a proper boron-rich glass standard, a low accelerating voltage, a high beam current, a large beam size, and a differential mode of PHA

Cohort size required for prognostic genes analysis of stage II/III esophageal squamous cell carcinoma

Background: Few overlaps between prognostic biomarkers are observed among different independently performed genomic studies of esophageal squamous cell carcinoma (ESCC). One of the reasons for this is the insufficient cohort size. How many cases are needed to prognostic genes analysis in ESCC?Methods: Here, based on 387 stage II/III ESCC cases analyzed by whole-genome sequencing from one single center, effects of cohort size on prognostic genes analysis were investigated. Prognostic genes analysis was performed in 100 replicates at each cohort size level using a random resampling method.Results: The number of prognostic genes followed a power-law increase with cohort size in ESCC patients with stage II and stage III, with exponents of 2.27 and 2.25, respectively. Power-law curves with increasing events number were also observed in stage II and III ESCC, respectively, and they almost overlapped. The probability of obtaining statistically significant prognostic genes shows a logistic cumulative distribution function with respect to cohort size. To achieve a 100% probability of obtaining statistically significant prognostic genes, the minimum cohort sizes required in stage II and III ESCC were approximately 95 and 60, respectively, corresponding to a number of outcome events of 33 and 36, respectively.Conclusion: In summary, the number of prognostic genes follows a power-law growth with the cohort size or events number in ESCC. The minimum events number required to achieve a 100% probability of obtaining a statistically significant prognostic gene is approximately 35

Inter-layer free cobalt-doped silica membranes for pervaporation of ammonia solutions

This study demonstrated the application of a new type of interlayer-free cobalt-doped silica membrane in treating ammonia solutions by pervaporation applied towards wastewater treatment. For enhanced hydrothermal stability, cobalt-doped silica (CoSi) membranes with increasing cobalt concentrations from 1 to 35 mol% were prepared and evaluated, namely CoSi-1, 5, 20 and 35. These membranes exhibited high water fluxes of 66 L m h for CoSi-1 and 15.5 L m h for CoSi-35 at 45 °C. The fluxes of the membranes decreased with increasing cobalt concentration; while the rejection to total nitrogen (TN, ammonia nitrogen) increased and hence allowed selective passage of water molecules. Enhanced thermostability was observed for the membranes, particularly CoSi-35 that exhibited TN rejection up to 99% at high temperature of 65 °C and highly alkaline environment (pH > 10). Also, the CoSi-35 membrane showed stable performance in treating ammonia present in industry wastewater by achieving stable TN and mineral rejections of 97% and 99%, respectively. Fouling was observed and confirmed by SEM morphological analysis and EDX elemental inspection. The results indicated the deposition of low solubility salts such as CaSO

A Magnetically and Thermally Controlled Liquid Metal Variable Stiffness Material

Smart materials that can actively tune their stiffness are of great interest to many fields, including the construction industry, medical devices, industrial machines, and soft robotics. However, developing a material that can offer a large range of stiffness change and rapid tuning remains a challenge. Herein, a liquid metal variable stiffness material (LMVSM) that can actively and rapidly tune its stiffness by applying an external magnetic field or by changing the temperature is developed. The LMVSM is composed of three layers: a gallium–iron magnetorheological fluid (Ga–Fe MRF) layer for providing variable stiffness, a nickel–chromium wire layer for Joule heating, and a soft heat dissipation layer for accelerating heating and rapid cooling. The stiffness can be rapidly increased by 4 times upon the application of a magnetic field or 10 times by solidifying the Ga–Fe MRF. Finally, the LMVSM is attached to a pneumatically controlled soft robotic gripper to actively tune its load capacity, demonstrating its potential to be further developed into smart components that can be widely adopted by smart devices

Fatty infiltration in the musculoskeletal system: pathological mechanisms and clinical implications

Fatty infiltration denotes the anomalous accrual of adipocytes in non-adipose tissue, thereby generating toxic substances with the capacity to impede the ordinary physiological functions of various organs. With aging, the musculoskeletal system undergoes pronounced degenerative alterations, prompting heightened scrutiny regarding the contributory role of fatty infiltration in its pathophysiology. Several studies have demonstrated that fatty infiltration affects the normal metabolism of the musculoskeletal system, leading to substantial tissue damage. Nevertheless, a definitive and universally accepted generalization concerning the comprehensive effects of fatty infiltration on the musculoskeletal system remains elusive. As a result, this review summarizes the characteristics of different types of adipose tissue, the pathological mechanisms associated with fatty infiltration in bone, muscle, and the entirety of the musculoskeletal system, examines relevant clinical diseases, and explores potential therapeutic modalities. This review is intended to give researchers a better understanding of fatty infiltration and to contribute new ideas to the prevention and treatment of clinical musculoskeletal diseases

Research on improved YOLOv8n based potato seedling detection in UAV remote sensing images

IntroductionAccurate detection of potato seedlings is crucial for obtaining information on potato seedlings and ultimately increasing potato yield. This study aims to enhance the detection of potato seedlings in drone-captured images through a novel lightweight model.MethodsWe established a dataset of drone-captured images of potato seedlings and proposed the VBGS-YOLOv8n model, an improved version of YOLOv8n. This model employs a lighter VanillaNet as the backbone network in-stead of the original YOLOv8n model. To address the small target features of potato seedlings, we introduced a weighted bidirectional feature pyramid network to replace the path aggregation network, reducing information loss between network layers, facilitating rapid multi-scale feature fusion, and enhancing detection performance. Additionally, we incorporated GSConv and Slim-neck designs at the Neck section to balance accuracy while reducing model complexity. ResultsThe VBGS-YOLOv8n model, with 1,524,943 parameters and 4.2 billion FLOPs, achieves a precision of 97.1%, a mean average precision of 98.4%, and an inference time of 2.0ms. Comparative tests reveal that VBGS-YOLOv8n strikes a balance between detection accuracy, speed, and model efficiency compared to YOLOv8 and other mainstream networks. Specifically, compared to YOLOv8, the model parameters and FLOPs are reduced by 51.7% and 52.8% respectively, while precision and a mean average precision are improved by 1.4% and 0.8% respectively, and the inference time is reduced by 31.0%.DiscussionComparative tests with mainstream models, including YOLOv7, YOLOv5, RetinaNet, and QueryDet, demonstrate that VBGS-YOLOv8n outperforms these models in terms of detection accuracy, speed, and efficiency. The research highlights the effectiveness of VBGS-YOLOv8n in the efficient detection of potato seedlings in drone remote sensing images, providing a valuable reference for subsequent identification and deployment on mobile devices

Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients’ quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host–microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area’s advancement towards precision PSCI treatment