Serum cystatin C and stroke risk: a national cohort and Mendelian randomization study

PurposeThe debate over the causal and longitudinal association between cystatin C and stroke in older adults persists. Our aim was to assess the link between cystatin C levels, both measured and genetically predicted, and stroke risk.MethodsThis study employed a retrospective cohort design using samples of the China Health and Retirement Longitudinal Study (CHARLS), which is a nationally representative cohort recruiting individuals aged 45 years or above. A multivariate logistic model and the two-sample Mendelian randomization framework were used to investigate the longitudinal and genetically predicted effect of serum cystatin C on stroke.ResultsThe study population had a mean age of 59.6 (SD ±9.5), with 2,996 (46.1%) women. After adjusting for confounding factors, compared to those in the first quartile of cystatin C, those in the last quartile had the greatest risk of stroke incidence [odds ratio (OR), 1.380; 95% confidence interval (CI), 1.046–1.825]. The Mendelian randomization analysis showed that a genetically predicted cystatin C level was positively associated with total stroke (OR by inverse variance-weighted method, 1.114; 95% CI, 1.041–1.192).ConclusionsThis national cohort study suggests that higher serum cystatin C is associated with an increased risk of total stroke, which is further supported by Mendelian randomization

A Power-Frequency Electric Field Sensor for Portable Measurement

In this paper, a new type of electric field sensor is proposed for the health and safety protection of inspection staff in high-voltage environments. Compared with the traditional power frequency electric field measurement instruments, the portable instrument has some special performance requirements and, thus, a new kind of double spherical shell sensor is presented. First, the mathematical relationships between the induced voltage of the sensor, the output voltage of the measurement circuit, and the original electric field in free space are deduced theoretically. These equations show the principle of the proposed sensor to measure the electric field and the effect factors of the measurement. Next, the characteristics of the sensor are analyzed through simulation. The simulation results are in good agreement with the theoretical analysis. The influencing rules of the size and material of the sensor on the measurement results are summarized. Then, the proposed sensor and the matching measurement system are used in a physical experiment. After calibration, the error of the measurement system is discussed. Lastly, the directional characteristic of the proposed sensor is experimentally tested

Dataset of cow manure by earthworm bio-composting process

Earthworm bio-composting is an environmentally friendly way of processing agricultural organic waste, especially cow manure. In order to observe the temperature, humidity and conductivity of cow manure during the biological composting process of earthworms, a composting data collection system was designed to collect the above data. The experiment was carried out in an earthworm breeding farm in Changqing District, Jinan City, and lasted for 50 days, from October 21, 2020 to December 10, 2020. The experiment data can be used for data comparison with conventional composting, and can also provide a reference for the exploration of the earthworm composting process

inGAP-family: Accurate Detection of Meiotic Recombination Loci and Causal Mutations by Filtering Out Artificial Variants due to Genome Complexities

Accurately identifying DNA polymorphisms can bridge the gap between phenotypes and genotypes and is essential for molecular marker assisted genetic studies. Genome complexities, including large-scale structural variations, bring great challenges to bioinformatic analysis for obtaining high-confidence genomic variants, as sequence differences between non-allelic loci of two or more genomes can be misinterpreted as polymorphisms. It is important to correctly filter out artificial variants to avoid false genotyping or estimation of allele frequencies. Here, we present an efficient and effective framework, inGAP-family, to discover, filter, and visualize DNA polymorphisms and structural variants (SVs) from alignment of short reads. Applying this method to polymorphism detection on real datasets shows that elimination of artificial variants greatly facilitates the precise identification of meiotic recombination points as well as causal mutations in mutant genomes or quantitative trait loci. In addition, inGAP-family provides a user-friendly graphical interface for detecting polymorphisms and SVs, further evaluating predicted variants and identifying mutations related to genotypes. It is accessible at https://sourceforge.net/projects/ingap-family/

Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph

This paper presents an improved clustering algorithm for categorizing data with arbitrary shapes. Most of the conventional clustering approaches work only with round-shaped clusters. This task can be accomplished by quickly searching and finding clustering methods for density peaks (DPC), but in some cases, it is limited by density peaks and allocation strategy. To overcome these limitations, two improvements are proposed in this paper. To describe the clustering center more comprehensively, the definitions of local density and relative distance are fused with multiple distances, including K-nearest neighbors (KNN) and shared-nearest neighbors (SNN). A similarity-first search algorithm is designed to search the most matching cluster centers for noncenter points in a weighted KNN graph. Extensive comparison with several existing DPC methods, e.g., traditional DPC algorithm, density-based spatial clustering of applications with noise (DBSCAN), affinity propagation (AP), FKNN-DPC, and K-means methods, has been carried out. Experiments based on synthetic data and real data show that the proposed clustering algorithm can outperform DPC, DBSCAN, AP, and K-means in terms of the clustering accuracy (ACC), the adjusted mutual information (AMI), and the adjusted Rand index (ARI)

Simulation Study of Allied In-Situ Injection and Production for Enhancing Shale Oil Recovery and CO2 Emission Control

The global greenhouse effect makes carbon dioxide (CO2) emission reduction an important task for the world, however, CO2 can be used as injected fluid to develop shale oil reservoirs. Conventional water injection and gas injection methods cannot achieve desired development results for shale oil reservoirs. Poor injection capacity exists in water injection development, while the time of gas breakthrough is early and gas channeling is serious for gas injection development. These problems will lead to insufficient formation energy supplement, rapid energy depletion, and low ultimate recovery. Gas injection huff and puff (huff-n-puff), as another improved method, is applied to develop shale oil reservoirs. However, the shortcomings of huff-n-puff are the low sweep efficiency and poor performance for the late development of oilfields. Therefore, this paper adopts firstly the method of Allied In-Situ Injection and Production (AIIP) combined with CO2 huff-n-puff to develop shale oil reservoirs. Based on the data of Shengli Oilfield, a dual-porosity and dual-permeability model in reservoir-scale is established. Compared with traditional CO2 huff-n-puff and depletion method, the cumulative oil production of AIIP combined with CO2 huff-n-puff increases by 13,077 and 17,450 m3 respectively, indicating that this method has a good application prospect. Sensitivity analyses are further conducted, including injection volume, injection rate, soaking time, fracture half-length, and fracture spacing. The results indicate that injection volume, not injection rate, is the important factor affecting the performance. With the increment of fracture half-length and the decrement of fracture spacing, the cumulative oil production of the single well increases, but the incremental rate slows down gradually. With the increment of soaking time, cumulative oil production increases first and then decreases. These parameters have a relatively suitable value, which makes the performance better. This new method can not only enhance shale oil recovery, but also can be used for CO2 emission control

High Glucose Induces Endothelial COX2 and iNOS Expression via Inhibition of Monomethyltransferase SETD8 Expression

Cyclooxygenase 2 (COX2) and inducible nitric oxide synthase (iNOS) overexpression results in endothelial apoptosis, thus mediating vascular endothelial injury in hyperglycaemia. E26 transformation-specific sequence transcription factor-1 (ESE-1), which belongs to the E26 transformation-specific family of transcription factors, has been demonstrated to be involved in COX2 and iNOS gene transcription. Our previous study indicated that SET domain-containing protein 8 (SETD8) downregulation is involved in high glucose-mediated endothelial inflammation in human umbilical vein endothelial cells (HUVECs). Here, we report that SETD8 plays a major role in hyperglycaemia-induced COX2 and iNOS expression. In HUVECs, upregulation of ESE-1 expression was related to high glucose-mediated apoptosis and COX2 and iNOS expression. High glucose inhibited SETD8 expression, and overexpression of SETD8 diminished the effects of high glucose treatment. Consistently, RNA silencing of SETD8 led to the opposite effect. Furthermore, SETD8 was found to interact with specificity protein 1 (SP1). Blockade of SP1 protected against high glucose-mediated endothelial injury. Mechanistically, we showed that H4K20me1, a downstream target of SETD8, and SP1 were enriched at the ESE-1 promoter region by ChIP assay. Luciferase reporter assays indicated that SETD8 overexpression attenuated ESE-1 promoter activity and augmented the inhibitory effect of siSP1 on ESE-1 promoter activity. In general, our data indicate that SETD8 interacts with SP1 to coregulate ESE-1 expression, which is involved in hyperglycaemia-mediated endothelial apoptosis in HUVECs

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management