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Background: Tinnitus treatment remains a global challenge, and current therapeutic approaches are still controversial. This study aims to elucidate the potential mechanisms of Xiao-Chai-Hu-Tang (XCHT) in treating tinnitus through the analysis of network pharmacology, mendelian randomization and molecular docking, and molecular dynamics simulation analysis. We hope to contribute to the research on the target of action of traditional Chinese medicine and exploration of the mechanism of tinnitus. Methods: We utilized network pharmacology to screen potential targets of action of XCHT on tinnitus. Mendelian randomization was employed to determine the causal relationship between potential targets of action and tinnitus. Finally, molecular docking and molecular dynamics simulation with clear targets and the combination of the active ingredient in effectiveness. Results: Through network pharmacology, we identified 38 potential targets of action. Mendelian randomization analysis revealed that HIF1A (OR [95 % CI] = 0.78 [0.65, 0.94], P = 0.008) and CCND1 (OR [95 % CI] = 1.22 [1.00, 1.49], P = 0.04) exhibited significant results with tinnitus. Molecular docking and molecular dynamics simulation of HIF1A and active ingredients demonstrated good binding efficacy. Conclusion: HIF1A may play a key role in the treatment of tinnitus by XCHT, which may play a certain protective role in tinnitus patients and may inhibit the occurrence and development of tinnitus. However, the specific mechanism and effect need to be further studied and verified.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai. [ABSTRACT FROM AUTHOR]

Magnetic data play a significant role in the interpretation of various geologic structures using depth estimation methods and edge detection filters. In this study, we applied methods based on directional derivatives such as tilt-depth (TD), signum transform (ST), source distance (SD) and classical Euler deconvolution (ED) to estimate the depth of the magnetic sources. Moreover, to enhance the edges of magnetic field anomalies, we utilize the total horizontal derivative (THD), analytical signal (AS), tilt angle (TA), theta map (TM), hyperbolic tilt angle (HTA), the tilt angle of horizontal derivative (TAHG), and logistic function of total horizontal gradient (LTHD). These algorithms are tested on a synthetic magnetic model with noise and free noise to understand their performance. Since the edge detection filters are sensitive to noise, we carry out an upward continuation (UC) filter before the reduction of data to magnetic the pole to reduce the noise effect. After comparing the efficiency of the approaches in estimating the depth and horizontal lateral boundaries, these methods were applied to the data surveyed from the Aji-Chai salt dome in East Azerbaijan Province, Iran. The results obtained by the depth determination methods were compared with the drilling report of Iran’s geological survey and three-dimensional classical Euler deconvolution method.

Rivers play an essential role in supplying high-quality water to diverse sectors. Understanding water quality indicators and systematic monitoring is crucial for water resources management and macro-level decision-making. In this context, the forthcoming article delves into the simulation of three crucial parameters, namely EC, SAR, and TDS, through a reach of 106 km length along the Aji-Chai River, Iran, encompassing stations from Markid, Khajeh, Akhola, and Serin Dizj. This simulation employs three advanced machine learning models: SVM, GEP, and MLP, in conjunction with the QUAL2Kw mathematical simulator. The study meticulously evaluates the performance of these models using four key indices: RMSE, MAE, R2, and DDR. The calculated results unequivocally establish the superiority of the SVM in simulating three essential water quality parameters across all stations. This is supported by consistently high R2 and DDR values, along with low RMSE and MAE values. While the mathematical model used in this study showed reasonable accuracy in simulating the parameters under investigation, it consistently performed less effectively than the SVM model. In summary, the SVM model with specific parameters (C = 68.5, ε = 4.55, and γ = 205) emerges as the optimal choice for accurately simulating river water quality parameters based on the conducted study.

Objective: Radix Bupleuri and Radix Paeoniae Alba are referred to as Chai-Hu and Bai-Shao, respectively, in Chinese. We used molecular docking and network pharmacological approaches to explore the active components of this Chinese herb combination which is commonly used for treating Alzheimer’s disease (AD), the Chai-Hu and Bai-Shao pair (CBP). Materials and Methods: The active compounds and action targets of Chai-Hu and Bai-Shao were identified in the Traditional Chinese Medicine Systems Pharmacology database, and the Therapeutic Target Database, DrugBank, GenCards, DisGeNET, and Online Mendelian Inheritance in Man databases were used to identify therapeutic targets in patients with AD. To identify a common target of CBP in the treatment of AD, we used the Metascape tool to perform Gene Ontology function enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. In addition, CBP-AD protein–protein interaction (PPI) and herb-component-target networks were created using Cytoscape 3.8.2. Molecular docking was verified using AutoDock tools. Results We identified 20 active compounds of CBP, 202 active targets, 1,323 disease targets, and 117 active compound disease intersection targets; the PPI network showed that the top eight key targets were AKT1, interleukin (IL) 6, JUN, MAPK1, tumor necrosis factor, TP53, vascular endothelial growth factor, and epidermal growth factor. These act mainly on the fluid shear stress, atherosclerosis, IL-17, hypoxia-inducible factor-1, and nonalcoholic fatty liver disease pathways, among others, which control immune inflammation, cell proliferation, and apoptosis. Based on molecular docking results, the active components of CBP bind well to its key targets. Conclusion Our results indicated that CBP was effective for treating AD by reducing the expression of inflammatory factors, promoting cell proliferation and differentiation, and preventing nerve cell death and anti-neuronal cell apoptosis.

IntroductionThe study of surface water quality control in water resources and environment management programs is very important. Surface water is one of the most important water sources that have crucial impact on agricultural, industrial, drinking and electricity production activities. Due to insufficient water sources with good quality and the increase in population growth rate and as a result of the increase in demand, the study of water quality parameters is very important. The Water Quality Index (WQI) serves as a prominent indicator in classifying surface water quality. Moreover, in recent years, the TOPSIS method has gained traction for evaluating water quality. This approach, known for its simplicity, is increasingly utilized in prioritizing river water and assessing its quality. Through this index, various components of water quality are condensed into a single numerical value, effectively expressing overall water quality. To ascertain the weight index, Shannon's entropy method was employed. Furthermore, to assess water suitability for drinking, agriculture, and industrial purposes, Schuler, Wilcox, and Piper diagrams were utilized. These diagrams provide valuable insights into the quality of water, aiding in decision-making processes regarding its utilization across different sectors. Therefore, the results of this study also confirmed the effectiveness of the TOPSIS method in identifying contaminated stations.Materials and MethodsThis research focuses on evaluating the water quality of three stations within the Aji Chai river watershed on an annual basis. These stations are identified as Arzanag, Akhola, and Markid. The assessment spans the years 2003 to 2021 and aims to classify water quality for both drinking and agricultural purposes. Utilizing the standards set forth by the World Health Organization, the surface water quality index of the Aji Chai basin is investigated to ascertain its suitability for drinking purposes. Shannon's entropy theory was used to prevent expert judgments in determining the weight of each parameter. TOPSIS method was used to classify eleven qualities including TDS, EC, pH, HCO3-, Cl-, , Ca2+, Mg2+, Na+ , K+ and TH. In all the three stations water quality were ranked, based on TOPSIS numerical values. Also, in order to check the quality of drinking, agricultural and industrial water, Schuler, Wilcox and Piper diagrams were used. Results and DiscussionThe initial findings from the %RE error analysis revealed that throughout the entire statistical period (2003-2021), the %RE values were consistently close to zero, with the majority being positive. This suggests that the total number of cations surpasses the total number. In terms of the Shannon water quality index, the results indicate that Markid station exhibited the highest index value at 945.92, while Arzanag station displayed the lowest value at 127.365 among the surveyed stations. The results of the water quality index showed that Arzanag and Akhola stations are in an average condition (100 < EWQI < 150) and Markid station is in a very poor condition (EWQI > 200). According to Schuler's diagram, it was found that the water of Arzanag station is in the average level in terms of water quality, which is in a good position in terms of quality compared to the other two stations, while the water of Akhola station is in a good position. In the range of poor quality, Markid water was undrinkable, which ranked worst among the three stations. According to the Wilcox diagram, it was found that the water quality of Markid is very poor, which is even outside the boundary of the Wilcox diagram, while the water of Arzanag station was ranked 1st in terms of quality. Arzanag water is in C4S2 class in terms of quality. Finally, the water class of Akhola station was placed in the C4S4 class (in the Wilcox chart), which shows very low water quality. According to the TOPSIS method, the first priority in terms of water quality pollution belonged to Markid station. Two other stations, including Akhola and Arzanag, were ranked second and third in this respect. Therefore, the most important station in this basin is Markid station. ConclusionThe results of Shannon water quality index showed that among the stations, the highest index value is related to Markid station with a value of 945.92 and the lowest one is related to Arzanag station with a value of 127.365. According to Schoeller diagram, it was found that the water quality of Arzanag station is average, compared to the other two stations, it was in the right place and the water of Akhola station was in the range of poor quality. The quality of Markid water was found to be undrinkable, which was the worst one among all the three stations. The range of TOPSIS values in different stations is between 0.054 and 0.894, which belonged to the Arzanag and Markid stations, respective ly. According to the results of the Arzanag station, the best water quality condition and the Markid station were assigned the worst water quality condition among all the three stations.

Objective: To explore the characteristics of Xiao Chai Hu (XCH) decoction in randomized controlled trials (RCTs) using a bibliometric method. Methods: Seven databases including PubMed, Web of Science, Embase, Cochrane library, China National Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP) and Wanfang database were retrieved from inception to May 27, 2022. In the study, XCH decoction (or modified) used alone or combined with conventional Western medicine as an intervention measure was included. The basic characteristics, funding support, relevant diseases, intervention methods, and adverse events (AEs) were analyzed. Data analysis was performed using SPSS 26.0 software. Results: A total of 813 RCTs were included, published from 1989 to 2022. There was only one English-language literature with the recent impact factor of 5.374. There were 147 studies were from Chinese-language core journals, with the highest impact factor of 2.414. Only 6.15% of the literatures mentioned funding support. 96.31% of the included literature reported the statistical significance of using XCH decoction. The diseases treated mainly included chronic hepatitis B (9.35%), cough variant asthma (5.66%), dizziness (5.54%), bile reflux gastritis (4.43%), and fever (4.18%). However, the overall research design of the included literature was poor, and large sample size, multicenter RCTs are needed. The incidence of AEs of XCH decoction alone was 8.86%, which was significantly lower than that of conventional Western medicine treatment. The combination of XCH decoction and conventional Western medicine treatment could reduce the incidence of AEs, and no serious adverse event was reported. Conclusion: Although the included studies show that XCH decoction is widely used, and has good efficacy and few AEs. Due to the low quality of the included RCTs, there may be some bias, and its rational use based on the specific conditions is recommended in clinical practice.

Fever of unknown origin (FUO) can be caused by many factors and usually presents as a high fever without a clear diagnosis. Although some treatments may relieve symptoms, they do not stabilise the condition and relapses are commonly reported. In this case, a 20-year-old male complained of recurring high fever (around 39-40 degrees Celsius) without reason for over two years. He had been hospitalised many times without a clear diagnosis. Applying fang zheng 方证 differentiation, the patient was prescribed Chai Ling Tang (Bupleurum and Poria Decoction) combined with Wai Tai Fu Ling Yin (Poria Decoction) as well as Ban Xia Hou Po Tang (Pinellia and Magnolia Bark Decoction). After three months of medication, the patient’s temperature stabilised, his body weight slowly increased and his other symptoms also improved. [ABSTRACT FROM AUTHOR]

AbstractEnvironmental DNA (eDNA) metabarcoding permits a new measurement to monitor biodiversity in aquatic ecosystems. This study utilized eDNA metabarcoding to examine the seasonal and spatial distribution of phytoplankton and their relationship with environmental factors in the Chai River, Kunming, China, in the dry, and wet seasons. The process detected 108 genera of phytoplankton, which were members of 11 phyla, 41 orders, and 60 families. Distinct differences in seasonal and spatial distribution patterns of phytoplankton diversity, including community structure, α-diversity, and representative functional groups, were observed. In the dry season, Pseudanabaena, Microcystis, and Synechococcus were the predominant genera in Cyanophyta, and Chlorophyta, Bacillariophyta and Euglenophyta were predominant in the eukaryotic phytoplankton. While in the wet season, Cylindrospermopsis, Microcystis, and Planktothrix predominated in the Cyanophyta, and Bacillariophyta, Chlorophyta and Xanthophyta predominated in the eukaryotic phytoplankton. Significant differences were shown by a principal coordinate analysis and PERMANOVA test among the upper, middle, and lower reaches of the phytoplankton communities except for the cyanobacterial community structure between the upper and lower reaches at the wet season (p > 0.05). The representative functional groups of phytoplankton varied from A, D, E, LM, L0, N, S1, MP, P, TB, TC, U, W0, W1, Xph, Y, and Z in the dry season to A, B, D, E, F, J, MP, LM, S1, SN, TC, P, W0, W1, X2, and Y in the wet season. Significant differences in the water temperature (WT), pH, chemical oxygen demand, oxygen consumed, nitrate nitrogen (NO3-N), and chlorophyll a were exhibited between the dry and wet seasons (p

In Tanzania, agriculture sector is known for employing more than 70% of the total population. Agriculture sector faces many challenges including climate change. Climate change causes low productivity in agriculture; low productivity is caused due to poor implementation of agricultural policies and strategies. This poor implementation of policies has also caused many farmers to be not competent in climate change adaptation. Over the years, provisions of agricultural advice and extension were provided by various approaches, including training and visit extension, participatory approaches, and farmers' field schools. However, provision of agricultural advisory and extension service is inefficient. Also, in most cases the usage of most agricultural innovations and technologies developed is limited. A literature review indicates that the main reasons given by Tanzanian farmers for not using improved technology are not lack of knowledge or skill, but rather that the technologies do not contribute towards improvements (e.g., the technologies are not profitable or they imply to high risk). Thus, agricultural extension service needs to be geared towards teaching farmers how to develop innovative and cost effective technologies that are contextualized. Limited numbers of agricultural extension staff and less interactivity of Information and Communication Technologies (ICTs), such as radio and television, have been mentioned to be among the factors limiting the provision of agricultural advisory and extension services to the majority of farmers in Tanzania. The advancements in ICTs have brought new opportunities for enhancing access to agricultural advisory and extension service for climate change adaptation. In Tanzania, farmers and other actors access agricultural information from various sources such as agricultural extension workers and use of various databases from Internet Services Providers. Also there are different web- and mobile-based farmers' advisory information systems to support conventional agricultural extension service. These systems are producing bulk amounts of data which makes it difficult for different stakeholders to make an informed decision after data analysis. This calls for the need to develop a tool for data visualization in order to understand hidden patterns from massive data. In this study, a semi-automated text classification was developed to determine the frequently asked keywords from a web and mobile based farmers' advisory system called "UshauriKilimo" after being in use for more than 2 years by more than 700 farmers.

Spi Rais anticline (East part of major Bekhair anticline) and western part of Chia Gara anticline were chosen to study joint paths. They are located within the highly folded zone of the western zagros fold and thrust belt, Iraqi segment. These anticlines arranged in an en-echelon pattern and they expose rocks range in age from Late Cretaceous to Late Miocene. The Late Cretaceous (Campanian – Maastrichtian) succession represented by Bekhme and Shiranish Formations. The lithologies of these rocks are competent and consist of limestone, dolomitic limestone and marly limestone whichare favorable to joint development. Different types of joints were developed in such rocks due to stresses resulted from collision between Arabian and Eurasian (Turkish and Iranian) plates. The joint sets and systems are the most abundant fractures in the rocks of the study area. The chronological relationships of the joints refer that the area had witnessed multiple tectonic pulses in the form of compressional and extensional stresses. The stereonet and the Win – Tensor programs were used to plot the attitudes of joints and deduce their paleostresss orientations. Furthermore, the orientations of deduced paleostresses were plotted onto geological map of investigated area and simulated as paleo trajectories as well. The deduced paleostress direction responsible for folding in traverse 1 was (356° - 195°), in traverse 2 (330° - 168°) and in traverse 3 (353°- 163°).