Evaluating analytical quality in clinical biochemistry laboratory using Six Sigma

Introduction: In recent years, Six Sigma metrics has become the hotspot in all trades and professions, which contributes a general procedure to explain the performance on sigma scale. Nowadays, many large companies, such as General Healthcare, Siemens, etc., have applied Six Sigma to clinical medicine and achieved satisfactory results. In this paper, we aim to evaluate the process performance of our laboratory by using Sigma metrics, thereby choosing the correct analytical quality control approach for each parameter. Materials and methods: This study was conducted in the clinical chemistry laboratory of Shandong Provincial Hospital. The five-months data of internal quality control were harvested for the parameters: amylase (AMY), lactate dehydrogenase (LD), potassium, total bilirubin (TBIL), triglyceride, aspartate aminotransferase (AST), uric acid, high density lipoprotein-cholesterol (HDL-C), alanine aminotransferase (ALT), urea, sodium, chlorine, magnesium, alkaline phosphatase (ALP), creatinine (CRE), total protein, creatine kinase (CK), total cholesterol, glucose (GLU), albumin (ALB). Sigma metrics were calculated using total allowable error, precision and percent bias for the above-mentioned parameters. Results: Sigma values of urea and sodium were below 3. Sigma values of total protein, CK, total cholesterol, GLU and ALB were in the range of 3 to 6. Sigma values of AMY, uric acid, HDL-C, TBIL, ALT, triglyceride, AST, ALP and CRE were more than 6. Conclusion: Amylase was the best performer with a Sigma metrics value of 19.93, while sodium had the least average sigma values of 2.23. Actions should be taken to improve method performance for these parameters with sigma below 3

Metastasis of human gastric adenocarcinoma partly depends on phosphoinositide-specific phospholipase γ1 expression

It is known that phosphoinositide-specific phospholipases γ1(PLCγ1) can trigger several signalling pathways to regulate cell proliferation, differentiation, and metastasis. However, whether this kinase is highly expressive and active in human gastric adenocarcinomas, and whether it can play an important role in the development of the cancer, have not yet been investigated. The aim of the study was to investigate the expression of PLCγ1 in human gastric adenocarcinoma, while the question of whether PLCγ1 can be activated through protein kinase B (Akt) signalling pathways to regulate cell migration was further explored using human gastric adenocarcinoma BGC-823 cell line. The expression of PLCγ1 in human adenocarcinoma was detected using immunohistochemical staining. The BGC-823 cells were cultured and treated with inhibitors or transfected with plasmid construction. The cell migration of BGC-823 cells was measured with wound healing assay, cell migration assay, and the ruffling assay. The expression levels of PLCγ1 and its related signal molecules in BGC-823 cells were assessed using Western blot analysis or gelatine zymography assay. PLCγ1 was highly expressed in humangastric adenocarcinomas, especially in the region with lymph node metastasis. It was shown that migration of BGC-823 cells in vitro depends on PLCγ1 activation. This activation is mediated through Akt, an upstream of PLCγ1 that triggers the PLCγ1/extracellular signal-regulated kinase (ERK)/matrix metalloproteinase (MMP) pathway in BGC-823 cells. PLCγ1 activities play an important role in the metastasis of gastric adenocarcinoma, and may serve as a potential therapeutic target in this type of cancer

Microalgal species variation at different successional stages in biological soil crusts of the Gurbantunggut Desert, Northwestern China

Biological soil crusts (BSC), most notably lichen crusts, develop and diversify in the Gurbantunggut Desert, the largest fixed and semi-fixed desert in China. Four different successional stages of BSC, including bare sand, microalgal crusts, lichen crusts, and moss crusts, were selected to determine successional changes in microalgal species composition and biomass and formation of BSC. A 10 x 10-m observation plot was established in an interdune region of the Gurbantunggut Desert and data were collected over an 8-year study period. The main results were: (1) different successional stages of BSC significantly affected the content of soil organic C and total and available N but not the total and available P and K content of soil; (2) composition of microalgal communities differed among the four successional stages; (3) significant differences in microalgal biomass were observed among the four successional stages; (4) bare sand was mainly uncompacted sand gains; (5) filamentous cyanobacteria, particularly Microcoleus vaginatus, were the dominant species in the early phase of crust succession. The presence of fungal mycelium and moss rhizoids prevented water and wind erosion

CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model

Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding non-English inputs for multi-lingual code-related tasks is still far from well studied. This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset that is carefully filtered by program analyzers and optimized during the training process. Extensive experiments are conducted using real-world usage scenarios, the industry-standard benchmark HumanEval-x, and the specially designed CodeFuseEval for Chinese prompts. To assess the effectiveness of CodeFuse, we actively collected valuable human feedback from the AntGroup's software development process where CodeFuse has been successfully deployed. The results demonstrate that CodeFuse-13B achieves a HumanEval pass@1 score of 37.10%, positioning it as one of the top multi-lingual code LLMs with similar parameter sizes. In practical scenarios, such as code generation, code translation, code comments, and testcase generation, CodeFuse performs better than other models when confronted with Chinese prompts.Comment: 10 pages with 2 pages for reference

Distribution and composition of cyanobacteria and microalgae associated with biological soil crusts in the Gurbantunggut Desert, China

In Gurbantunggut Desert, cyanobacterial and microalgal components were characterized within 60 soil samples collected from sand dunes. Fifty-one taxa of cyanobacteria and algae were identified. Without exception, the soils were alkaline, poor in nutrients, and showed large variations in other soil properties. Spatial heterogeneity for distribution of cyanobacteria and microalgae (diversity of morphotypes, species composition, and microbiomass) existed. Compared with other deserts in the world, the Gurbantunggut Desert has a greater diversity of cyanobacterial-microalgal morphotypes. Results from step regression showed that the diversity of morphotype was determined by total P, available P, and soil layer. Filamentous cyanobacteria dominated the community. Microcoleus vaginatus (Vauch.) Gom was the dominant species in most positions on sand dune, while the abundance of other dominant species varied depending on the sand dune position and the soil layer in which they occurred. The microalgal biomass was influenced by the content of Mg, crust type, soil moisture, sunlight, and oxygen concentration. A significant positive relation was found between microalgal biomass and diversity of morphotype. Species composition, diversity of morphotype, and microalgal biomass intera cted with each other. The contents of P and Mg ion, soil texture, and soil moisture may be the main factors responsible for cyanobacterial-microalgal distribution.19 page(s

Multiobjective Collaborative Optimization of Argon Bottom Blowing in a Ladle Furnace Using Response Surface Methodology

In order to consider both the refining efficiency of the ladle furnace (LF) and the quality of molten steel, the water model experiment is carried out. In this study, the single factor analysis, central composite design principle, response surface methodology, visual analysis of response surface, and multiobjective optimization are used to obtain the optimal arrangement scheme of argon blowing of LF, design the experimental scheme, establish the prediction models of mixing time (MT) and slag eye area (SEA), analyze the comprehensive effects of different factors on MT and SEA, and obtain the optimal process parameters, respectively. The results show that when the identical porous plug radial position is 0.6R and the separation angle is 135°, the mixing behavior is the best. Moreover, the optimized parameter combination is obtained based on the response surface model to simultaneously meet the requirements of short MT and small SEA in the LF refining process. Meanwhile, compared with the predicted values, the errors of MT and SEA for different conditions from the experimental values are 1.3% and 2.1%, 1.3% and 4.2%, 2.5% and 3.4%, respectively, which is beneficial to realizing the modeling of argon bottom blowing in the LF refining process and reducing the interference of human factors

Dynamic state estimation for power networks using distributed MAP technique

This paper studies a distributed state estimation problem for a network of linear dynamic systems (called nodes), which evolve autonomously, but their measurements are coupled through neighborhood interactions. Power networks are typical networked systems obeying such features, with other examples including traffic networks, sensor networks and many multi-agent systems. We develop a new distributed state estimation approach, for each node to update its local state. The core of this distributed approach is a distributed maximum a posteriori (MAP) estimation technique, which delivers a globally optimal estimate under certain assumptions. We apply the distributed approach to an IEEE 118-bus system, and compare it with a centralized approach, which provides the optimal state estimate using all the measurements, and with a local state estimation approach, which uses only local measurements to estimate local states. Simulation results show that under different scenarios including normal operation, bad measurements and sudden load change, the distributed approach is clearly more accurate than the local state estimation approach and distributed static state estimation approach. Although the result is a bit less accurate than that by a centralized algorithm, the distributed algorithm enjoys low computational complexity and communication load, and is scalable to large power networks.Fil: Sun, Yibing. Shandong University; ChinaFil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; ChinaFil: Wang, Bingchang. Shandong University; ChinaFil: Zhang, Huanshui. Shandong University; ChinaFil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Guangdong University of Technology; Chin