Analysis Precipitation to Seek a Good Place for Farm

In this poster, we present how to employ Hadoop System including HDFS and MapReduce to analyze the precipitation data to find good places for farming. The precipitation data are collected from National Oceanic and Atmospheric Administration (NOAA) and some formulas form Food and Agriculture Organization of the United Nations (FAO) to help to find places which have good precipitation for specific plant. In order to address this, we employ Hadoop Distributed File System (HDFS) and MapReduce programming with banana as an example. Combining the weather data with the precipitation data, we can figure out the places which are good for banana grows. The implemented system uses two MR programs and Google Earth to implement the visualization

Exploration of Ideological and Political Education in the Course of “Biopharmaceutical Testing and Testing Technology” under the Post Pandemic Situation

Motivated by curriculum reform and the traditional theoretical teaching content system of drug analysis and testing, we will strengthen experimental teaching in accordance with enterprise needs of vocational colleges. And then, under the direction of content and task driven, we will build an ACQUIN quality certification system for the Biopharmaceutical Testing and Testing Technology course to strengthen students’ independent participation, pique their interest in learning, and cultivate high-quality skilled talents who are capable of doing things and being good person, by thoroughly examining the ideological and political components of the course

Fine-grained Appearance Transfer with Diffusion Models

Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coherence, presents formidable challenges. Despite significant advancements brought by diffusion models, achieving fine-grained transfer remains complex, particularly in terms of retaining detailed structural elements and ensuring information fidelity. This paper proposes an innovative framework designed to surmount these challenges by integrating various aspects of semantic matching, appearance transfer, and latent deviation. A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes. This is identified as a crucial element for the precise and natural transfer of fine-grained details. Our framework exploits this space to accomplish semantic alignment between source and target images, facilitating mask-wise appearance transfer for improved feature acquisition. A significant advancement of our method is the seamless integration of these features into the latent space, enabling more nuanced latent deviations without necessitating extensive model retraining or fine-tuning. The effectiveness of our approach is demonstrated through extensive experiments, which showcase its ability to adeptly handle fine-grained appearance transfers across a wide range of categories and domains. We provide our code at https://github.com/babahui/Fine-grained-Appearance-TransferComment: 14 pages, 15 figure

Detection of Pine Nut Allergen in Three Kinds of Food by Ultra-high Performance Liquid Chromatography-Tandem Mass Spectrometry

An ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was established for the detection of pine nut allergen Pin k 2 in food matrices. Pine nuts were ground, degreased, and enzymatically extracted and the hydrolysate was separated and analyzed by using an Easy-nLC 1000-QExecutive high-resolution mass spectrometer, and the mass spectral data obtained were processed using Protein Pilot TM software and the Uniprot protein database. The specificity was verified by Basic Local Alignment Search Tool (BLAST), and three pine nut-specific peptides were selected finally. The developed method exhibited a good linear relationship in the concentration range of 0.001–50 mg/mL, and the limit of quantification was 1 mg/kg. The average recoveries for blank biscuit, chocolate and beverage were 88.50%–107.57%, with a relative standard deviation (RSD) not exceeding 6.08%, and the matrix effect was 89.77%–96.13%. This method has the advantages of high sensitivity and good specificity, and can be applied to the detection of pine nut allergens in food samples such as biscuits, chocolate, and beverages, which provides technical support for the authentication of food labels and the detection of latent allergens in foods

CXCR2 is essential for cerebral endothelial activation and leukocyte recruitment during neuroinflammation

Chemokines and chemokine receptors cooperate to promote immune cell recruitment to the central nervous system (CNS). In this study, we investigated the roles of CXCR2 and CXCL1 in leukocyte recruitment to the CNS using a murine model of neuroinflammation. Wild-type (WT), CXCL1−/−, and CXCR2−/− mice each received an intracerebroventricular (i.c.v.) injection of lipopolysaccharide (LPS). Esterase staining and intravital microscopy were performed to examine neutrophil recruitment to the brain. To assess endothelial activation in these mice, the expression of adhesion molecules was measured via quantitative real-time polymerase chain reaction (PCR) and Western blotting. To identify the cellular source of functional CXCR2, chimeric mice were generated by transferring bone marrow cells between the WT and CXCR2−/− mice. Expression levels of the chemokines CXCL1, CXCL2, and CXCL5 were significantly increased in the brain following the i.c.v. injection of LPS. CXCR2 or CXCL1 deficiency blocked neutrophil infiltration and leukocyte recruitment in the cerebral microvessels. In the CXCR2−/− and CXCL1−/− mice, the cerebral endothelial expression of adhesion molecules such as P-selectin and VCAM-1 was dramatically reduced. Furthermore, the bone marrow transfer experiments demonstrated that CXCR2 expression on CNS-residing cells is essential for cerebral endothelial activation and leukocyte recruitment. Compared with microglia, cultured astrocytes secreted a much higher level of CXCL1 in vitro. Astrocyte culture conditioned medium significantly increased the expression of VCAM-1 and ICAM-1 in cerebral endothelial cells in a CXCR2-dependent manner. Additionally, CXCR2 messenger RNA (mRNA) expression in cerebral endothelial cells but not in microglia or astrocytes was increased following tumor necrosis factor-α (TNF-α) stimulation. The intravenous injection of the CXCR2 antagonist SB225002 significantly inhibited endothelial activation and leukocyte recruitment to cerebral microvessels. CXCL1 secreted by astrocytes and endothelial CXCR2 play essential roles in cerebral endothelial activation and subsequent leukocyte recruitment during neuroinflammation.https://doi.org/10.1186/s12974-015-0316-

A mutual information theory-based approach for assessing uncertainties in deterministic multi-category precipitation forecasts

The very nature of weather forecasts and verifications and the way they are used make it impossible for one single or absolute standard of evaluation. However, little research has been conducted on verifying deterministic multi-category forecasts, which is based on the attribute of uncertainty. The authors propose a new approach using two mutual information theory-based scores for assessing the comprehensive uncertainty of all categories and the uncertainty for a certain category in deterministic multi-category precipitation forecasts, respectively. Specifically, the comprehensive uncertainty is defined as the average reduction in uncertainty about the observations resulting from the use of a predictive model to provide all categories forecasts; the uncertainty of a certain category is defined as the reduction in uncertainty about the observations resulting from the use of a predictive model to provide a certain category forecast. By applying the proposed approach and traditional verification methods, the four precipitation forecasting products from the China Meteorological Administration (CMA), European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) and United Kingdom Meteorological Office (UKMO) were verified in the Dahuofang Reservoir Drainage Basin, China. The results indicate that: (1) the proposed approach can better capture the changing patterns of uncertainties with lead times and distinguish the forecasting performance among different forecast products; (2) the proposed approach is resistant to the extreme bias; (3) the proposed approach needs a careful choice of bin width; and (4) the bias analysis is necessary before verifying the uncertainties in precipitation forecasts