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Cheese is a product of animal origin with a high nutritional value, and it is one of the most consumed dairy foods in Mexico. In addition, Chihuahua cheese is the most consumed matured cheese in Mexico. In the production process of Chihuahua cheese, maturation is carried out by adding acid lactic microorganisms, mainly of the Lactococcus genus and, in some cases, also the Streptococcus and Lactobacillus genus. As part of the metabolism of fermenting microorganisms, biogenic amines can develop in matured foods, which result from the activity of amino decarboxylase enzymes. In cheeses, histamine and tyramine are the main amines that are formed, and the consumption of these represents a great risk to the health of consumers. In this work, the presence of biogenic amines (histamine and tyramine) was determined by HPLC at different times of the shelf life of Chihuahua cheeses. In addition, the presence of genes hdc and tdc that code for the enzymes responsible for the synthesis of these compounds (histidine and tyrosine decarboxylase, or HDC and TDC) was determined by molecular techniques. A significant correlation was observed between the presence of both histamine and tyramine at the end of shelf life with the presence of genes that code for the enzymes responsible for their synthesis.

Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their decision. Unfortunately, the existing deep learning algorithms have a weak and non-transparent notion of memorization. Unlike existing deep learning models inspired by the brain's computing capability, HyperDimensional Computing (HDC) is introduced as a model of human memory. Therefore, it mimics several important functionalities of the brain memory by operating with a vector that is computationally tractable and mathematically rigorous in describing human cognition. In this manuscript, we introduce a brain-inspired system that represents HDC memorization capability over a graph of relations. We propose GrapHD, hyperdimensional memorization that represents graph-based information into high-dimensional space and enables reasoning. GrapHD defines an encoding method representing complex graph structure into high-dimensional space, supporting weighted and unweighted graphs. Our encoder spreads the information of all nodes and edges across into a full holistic representation so that no component is more responsible for storing any piece of information than another. Then, GrapHD defines several important cognitive functionalities over the encoded memory graph. These operations include memory reconstruction, information retrieval, graph matching, and shortest path. Our extensive evaluation shows that GrapHD: (1) significantly enhances learning capability by giving the notion of short/long term memorization to learning algorithms, (2) enables cognitive computing and reasoning over memorization graph, and (3) enables holographic brain-like computation with substantial robustness to noise and failure.

The tight-tuff heavy oil reservoir exhibits severe heterogeneity and is characterized by high density, high viscosity, and a high wax content, posing significant challenges for its development. While CO2 huff-and-puff (H-n-P) enhances oil recovery, these reservoirs struggle with low displacement efficiency. This study proposes a method that combines CO2 with an oil-soluble viscosity reducer to improve displacement efficiency in the H-n-P process for tight-tuff heavy oil reservoirs. It also focuses on evaluating pore utilization limits and optimizing the injection strategy. Core samples and crude oil from the TH oilfield (a tight-tuff heavy oil reservoir) were used to conduct online NMR core flooding experiments, including depletion development, water, CO2, and HDC (CO2 combined with an oil-soluble viscosity reducer) H-n-P injection processes. A single-porosity model accurately reflecting its geological characteristics was developed using the GEM component simulator within the CMG numerical simulation software to investigate the optimized schemes and the enhanced oil recovery potential for a tight-tuff heavy oil reservoir in the TH oilfield. This model was utilized to evaluate the impact of various injection strategies on oilfield recovery efficiency. The study was designed and implemented with five distinct injection schemes. Results showed that oil was produced primarily from large and medium pores during the depletion stage, while water H-n-P, with CO2 H-n-P, first targeted macropores, then mesopores, and micropores. The lower pore utilization limit was 0.0267 μm. In the HDC H-n-P process, most oil was recovered from water-flooded pores. Still, HDC's lower injection capacity increased the pore utilization limit to 0.03 μm, making micropore recovery difficult. Experimental and modeling results suggest that the optimal development plan for the TH oilfield is one cycle of HDC H-n-P followed by two cycles of CO2 H-n-P. This strategy leverages HDC's ability to promote water and oil recovery in the early stage and mass transfer and extraction capacity of CO2 in later cycles. Additionally, the characteristics of CO2 and HDC H-n-P processes, pore utilization, and recoverable oil (at the pore scale) were evaluated. The results of this study are crucial for refining the reservoir development plan.

The catalytic hydrodechlorination (HDC) technology exhibits great flexibility and safety under mild conditions, and shows extremely promising application prospects for the degradation of 4-Chlorophenol (4-CP). Prepare the N-doped phenolic resin carbon support (PMF) using phenol, melamine and formaldehyde as raw materials, and load Pd nanoparticles (NPs) on it. The XPS results indicate that the Pd/PMF-800 has a higher Pyridine-N (24.8%) and a higher Pd 0 /(Pd 2+ +Pd 0 ) ratio (65.4%). Moreover, the difference in electronegativity between the N atom and the resin carbon support enhances the binding energy between them. This enhancement promotes the nucleation of Pd NPs on the surface of the resin carbon support, thereby imparting higher stability to the Pd NPs. Due to these comprehensive advantages, Pd/PMF-800 has the highest dechlorination activity (k obs = 0.0594 min⁻¹) and stability (dechlorination rate is 91.56% after 5 cycle). Additionally, it also demonstrates efficient dehalogenation rates for 2-Chlorophenol and 4-Bromophenol. It can provide a catalyst that has high-efficiency dehalogenation performance, strong acid and alkali stability and adaptability, and can be recycled for the degradation of halogenated phenols in the environment.

Inflammatory bowel disease (IBD) is increasingly recognized as a serious, worldwide public health concern. It is generally acknowledged that a variety of factors play a role in the pathogenesis of this group of chronic inflammatory diseases. The diversity of molecular actors involved in IBD does not allow us to fully assess the causal relationships existing in such interactions. Given the high immunomodulatory activity of histamine and the complex immune-mediated nature of inflammatory bowel disease, the role of histamine and its receptors in the gut may be significant. This paper has been prepared to provide a schematic of the most important and possible molecular signaling pathways related to histamine and its receptors and to assess their relevance for the development of therapeutic approaches.

A unique hollow core PCF with a decagonal shape, produced by an optical waveform, is created and analytically evaluated. In this paper, on the basis of refractive indices (RI), we detected the most common cancer cells, especially cervical carcinoma, which emerged from HeLa. Additionally, the properties of the guiding of the designed waveguide have been examined. In terms of FEM, a computational tool Comsol Multiphysics 5.6 Software’s are utilized for analyse salient features of the proposed cancer cell sensor. Additionally, to construct the graph of every result, Paint and MATLAB 18a are also used. With regard to cancer cells and normal cells, this detector achieves a relative sensitivity of around 99.845% and 99.50% and CL is 6.24 × 10 − 11 dB/m and 2.66 × 10 − 12 dB/m at 3.2 THz. This biosensor offers an extremely reliable and precise method for fast detection of Cancer Cell. The innovative method enhances the capability to seance and identify certain diseases, resulting for rapid treatment and good outcomes for patient. The PCF sensor is unique due to its hollow core decagonal-core structure, which significantly enhances light-matter interaction in the terahertz range and enables accurate cervical cancer cell detection. This innovative method offers a non-invasive, label-free testing procedure with great depth resolution and selectivity, compared to traditional detection approaches.

Background Automatic segmentation of brain tumours using deep learning algorithms is currently one of the research hotspots in the medical image segmentation field. An improved U-Net network is proposed to segment brain tumours to improve the segmentation effect of brain tumours. Methods To solve the problems of other brain tumour segmentation models such as U-Net, including insufficient ability to segment edge details and reuse feature information, poor extraction of location information and the commonly used binary cross-entropy and Dice loss are often ineffective when used as loss functions for brain tumour segmentation models, we propose a serial encoding–decoding structure, which achieves improved segmentation performance by adding hybrid dilated convolution (HDC) modules and concatenation between each module of two serial networks. In addition, we propose a new loss function to focus the model more on samples that are difficult to segment and classify. We compared the results of our proposed model and the commonly used segmentation models under the IOU, PA, Dice, precision, Hausdorf95, and ASD metrics. Results The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. Conclusions Our algorithm has better semantic segmentation performance than other commonly used segmentation algorithms. The technology we propose can be used in the brain tumour diagnosis to provide better protection for patients' later treatments.

Histamine decarboxylase (HDC) catalyzes decarboxylation of histidine to generate histamine. This enzyme affects several biological processes including inflammation, allergy, asthma, and cancer, although the underlying mechanism is not fully understood. The present study provides a novel insight into the relationship between the transcription factor FLI1 and its downstream target HDC, and their effects on inflammation and leukemia progression. Promoter analysis combined with chromatin immunoprecipitation (ChIp) was used to demonstrate binding of FLI1 to the promoter of in leukemic cells. Western blotting and RT-qPCR were used to determine expression of HDC and allergy response genes, and lentivirus shRNA was used to knock-down target genes. Proliferation, cell cycle, apoptosis assays and molecular docking were used to determine the effect of HDC inhibitors in culture. An animal model of leukemia was employed to test the effect of HDC inhibitory compounds in vivo. Results presented herein demonstrate that FLI1 transcriptionally regulates by direct binding to its promoter. Using genetic and pharmacological inhibition of HDC, or the addition of histamine, the enzymatic product of HDC, we show neither have a discernable effect on leukemic cell proliferation in culture. However, HDC controls several inflammatory genes including IL1B and CXCR2 that may influence leukemia progression in vivo through the tumor microenvironment. Indeed, diacerein, an IL1B inhibitor, strongly blocked Fli-1-induced leukemia in mice. In addition to allergy, FLI1 is shown to regulate genes associated with asthma such as IL1B, CPA3 and CXCR2. Toward treatment of these inflammatory conditions, epigallocatechin (EGC), a tea polyphenolic compound, is found strongly inhibit HDC independently of FLI1 and its downstream effector GATA2. Moreover, the HDC inhibitor, tetrandrine, suppressed HDC transcription by directly binding to and inhibiting the FLI1 DNA binding domain, and like other FLI1 inhibitors, tetrandrine strongly suppressed cell proliferation in culture and leukemia progression in vivo. These results suggest a role for the transcription factor FLI1 in inflammation signaling and leukemia progression through HDC and point to the HDC pathway as potential therapeutics for FLI1-driven leukemia.

Cheng R, Wang J, Wu Q, et al. Int J Gen Med. 2024;17:1937-1948. The authors have advised that the funding statement on page 1946 is incorrect. The correct funding statement is as follows. FundingThis study was funded by Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation under Grant No. 2023GXNSFAA026455, the Guangxi Natural Science Foundation No. 2023GXNSFAA026165, Health Commission of Guangxi Zhuang Autonomous Region Self-funded research projects (Z20210625), Youth Science Foundation of Guangxi Medical University (GXMUYSF202347) and Innovation Project of Guangxi Graduate Education (YCBZ2022085). The authors apologize for this error.