Improvements In Processing Research Articles

Vibrio parahaemolyticus Foodborne Illness Associated with Oysters, Australia, 2021-2022.

The bacterium Vibrio parahaemolyticus is ubiquitous in tropical and temperate waters throughout the world and causes infections in humans resulting from water exposure and from ingestion of contaminated raw or undercooked seafood, such as oysters. We describe a nationwide outbreak of enteric infections caused by Vibrio parahaemolyticus in Australia during September 2021-January 2022. A total of 268 persons were linked with the outbreak, 97% of whom reported consuming Australia-grown oysters. Cases were reported from all states and territories of Australia. The outbreak comprised 2 distinct strains of V. parahaemolyticus, sequence types 417 and 50. We traced oysters with V. parahaemolyticus proliferation back to a common growing region within the state of South Australia. The outbreak prompted a national recall of oysters and subsequent improvements in postharvest processing of the shellfish.

Enhancing catalytic oxidation of benzene with Cu-Doped MnO2: Insights into structural defects and electronic modifications

The present research focuses on the role of copper (Cu) doping in enhancing the catalytic efficacy of manganese dioxide (α-MnO2) for benzene oxidation, a representative volatile organic compound (VOC). Despite the abundance and low cost of MnO2, its pristine form requires improvement for industrial VOC oxidation processes. Utilizing a range of characterization methods, the study reveals that Cu doping generates oxygen vacancies and increases the Mn3+ content within the catalyst structure, which are crucial for enhancing the adsorption and activation of reactants. The findings indicate that Cu-doped α-MnO2 exhibits superior catalytic activity and lower activation energy for benzene oxidation compared to the undoped counterpart. The electronic interactions induced by the doping of Cu and the effect on the catalytic process are demonstrated and discussed in depth. This work emphasizes the strategic importance of dopant optimization and contributes to the advancement of MnO2-based catalysts for environmental remediation, aligning with goals of environmental protection and human health safety.

Fusion innovation: Multi-scale dilated collaborative model of ConvNeXt and MSDA for fault diagnosis

Bearings and gears are critical components in modern industry, and cross-domain diagnosis of these elements is of great significance. However, in practical applications, challenges such as insufficient training data and variability between equipment arise. To address this issue, this study proposes an innovative neural network structure, ConvNeXt, and a Multi-Scale Dilated Attention (MSDA) mechanism to improve the accuracy problem caused by inadequate feature extraction. ConvNeXt improves upon traditional convolutional neural networks by introducing a multi-scale attention mechanism to enhance the model's performance and expressiveness. Through parallel multi-channel convolution operations, ConvNeXt can capture dependencies between different channels and reduce the number of parameters. Meanwhile, the MSDA mechanism allows signals to interact and exchange information at different scales, effectively extracting complex features in one-dimensional signals. Experimental results demonstrate a significant performance improvement in one-dimensional signal processing using ConvNeXt and MSDA, better capturing relationships between global and local features in one-dimensional signals and enhancing model accuracy. The joint application of ConvNeXt and MSDA brings new solutions to one-dimensional signal processing, offering potential opportunities for effective monitoring of critical components in rotating machinery. Experimental results show that this method achieves high diagnostic accuracy in various transfer tasks, with an average accuracy of 94.28%, providing reliable support for bearing fault diagnosis.

S487 Modification of the Fecal Immunochemical Test (FIT) Return Envelope Process Improves Lab Acceptance Rate

S487 Modification of the Fecal Immunochemical Test (FIT) Return Envelope Process Improves Lab Acceptance Rate

NRN1 genetic variability and methylation changes as biomarkers for cognitive remediation therapy response in schizophrenia

Cognitive remediation therapy (CRT) demonstrates potential in enhancing cognitive function in schizophrenia (SZ), though the identification of molecular biomarkers remains challenging. The Neuritin-1 gene (NRN1) emerges as a promising candidate gene due to its association with SZ, cognitive performance and response to neurotherapeutic treatments. We aimed to investigate whether NRN1 genetic variability and methylation changes following CRT are related to cognitive improvements.Twenty-five SZ patients were randomly assigned to CRT or treatment-as-usual (TAU) groups, with cognitive function and NRN1 methylation assessed pre- and post-intervention using the MATRICS Consensus Cognitive Battery and EpiTYPER. Besides, eleven NRN1 polymorphisms were genotyped. Methylation changes (Δm = post - pre) were analyzed via sparse Partial Least Square Discriminant Analysis (sPLS-DA) to identify latent components (LCs) distinguishing CRT from TAU. To further explore methylation patterns of these LCs, CpG units were grouped into two subsets, yielding Δm means for those with increased and decreased methylation. Cognitive changes (Δcog = post - pre) were used to identify CRT improvers (CRTI, Δcog ≥1), and the association between methylation changes and cognitive improvements post-therapy was also tested.We identified two LCs that differentiated CRT from TAU with a classification error rate of 0.28. The main component, LC1, included 25 CpG units. The subsets of CpG units with increased and decreased post-therapy methylation differed significantly between the two treatment arms, suggesting that differences were not merely data-driven but reflected meaningful biological variation. Additionally, CpG units linked to therapy were also associated with cognitive improvement, with LC1 and the subset of CpG units showing increased methylation post-therapy distinguishing CRT-I from the rest of the patients across multiple cognitive domains. Furthermore, the effect of LC1 on speed processing improvement after CRT was enhanced by considering the NRN1-rs9405890 polymorphism. Notably, these CpG units, particularly those with increased methylation after CRT, overlapped with key gene regulatory elements.Our model, integrating genetics and epigenetics, boosts the understanding of CRT response variability and highlights this multi-level approach as a promising strategy for identifying potential NRN1-related biomarkers of CRT effects, though further studies with larger samples are needed.

Daya Saing Kacang Mete Indonesia Di Pasar Negara Tujuan Ekspor

Cashew nuts (Anacardium occidentale) are a leading plantation commodity with high economic value, essential for the development of the agricultural industry. Despite their significant potential as an export commodity, the export value of Indonesian cashew nuts has tended to decline during the period from 2013 to 2022. The aim of this study is to measure the competitiveness and development position of Indonesian cashew nuts from 2013 to 2022 in export destination countries. The data used in this research are secondary data in the form of time series data. The time series data includes annual data over 10 years (2013-2022) with export destination countries being Vietnam, India, Malaysia, Singapore, Sri Lanka, Japan, and China. The methods used in this study are the Revealed Comparative Advantage (RCA), Export Product Dynamics (EPD), and X-Model Potential Export Product methods. The results of the study show the Revealed Comparative Advantage (RCA) and Export Product Dynamics (EPD) values of Indonesian cashew nuts for the period 2013-2022 in the Vietnamese market with an RCA of 4.47 and a Lost Opportunity market position, India at 0.88 with a Retreat status, Singapore at 0.019 with a Falling Star status, Malaysia at 0.43 with a Lost Opportunity status, Sri Lanka at 21.55 with a Falling Star status, Japan at 7.36 with a Falling Star status, and China at 0.21 with a Rising Star market position. Meanwhile, the X-Model analysis shows that the market potential in Vietnam, Singapore, Sri Lanka, Japan, and Malaysia is classified as potential markets, the market potential in China is classified as an optimistic market, and the market potential in India is classified as a less potential market. As an export commodity, cashew nuts require proper handling and special attention. To ensure the quality, selling value, and strong competitiveness of cashew nuts, the government must continuously strive to enhance the selling value of cashew nuts through plantation development, garden and plant maintenance, and processing improvements.

Optimizing Lactic Acid Bacteria Fermentation for Enhanced Summer and Autumn Tea Quality

The level of consumption of summer tea is a problem in the development of China’s tea industry. Current strategies to enhance the quality of summer and autumn teas primarily target the cultivation environment, with less emphasis on processing improvements. This study aimed to optimize the fermentation parameters to impact the quality of summer and autumn teas. We screened four strains of lactic acid bacteria (LAB) suitable for tea fermentation and determined their optimal mix. This optimized blend was applied to ferment summer and autumn teas. Through single-factor experiments, we evaluated the impact of various processing parameters, including the fixation method, rolling degree, inoculation amount, glucose concentration, fermentation temperature, and fermentation duration, on LAB growth and tea quality. The optimal processing conditions were established as microwave fixation, heavy rolling, an inoculation rate of 1.8% LAB, glucose addition at 8.8%, and fermentation at 36.5 °C for five days. Analysis revealed that the fermentation process significantly reduced the levels of polyphenols and ester-type catechins, which are associated with astringency and bitterness while enhancing the content of gamma-aminobutyric acid (GABA). Specifically, after five days, polyphenol content decreased by 26.89%, and GABA levels increased from 0.051 mg/g to 0.126 mg/g. The predominant aroma compounds in the fermented tea were alcohols with floral and fruity scents, constituting 54.63% of the total aroma profile. This research presents a methodical approach to reduce the astringency and bitterness of summer and autumn teas while concurrently increasing GABA levels.

SEISMICgraph: a web-based tool for RNA structure data visualization.

In recent years, RNA has been increasingly recognized for its essential roles in biology, functioning not only as a carrier of genetic information but also as a dynamic regulator of gene expression through its interactions with other RNAs, proteins, and itself. Advances in chemical probing techniques have significantly enhanced our ability to identify RNA secondary structures and understand their regulatory roles. These developments, alongside improvements in experimental design and data processing, have greatly increased the resolution and throughput of structural analyses. Here, we introduce SEISMICgraph, a web-based tool designed to support RNA structure research by offering data visualization and analysis capabilities for a variety of chemical probing modalities. SEISMICgraph enables simultaneous comparison of data across different sequences and experimental conditions through a user-friendly interface that requires no programming expertise. We demonstrate its utility by investigating known and putative riboswitches and exploring how RNA modifications influence their structure and binding. SEISMICgraph's ability to rapidly visualize adenine-dependent structural changes and assess the impact of pseudouridylation on these transitions provides novel insights and establishes a roadmap for numerous future applications.

Advancing semantic segmentation of two-dimensional materials using a semantic-adaptive transformer model

Accurate detection and characterization of two-dimensional (2D) materials are essential for their effective utilization in various applications. Traditional techniques, such as chemical vapor deposition, often produce materials with high defect density, while mechanical exfoliation is hindered by its labor-intensive and time-consuming nature. In this Letter, we propose a semantic-adaptive transformer model, termed Semptive, designed specifically for the precise detection of monolayer MoS2. Our approach integrates a semantic adaptation module with a multi-head self-attention mechanism, incorporating deep supervision and leveraging prior knowledge to enhance model performance. The model was trained on a dataset obtained through mechanical exfoliation and validated using photoluminescence spectroscopy. The experimental results reveal that Semptive significantly enhances segmentation performance compared to conventional models, achieving higher Intersection over Union and Dice scores while reducing computational demands. This method represents a notable advancement in the efficient and precise identification of 2D materials, providing substantial improvements for material characterization and device fabrication processes.

How structural biology has changed our understanding of icosahedral viruses.

Cryo-electron microscopy and tomography have allowed us to unveil the remarkable structure of icosahedral viruses. However, in the past few years, the idea that these viruses must have perfectly symmetric virions, but in some cases, it might not be true. This has opened the door to challenging paradigms in structural virology and raised new questions about the biological implications of "unusual" or "defective" symmetries and structures. Also, the continual improvement of these technologies, coupled with more rigorous sample purification protocols, improvements in data processing, and the use of artificial intelligence, has allowed solving the structure of sub-viral particles in highly heterogeneous samples and finding novel symmetries or structural defects. In this review, I initially analyzed the case of the symmetry and composition of hepatitis B virus-produced spherical sub-viral particles. Then, I focused on Alphaviruses as an example of "imperfect" icosahedrons and analyzed how structural biology has changed our understanding of the Alphavirus assembly and some biological implications arising from these discoveries.

Improvement in Math Ability and Cognitive Processing in Children with Low Attention: An Intervention Based on PASS Theory.

This study investigates the effects of math training on math and cognitive performance among 8-9 year-old students with low attention. Fifty-six students with low attention were randomly assigned to a training group (n = 24) and a passive control group (n = 32). They completed math problem-solving, calculation fluency and PASS cognitive processing tests both before and after training. The children in the training group received 3 days of training per week for a total of 21 days using the math modules of The Children's Mathematics and Cognition Training Manual in Chinese. The results showed that the training group's math problem-solving performance improved significantly. Moreover, the cognitive performance on the CAS-2 in the planning and simultaneous processing tests for the training group was enhanced. The implications of these findings are discussed with consideration of the interpretability being constrained by the fact that no active control condition was applied.

Investigation of surface finish and fatigue life of laser Powder Bed fused Ti-6Al-4V

In this fact-pacing world of technological advancement, the rise of additive manufacturing (AM) has found immense applications of metallic alloys such as Ti6Al4V to manufacture key components in automotive, aerospace and healthcare sectors. However, fatigue characteristics of such additively manufactured metal alloys is noticeably weak, and as such requires post-processing. In this study, AM Ti6Al4V manufactured by Laser Powder Bed Fusion (L-PBF) was treated by Hydrodynamic Cavitation Abrasive Finishing (HCAF) and Laser Cavitation Peening (LCP) to observe the improvement in surface roughness as well as fatigue performance. Our investigations revealed a substantial improvement in surface quality post-HCAF processing. Notably, the surface roughness of AM Ti6Al4V specimens showed a significant reduction of about 91.2 %, post surface treatment. However, Laser Cavitation Peening (LCP) treated sample demonstrates a significantly enhanced fatigue strength of 393 ± 22 MPa at 107, which was 1.75 times better than that of as-built one, i.e., 224 ± 16 MPa. This difference can be attributed to the distinct surface modification mechanisms of each technique. LCP, utilizing laser-induced shock waves, likely induces large compressive residual stresses, leading to better fatigue resistance.

DESI Early Data Release Milky Way Survey value-added catalogue

ABSTRACT We present the stellar value-added catalogue based on the Dark Energy Spectroscopic Instrument (DESI) Early Data Release. The catalogue contains radial velocity and stellar parameter measurements for $\simeq$ 400 000 unique stars observed during commissioning and survey validation by DESI. These observations were made under conditions similar to the Milky Way Survey (MWS) currently carried out by DESI but also include multiple specially targeted fields, such as those containing well-studied dwarf galaxies and stellar streams. The majority of observed stars have $16\lt r\lt 20$ with a median signal-to-noise ratio in the spectra of $\sim$ 20. In the paper, we describe the structure of the catalogue, give an overview of different target classes observed, as well as provide recipes for selecting clean stellar samples. We validate the catalogue using external high-resolution measurements and show that radial velocities, surface gravities, and iron abundances determined by DESI are accurate to 1 km s−1, 0.3 dex, and $\sim$ 0.15 dex respectively. We also demonstrate possible uses of the catalogue for chemo-dynamical studies of the Milky Way stellar halo and Draco dwarf spheroidal. The value-added catalogue described in this paper is the very first DESI MWS catalogue. The next DESI data release, expected in less than a year, will add the data from the first year of DESI survey operations and will contain approximately 4 million stars, along with significant processing improvements.

A Super-Resolution and 3D Reconstruction Method Based on OmDF Endoscopic Images.

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Emerging Applications of Picture Archiving and Communication Systems and Their Impact on Research and Education: A Literature Review.

In recent times, technological advancements have remarkably improved picture archiving and communication system(PACS) capabilities beyond their conventional use in radiology departments. Researchers and instructors have started employing PACS functionalities to improve medical research processes, promote interdisciplinary collaborations, and facilitate learning. To illustrate this point further, the PACS enables researchers to handle and analyze huge amounts of imaging data with superior precision and speed, supporting innovative studies in areas like disease progression, treatment outcomes, and imaging modalities. Moreover, a PACSintegrated with artificial intelligence (AI) algorithms leads to significant improvements in image processing, diagnostic accuracy, and personalized treatment, thus marking a new approach to medical imaging. The PACSsupported by AI is mostly transformative since they allow for improved early disease detection capabilities as well as automated image processing and decision assistance, which increase diagnostic accuracy and clinical outcomes. Such systems can rapidly process large quantities of visual data with an accuracy rate surpassing earlier endeavors. In medical research, however, combining PACSwith AI allows challenging imaging datasets to be examined, thereby making findings that were not previously possible. The capacity to combine imaging outcomes with clinical information is valuable for medical students and professionals in the field of education. They can access extensive medical image collections and case studies using PACS. This link is critical for teaching and learning as it allows students to interact with concrete events and improve their diagnostic accuracy in a controlled environment. This review discusses how the PACS affects educational courses and clinical outcomes based on the available literature. Our aim was not only to outline recent research or developments but also to present a comprehensive overview regarding the growing role played by PACS in the modern healthcare system and academics. Similarly, we look at the challenges and opportunities associated with the wide adoption of PACS, highlighting possible future areas of study or teaching methodologies. Issues such as data security, interoperability, and the need for defined protocols are included to give an exhaustive understanding of what PACS can and cannot do. Through this study, we stress PACS's revolutionary potential in advancing research methodology and educational practices, eventually contributing to enhanced patient care and knowledge dissemination in healthcare areas. The continual growth of PACS technology and its applications is expected to reshape the landscape of medical research and education, making it a vital component in the quest for medical excellence. By knowing the present trends and future potential, stakeholders in healthcare and education may better employ PACS to reach their objectives and boost overall results.

GPU-accelerated OCT imaging: Real-time data processing and artifact suppression for enhanced monitoring of 3D bioprinted tissues and vascular-like networks

Optical coherence tomography (OCT) imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues. However, it still faces the following challenges: including data processing speed, image quality, and improvements in three-dimensional (3D) visualization effects. OCT technology, especially functional imaging techniques like optical coherence tomography angiography (OCTA), requires a long acquisition time and a large data size. Despite the substantial increase in the acquisition speed of swept source optical coherence tomography (SS-OCT), it still poses significant challenges for data processing. Additionally, during in situ acquisition, image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis. Firstly, a customized frequency domain filter with anti-banding suppression parameters was designed to suppress artifact noises. Then, this study proposed a graphics processing unit (GPU)-based real-time data processing pipeline for SS-OCT, achieving a measured line-process rate of 800[Formula: see text]kHz for 3D fast and high-quality data visualization. Furthermore, a GPU-based real-time data processing for CC-OCTA was integrated to acquire dynamic information. Moreover, a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials, with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network. OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation. Therefore, GPU-based OCT enables real-time processing and visualization with artifact suppression, making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

Evaluating the Benefits of Occupational Therapy in Children With Autism Spectrum Disorder Using the Autism Behavior Checklist.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder typically manifesting before the age of three years, characterized by significant impairments in social interaction and communication, as well as restricted and repetitive patterns of interests and activities. In our study, we assessed the benefits of sensory integration therapy in children with ASDusing the Autism Behavior Checklist (ABC), focusing on improvements in sensory processing, relationship-building, language skills, and social and self-care abilities. The study was conducted with 40 children aged three to nine years, diagnosed with ASD, and whose parents provided consent for their participation in therapy between December 2022 and March 2023 at the Private Adana Metro Hospital Child Psychiatry Clinic. The ages and genders of the patients were recorded. Before occupational therapy, after five sessions of occupational therapy, and after 10 sessions of occupational therapy, the ABC test was administered under the supervision of a child psychiatrist and an occupational therapist. The results were recorded, and statistical analyses were performed. In the ABC test conducted after 10 sessions, a decreasing trend was observed in sensory, relationship-building, body and object usage, language skills, social and self-care, and total scale scores compared to pre-occupational therapy test scores (p < 0.000). Occupational therapy improves sensory skills, relationship-building skills, body and object usage abilities, language skills, and social and self-care skills.

Low Resource Summarization using Pre-trained Language Models

With the advent of Deep Learning based Artificial Neural Networks models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. However, the research is mostly restricted to high-resource languages such as English and low-resource languages still suffer from a lack of available resources in terms of training datasets as well as models with even baseline evaluation results. Considering the limited availability of resources for low-resource languages, we propose a methodology for adapting self-attentive transformer-based architecture models (mBERT, mT5) for low-resource summarization, supplemented by the construction of a new baseline dataset (76.5k article, summary pairs) in a low-resource language Urdu. Choosing news (a publicly available source) as the application domain has the potential to make the proposed methodology useful for reproducing in other languages with limited resources. Our adapted summarization model urT5 with up to 44.78% reduction in size as compared to mT5 can capture contextual information of low resource language effectively with evaluation score (up to 46.35 ROUGE-1, 77 BERTScore) at par with state-of-the-art models in high resource language English (PEGASUS: 47.21, BART: 45.14 on XSUM Dataset) . The proposed method provided a baseline approach towards extractive as well as abstractive summarization with competitive evaluation results in a limited resource setup.

Investigation of the role of jiaotou in the processing of Jinhua Dajiao steamed bread using metagenomic and metabolomic analyses

ABSTRACT Zhejiang, China, has a tradition of wine-fermented foods, including Jinhua Dajiao steamed bread (JDSB). The traditional leavening agent, jiaotou, confers its properties and taste to JDSB. We examined microorganisms in jiaotou, their effects on processing, and the metabolome using metagenomic and metabolomic analyses. Metagenomics of microorganisms in jiaotou revealed a high average diversity, with Rhizopus sp. being the dominant bacteria. The enriched genes were most abundant in genes related to carbohydrate and amino acid metabolism, with a greater number of non-redundant genes for glycoside hydrolases and glycosyl transferases. The enriched genes may have a direct impact on the organoleptic quality of JDSB. Functional enrichment analysis of different metabolites revealed that pathways associated with linoleic acid, ABC transporter, alanine, aspartate, glutamate, and galactose metabolism were enriched during JDSB processing. We identified a strong correlation between metagenomic functional level and metabolomics pathway enrichment analyses.The results contribute to the improvement of JDSB processing.