VALUE CREATION THROUGH INTER-ORGANIZATIONAL SYSTEMS (IOS): FROM GOVERNANCE PROCESS VIEW

In this study, we seek to reveal the mechanism of value creation between firms and their partners in B2B context. An integrated conceptual model is constructed grounded in the co-creation theory and the process view, which proposes the interaction between relational assets, IOS support and governance process, as well as their impacts on co-created value. It differs from previous studies by highlighting the mediating effect of governance on value creation. Using a sample of 181 collected from China, our analysis indicates the contribution of governance to co-created value, which is generated through IOS and relational assets. In fact, the IOS support and relational assets alone don’t hold the answer to value co-creation, but they affect the mediating process and enable governance to create value. Especially, the IOS could accommodate the use of relational assets and significantly affect governance process, which is found to be fundamental in value creation. While commodity-like resources have diminishing value in co-creation, governance process with causal ambiguity, social complexity and organizational interconnectedness, becomes the main source of co-created value. Overall, our research sheds light on the key drivers of value co-creation, and provides insights to their impacts on value creation

N-acetylcysteine Protects against Apoptosis through Modulation of Group I Metabotropic Glutamate Receptor Activity

The activation of group I metabotropic glutamate receptor (group I mGlus) has been shown to produce neuroprotective or neurotoxic effects. In this study, we investigated the effects of N-acetylcysteine (NAC), a precursor of the antioxidant glutathione, on group I mGlus activation in apoptosis of glial C6 and MN9D cell lines, and a rat model of Parkinson's disease (PD). We demonstrated that NAC protected against apoptosis through modulation of group I mGlus activity. In glial C6 cells, NAC promoted phosphorylation of ERK induced by (s)-3,5- dihydroxy-phenylglycine (DHPG), an agonist of group I mGlus. NAC enhanced the group I mGlus-mediated protection from staurosporine (STS)-induced apoptosis following DHPG treatment. Moreover, in rotenone-treated MN9D cells and PD rat model, NAC protected against group I mGlus-induced toxicity by compromising the decrease in phosphorylation of ERK, phosphorylation or expression level of TH. Furthermore, the results showed that NAC prohibited the level of ROS and oxidation of cellular GSH/GSSG (Eh) accompanied by activated group I mGlus in the experimental models. Our results suggest that NAC might act as a regulator of group I mGlus-mediated activities in both neuroprotection and neurotoxicity via reducing the oxidative stress, eventually to protect cell survival. The study also suggests that NAC might be a potential therapeutics targeting for group I mGlus activation in the treatment of PD

Large Language Models for Information Retrieval: A Survey

As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, thereby reshaping the IR landscape, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs), typified by ChatGPT and GPT-4, has revolutionized natural language processing due to their remarkable language understanding, generation, generalization, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers. Additionally, we explore promising directions within this expanding field

Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability.Comment: 18 pages, 12 figures, 8 table

Broiler Genetics Influences Proteome Profiles of Normal and Woody Breast Muscle

Wooden or woody breast (WB) is a myopathy of the pectoralis major in fast-growing broilers that influences the quality of breast meat and causes an economic loss in the poultry industry. The objective of this study was to evaluate growth and proteome differences between 5 genetic strains of broilers that yield WB and normal breast (NB) meat. Eight-week-old broilers were evaluated for the WB myopathy and divided into NB and WB groups. Differential expression of proteins was analyzed using 2-dimensional gel electrophoresis and LC-MS/MS to elucidate the mechanism behind the breast myopathy because of the genetic backgrounds of the birds. The percentages of birds with WB were 61.3, 68.8, 46.9, 45.2, and 87.5% for strains 1-5, respectively, indicating variability in WB myopathy among broiler strains. Birds from strains 1, 3, and 5 in the WB group were heavier than those in the NB group (P \u3c 0.05). Woody breast meat from all strains were heavier than NB meat (P \u3c 0.05). Within WB, strain 5 had a greater breast yield than strains 1, 3, and 4 (P \u3c 0.0001). Woody breast from strains 2, 3, 4, and 5 had a greater breast yield than NB (P \u3c 0.05). Six proteins were more abundant in NB of strain 5 than those of strains 2, 3, and 4, and these proteins were related to muscle growth, regeneration, contraction, apoptosis, and oxidative stress. Within WB, 14 proteins were differentially expressed between strain 5 and other strains, suggesting high protein synthesis, weak structural integrity, intense contraction, and oxidative stress in strain 5 birds. The differences between WB from strain 3 and strains 1, 2, and 4 were mainly glycolytic. In conclusion, protein profiles of broiler breast differed because of both broiler genetics and the presence of WB myopathy

Improving Performance of All-Polymer Solar Cells Through Backbone Engineering of Both Donors and Acceptors

All-polymer solar cells (APSCs), composed of semiconducting donor and acceptor polymers, have attracted considerable attention due to their unique advantages compared to polymer-fullerene-based devices in terms of enhanced light absorption and morphological stability. To improve the performance of APSCs, the morphology of the active layer must be optimized. By employing a random copolymerization strategy to control the regularity of the backbone of the donor polymers (PTAZ-TPDx) and acceptor polymers (PNDI-Tx) the morphology can be systematically optimized by tuning the polymer packing and crystallinity. To minimize effects of molecular weight, both donor and acceptor polymers have number-average molecular weights in narrow ranges. Experimental and coarse-grained modeling results disclose that systematic backbone engineering greatly affects the polymer crystallinity and ultimately the phase separation and morphology of the all-polymer blends. Decreasing the backbone regularity of either the donor or the acceptor polymer reduces the local crystallinity of the individual phase in blend films, affording reduced short-circuit current densities and fill factors. This two-dimensional crystallinity optimization strategy locates a PCE maximum at highest crystallinity for both donor and acceptor polymers. Overall, this study demonstrates that proper control of both donor and acceptor polymer crystallinity simultaneously is essential to optimize APSC performance

Effect of Tanshinone IIA on gut microbiome in diabetes-induced cognitive impairment

Diabetes-induced cognitive impairment (DCI) presents a major public health risk among the aging population. Previous clinical attempts on known therapeutic targets for DCI, such as depleted insulin secretion, insulin resistance, and hyperglycaemia have delivered poor patient outcomes. However, recent evidence has demonstrated that the gut microbiome plays an important role in DCI by modulating cognitive function through the gut–brain crosstalk. The bioactive compound tanshinone IIA (TAN) has shown to improve cognitive and memory function in diabetes mellitus models, though the pharmacological actions are not fully understood. This study aims to investigate the effect and underlying mechanism of TAN in attenuating DCI in relation to regulating the gut microbiome. Metagenomic sequencing analyses were performed on a group of control rats, rats with diabetes induced by a high-fat/high-glucose diet (HFD) and streptozotocin (STZ) (model group) and TAN-treated diabetic rats (TAN group). Cognitive and memory function were assessed by the Morris water maze test, histopathological assessment of brain tissues, and immunoblotting of neurological biomarkers. The fasting blood glucose (FBG) level was monitored throughout the experiments. The levels of serum lipopolysaccharide (LPS) and tumor necrosis factor-α (TNF-α) were measured by enzyme-linked immunoassays to reflect the circulatory inflammation level. The morphology of the colon barrier was observed by histopathological staining. Our study confirmed that TAN reduced the FBG level and improved the cognitive and memory function against HFD- and STZ-induced diabetes. TAN protected the endothelial tight junction in the hippocampus and colon, regulated neuronal biomarkers, and lowered the serum levels of LPS and TNF-α. TAN corrected the reduced abundance of Bacteroidetes in diabetic rats. At the species level, TAN regulated the abundance of B. dorei, Lachnoclostridium sp. YL32 and Clostridiodes difficile. TAN modulated the lipid metabolism and biosynthesis of fatty acids in related pathways as the main functional components. TAN significantly restored the reduced levels of isobutyric acid and butyric acid. Our results supported the use of TAN as a promising therapeutic agent for DCI, in which the underlying mechanism may be associated with gut microbiome regulation

Associations of the Triglyceride and Glucose Index With Hypertension Stages, Phenotypes, and Their Progressions Among Middle-Aged and Older Chinese

Objectives: To assess the associations of the triglyceride and glucose (TyG) index with hypertension stages, phenotypes, and their progressions.Methods: The data originated from the China Health and Retirement Longitudinal Study. Multinomial logistic regression investigated the associations of the TyG index with hypertension stages (stage 1, stage 2), phenotypes (isolated systolic hypertension [ISH], isolated diastolic hypertension [IDH], systolic diastolic hypertension [SDH]), their progressions.Results: Compared with the lowest quartile of TyG index, the highest quartile was associated with increased risks of stage 1 hypertension (OR 1.71, 95% CI 1.38–2.13), stage 2 (1.74, 1.27–2.38), ISH (1.66, 1.31–2.11), IDH (2.52, 1.26–5.05), and SDH (1.65, 1.23–2.23). Similar results were found when TyG index was a continuous variable. From 2011 to 2015, a higher baseline TyG index was associated with normotension to stage 1 (per-unit: 1.39, 1.16–1.65), normotension to ISH (per-unit: 1.28, 1.04–1.56), and normotension to IDH (per-unit: 1.94, 1.27–2.97).Conclusion: The TyG index was associated with different hypertension stages, phenotypes, their progressions, and could be served as a surrogate indicator for early hypertension management

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis

Multiomics and bioinformatics identify differentially expressed effectors in the brain of Toxoplasma gondii infected masked palm civet

IntroductionThe masked palm civet (Paguma larvata) serves as a reservoir in transmitting pathogens, such as Toxoplasma gondii, to humans. However, the pathogenesis of T. gondii infection in masked palm civets has not been explored. We studied the molecular changes in the brain tissue of masked palm civets chronically infected with T. gondii ME49.MethodsThe differentially expressed proteins in the brain tissue were investigated using iTRAQ and bioinformatics.ResultsA total of 268 differential proteins were identified, of which 111 were upregulated and 157 were downregulated. KEGG analysis identified pathways including PI3K-Akt signaling pathway, proteoglycans in cancer, carbon metabolism, T-cell receptor signaling pathway. Combing transcriptomic and proteomics data, we identified 24 genes that were differentially expressed on both mRNA and protein levels. The top four upregulated proteins were REEP3, REEP4, TEP1, and EEPD1, which was confirmed by western blot and immunohistochemistry. KEGG analysis of these 24 genes identified signaling cascades that were associated with small cell lung cancer, breast cancer, Toll-like receptor signaling pathway, Wnt signaling pathways among others. To understand the mechanism of the observed alteration, we conducted immune infiltration analysis using TIMER databases which identified immune cells that are associated with the upregulation of these proteins. Protein network analysis identified 44 proteins that were in close relation to all four proteins. These proteins were significantly enriched in immunoregulation and cancer pathways including PI3K-Akt signaling pathway, Notch signaling pathway, chemokine signaling pathway, cell cycle, breast cancer, and prostate cancer. Bioinformatics utilizing two cancer databases (TCGA and GEPIA) revealed that the four genes were upregulated in many cancer types including glioblastoma (GBM). In addition, higher expression of REEP3 and EEPD1 was associated with better prognosis, while higher expression of REEP4 and TEP1 was associated with poor prognosis in GBM patients.DiscussionWe identified the differentially expressed genes in the brain of T. gondii infected masked palm civets. These genes were associated with various cellular signaling pathways including those that are immune- and cancer-related