NHERF1 regulates the progression of colorectal cancer through the interplay with VEGFR2 pathway

The oncogenic role of ectopic expression of Na+/H+ exchanger regulatory factor 1 (NHERF1) was recently suggested in colorectal cancer, where it was implicated in playing a role in the tumor hypoxia microenvironment. Here we showed that a high level expression of NHERF1 was found in colorectal cancer tissues and that the expression of NHERF1 was positively correlated with VEGFR2 expression. The prognostic value of VEGFR2 expression in colorectal cancer relied on the expression of NHERF1. The up-regulation of NHERF1 induced by the exposure to hypoxia in colon cancer cells depended on the activation of VEGFR2 signaling. NHERF1 in turn inhibited the activation of VEGFR2 signaling which could be regulated by the interaction between NHERF1 and VEGFR2, resulting in the reduction of migration and invasion of colon cancer cells. These results suggest a dynamic interplay between NHERF1 and VEGFR2 signaling in colorectal cancer, which could explain the contribution of NHERF1 to the regulation of tumor cell responses to the hypoxia microenvironment

The cellular distribution of Na+/H+ exchanger regulatory factor 1 is determined by the PDZ-I domain and regulates the malignant progression of breast cancer

The oncogenic role of ectopic expression of Na+/H+ exchanger regulatory factor 1 (NHERF1) was recently suggested. Here, we show that NHERF1 was upregulated in high grades compared with low grades. Increased NHERF1 expression was correlated with poor prognosis and poor survival. NHERF1 expression was higher in the nucleus of cancer cells than in contiguous non- mammary epithelial cells. A novel mutation, namely NHERF1 Y24S, was identified in human breast cancer tissues and shown to correspond to a conserved residue in the PDZ-I domain of NHERF1. Truncation and mutation of the PDZ-I domain of NHERF1 increased the nuclear distribution of the NHERF1 protein, and this redistribution was associated with the malignant phenotype of breast cancer cells, including growth, migration, and adhesion. The present results suggest a role for NHERF1 in the progression of breast cancer mediated by the nuclear distribution of the NHERF1 protein, as determined by the truncation or key site mutation of the PDZ-I domain

The Effect of Organic-cr Dietary Supplementation on Stress Response in Transport-stressed Beef Cattle

Transportation over long distances resulted in stress at animal. Under these circumstances, animalusually manifest depression and the impact on physiological condition changes and loss of body weight.The objectives of the research were to examine effect supplementation of organic-Cr type into diets intransport-stress beef cattle on physiological condition, haematochemical (included were haematologicalcondition and blood chemical) and body weight changes. The experiment was conducted using 16 beefcattle those were transported by truck for a distance of 400 km from Malangbong to Tangerang. Theexperiment was arranged by Completely Randomized Design with four treatments and four replications.The dietary treatments consisted of R0 (basal diet without Cr supplemented), R1 (R0+3ppm organic-Crresulted of alkali hydrolysis), R2 (R0+3 ppm organic-Cr resulted from bioprocess), R3 (R0+3 ppmorganic-Cr resulted from bioremediation). The result indicated that type of organic-Cr supplementationat 3 ppm in diet did not influence physiological condition, haematochemical and body weight at beefcattle transported for seven hours. There was indication that beef cattle fed on control diet (without Cr)showed a stress symptom, their loss of body weight were higher (5.41%) compared to beef given dietcontains organic-Cr (3.72%, 5.04% and 4.83%, respectively for R1, R2 and R3)

Probing topcolor-assisted technicolor from top charge asymmetry and triple-top production at the LHC

In a topcolor-assisted technicolor model (TC2) with large FCNC top quark couplings, we study its correlated contributions to the top quark forward-backward asymmetry ($A_{FB}$) at the Tevatron, the top charge asymmetry ($A_{C}$) and the triple-top production at the LHC. Under current constraints on the top quark from the LHC and Tevatron(such as the total and differential production rates), we scan the parameter space of such a TC2 model. We find that in the allowed parameter space the TC2 model can explain the Tevatron measured $A_{FB}$ at $2\sigma$ level, but meanwhile significantly enhance $A_{C}$ at the LHC. Such enhanced $A_{C}$, albeit currently allowed by the LHC measurement at $2\sigma$ level, will serve as a test of TC2 with the improvement of measurement precision at the LHC. Then with all the constraints (including the requirement to explain $A_{FB}$ at $2\sigma$ level and satisfying the current LHC measurement of $A_{C}$ at $2\sigma$ level), we find that the TC2 model can induce sizable triple-top production at the 14 TeV LHC (the production rate can maximally reach 16 pb). Due to the low SM backgrounds, the triple-top production can also be a good probe for TC2 model, complementary to $A_{C}$.Comment: 15 pages, 5 figures, new constraints from LHC addded, published version(Phys. Lett. B

Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition

The development of foundation vision models has pushed the general visual recognition to a high level, but cannot well address the fine-grained recognition in specialized domain such as invasive species classification. Identifying and managing invasive species has strong social and ecological value. Currently, most invasive species datasets are limited in scale and cover a narrow range of species, which restricts the development of deep-learning based invasion biometrics systems. To fill the gap of this area, we introduced Species196, a large-scale semi-supervised dataset of 196-category invasive species. It collects over 19K images with expert-level accurate annotations Species196-L, and 1.2M unlabeled images of invasive species Species196-U. The dataset provides four experimental settings for benchmarking the existing models and algorithms, namely, supervised learning, semi-supervised learning, self-supervised pretraining and zero-shot inference ability of large multi-modal models. To facilitate future research on these four learning paradigms, we conduct an empirical study of the representative methods on the introduced dataset. The dataset is publicly available at https://species-dataset.github.io/.Comment: Accepted by NeurIPS 2023 Track Datasets and Benchmark

Model-free False Data Injection Attack in Networked Control Systems: A Feedback Optimization Approach

Security issues have gathered growing interest within the control systems community, as physical components and communication networks are increasingly vulnerable to cyber attacks. In this context, recent literature has studied increasingly sophisticated \emph{false data injection} attacks, with the aim to design mitigative measures that improve the systems' security. Notably, data-driven attack strategies -- whereby the system dynamics is oblivious to the adversary -- have received increasing attention. However, many of the existing works on the topic rely on the implicit assumption of linear system dynamics, significantly limiting their scope. Contrary to that, in this work we design and analyze \emph{truly} model-free false data injection attack that applies to general linear and nonlinear systems. More specifically, we aim at designing an injected signal that steers the output of the system toward a (maliciously chosen) trajectory. We do so by designing a zeroth-order feedback optimization policy and jointly use probing signals for real-time measurements. We then characterize the quality of the proposed model-free attack through its optimality gap, which is affected by the dimensions of the attack signal, the number of iterations performed, and the convergence rate of the system. Finally, we extend the proposed attack scheme to the systems with internal noise. Extensive simulations show the effectiveness of the proposed attack scheme

OPDAI at SemEval-2024 Task 6: Small LLMs can Accelerate Hallucination Detection with Weakly Supervised Data

This paper mainly describes a unified system for hallucination detection of LLMs, which wins the second prize in the model-agnostic track of the SemEval-2024 Task 6, and also achieves considerable results in the model-aware track. This task aims to detect hallucination with LLMs for three different text-generation tasks without labeled training data. We utilize prompt engineering and few-shot learning to verify the performance of different LLMs on the validation data. Then we select the LLMs with better performance to generate high-quality weakly supervised training data, which not only satisfies the consistency of different LLMs, but also satisfies the consistency of the optimal LLM with different sampling parameters. Furthermore, we finetune different LLMs by using the constructed training data, and finding that a relatively small LLM can achieve a competitive level of performance in hallucination detection, when compared to the large LLMs and the prompt-based approaches using GPT-4

Food4healthKG: Knowledge graphs for food recommendations based on gut microbiota and mental health

Food is increasingly acknowledged as a powerful means to promote and maintain mental health. The introduction of the gut-brain axis has been instrumental in understanding the impact of food on mental health. It is widely reported that food can significantly influence gut microbiota metabolism, thereby playing a pivotal role in maintaining mental health. However, the vast amount of heterogeneous data published in recent research lacks systematic integration and application development. To remedy this, we construct a comprehensive knowledge graph, named Food4healthKG, focusing on food, gut microbiota, and mental diseases. The constructed workflow includes the integration of numerous heterogeneous data, entity linking to a normalized format, and the well-designed representation of the acquired knowledge. To illustrate the availability of Food4healthKG, we design two case studies: the knowledge query and the food recommendation based on Food4healthKG. Furthermore, we propose two evaluation methods to validate the quality of the results obtained from Food4healthKG. The results demonstrate the system's effectiveness in practical applications, particularly in providing convincing food recommendations based on gut microbiota and mental health. Food4healthKG is accessible at https://github.com/ccszbd/Food4healthKG.</p