Helicobacter suis affects the health and function of porcine gastric parietal cells

The stomach of pigs at slaughter age is often colonized by Helicobacter (H.) suis, which is also the most prevalent gastric non-H. pylori Helicobacter (NHPH) species in humans. It is associated with chronic gastritis, gastric ulceration and other gastric pathological changes in both hosts. Parietal cells are highly specialized, terminally differentiated epithelial cells responsible for gastric acid secretion and regulation. Dysfunction of these cells is closely associated with gastric pathology and disease. Here we describe a method for isolation and culture of viable and responsive parietal cells from slaughterhouse pigs. In addition, we investigated the interactions between H. suis and gastric parietal cells both in H. suis-infected six-month-old slaughter pigs, as well as in our in vitro parietal cell model. A close interaction of H. suis and parietal cells was observed in the fundic region of stomachs from H. suis positive pigs. The bacterium was shown to be able to directly interfere with cultured porcine parietal cells, causing a significant impairment of cell viability. Transcriptional levels of Atp4a, essential for gastric acid secretion, showed a trend towards an up-regulation in H. suis positive pigs compared to H. suis-negative pigs. In addition, sonic hedgehog, an important factor involved in gastric epithelial differentiation, gastric mucosal repair, and stomach homeostasis, was also significantly up-regulated in H. suis positive pigs. In conclusion, this study describes a successful approach for the isolation and culture of porcine gastric parietal cells. The results indicate that H. suis affects the viability and function of this cell type

Promoting Information Systems Major to Undergraduate Students - A Comprehensive Investigation

Weak enrollment growth has been a concern for many Information Systems (IS) programs in recent years although the IT/IS job market remains strong. Stimulating undergraduate students’ interest to IS programs have been a challenge. In this paper, the researchers took a comprehensive approach to study how to effectively promote a Management Information Systems (MIS) program to undergraduate students at a medium-size public university in the southeastern US. Using a survey-based method, the researchers first investigated the factors that impact students’ selection of majors and identified students’ perceptions on an MIS program. In this paper, an MIS program promotion strategy was then developed and empirically validated. The research results showed that the promotion strategy can successfully stimulate participants’ positive perceptions on the MIS program. The approach presented in this study could serve as an exemplar to other IS programs or other major fields to tackle enrollment challenges

Developing Business Intelligence Competency In Health It: Perspectives From Health It Professionals

Business intelligence (BI) is a set of methods and technologies that can provide analytical power to help the healthcare industry to tackle the challenges brought by ever-growing and complex health data. To develop a successful Health Information Technology (HIT) or Health Informatics (HI) curriculum with the component of BI or health data analytics, it is critical to first identify the sets of important skills that a HIT student should possess upon graduation, especially from HIT professionals’ perspective. In this paper, we reported findings from a pilot study in which we surveyed a group of HIT practitioners. The implications of the pilot study are discussed

Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer Generator

The incorporation of biasing words obtained through contextual knowledge is of paramount importance in automatic speech recognition (ASR) applications. This paper proposes an innovative method for achieving end-to-end contextual ASR using graph neural network (GNN) encodings based on the tree-constrained pointer generator method. GNN node encodings facilitate lookahead for future word pieces in the process of ASR decoding at each tree node by incorporating information about all word pieces on the tree branches rooted from it. This results in a more precise prediction of the generation probability of the biasing words. The study explores three GNN encoding techniques, namely tree recursive neural networks, graph convolutional network (GCN), and GraphSAGE, along with different combinations of the complementary GCN and GraphSAGE structures. The performance of the systems was evaluated using the Librispeech and AMI corpus, following the visual-grounded contextual ASR pipeline. The findings indicate that using GNN encodings achieved consistent and significant reductions in word error rate (WER), particularly for words that are rare or have not been seen during the training process. Notably, the most effective combination of GNN encodings obtained more than 60% WER reduction for rare and unseen words compared to standard end-to-end systems.Comment: Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processin

Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment

In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs

Bacillus megaterium BMJBN02 induces the resistance of grapevine against downy mildew

Grape downy mildew caused by Plasmopara viticola is one of the most destructive diseases of grapes. All grape cultivars are susceptible to P. viticola. However, the resistance of grape plants could be induced in plant defense with some help of microbes. In this study, Bacillus megaterium BMJBN02 obtained from farmland soil was shown to regulate the resistance of grapevine against downy mildew. The salicylic acid (SA) content and the expression of pathogenesis-related (PR) genes of grapes under different treatments were examined using high-performance liquid chromatography-mass spectrometry (HPLC-MS) and reverse transcription- quantitative polymerase chain reaction (RT-qPCR), and it was found that SA content and the expression of PR genes could play a role in regulating the resistance of grapevine against downy mildew. The five-year plot experiment showed that the resistance effectiveness of isolate BMJBN02 was approximately equal to that of 0.1 % nicotinyl morpholine (commercial fungicide). Therefore, this study provides a valuable candidate method that uses B. megaterium BMJBN02 by regulating the resistance of grape against downy mildew for quality and yield of grape in commercial productivity

Affect Recognition in Conversations Using Large Language Models

Affect recognition, encompassing emotions, moods, and feelings, plays a pivotal role in human communication. In the realm of conversational artificial intelligence (AI), the ability to discern and respond to human affective cues is a critical factor for creating engaging and empathetic interactions. This study delves into the capacity of large language models (LLMs) to recognise human affect in conversations, with a focus on both open-domain chit-chat dialogues and task-oriented dialogues. Leveraging three diverse datasets, namely IEMOCAP, EmoWOZ, and DAIC-WOZ, covering a spectrum of dialogues from casual conversations to clinical interviews, we evaluated and compared LLMs' performance in affect recognition. Our investigation explores the zero-shot and few-shot capabilities of LLMs through in-context learning (ICL) as well as their model capacities through task-specific fine-tuning. Additionally, this study takes into account the potential impact of automatic speech recognition (ASR) errors on LLM predictions. With this work, we aim to shed light on the extent to which LLMs can replicate human-like affect recognition capabilities in conversations