Search

Purpose: This research project aimed to discuss the effect of early enteral nutrition (EEN) plus probiotics on intestinal function of senile patients with sepsis. Methods: 108 senile sepsis patients admitted to our hospital from January 2019 to January 2022 were selected in this retrospective study. These patients including 50 cases in a control group (CG) and 58 cases in a research group (RG). Both groups received EEN, but the research group was given EEN plus probiotics. The two cohorts of patients were compared with respect to treatment efficacy, intestinal mucosal barrier, nutritional status and 28-day mortality. Cox regression was performed to analyze the prognostic factors of elderly patients with sepsis. Results: Compared to the CG, the RG had evidently higher overall response rate and post-treatment albumin (Alb) and prealbumin (PA) levels, as well as statistically lower intestinal fatty acid binding protein, diamine oxidase, D-lactate and 28-day mortality. Furthermore, Alb and PA were identified as independent predictors of prognosis in elderly patients with sepsis. Conclusions: EEN supplemented with probiotics is superior to EEN alone in the treatment of senile patients with sepsis. This combined regimen can significantly improve intestinal function, nutritional status and prognosis of patients. Moreover, Alb and PA are independently related to the prognosis of elderly patients with sepsis.

In this report, we present the technical details of our submission to the 2022 EPIC-Kitchens Unsupervised Domain Adaptation (UDA) Challenge. Existing UDA methods align the global features extracted from the whole video clips across the source and target domains but suffer from the spatial redundancy of feature matching in video recognition. Motivated by the observation that in most cases a small image region in each video frame can be informative enough for the action recognition task, we propose to exploit informative image regions to perform efficient domain alignment. Specifically, we first use lightweight CNNs to extract the global information of the input two-stream video frames and select the informative image patches by a differentiable interpolation-based selection strategy. Then the global information from videos frames and local information from image patches are processed by an existing video adaptation method, i.e., TA3N, in order to perform feature alignment for the source domain and the target domain. Our method (without model ensemble) ranks 4th among this year's teams on the test set of EPIC-KITCHENS-100., Technical report for CVPR 2022 EPIC-Ego4D Workshop (EPIC Challenge)

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution., Validation code of the baseline model is available at https://github.com/ofsoundof/IMDN. Validation of all submitted models is available at https://github.com/ofsoundof/NTIRE2022_ESR

Additional file 1. Ferroptosis-KIRC data.

The report describes the results of research in the food manufacturing, distribution and retail sectors, focusing on the work of union health and safety representatives during the Covid-19 pandemic in 2020. The research reported in this report is congruent with a wider study supported by UK Research and Innovation. This research, focusing on the food and drinks sectors, is supported by the TUC. It examines health and safety representatives and management actions in the pandemic, in food processing, manufacturing, distribution and retail sectors. Many workers in these sectors worked throughout the pandemic and increased demand meant increased recruitment in the sector, but also a measure of work intensification. \ud \ud Case studies suggest substantial increases in the utilisation of agency workers. Four out of five workers in the food industry said they had been identified as "essential workers" by their employers. Over half of workers reported fears about the transmission of Covid19 at work, with the figure rising to 63% for those classed as ‘essential workers’. A third of workers (and 40% of essential workers) worried about the impact of continued attendance at the workplace on their mental health. \ud \ud The research suggests an absence of health and safety infrastructure in UK workplaces. Under half of managers reported that there was a health and safety committee representing management and workers at the workplace level or at the organisational level. Just over one third of managers in the food manufacturing and distribution sector said there was such a committee at the organisational level and a half at workplace level. (For food retail, these figures are reversed with half of them saying that there is an organisational level health and safety committee and over a third reporting that a committee was established at the workplace level. Under half of managers (34% in food retail and 24% in food manufacturing and distribution) reported a dedicated health and safety officer in their workplace. Under half said that their workplaces had a health and safety management team.\ud \ud Other than in isolated cases, there was little evidence of expansion of health and safety representation under the Covid-19 pandemic. Yet it is clear from the case studies that existing structures provide a necessary basis for informal and frequent dialogue between union reps and managers under Covid, often on a day-today basis. Workers in workplaces with union health and safety reps were more likely to report that risk assessments had been carried out. Two thirds of workers in a workplace with union health and safety representatives said that a risk assessment had been carried out, while fewer (58% in workplaces with non-union representatives and 43% where there was no health and safety representative) said that a risk assessment had been carried out. Twice as many workers in workplaces with no health and safety rep said that they did not know if a risk assessment had been carried out in comparison with those in a workplace\ud with a union rep. The evidence points therefore, to the presence of union health and safety reps having a beneficial impact on health and safety in an organisation. These findings from the quantitative survey are supported by detailed case study interviews with managers and union reps, in which safety reps in particular, attest to energetic work which they undertook to keep members safe during the pandemic. The study affirms the value of union health and safety representatives, highlights some worrying consequences of the erosion of health and safety structures and their replacement by direct channels of communication.

This work tackles the unsupervised cross-domain object detection problem which aims to generalize a pre-trained object detector to a new target domain without labels. We propose an uncertainty-aware model adaptation method, which is based on two motivations: 1) the estimation and exploitation of model uncertainty in a new domain is critical for reliable domain adaptation; and 2) the joint alignment of distributions for inputs (feature alignment) and outputs (self-training) is needed. To this end, we compose a Bayesian CNN-based framework for uncertainty estimation in object detection, and propose an algorithm for generation of uncertainty-aware pseudo-labels. We also devise a scheme for joint feature alignment and self-training of the object detection model with uncertainty-aware pseudo-labels. Experiments on multiple cross-domain object detection benchmarks show that our proposed method achieves state-of-the-art performance., Comment: 11 pages, 4 figures

Millennial survey data published by consultancy firms suggest a dominant role of workplace diversity and inclusion in influencing Generation Y’s perception of and commitment to employers. While diversity practices have been gradually introduced by many organisations to address inequality issues in recruitment and promotion, the impact of these practices on employees’ actual perception of inclusion remains unclear. There is also mixed evidence on generational differences, which raises questions about whether the younger workforce, especially those who belong to historically excluded identity groups, interpret workplace inclusion differently from other generational cohorts. This developmental paper explores the subject with a structured review of the existing evidence and outlines the research design of a wider empirical project. It aims to shift the popular focus on surface diversity to inclusion and contribute to the understanding of how organisations can adapt their strategies to attract and retain the increasingly diverse future workforce.

This paper proposes a novel method for understanding daily hand-object manipulation by developing computer vision-based techniques. Specifically, we focus on recognizing hand grasp types, object attributes and manipulation actions within an unified framework by exploring their contextual relationships. Our hypothesis is that it is necessary to jointly model hands, objects and actions in order to accurately recognize multiple tasks that are correlated to each other in hand-object manipulation. In the proposed model, we explore various semantic relationships between actions, grasp types and object attributes, and show how the context can be used to boost the recognition of each component. We also explore the spatial relationship between the hand and object in order to detect the manipulated object from hand in cluttered environment. Experiment results on all three recognition tasks show that our proposed method outperforms traditional appearance-based methods which are not designed to take into account contextual relationships involved in hand-object manipulation. The visualization and generalizability study of the learned context further supports our hypothesis., 14 pages, 13 figures

We present a new computational model for gaze prediction in egocentric videos by exploring patterns in temporal shift of gaze fixations (attention transition) that are dependent on egocentric manipulation tasks. Our assumption is that the high-level context of how a task is completed in a certain way has a strong influence on attention transition and should be modeled for gaze prediction in natural dynamic scenes. Specifically, we propose a hybrid model based on deep neural networks which integrates task-dependent attention transition with bottom-up saliency prediction. In particular, the task-dependent attention transition is learned with a recurrent neural network to exploit the temporal context of gaze fixations, e.g. looking at a cup after moving gaze away from a grasped bottle. Experiments on public egocentric activity datasets show that our model significantly outperforms state-of-the-art gaze prediction methods and is able to learn meaningful transition of human attention., Accepted as oral presentation in ECCV 2018

Guanxi, the Chinese term for social connections, has long been a key subject of interest in Chinese management research. Despite the conceptual and empirical efforts of earlier studies to address the nature, instrumentality, and managerial implications of guanxi, the extent to and the ways in which guanxi is deployed in the actual decision-making process in contemporary Chinese organisations remains ambiguous. Cont/d...