FORECASTING THE WORKLOAD WITH A HYBRID MODEL TO REDUCE THE INEFFICIENCY COST

Time series forecasting and modeling are challenging problems during the past decades, because of its plenty of properties and underlying correlated relationships. As a result, researchers proposed a lot of models to deal with the time series. However, the proposed models such as Autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs) only describe part of the properties of time series. In this thesis, we introduce a new hybrid model integrated filter structure to improve the prediction accuracy. Case studies with real data from University of Kentucky HealthCare are carried out to examine the superiority of our model. Also, we applied our model to operating room (OR) to reduce the inefficiency cost. The experiment results indicate that our model always outperforms compared with other models in different conditions

Facile sand enhanced electro-flocculation for cost-efficient harvesting of Dunaliella salina

通讯作者地址: Jia, LSEnergy consumption and water resource in the cultivation and harvesting steps still need to be minimized for the popularization of the microalgae-based products. An efficient electro-flocculation method for harvesting Dunaliella Salina integrated with local sand has been successfully applied. Sand was effective for speeding up the processes of flocculation and sedimentation of algal flocs and the electrolytic hydroxides was essential to bridge the sand and small flocs into large dense flocs. The maximal recovery effective improved from 95.13% in 6 min to 98.09% in 4.5 min and the optimal electrical energy consumption decreased 51.03% compared to conventional electro-flocculation in a laboratory ambient condition. Furthermore, reusing the flocculated medium in cultivation of the D. Salina with nitrogen supplemented performed no worse than using fresh medium. This sand enhanced electro-flocculation (SEF) technology provides a great potential for saving time and energy associated with improving microalgae harvesting.Xiamen Southern Oceanographic Center of Fujian Province of China 14CZP046HJ20 talents introduction project of Shenzhen City Science and Technology R&D funds recruit research of China ZYD201111080010

A Locality-based Neural Solver for Optical Motion Capture

We present a novel locality-based learning method for cleaning and solving optical motion capture data. Given noisy marker data, we propose a new heterogeneous graph neural network which treats markers and joints as different types of nodes, and uses graph convolution operations to extract the local features of markers and joints and transform them to clean motions. To deal with anomaly markers (e.g. occluded or with big tracking errors), the key insight is that a marker's motion shows strong correlations with the motions of its immediate neighboring markers but less so with other markers, a.k.a. locality, which enables us to efficiently fill missing markers (e.g. due to occlusion). Additionally, we also identify marker outliers due to tracking errors by investigating their acceleration profiles. Finally, we propose a training regime based on representation learning and data augmentation, by training the model on data with masking. The masking schemes aim to mimic the occluded and noisy markers often observed in the real data. Finally, we show that our method achieves high accuracy on multiple metrics across various datasets. Extensive comparison shows our method outperforms state-of-the-art methods in terms of prediction accuracy of occluded marker position error by approximately 20%, which leads to a further error reduction on the reconstructed joint rotations and positions by 30%. The code and data for this paper are available at https://github.com/non-void/LocalMoCap.Comment: Siggraph Asia 2023 Conference Pape

A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions

Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic

26S Proteasome Activity Is Down-Regulated in Lung Cancer Stem-Like Cells Propagated In Vitro

Cancer stem cells (CSCs) are a small subset of cancer cells capable of self-renewal and tumor maintenance. Eradicating cancer stem cells, the root of tumor origin and recurrence, has emerged as one promising approach to improve lung cancer survival. Cancer stem cells are reported to reside in the side population (SP) of cultured lung cancer cells. We report here the coexistence of a distinct population of non-SP (NSP) cells that have equivalent self-renewal capacity compared to SP cells in a lung tumor sphere assay. Compared with the corresponding cells in monolayer cultures, lung tumor spheres, formed from human non-small cell lung carcinoma cell lines A549 or H1299, showed marked morphologic differences and increased expression of the stem cell markers CD133 and OCT3/4. Lung tumor spheres also exhibited increased tumorigenic potential as only 10,000 lung tumor sphere cells were required to produce xenografts tumors in nude mice, whereas the same number of monolayer cells failed to induce tumors. We also demonstrate that lung tumor spheres showed decreased 26S proteasome activity compared to monolayer. By using the ZsGreen–cODC (C-terminal sequence that directs degradation of Ornithine Decarboxylase) reporter assay in NSCLC cell lines, only less than 1% monolayer cultures were ZsGreen positive indicating low 26S proteasome, whereas lung tumor sphere showed increased numbers of ZsGreen-positive cells, suggesting the enrichment of CSCs in sphere cultures

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Anti-inflammatory Components from Functional Foods for Obesity

Obesity, defined as excessive fat accumulation that may impair health, has been described throughout human history, but it has now reached epidemic proportions with the WHO estimating that 39% of the world’s adults over 18 years of age were overweight or obese in 2016. Obesity is a chronic low-grade inflammatory state leading to organ damage with an increased risk of common diseases including cardiovascular and metabolic disease, non-alcoholic fatty liver disease, osteo-arthritis and some cancers. This inflammatory state may be influenced by adipose tissue hypoxia and changes in the gut microbiota. There has been an increasing focus on functional foods and nutraceuticals as treatment options for obesity as drug treatments are limited in efficacy. This chapter summarises the importance of anthocyanin-containing fruits and vegetables, coffee and its components, tropical fruit and food waste as sources of phytochemicals for obesity treatment. We emphasise that preclinical studies can form the basis for clinical trials to determine the effectiveness of these treatments in humans

Optimal strategy for a dose-escalation vaccination against COVID-19 in refugee camps

An immunogenic and safe vaccine against COVID-19 for use in the healthy population will become available in the near future. In this paper, we aim to determine the optimal vaccine administration strategy in refugee camps considering maximum daily administration and limited total vaccine supply. For this purpose, extended SEAIRD compartmental models are established to describe the epidemic dynamics with both single-dose and double-dose vaccine administration. Taking the vaccination rates in different susceptible compartments as control variables, the optimal vaccine administration problems are then solved under the framework of nonlinear constrained optimal control problems. To the best of our knowledge, this is the first paper that addresses an optimal vaccine administration strategy considering practical constraints on limited medical care resources. Numerical simulations show that both the single-dose and double-dose strategies can successfully control COVID-19. By comparison, the double-dose vaccination strategy can achieve a better reduction in infection and death, while the single-dose vaccination strategy can postpone the infection peak more efficiently. Further studies of the influence of parameters indicate that increasing the number of medical care personnel and total vaccine supply can greatly contribute to the fight against COVID-19. The results of this study are instructive for potential forthcoming vaccine administration. Moreover, the work in this paper provides a general framework for developing epidemic control strategies in the presence of limited medical resources