Selecting the appropriate speed for rotational elements in human-machine interfaces: A quantitative study

The motion of rotation, which served as a dynamic symbol within human-computer interfaces, has garnered extensive attention in interface and graphic design. This study aimed to establish speed benchmarks for interface design by exploring visual system preferences for the perception of both simple and complex rotating icons within the velocity range of 5-1800 degrees per second. The research conducted two experiments with 12 participants to examine the observers’ just noticeable difference in speed (JNDS) and perceived speed for rotational icons. Experiment one measured the JNDS over eight-speed levels using a constant stimulus method, achieving a range of 14.9-29%. Building on this, experiment two proposed a sequence of speeds within the given range and used a rating scale method to assess observers ' subjective perception of the speed series' rapidity. The findings indicated that speed increases impacted the ability to differentiate between speeds; key points for categorizing low, medium, and high speeds were identified at 10, 180, and 720 degrees/s, respectively. Shape complexity was found to modulate the visual system's perception of actual speed, such that at rotation speeds above 180 degrees/s, complex icons appeared to rotate faster than simpler ones. Most importantly, the study applied quantitative methods and metrology to interface design, offering a more scientific approach to the design workflow

Biological current source imaging method based on acoustoelectric effect: A systematic review

Neuroimaging can help reveal the spatial and temporal diversity of neural activity, which is of utmost importance for understanding the brain. However, conventional non-invasive neuroimaging methods do not have the advantage of high temporal and spatial resolution, which greatly hinders clinical and basic research. The acoustoelectric (AE) effect is a fundamental physical phenomenon based on the change of dielectric conductivity that has recently received much attention in the field of biomedical imaging. Based on the AE effect, a new imaging method for the biological current source has been proposed, combining the advantages of high temporal resolution of electrical measurements and high spatial resolution of focused ultrasound. This paper first describes the mechanism of the AE effect and the principle of the current source imaging method based on the AE effect. The second part summarizes the research progress of this current source imaging method in brain neurons, guided brain therapy, and heart. Finally, we discuss the problems and future directions of this biological current source imaging method. This review explores the relevant research literature and provides an informative reference for this potential non-invasive neuroimaging method

Using risk data as a source for human reliability assessment during shipping LNG offloading work

This manuscript has been made open access under a Creative Commons Attribution (CC BY) licence under the terms of the University of Aberdeen Research Publications Policy. https://creativecommons.org/licenses/by/4.0/Peer reviewe

Comparison of the protective effects of ginsenosides Rb1 and Rg1 on improving cognitive deficits in SAMP8 mice based on anti-neuroinflammation mechanism

This present study was designed to investigate the different effects of ginsenosides Rb1 and Rg1 on improving cognitive deficits in 4-month-old SAMP8 mice. Mice were divided into six groups, including the SAMP8 group, the SAMP8 + Donepezil (1.6 mg/kg) group, the SAMP8 + Rb1 (30 and 60 µmol/kg), and SAMP8 + Rg1 (30 and 60 µmol/kg) groups. SAMR1 mice of the same age were used as the control group. Ginsenosides and donepezil were administrated orally to animals for 8 weeks, then the learning and memory ability of mice were measured by using Morris water maze (MWM) test, object recognition test and passive avoidance experiments. The possible mechanisms were studied including the anti-glial inflammation of Rb1 and Rg1 using HE staining, immunohistochemistry and western blot experiments. Results revealed that Rb1 and Rg1 treatment significantly improved the discrimination index of SAMP8 mice in the object recognition test. Rb1 (60 µmol/kg) and Rg1 (30, 60 µmol/kg) could significantly shorten the escape latency in the acquisition test of the MWM test in SAMP8 mice. Furthermore, Rb1 and Rg1 treatments effectively reduced the number of errors in the passive avoidance task in SAMP8 mice. Western blot experiments revealed that Rb1 showed higher effect than Rg1 in decreasing protein expression levels of ASC, caspase-1 and Aβ in the hippocampus of SAMP8 mice, while Rg1 was more effective than Rb1 in decreasing the protein levels of iNOS. In addition, although Rb1 and Rg1 treatments showed significant protective effects in repairing neuronal cells loss and inhibiting the activation of astrocyte and microglia in hippocampus of SAMP8 mice, Rb1 was more effective than Rg1. These results suggest that Rb1 and Rg1 could improve the cognitive impairment in SAMP8 mice, and they have different mechanisms for the treatment of Alzheimer's disease.This work was supported by the National Key Research and Development Program of China (2016YFE0131800), Science & Technology department of Sichuan province (2019YFH0023), Office of Sciences & Technology and Talent work of Luzhou (2018LZXNYD-ZK32), the High - end Talents Recruitment Program (Liu Xinmin group) of Luzhou Municipal People's Government

In silico Genetic Network Models for Pre-clinical Drug Prioritization

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.
&#xa

Fresh Gastrodia elata Blume alleviates simulated weightlessness-induced cognitive impairment by regulating inflammatory and apoptosis-related pathways

In aerospace medicine, the influence of microgravity on cognition has always been a risk factor threatening astronauts’ health. The traditional medicinal plant and food material Gastrodia elata Blume has been used as a therapeutic drug for neurological diseases for a long time due to its unique neuroprotective effect. To study the effect of fresh Gastrodia elata Blume (FG) on cognitive impairment caused by microgravity, hindlimb unloading (HU) was used to stimulate weightlessness in mice. The fresh Gastrodia elata Blume (0.5 g/kg or 1.0 g/kg) was intragastrically administered daily to mice exposed to HU and behavioral tests were conducted after four weeks to detect the cognitive status of animals. The behavioral tests results showed that fresh Gastrodia elata Blume therapy significantly improved the performance of mice in the object location recognition test, Step-Down test, and Morris Water Maze test, including short-term and long-term spatial memory. According to the biochemical test results, fresh Gastrodia elata Blume administration not only reduced serum factor levels of oxidative stress but also maintained the balance of pro-inflammatory and anti-inflammatory factors in the hippocampus, reversing the abnormal increase of NLRP3 and NF-κB. The apoptosis-related proteins were downregulated which may be related to the activation of the PI3K/AKT/mTOR pathway by fresh Gastrodia elata Blume therapy, and the abnormal changes of synapse-related protein and glutamate neurotransmitter were corrected. These results identify the improvement effect of fresh Gastrodia elata Blume as a new application form of Gastrodia elata Blume on cognitive impairment caused by simulated weightlessness and advance our understanding of the mechanism of fresh Gastrodia elata Blume on the neuroprotective effect

Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration

BACKGROUND: The automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets. RESULTS: We have demonstrated the capabilities of this method in two ways. We first collected various experimental datasets associated with yeast (Saccharomyces cerevisiae) and applied the technique to a set of previously annotated open reading frames (ORFs). These ORFs were divided into training and test sets and were used to examine the accuracy of the predictions made by our method. Then we applied GMUP to previously un-annotated ORFs and made 1980, 836 and 1969 predictions corresponding to the GO Biological Process, Molecular Function and Cellular Component sub-categories respectively. We found that GMUP was particularly successful at predicting ORFs with functions associated with the ribonucleoprotein complex, protein metabolism and transportation. CONCLUSION: This study presents a global and generic gene knowledge discovery approach based on evidence integration of various genome-scale data. It can be used to provide insight as to how certain biological processes are implemented by interaction and coordination of proteins, which may serve as a guide for future analysis. New data can be readily incorporated as it becomes available to provide more reliable predictions or further insights into processes and interactions

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies