Lentinan Impairs the Early Development of Zebrafish Embryos, Possibly by Disrupting Glucose and Lipid Metabolism

LNT is the major biologically active substance extracted from Lentinus edodes (L. edodes). Although functional and pharmacological studies have demonstrated that LNT has multiple benefits for animals and humans, the safety assessment is far from sufficient. To evaluate the potential safety risk, larval zebrafish were continuously exposed to varying concentrations of LNT for 120 h. The 96 h LC50 of LNT was determined to be 1228 μg/mL, and morphological defects including short body length, reduced eye and swim bladder sizes and yolk sac edema were observed. In addition, LNT exposure significantly reduced the blood flow velocity and locomotor activity of larval zebrafish. The biochemical parameters were also affected, showing reduced glucose, triglyceride and cholesterol levels in zebrafish larvae after being exposed to LNT. Correspondingly, the genes involved in glucose and lipid metabolism were disrupted. In conclusion, the present study demonstrates the adverse potential of high concentrations of LNT on the development of zebrafish larvae in the early life stage.

The Modelling of Digital Twins Technology in the Construction Process of Prefabricated Buildings

In the process of building construction, traditional architectural design and construction methods take a long time. The built buildings perform poorly in terms of energy usage and energy conservation. The study expects to explore the potential safety hazards of prefabricated buildings during the construction process. On this basis, a modelling study of the construction process is carried out. The study uses Digital Twins (DTs) technology and prefabricated Building Information Modelling (BIM) to conduct in-depth modelling research on the building construction process. The prefabricated building construction system oriented to DTs technology can well solve the problems of structural damage and deformation in the production, transportation, and assembly process of building components. Especially in prefabricated buildings, it can monitor and accurately predict the damage of building components that may occur in the entire system due to structural problems and material problems in real time. Regarding the building information model, the study uses third-party software to transfer the assembly information to the network cloud to further realize the display of the BIM. The study shows that the maximum value of the effective risk cases selected is 130, and its effective rate is 100%; after processing the data, it is found that the initial value is always stable, and its value is 1; the extracted value is always changing, the maximum value is 0.86, and the minimum is 0.75. By this result, the conclusion is that DTs technology and BIM can effectively monitor the indicators of risk problems during the construction of prefabricated buildings and can further reduce potential safety hazards. Through building information modelling, the development of intelligent industrialization of building construction design and the in-depth study of construction modelling has practical application value.

Knowledge pattern assessment of potential safety threats of compact fluorescent lamps in Kuwait

Globally, compact fluorescent lamps (CFLs) are increasing consistently, and Kuwait is not an exception. However, these lamps contain mercury, which is highly injurious to human health and the environment. This study assessed Kuwaiti respondents' awareness using a large-scale national survey conducted on a random sample of 6210 individuals (response rate 84.3%). The questionnaire was comprised of four sections and utilized skip logic branching. The modes were paper-based, face-to-face interviews, and electronic structured questionnaires. Data were also analyzed through the Pearson chi-square test to know the significant differences in lamp type preferences and the reasons for the preferences. Almost half of the participants (51.4%) knew the difference between incandescent and fluorescent lamps. Only 11.1% were using incandescent lamps solely in their houses. The remaining 88.9% used fluorescent lamps (38.4%) or both types (50.5%). The results showed that 48.3% think fluorescent lamps save energy, whereas 81.3% of people were unaware of their mercury content. The knowledge patterns towards breakage showed that respondents who chose the proper response were 31.9% for evacuation, 14.6% for aeration, and 7.3% for turning off the AC. The awareness of populations to take appropriate actions towards proper disposal was very poor in case of fluorescent lamp accidental breakage or when it completes its life cycle because most of them did not know about the proper evacuation, aeration, and cleanup measures. These findings are beneficial for the government and policymakers to take essential steps to create relevant awareness channels among the country's communities for safety from expected health hazards.

A Predictive Pedestrian Crash Model Based on Artificial Intelligence Techniques

Every year in Italy, there are about 20,000 road accidents involving pedestrians, with a significant number of injuries and deaths. Out of these, about 30% occur at pedestrian crossings, where pedestrians should be protected the most. Here, we propose a new accident prediction model to improve pedestrian safety assessments that allows us to accurately identify the sites with the largest potential safety improvements and define the best treatments to be applied. The accident prediction model was developed using the ISTAT dataset, including information about the fatal and injurious crashes that occurred in Italy in a 5-year period. The model allowed us to estimate the risk level of a road section through a machine-learning approach. Gradient Boosting seems to be an appropriate tool to fit classification models for its flexibility that allows us to capture non-linear relationships that would be difficult to detect via a classical approach. The results show the ability of the model to perform an accurate analysis of the sites included in the dataset. The locations analyzed have been classified based on the potential risk in the following three classes: High, medium, and low. The proposed model represents a solid and reliable tool for practitioners to perform accident analysis with pedestrian involvement.

MILAD: Robust Anomaly Detection for Electric Vehicles with Label Noise

One of the bottlenecks restricting the development of electric vehicle industry is the safety problem. Although numerous of anomaly detection algorithms for electric vehicles have been proposed, most of them may perform poorly due to the complexity and unpredictability of real scenes. We consider that there may be a certain degree of potential safety hazard in the battery system of electric vehicles before, during and after the process of faults in the real scenes, that is, label noise. In order to solve this problem, we propose a Multi-Instance Learning based Anomaly Detection (MILAD) framework, to perform anomaly detection for electric vehicles with label noise problem. Extensive cross validation experiments fully verify that the framework can effectively detect the existence of abnormal conditions in the presence of label noise in multivariate time series data.

Applications of genome editing tools in stem cells towards regenerative medicine: An update

: Precise and site specific genome editing through application of emerging and modern gene engineering techniques, namely zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) have swiftly progressed the application and use of the stem cell technology in the sphere of in-vitro disease modelling and regenerative medicine. Genome editing tools facilitate the manipulating of any gene in various types of cells with target specific nucleases. These tools aid in elucidating the genetics and etiology behind different diseases and have immense promise as novel therapeutics for correcting the genetic mutations, make alterations and cure diseases permanently that are not responding and resistant to traditional therapies. These genome engineering tools have evolved in the field of biomedical research and have also shown to have a significant improvement in clinical trials. However, their widespread use in research revealed potential safety issues, which need to be addressed before implementing such techniques in clinical purposes. Significant and valiant attempts are being made in order to surpass those hurdles. The current review outlines the advancements of several genome engineering tools and describes suitable strategies for their application towards regenerative medicine.