Effects of enzyme-induced carbonate precipitation technique on multiple heavy metals immobilization and unconfined compressive strength improvement of contaminated sand.

Enzyme-induced carbonate precipitation (EICP) has been studied in remediation of heavy metal contaminated water or soil in recent years. This paper aims to investigate the immobilization mechanism of Zn2+, Ni2+, and Cr(VI) in contaminated sand, as well as strength enhancement of sand specimens by using EICP method with crude sword bean urease extracts. A series of liquid batch tests and artificially contaminated sand remediation experiments were conducted to explore the heavy metal immobilization efficacy and mechanisms. Results showed that the urea hydrolysis completion efficiency decreased as the Ca2+ concentration increased and the heavy metal immobilization percentage increased with the concentration of Ca2+ and treatment cycles in contaminated sand. After four treatment cycles with 0.5 mol/L Ca2+ added, the immobilization percentage of Zn2+, Ni2+, and Cr(VI) were 99.99 %, 86.38 %, and 75.18 %, respectively. The microscale analysis results presented that carbonate precipitates and metallic oxide such as CaCO3, ZnCO3, NiCO3, Zn(OH)2, and CrO(OH) were generated in liquid batch tests and sand remediation experiments. The SEM-EDS and FTIR results also showed that organic molecules and CaCO3 may adsorb or complex heavy metal ions. Thus, the immobilization mechanism of EICP method with crude sword bean urease can be considered as biomineralization, as well as adsorption and complexation by organic matter and calcium carbonate. The unconfined compressive strength of EICP-treated contaminated sand specimens demonstrated a positive correlation with the increased generation of carbonate precipitates, being up to 306 kPa after four treatment cycles with shear failure mode. Crude sword bean urease with 0.5 mol/L Ca2+ added is recommended to immobilize multiple heavy metal ions and enhance soil strength.

Hypobaric hypoxia causes low fecundity in zebrafish parents and impairment of skeletal development in zebrafish embryos and rat offspring.

Hypobaric Hypoxia (HH) negatively affects the cardiovascular and respiratory systems as well as gonadal development and the therefore next generation. This study investigated the effects of HH on zebrafish and SD rats, by exposing them to a low-pressure environment at 6000 m elevation for 30 days to simulate high-altitude conditions. It was indicated that parental zebrafish reared amh under HH had increased embryo mortality, reduced hatchability, and abnormal cartilage development in the offspring. Furthermore, the HH-exposed SD rats had fewer reproductive cells and smaller litters. Moreover, the transcriptome analysis revealed the down-regulation of steroid hormone biosynthesis pathways. The expression of the gonad-associated genes (amh, pde8a, man2a2 and lhcgr), as well as the gonad and cartilage-related gene bmpr1a, were also down-regulated. In addition, Western blot analysis validated reduced bmpr1a protein expression in the ovaries of HH-treated rats. In summary, these data indicate the negative impact of HH on reproductive organs and offspring development, emphasizing the need for further research and precautions to protect future generations' health.

Has the COVID-19 pandemic lockdown worsened eating disorders symptoms among patients with eating disorders? A systematic review.

Objective: During the coronavirus pandemic lockdowns, general medical complications have received the most attention, and few studies have examined the association between the COVID-19 lockdown and eating disorders (ED). This study aimed to investigate the impact of the coronavirus lockdowns on ED symptoms severity and summarize factors associated with lockdowns that led to changes in eating disorders. Method: PubMed, Scopus, and Cochrane Library databases were searched for studies measuring the impact of coronavirus lockdowns on ED symptoms. Results: A total of 132 studies were retrieved, after abstract screening and removal of duplicates, 21 papers were full-text screened, and 11 eligible papers were identified. Factors associated with symptomatic deterioration in ED patients during COVID-19 lockdowns included disruption of lifestyle routine, social isolation, reduced access to usual support networks, limited or no access to healthcare and mental care services, and social anxiety. Discussion: Overall, the pandemic lockdowns were associated with worsening of eating disorders.This triggering environment can lead to increased anxiety and depression symptoms, change in dietary habits, and eventually result in worsening eating disorder symptoms.

FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction.

The implementation of neural network regression prediction based on digital circuits is one of the challenging problems in the field of machine learning and cognitive recognition, and it is also an effective way to relieve the pressure of the Internet in the era of intelligence. As a nonlinear network, the stochastic configuration network (SCN) is considered to be an effective method for regression prediction due to its good performance in learning and generalization. Therefore, in this paper, we adapt the SCN to regression analysis, and design and verify the field programmable gate array (FPGA) framework to implement SCN model for the first time. In addition, in order to improve the performance of the SCN model based on the FPGA, the implementation of the nonlinear activation function on the FPGA is optimized, which effectively improves the prediction accuracy while considering the utilization rate of hardware resources. Experimental results based on the simulation data set and the real data set prove that the proposed FPGA framework successfully implements the SCN regression prediction model, and the improved SCN model has higher accuracy and a more stable performance. Compared with the extreme learning machine (ELM), the prediction performance of the proposed SCN implementation model based on the FPGA for the simulation data set and the real data set is improved by 56.37% and 17.35%, respectively.