During the COVID-19 pandemic, many employees were asked to start working from home for an extended time. The current study investigated how well employees worked and felt in this novel situation by following n = 199 German employees-56% of them female, 24% with childcare duties-over the course of two working weeks in which they reported once daily on their well-being (PANAS-20, detachment) and motivation (work engagement, flow). Participants reported on organizational and personal resources (emotional exhaustion, emotion regulation, segmentation preference, role clarity, job control, social support). Importantly, they indicated how well their work-related basic needs, i.e., autonomy, competence, and relatedness, were met when working from home and how these needs had been met in the office. Multilevel models of growth showed that work engagement, flow, affect and detachment were on average positive and improving over the two weeks in study. Higher competence need satisfaction predicted better daily work engagement, flow, and affect. In a network model, we explored associations and dynamics between daily variables. Overall, the results suggest that people adapted well to the novel situation, with their motivation and well-being indicators showing adequate levels and increasing trajectories. Avenues for improving work from home are job control and social support.Air pollution is becoming an increasingly important global issue. Toxic gases such as ammonia, nitrogen dioxide, and volatile organic compounds (VOCs) like phenol are very common air pollutants. To date, various sensing methods have been proposed to detect these toxic gases. Researchers are trying their best to build sensors with the lowest detection limit, the highest sensitivity, and the best selectivity. As a 2D material, graphene is very sensitive to many gases and so can be used for gas sensors. Recent studies have shown that graphene with a 3D structure can increase the gas sensitivity of the sensors. The limit of detection (LOD) of the sensors can be upgraded from ppm level to several ppb level. In this review, the recent progress of the gas sensors based on 3D graphene frameworks in the detection of harmful gases is summarized and discussed.The selectivity in the simultaneous detection of ascorbic acid (AA), dopamine (DA), and uric acid (UA) has been an open problem in the biosensing field. Many surface modification methods were carried out for glassy carbon electrodes (GCE), including the use of graphene oxide and amino acids as a selective layer. In this work, molecular dynamics (MD) simulations were performed to investigate the role of serine oligomers on the selectivity of the AA, DA, and UA analytes. Our models consisted of a graphene oxide (GO) sheet under a solvent environment. Serine tetramers were added into the simulation box and were adsorbed on the GO surface. Then, the adsorption of each analyte on the mixed surface was monitored from MD trajectories. It was found that the adsorption of AA was preferred by serine oligomers due to the largest number of hydrogen-bond forming functional groups of AA, causing a 10-fold increase of hydrogen bonds by the tetraserine adsorption layer. UA was the least preferred due to its highest aromaticity. Finally, the role of hydrogen bonds on the electron transfer selectivity of biosensors was discussed with some previous studies. AA radicals received electrons from serine through hydrogen bonds that promoted oxidation reaction and caused the negative shifts and separation of the oxidation potential in experiments, as DA and UA were less affected by serine. Agreement of the in vitro and in silico results could lead to other in silico designs of selective layers to detect other types of analyte molecules.The additive fillers in bioretention facilities play a leading role in stormwater treatment to purify polluted runoff. At present, many traditional materials could not meet the requirements at the same time, including low ammonium leaching quantities, high water storage volume and strong ammonium adsorption. This study investigated a polymer material, polyurethane-biochar crosslinked material (PCB), to evaluate the feasibility of using it as an additive filler in stormwater treatment compared with its raw material hardwood biochar (HB), and two traditional fillers. Successive leaching and ammonium isothermal adsorption experiments were conducted in deionized water and artificial stormwater. PCB leached 4.98-5.31 μmol/g NH4-N, less than the leaching quantities of compost, the traditional filler. After polyurethane modification, ammonium adsorption of PCB was improved at a typical ammonium concentration of 2 mg/L in stormwater, PCB could adsorb 43.6 mg/kg ammonium versus 34.6 mg/kg for HB. With the addition of PCB in sand column, the ammonium adsorption improved from 31.34 to 84.72%. SW033291 molecular weight To improve the performance of bioretention facilities, PCB is recommended to be added into filter layers in stormwater treatment, taking advantage of its high cation exchange capacity and spongy internal structure to minimize overland flooding and enhance removal of ammonium from stormwater.Perinatal asphyxia is mainly a brain disease leading to the development of neurodegeneration, in which a number of peripheral lesions have been identified; however, little is known about the expression of key genes involved in amyloid production by peripheral cells, such as lymphocytes, during the development of hypoxic-ischemic encephalopathy. We analyzed the gene expression of the amyloid protein precursor, β-secretase, presenilin 1 and 2 and hypoxia-inducible factor 1-α by RT-PCR in the lymphocytes of post-asphyxia and control neonates. In all examined periods after asphyxia, decreased expression of the genes of the amyloid protein precursor, β-secretase and hypoxia-inducible factor 1-α was noted in lymphocytes. Conversely, expression of presenilin 1 and 2 genes decreased on days 1-7 and 8-14 but increased after survival for more than 15 days. We believe that the expression of presenilin genes in lymphocytes could be a potential biomarker to determine the severity of the post-asphyxia neurodegeneration or to identify the underlying factors for brain neurodegeneration and get information about the time they occurred.