Fabrication of magnetic CuFe2O4@PBC composite and efficient removal of metronidazole by the photo-Fenton process in a wide pH range.

CuFe2O4-coated pretreated biochars (CuFe2O4@PBC) were synthesized for the first time via a facile method by impregnating and calcinating Cu-Fe-ethanol solution to activate H2O2 for the degradation of metronidazole (MNZ) at a wide pH range. CuFe2O4@PBC samples were characterized by XRD, SEM, VSM, XPS, and BET. The results showed that CuFe2O4 coating, which is evenly distributed on the surface of HNO3-pretreated biochar, can provide more active sites to enable CuFe2O4@PBC to be activated by visible light. The introduction of biochar by impregnating and calcinating method effectively suppressed the aggregation of CuFe2O4 and maintained its high surface area and pore structure. CuFe2O4@PBC composite can be separated easily by an external magnetic field. The PBC-400CuFe sample calcined under 400 °C showed superior photo-Fenton catalytic ability in MNZ degradation at a wide pH range (pH = 3-7) and exhibited high-efficiency degradation of about 96.3% with the dosage concentration of catalyst 0.4 g/L in the presence of H2O2 at pH 3.0 within 60 min. While, at pH 7.0, the PBC-400CuFe material removed 91.1% MNZ within 120 min, and the degradation efficiency was still higher than that of traditional Fenton reaction and some Fenton-like reaction. The PBC-400CuFe showed good stability. After 5 times of repeated use, its removal rate was still above 89.1%. This study confirmed that O2•- and h+ are both important radicals, but the •OH played a key role in the visible photo/CuFe2O4@PBC- H2O2 system. The results indicate that CuFe2O4@PBC is highly suitable for the wastewaters with high MNZ content under mild conditions.

Study on the Adsorption of CuFe2O4-Loaded Corncob Biochar for Pb(II).

A series of the magnetic CuFe2O4-loaded corncob biochar (CuFe2O4@CCBC) materials was obtained by combining the two-step impregnation of the corncob biochar with the pyrolysis of oxalate. CuFe2O4@CCBC and the pristine corncob biochar (CCBC) were characterized using XRD, SEM, VSM, BET, as well as pHZPC measurements. The results revealed that CuFe2O4 had a face-centered cubic crystalline phase and was homogeneously coated on the surface of CCBC. The as-prepared CuFe2O4@CCBC(5%) demonstrated a specific surface area of 74.98 m2·g-1, saturation magnetization of 5.75 emu·g-1 and pHZPC of 7.0. The adsorption dynamics and thermodynamic behavior of Pb(II) on CuFe2O4@CCBC and CCBC were investigated. The findings indicated that the pseudo-second kinetic and Langmuir equations suitably fitted the Pb(II) adsorption by CuFe2O4@CCBC or CCBC. At 30 °C and pH = 5.0, CuFe2O4@CCBC(5%) displayed an excellent performance in terms of the process rate and adsorption capacity towards Pb(II), for which the theoretical rate constant (k2) and maximum adsorption capacity (qm) were 7.68 × 10-3 g·mg-1··min-1 and 132.10 mg·g-1 separately, which were obviously higher than those of CCBC (4.38 × 10-3 g·mg-1·min-1 and 15.66 mg·g-1). The thermodynamic analyses exhibited that the adsorption reaction of the materials was endothermic and entropy-driven. The XPS and FTIR results revealed that the removal mechanism could be mainly attributed to the replacement of Pb2+ for H+ in Fe/Cu-OH and -COOH to form the inner surface complexes. Overall, the magnetic CuFe2O4-loaded biochar presents a high potential for use as an eco-friendly adsorbent to eliminate the heavy metals from the wastewater streams.

The potential causal relationship between various lifestyles and depression: a univariable and multivariable Mendelian randomization study.

Background: Previous studies have shown that lifestyle was associated with depression. Thus, the aim of this study was to examine the causality between multiple lifestyles and depression by Mendelian randomization (MR) analysis. Methods: The single-nucleotide polymorphisms (SNPs) of depression, alcoholic drinks per week, sleeplessness or insomnia, body mass index (BMI), mood swings, weekly usage of mobile phone in the last 3 months, beef intake, cooked vegetable intake, and "smoking status: never" were acquired from the Integrative Epidemiology Unit Open genome-wide association study database. Causal effects of eight exposure factors and depression were investigated using MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, and results were primarily referred to IVW. Subsequently, univariable MR (UVMR) analysis was performed on eight exposure factors and depression, separately. In addition, sensitivity analysis, including heterogeneity test, horizontal pleiotropy, and leave-one-out (LOO) methods, was conducted to evaluate the stability of MR results. Furthermore, multivariable MR (MVMR) analysis was carried out. Results: UVMR analysis revealed that all eight exposure factors were causally associated with depression; alcoholic drinks per week, sleeplessness or insomnia, BMI, mood swings, weekly usage of mobile phone in the last 3 months, and cooked vegetable intake were risk factors, and beef intake and "smoking status: never" were protection factors. Heterogeneity tests revealed no heterogeneity for alcoholic drinks per week, sleeplessness or insomnia, mood swings, weekly usage of mobile phone in the last 3 months, and cooked vegetable intake. Meanwhile, there was no horizontal pleiotropy in UVMR, and LOO analysis verified that univariable analysis results were reliable. Moreover, MVMR analysis indicated that mood swings and weekly usage of mobile phone in the last 3 months were risk factors, and beef intake was a protection factor for depression when multiple factors occurred at the same time. Conclusion: Alcoholic drinks per week, sleeplessness or insomnia, BMI, mood swings, weekly usage of mobile phone in the last 3 months, and cooked vegetable intake were risk factors, and beef intake and "smoking status: never" were protection factors. In addition, mood swings, weekly usage of mobile phone in the last 3 months, and beef intake had a direct effect on depression when multiple factors occurred simultaneously.

Meeting report: The first soil viral workshop 2022.

Soil viral ecology is a growing research field; however, the state of knowledge still lags behind that of aquatic systems. Therefore, to facilitate progress, the first Soil Viral Workshop was held to encourage international scientific discussion and collaboration, suggest guidelines for future research, and establish soil viral research as a concrete research area. The workshop took place at Søminestationen, Denmark, between 15 and 17th of June 2022. The meeting was primarily held in person, but the sessions were also streamed online. The workshop was attended by 23 researchers from ten different countries and from a wide range of subfields and career stages. Eleven talks were presented, followed by discussions revolving around three major topics: viral genomics, virus-host interactions, and viruses in the soil food web. The main take-home messages and suggestions from the discussions are summarized in this report.

Experimental evaluation of deep learning method in reticulocyte enumeration in peripheral blood.

INTRODUCTION: Reticulocytes (RET) are immature red blood cells, and RET enumeration in peripheral blood has important clinical value in diagnosis, treatment efficacy observation, and prognosis of anemic diseases. For RET enumeration, flow cytometric reference method has shown to be more precise than the manual method by light microscopy. However, flow cytometric method generates occasionally spurious RET counts in some situations. The manual method, which is subjective, imprecise, and tedious, currently remains as an accepted reference method. As a result, there is a need for manual method to be more objective, precise, and rapid. METHODS: 40 EDTA-K2 anticoagulated whole blood samples were randomly selected for the study. 784 microscopic images were taken from blood slides as dataset, and all mature RBCs and RETs in these images were located and labeled by experienced experts. Then, we leverage a Faster R-CNN deep neural network to train a RET detection model and evaluate the model. RESULTS: Both the recall and precision rate of the model are more than 97%, and average analysis time of a single image is 0.21 seconds. CONCLUSION: The deep learning method shows outstanding performance including high accuracy and fast speed. The experimental results show that the deep learning method holds the potential to act as a rapid computer-aid method for manual RET enumeration for cytological examiners.

Grazing and Cultivated Grasslands Cause Different Spatial Redistributions of Soil Particles.

The distribution of soil particle sizes is closely related to soil health condition. In this study, grasslands under different grazing intensities and different cultivation ages grasslands were selected to evaluate the dynamics of soil particle size redistribution in different soil layers. When the grazing intensity increased, the percentage of 2000~150-µm soil particles in the 0-10-cm soil layer decreased; 150~53-µm soil particles remained relatively stable among the grazing intensities-approximately 28.52%~35.39%. However, the percentage of less than 53-µm soil particles increased. In cultivated grasslands, the larger sizes (>53 µm) of soil particles increased and the smaller sizes (<53 µm) decreased significantly (p < 0.05) in the 0-10 cm-soil layer with increasing cultivation ages. The increase in small soil particles (<53 µm) in topsoil associated with grazing intensity increased the potential risk of further degradation by wind erosion. The increase in big soil particles (>53 µm) in topsoil associated with cultivation ages decreased the soil capacity of holding water and nutrient. Therefore, to maintain the sustainability of grassland uses, grazing grasslands need to avoid heavy grazing, and cultivated grasslands need to change current cultivation practices.