Effects of remediation agents on rice and soil in toxic metal(loid)s contaminated paddy fields: A global meta-analysis.

Toxic metal(loid)s contamination of paddy soil is a nonnegligible issue and threatens food safety considering that it is transmitted via the soil-plant system. Applying remediation agents could effectively inhibit the soil available toxic metal(loid)s and reduce their accumulation in rice. To comprehensively quantify how remediation agents impact the accumulation of Cd/Pb/As in rice, rice growth and yield, the accumulation of available Cd/Pb/As in paddy soil, and soil characteristics, 50 peer-reviewed publications were selected for meta-analysis. Overall, the application of remediation agents exhibited significant positive effects on rice plant length (ES = 0.05, CI = 0.01-0.08), yield (ES = 0.20, CI = 0.13-0.27), peroxidase (ES = 0.56, CI = 0.18-0.31), photosynthetic rate (ES = 0.47, CI = 0.34-0.61), and respiration rate (ES = 0.68, CI = 0.47-0.88). Among the different types of remediation agents, biochar was the most effective in controlling the accumulation of Cd/Pb/As in all portions of rice, and was also superior in inhibiting the accumulation of Pb in rice grains (ES = -0.59, 95 % CI = -1.04-0.13). This study offers an essential contribution for the remediation strategies of toxic metal(loid)s contaminated paddy fields.

The effects of iron-based nanomaterials (Fe NMs) on plants under stressful environments: Machine learning-assisted meta-analysis.

Mitigating the adverse effects of stressful environments on crops and promoting plant recovery in contaminated sites are critical to agricultural development and environmental remediation. Iron-based nanomaterials (Fe NMs) can be used as environmentally friendly nano-fertilizer and as a means of ecological remediation. A meta-analysis was conducted on 58 independent studies from around the world to evaluate the effects of Fe NMs on plant development and antioxidant defense systems in stressful environments. The application of Fe NMs significantly enhanced plant biomass (mean = 25%, CI = 20%-30%), while promoting antioxidant enzyme activity (mean = 14%, CI = 10%-18%) and increasing antioxidant metabolite content (mean = 10%, CI = 6%-14%), reducing plant oxidative stress (mean = -15%, CI = -20%∼-10%), and alleviating the toxic effects of stressful environments. The observed response was dependent on a number of factors, which were ranked in terms of a Random Forest Importance Analysis. Plant species was the most significant factor, followed by Fe NM particle size, duration of application, dose level, and Fe NM type. The meta-analysis has demonstrated the potential of Fe NMs in achieving sustainable agriculture and the future development of phytoremediation.

Effects of the application of nanoscale zero-valent iron on plants: Meta analysis, mechanism, and prospects.

In order to determine the ideal conditions for the application of nanoscale zero-valent iron (nZVI) in agricultural production, this review studies the effects of nZVI application on plant physiological parameters, presents its mechanism and prospective outcomes. In this research, it was observed that the application of nZVI had both favorable and unfavorable effects on plant growth, photosynthesis, oxidative stress, and nutrient absorption levels. Specifically, the application of nZVI significantly increased the biomass and length of plants, and greatly reduced the germination rate of seeds. In terms of photosynthesis, there was no significant effect for the application of nZVI on the synthesis of photosynthetic pigments (chlorophyll and carotenoids). In terms of oxidative stress, plants respond by increasing the activity of antioxidant enzyme under mild nZVI stress and trigger oxidative burst under severe stress. In addition, the application of nZVI significantly increased the absorption of nutrients (B, K, P, S, Mg, Zn, and Fe). In summary, the application of nZVI can affect the plant physiological parameters, and the degree of influence varies depending on the concentration, preparation method, application method, particle size, and action time of nZVI. These findings are important for evaluating nZVI-related risks and enhancing nZVI safety in agricultural production.

A multistage multimodal deep learning model for disease severity assessment and early warnings of high-risk patients of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic research on leveraging consecutive rounds of multimodal clinical examination and laboratory test data to facilitate clinical decision-making for the treatment of COVID-19. To address this issue, we propose a multistage multimodal deep learning (MMDL) model to (1) first assess the patient's current condition (i.e., the mild and severe symptoms), then (2) give early warnings to patients with mild symptoms who are at high risk to develop severe illness. In MMDL, we build a sequential stage-wise learning architecture whose design philosophy embodies the model's predicted outcome and does not only depend on the current situation but also the history. Concretely, we meticulously combine the latest round of multimodal clinical data and the decayed past information to make assessments and predictions. In each round (stage), we design a two-layer multimodal feature extractor to extract the latent feature representation across different modalities of clinical data, including patient demographics, clinical manifestation, and 11 modalities of laboratory test results. We conduct experiments on a clinical dataset consisting of 216 COVID-19 patients that have passed the ethical review of the medical ethics committee. Experimental results validate our assumption that sequential stage-wise learning outperforms single-stage learning, but history long ago has little influence on the learning outcome. Also, comparison tests show the advantage of multimodal learning. MMDL with multimodal inputs can beat any reduced model with single-modal inputs only. In addition, we have deployed the prototype of MMDL in a hospital for clinical comparison tests and to assist doctors in clinical diagnosis.

How culture orientation influences the COVID-19 pandemic: An empirical analysis.

Purpose: This study aims to investigate the mediational path of the influence of cultural orientation on the COVID-19 pandemic outcome at the national level and find out whether some culture-related factors can have a moderating effect on the influence of culture. Methodology: Cultural dimension theory of Hofstede is used to quantify the degree of each dimension of culture orientation. The cross-section regression model is adopted to test if culture orientations affect the pandemic outcome, controlling for democracy, economy, education, population, age, and time. Then, a mediational analysis is conducted to examine if policy response is the mediator that culture makes an impact on the pandemic outcome. Finally, a moderation analysis is carried out to determine how each control variable has moderated the influence. Findings: The cross-section regression results showed that culture orientation influences the outcome of the COVID-19 pandemic at the 99% confidence level and that among the six cultural dimensions, collectivism-individualism has the most significant impact. It has also been found that policy response is the mediator of cultural influence, and culture-related factors can moderate the influence. Contribution: The contribution of this research lies in developing the assertion that culture influences pandemic outcomes. Our findings indicate that collectivism-individualism culture orientation affects the effectiveness of epidemic controls the most among the six culture dimensions. Additionally, our research is the first to study the mediating effect of policy responses and the moderating effect of culture-related factors on the influence of cultural orientation on the pandemic outcome.

Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm.

Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(p n2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.

Establishing Antibacterial Multilayer Films on the Surface of Direct Metal Laser Sintered Titanium Primed with Phase-Transited Lysozyme.

Direct metal laser sintering is a technology that allows the fabrication of titanium (Ti) implants with a functional gradation of porosity and surface roughness according to three-dimensional (3D) computer data. The surface roughness of direct metal laser sintered titanium (DMLS-Ti) implants may provide abundant binding sites for bacteria. Bacterial colonization and subsequent biofilm formation can cause unsatisfactory cell adhesion and implant-related infections. To prevent such infections, a novel phase-transited lysozyme (PTL) was utilized as an initial functional layer to simply and effectively prime DMLS-Ti surfaces for subsequent coating with antibacterial multilayers. The purpose of the present study was to establish a surface with dual biological functionality. The minocycline-loaded polyelectrolyte multilayers of hyaluronic acid (HA) and chitosan (CS) formed via a layer-by-layer (LbL) self-assembly technique on PTL-functionalized DMLS-Ti were designed to inhibit pathogenic microbial infections while allowing the DMLS-Ti itself and the modified coatings to retain acceptable biocompatibility. The experimental results indicate that the DMLS-Ti and the hydrogel treated surfaces can inhibit early bacterial adhesion while completely preserving osteoblast functions. This design is expected to gain considerable interest in the medical field and to have good potential for applications in multifunctional DMLS-Ti implants.

[Method for determining nitrogen dioxide in workplace air by ion-exchange chromatography].

OBJECTIVE: To evaluate the method for collecting NO2 in workplace air using a solid adsorbent and determining the concentration of NO2 by ion-exchange chromatography. METHODS: NO2 in workplace air was collected using sampling tubes filled with 13X molecular sieve soaked with triethanolamine, and the samples were desorbed with a certain concentration of triethanolamine solution to obtain NO2(-). NO2(-) was separated with an anion exchange chromatography column and quantified by a conductivity detector. The concentration of NO2 was determined based on the conversion coefficient of NO2 and NO2(-). RESULTS: Different concentrations of NO2 (standard gas) was collected using the sampling tubes at different time points, and the sampling efficiency and desorption efficiency could reach more than 90%. The penetrating capacity of sampling tubes was more than 1.1 mg. The comparative test shows that there was no significant difference between the new method and national standard method (solution absorption) (P > 0.05). The samples could be stored at room temperature for more than 16 days. The calibration curve plotted in the new method was linear in the range of 0.1∼20.0 µg/ml, with a correlation coefficient of 0.9998; the detection limit was 0.01 µg/ml; the minimum detectable concentration was 0.05 mg/m(3) (V0 = 3.0 L). CONCLUSION: This determination method meets the requirements of Guide for establishing occupational heath standards-Part 4 Determination methods of air chemicals in workplace (GBZ/T 210.4-2008) and shows obvious advantages for determination of NO2 in workplace air.

Synthesis and bioactivity of 4-alkyl(aryl)thioquinazoline derivatives.

Some S'-substituted 4-alkyl(aryl)thioquinazoline derivatives were synthesized through thioetherification reaction of 4-chloroquinazolines 2 and thiol compounds 1 refluxed in acetone in the presence of K(2)CO(3). Their structures were verified by elemental analysis, IR, (1)H NMR, and (13)C NMR. The compounds were evaluated for their anti-proliferative activities against some cancer cells in vitro by MTT method. Among them, 3c, 3a, 3d, 3f, and 3l were highly effective against PC3 cells and 3a-3m showed weak activities against Bcap37 and BGC823 cells. The IC(50) value of 3c, 3a, 3d, 3f, and3l against PC3 cell was 1.8, 5.6, 8.1, 8.7, and 8.9 microM, respectively.

Synthesis, structure, and bioactivity of N'-substituted benzylidene-3,4,5-trimethoxybenzohydrazide and 3-acetyl-2-substituted phenyl-5-(3,4,5-trimethoxyphenyl)-2,3-dihydro-1,3,4-oxadiazole derivatives.

Some 3-acetyl-2-substituted phenyl-5-(3,4,5-trimethoxyphenyl)-2,3-dihydro-1,3,4-oxadiazole derivatives were synthesized by cyclization reaction of N'-substituted benzylidene-3,4,5-trimethoxybenzohydrazide in acetic anhydride. Their structures were verified by elemental analysis, IR, (1)H NMR, and (13)C NMR. Compound 3i was provided with X-ray crystallographic data. The compounds were evaluated for their antiproliferative activities against some cancer cells in vitro by MTT method. Among them, 2a, 2b, 2c, 2f, 3l, and 3m were highly effective against PC3 cells and 2a, 2c, and 2f showed moderate activities against Bcap37 and BGC823 cells. The IC(50) values of high active compounds 2a, 2b, 2c, 2f, 3l, and 3m against PC3 cells were 0.2, 1.8, 0.2, 1.2, 1.7, and 0.3muM, respectively.

Synthesis, X-ray crystallographic analysis, and antitumor activity of N-(benzothiazole-2-yl)-1-(fluorophenyl)-O,O-dialkyl-alpha-aminophosphonates.

Alpha-aminophosphonates containing benzothiazole and fluorine moiety, 4a-4m, were synthesized by Mannich-type addition in ionic liquid media with high yield and short reaction time. Their structures were established by IR, (1)H NMR, (13)C NMR, and elemental analysis. The X-ray crystallographic data of compounds 4j and 4m were provided. The newly synthesized compounds were evaluated for their anticancer activities against PC3, A431, A375, and Bcap37 cells in vitro by the MTT method. Compound 4c is highly effective against PC3 cells and moderate to A431 cells. Hence, further study is necessary to find out the potential antitumor activities.

[Determination of Rh in removal solution of the catalyst from HNBR by FAAS].

The content of Rhodium in extraction solution of the catalyst from HNBR was determined by FAAS. The results showed that this method was simple, rapid, accurate and convenient. Furthermore, this method was used as a means to investigate the optimum process conditions for removing catalyst from HNBR by ligation-extraction technology. When sulfocarbmide was used as a ligation agent and 88% formic acid as an extraction agent, the removal rate of Rh was greater than 98% under the condition of 100 degrees C and two hours of reaction time.