Adsorption performance of ammonium molybdate modified Salix wood flour biochar for the treatment of monosodium glutamate wastewater.

ABSTRACTThe problem of wastewater pollution in the production of monosodium glutamate (MSG) is becoming more and more serious. A novel type of chemically modified Salix psammophila powder charcoal (SPPCAM) was synthesized to address the chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) in MSG wastewater. SPPCAM was prepared by carbonization method, in which inorganic ammonium molybdate (AM) was used as modifier and Salix psammophila powder (SPP) was used as raw material. Under optimal treatment conditions, maximum removal rates (removal capacities) of 45.9% (3313.2 mg·L-1) for COD and 29.4% (23.2 mg·L-1) for NH3-N in MSG wastewater were achieved. The treatment results significantly outperforming the unmodified Salix psammophila powder charcoal (SPPC), which only achieved removal rates (removal capacities) of 10.6% (763.9 mg·L-1) for COD and 12.9% (10 mg·L-1) for NH3-N. SPPC and SPPCAM before and after preparation were analysed by FT-IR and XRD, and Mo ions in the form of Mo2C within SPPCAM were successfully loaded. SEM, EDS-Mapping, BET, and other methods were used to analyse SPPCAM before and after MSG wastewater treatment, demonstrating that SPPCAM effectively treated organic pollutants in monosodium glutamate wastewater. The NH3-N in the treated MSG wastewater has reached the standard of safe discharge.

Effect of lignocellulose/montmorillonite immobilized Bacillus sp. on monosodium glutamate wastewater.

A group of Bacillus sp. was extracted from monosodium glutamate wastewater. Lignocellulose/montmorillonite composite was selected as the carrier. Lignocellulose/montmorillonite composite immobilized Bacillus sp./calcium alginate microspheres were prepared by immobilized microorganism techniques. The microspheres were used to treat monosodium glutamate wastewater with significantly reduced ammonia nitrogen (NH3-N) and chemical oxygen demand (COD) concentrations. The optimum preparation conditions of microspheres in the treatment of NH3-N and COD of monosodium glutamate wastewater were studied. The concentration of sodium alginate was 2.0 wt%, lignocellulose/montmorillonite was 0.06 wt%, Bacillus sp. was 1.0 wt%, CaCl2 solution was 2.0 wt%, coagulation time was 12 h, and the removal capacities of NH3-N and COD were 44832 and 78345 mg/L, respectively. The surface structure, element content, functional group change, and crystal structure of the microspheres were characterized by SEM, EDS, and other methods. The results showed that the -COOH in lignocellulose/montmorillonite and the -OH in the Bacillus sp. form intermolecular hydrogen bonds. The Si-O and Al-O bonds in lignocellulose/montmorillonite reacted with sodium ions in sodium alginate. New crystal structures appear inside the material after crosslinking, and the microspheres was formed. Thus, the study has shown that the microspheres were successfully prepared and contributes to the treatment of NH3-N and COD in monosodium glutamate wastewater. This work can provide an interesting strategy for the removal of COD and NH3-N in industrial wastewater by reasonably combining bio-physicochemical processes.

Aristolochic Acid-Induced Genotoxicity and Toxicogenomic Changes in Rodents.

Aristolochic acid (AA) is a group of structurally related nitrophenanthrene carboxylic acids found in many plants that are widely used by many cultures as traditional herbal medicines. AA is a causative agent for Chinese herbs nephropathy, a term replaced later by AA nephropathy. Evidence indicates that AA is nephrotoxic, genotoxic, and carcinogenic in humans; and it also induces tumors in the forestomach, kidney, renal pelvis, urinary bladder, and lung of rats and mice. Therefore, plants containing AA have been classified as carcinogenic to humans (Group 1) by the International Agency for Research on Cancer. In our laboratories, we have conducted a series of genotoxicity and toxicogenomic studies in the rats exposed to AA of 0.1-10 mg/kg for 12 weeks. Our results demonstrated that AA treatments induced DNA adducts and mutations in the kidney, liver, and spleen of rats, as well as significant alteration of gene expression in both its target and nontarget tissues. AA treatments altered mutagenesis- or carcinogenesis-related microRNA expression in rat kidney and resulted in significant changes in protein expression profiling. We also applied benchmark dose (BMD) modeling to the 3-month AA-induced genotoxicity data. The obtained BMDL10 (the lower 95% confidence interval of the BMD10 that is a 10% increase over the background level) for AA-induced mutations in the kidney of rats was about 7 µg/kg body weight per day. This review constitutes an overview of our investigations on AA-induced genotoxicity and toxicogenomic changes including gene expression, microRNA expression, and proteomics; and presents updated information focused on AA-induced genotoxicity in rodents.

Clinical Characteristics of Children with Coronavirus Disease 2019 in Hubei, China.

Since December 2019, COVID-19 has occurred unexpectedly and emerged as a health problem worldwide. Despite the rapidly increasing number of cases in subsequent weeks, the clinical characteristics of pediatric cases are rarely described. A cross-sectional multicenter study was carried out in 10 hospitals across Hubei province. A total of 25 confirmed pediatric cases of COVID-19 were collected. The demographic data, epidemiological history, underlying diseases, clinical manifestations, laboratory and radiological data, treatments, and outcomes were analyzed. Of 25 hospitalized patients with COVID-19, the boy to girl ratio was 1.27:1. The median age was 3 years. COVID-19 cases in children aged <3 years, 3.6 years, and ≥6-years patients were 10 (40%), 6 (24%), and 9 (36%), respectively. The most common symptoms at onset of illness were fever (13 [52%]), and dry cough (11 [44%]). Chest CT images showed essential normal in 8 cases (33.3%), unilateral involvement of lungs in 5 cases (20.8%), and bilateral involvement in 11 cases (45.8%). Clinical diagnoses included upper respiratory tract infection (n=8), mild pneumonia (n=15), and critical cases (n=2). Two critical cases (8%) were given invasive mechanical ventilation, corticosteroids, and immunoglobulin. The symptoms in 24 (96%) of 25 patients were alleviated and one patient had been discharged. It was concluded that children were susceptible to COVID-19 like adults, while the clinical presentations and outcomes were more favorable in children. However, children less than 3 years old accounted for majority cases and critical cases lied in this age group, which demanded extra attentions during home caring and hospitalization treatment.

Preconditioned Stochastic Gradient Descent.

Stochastic gradient descent (SGD) still is the workhorse for many practical problems. However, it converges slow, and can be difficult to tune. It is possible to precondition SGD to accelerate its convergence remarkably. But many attempts in this direction either aim at solving specialized problems, or result in significantly more complicated methods than SGD. This paper proposes a new method to adaptively estimate a preconditioner, such that the amplitudes of perturbations of preconditioned stochastic gradient match that of the perturbations of parameters to be optimized in a way comparable to Newton method for deterministic optimization. Unlike the preconditioners based on secant equation fitting as done in deterministic quasi-Newton methods, which assume positive definite Hessian and approximate its inverse, the new preconditioner works equally well for both convex and nonconvex optimizations with exact or noisy gradients. When stochastic gradient is used, it can naturally damp the gradient noise to stabilize SGD. Efficient preconditioner estimation methods are developed, and with reasonable simplifications, they are applicable to large-scale problems. Experimental results demonstrate that equipped with the new preconditioner, without any tuning effort, preconditioned SGD can efficiently solve many challenging problems like the training of a deep neural network or a recurrent neural network requiring extremely long-term memories.

Automatic identification of functional clusters in FMRI data using spatial dependence.

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependence--mutual information--among spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.

Time delay and Doppler estimation for wideband acoustic signals in multipath environments.

Estimation of the parameters of a multipath underwater acoustic channel is of great interest for a variety of applications. This paper proposes a high-resolution method for jointly estimating the multipath time delays, Doppler scales, and attenuation amplitudes of a time-varying acoustical channel. The proposed method formulates the estimation of channel parameters into a sparse representation problem. With the [script-l](1)-norm as the measure of sparsity, the proposed method makes use of the basis pursuit (BP) criterion to find the sparse solution. The ill-conditioning can be effectively reduced by the [script-l](1)-norm regularization. Unlike many existing methods that are only applicable to narrowband signals, the proposed method can handle both narrowband and wideband signals. Simulation results are provided to verify the performance and effectiveness of the proposed algorithm, indicating that it has a super-resolution in both delay and Doppler domain, and it is robust to noise.

[Structural characterization of a glucan isolated from Gastrodia elata].

OBJECTIVE: To study a glucan (GB II) isolated from Gastrodia elata. METHODS: The glucan was obtained with water extraction, alcohol precipitate, DEAE-Sepharose Fast Flow column and Sepharose CL-6B column chromatography; sugar composition analysis, IR and NMR were used to determine the structural feature. RESULTS: The molecular weight of the glucan was 4 300 dalton estimated by HPGPC; it contained 27 glucose residues, which mainly composed of alpha-D-(1-->4)-glucose with little glucuronic acid and branch O-6 points. CONCLUSION: The glucan was a new glucan for the first report from Gastrodia elata.

Deconvolution of sparse underwater acoustic multipath channel with a large time-delay spread.

The deconvolution of multipath underwater acoustic channel with a large time-delay spread is investigated. The channel deconvolution involves estimating the multipath time-delays and attenuation factors from a noisy received signal consisting of multiple overlapped signals. Similar to conventional deconvolution methods, the proposed method estimates channel impulse response based on least-squares criterion. However, the proposed method harnesses the sparse structure of an underwater acoustic channel, and [script-l](1)-norm of the channel impulse response is adopted as the cost function to be minimized. In addition, the available a priori knowledge of support constraint and attenuation factor constraint are imposed and channel deconvolution problem is converted to a convex optimization problem. Instead of employing the existing standard algorithms, which require huge storage space and high computational complexity, a simple iterative algorithm for solving the optimization problem with fast convergence rate and low complexity is developed. The computational complexity of the proposed algorithm is O(N log(2)(N)) per iteration with N being the length of the received signal. Simulation results confirm that the proposed method provides better performance in terms of temporal resolution and robustness to noise compared with other extant multipath channel deconvolution techniques.