Water is a limited and valuable resource. Singapore has four national sources of water supply, one of which is natural precipitation. Pollutants collected in stormwater runoff are deposited into drainage systems and reservoirs. Major nutrient pollutants found in local stormwater runoff include nitrate and phosphate, which may cause eutrophication. Bioretention systems are efficient in removing these pollutants in the presence of plants. This paper discusses plant traits that can enhance the phytoremediation of nutrient pollutants in stormwater runoff for application in bioretention systems. The plant species studied showed variations in chlorophyll florescence, leaf greenness, biomass production, and nitrate and phosphate removal. In general, dry biomass was moderately correlated to nitrate and phosphate removal (r = 0.339–0.501). Root, leaf, and total dry biomass of the native tree species showed a moderate to strong correlation with nitrate removal (r = 0.811, 0.657, and 0.727, respectively). Leaf dry biomass of fastgrowing plants also showed a moderate to strong relationship with the removal of both pollutants (r = 0.707 and 0.609, respectively). Root dry biomass of slow-growing plants showed a strong relationship with phosphate removal (r = 0.707), but the correlation was weaker for nitrate removal (r = 0.557). These results are valuable for choosing plants for application in bioretention systems.
2,4-dinitroanisole (DNAN) is an important component of insensitive munitions that is anticipated to replace 2,4,6-trinitrotoluene (TNT) in munitions formulations. Photocatalyzed hydrogen peroxide (H2O2) oxidation experiments and chemical analyses were conducted to study the effect of initial pH and H2O2 dosage on the kinetics of DNAN decomposition and the reaction pathways. The results show that DNAN degradation followed zero-order kinetics when a 250 ppm DNAN solution was treated with ultraviolet (UV) light and 1500–4500 ppm H2O2 in an initial pH range of 4–7. However, when the H2O2 concentration was 750 ppm, DNAN degradation followed pseudo-first-order kinetics. The results indicate that DNAN can easily be oxidized by UV/H2O2 treatment. When the H2O2 dosage was 1500 ppm and the initial pH was 7, DNAN was reduced from 250 ppm to less than 1 ppm in 3 h. However, the total organic carbon (TOC) and total carbon (TC) concentrations were reduced slowly from 100 to less than 70 ppm carbon (C) in 3 h, and decreased to about 5 ppm after 9 h of treatment, suggesting the formation of other organic compounds. Those reaction intermediates were oxidized to carbon dioxide (CO2) at a slower rate than the oxidation of DNAN. CO2 was emitted from the solution because the solution pH decreased rapidly to about 3 during the UV/H2O2 oxidation. Most of the nitrogen in DNAN was converted to nitrate by UV/H2O2 oxidation after 9 h of treatment. The research results indicate that UV/H2O2 oxidation is a promising technique for the treatment of DNAN in wastewater.
The Chinese Loess Plateau is the most seriously eroded area in the world and contributes the vast majority of the sediment that goes into the Yellow River. Since the 1950s, progressive soil and water conservation measures have been implemented—in particular, large-scale ecological restoration has been ongoing since 1999—resulting in a significant reduction of the sediment load. However, the mechanism
of the sediment transport dynamics is not fully understood due to multiple and complicated influencing factors including climate change and human activities (e.g., ecological restoration). A challenging question, then, arises: Is the current low sediment level a ″new normal″ in this era and in the future? To address this question, we selected a typical loess hilly region where considerable ecological restoration has been implemented, and which is regarded as the site of the first and most representative Grain-for-Green program in the Loess Plateau. We investigated the evolution of discharge–sediment relationships in the past decades (1960–2010) and their association with the soil and water conservation measures in this area. The results showed that there was a distinct change in the regression parameters of the commonly used annual discharge–sediment regression equation—a continuously increasing trend of parameter b and a decreasing trend of parameter a, accompanying the ecological restoration. The increase in exponent b (i.e., a steeper slope) implies a potential lower sediment load resulting from low discharge and a potential higher sediment load resulting from large discharge. This finding may question the new
normal of a low sediment level and implies the potential risk of a large sediment load during extremely wet years.
In this study, a copper ferrocyanide/silica/polyvinylidene fluoride (CuFC/SiO2/PVDF) hollow-fiber composite membrane was successfully synthesized through a facile and effective crosslinking strategy. The PVDF hollow-fiber membrane with embedded SiO2 was used to fix the dispersion of CuFC nanoparticles for cesium (Cs) removal. The surface morphology and chemical composition of the composite membrane were analyzed using scanning electron microscopy and X-ray photoelectron spectroscopy (XPS). The composite membrane showed a high Cs rejection rate and membrane flux at the three layers of CuFC and 0.5% SiO2, and its Cs rejection rate was not affected by variation in the pH (pH = 4–10). The modified membrane could be effectively regenerated many times using ammonium nitrate (NH4NO3). The Cs selectivity performance was verified by an efficient Cs rejection rate (76.25% and 88.67% in 8 h) in a solution of 100 μg·L–1 of Cs with 1 mmol·L–1 of competing cations (K+ and Na+). The CuFC/SiO2/PVDF hollowfiber composite membrane showed a particularly superior removal performance (greater than 90%) in natural surface water and simulated water with a low Cs concentration. Therefore, the CuFC/SiO2/PVDF hollow-fiber composite membrane can be used directly in engineering applications for the remediation of radioactive Cs-contaminated water.
Illicit and pharmaceutical drugs are considered to be emerging contaminants of concern, and much research effort has gone into assessing their occurrence in wastewater. However, little information exists on their presence in treated sludge or biosolids. In this study, we examined sludge and biosolids from a large metropolitan wastewater treatment plant (WWTP) in Australia to determine the occurrence of five drugs of abuse, including benzoylecgonine as indicator of cocaine consumption, methamphetamine and 3,4-methylenedioxy methamphetamine (MDMA) as representative illicit stimulants, and codeine and morphine as pharmaceuticals with potential environmental risk. The samples were solid-phase extracted and analyzed by liquid chromatography-tandem mass spectrometry (LCMS/MS). Benzoylecgonine and MDMA were present in raw sludge but were notably degraded during solids treatment processes, and were not detected in the dewatered sludge (after treatment) or in biosolids. Methamphetamine, codeine, and morphine were detected in all biosolids samples at mean concentrations of 20–50 μg·kg−1. The presence of these three drugs in biosolids shows that these compounds are relatively stable in the solids and in soil, and can persist in biosolids for at least several years. A simple environmental risk assessment based on
estimated risk quotients (RQs) for these compounds indicated that the potential environmental risks associated with the land application of biosolids are very low at typical Australian biosolids application rates.
Groundwater treatment sludge is an industrial waste that is massively produced from groundwater treatment plants. Conventional methods for treatment of this sludge, such as discharge into deep wells or the sea, or disposal at landfills, are not environmentally sustainable. Here, we demonstrate an alternative strategy to recycle the sludge by preparing a magnetic maghemite adsorbent via a one-step hydrothermal method with NaOH solution as the only solvent. With this method, the weakly magnetized sludge, which contained 33.2% iron (Fe) and other impurities (e.g., silicon (Si), aluminum (Al), and manganese (Mn)), was converted to magnetic adsorbent (MA) with the dissolution of Si/Al oxides (e.g., quartz and albite) into the liquid fraction. At a NaOH concentration of 2 mol·L−1, approximately 18.1% of the ferrihydrite in the Fe oxides of the sludge was converted into 11.2% maghemite and 6.9% hematite after the hydrothermal treatment. MA2 (MA produced by a 2 mol·L−1 NaOH concentration) exhibited a good magnetic response of 8.2 emu·g−1 (1 emu = 10−3 A·m2), and a desirable surface site concentration of 0.75 mmol·g−1. The synthesized MA2 was used to adsorb the cationic pollutant tetracycline (TC). The adsorption kinetics of TC onto MA2 fitted well with a pseudo-second-order model, and the adsorption isotherms complied well with the Langmuir model. The maximum adsorption capacity of MA2 for TC was 362.3 mg·g−1, and the main mechanism for TC adsorption was cationic exchange. This study is the first to demonstrate the preparation of an MA from recycled sludge without a reductant and/or exogenous Fe source. The prepared adsorbent can be used as a low-cost adsorbent with high capacity for TC sorption in the treatment of TC-containing wastewater.
As the most important complementary medication against a variety of diseases, traditional Chinese medicines (TCMs) have been extensively applied over thousands of years. Current quality control of herbal medicines, however, is in great dispute. Unlike chemical drugs, which have clear and validated quality standards, the content of only one (or a few) compounds of many herbs and preparations is currently assessed as an indicator of quality, even though the assessed compound(s) is neither closely associated with the efficacy nor representative of the medicine as a whole. Based on the clinical use, compatibility of multiple component prescriptions, and manufacturing process of TCM, the new concept of a TCM quality marker that was proposed in previous work is discussed further here. In addition, practical technological approaches are described for the qualitative analysis and quantification of TCMs including herbs, processed products, and preparations, which lead to the discovery and identification of specific chemicals as quality markers and new quality control patterns. The progress that has been made in TCM quality control is also addressed. This work provides useful information for the quality control of herbal medicines in the future.
Rheumatoid arthritis (RA) is a common autoimmune condition with an elusive etiology. Conventional and biological disease-modifying drugs sometimes fail or produce only partial responses. Traditional Chinese medicine (TCM) has long been used in China as a treatment for RA and is achieving everincreasing acceptance worldwide. TCM treatments are traditionally guided by the theory of treatment based on TCM syndrome differentiation; however, they remain a matter of empirical practice relying on TCM theories and doctors' own experience, which places severe restrictions on worldwide TCM application. Nevertheless, TCM is a treasure trove for drug discovery, particularly as a treatment for complicated human conditions. The discoveries of artemisinin as a treatment for malaria and of TCM–arsenic trioxide (As2O3) combination therapy as a treatment for acute promyelocytic leukemia (APL) are excellent examples of the great value of TCM. Regarding RA treatments, many Chinese medicinal herbs and their formulas, extracts, ingredients, and even single compounds have been used in clinical applications. Several Chinese proprietary medicines (CPMs) derived from TCM formulas or herbal bioactive components, such as the controlled-release tablets of ZhengQingFengTongNing (ZQFTN), tripterygium glycoside tablets, and capsules of total glucosides of peony (TGP), have been included in the National Health Insurance Directory of China, and show comparable therapeutic efficacies to those of western chemical drugs with fewer side effects. As TCM research has advanced, particularly in the use of multidisciplinary technologies, the scientific foundations and characteristics of the use of TCM to treat RA have been revealed, and the quality of TCM treatments have been increasingly enhanced. However, TCM generally lacks sufficient clinical and laboratory data to be consistent with international standards for quality, safety, and efficacy in order to support its application worldwide. Therefore, intensive basic and clinical studies on TCM are required. In particular, investigations that use cutting-edge technologies in analytical chemistry, biology, and biomedical sciences, and the development of randomized clinical trials (RCTs) and personalized pragmatic randomized controlled trials (PPRCTs) are necessary. Researchers should also collaborate to advance TCM from empirical practice to evidence-based therapy, thus consistently promoting TCM development and globalization in a vital, beneficial, and contributable manner.
Herbgenomics is an emerging field of traditional Chinese medicine (TCM) research and development. By combining TCM research with genomics, herbgenomics can help to establish the scientific validity of TCM and bring it into wider usage within the field of medicine. Salvia Linn. is a large genus of Labiatae that includes important medicinal plants. In this herbgenomics study, the complete chloroplast (cp) genomes of two Salvia (S.) spp.—namely, S. przewalskii and S. bulleyana, which are used as a surrogate for S. miltiorrhiza—were sequenced and compared with those of two other reported Salvia spp.—namely, S. miltiorrhiza and S. japonica. The genome organization, gene number, type, and repeat sequences were compared. The annotation results showed that both Salvia plants contain 114 unique genes, including 80 protein-coding, 30 transfer RNA (tRNA), and four ribosomal RNA (rRNA) genes. Repeat sequence analysis revealed 21 direct and 22 palindromic
sequences in both Salvia cp genomes, and 17 and 21 tandem repeats in S. przewalskii and S. bulleyana, respectively. A synteny comparison of the Salvia spp. cp genomes showed a high degree of sequence similarity in the coding regions and a relatively high divergence of the intergenic spacers. Pairwise alignment and singlenucleotide polymorphism (SNP) analyses found some candidate fragments to identify Salvia spp., such as the intergenic region of the trnV-ndhC, trnQ-rps16, atpI-atpH, psbA-ycf3, ycf1, rpoC2, ndhF, matK, rpoB, rpoA and accD genes. All of the results—including the repeat sequences and SNP sites, the inverted repeat (IR) region border, and the phylogenetic analysis—showed that S. przewalskii and S. bulleyana are extremely similar from a genetic standpoint. The cp genome sequences of the two Salvia spp. reported here will pave the way for breeding, species identification, phylogenetic evolution, and cp genetic engineering studies of Salvia medicinal plants.
The prevalence of type 2 diabetes mellitus (T2DM) is increasing rapidly worldwide. Because of the limited success of generic interventions, the focus of the disease study has shifted toward personalized strategies, particularly in the early stages of the disease. Traditional Chinese medicine (TCM) is based on a systems view combined with personalized strategies and has improved our knowledge of personalized diagnostics. From a systems biology perspective, the understanding of personalized diagnostics can be improved to yield a biochemical basis for such strategies; for example, metabolomics can be used in combination with other systembased diagnostic methods such as ultra-weak photon emission (UPE). In this study, we investigated the feasibility of using plasma metabolomics obtained from 44 pre-diabetic subjects to stratify the following TCM-based subtypes: Qi-Yin deficiency, Qi-Yin deficiency with dampness, and Qi-Yin deficiency with stagnation. We studied the relationship between plasma metabolomics and UPE with respect to TCM-based subtyping in order to obtain biochemical information for further interpreting disease subtypes. Principal component analysis of plasma metabolites revealed differences among the TCM-based pre-T2DM subtypes. Relatively high levels of lipids (e.g., cholesterol esters and triglycerides) were important discriminators of two of the three subtypes and may be associated with a higher risk of cardiovascular disease. Plasma metabolomics data indicate that the lipid profile is an essential component captured by UPE with respect to stratifying subtypes of T2DM. The results suggest that metabolic differences exist among different TCM-based subtypes of pre-T2DM, and profiling plasma metabolites can be used to discriminate among these subtypes. Plasma metabolomics thus provides biochemical insights into system-based UPE measurements.
Exploring the physical mechanisms of complex systems and making effective use of them are the keys to dealing with the complexity of the world. The emergence of big data and the enhancement of computing power, in conjunction with the improvement of optimization algorithms, are leading to the development of artificial intelligence (AI) driven by deep learning. However, deep learning fails to reveal the underlying logic and physical connotations of the problems being solved. Mesoscience provides a concept to understand the mechanism of the spatiotemporal multiscale structure of complex systems, and its capability for analyzing complex problems has been validated in different fields. This paper proposes a research paradigm for AI, which introduces the analytical principles of mesoscience into the design of deep learning models. This is done to address the fundamental problem of deep learning models detaching the physical prototype from the problem being solved; the purpose is to promote the sustainable development of AI.
It has long been a challenging task to detect an anomaly in a crowded scene. In this paper, a self-supervised framework called the abnormal event detection network (AED-Net), which is composed of a principal component analysis network (PCAnet) and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, the PCAnet is trained to extract high-level semantics of the crowd's situation. Next, kPCA, a one-class classifier, is trained to identify anomalies within the scene. In contrast to some prevailing deep learning methods, this framework is completely self-supervised because it utilizes only video sequences of a normal situation. Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota (UMN dataset) and Anomaly Detection dataset from University of California, San Diego (UCSD dataset), and competitive results that yield a better equal error rate (EER) and area under curve (AUC) than other state-of-the-art methods are observed. Furthermore, by adding a local response normalization (LRN) layer, we propose an improvement to the original AED-Net. The results demonstrate that this proposed version performs better by promoting the framework's generalization capacity.
Middle East respiratory syndrome (MERS) is a viral respiratory disease caused by a de novo coronavirus—Middle East respiratory syndrome coronavirus (MERS-CoV)—that is associated with high mortality. However, the mechanism by which MERS-CoV infects humans remains unclear. To date, there is no effective vaccine or antibody for human immunity and treatment, other than the safety and tolerability of the fully human polyclonal Immunoglobulin G (IgG) antibody (SAB-301) as a putative therapeutic agent specific for MERS. Although rapid diagnostic and public health measures are currently being implemented, new cases of MERS-CoV infection are still being reported. Therefore, various effective measures should be taken to prevent the serious impact of similar epidemics in the future. Further investigation of the epidemiology and pathogenesis of the virus, as well as the development of effective therapeutic and prophylactic anti-MERS-CoV infections, is necessary. For this purpose, detailed information on MERS-CoV proteins is needed. In this review, we describe the major structural and nonstructural proteins of MERS-CoV and summarize different potential strategies for limiting the outbreak of MERS-CoV. The combination
of computational biology and virology can accelerate the advanced design and development of effective peptide therapeutics against MERS-CoV. In summary, this review provides important information about the progress of the elimination of MERS, from prevention to treatment.
Brain encoding and decoding via functional magnetic resonance imaging (fMRI) are two important aspects of visual perception neuroscience. Although previous researchers have made significant advances in brain encoding and decoding models, existing methods still require improvement using advanced machine learning techniques. For example, traditional methods usually build the encoding and decoding models separately, and are prone to overfitting on a small dataset. In fact, effectively unifying the encoding and decoding procedures may allow for more accurate predictions. In this paper, we first review the existing encoding and decoding methods and discuss
the potential advantages of a ″bidirectional″ modeling strategy. Next, we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules Furthermore, deep generative models (e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs)) have produced promising results in studies on brain encoding and decoding. Finally, we propose that the dual learning method, which was originally designed for machine translation tasks, could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.
Reducing heat accumulation within vehicles and ensuring appropriate vehicular temperature levels can lead to enhanced
vehicle fuel economy, range, reliability, longevity, passenger comfort, and safety. Advancements in vehicle thermal management remain key as new technologies, consumer demand, societal concerns, and government regulations emerge and evolve. This study summarizes several recent advances in vehicle thermal management technology and modeling, with a focus on three key areas: the cabin, electronics, and exterior components of vehicles. Cabin-related topics covered include methods for reducing thermal loads and improving heating, ventilation, and air-conditioning (HVAC) systems; and advancements in window glazing/tinting and vehicle surface treatments. For the thermal management of electronics, including batteries and insulated-gate bipolar transistors (IGBTs), active and passive cooling methods that employ heat pipes, heat sinks, jet impingement, forced convection, and phase-change materials are discussed. Finally, efforts to model and enhance the heat transfer of exterior vehicular components are reviewed while considering drag/friction forces and environmental effects. Despite advances in the field of vehicle thermal management, challenges still exist; this article provides a broad summary of the major issues, with recommendations for further study.
Waste disposal management and the energy crisis are important challenges facing most countries. The fruit-processing industry generates daily several tons of wastes, of which the major share comes from banana farms. Anaerobic digestion (AD) technology has been applied to the treatment of wastewater, animal slurry, food waste, and agricultural residues, with the primary goals of energy production and waste elimination. This study examines the effect of organic loading (OL) and cow manure (CM) addition on AD performance when treating banana peel waste (BPW). The maximum daily biogas production rates of banana peels (BPs) with a CM content of 10%, 20%, and 30% at 18 and 22 g of volatile solids (gvs) per liter were 50.20, 48.66, and 62.78 mL·(gvs·d)-1 and 40.49, 29.57, and 46.54 mL·(gvs·d)-1, respectively. However, the daily biogas yield showed no clear interdependence with OL or CM content. In addition, a kinetic analysis using first-order and cone models showed that the kinetic parameters can be influenced by the process parameters.