Bacterial infection is amongst the most common diseases in community and hospital settings. Fluoroquinolones, exerting the antibacterial activity through binding to type II bacterial topoisomerase enzymes, DNA gyrase and topoisomerase IV, are mainstays of chemotherapy. At present, fluoroquinolones are the most valuable antibacterial agents used popularly. However, the emergence of more virulent and resistant pathogens by the development of either mutated DNA-binding proteins or efflux pump mechanism for fluoroquinolones results in an urgent demand to develop new fluoroquinolones to withstand the drug resistance and to obtain a broader spectrum of activity. This review aims to outline the recent advances of fluoroquinolone derivatives with antibacterial potential and to summarize the structure-activity relationship (SAR) so as to provide an insight for rational design of more active candidates, covering articles published between January 2018 and June 2021.
The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.
Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.
Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.
Weedy rice is a conspecific form of cultivated rice (Oryza sativa L.) that infests rice fields and results in severe crop losses. Weed strains in different world regions appear to have originated multiple times from different domesticated and/or wild rice progenitors. In the case of Malaysian weedy rice, a multiple-origin model has been proposed based on neutral markers and analyses of domestication genes for hull color and seed shattering. Here, we examined variation in pericarp (bran) color and its molecular basis to address how this trait evolved in Malaysian weeds and its possible role in weed adaptation. Functional alleles of the Rc gene confer proanthocyanidin pigmentation of the pericarp, a trait found in most wild and weedy Oryzas and associated with seed dormancy; nonfunctional rc alleles were strongly favored during rice domestication, and most cultivated varieties have nonpigmented pericarps. Phenotypic characterizations of 52 Malaysian weeds revealed that most strains are characterized by the pigmented pericarp; however, some weeds have white pericarps, suggesting close relationships to cultivated rice. Phylogenetic analyses indicate that the Rc haplotypes present in Malaysian weeds likely have at least three distinct origins: wild O. rufipogon, white-pericarp cultivated rice, and red-pericarp cultivated rice. These diverse origins contribute to high Rc nucleotide diversity in the Malaysian weeds. Comparison of Rc allelic distributions with other rice domestication genes suggests that functional Rc alleles may confer particular fitness benefits in weedy rice populations, for example, by conferring seed dormancy. This may promote functional Rc introgression from local wild Oryza populations.
Inonotus obliquus is a traditional mushroom well known for its therapeutic value. In this study, various solvent fractions of I. obliquus were preliminarily screened for their antioxidant, α-amylase and α-glucosidase inhibition properties. To improve the drug delivery, the active fraction (ethyl acetate fraction) of I. obliquus was synthesized into fungisome (ethyl acetate phophotidyl choline complex, EAPC) and its physical parameters were assessed using Fourier transform infrared spectroscopy (FTIR), High performance liquid chromatography (HPLC), Scanning electron microscope (SEM), and ς potential analysis. Then normal human hepatic L02 cells was used to evaluate the cytotoxicity of EAPC. The results showed that EA fraction possesses significant free radical scavenging, α-amylase and α-glucosidase inhibition properties. FTIR, SEM, and HPLC analysis confirmed the fungisome formation. The particle size of EAPC was 102.80 ± 0.42 nm and the ς potential was -54.30 ± 0.61 mV. The percentage of drug entrapment efficiency was 97.13% and the drug release rates of EAPC in simulated gastric fluid and simulated intestinal fluid were 75.04 ± 0.29% and 93.03 ± 0.36%, respectively. EAPC was nontoxic to L02 cells, however it could selectively fight against the H2 O2 induced oxidative damage in L02 cells. This is the first study to provide scientific information to utilize the active fraction of I. obliquus as fungisome. PRACTICAL APPLICATIONS: Inonotus obliquus (IO) is a traditional medicinal fungus. The extracts of IO have obvious antioxidant and hypoglycemic activities. Ethyl acetate (EA) fraction of IO was encapsulated in liposomes to form EAPC. EAPC has a sustained-release effect. It has nontoxic to L02 cells and could protect L02 cells from oxidative damage caused by hydrogen peroxide. This study could provide new ideas for the treatment of diabetes.
This work established a high-speed camera-assisted visualization system that investigated the effect of volatile matter and fixed carbon content in biomass particles on single-particle combustion phases and their luminous properties. Three types of biomass particles, namely, sawdust (a mixture of pine and willow), corncob, and rice husk, were examined on a Hencken flat-flame burner. The luminous region and intensity of single biomass particles were closely related to the flammability and calorific value of biomass fuel and derived by analyzing a sequence of images captured using a high-speed camera. The combustion temperature was determined through analysis of its radiant energy. The results showed that the ignition mechanisms of volatile matter and fixed carbon corresponded to homogeneous and heterogeneous reactions, respectively. The maximum luminous region values of 1.75 × 106, 2.1 × 106, and 1.0 × 106 μm2 for sawdust (SD), corncob (CC), and rice husk (RH) correlated to the volatile matter content of each biomass sample, which was 69.38, 74.15, and 64.56%, respectively. Because of the high fixed carbon content, the peak temperature of the SD particles could reach 1549 °C. The luminous region and intensity of the combusting particles were significantly affected by the volatile matter and fixed carbon, respectively.
Agricultural weeds serve as productive models for studying the genetic basis of rapid adaptation, with weed-adaptive traits potentially evolving multiple times independently in geographically distinct but environmentally similar agroecosystems. Weedy relatives of domesticated crops can be especially interesting systems because of the potential for weed-adaptive alleles to originate through multiple mechanisms, including introgression from cultivated and/or wild relatives, standing genetic variation, and de novo mutations. Weedy rice populations have evolved multiple times through dedomestication from cultivated rice. Much of the genomic work to date in weedy rice has focused on populations that exist outside the range of the wild crop progenitor. In this study, we use genome-wide SNPs generated through genotyping-by-sequencing to compare the evolution of weedy rice in regions outside the range of wild rice (North America, South Korea) and populations in Southeast Asia, where wild rice populations are present. We find evidence for adaptive introgression of wild rice alleles into weedy rice populations in Southeast Asia, with the relative contributions of wild and cultivated rice alleles varying across the genome. In addition, gene regions underlying several weed-adaptive traits are dominated by genomic contributions from wild rice. Genome-wide nucleotide diversity is also much higher in Southeast Asian weeds than in North American and South Korean weeds. Besides reflecting introgression from wild rice, this difference in diversity likely reflects genetic contributions from diverse cultivated landraces that may have served as the progenitors of these weedy populations. These important differences in weedy rice evolution in regions with and without wild rice could inform region-specific management strategies for weed control.
A flame retardant high-performance gelatinized starch (GS)-ammonium dihydrogen phosphate (ADP) wood adhesive, named GS-ADP adhesive was prepared by condensation of GS and ADP under acidic condition. The preparation process of GS-ADP adhesive is very simple by mixing and stirring GS and ADP evenly at room temperature. The results revealed that the GS-ADP adhesive has good storage stability and water resistance, and its wet shear strength is much higher than that of phenolic resin (PF) adhesive. Markedly, the cone calorimeter test results show that G-ADP adhesive has good flame retardancy, and the plywood based on GS-ADP adhesive has good flame retardancy. Meanwhile, it can be seen from dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) that GS-ADP has excellent modulus of elasticity (MOE), high glass transition temperature (Tg) and good thermal stability. The findings suggest that GS-ADP could be a viable substitute for PF resin in structural wood fabrication.
The water-soluble bioactive polysaccharides can contribute to the health benefits of Lycium barbarium fruit. However, the structure characteristics of these polysaccharides remain unclear yet. An important polysaccharide (LBPA) was isolated and purified from L. barbarium in this work. It was identified by chemical and spectroscopic methods as arabinogalactan with β-d-(1→6)-galactan as backbone, which was different to any reported polysaccharides from this species before. This arabinogalactan was comprised of Araf, Galp, GlcpA and Rhap with a molar ratio of 9.2:6.6:1.0:0.9. The side chains, including α-l-Araf-(1→, α-l-Araf-(1→5)-α-l-Araf-(1→, β-l-Araf-(1→5)-α-l-Araf-(1→ and α-l-Rhap-(1→4)-β-d-GlcpA-(1→6)-β-d-Galp-(1→, were linked to β-d-(1→6)-galactan at O-3. The putative structure was drawn as below. The molecular weight was determined to be 470,000g/mol by gel permeation chromatography.