High mono-rhamnolipids production by a novel isolate Pseudomonas aeruginosa LP20 from oily sludge: characterization, optimization, and potential application.

This study aims to isolate microbial strains for producing mono-rhamnolipids with high proportion. Oily sludge is rich in petroleum and contains diverse biosurfactant-producing strains. A biosurfactant-producing strain LP20 was isolated from oily sludge, identified as Pseudomonas aeruginosa based on phylogenetic analysis of 16S rRNA. High-performance liquid chromatography-mass spectrometry results indicated that biosurfactants produced from LP20 were rhamnolipids, mainly containing Rha-C8-C10, Rha-C10-C10, Rha-Rha-C8-C10, Rha-Rha-C10-C10, Rha-C10-C12:1, and Rha-C10-C12. Interestingly, more mono-rhamnolipids were produced by strain LP20 with a relative abundance of 64.5%. Pseudomonas aeruginosa LP20 optimally produced rhamnolipids at a pH of 7.0 and a salinity of 0.1% using glycerol and nitrate. The culture medium for rhamnolipids by strain LP20 was optimized by response surface methodology. LP20 produced rhamnolipids up to 6.9 g L-1, increased by 116%. Rhamnolipids produced from LP20 decreased the water surface tension to 28.1 mN m-1 with a critical micelle concentration of 60 mg L-1. The produced rhamnolipids emulsified many hydrocarbons with EI24 values higher than 56% and showed antimicrobial activity against Staphylococcus aureus and Cladosporium sp. with inhibition rates 48.5% and 17.9%, respectively. Pseudomonas aeruginosa LP20 produced more proportion of mono-rhamnolipids, and the LP20 rhamnolipids exhibited favorable activities and promising potential in microbial-enhanced oil recovery, bioremediation, and agricultural biocontrol.

When Machine Learning Meets 2D Materials: A Review.

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

Clinical characteristics and prognosis of patients with primary squamous cell carcinoma of the retromolar trigone: A SEER-based analysis.

BACKGROUND: Primary squamous cell carcinoma of the retromolar trigone (primary SCC RMT) is an uncommon malignant tumor. There is still much to learn about its clinicopathological characteristics and prognosis. In order to better understand the clinicopathological features and predictive survival aspects of primary SCC RMT, this study examined data from the SEER database from 2000 to 2020. Furthermore, in order to forecast the overall survival (OS) and cancer-specific survival (CSS) of patients with primary SCC RMT, we created nomograms. METHOD: The Surveillance, Epidemiology and End Results (SEER) database was used to retrieve the information on individuals with primary SCC RMT who received a diagnosis between 2000 and 2020. Both univariate and multivariate analyses were conducted using the Cox proportional risk regression model. Using R software, prognostic nomograms were created to forecast the OS and CSS likelihood. The nomograms' prediction abilities were evaluated using the consistency index (C-index), calibration curve, and receiver operating characteristic (ROC) curve. RESULT: A total of 1717 patients with primary SCC RMT were included, they were randomly assigned to the primary and validation cohorts in a 7:3 ratio using R software. Multivariate Cox regression revealed that age, marital status, regional nodes positive, Summary stage, TNM stage, T stage, N stage, surgery were independent prognostic factors of OS, and age, marital status, regional nodes positive, tumor sizes, Summary stage, N stage, surgery were independent prognostic factors of CSS in the primary cohort. The C-index of the nomogram OS was 0.705 (95 % CI: 0.685-0.725) and the C-index of CSS was 0.734 (95 % CI:0.714-0.754) in the primary cohort. In validation cohort, the C-index of the nomogram OS and CSS were 0.730 (95 % CI: 0.710-0.750) and 0.723 (95 % CI: 0.684-0.762), respectively. The 1-, 3-, and 5-year OS and CSS rates in the primary cohort and validation cohort were approximately in line with the nomogram estimations, in accordance to the calibration curves. CONCLUSION: We conducted an analysis using the SEER database to investigate the features, survival outcomes, and prognostic parameters of patients with primary SCC RMT. And we developed two prognostic nomograms that can be used by clinicians to forecast the 1-, 3-, and 5-year overall survival and cancer-specific survival of patients with primary SCC RMT.

A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China.

In spite of the state-of-the-art performances of machine learning in the PM2.5 estimation, the high-value PM2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM2.5 with R2 of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM2.5 concentrations, the spatial evolution of PM2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM2.5 (PM2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM2.5 (PM2.5_D) were CO, AOD and NO2. This study not only provided an effective solution to the PM2.5 underestimation and AOD missing problems in the PM2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM2.5 and some spatiotemporal variables.

Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis.

PM2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM2.5 from 2015 to 2018 in the YRDUA. Results showed that PM2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM2.5, an annual cycle on the time scale of 250-480 d and a semi-annual cycle on the time scale of 130-220 d. In addition, PM2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM2.5 and CO was the most consistent, and the correlation between PM2.5 and SO2/NO2 improved with the increase of time scale, while the correlation between PM2.5 and O3 was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

[Minimally invasive external fixation of humeral supracondylar angular rotation center for the treatment of cubitus varus deformity in adolescents].

OBJECTIVE: To investigate clinical effect of minimally invasive osteotomy and external fixation with the center of roration of angulation (CORA) in treating cubitus varus in adolescents. METHODS: From August 2013 to August 2017, 15 patients with cubitus varus caused by supracondylar fracture of humerus were treated with minimally invasive osteotomy and external fixation with the CORA. Among them, including 9 males and 6 females; 11 patients on the left side and 4 patients on the right side; aged from 13 to 16 years old with an average of 14.5 years old. The time from injury to operation was for 6 to 10 years with an average of 7.5 years. Five patients had a history of recurrence after cubitus varus surgery. Correction time. fracture healing time, carrying angle were observed, Laupattarakasem standard was used to evaluate clinical effect. RESULTS: All patients were followed up from 12 to 30 months with an average of 24 months; correction time ranged from 3 to 5 weeks with an average of 4 weeks; fracture healing time ranged from 4 to 6 months with an average of 5 months; carrying angle before operation ranged from -12° to -23°, and improved 9° to 14° after operation. According to Laupattarakasem evaluation criteria, 11 patients got an excellent result, 3 good and 1 fair. CONCLUSIONS: Minimally invasive osteotomy and external fixation with CORA in treating cubitus varus deformity in adolescents has advantages of less trauma, less blood loss, earlier exercise, speed and angle of correction could controlled without hospitalized for fixation.

Exploring the mechanisms of heat wave vulnerability at the urban scale based on the application of big data and artificial societies.

Rapid urbanisation has altered the vulnerability of urban areas to heat wave disasters. There is an urgent need to identify the factors underlying the effect of heat waves on human health and the areas that are most vulnerable to heat waves. In this study, we plan to integrate indices associated with heat wave vulnerability based on meteorological observation data, remote sensing data and point of interest (POI) data; analyse the influence of urbanisation on the urban vulnerability environment; and explore the relationship between the vulnerability environment and heat-wave-related mortality. Finally, we attempt to map the spatial distribution of high heat-wave-related mortality risk based on the results of heat wave vulnerability study and artificial society. The results reveal that 1) there are differences in the influence of urbanisation on heat wave exposure, sensitivity and adaptability; 2) the exposure and sensitivity level effects on the lower limit of health impacts and the adaptability level effects on the upper limit of the health impact from heat wave in a given study area; and 3) areas vulnerable to the effects of heat waves are not confined to the city centre, which implies that residents living in suburban areas are also vulnerable to heat waves. Finally, this study not only explores the factors contributing to the impacts of heat waves but also describes the spatial distribution of the risk of disaster-associated mortality, thereby providing direct scientific guidance that can be used by cities to address heat wave disasters in the future.

Separability and Compactness Network for Image Recognition and Superresolution.

Convolutional neural networks (CNNs) have wide applications in pattern recognition and image processing. Despite recent advances, much remains to be done for CNNs to learn a better representation of image samples. Therefore, constant optimizations should be provided on CNNs. To achieve a good performance on classification, intuitively, samples' interclass separability, or intraclass compactness should be simultaneously maximized. Accordingly, in this paper, we propose a new network, named separability and compactness network (SCNet) to rectify this problem. SCNet minimizes the softmax loss and the distance between features of samples from the same class under a jointly supervised framework, resulting in simultaneous maximization of interclass separability and intraclass compactness of samples. Furthermore, considering the convenience and the efficiency of the cosine similarity in face recognition tasks, we incorporate it into SCNet's distance metric to enable sample features from the same class to line up in the same direction and those from different classes to have a large angle of separation. We apply SCNet to three different tasks: visual classification, face recognition, and image superresolution. Experiments on both public data sets and real-world satellite images validate the effectiveness of our SCNet.

Carbon exchanges and their responses to temperature and precipitation in forest ecosystems in Yunnan, Southwest China.

Forest ecosystems play an increasingly important role in the global carbon cycle. However, knowledge on carbon exchanges, their spatio-temporal patterns, and the extent of the key controls that affect carbon fluxes is lacking. In this study, we employed 29-site-years of eddy covariance data to observe the state, spatio-temporal variations and climate sensitivity of carbon fluxes (gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem carbon exchange (NEE)) in four representative forest ecosystems in Yunnan. We found that 1) all four forest ecosystems were carbon sinks (the average NEE was -3.40tCha-1yr-1); 2) contrasting seasonality of the NEE among the ecosystems with a carbon sink mainly during the wet season in the Yuanjiang savanna ecosystem (YJ) but during the dry season in the Xishuangbanna tropical rainforest ecosystem (XSBN), besides an equivalent NEE uptake was observed during the wet/dry season in the Ailaoshan subtropical evergreen broad-leaved forest ecosystem (ALS) and Lijiang subalpine coniferous forest ecosystem (LJ); 3) as the GPP increased, the net ecosystem production (NEP) first increased and then decreased when the GPP>17.5tCha-1yr-1; 4) the precipitation determines the carbon sinks in the savanna ecosystem (e.g., YJ), while temperature did so in the tropical forest ecosystem (e.g., XSBN); 5) overall, under the circumstances of warming and decreased precipitation, the carbon sink might decrease in the YJ but maybe increase in the ALS and LJ, while future strength of the sink in the XSBN is somewhat uncertain. However, based on the redundancy analysis, the temperature and precipitation combined together explained 39.7%, 32.2%, 25.3%, and 29.6% of the variations in the NEE in the YJ, XSBN, ALS and LJ, respectively, which indicates that considerable changes in the NEE could not be explained by variations in the temperature and precipitation. Therefore, the effects of other factors (e.g., CO2 concentration, N/P deposition, aerosol and other variables) on the NEE still require extensive research and need to be considered seriously in carbon-cycle-models.

Comparison of infrared canopy temperature in a rubber plantation and tropical rain forest.

Canopy temperature is a result of the canopy energy balance and is driven by climate conditions, plant architecture, and plant-controlled transpiration. Here, we evaluated canopy temperature in a rubber plantation (RP) and tropical rainforest (TR) in Xishuangbanna, southwestern China. An infrared temperature sensor was installed at each site to measure canopy temperature. In the dry season, the maximum differences (Tc - Ta) between canopy temperature (Tc) and air temperature (Ta) in the RP and TR were 2.6 and 0.1 K, respectively. In the rainy season, the maximum (Tc - Ta) values in the RP and TR were 1.0 and -1.1 K, respectively. There were consistent differences between the two forests, with the RP having higher (Tc - Ta) than the TR throughout the entire year. Infrared measurements of Tc can be used to calculate canopy stomatal conductance in both forests. The difference in (Tc - Ta) at three gc levels with increasing direct radiation in the RP was larger than in the TR, indicating that change in (Tc - Ta) in the RP was relatively sensitive to the degree of stomatal closure.

Research progress of photoperiod regulated genes on flowering time in rice.

Rice flowering regulation is an extremely complex process, which is controlled by genetic factors and external environment. Photoperiodic regulatory pathway is pivotal to control flowering in rice, in which florigen genes Hd3a and RTF1 are at the core and they are regulated by upstream Hd1-dependent, Ehd1-dependent, as well as both Hd1- and Ehd1-independent pathways. The three pathways bring a variety of light signal information together to Hd3a and RTF1 for further integration, and then transmit the signals in the form of florigen to the downstream flowering related genes. In this review, we summarize the research progress of photoperiod regulated genes on flowering time in rice, including the photoreceptors and circadian rhythm genes, the florigens, its upstream, downstream and interacting genes. We hope to provide a reference for in-depth study of rice flowering regulation.

A novel gene OsAHL1 improves both drought avoidance and drought tolerance in rice.

A novel gene, OsAHL1, containing an AT-hook motif and a PPC domain was identified through genome-wide profiling and analysis of mRNAs by comparing the microarray of drought-challenged versus normally watered rice. The results indicated OsAHL1 has both drought avoidance and drought tolerance that could greatly improve drought resistance of the rice plant. Overexpression of OsAHL1 enhanced multiple stress tolerances in rice plants during both seedling and panicle development stages. Functional studies revealed that OsAHL1 regulates root development under drought condition to enhance drought avoidance, participates in oxidative stress response and also regulates the content of chlorophyll in rice leaves. OsAHL1 specifically binds to the A/T rich sequence region of promoters or introns, and hence directly regulates the expression of many stress related downstream genes.

[Repairing tibial post-traumatic osteomyelitis with bone and skin defect by Ilizarov technique at stage I].

OBJECTIVE: To explore clinical effects of Ilizarov technique at stage I for repairing tibial post-traumatic osteomyelitis with bone and skin defect. METHODS: From June 2010 to December 2013,44 patients with tibial post-traumatic osteomyelitis with bone and skin defect were treated with Ilizarov technique at stage I . Among them, there were 35 males and 9 females aged from 18 to 70 years old with an average of 42.5 years old. Bone defect ranged from 4 to 16 cm, skin defect ranged from 3 cm x 4 cm to 5 cm x 16 cm. The operation was performed debridement thoroughly, removed inflammatory bone section, osteotomy invasively, install circular external fixator by Ilizarow technique; screw nut were rotated at 1 week after operation, and prolonged 0.5 to 1.0 mm everyday. Wound surface, new born callus and bone healing were observed to evaluate clinical effects. RESULTS: All patients were followed up from 11 to 36 months with an average of 18.5 months. Bone defect after osteotomy was from 6 to 22 cm with an average of 11.5 cm; the time of wound healing time ranged from 21 to 79 d with an average of 38 d; bone defect healing time was from 8 to 15 months with an average of 12.5 months. All patients were cured, no recurrent infection, refracture and shorten of calf deformity were occurred. CONCLUSION: Repairing tibial post-traumatic osteomyelitis with bone and skin defect by llizarov technique at stage I has advantages of less trauma, low inflammatory recurrence rate, could avoid multiple complex operation, and receive definite curative effect.

[Atmospheric correction of HJ-1 CCD data for water imagery based on dark object model].

The CCD multi-band data of HJ-1A has great potential in inland water quality monitoring, but the precision of atmospheric correction is a premise and necessary procedure for its application. In this paper, a method based on dark pixel for water-leaving radiance retrieving is proposed. Beside the Rayleigh scattering, the aerosol scattering is important to atmospheric correction, the water quality of inland lakes always are case II water and the value of water leaving radiance is not zero. So the synchronous MODIS shortwave infrared data was used to obtain the aerosol parameters, and in virtue of the characteristic that aerosol scattering is relative stabilized in 560 nm, the water-leaving radiance for each visible and near infrared band were retrieved and normalized, accordingly the remotely sensed reflectance of water was computed. The results show that the atmospheric correction method based on the imagery itself is more effective for the retrieval of water parameters for HJ-1A CCD data.

Land use dynamics of the fast-growing Shanghai Metropolis, China (1979-2008) and its implications for land use and urban planning policy.

Through the integrated approach of remote sensing and geographic information system (GIS) techniques, four Landsat TM/ETM+ imagery acquired during 1979 and 2008 were used to quantitatively characterize the patterns of land use and land cover change (LULC) and urban sprawl in the fast-growing Shanghai Metropolis, China. Results showed that, the urban/built-up area grew on average by 4,242.06 ha yr(-1). Bare land grew by 1,594.66 ha yr(-1) on average. In contrast, cropland decreased by 3,286.26 ha yr(-1) on average, followed by forest and shrub, water, and tidal land, which decreased by 1,331.33 ha yr(-1), 903.43 ha yr(-1), and 315.72 ha yr(-1) on average, respectively. As a result, during 1979 and 2008 approximately 83.83% of the newly urban/built-up land was converted from cropland (67.35%), forest and shrub (9.12%), water (4.80%), and tidal land (2.19%). Another significant change was the continuous increase in regular residents, which played a very important role in contributing to local population growth and increase in urban/built-up land. This can be explained with this city's huge demand for investment and qualified labor since the latest industrial transformation. Moreover, with a decrease in cropland, the proportion of population engaged in farming decreased 13.84%. Therefore, significant socio-economic transformation occurred, and this would lead to new demand for land resources. However, due to very scarce land resources and overload of population in Shanghai, the drive to achieve economic goals at the loss of cropland, water, and the other lands is not sustainable. Future urban planning policy aiming at ensuring a win-win balance between sustainable land use and economic growth is urgently needed.

Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa.

In addition to regulating growth and development, the most important function of microRNAs (miRNAs) in plants is the regulation of a variety of cellular processes underlying plant adaptation to environmental stresses. To gain a deep understanding of the mechanism of drought tolerance in rice, genome-wide profiling and analysis of miRNAs was carried out in drought-challenged rice across a wide range of developmental stages, from tillering to inflorescence formation, using a microarray platform. Among the 30 miRNAs identified as significantly down- or up-regulated under the drought stress, 11 down-regulated miRNAs (miR170, miR172, miR397, miR408, miR529, miR896, miR1030, miR1035, miR1050, miR1088, and miR1126) and eight up-regulated miRNAs (miR395, miR474, miR845, miR851, miR854, miR901, miR903, and miR1125) were revealed for the first time to be induced by drought stress in plants, and nine (miR156, miR168, miR170, miR171, miR172, miR319, miR396, miR397, and miR408) showed opposite expression to that observed in drought-stressed Arabidopsis. The most conserved down-regulated miRNAs were ath-miR170, the miR171 family, and ath-miR396, and the most conserved up-regulated miRNAs were ptc-miR474 and ath-miR854a. The identification of differentially expressed novel plant miRNAs and their target genes, and the analysis of cis-elements provides molecular evidence for the possible involvement of miRNAs in the process of drought response and/or tolerance in rice.