GIS-based time series study of soil erosion risk using the Revised Universal Soil Loss Equation (RUSLE) model in a micro-catchment on Mount Elgon, Uganda

Land degradation has already been treated as one of the most serious problem all around the world. This study is a GIS-based time series study which devotes to calculate annual soil loss value, seek for soil erosion trends linked with precipitation and land use in Manafwa micro-catchment, Mount Elgon region, Uganda. Two different versions of Revised Universal Soil loss Equation (RUSLE) are implemented and compared, one using flow length and the other using flow accumulation to estimate the slope length and steepness (LS) factor. The modeling is carried out for the years 2000, 2006, and 2012, and is based on ASTER remotely sensed data, digital elevation models, precipitation data from the study area, as well as existing soil maps. After running RUSLE model and analyzing the result maps, no significant soil erosion trends or patterns are found, as well as significant trends in precipitation and land cover changes during last decade. Over exploitation of land is probably compensated by improved agricultural management and no significant increase in precipitation. Even if there are reports of more intense and increasing amounts of rainfall in the area, this could not be verified, neither through analysis of climate data, nor by trends in estimated soil loss.Land degradation has already been treated as one of the most serious problem all around the world. This study is a GIS-based time series study which devotes to calculate annual soil loss value, seek for soil erosion trends linked with precipitation and land use in Manafwa micro-catchment, Mount Elgon region, Uganda. Revised Universal Soil loss Equation (RUSLE) is a world popular soil erosion model with five influencing factors, rainfall erosivity, soil erodability, slope length and steepness factor, cover management factor, and conservation practice factor. Two different versions of RUSLE which present two different calculation methods for slope length and steepness factor are implemented and compared. The modeling is carried out for the years 2000, 2006, and 2012, and is based on remotely sensed data, digital elevation models, precipitation data from the study area, as well as existing soil maps. After running RUSLE model six result maps showing soil erosion risk level are obtained, two for each year with two different methods. By analyzing the result maps, no significant soil erosion trends or patterns are found, as well as significant trends in precipitation and land cover changes during last decade. Over exploitation of land is probably compensated by improved agricultural management and no significant increase in precipitation. Even if there are reports of more intense and increasing amounts of rainfall in the area, this could not be verified, neither through analysis of climate data, nor by trends in estimated soil loss

CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 201

Septic arthritis with osteomyelitis due to Salmonella enterica serotype Dublin: A case series

BackgroundSeptic arthritis with osteomyelitis due to Salmonella enterica serotype Dublin is rare. We reviewed and analyzed cases of septic arthritis with osteomyelitis due to Salmonella enterica serotype Dublin seen at our institution.MethodsThe medical records of all patients diagnosed with Salmonella septic arthritis and/or Salmonella osteomyelitis during 2017–2022 were included. We reviewed the diagnosis, medical history, clinical management, and outcome of all cases.ResultsFive patients with Salmonella septic arthritis or Salmonella osteomyelitis were identified during the 5-year study period. They were all male; the median age was 53 years (range 15–56). Only one was immunodeficient. All five patients were infected at the hip joint and ipsilateral femur, while two suffered bilateral hip septic arthritis with femoral osteomyelitis. Salmonella Dublin was isolated from the hip joint fluid of all patients. Four presented with fever and constitutional signs within four weeks of symptom onset. Four had positive blood cultures, and only one patient had gastrointestinal symptoms. Four patients underwent surgical debridement as the primary surgical plan, and two underwent secondary two-stage exchange after primary surgical debridement failure. The last patient had a two-stage exchange directly as the first surgical treatment. All patients received intravenous antimicrobial therapy for a median duration of 6 (range 4–12) weeks and oral antimicrobial therapy for a median duration of 4 (range 4–6) weeks. All patients had a median duration of follow-up of 12 months (range 9–25), and none had evidence of recurrence of infection.ConclusionsSeptic arthritis due to Salmonella Dublin remains rare. It frequently occurs with ipsilateral femur osteomyelitis adjacent to the infected hip joint in our cases. Surgical debridement or two-stage exchange, along with 4–12 weeks of effective intravenous and followed by 4–6 oral antimicrobial therapy, could successfully eradicate the infection

A stream processing framework based on linked data for information collaborating of regional energy networks

© 2005-2012 IEEE. Coordinating of energy networks to form a city-level multidimensional integrated energy system becomes a new trend in Energy Internet (EI). The collaborating in the information layer is a core issue to achieve smart integration. However, the heterogeneity of multiagent data, the volatility of components, and the real-time analysis requirement in EI bring significant challenges. To solve these problems, in this article we propose a stream processing framework based on linked data for information collaboration among multiple energy networks. The framework provides a universal data representation based on linked data and semantic relation discovery approach to model and semantically fuse heterogeneous data. Semantics-based information transmission contracts and channels are automatically generated to adapt to structural changes in EI. A multimodel-based dynamic adjusting stream processing is implemented using data semantics. A real-world case study is implemented to demonstrate the adaptability, feasibility, and flexibility of the proposed framework

Are the preoperative albumin levels and the albumin to fibrinogen ratio the risk factors for acute infection after primary total joint arthroplasty?

BackgroundAcute infection, such as periprosthetic joint infection and superficial surgical site infection, after primary total joint arthroplasty (TJA) is a serious complication, and its risk factors remain controversial. This study aimed to identify the risk factors for acute infection after primary TJA, especially the serological indicators that reflect preoperative nutritional statuses, such as albumin level and albumin to fibrinogen ratio (AFR).MethodsWe retrospectively reviewed patients who underwent elective primary hip or knee arthroplasty at our institution from 2009 to 2021. Potential risk factors of acute infection and demographic information were extracted from an electronic health record. Patients who suffered acute infection, such as PJI or SSI, after TJA were considered the study group. Non-infected patients were matched 1:2 with the study group according to sex, age, the involved joint (hip or knee), and year of surgery (control group). The variables of potential risk factors for acute postoperative infection (demographic characteristics, preoperative comorbidities and drug use, operative variables, and laboratory values) were collected and evaluated by regression analysis. Restrictive cubic spline regression analysis was also used to examine the relationship between preoperative serum albumin levels and acute postoperative infection.ResultsWe matched 162 non-infected patients with 81 patients who suffered from acute postoperative infection. Among the patients who suffered from acute infection within 90 days after TJA, 18 were diagnosed with periprosthetic joint infection and 63 with surgical site infection. Low albumin levels were strongly associated with acute postoperative infection (95% confidence interval, 0.822–0.980; P = 0.015). This risk increased as preoperative albumin levels decreased, with a negative dose-response relationship (Poverall = 0.002; Pnonlinear = 0.089). However, there was no significant association between the AFR and acute infection after primary TJA (P = 0.100).ConclusionThere is currently insufficient evidence to confirm the relationship between preoperative AFR and acute infection after elective primary TJA, while a lower preoperative albumin level is an independent risk factor for acute infection with a negative dose-response relationship. This suggests that optimal nutritional management may be benefited before elective primary TJA

A comparative study of data-driven MHD simulations of solar coronal evolution with photospheric flows derived from two different approaches

Data-driven simulation proves to be a powerful tool in revealing the dynamic process of the solar corona, but it remains challenging to implement the driving boundary conditions in a self-consistent way and match the observables at the photosphere. Here, we test two different photospheric velocity-driven MHD simulations in studying the quasi-static evolution of solar active region NOAA 11158. The two simulations were identically initialized with an MHD equilibrium as relaxed from a non-linear force-free field extrapolation from a vector magnetogram. Then, we energized the MHD system by applying the time series of photospheric velocity at the bottom boundary as derived by two different codes, the DAVE4VM and PDFI, from the observed vector magnetograms. To mimic the small-scale flux cancellation on the photosphere, the magnetic diffusion at the bottom boundary was set to be inversely proportional to the local scale length of the magnetic field. The result shows the evolution curves of the total magnetic energy and unsigned magnetic flux generated by the PDFI velocity match the corresponding curves from the observations much better than those by the DAVE4VM one. The structure of the current layer and synthetic image in PDFI simulation also has a more reasonable consistency with SDO/AIA 131 Å observation. The only shortage of the PDFI velocity is its capability in reproducing the morphology of sunspots, as characterized by a slightly lower correlation coefficient for the bottom magnetic field in simulations and magnetograms. Overall, this study suggests the superiority of each method in the models driven by the bottom velocity, which represents a further step toward the goal of reproducing more realistically the evolution of coronal magnetic fields using data-driven modeling

Electroacupuncture Regulates Pain Transition Through Inhibiting PKCε and TRPV1 Expression in Dorsal Root Ganglion

Many cases of acute pain can be resolved with few side effects. However, some cases of acute pain may persist beyond the time required for tissue injury recovery and transit to chronic pain, which is hard to treat. The mechanisms underlying pain transition are not entirely understood, and treatment strategies are lacking. In this study, the hyperalgesic priming model was established on rats to study pain transition by injection of carrageenan (Car) and prostaglandin E2 (PGE2). The expression levels of protein kinase C epsilon (PKCε) and transient receptor potential vanilloid 1 (TRPV1) in the L4-L6 dorsal root ganglion (DRG) were investigated. Electroacupuncture (EA) is a form of acupuncture in which a small electric current is passed between a pair of acupuncture needles. EA was administrated, and its effect on hyperalgesia and PKCε and TRPV1 expression was investigated. The PKCε-TRPV1 signaling pathway in DRG was implicated in the pain transition. EA increased the pain threshold of model animals and regulated the high expression of PKCε and TRPV1. Moreover, EA also regulated hyperalgesia and high TRPV1 expression induced by selective PKCε activation. We also found that EA partly increased chronic pain threshold, even though it was only administered between the Car and PGE2 injections. These findings suggested that EA could prevent the transition from acute to chronic pain by inhibiting the PKCε and TRPV1 expression in the peripheral nervous system