Endoscopic rhizotomy for chronic lumbar zygapophysial joint pain.

BACKGROUND: Chronic lumbar zygapophysial joint pain is a common cause of chronic low back pain. Percutaneous radiofrequency ablation (RFA) is one of the effective management options; however, the results from the traditional RFA need to be improved in certain cases. The aim of this study is to investigate the effect of percutaneous radiofrequency ablation under endoscopic guidance (ERFA) for chronic low back pain secondary to facet joint arthritis. METHODS: This is a prospective study enrolled 60 patients. The cases were randomized into two groups: 30 patients in the control group underwent traditional percutaneous radiofrequency ablation, others underwent ERFA. The lumbar visual analog scale (VAS), MacNab score, and postoperative complications were used to evaluate the outcomes. All outcome assessments were performed at postoperative 1 day, 1 month, 3 months, 6 months, and 12 months. RESULTS: There was no difference between the two groups in preoperative VAS (P \u3e 0.05). VAS scores, except the postoperative first day, in all other postoperative time points were significantly lower than preoperative values each in both groups (P \u3c 0.05). There was no significant difference between the two groups in VAS at 1 day, 1 month, and 3 months after surgery (P \u3e 0.05). However, the EFRA demonstrated significant benefits at the time points of 3 months and 6 months (P \u3e 0.05). The MacNab scores of 1-year follow-up in the ERFA group were higher than that in the control group (P \u3c 0.05). The incidence of complications in the ERFA group was significantly less than that in the control group (P \u3c 0.05). CONCLUSIONS: ERFA may achieve more accurate and definite denervation on the nerves, which leads to longer lasting pain relief

Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

Analyzing the driving factors of regional carbon emissions is important for achieving emissions reduction. Based on the Kaya identity and Logarithmic Mean Divisia Index method, we analyzed the effect of population, economic development, energy intensity, renewable energy penetration, and coefficient on carbon emissions during 1990–2016. Afterwards, we analyzed the contribution rate of sectors’ energy intensity effect and sectors’ economic structure effect to the entire energy intensity. The results showed that the influencing factors have different effects on carbon emissions under different stages. During 1990–2000, economic development and population were the main factors contributing to the increase in carbon emissions, and energy intensity was an important factor to curb the carbon emissions increase. The energy intensity of industry and the economic structure of agriculture were the main factors to promote the decline of entire energy intensity. During 2001–2010, economic growth and emission coefficient were the main drivers to escalate the carbon emissions, and energy intensity was the key factor to offset the carbon emissions growth. The economic structure of transportation, and the energy intensity of industry and service were the main factors contributing to the decline of the entire energy intensity. During 2011–2016, economic growth and energy intensity were the main drivers of enhancing carbon emissions, while the coefficient was the key factor in curbing the growth of carbon emissions. The industry’s economic structure and transportation’s energy intensity were the main factors to promote the decline of the entire energy intensity. Finally, the suggestions of emissions reductions are put forward from the aspects of improving energy efficiency, optimizing energy structure and adjusting industrial structure etc. View Full-Tex

Kooperativna evolucija za kvalitetno pružanje usluga u paradigmi Interneta stvari

To facilitate the automation process in the Internet of Things, the research issue of distinguishing prospective services out of many “similar” services, and identifying needed services w.r.t the criteria of Quality of Service (QoS), becomes very important. To address this aim, we propose heuristic optimization, as a robust and efficient approach for solving complex real world problems. Accordingly, this paper devises a cooperative evolution approach for service composition under the restrictions of QoS. A series of effective strategies are presented for this problem, which include an enhanced local best first strategy and a global best strategy that introduces perturbations. Simulation traces collected from real measurements are used for evaluating the proposed algorithms under different service composition scales that indicate that the proposed cooperative evolution approach conducts highly efficient search with stability and rapid convergence. The proposed algorithm also makes a well-designed trade-off between the population diversity and the selection pressure when the service compositions occur on a large scale.Kako bi se automatizirali procesi u internetu stvati, nužno je rezlikovati bitne usluge u moru sličnih kao i identificirati potrebne usluge u pogledu kvalitete usluge (QoS). Kako bi doskočili ovome problemu prdlaže se heuristička optimizacija kao robustan i efikasan način rješavajne kompleksnih problema. Nadalje, u članku je predložen postupak kooperativne evolucije za slaganje usluga uz ograničenja u pogledu kvalutete usluge. Predstavljen je niz efektivnih strategija za spomenuti problem uključujući strategije najboljeg prvog i najboljeg globalnog koje unose perturbacije u polazni problem. Simulacijski rezultati kao i stvarni podatci su korišteni u svrhu evaluacije prodloženog algoritma kako bi se osigurala efikasna pretraga uz stabilnost i brzu konvergenciju. Predloženi algoritam tako.er vodi računa o odnosu izme.u različitosti populacije i selekcijskog pritiska kada je potrebno osigurati slaganje usluga na velikoj skali

Description and attribution analysis of the 2017 spring anomalous high temperature causing floods in Kazakhstan

It is speculated that floods in many areas of the world have become more severe with global warming. This study describes the 2017 spring floods in Kazakhstan, which, with about six people dead or missing, prompted the government to call for more than 7,000 people to leave their homes. Then, based on the Climatic Research Unit (CRU), the NCEP/NCAR Reanalysis 1, and the Coupled Model Intercomparison Project 5 (CMIP5) simulations, the seasonal trends of temperature were calculated using the linear least-squares regression and the Mann–Kendall trend test. The correlation between the surface air temperature and atmospheric circulation was explored, and the attributable risk of the 2017 spring floods was evaluated using the conventional fraction of the attributable risk (FAR) method. The results indicate that the north plains of Kazakhstan had a higher (March–April) mean temperature anomaly compared to the south plains, up to 3°C, relative to the 1901-2017 average temperature. This was the primary cause of flooding in Kazakhstan. March and April were the other months with a higher increasing trend in temperature from 1901 to 2017 compared with other months. In addition, a positive anomaly of the geopotential height and air temperature for the March–April 2017 period (based on the reference period 1961-1990) was the reason for a warmer abnormal temperature in the northwest region of Kazakhstan. Finally, the FAR value was approximately equal to 1, which supported the claim of a strong anthropogenic influence on the risk of the 2017 March–April floods in Kazakhstan. The results presented provide essential information for a comprehensive understanding of the 2017 spring floods in Kazakhstan and will help government officials identify flooding situations and mitigate damage in future

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350–2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745–910 nm and 1,911–2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSEt and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSEt was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions

Resveratrol reduces the inflammatory response in adipose tissue and improves adipose insulin signaling in high-fat diet-fed mice

Background Obesity-induced glucose metabolism disorder is associated with chronic, low-grade, systemic inflammation and is considered a risk factor for diabetes and metabolic syndrome. Resveratrol (RES), a natural anti-inflammatory compound, is observed to improve glucose tolerance and insulin sensitivity in obese rodents and humans. This study aimed to test the effects of RES administration on insulin signaling and the inflammatory response in visceral white adipose tissue (WAT) caused by a high-fat diet (HFD) in mice. Methods A total of 40 wild-type C57BL/6 male mice were divided into four groups (10 in each group): the standard chow diet (STD) group was fed a STD; the HFD group was fed a HFD; and the HFD-RES/L and HFD-RES/H groups were fed a HFD plus RES (200 and 400 mg/kg/day, respectively). The L and H in RES/L and RES/H stand for low and high, respectively. Glucose tolerance, insulin sensitivity, circulating inflammatory biomarkers and lipid profile were determined. Quantitative PCR and Western blot were used to determine the expression of CC-chemokine receptor 2 (CCR2), other inflammation markers, glucose transporter 4 (GLUT4), insulin receptor substrate 1 (IRS-1) and pAkt/Akt and to assess targets of interest involving glucose metabolism and inflammation in visceral WAT. Results HFD increased the levels of total cholesterol, triglycerides, low-density lipoprotein cholesterol and proinflammatory cytokines in serum, decreased the high-density lipoprotein cholesterol level in serum, and induced insulin resistance and WAT inflammation in mice. However, RES treatment alleviated insulin resistance, increased the expressions of pAkt, GLUT4 and IRS-1 in WAT, and decreased serum proinflammatory cytokine levels, macrophage infiltration and CCR2 expression in WAT. Conclusion Our results indicated that WAT CCR2 may play a vital role in macrophage infiltration and the inflammatory response during the development of insulin resistance in HFD-induced obesity. These data suggested that administration of RES offers protection against abnormal glucose metabolism and inflammatory adaptations in visceral WAT in mice with HFD-induced obesity

Separating and characterizing functional alkane degraders from crude-oil-contaminated sites via magnetic nanoparticle-mediated isolation

Uncultivable microorganisms account for over 99% of all species on the planet, but their functions are yet not well characterized. Though many cultivable degraders for n-alkanes have been intensively investigated, the roles of functional n-alkane degraders remain hidden in the natural environment. This study introduces the novel magnetic nanoparticle-mediated isolation (MMI) technology in Nigerian soils and successfully separates functional microbes belonging to the families Oxalobacteraceae and Moraxellaceae, which were dominant and responsible for alkane metabolism in situ. The alkR-type n-alkane monooxygenase genes, instead of alkA- or alkP-type, were the key functional genes involved in the n-alkane degradation process. Further physiological investigation via a BIOLOG PM plate revealed some carbon (Tween 20, Tween 40 and Tween 80) and nitrogen (tyramine, L-glutamine and D-aspartic acid) sources promoting microbial respiration and n-alkane degradation. With further addition of promoter carbon or nitrogen sources, the separated functional alkane degraders significantly improved n-alkane biodegradation rates. This suggests that MMI is a promising technology for separating functional microbes from complex microbiota, with deeper insight into their ecological functions and influencing factors. The technique also broadens the application of the BIOLOG PM plate for physiological research on functional yet uncultivable microorganisms

Assessing real-world safety concerns of Sacituzumab govitecan: a disproportionality analysis using spontaneous reports in the FDA adverse event reporting system

AimThe aim of this study was to identify potential safety concerns associated with Sacituzumab Govitecan (SG), an antibody-drug conjugate targeting trophoblastic cell-surface antigen-2, by analyzing real-world safety data from the largest publicly available worldwide pharmacovigilance database.MethodsAll data obtained from the FDA Adverse Event Reporting System (FAERS) database from the second quarter of 2020 to the fourth quarter of 2022 underwent disproportionality analysis and Bayesian analysis to detect and assess the adverse event signals of SG, considering statistical significance when the lower limit of the 95% CI >1, based on at least 3 reports.ResultsTotal of 1072 cases were included. The main safety signals were blood and lymphatic system disorders [ROR(95CI)=7.23 (6.43-8.14)], gastrointestinal disorders [ROR(95CI)=2.01 (1.81-2.22)], and relative infection adverse events, such as neutropenic sepsis [ROR(95CI)=46.02 (27.15-77.99)] and neutropenic colitis [ROR(95CI)=188.02 (120.09-294.37)]. We also noted unexpected serious safety signals, including large intestine perforation [ROR(95CI)=10.77 (3.47-33.45)] and hepatic failure [ROR(95CI)=3.87 (1.45-10.31)], as well as a high signal for pneumonitis [ROR(95CI)=9.93 (5.75-17.12)]. Additionally, age sub-group analysis revealed that geriatric patients (>65 years old) were at an increased risk of neutropenic colitis [ROR(95CI)=282.05 (116.36-683.66)], neutropenic sepsis [ROR(95CI)=101.11 (41.83-244.43)], acute kidney injury [ROR(95CI)=3.29 (1.36-7.94)], and atrial fibrillation [ROR(95CI)=6.91 (2.86-16.69)].ConclusionThis study provides crucial real-world safety data on SG, complementing existing clinical trial information. Practitioners should identify contributing factors, employ monitoring and intervention strategies, and focus on adverse events like neutropenic sepsis, large intestine perforation, and hepatic failure. Further prospective studies are needed to address these safety concerns for a comprehensive understanding and effective management of associated risks

Observation of TeV gamma rays from the Cygnus region with the ARGO-YBJ experiment

We report the observation of TeV gamma-rays from the Cygnus region using the ARGO-YBJ data collected from 2007 November to 2011 August. Several TeV sources are located in this region including the two bright extended MGRO J2019+37 and MGRO J2031+41. According to the Milagro data set, at 20 TeV MGRO J2019+37 is the most significant source apart from the Crab Nebula. No signal from MGRO J2019+37 is detected by the ARGO-YBJ experiment, and the derived flux upper limits at 90% confidence level for all the events above 600 GeV with medium energy of 3 TeV are lower than the Milagro flux, implying that the source might be variable and hard to be identified as a pulsar wind nebula. The only statistically significant (6.4 standard deviations) gamma-ray signal is found from MGRO J2031+41, with a flux consistent with the measurement by Milagro.Comment: 14 pages, 4 figure