Monitoring recent lake variations under climate change around the Altai Mountains using multimission satellite data

Estimating lake dynamics is vital for the accurate evaluation of climate change and water resources monitoring. However, it remains a challenge to estimate the lake mass budget due to extremely scarce in situ data, especially for alpine regions. In this article, multimission remote sensing observations were blended to examine recent lake variations and their responses to climate change around the Altai Mountains during 2001–2009 and 2010–1018. First, the multitemporal Landsat images were used to enable the detailed monitoring of the surface extent of 43 lakes (> 5 km 2 ) around the Altai Mountains from 2001 to 2018. The results presented that the total lake surface extent shrunk from 9835 km 2 in 2001 to a minimum of 9652 km 2 in 2009, while subsequently rose to 9714 km 2 in 2018. By combining the lake area with the water level derived from the ICESat and CryoSat-2 altimetry data, the water storage of seven lakes covering ∼84% of the overall lake area in the region was obtained. The total water storage was detected with a decrease of 4.86 ± 1.17 km 3 from 2003 to 2009 and a decrease of 3.65 ± 1.16 km 3 from 2010 to 2018, respectively. Although most of the glaciers in this region had a significant mass loss in the past decades, the factor analysis indicated that most of the lakes had maintained a steady or slightly changing tendency because the glacial melting water was counteracted by the negative impact of high evapotranspiration amount. For the lakes with a few glacier melting supplies, e.g., the Uvs lake and Hyargas lake, the significant water budget loss was caused by the increasing evapotranspiration, decreased precipitation, and developed animal husbandry, which mainly dominated the overall decreasing trend of lake water storage in the Altai Mountains

TBR2 coordinates neurogenesis expansion and precise microcircuit organization via Protocadherin 19 in the mammalian cortex.

Cerebral cortex expansion is a hallmark of mammalian brain evolution; yet, how increased neurogenesis is coordinated with structural and functional development remains largely unclear. The T-box protein TBR2/EOMES is preferentially enriched in intermediate progenitors and supports cortical neurogenesis expansion. Here we show that TBR2 regulates fine-scale spatial and circuit organization of excitatory neurons in addition to enhancing neurogenesis in the mouse cortex. TBR2 removal leads to a significant reduction in neuronal, but not glial, output of individual radial glial progenitors as revealed by mosaic analysis with double markers. Moreover, in the absence of TBR2, clonally related excitatory neurons become more laterally dispersed and their preferential synapse development is impaired. Interestingly, TBR2 directly regulates the expression of Protocadherin 19 (PCDH19), and simultaneous PCDH19 expression rescues neurogenesis and neuronal organization defects caused by TBR2 removal. Together, these results suggest that TBR2 coordinates neurogenesis expansion and precise microcircuit assembly via PCDH19 in the mammalian cortex

Novel tumor necrosis factor-related long non-coding RNAs signature for risk stratification and prognosis in glioblastoma

BackgroundTumor necrosis factor (TNF) is an inflammatory cytokine that can coordinate tissue homeostasis by co-regulating the production of cytokines, cell survival, or death. It widely expresses in various tumor tissues and correlates with the malignant clinical features of patients. As an important inflammatory factor, the role of TNFα is involved in all steps of tumorigenesis and development, including cell transformation, survival, proliferation, invasion and metastasis. Recent research has showed that long non-coding RNAs (lncRNAs), defined as RNA transcripts >200 nucleotides that do not encode a protein, influence numerous cellular processes. However, little is known about the genomic profile of TNF pathway related-lncRNAs in GBM. This study investigated the molecular mechanism of TNF related-lncRNAs and their immune characteristics in glioblastoma multiforme (GBM) patients.MethodsTo identify TNF associations in GBM patients, we performed bioinformatics analysis of public databases - The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). The ConsensusClusterPlus, CIBERSORT, Estimate, GSVA and TIDE and first-order bias correlation and so on approaches were conducted to comprehensively characterize and compare differences among TNF-related subtypes.ResultsBased on the comprehensive analysis of TNF-related lncRNAs expression profiles, we constructed six TNF-related lncRNAs (C1RL-AS1, LINC00968, MIR155HG, CPB2-AS1, LINC00906, and WDR11-AS1) risk signature to determine the role of TNF-related lncRNAs in GBM. This signature could divide GBM patients into subtypes with distinct clinical and immune characteristics and prognoses. We identified three molecular subtypes (C1, C2, and C3), with C2 showing the best prognosis; otherwise, C3 showing the worst prognosis. Moreover, we assessed the prognostic value, immune infiltration, immune checkpoints, chemokines cytokines and enrichment analysis of this signature in GBM. The TNF-related lncRNA signature was tightly associated with the regulation of tumor immune therapy and could serve as an independent prognostic biomarker in GBM.ConclusionThis analysis provides a comprehensive understanding of the role of TNF-related characters, which may improve the clinical outcome of GBM patients

Analysis of COVID-19 Guideline Quality and Change of Recommendations: A Systematic Review.

Background Hundreds of coronavirus disease 2019 (COVID-19) clinical practice guidelines (CPGs) and expert consensus statements have been developed and published since the outbreak of the epidemic. However, these CPGs are of widely variable quality. So, this review is aimed at systematically evaluating the methodological and reporting qualities of COVID-19 CPGs, exploring factors that may influence their quality, and analyzing the change of recommendations in CPGs with evidence published. Methods We searched five electronic databases and five websites from 1 January to 31 December 2020 to retrieve all COVID-19 CPGs. The assessment of the methodological and reporting qualities of CPGs was performed using the AGREE II instrument and RIGHT checklist. Recommendations and evidence used to make recommendations in the CPGs regarding some treatments for COVID-19 (remdesivir, glucocorticoids, hydroxychloroquine/chloroquine, interferon, and lopinavir-ritonavir) were also systematically assessed. And the statistical inference was performed to identify factors associated with the quality of CPGs. Results We included a total of 92 COVID-19 CPGs developed by 19 countries. Overall, the RIGHT checklist reporting rate of COVID-19 CPGs was 33.0%, and the AGREE II domain score was 30.4%. The overall methodological and reporting qualities of COVID-19 CPGs gradually improved during the year 2020. Factors associated with high methodological and reporting qualities included the evidence-based development process, management of conflicts of interest, and use of established rating systems to assess the quality of evidence and strength of recommendations. The recommendations of only seven (7.6%) CPGs were informed by a systematic review of evidence, and these seven CPGs have relatively high methodological and reporting qualities, in which six of them fully meet the Institute of Medicine (IOM) criteria of guidelines. Besides, a rapid advice CPG developed by the World Health Organization (WHO) of the seven CPGs got the highest overall scores in methodological (72.8%) and reporting qualities (83.8%). Many CPGs covered the same clinical questions (it refers to the clinical questions on the effectiveness of treatments of remdesivir, glucocorticoids, hydroxychloroquine/chloroquine, interferon, and lopinavir-ritonavir in COVID-19 patients) and were published by different countries or organizations. Although randomized controlled trials and systematic reviews on the effectiveness of treatments of remdesivir, glucocorticoids, hydroxychloroquine/chloroquine, interferon, and lopinavir-ritonavir for patients with COVID-19 have been published, the recommendations on those treatments still varied greatly across COVID-19 CPGs published in different countries or regions, which may suggest that the CPGs do not make sufficient use of the latest evidence. Conclusions Both the methodological and reporting qualities of COVID-19 CPGs increased over time, but there is still room for further improvement. The lack of effective use of available evidence and management of conflicts of interest were the main reasons for the low quality of the CPGs. The use of formal rating systems for the quality of evidence and strength of recommendations may help to improve the quality of CPGs in the context of the COVID-19 pandemic. During the pandemic, we suggest developing a living guideline of which recommendations are supported by a systematic review for it can facilitate the timely translation of the latest research findings to clinical practice. We also suggest that CPG developers should register the guidelines in a registration platform at the beginning for it can reduce duplication development of guidelines on the same clinical question, increase the transparency of the development process, and promote cooperation among guideline developers all over the world. Since the International Practice Guideline Registry Platform has been created, developers could register guidelines prospectively and internationally on this platform

A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance

In this paper, a novel state estimation approach based on the variational Bayesian adaptive Kalman filter (VBAKF) and road classification is proposed for a suspension system with time-varying and unknown noise covariance. Using the VB approach, the time-varying noise covariance can be inferred from the inverse-Wishart distribution and then optimized state estimation by the finite sampling posterior probability distribution function (PDF) of noise covariance and backward Kalman smoothing. In addition, a new road classification algorithm based on multi-objective optimization and the linear classifier is proposed to identify the unknown noise covariance. Simulation results for a suspension model with time-varying and unknown noise covariance show that the proposed approach has a higher performance in state estimation accuracy than other filters

Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations

Non-intrusive image-based techniques for measuring surface river velocities have rapidly evolved as a cost-effective and safe means for quantifying flow patterns. Large-scale particle image velocimetry (LSPIV) can provide instantaneous surface velocities over a large spatial footprint rapidly and with little pre-calibration as compared to traditional techniques. Assessment of the spatial distribution of flow velocities in hydraulic models has been comparatively harder to achieve than assessment of depth due to logistical challenges but would be aided using large observational datasets that represent the variability and distribution of flow hydraulics. Additionally, the efficacy of image velocimetry in assessing the accuracy of outputs from 2D hydraulic models has not been addressed. Here, we demonstrate how LSPIV can be used to calibrate and validate 2D model predictions in a gravel bed river reach. LSPIV velocities are depth-averaged using standard velocity coefficients (α) and then using the Probability Concept (PC) - a probabilistic formulation of velocity distributions that accounts for non-standard velocity profiles, typical in field settings. UAV surveys were used to acquire video for LSPIV and imagery for Structure from Motion (SfM) topographic modelling. We use spatially dense acoustic doppler current profiler (aDcp) velocity data for benchmark assessment of the velocity outputs of HEC-RAS 2D model simulations. 2D model prediction error, based on seeded LSPIV velocities, was within range (4.2%) of the aDcp parametrised model, with improvements in modelled versus predicted velocity correlations (up to 7.7%) when using PC to depth average LSPIV velocities. Validation bias reduced significantly (11%) with tighter error distributions when compared to the aDcp based model. Although additional hydraulic measurements are required to parametrise the Probability Concept algorithm, the performance of 2D hydraulic models calibrated/validated with LSPIV velocities is on par with traditional techniques, demonstrating the potential of this non-intrusive, low-cost approach

Temperature-Sensitive Points Optimization of Spindle on Vertical Machining Center with Improved Fuzzy C-Means Clustering

The heat generated by motors and bearings of machine tools has a significant impact on machining accuracy. Error modeling and compensation has proven to be effective ways to reduce thermal errors and improve accuracy. An improved fuzzy c-means (FCM) clustering algorithm is proposed to determine the optimized temperature sensitive points for thermal error modeling of a spindle on the vertical machining center. The sensors are deployed to measure the temperature of different positions of machine tools, and the improved FCM algorithm is used to classify the measured data. Combined with the F-test statistics of multiple linear regression, the optimal temperature points of each group are selected. The improved FCM clustering algorithm significantly reduces the multicollinearity problem among temperature measuring points and avoids them falling into local optimization. The modeling method was verified through experiments on two types of vertical machining centers. The results show that the accuracy of the spindle in Y and Z directions of the machine tools was increased by more than 75%, and the model has good robustness, demonstrating application prospects in the selection of temperature measuring points of the spindle system of vertical machining centers

Model based calibration for improving fuel economy of a turbocharged diesel engine

Fuel economy is the key performance for the vehicle besides emission, which has been compulsory controlled by the legislation. For the electronic controlled Diesel engines, the mentioned properties could be satisfied not only by engine design but also by engine performance tuning. Fuel economy may be influenced by many coupled factors, such as injection timing, speed, load and under the limitation of cylinder peak pressure and exhaust temperature. To achieve a high efficient calibration, a model based calibration was performed on a four cylinder electronic unit pump Diesel engine with exhaust gas recirculation. The objective of the study is to solve the complexity of the interactions among the engine running parameters and the best fuel economy performance in order to meet under the restriction of NOx emission performance. The study was carried out in four stages. First, the experiment design has been proposed to identify designed experiment operating points and weighting factors. Second, two-stage statistical engine responses and boundary models have been established. Third, the global optimization and European steady-state cycle operating point optimization have been carried out. Finally, the bench test has been conducted on the Diesel engine. The global operating points results show that the fuel consumption rate has decreased at most test operating points by model based calibration. The fuel consumption rate has decreased by 3.5%, and 13 mode cycle test results indicate that the proposed model based calibration method is effective and can improve the fuel efficiency by 2.72% compared with the traditional calibratio