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Abstract Background Chronic cerebral hypoperfusion (CCH) is a critical pathophysiological mechanism underlying cerebral small vessel disease (CSVD). Accumulating evidence have demonstrated that resident pericytes and deposit extracellular matrix (ECM) and play a key role in mediating fibrosis in hypoxic changes. Edaravone dexborneol (EDB) is known to target multiple pathways involved in fibrosis. Methods We constructed the CCH mouse models that were subjected to either PBS or EDB at different concentrations. Measures of cognitive function, neuronal damage, white matter lesion (WML), the fibrous profiles of pericytes and ECM protein were investigated to assess the effect of EDB. RNA sequencing of OGD in pericytes was performed to identify a key signaling pathway. Results We observed that both medium and high concentrations of EDB could ameliorate CCH-induced cognitive impairment and emotional disorders. Neuronal damage in cortical layer and hippocampus and WML in corpus callosum were improved by EDB, which was consistent with the tends of fibrous pericytes and ECM proteins in these regions. RNA sequencing suggested that TGF-β1/IL-11 plays an important role in mechanism of pericytes fibrosis. Subsequently, the results of sequencing were confirmed in both cellular and mouse model. Conclusions Our findings reveal the role of pericyte-mediated fibrosis in depositing ECM in the pathogenesis of CSVD. EDB could improve symptoms and the underlying pathogenesis of CCH mice and decrease the expression of the fibrous profiles of pericytes and ECM proteins, which may be regulated by TGF-β1/ IL-11. EDB treatment, targeting pericytes fibrosis, may be a novel therapeutic strategy for CSVD.

Lung cancer remains a major global health problem with high mortality rates. Early diagnosis can significantly improve prognosis. Hence alternative diagnostic platforms facilitating rapid, noninvasive, point-of-care screening are needed. Nanomaterial-assisted electrochemical biosensors present a promising technology. Unique properties of nanomaterials like high electrical conductivity, catalytic activity, and large surface area enhance achieved detection limits and kinetics. Recent works have combined nanomaterials like graphene, metal nanoparticles and conducting polymers with recognition elements like antibodies and aptamers. Considerable progress has focused on improving analytical parameters including sensitivity, linear ranges, reproducibility and response times. Initial embodiments also demonstrate multiplexed detection, microfluidic integration and smartphone connectivity towards viable clinical practice. However considerable efforts on large sample size validation, standardization of testing protocols and viable commercialization pathways integrating regulatory guidelines and reimbursement ecosystem partnerships remain before such lab-on-chip platforms can enter mainstream and guide therapeutic interventions through affordable decentralized personalized monitoring.

Accidents induced by natural disasters at sports sites may cause catastrophic loss of great concern. However, previous studies on risk assessments of sports sites have only focused on operational risk and equipment failure. With the frequent occurrence of extreme disasters, the risk of domino chains caused by natural disasters at large-scale events, such as large-scale winter sports sites, cannot be ignored. In this study, a natural disaster-induced accident-chain evolution analysis model (NAEA model) is proposed. Based on the results of the NAEA model, a fuzzy Bayesian network for domino accidents triggered by an earthquake at large-scale winter sports sites was established. Through sensitivity analysis and scenario analysis, it was found that fire and explosion accidents and crowded stampede accidents are the main causes of serious loss in domino disaster chains in large-scale sports sites. Simultaneously, improving the early warning capability, reliability of electrical equipment, and automatic sprinkler systems are the most effective ways to prevent and control major accidents. In addition, an optimal safety strategy improvement analysis was performed to facilitate the decision-making of safety managers to prevent serious accidents and reduce accident loss.

The upper-tropospheric carbonaceous aerosol layer (TCAL) represents the increase of aerosols in the upper-troposphere. It was first discovered over Asia but was found in this study to also occur over South America and Africa. The TCALs over three regions typically exist during the strong deep convection season, with the Asian, South American, and African TCALs showing peak intensity during July–August, October–December, and November–December, respectively. Over Asia, the TCAL has the highest altitude and widest spread due to strongest deep convection and upper-troposphere anticyclonic system. TCAL intensity is highest in South America maybe due to heaviest pollutant emissions. Anthropogenic pollution from India and western China produces two Asian TCAL centers, whereas widespread wildfires result in single centers over South America and Africa. TCAL radiative effect at the top of the atmosphere has warming effects over Asia (+0.23 W m ^−2 ), whereas cooling effects perform over South America (−0.54 W m ^−2 ) and Africa (−0.20 W m ^−2 ) owing to its altitude and the divergent strengths of black-carbon absorption and organic-carbon scattering.

ObjectiveTo describe the characteristics of large vestibular aqueduct syndrome (LVAS) in wideband acoustic immittance (WAI) and to explore whether inner ear deformity has an impact on WAI results.MethodsSubjects with typical LVAS (LVAS group) and control subjects with a normal anatomical structure of the inner ear (control group) were screened from pediatric patients with cochlear implants using thin-slice computed tomography (CT) images of the temporal bone. With inflammation of the auditory canal and middle ear excluded by routine ear examination and 226 Hz acoustic immittance, WAI data were acquired. Then, the maximum absorbance as the major observation indicator on the mean tympanogram was compared between the LVAS group and control group, and a descriptive comparison of the mean tympanogram and frequency-absorbance curve at peak pressure was performed between the two groups.ResultsThe LVAS group included 21 cases (38 ears), and the control group included 27 cases (45 ears). All LVAS subjects met the Valvassori criteria, and the VA at the horizontal semicircular canal displayed flared expansion. On the mean tympanogram, the maximum absorbance in the LVAS group (0.542 ± 0.087) was significantly higher than that in the control group (0.455 ± 0.087) (p < 0.001). The tympanogram in the LVAS group showed an overall elevation, and the absorbance at all pressure sampling points was significantly higher than that in the control group (p < 0.001). The frequency-absorbance curve at peak pressure first increased and then decreased in both groups, and the LVAS group showed higher absorbance than the control group in the frequency range below 2,828 Hz. The absorbance at 343–1,124 Hz was significantly different between the two groups (p < 0.001), and 343–1,124 Hz was the major frequency range at which the maximum absorbance on the mean tympanogram increased in the LVAS group.ConclusionLarge vestibular aqueduct syndrome (LVAS) shows increased absorbance in low and medium frequency ranges in WAI. The maximum absorbance on the mean tympanogram can serve as a reliable evaluation indicator. Inner ear factors must be considered when middle ear lesions are analyzed by WAI.

Wildfire events have recently shown a rapid increase in frequency and scale due to the warmer present-day climate; however, their potential effects on the cryosphere are difficult to assess. Catastrophic wildfires in Australia during 2019–2020 emitted large amounts of light-absorbing particles (LAPs) to the atmosphere. Satellite observations indicate that these LAPs caused unprecedented snow-darkening of glaciers in New Zealand through long-range transport and deposition, with their effects lasting for up to three months in January–March 2020, influencing >90% of total glacier/snow and leading to a mean broadband snow-reflectance reduction of 0.08 ± 0.03. This snow darkening accelerated snowmelt by ~0.41 ± 0.2 cm day–1 during the southern summer, equivalent to that caused by a ~1.8 °C increase in air temperature. This indicates the significant impact of the 2019–2020 Australian wildfires on the hydrologic cycle in New Zealand, exceeding that of the local climate warming of ~1.5 °C since the preindustrial period. Wildfire-induced snow darkening is not limited to New Zealand. Future projections of wildfire incidence indicate widespread effects of snow darkening on the global cryosphere.

Abstract Australia experienced record‐breaking wildfires during 2019–2020, which emitted large amounts of carbonaceous aerosols (CAs) to the atmosphere. In this study, we explored the atmospheric dynamics and thermodynamic mechanism of the long‐range transport of CAs during November 2019–February 2020. The results indicate that the emitted CAs had a ternary advective spreading pattern across the Pacific Ocean in the Southern Hemisphere (SH), influenced mainly by the westerlies and anticyclonic systems. Upward vertical motion over the convergence zone and heat released from smoke pyro‐cumulonimbus clouds and strong light absorption of CAs provided strong vertical lift as the predominant injection pathway of the CAs into the stratosphere in the central Pacific Ocean in the SH. The abundant tropospheric CAs induced atmospheric heating (and potentially surface cooling), with increasing air temperature and decreasing of surface temperature. The anomalous thermal structure led to changes in atmospheric circulation, which weakened the upward vertical motion.

Abstract Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones.

Abstract We quantified the combined effects of mineral dust nonsphericity and size on snow albedo reduction using the MOPSMAP (Modeled optical properties of ensembles of aerosol particles) package and SAMDS (Spectral Albedo Model for Dirty Snow) with the consideration of dust from Sahara, Greenland, San Juan Mountains, and Tibetan Plateau. Results indicate that the dust‐induced albedo reduction decreases by up to 30% as the effective radii of dust particles increase in 1–5 μm. Nonspherical dust enhances snow albedo reduction relative to spherical dust by up to 20%. Stronger enhancements are obtained for higher dust concentration and larger dust size. Furthermore, the dust nonsphericity‐induced enhancement of snow albedo reduction is more pronounced for more‐absorptive dust. Finally, we develop a new parameterization for quantifying the dependence of snow albedo reduction on dust nonsphericity and size, and provide a convenient way for assessing the climate impacts of dust in snow.

Bayesian network is an effective method for quantitative risk assessment, but most existing studies are either heavily data-dependent or excessively expert-dependent. In this paper, knowledge graph, complex network theory and Bayesian network are integrated into a KCB model for data-driven risk assessment, especially small data situations. By applying knowledge graph with natural language processing, a causation graph could be extracted and illustrated from accident reports. Some indexes from complex network theory are introduced to identify critical nodes to simplify the huge graph. Based on the simplified network, a Bayesian network is established to quantitatively demonstrate accidents from causes to consequences. Moreover, sensitivity analysis and scenario analysis are conducted to support the decision-making of safety management. In all, the expert involvement of Bayesian network can be reduced by applying the KCB model. Besides, the KCB model can be further applied to many other areas to reach uncertainty modelling.

Under the background of implementing green travel in response to the national energy conservation and emission reduction policy, the concept of shared travel as a new transportation mode has been promoted. This paper aims at providing a more scientific and quantitative method to explore the shared travel traffic mode and evaluating efficiency with data mining technology. Based on the analysis of the evaluation index system of the existing intelligent shared mobility, this paper firstly points out some issues in reflecting the operation efficiency of a specific shared mobility service. In order to find out the characteristics of passenger flow and propose recommend indicators, this study presents a detail analysis using data in Xiong’an new area from September 2020 to September 2021. Then, this paper proposes a time-series algorithm to identify ridesharing behavior of demand responsive (DR) bus and to recommend indicators on operation efficiency considering capacity, turnover, and time. Moreover, the definition of indicators and calculation of case study are carried out. Results show that the utilization rate of vehicle seat for DR bus was increased by 1.6–2.5 times, and the turnover efficiency was increased by about 2 times compared to private cars and taxi. In general, this paper quantitatively describes the improvement of operation efficiency brought by bus sharing, which shows that this kind of shared mobility has the attributes of public transport in a certain sense. Also, this paper shows that the above indicators are quantifiable and comparable, which is a useful supplement to the existing evaluation index system.

Light-absorbing particles (LAPs) deposited on snow can significantly reduce surface albedo and contribute to positive radiative forcing. This study firstly estimated and attributed the spatio-temporal variability in the radiative forcing (RF) of LAPs in snow over the northern hemisphere during the snow-covered period 2003–2018 by employing Moderate Resolution Imaging Spectroradiometer (MODIS) data, coupled with snow and atmospheric radiative transfer modelling. In general, the RF for the northern hemisphere shows a large spatial variability over the whole snow-covered areas and periods, with the highest value (12.7 W m−2) in northeastern China (NEC) and the lowest (1.9 W m−2) in Greenland (GRL). The concentration of LAPs in snow is the dominant contributor to spatial variability in RF in spring (~73%) while the joint spatial contributions of snow water equivalent (SWE) and solar irradiance (SI) are the most important (>50%) in winter. The average northern hemisphere RF gradually increases from 2.1 W m−2 in December to 4.1 W m−2 in May and the high-value area shifts gradually northwards from mid-altitude to high-latitude over the same period, which is primarily due to the seasonal variability of SI (~58%). More interestingly, our data reveal a significant decrease in RF over high-latitude Eurasia (HEUA) of −0.04 W m−2 a−1 and northeastern China (NEC) of −0.14 W m−2 a−1 from 2003 to 2018. By employing a sensitivity test, we find the concurrent decline in the concentration of LAPs in snow accounted for the primary responsibility for the decrease in RF over these two areas, which is further confirmed by in situ observations.

Black carbon in snow (BCS) has a significant impact on global climate and is an important component of Earth system modeling. Here, we provide a comprehensive evaluation of BCS simulations in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and its radiative forcing on a global scale. Overall, the multi-model mean generally captures the characteristics of BCS spatial patterns, with maximum concentrations in East Asia and the Tibetan Plateau (~120 ng·g−1), and the lowest in Antarctica (~0.05 ng·g−1). The BCS concentrations in all CMIP6 multi-model mean and individual models generally exhibit a temporally increasing trend globally, with particularly large increases after the 1940s. In terms of seasonal cycles, individual models are generally consistent in most regions. Globally, BCS concentrations are highest around January and lowest in September. The albedo reduction in the Tibetan Plateau and East Asia simulated by the CMIP6 multi-model mean reached ~0.06 in 2014 and may influence climate more than expected.

The general volume of data has exploded to unimaginable levels in the past decade. Therefore, the big data analytics has become an area of focus. Many frameworks have been developed for data analytics, such as Hadoop, Spark, etc. Most of the frameworks are built on multi-core or many-core memory systems, requiring developers and users to have an in-depth understanding of the architectures to take full advantage of the hardware. In this paper, we present a comprehensive study of both multi-core and many-core memory systems and discuss the different characteristics, including core, cache, memory, and the on-chip network. Furthermore, we propose a simple but effective mechanism for cache false-sharing overhead that can reduce a large number of LLC-load/store instructions and LLC cache-misses. In addition, we conduct detailed experiments with Ligra, a graph analytics framework, on four different-sized datasets. The results show that it can achieve up to a 2.5× and 9.5× speed-up for multi-core and many-core memory systems, respectively. We finally share our key findings and discuss the platform development on multi-core and many-core memory systems.

Graphs are ubiquitous, and graph analytics has been widely adopted in many big data applications such as social computation and natural language processing, as well as web-search and recommendation systems. Prior research focuses on processing large-scale graphs on distributed environments or a single multi-core machine with several terabytes of RAM. Increasing complex memory systems and on-chip interconnects are developed to mitigate the data movement bottlenecks in manycore processors such as Xeon Phi KNL CPU with heterogeneous memory, with up to 72 dual-core tiles. This paper presents a detailed study on the characteristics of manycore memory systems and their impact on the efficiency of graph analytics. Based on this paper, we introduce Ants, the first graph analytics platform on manycore memory systems. First, Ants differentially allocates graph data according to their access patterns and the behavior of heterogeneous memory. Second, to reduce excessive memory access and ease congestion on interconnects and memory controllers, Ants develops a fine-grained and effective task partitioning strategy for many cores. A detailed experiment on a 64 dual-core tile machine shows that Ants outperforms the state-of-the-art graph analytics platform-Ligra by up to 8.97X for real-world graphs.

The extensive snow cover across the Tibetan Plateau (TP) regions has a major influence on the climate and water supply for over one billion downstream inhabitants. However, an adequate evaluation of snow cover fraction (SCF) variability over the TP simulated by global multiple reanalysis datasets has yet to be undertaken. In this study, we examined eight global reanalysis SCF datasets using the Snow Property Inversion from Remote Sensing (SPIReS) product spanning the period 2001-2020. The results reveal that the HMASR generated the best SCF simulations because of its outstanding spatial and temporal accuracy. The GLDAS and CFSR demonstrated acceptable SCF accuracy with respect to spatial variability, but struggled to reproduce the annual trend. Pronounced SCF overestimations were found when using the ERA5, ERA5L, and JRA55, but SCF was underestimated by MERRA2, and CRAL generated poor spatial pattern. Overall biases were related to the combined effect of precipitation forcing, temperature forcing, snow data assimilation, and SCF parameterization methods, with the dominant factor changing across datasets. In ERA5 and ERA5L, temperature and snowfall bias exhibited significant correlations with SCF bias over most TP areas, therefore having a greater impact on the accuracy of SCF in terms of spatial variability and temporal evolution. On the other hand, the impact of snow assimilation was possibly more pronounced in MERRA2 and CRAL. Although parameterization methods can improve SCF simulation accuracy, their influence was weaker than those of other factors, except for JRA55. To further improve the accuracy of SCF simulation, an ensemble average method was developed. The ensemble based on HMASR and GLDAS generated the most accurate SCF spatial distribution, whereas the ensemble containing ERA5L, CFSR, CRAL, GLDAS, ERA5, and MERRA2 proved optimal for capturing the annual trend. [ABSTRACT FROM AUTHOR]

Neuroinflammation plays a crucial role in the pathogenesis and progression of Alzheimer's disease (AD). The Sterile Alpha and Toll Interleukin Receptor Motif-containing protein 1 (SARM1) has been shown to promote axonal degeneration and is involved in neuroinflammation. However, the role of SARM1 in AD remains unclear. In this study, we found that SARM1 was reduced in hippocampal neurons of AD model mice. Interestingly, conditional knockout (CKO) of SARM1 in the central nervous system (CNS, SARM1Nestin -CKO mice) delayed the cognitive decline in APP/PS1 AD model mice. Furthermore, SARM1 deletion reduced the Aß deposition and inflammatory infiltration in the hippocampus and inhibited neurodegeneration in APP/PS1 AD model mice. Further investigation into the underlying mechanisms revealed that the signaling of tumor necrosis factor-α (TNF-α) was downregulated in the hippocampus tissues of APP/PS1;SARM1Nestin-CKO mice, thereby alleviating the cognitive decline, Aβ deposition and inflammatory infiltration. These findings identify unrecognized functions of SARM1 in promoting AD and reveal the SARM1-TNF-α pathway in AD model mice. [ABSTRACT FROM AUTHOR]