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Significant progress has been achieved in the field of oriented object detection (OOD) in recent years. Compared to natural images, objects in remote sensing images exhibit characteristics of dense arrangement and arbitrary orientation while also containing a large amount of background information. Feature extraction in OOD becomes more challenging due to the diversity of object orientations. In this paper, we propose a semantic-driven rotational feature enhancement method, termed SREDet, to fully leverage the joint semantic and spatial information of oriented objects in the remote sensing images. We first construct a multi-rotation feature pyramid network (MRFPN), which leverages a fusion of multi-angle and multiscale feature maps to enhance the capability to extract features from different orientations. Then, considering feature confusion and contamination caused by the dense arrangement of objects and background interference, we present a semantic-driven feature enhancement module (SFEM), which decouples features in the spatial domain to separately enhance the features of objects and weaken those of backgrounds. Furthermore, we introduce an error source evaluation metric for rotated object detection to further analyze detection errors and indicate the effectiveness of our method. Extensive experiments demonstrate that our SREDet method achieves superior performance on two commonly used remote sensing object detection datasets (i.e., DOTA and HRSC2016).

BackgroundNo previous studies have reported on the use of minimally invasive endoscopic therapy for colon cancer in older patients.Case presentationAn 80-year-old man was admitted to our hospital with haematochezia and diagnosed with advanced colon cancer in 2018. Traditional surgical care was rejected by his family. We successfully treated the patient with multiple minimally invasive endoscopic therapies, such as argon plasma coagulation, from 2018 to 2021.ConclusionInvasive endoscopic therapy is a feasible way to treat colon cancer in older patients.

Abstract Background Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. Methods We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations. Results The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). Conclusion The study provides empirical evidence for the task by applying a weakly supervised NLP pipeline on clinical notes. The proposed weak supervised deep learning approach requires no human annotation except for validation and testing, by leveraging ontologies, NER+L tools, and contextual representations. The study also demonstrates that Natural Language Processing (NLP) can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes. We discuss the usefulness and limitations of the weak supervision approach and propose directions for future studies.

Deformation monitoring plays a pivotal role in assessing dam safety. Interferometric Synthetic Aperture Radar (InSAR) has the advantage of obtaining an extensive range of deformation, regardless of weather conditions. The Datengxia Water Conservancy Hub is the largest in-construction dam in China. To effectively assess the in-construction dam safety, the SBAS-InSAR (Small Baseline Subset-InSAR) technique and 86 Sentinel-1 images (from 11 February 2020, to 16 January 2023) have been employed in this study to monitor the deformation over the reservoir and its surrounding areas. The reliability of the SBAS-InSAR monitoring results over the study area was demonstrated by the in situ monitoring results. And the InSAR results show that the central section of the left dam exhibits the most substantial cumulative deformation, attributed to the maximal water pressure. This is closely followed by the left end of the dam, which reflects a similar but smaller deformation. However, the in-construction cofferdam facilities make the right-end section of the left dam more robust, and the deformation is the most stable. Additionally, significant deformation of the auxiliary dam slope has been identified. Moreover, the analysis indicated that the deformation of the four upstream slopes is closely related to the precipitation, which potentially poses a threat to the safety of the Datengxia Dam.

IntroductionLinking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set.MethodsIn this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s (n=771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB).ResultsWe achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n=3,876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement.DiscussionIn conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.

BackgroundNatural language processing (NLP) has the potential to automate the reading of radiology reports, but there is a need to demonstrate that NLP methods are adaptable and reliable for use in real-world clinical applications.MethodsWe tested the F1 score, precision, and recall to compare NLP tools on a cohort from a study on delirium using images and radiology reports from NHS Fife and a population-based cohort (Generation Scotland) that spans multiple National Health Service health boards. We compared four off-the-shelf rule-based and neural NLP tools (namely, EdIE-R, ALARM+, ESPRESSO, and Sem-EHR) and reported on their performance for three cerebrovascular phenotypes, namely, ischaemic stroke, small vessel disease (SVD), and atrophy. Clinical experts from the EdIE-R team defined phenotypes using labelling techniques developed in the development of EdIE-R, in conjunction with an expert researcher who read underlying images.ResultsEdIE-R obtained the highest F1 score in both cohorts for ischaemic stroke, ≥93%, followed by ALARM+, ≥87%. The F1 score of ESPRESSO was ≥74%, whilst that of Sem-EHR is ≥66%, although ESPRESSO had the highest precision in both cohorts, 90% and 98%. For F1 scores for SVD, EdIE-R scored ≥98% and ALARM+ ≥90%. ESPRESSO scored lowest with ≥77% and Sem-EHR ≥81%. In NHS Fife, F1 scores for atrophy by EdIE-R and ALARM+ were 99%, dropping in Generation Scotland to 96% for EdIE-R and 91% for ALARM+. Sem-EHR performed lowest for atrophy at 89% in NHS Fife and 73% in Generation Scotland. When comparing NLP tool output with brain image reads using F1 scores, ALARM+ scored 80%, outperforming EdIE-R at 66% in ischaemic stroke. For SVD, EdIE-R performed best, scoring 84%, with Sem-EHR 82%. For atrophy, EdIE-R and both ALARM+ versions were comparable at 80%.ConclusionsThe four NLP tools show varying F1 (and precision/recall) scores across all three phenotypes, although more apparent for ischaemic stroke. If NLP tools are to be used in clinical settings, this cannot be performed “out of the box.” It is essential to understand the context of their development to assess whether they are suitable for the task at hand or whether further training, re-training, or modification is required to adapt tools to the target task.

In order to meet the development requirements of time-sensitive and computing-intensive businesses, the guarantee problem of real-time transmission and real-time computing of computing tasks was studied.Firstly, the research progress of computing power network and deterministic networking was briefly overviewed.Then, the technical scheme of deterministic computing power network was proposed, and the technical architecture and working mechanism were designed.Real-time transmission and real-time computing of computing tasks were realized through the technical capabilities such as computing-network perception, planning and scheduling, resource management and control.The simulation results also verified the effectiveness of the proposed technical scheme.Finally, the representative application scenarios of deterministic computing power network were analyzed, and the future development trends and technical challenges were discussed.

Glacial lake outburst floods (GLOFs) are one of the most severe disasters in alpine regions, releasing a large amount of water and sediment that can cause fatalities and economic loss as well as substantial damage to downstream infrastructures. The risk of GLOFs in the Himalayas is exacerbated by glacier retreat caused by global warming. Critical economic corridors, such as the Rongxer Watershed, are threatened by GLOFs, but the lack of risk assessment specific to the watershed hinders hazard prevention. In this study, we propose a novel model to evaluate the risk of GLOF using a combination of remote sensing observations, GIS, and hydrological models and apply this model to the GLOF risk assessment in the Rongxer Watershed. The results show that (1) the area of glacial lakes in the Rongxer Watershed increased by 31.19% from 11.35 km2 in 1990 to 14.89 km2 in 2020, and (2) 18 lakes were identified as potentially dangerous glacial lakes (PDGLs) that need to be assessed for the GLOF risk, and two of them were categorized as very high risk (Niangzongmajue and Tsho Rolpa). The proposed model was robust in a GLOF risk evaluation by historical GLOFs in the Himalayas. The glacial lake data and GLOF risk assessment model of this study have the potential to be widely used in research on the relationships between glacial lakes and climate change, as well as in disaster mitigation of GLOFs.

Taxa are traditionally identified using morphological proxies for groups of evolutionarily isolated populations. These proxies are common characters deemed by taxonomists as significant. However, there is no general rule on which character or sets of characters are appropriate to circumscribe taxa, leading to discussions and uncertainty. Birch species are notoriously hard to identify due to strong morphological variability and factors such as hybridization and the existence of several ploidy levels. Here, we present evidence for an evolutionarily isolated line of birches from China that are not distinguishable by traditionally assumed taxon recognition proxies, such as fruit or leaf characters. We have discovered that some wild material in China and some cultivated in the Royal Botanic Gardens Edinburgh, formerly recognized as Betula luminifera, differ from other individuals by having a peeling bark and a lack of cambial fragrance. We use restriction site-associated DNA sequencing and flow cytometry to study the evolutionary status of the unidentified Betula samples to assess the extent of hybridization between the unidentified Betula samples and typical B. luminifera in natural populations. Molecular analyses show the unidentified Betula samples as a distinct lineage and reveal very little genetic admixture between the unidentified samples and B. luminifera. This may also be facilitated by the finding that B. luminifera is tetraploid, while the unidentified samples turned out to be diploid. We therefore conclude that the samples represent a yet unrecognized species, which is here described as Betula mcallisteri.

Abstract Background Skin innervation is crucial for normal wound healing. However, the relationship between nerve receptors and wound healing and the intrinsic mechanism remains to be further identified. In this study, we investigated the role of a calcitonin gene-related peptide (CGRP) receptor component, receptor activity‐modifying protein 1 (RAMP1), in mouse skin fibroblast (MSF) proliferation. Methods In vivo, Western blotting and immunohistochemical (IHC) staining of mouse skin wounds tissue was used to detect changes in RAMP1 expression. In vitro, RAMP1 was overexpressed in MSF cell lines by infection with Tet-On-Flag-RAMP1 lentivirus and doxycycline (DOX) induction. An IncuCyte S3 Live-Cell Analysis System was used to assess and compare the proliferation rate differences between different treatment groups. Total protein and subcellular extraction Western blot analysis, quantitative real-time-polymerase chain reaction (qPCR) analysis, and immunofluorescence (IF) staining analysis were conducted to detect signalling molecule expression and/or distribution. The CUT & RUN assay and dual-luciferase reporter assay were applied to measure protein-DNA interactions. Results RAMP1 expression levels were altered during skin wound healing in mice. RAMP1 overexpression promoted MSF proliferation. Mechanistically, total Yes-associated protein (YAP) and nuclear YAP protein expression was increased in RAMP1-overexpressing MSFs. RAMP1 overexpression increased inhibitory guanine nucleotide-binding protein (G protein) α subunit 3 (Gαi3) expression and activated downstream protein kinase A (PKA), and both elevated the expression of cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB) and activated it, promoting the transcription of YAP, elevating the total YAP level and promoting MSF proliferation. Conclusions Based on these data, we report, for the first time, that changes in the total RAMP1 levels during wound healing and RAMP1 overexpression alone can promote MSF proliferation via the Gαi3-PKA-CREB-YAP axis, a finding critical for understanding RAMP1 function, suggesting that this pathway is an attractive and accurate nerve target for skin wound treatment. Video Abstract

Abstract Background Several common conditions have been widely recognised as risk factors for COVID-19 related death, but risks borne by people with rare diseases are largely unknown. Therefore, we aim to estimate the difference of risk for people with rare diseases comparing to the unaffected. Method To estimate the correlation between rare diseases and COVID-19 related death, we performed a retrospective cohort study in Genomics England 100k Genomes participants, who tested positive for Sars-Cov-2 during the first wave (16-03-2020 until 31-July-2020) of COVID-19 pandemic in the UK (n = 283). COVID-19 related mortality rates were calculated in two groups: rare disease patients (n = 158) and unaffected relatives (n = 125). Fisher’s exact test and logistic regression was used for univariable and multivariable analysis, respectively. Results People with rare diseases had increased risk of COVID19-related deaths compared to the unaffected relatives (OR [95% CI] = 3.47 [1.21– 12.2]). Although, the effect was insignificant after adjusting for age and number of comorbidities (OR [95% CI] = 1.94 [0.65–5.80]). Neurology and neurodevelopmental diseases was significantly associated with COVID19-related death in both univariable (OR [95% CI] = 4.07 [1.61–10.38]) and multivariable analysis (OR [95% CI] = 4.22 [1.60–11.08]). Conclusions Our results showed that rare disease patients, especially ones affected by neurology and neurodevelopmental disorders, in the Genomics England cohort had increased risk of COVID-19 related death during the first wave of the pandemic in UK. The high risk is likely associated with rare diseases themselves, while we cannot rule out possible mediators due to the small sample size. We would like to raise the awareness that rare disease patients may face increased risk for COVID-19 related death. Proper considerations for rare disease patients should be taken when relevant policies (e.g., returning to workplace) are made.

The electric vehicle (EV) market is expanding rapidly to achieve the future goal of eco-friendly transportation. The scientific planning of energy supplement infrastructures (ESIs), with appropriate locations and capacity, is imperative to develop the EV industry. In this research, a mixed integer linear programming (MILP) model is proposed to optimize the location and capacity of ESIs, including vehicle charging stations (VCSs), battery swapping stations (BSSs), and battery charging stations (BCSs), in highway networks. The objective of this model is to minimize the total cost with the average waiting time for EVs being constrained. In this model, battery swapping and transportation behaviors are optimized such that the EV average waiting time can be reduced, and the average queue and service process waiting time is estimated by the M/M/1 model. Real-world data, i.e., from the London M25 highway network system, are used as a case study to test the effectiveness of the proposed method. The results show that considering battery transportation behaviors is more cost efficient, and the results are sensitive to the EV average waiting time tolerance, battery cost, and charging demand.

In recent years, with the development of TSN, its low latency, low jitter and predictable maximum delay can be widely used in automatic driving technology and industrial Internet.To solve the 75% (empirical value) bandwidth problem of stream reservation technology in TSN protocols, and combine with the separation of data plane and control plane in SDN, a stream reservation solution was put forward, which was combined with TSN-SDN controller and scheduling the transmission path and reservation of the entire topology in the control plane.And two reservation computing methods was put forward, which would give out a bandwidth with time slot.The simulation experimental results show that it can control the jitter to 0 based on require latency, and also use the bandwidth based on time slot to guarantee the utilization of bandwidth.

Abstract Background The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. Methods Training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s and St Thomas’ Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. Results A baseline model of ‘NEWS2 + age’ had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. Conclusions NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Abstract Background ADHD is one of the most common psychiatric disorders in children and adolescents. Altered functional connectivity has been associated with ADHD symptoms. This study aimed to investigate abnormal changes in the functional connectivity of resting-state brain networks (RSNs) among adolescent patients with different subtypes of ADHD. Methods The data were obtained from the ADHD-200 Global Competition, including fMRI data from 88 ADHD patients (56 patients of ADHD-Combined, ADHD-C and 32 patients of ADHD-Inattentive, ADHD-I) and 67 typically developing controls (TD-C). Group ICA was utilized to research aberrant brain functional connectivity within the different subtypes of ADHD. Results In comparison with the TD-C group, the ADHD-C group showed clusters of decreased functional connectivity in the left inferior occipital gyrus (p = 0.0041) and right superior occipital gyrus (p = 0.0011) of the dorsal attention network (DAN), supplementary motor area (p = 0.0036) of the executive control network (ECN), left supramarginal gyrus (p = 0.0081) of the salience network (SN), middle temporal gyrus (p = 0.0041), and superior medial frontal gyrus (p = 0.0055) of the default mode network (DMN), while the ADHD-I group showed decreased functional connectivity in the right superior parietal gyrus (p = 0.0017) of the DAN and left middle temporal gyrus (p = 0.0105) of the DMN. In comparison with the ADHD-I group, the ADHD-C group showed decreased functional connectivity in the superior temporal gyrus (p = 0.0062) of the AN, inferior temporal gyrus (p = 0.0016) of the DAN, and the dorsolateral superior frontal gyrus (p = 0.0082) of the DMN. All the clusters surviving at p

Ultrafine W–10 wt.%Cu powders synthesized by thermo-chemical method have a good sinterability compared with the mechanically mixed powers, which are suitable for powder injection molding (PIM). The deagglomeration, however, is necessary to improve the flowability of the ultrafine powder to obtain a PIM feedstock with high powder loading. Jet milling (JM) was employed in this study to reduce the strong agglomerations among the particles, which were subsequently used for PIM. Results showed that JM was a proper process to modify the ultrafine powders for mass production with negligible contamination. JM modified the powder characteristics, including agglomeration, size distribution, surface morphology, and tap density, which considerably improved the rheological properties of feedstock. With the increasing of powder fraction in the feedstock from 46 vol.% to 52 vol.%, the shrinkage of the parts during sintering reduced accordingly, which is favorable for dimensional stability. The resulted W–10 wt.%Cu sintered composite has excellent physical properties, such as high thermal and electric conductivity, as well as low coefficient of thermal expansion, which can be attributed to its near full density and homogeneous microstructure.

Abstract Background A novel coronavirus pneumonia outbreak began in Wuhan, Hubei Province, in December 2019; the outbreak was caused by a novel coronavirus previously never observed in humans. China has imposed the strictest quarantine and closed management measures in history to control the spread of the disease. However, a high level of evidence to support the surgical management of potential trauma patients during the novel coronavirus outbreak is still lacking. To regulate the emergency treatment of trauma patients during the outbreak, we drafted this paper from a trauma surgeon perspective according to practical experience in Wuhan. Main body The article illustrates the general principles for the triage and evaluation of trauma patients during the outbreak of COVID-19, indications for emergency surgery, and infection prevention and control for medical personnel, providing a practical algorithm for trauma care providers during the outbreak period. Conclusions The measures of emergency trauma care that we have provided can protect the medical personnel involved in emergency care and ensure the timeliness of effective interventions during the outbreak of COVID-19.

Most of the time-sensitive networking standards are based on a high precise and reliability time reference, which is accomplished by IEEE 802.1 AS. However, the accuracy and reliability of clock synchronization could be affected in practice. To conquer the inaccuracy resulting from the collision between different packets, an algorithm that combines time-slot-based synchronization and priority scheduling is proposed. To improve the reliability, an abnormal recovery strategy of the master clock is proposed. The proposed algorithms are implemented on the OMNet++ simulation platform with an typical in-vehicle network topology. The simulation results show that the clock synchronization accuracy could achieve sub-microsecond level even if there are interference flows, and the abnormal recovery mechanism can maintain the stable state of the clock.

As one of the effective solutions to recover waste heat, absorption refrigeration systems are used in various industrial or refrigeration places. Flat-plate falling-film absorption is one of the newer types, and the lattice Boltzmann method (LBM) has certain advantages compared with the traditional numerical simulation method. In this work, an LBM is used to analyze flat-plate falling-film absorption. Using the additional calculation of the pressure by the pseudo-force model, a lithium bromide–water working fluid–heat and mass transfer model driven by steam partial pressure is realized. The results show that the turbulence generated in the surface wave has a favorable effect on the absorption process; the degree of turbulence gradually decreases with the increase in the Reynolds number, which weakens the increasing effect of the surface wave on the absorption. When the Reynolds number is moderate, the solitary wave flows forward relative to the front thin liquid film, which promotes concentration and temperature diffusion inside the liquid film and inside the solitary wave. The model of falling-film flow under vibration environment is realized by using the characteristic of imposing inertial force in the model by pseudo-force method. The results show that vibration has a favorable effect on liquid film absorption, increasing the amplitude can increase the gas–liquid contact area and obtain a lower average film thickness, while increasing the vibration frequency can promote the internal diffusion of the solution.

Cloud computing provides blockchain a flexible and cost-effective service by on-demand resource sharing, which also introduces additional security risks. Adaptive Cyber Defense (ACD) provides a solution that continuously changes the attack surface according to the cloud environments. The dynamic characteristics of ACDs give defenders a tactical advantage against threats. However, when assessing the effectiveness of ACDs, the structure of traditional security evaluation methods becomes unstable, especially when combining multiple ACD techniques. Therefore, there is still a lack of standard methods to quantitatively evaluate the effectiveness of ACDs. In this paper, we conducted a thorough evaluation with a hierarchical model named SPM. The proposed model is made up of three layers integrating Stochastic Reward net (SRN), Poisson process, and Martingale theory incorporated in the Markov chain. SPM provides two main advantages: (1) it allows explicit quantification of the security with a straightforward computation; (2) it helps obtain the effectiveness metrics of interest. Moreover, the hierarchical architecture of SPM allows each layer to be used independently to evaluate the effectiveness of each adopted ACD method. The simulation results show that SPM is efficient in evaluating various ACDs and the synergy effect of their combination, which thus helps improve the system configuration accordingly.

Air pollution is one of the severe environmental issues in Chongqing. Many measures made by the government for improving air quality have been put into use these past few years, while the influence of these measures remains unknown. This study analyzed the changes in the air quality of the urban area of Chongqing between 2015 and 2021 using a complete in situ observation dataset that all missing data were filled by the interpolation of a low-rank tensor completion model with truncate nuclear norm minimization (LRTC-TNN). The results include: (1) the LRTC-TNN model robustly performs to reconstruct missing data of pollutant concentrations with an R2 of 0.93 and an RMSE of 7.78; (2) the air quality index (AQI) decreases by 15.96%, and the total polluted days decrease by 21.05% from 2015 to 2021, showing an obvious promotion in air quality; and (3) the changing air quality is attributed to decreasing concentrations of PM2.5 (34.10%), PM10 (25.03%), and NO2 (5.53%) from 2015 to 2021, whereas an increasing concentration of O3 (10.49%) is observed. The processing method for missing data, intact AQI datasets, and analysis of changes are beneficial to policy-making for environmental improvement and fill the gap in the field of data interpolation for air quality datasets in mountainous areas.

Objective: Besides hyperlipidemia, inflammation is an important determinant in the initiation and the progression of atherosclerosis. As Neuroimmune Guidance Cues (NGCs) are emerging as regulators of atherosclerosis, we set out to investigate the expression and function of inflammation-regulated NGCs.Methods and results: NGC expression in human monocytes and endothelial cells was assessed using a publicly available RNA dataset. Next, the mRNA levels of expressed NGCs were analyzed in primary human monocytes and endothelial cells after stimulation with IL1β or TNFα. Upon stimulation a total of 14 and 19 NGCs in monocytes and endothelial cells, respectively, were differentially expressed. Since plexin A4 (PLXNA4) was strongly downregulated in endothelial cells under inflammatory conditions, the role of PLXNA4 in endothelial function was investigated. Knockdown of PLXNA4 in endothelial cells markedly impaired the integrity of the monolayer leading to more elongated cells with an inflammatory phenotype. In addition, these cells showed an increase in actin stress fibers and decreased cell-cell junctions. Functional assays revealed decreased barrier function and capillary network formation of the endothelial cells, while vascular leakage and trans-endothelial migration of monocytes was increased.Conclusion: The current study demonstrates that pro-inflammatory conditions result in differential expression of NGCs in endothelial cells and monocytes, both culprit cell types in atherosclerosis. Specifically, endothelial PLXNA4 is reduced upon inflammation, while PLXNA4 maintains endothelial barrier function thereby preventing vascular leakage of fluids as well as cells. Taken together, PLXNA4 may well have a causal role in atherogenesis that deserves further investigation.

In response to inflammatory cytokines and chemokines, monocytes differentiate into macrophages. Comprehensive analysis of gene expression regulation of neuronal guidance cue (NGC) ligands and receptors in the monocyte-to-macrophage differentiation process is not available yet. We performed transcriptome profiling in both human primary PBMCs/PBMC-derived macrophages and THP-1 cells/THP-1-macrophages using microarray or RNA sequencing methods. Pathway analysis showed that the axonal guidance pathway is significantly regulated upon monocyte differentiation. We confirmed NGC ligands and receptors which were consistently regulated, including SEMA4D, SEMA7A, NRP1, NRP2, PLXNA1 and PLXNA3. The involvement of RNA-binding protein quaking (QKI) in the regulation of NGC expression was investigated using monocytes and macrophages from a QKI haplo-insufficient patient and her healthy sibling. This revealed a positive correlation of SEMA7A expression with QKI expression. In silico analysis of 3′UTRs of NGCs proposed the competitive binding of QKI to proximal microRNA targeting sites as the mechanism of QKI-dependent regulation of SEMA7A. RNA immunoprecipitation confirmed an interaction of QKI with the 3′UTR of SEMA7A. Loss of SEMA7A resulted in monocyte differentiation towards a more anti-inflammatory macrophage. Taken together, the axonal guidance pathway is regulated during monocyte-to-macrophage differentiation, and the regulation is in line with the necessary functional adaption for the specialised role of macrophages.

Long noncoding RNAs (LncRNAs) are involved in multiple processes of human malignancy, and emerge as crucial molecules in RNA biology. However, the function of lncRNAs has not been well illustrated in abdominal aortic aneurysm (AAA). In this research, the effects of dysregulated ladybird homeobox 2 antisense RNA 1 (LBX2-AS1) or ladybird homeobox 2 (LBX2) on vascular smooth muscle cell (VSMC) biological processes were surveyed via cell counting kit-8 (CCK-8), methyl thiazolyl tetrazolium (MTT), terminal-deoxynucleoitidyl transferase mediated nick end labeling (TUNEL) and caspase-3 activity assays. LBX2-AS1 and LBX2 both possessed pro-apoptosis and anti-proliferation functions in AAA. Mechanically, the regulation role of LBX2-AS1 on miR-4685-5p or that of miR-4685-5p on LBX2 was investigated by quantitative real-time polymerase chain reaction (qRT-PCR). Additionally, the competing endogenous RNA (ceRNA) network was confirmed by luciferase reporter, RNA pull-down, and RNA immunoprecipitation (RIP) assays. LBX2-AS1 sequestered miR-4685-5p to release LBX2 expression via ceRNA mechanism. Further, LBX2 could act as a transcriptional activator of LBX2-AS1. A positive feedback loop was formed by LBX2-AS1, miR-4685-5p and LBX2, deteriorating AAA formation and progression. To sum up, our data suggested that LBX2-AS1, miR-4685-5p and LBX2 constituted a positive feedback loop in promoting AAA development, implying a potential usage of LBX2-AS1/miR-4685-5p/LBX2 axis in AAA management.

Recently, more and more mobile devices have been connected to the Internet. The Internet environment is complicated, and network security incidents emerge endlessly. Traditional blocking and killing passive defense measures cannot fundamentally meet the network security requirements. Inspired by the heuristic establishment of multiple lines of defense in immunology, we designed and prototyped a Double Defense strategy with Endogenous Safety and Security (DDESS) based on multi-identifier network (MIN) architecture. DDESS adopts the idea of a zero-trust network, with identity authentication as the core for access control, which solves security problems of traditional IP networks. In addition, DDESS achieves individual static security defense through encryption and decryption, consortium blockchain, trusted computing whitelist, and remote attestation strategies. At the same time, with the dynamic collection of data traffic and access logs, as well as the understanding and prediction of the situation, DDESS can realize the situation awareness of network security and the cultivation of immune vaccines against unknown network attacks, thus achieving the active herd defense of network security.

Regenerating codes (RGCs) have recently been proposed to reduce the repair traffic of (n, k) erasure-coded distributed storage systems. Moreover, RGCs can also be used in a scalable distributed storage scenario where n is increased (decreased) to upgrade (degrade) redundancy while maintaining the maximum distance separable property of erasure codes. In this paper, we propose a new application of minimum storage regenerating (MSR) codes in storage scalability. The connection between repairing invalid nodes and adding new nodes suggests that the two processes can be unified in the same framework. We consider both single and multiple node situations, and two methods for constructing multiple nodes are proposed: concurrent and sequential. We focus on proving the achieved capability of concurrent MSR that can consume minimum traffic for generating multiple nodes. Because concurrent MSR is sensitive to both the number of helpers and added nodes, sequential methods make scalable MSR generalizable. The examples show that the scalable MSR codes have the same advantage of saving network traffic as repairing failures.

Netrins form a family of secreted and membrane-associated proteins. Netrins are involved in processes for axonal guidance, morphogenesis, and angiogenesis by regulating cell migration and survival. These processes are of special interest in tumor biology. From the netrin genes various isoforms are translated and regulated by alternative splicing. We review here the diversity of isoforms of the netrin family members and their known and potential roles in cancer.

W-20 wt.% Cu pseudo-alloys were produced via powder injection molding (PIM) with powders prepared thermochemically. Bonding treatment and ball milling (BTBM) were used, and the effects of BTBM on the characteristics of the powders, rheological properties of the feedstock, shrinkage and properties of the sintered samples were studied. The morphology of the powder changed from extremely agglomerated small particles to pebble-shaped smooth large particles which were composed of several small particles combined tightly. The tap density increased from 3.25 g/cm3 to 7.22 g/cm3, and the specific surface area decreased from 0.86 m2/g to 0.45 m2/g. The critical powder loading of the feedstock increased from 45 vol.% to 56 vol.% due to the change in powder characteristics, thereby improving densification and dimension precision. For the PIM samples sintered at 1290 °C for 120 min in a hydrogen gas, the oversizing factor decreased from 1.297 to 1.216, and the dimension fluctuation ratio decreased from ±0.61% to ±0.33%. At the same time, the relative density increased from 97.8% to 98.6%, the thermal conductivity increased from 218 W/(m·K) to 233 W/(m·K), and the average coefficients of thermal expansion were roughly similar, within the range of 8.43–8.52 × 10−6/K.