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Due to the scale of data and the complexity of analysis tasks, insight discovery often requires coordinating multiple visualizations (views), with each view displaying different parts of data or the same data from different perspectives. For example, to analyze car sales records, a marketing analyst uses a line chart to visualize the trend of car sales, a scatterplot to inspect the price and horsepower of different cars, and a matrix to compare the transaction amounts in types of deals. To explore related information across multiple views, current visual analysis tools heavily rely on brushing and linking techniques, which may require a significant amount of user effort (e.g., many trial-and-error attempts). There may be other efficient and effective ways of displaying cross-view data relationships to support data analysis with multiple views, but currently there are no guidelines to address this design challenge. In this article, we present systematic design considerations for visualizing cross-view data relationships, which leverages descriptive aspects of relationships and usable visual context of multi-view visualizations. We discuss pros and cons of different designs for showing cross-view data relationships, and provide a set of recommendations for helping practitioners make design decisions.

The booming photothermal therapy (PTT) has achieved great progress in non-invasive oncotherapy, and paves a novel way for clinical oncotherapy. Of note, mild temperature PTT (mPTT) of 42–45 °C could avoid treatment bottleneck of the traditional PTT, including nonspecific injury to normal tissues, vasculature and host antitumor immunity. However, cancer cells can resist mPTT via heat shock response and autophagy, thus leading to insufficient mPTT monotherapy to ablate tumor. To overcome the deficient antitumor efficacy caused by thermo-resistance of cancer cells and mono mPTT, synergistic therapies towards cancer cells have been conducted with mPTT. This review summarizes the recent advances in nanomedicine-potentiated mPTT for cancer treatment, including strategies for enhanced single-mode mPTT and mPTT plus synergistic therapies. Moreover, challenges and prospects for clinical translation of nanomedicine-potentiated mPTT are discussed., Graphical Abstract Graphical Abstract Image, graphical abstract

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy.

Objectives Shenzhen is suffering severe HIV epidemic. No systematic surveillance on high risk populations, HIV genetic diversity, transmitted drug resistance (TDR) and molecular transmission clusters (MTCs) have been reported yet. In this study, we described them based on newly diagnosed HIV positive cases from 2011 to 2018 in Shenzhen city, China. Methods Plasma samples of newly reported HIV positive cases in Shenzhen, China were collected from 2011 to 2018. The HIV pol gene was amplified and sequenced for subtyping, genetic characterization, TDR and phylogenetic analysis. Demographic and risk characteristics associated with transmitted drug resistance-associated mutations (TDRAMs) and MTCs were explored by using logistic regression analyses. Results 10,378 HIV pol sequences were successfully obtained from newly diagnosed patients with available background information. The most prevalent HIV-1 subtype was CRF07_BC (40.92%). CRF07_BC, CRF55_01B and URFs increased across years. Total TDR was 6.02% during 2011 to 2018. CRF01_AE, CRF08_BC, CRF55_01B and subtype B were more likely to be associated with TDRAMs than CRF07_BC. 4460 (42.98%) patients were infected with strains included in MTCs. Patients younger than 30 and over 50 years were more likely to cluster. Conclusions The prevalence of HIV-1 drug resistance and molecular transmission clusters in Shenzhen should raise a high alert. Interventions targeting on patients with strains locating in MTCs should be considered to improve prevention effect in Shenzhen.

Background Stroke in children easily causes long-term dysfunction. Whether the prognoses of motor and anxiety disorders are related to the affected stroke area has not been reported. Methods One hundred nine cases of children with ischaemic stroke were reviewed and divided into three groups: lenticular nucleus lesions only (lenticular nucleus group), lenticular nucleus and caudate head lesions (caudate head group), and lenticular nucleus and thalamus lesions (thalamus group). Overall prognosis was evaluated by the mRS score. The SCAS-P was used to evaluate anxiety in children aged ≥6 years. Results mRS scores were ≤ 2 points (mean: 0.62), no significant difference among groups. 3/21 (14.2%) patients in the caudate head group changed handedness, which is significantly higher than other groups. Patients with lesions in thalamus group had significantly higher SCAS-P scores. Conclusions The overall prognosis of children with basal ganglia ischaemic stroke is good. However, hand preference changes and anxiety disorders may develop. Patients in the caudate head groups are more likely to suffer from fine motor disorders and changes in handedness. Patients within the thalamus group are more prone to anxiety than patients in the other groups. Anxiety disorders should be noted in children with basal ganglia stroke.

Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient’s neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients’ recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients’ recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients’ recovery with R2 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.

Versatile Video Coding (VVC), as the latest standard, significantly improves the coding efficiency over its ancestor standard High Efficiency Video Coding (HEVC), but at the expense of sharply increased complexity. In VVC, the quad-tree plus multi-type tree (QTMT) structure of coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization. Instead of the brute-force QTMT search, this paper proposes a deep learning approach to predict the QTMT-based CU partition, for drastically accelerating the encoding process of intra-mode VVC. First, we establish a large-scale database containing sufficient CU partition patterns with diverse video content, which can facilitate the data-driven VVC complexity reduction. Next, we propose a multi-stage exit CNN (MSE-CNN) model with an early-exit mechanism to determine the CU partition, in accord with the flexible QTMT structure at multiple stages. Then, we design an adaptive loss function for training the MSE-CNN model, synthesizing both the uncertain number of split modes and the target on minimized RD cost. Finally, a multi-threshold decision scheme is developed, achieving desirable trade-off between complexity and RD performance. Experimental results demonstrate that our approach can reduce the encoding time of VVC by 44.65%-66.88% with the negligible Bj{\o}ntegaard delta bit-rate (BD-BR) of 1.322%-3.188%, which significantly outperforms other state-of-the-art approaches., Comment: 14 pages, 10 figures, 7 tables. Published in IEEE Transactions on Image Processing (TIP), 2021

A non-local, ordinary state-based, peridynamic elastoplastic model is formulated to numerically simulate the fracture of reinforced concrete materials. Several basic definitions are first discussed to avoid confusion; and then, a detailed derivation of the force vector state is presented, leading to a unified expression of force state for one-, two- and three-dimensional elasticity problems. Furthermore, an ordinary state-based peridynamic (OSB PD) elastoplastic analysis approach is developed for both plastic compressible and incompressible materials, including the constitutive relationship, the yield function, the consistency condition and the plasticity flow rule. The peridynamic predictions of a quasi-static deformation of the steel rods are in good agreement with the analytical solution. Moreover, the OSB PD plasticity is verified by analyzing a square plate with or without a central hole suffering different loading-unloading paths. Finally, a two dimensional reinforced concrete clamped beam subjected to impact loading is simulated with the proposed OSB PD elastoplasticity, demonstrating its capability in capturing the damage characteristics and structural failure behavior. Simulation results show good accuracy of the peridynamics in simulating elastoplastic problems.

Cardiovascular disease is the leading cause of mortality worldwide, and mitochondrial dysfunction is the primary contributor to these disorders. Recent studies have elaborated on selective autophagy-mitophagy, which eliminates damaged and dysfunctional mitochondria, stabilizes mitochondrial structure and function, and maintains cell survival and growth. Numerous recent studies have reported that mitophagy plays an important role in the pathogenesis of various cardiovascular diseases. This review summarizes the mechanisms underlying mitophagy and advancements in studies on the role of mitophagy in cardiovascular disease.

Personality heterogeneity is an important topic in team management. In many working groups, there exists certain type of people that are talented but under-disciplined, who could occasionally make extraordinary contributions for the team, but often have less satisfactory overall performance. It is interesting to investigate whether the existence of such people in the team does help improve the overall team performance, and if it does so, what are the conditions for their existence to be positive, and through which channel their benefits for the team are manifested. This study proposes an analytical model with a simple structure that sets up an environment to study these questions. It is shown that: (1) feedback learning could be the mechanism through which outliers' benefits to the team are established, and thus could be a prerequisite for outliers' positive existence; (2) different types of teamwork settings have different outlier-positivity conditions: a uniform round-wise punishment for teamwork failures could be the key idea to encourage outliers' existence; for two specific types of teamwork, teamwork that highlights assistance in interactions are more outliers-friendly than teamwork that consists internal competitions. These results well match empirical observations and may have further implications for managerial practice.

Crowdsourced sensemaking has shown great potential for enabling scalable analysis of complex data sets, from planning trips, to designing products, to solving crimes. Yet, most crowd sensemaking approaches still require expert intervention because of worker errors and bottlenecks that would otherwise harm the output quality. Mitigating these errors and bottlenecks would significantly reduce the burden on experts, yet little is known about the types of mistakes crowds make with sensemaking micro-tasks and how they propagate in the sensemaking loop. In this paper, we conduct a series of studies with 325 crowd workers using a crowd sensemaking pipeline to solve a fictional terrorist plot, focusing on understanding why errors and bottlenecks happen and how they propagate. We classify types of crowd errors and show how the amount and quality of input data influence worker performance. We conclude by suggesting design recommendations for integrated crowdsourcing systems and speculating how a complementary top-down path of the pipeline could refine crowd analyses.

Lane-level localization is a fundamental task for autonomous driving. As front cameras are easily disturbed by dynamic objects in urban environments, this paper presents an accurate lane-level localization approach using the around view monitoring (AVM) system. The paper proposes to detect the road features (i.e., road boundaries and road markings) based on pixel-wise semantic segmentation of raw fisheye images. The method can detect various types of road features and exclude dynamic objects from the localization. To address the problem of AVM-based localization with road features of different characteristics, this paper proposes coarse-scale localization (CSL) and fine-scale localization (FSL) methods for high-accurate localization. The CSL method leverages the road boundaries to provide an initial position; the FSL method estimates a high-accuracy position by matching nearby road markings with the map. The experiments in urban environments demonstrate that the proposed approach achieves centimeter-level localization accuracy with five centimeters in the lateral direction and seventeen centimeters in the longitudinal direction.

An extensive study on the in-loop filter has been proposed for a high efficiency video coding (HEVC) standard to reduce compression artifacts, thus improving coding efficiency. However, in the existing approaches, the in-loop filter is always applied to each single frame, without exploiting the content correlation among multiple frames. In this paper, we propose a multi-frame in-loop filter (MIF) for HEVC, which enhances the visual quality of each encoded frame by leveraging its adjacent frames. Specifically, we first construct a large-scale database containing encoded frames and their corresponding raw frames of a variety of content, which can be used to learn the in-loop filter in HEVC. Furthermore, we find that there usually exist a number of reference frames of higher quality and of similar content for an encoded frame. Accordingly, a reference frame selector (RFS) is designed to identify these frames. Then, a deep neural network for MIF (known as MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from its improved generalization capacity and computational efficiency. In addition, a novel block-adaptive convolutional layer is designed and applied in the MIF-Net, for handling the artifacts influenced by coding tree unit (CTU) structure in HEVC. Extensive experiments show that our MIF approach achieves on average 11.621% saving of the Bjøntegaard delta bit-rate (BD-BR) on the standard test set, significantly outperforming the standard in-loop filter in HEVC and other state-of-the-art approaches.

Composite semiconductor photocatalysts with hollow structures can present excellent photocatalytic performance by combining the advantages of composite photocatalyst and hollow structures. However, it is a great challenge to develop a facile synthetic route to realize the composite hollow nanostructures. In the paper, AgCl/TiO2 composite hollow octahedra were synthesized by a facile self-templated method via the direct addition of Ag2O octahedron template into TiCl4 ethanol solution. In the case, AgCl and TiO2 nanoparticles can be simultaneously formed to construct the shell of hollow octahedra along with the alcoholysis and hydrolysis of TiCl4 and the reaction of releasing H+ and Cl− ions with Ag2O template. Obviously, the synthetic method showed some advantages of economic reaction steps and uniform component distribution. After AgCl/TiO2 is illuminated by visible light, partial Ag metal can form in the sample due to the photosensitive property. Further photocatalytic results indicated that the Ag/AgCl/TiO2 hollow octahedron photocatalyst exhibited excellent photocatalytic activity and stability of performance. Importantly, the present Ag2O self-template method was also extended for the synthesis of other hollow octahedra such as AgCl/Bi2O3, AgCl/SnO2 and AgCl. Thus, the present work may provide new insights into design and synthesis of other hollow octahedron photocatalysts.

ObjectivesOur objective was to determine the antibody and cytokine profiles in different COVID-19 patients.MethodsCOVID-19 patients with different clinical classifications were enrolled in this study. The level of IgG antibodies, IgA, IgM, IgE, and IgG subclasses targeting N and S proteins were tested using ELISA. Neutralizing antibody titers were determined by using a toxin neutralization assay (TNA) with live SARS-CoV-2. The concentrations of 8 cytokines, including IL-2, IL-4, IL-6, IL-10, CCL2, CXCL10, IFN-γ, and TNF-α, were measured using the Protein Sample Ella-Simple ELISA system. The differences in antibodies and cytokines between severe and moderate patients were compared by t-tests or Mann-Whitney tests.ResultsA total of 79 COVID-19 patients, including 49 moderate patients and 30 severe patients, were enrolled. Compared with those in moderate patients, neutralizing antibody and IgG-S antibody titers in severe patients were significantly higher. The concentration of IgG-N antibody was significantly higher than that of IgG-S antibody in COVID-19 patients. There was a significant difference in the distribution of IgG subclass antibodies between moderate patients and severe patients. The positive ratio of anti-S protein IgG3 is significantly more than anti-N protein IgG3, while the anti-S protein IgG4 positive rate is significantly less than the anti-N protein IgG4 positive rate. IL-2 was lower in COVID-19 patients than in healthy individuals, while IL-4, IL-6, CCL2, IFN-γ, and TNF-α were higher in COVID-19 patients than in healthy individuals. IL-6 was significantly higher in severe patients than in moderate patients. The antibody level of anti-S protein was positively correlated with the titer of neutralizing antibody, but there was no relationship between cytokines and neutralizing antibody.ConclusionsOur findings show the severe COVID-19 patients’ antibody levels were stronger than those of moderate patients, and a cytokine storm is associated with COVID-19 severity. There was a difference in immunoglobulin type between anti-S protein antibodies and anti-N protein antibodies in COVID-19 patients. And clarified the value of the profile in critical prevention.

Metallization plays a very important role in fabricating low cost and high efficiency silicon solar cells. Silver (Ag) metallization of industrial crystalline silicon (c-Si) solar cells comprises a large share of the total cell production cost. However, in amorphous silicon based (a-Si:H) c-Si heterojunction solar cell (SHJ) fabrication, the metallization process costs more compared to the metallization process for traditional crystalline silicon (c-Si) solar cells since it requires expensive, low temperature Ag pastes [1]. These specialized, low temperature Ag pastes are required because the passivation quality at the a-Si:H(i)/c-Si interface degrades if annealed at temperatures above 200°C. This temperature limitation not only increases the fabrication cost, but also results in poor sintering of the metal contacts which increases the resistive loss [2]. In this work, we introduce a novel Ag metal contact printing technique for SHJ solar cells using a Ag nanoparticle ink and an in-line laser sintering process with the goal of reducing the bulk resistivity of the printed Ag without requiring any ex situ annealing. In this process, the Ag nanoparticle ink is dispensed through a very small diameter needle on the SHJ substrate with simultaneous sintering using a focused continuous carbon dioxide (CO 2 ) laser beam. We have been able to print Ag lines with width as low as 50 µm and bulk resistivity as low as 8 µΩ·cm using this printing technique.

IntroductionAcute myocardial infarction (AMI) has become one of the major causes of mortality and morbidity in China. However, little is known about the characteristics, medical care and outcomes of patients with AMI in Northeastern China. The Acute Myocardial Infarction Study in Northeastern China (AMINoC) is aimed at obtaining timely real-world knowledge in terms of characteristics, clinical care and outcomes of patients with AMI and at providing care-quality improvement efforts in Northeastern China.Methods and analysisThe AMINoC is a real-world, prospective, multicentre cohort study. The study selected 20 hospitals using stratified cluster sampling from different levels of hospitals among nine districts throughout Jilin Province. Hospitalised patients with a primary diagnosis of AMI in each site are consecutively enrolled for 1 year. Demographic characteristics, clinical data, treatments, outcomes and cost are collected by local investigators. Patient follow-up after discharge is planned for up to 2 years.Ethics and disseminationThe protocol has been approved by the ethics committee at the Second Hospital of Jilin University. The findings of this study will be published in peer-reviewed journals and medical conferences.Trial registrationNCT04451967.

Density-peaks-clustering (DPC) algorithm plays an important role in clustering analysis with the advantages of easy realization and comprehensiveness whereas without the requirement of any iteration or optimization. However, the DPC accuracy depends on two user-specified parameters, and each of them can greatly affect clustering results. To solve this problem, we extend DPC to a general and hierarchical form. Without the need of any parameters, the proposed E-DPC algorithm can effectively cluster points in any dataset with various characteristics. The results of experiments show that the proposed algorithm is more accurate and general in comparison with two mostly used algorithms.

Since the carrier density and resistivity of graphene are heavily dependent on the Fermi level, Josephson junctions with graphene as the weak link can have their I – V properties easily tuned by the gate voltage. Most of the previous work on superconductor–graphene–superconductor (SGS) junctions and superconducting quantum interference devices (SQUIDs) were based on mechanically exfoliated graphene, which is not compatible with large scale production. Here, we show that SGS junctions and dc SQUIDs can be easily fabricated from chemical vapor deposition (CVD) graphene and exhibit good electronic properties. The SGS junctions can work without any hysteresis in their electrical characteristics from 1.5 K down to a base temperature of 320 mK, and the critical current can be effectively tuned by the gate voltage by up to an order of magnitude. As a result, dc SQUIDs made up of these junctions can have their critical current tuned by both the magnetic field and the gate voltage.

The morphological evolution of tin particles with different sizes during the first lithiation and delithiation processes has been visualized by an in operando synchrotron transmission X-ray microscope (TXM). The in operando lithium ion battery cell was operated at constant current condition during TXM imaging. Two-dimensional projection images with 40 nm resolution showing morphological evolution were obtained and analyzed. The analysis of relative area change shows that the morphology of tin particles with different sizes changed simultaneously. This phenomenon is mainly due to a negative feedback mechanism among tin particles in the battery electrode at a constant current operating condition. For irregular-shaped tin particles, the contour analysis shows that the regions with higher curvature started volume expansion first, and then the entire particle expanded almost homogeneously. This study provides insights for understanding the dynamic morphological change and the particle-particle interactions in high capacity lithium ion battery electrodes.

Terrain-based vehicle localization is a valuable alternative to GPS when the signal is blocked or dynamic occlusions exist. However, braking events can induce the slips and vibrations of the vehicle and, thus, increase the errors of terrain-based position estimations. This paper develops a terrain-based localization approach which is robust to braking events. First, the terrain map is generated and stored before localization, which includes distance measurements, terrain feature data, and the geographic locations. The terrain feature data are pitch differences to eliminate the accumulated error. Next, particle-filter-based and acceleration-considered vehicle localization is proposed for position estimation. It takes into account the influence of braking in the vehicle model and the inference process. Experimental results demonstrate the repeatability of pitch difference measurements and the robustness of the proposed approach in the cases of smooth driving and braking.

Objective The morbidity and prevalence of type 2 diabetes mellitus (DM) are increasing in the elderly population. Interleukin 37 (IL-37) play important roles in anti-inflammatory and anti-bacteria immune responses, but its role in the development of type 2 DM in the elderly is unclear. Therefore, we investigated whether IL-37 is associated with type 2 DM in the elderly and the underlying mechanism. Methods Hospitalized patients (aged 65–95 years) with recently diagnosed type 2 diabetes mellitus were studied retrospectively and compared with healthy subjects without glucose metabolism abnormalities. A diabetic mouse model was established by feeding ob/ob mice (C57BL/6) a high-fat, carbohydrate-free diet. Plasma glucose and insulin levels were determined by glucose oxidase assay and radioimmunoassay, respectively. The IL-37 expression level was determined by real-time PCR, western blot and ELISA (Enzyme-linked immunoassay). Results Statistic analysis showed that the IL-37 level was significantly associated with type 2 DM and insulin resistance in the elderly. The patients were then divided into insulin therapy sensitive and resistant group according to their response to insulin therapy. Data showed that the IL-37 was highly expressed in the insulin therapy sensitive group. And this was related to the less severe gut microbiota dysbiosis. In the mice model, overexpressing the IL-37 could suppress the gut microbiota dysbiosis and also the diabetes development. Conclusion Thus our results showed that higher IL-37 was associated with increased insulin sensitive in elderly type 2 DM patients through suppressing the gut microbiota dysbiosis.

Despite remarkable advances in epilepsy research, prevention and reversal of cognitive deficits following epilepsy remain a challenge. It was reported that the Rho kinase (ROCK) inhibitor fasudil hydrochloride (FH) could improve cognitive deficits in animal models of Alzheimer's disease (AD). Thus, the aim of the present study was to determine whether FH-mediated inhibition of the effects of ROCK signaling could improve cognitive deficits in male rats (postnatal 21-day old) following status epilepticus (SE) induced by lithium-pilocarpin, the therapeutic window of opportunity and to elucidate the underlying mechanisms. Western blotting analysis showed upregulation of phosphorylated RhoA (p-RhoA) expression, and indicated activation of Rho/ROCK signaling after SE. The Morris water maze (MWM) test was used to analyze learning-memory ability. HE staining, immunofluorescence staining with antineuronal nuclei (NeuN) and anti-neurofilament proteins 200 kD (NF200), transmission electron microscopy, and quantitative analysis of NeuN and synaptophysin by western blotting were performed to observe alterations in neurons, axons, and synapses in the hippocampi. Electroencephalogram (EEG) monitoring was used to record electrophysiological activities after SE. Our results indicated that treatment with FH at the first day following SE or 5 days later both could ameliorate cognitive dysfunction by reducing neuron, axon, and synapse damage, and mitigating EEG discharges, suggesting various roles for the Rho/ROCK signaling pathway in the pathological processes of brain damages following SE induced by lithium-pilocarpine. The Rho/ROCK signaling pathway is, therefore, a potential therapeutic target for the prevention or reversal of epilepsy induced brain damages.

The increasing volume of text data is challenging the cognitive capabilities of expert analysts. Machine learning and crowdsourcing present new opportunities for large-scale sensemaking, but we must overcome the challenge of modeling the overall process so that many distributed agents can contribute to suitable components asynchronously and meaningfully. In this paper, we explore how to crowdsource the sensemaking process via a pipeline of modularized steps connected by clearly defined inputs and outputs. Our pipeline restructures and partitions information into "context slices" for individual workers. We implemented CrowdIA, a software platform to enable unsupervised crowd sensemaking using our pipeline. With CrowdIA, crowds successfully solved two mysteries, and were one step away from solving the third. The crowd's intermediate results revealed their reasoning process and provided evidence that justifies their conclusions. We suggest broader possibilities to optimize each component, as well as to evaluate and refine previous intermediate analyses to improve the final result.

Managing multiple ecosystem services (ES) is a pressing field in sustainability research. ES bundles (ESBs), which closely link ES trade-offs and synergies, provide a comprehensive approach to exploring the relationship between natural ecosystems and human well-being. In this study, we quantified eight ES in terms of provisioning, regulating and cultural services using geographical data and other available information (both ecological and social) in the Pearl River Delta, China. We identified ESBs based on K-means clustering and redundancy analysis. The results showed that spatial patterns of each ES were quite heterogeneous at watershed level. Provisioning services were mainly distributed in watersheds with high proportion of cultivated land and waterbody. Remote forest areas provided high regulating services. Moreover, densely populated urban areas provide high cultural services. Five ESBs were detected ranging from 2941 to 16,249 km2. According to the Pearson correlations and the Root Mean Squared Error (RMSE), we detected the trade-offs between provisioning and cultural services, and between regulating and cultural services, and synergies happened within regulating services. Intensive land-use and management in urban areas contributed to ES trade-offs. These results provide deeper understanding of the relationship between ES and land-use type at watershed level and detailed guidelines for ecosystem management.

BACKGROUND: The volume and thickness of intravesical prostatic protrusion and other characteristics of benign prostatic hyperplasia have not been investigated. We determine the effects of age and prostate volume on anatomical features of benign prostatic hyperplasia using three-dimensional measurement in this study. METHODS: This retrospective study included a total of 98 patients with benign prostatic hyperplasia. Three-dimensional models of prostate, central gland, peripheral zone, intravesical prostatic protrusion, prostatic urethra and bladder were reconstructed according to pelvic T2-weighted magnetic resonance imaging of these patients. The models were used to measure the intravesical prostatic protrusion volume, intravesical prostatic protrusion thickness, intravesical prostatic protrusion index, intravesical prostatic protrusion, prostate volume, peripheral zone volume, peripheral zone thickness, peripheral zone index, prostatic urethra thickness, the angle and distance of distal prostatic urethra with regard to coronal plane and sagittal plane and so on. RESULTS: Intravesical prostatic protrusion volume, intravesical prostatic protrusion thickness and peripheral zone volume of prostate volume >80 mL group were significantly higher than these in prostate volume 80 mL group than these in prostate volume

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. Atherosclerosis (AS) is a major cause of CVD. Oxidative stress, endothelial dysfunction, and inflammation are key factors involved in the development and progression of AS. Exosomes are nano-sized vesicles secreted into the extracellular space by most types of cells, and are ideal substances for the transmission and integration of signals between cells. Cells can selectively encapsulate biologically active substances, such as lipids, proteins and RNA in exosomes and act through paracrine mechanisms. Non-coding RNAs (ncRNAs) are important for communication between cells. They can reach the recipient cells through exosomes, causing phenotypic changes and playing a molecular regulatory role in cell function. Elucidating their molecular mechanisms can help identify therapeutic targets or strategies for CVD. Coronary artery disease (CAD) is the most important disease in CVD. Here, we review the role and the regulatory mechanism of exosomal ncRNAs in the pathophysiology of CAD, as well as the potential contribution of exosomal ncRNA to diagnosis and treatment of CAD.

The dynamic information of lithium-ion battery active materials obtained from coin cell-based in-situ characterizations might not represent the properties of the active material itself because many other factors in the cell could have impacts on the cell performance. To address this problem, a single particle cell was developed to perform the in-situ characterization without the interference of inactive materials in the battery electrode as well as the X-ray-induced damage. In this study, the dynamic morphological and phase changes of selenium-doped germanium (Ge0.9 Se0.1 ) at the single particle level were investigated via synchrotron-based in-situ transmission X-ray microscopy. The results demonstrate the good reversibility of Ge0.9 Se0.1 at high cycling rate that helps understand its good cycling performance and rate capability. This in-situ and operando technique based on a single particle battery cell provides an approach to understanding the dynamic electrochemical processes of battery materials during charging and discharging at the particle level.

A 0.85 THz regenerative feedback oscillator is proposed as a compact and frequency-tunable source. The circuit design and simulation is presented including both the folded waveguide slow wave structure and the feedback circuit. The bandwidth of the circuit is over 70 GHz and the output power is over 200 mW. The circuit is being fabricated using UV-LIGA micromachining process.

The current widely spread of micro-form video is undeniable. However, neither the music-orienting nor the video-orienting way to make a video can totally express the idea of the video maker because of the fixed music types and the cost of time to find a proper music. Based on the deep learning method, this paper studies the automatic matching algorithm of background music for micro-videos which analyzes the background information and the emotions of characters in micro-videos and establishes the model to select the proper background music according to the video contents. Experiments are carried out on the database obtained from TikTok and the result shows that the current Micro-Video Background Music Automatic Matching model in this paper is effective.

Capturing analytic provenance is important for refining sensemaking analysis. However, understanding this provenance can be difficult. First, making sense of the reasoning in intermediate steps is time-consuming. Especially in distributed sensemaking, the provenance is less cohesive because each analyst only sees a small portion of the data without an understanding of the overall collaboration workflow. Second, analysis errors from one step can propagate to later steps. Furthermore, in exploratory sensemaking, it is difficult to define what an error is since there are no correct answers to reference. In this paper, we explore provenance analysis for distributed sense-making in the context of crowdsourcing, where distributed analysis contributions are captured in microtasks. We propose crowd auditing as a way to help individual analysts visualize and trace provenance to debug distributed sensemaking. To evaluate this concept, we implemented a crowd auditing tool, CrowdTrace. Our user study-based evaluation demonstrates that CrowdTrace offers an effective mechanism to audit and refine multi-step crowd sensemaking.

This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.

Inspired by the visual properties of the human eyes, the depth information of visual attention is integrated into the saliency detection to effectively solve problems such as low accuracy and poor stability under similar or complex background interference. Firstly, the improved SLIC algorithm was used to segment and cluster the RGBD image. Secondly, the depth saliency of the image region was obtained according to the anisotropic center-surround difference method. Then, the global feature saliency of RGB image was calculated according to the colour perception rule of human vision. The obtained multichannel saliency maps were weighted and fused based on information entropy to highlighting the target area and get the final detection results. The proposed method works within a complexity of O(N), and the experimental results show that our algorithm based on visual bionics effectively suppress the interference of similar or complex background and has high accuracy and stability.

Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. Recently, extensive approaches have been proposed to reduce image compression artifacts at the decoder side; however, they require a series of architecture-identical models to process images with different quality, which are inefficient and resource-consuming. Besides, it is common in practice that compressed images are with unknown quality and it is intractable for existing approaches to select a suitable model for blind quality enhancement. In this paper, we propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images. Specifically, our approach blindly and progressively enhances the quality of compressed images through a dynamic deep neural network (DNN), in which an early-exit strategy is embedded. Then, our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images. Consequently, slight artifacts can be removed in a simpler and faster process, while the severe artifacts can be further removed in a more elaborate process. Extensive experiments demonstrate that our RBQE approach achieves state-of-the-art performance in terms of both blind quality enhancement and resource efficiency.

In order to enable enterprise decision–makers to objectively and accurately position the market, improve the vital interests of investors, improve the efficiency of corporate investment, and ensure...

Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate coherent and meaningful stories. To pick the images which may provide textual experience, we propose a two-step ranking method based on image object recognition techniques. To utilize textual information, we design an extended Seq2Seq model with two-channel encoder and attention. Experiments on the VIST dataset show that our method outperforms state-of-the-art baseline models without heavy engineering.

The increasing volume of text data is challenging the cognitive capabilities of experts. Machine learning and crowdsourcing present opportunities for large-scale distributed sensemaking, but we must overcome the challenge of modeling the holistic process so that many distributed agents can contribute to suitable components asynchronously and meaningfully. My dissertation work is devoted to addressing this challenge from a crowdsourcing perspective. Specifically, I study 1) how novice crowds can build theories from raw datasets without expert intervention; 2) what bottlenecks exist for crowds in the theory-building process; and 3) how previous crowd analyses can be refined to enable iterative crowdsourced sensemaking.

HIV-1 dual infection occurs when an individual is simultaneously or sequentially infected with two or more genetically distinct HIV-1 strains. According to the number of infected strains, HIV-1 dual infection can be divided in double infection and triple infection and so on. Currently, the majority of dual infection cases have been reported to be double infections which can result in detrimental clinical outcomes. The high incidence of double infection among specific high-risk populations increases the likelihood of triple infection, which has been sporadically described. There is no doubt that we are concerned about the association between triple infection and disease progression. However, this relationship is still unclear on the population level. In this study, 70 individuals from the Beijing PRIMO cohort were longitudinally followed up with a median time of 15.75 months for the purpose of investigating the incidence of dual infection. Phylogenetic analyses using bulk and single-genome sequences showed that nine individuals acquired double infection, with the incidence of 9.21 per 100 person-years, and three individuals with triple infection were identified, with the incidence of 3.07 per 100 person-years. The further survival analysis demonstrated that the triple infection group exhibited faster CD4+ T-cell decline. In summary, these results demonstrate for the first time that the triple HIV-1 infection might reduce CD4+ T-cell counts, which would predict a more rapid disease progression.

A semi-analytical solution for the concentration of photosensitive suspension is developed in a hemispherical droplet illuminated with ultraviolet (UV) laser. A biharmonic equation in stream function is analytically solved using toroidal coordinates, which is used to solve the transport equation for concentration. Flow pattern and photosensitive material concentration are affected by the peak location of the UV light intensity, which corresponds to the surface tension profile. When the laser beam is moved from the droplet center to its edge, a rotationally symmetric flow pattern changes from a single counter clockwise circulation to a circulation pair and finally to a single clockwise circulation. This modulation in the orientation of circulation modifies the concentration distribution of the photosensitive material. The distribution depends on both diffusion from the droplet surface and the Marangoni convection. The region beneath the droplet surface away from the UV light intensity peak has low concentration, while the region near the downward dividing streamline has the highest concentration. When the UV light peak reaches the droplet edge, the concentration is high everywhere in the droplet.

A synchrotron transmission X-ray microscopy tomography system with a spatial resolution of 58.2 nm at the Advanced Photon Source was employed to obtain three-dimensional morphological data of all-solid Li-ion battery electrodes. The three-phase electrode was fabricated from a 47:47:6 (wt %) mixture of Li(Ni1/3Mn1/3Co1/3)O2 as active material, Li1.3Ti1.7Al0.3(PO4)3 as Li-ion conductor, and Super-P carbon as electron conductor. The geometric analysis show that particle-based all-solid Li-ion battery has serious contact interface problem which significantly impact the Li-ion transport and intercalation reaction in the electrode, leading to low capacity, poor rate capability and cycle life.

We report the presence of the second human pegivirus (HPgV-2) in Guangdong and Sichuan Provinces in China. The prevalence of HPgV-2 in hepatitis C virus/HIV-1–co-infected persons who inject drugs was 12.9% in Guangdong and 15.9% in Sichuan. This population is at high risk for HPgV-2 infection.

Vonoprazan is a new potassium-competitive acid blocker to treat acid-related diseases. However, its safety during pregnancy is unclear. The aim of the study was to investigate the potential reproductive toxicity on the embryo-fetal development of vonoprazan. Vonoprazan acetate was administered by intravenous injection to pregnant rats (0, 2, 6 and 20 mg kg-1 day-1 ) and rabbits (0, 1.2, 3.6 and 12 mg kg-1 day-1 ) during the organogenetic period (gestation day 6-15 [rats] and 6-18 [rabbits]). Maternal reproductive endpoints were evaluated, together with effects on fetal growth and morphological development. In rats, no treatment-related effects were found in the highest dose group (20 mg kg-1 ) and the maternal plasma exposure was ≥50-fold the expected clinical human exposure. However, in rabbits, dose-related clinical signs (soft or liquid feces) occurred in the 12 mg kg-1 group, which was regarded as a maternal toxicity. Besides, decreased maternal weight gain also was considered as a minimal maternal toxicity. At 12 mg kg-1 , delayed fetal ossification was found as evidence of embryo-fetal growth retardation, which was related to decreased fetal and placental weights. There was no maternal and developmental toxicity in the 1.2 and 3.6 mg kg-1 groups. Thus, the no-observed-adverse-effect levels of vonoprazan acetate in rabbits are considered 3.6 mg kg-1 day-1 , which produced plasma exposure that was about 18-fold human clinical exposure.

As the pharmaceutical industry increasingly adopts continuous manufacturing technology, significant attention must be paid to process analytical technology (PAT), process integration, and process control. Published information is no substitute for hands-on comprehensive training, which is critical to implementing and operating continuous pharmaceutical manufacturing systems effectively and efficiently. In this article, an intensive hands-on training course has been developed and implemented at the Engineering Research Center for Structured Organic Particulate Systems (C-SOPS) based on 15 years of experience and several implemented systems. Here, we brought the details of the four integral components in a continuous direct compression manufacturing process: (1) unit operations, (2) PAT, (3) modeling and process controls, and (4) material characterization. Each section built-in classroom lectures with a brief overview on the theoretical aspects of each topic, followed by a hands-on session covering the classroom theory. The training program is described here in sufficient detail to enable creation of similar programs at other institutions.

We discuss steps toward the readout of the position of a nanoelectromagnetic system (NEMS) beam resonator using a Nb nanosuperconducting quantum interference device (nanoSQUID). We describe our fabrication procedure for coupling the nanoSQUID and a suspended Al-coated Si3N4 NEMS resonator together by a combination of focused ion beam lithography and nanomanipulation. We discuss typical electrical characteristics of the integrated devices, and independent postfabrication atomic force microscope nanoindentation measurements of the elastic properties of the integrated resonator to estimate its natural frequencies of vibration. We compare and discuss the response of a nanoSQUID with current-carrying and superconducting screening (noncurrent carrying) modes of operation of the resonator

Purpose Accurate and consistent delivery of materials by well-designed feeders ensures overall process stability. Importantly, feeding performance is strongly dependent on material flow properties. The purpose of this study is to develop a methodology that identifies predictive correlation between material flow properties and feeder performance. Method The proposed methodology includes techniques to characterize material flow properties, methods to quantify feeding performance of a loss-in-weight feeder, and predictive multivariate analysis. Two approaches to correlate feeding performance and material flow properties were examined in the study: principal component analysis, followed by similarity scoring (PCA-SS), and partial least squares regression (PLSR). Results Experimental results showed that selection of the optimal feeder screw to achieve optimum feeding performance is heavily dependent on material flow properties. Both approaches to predict feeding performance based on material properties were validated. In addition, a strong correlation between the initial feed factor of each material and its flow properties were observed. Conclusion The work presented here has demonstrated an efficient approach to correlate material properties with gravimetric feeder performance. This approach is especially powerful in the early phase of process and product development, when the amount of a material is limited.

Quantitative analysis of the response of ecosystem health to rural land use change is required to comprehend the human-nature coupling mechanism and to explore the process of global environmental change, which can interpret the ecological effects of regional land use and land cover change comprehensively. However, the existing regional ecosystem health assessment largely ignored either the internal connection of ecosystem health to land use patterns or the internal representation of ecosystem services to ecosystem health. Using Lijiang City of China as a study area, the average normalized difference vegetation index (NDVI), landscape metrics, and ecosystem elasticity coefficient based on different land use types were used as quantitative indicators. Then the coefficient of spatial neighboring effect was introduced to characterize the adjacency effect on ecosystem services, and to generate the index of integrated ecosystem health. The results showed the change of land use was close to 30% at county level from 1986 to 2006, and forest land was the primary land use type. With respect to the declining physical health of ecosystems in all the four counties, the integrated health experienced a slight increase in Lijiang County. The vast majority of towns’ ecosystem physical health and integrated health declined, while more than 70% of towns did not change distinctly. Ecosystem physical health had distinct influence on the integrated ecosystem health, and ecosystem vitality was the main factor affecting the condition of physical health. Emphasized in the interconnection of pattern and process, this study provided an ecosystem health approach to assessing the integrated ecological effects of regional land use change.