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The latest near-infrared light-activated nanomedicine in the direction of accuracy most cancers therapy.
We present approximation algorithms for the problems and an NP-hardness proof.We consider the problem of determining the mutational support and distribution of the SARS-CoV-2 viral genome in the small-sample regime. The mutational support refers to the unknown number of sites that may eventually mutate in the SARS-CoV-2 genome while mutational distribution refers to the distribution of point mutations in the viral genome across a population. The mutational support may be used to assess the virulence of the virus and guide primer selection for real-time RT-PCR testing. Estimating the distribution of mutations in the genome of different subpopulations while accounting for the unseen may also aid in discovering new variants. To estimate the mutational support in the small-sample regime, we use GISAID sequencing data and our state-of-the-art polynomial estimation techniques based on new weighted and regularized Chebyshev approximation methods. For distribution estimation, we adapt the well-known Good-Turing estimator. Our analysis reveals several findings First, the mutational supports exhibit significant differences in the ORF6 and ORF7a regions (older vs younger patients), ORF1b and ORF10 regions (females vs males) and in almost all ORFs (Asia/Europe/North America). Second, even though the N region of SARS-CoV-2 has a predicted 10% mutational support, mutations fall outside of the primer regions recommended by the CDC.The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policymakers have expressed the urgency to effectively evaluate the outcome of human restriction policies with the aid of big data and information technology. UNC0642 order Thus, based on big human mobility data and city POI data, an interactive visual analytics system called Epidemic Mobility (EpiMob) was designed in this study. The system interactively simulates the changes in human mobility and infection status in response to the implementation of a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies. Then, the results reflecting the infection situation under different policies are dynamically displayed and can be flexibly compared and analyzed in depth. Multiple case studies consisting of interviews with domain experts were conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area) to demonstrate that the system can provide insight into the effects of different human mobility restriction policies for epidemic control, through measurements and comparisons.In this paper, we propose a dynamic graph modeling approach to learn spatial-temporal representations for video summarization. Most existing video summarization methods extract image-level features with ImageNet pre-trained deep models. Differently, our method exploits object-level and relation-level information to capture spatial-temporal dependencies. Specifically, our method builds spatial graphs on the detected object proposals. Then, we construct a temporal graph by using the aggregated representations of spatial graphs. Afterward, we perform relational reasoning over spatial and temporal graphs with graph convolutional networks and extract spatial-temporal representations for importance score prediction and key shot selection. To eliminate relation clutters caused by densely connected nodes, we further design a self-attention edge pooling module, which disregards meaningless relations of graphs. We conduct extensive experiments on two popular benchmarks, including the SumMe and TVSum datasets. Experimental results demonstrate that the proposed method achieves superior performance against state-of-the-art video summarization methods.In this paper, a Multi-scale Contrastive Graph Convolutional Network (MC-GCN) method is proposed for unconstrained face recognition with image sets, which takes a set of media (orderless images and videos) as a face subject instead of single media (an image or video). Due to factors such as illumination, posture, media source, etc., there are huge intra-set variances in a face set, and the importance of different face prototypes varies considerably. How to model the attention mechanism according to the relationship between prototypes or images in a set is the main content of this paper. In this work, we formulate a framework based on graph convolutional network (GCN), which considers face prototypes as nodes to build relations. Specifically, we first present a multi-scale graph module to learn the relationship between prototypes at multiple scales. Moreover, a Contrastive Graph Convolutional (CGC) block is introduced to build attention control model, which focuses on those frames with similar prototypes (contrastive information) between pair of sets instead of simply evaluating the frame quality. The experiments on IJB-A, YouTube Face, and an animal face dataset clearly demonstrate that our proposed MC-GCN outperforms the state-of-the-art methods significantly.Convolutional neural network (CNN)-based filters have achieved great success in video coding. However, in most previous works, individual models were needed for each quantization parameter (QP) band, which is impractical due to limited storage resources. To explore this, our work consists of two parts. First, we propose a frequency and spatial QP-adaptive mechanism (FSQAM), which can be directly applied to the (vanilla) convolution to help any CNN filter handle different quantization noise. From the frequency domain, a FQAM that introduces the quantization step (Qstep) into the convolution is proposed. When the quantization noise increases, the ability of the CNN filter to suppress noise improves. Moreover, SQAM is further designed to compensate for the FQAM from the spatial domain. Second, based on FSQAM, a QP-adaptive CNN filter called QA-Filter that can be used under a wide range of QP is proposed. By factorizing the mixed features to high-frequency and low-frequency parts with the pair of pooling and upsampling operations, the QA-Filter and FQAM can promote each other to obtain better performance. Compared to the H.266/VVC baseline, average 5.25% and 3.84% BD-rate reductions for luma are achieved by QA-Filter with default all-intra (AI) and random-access (RA) configurations, respectively. Additionally, an up to 9.16% BD-rate reduction is achieved on the luma of sequence BasketballDrill. Besides, FSQAM achieves measurably better BD-rate performance compared with the previous QP map method.Zero-shot recognition has been a hot topic in recent years. Since no direct supervision is available, researchers use semantic information as the bridge instead. However, most zero-shot recognition methods jointly model images on the class level without considering the distinctive character of each image. To solve this problem, in this paper, we propose a novel exemplar-based, semantic guided zero-shot recognition method (EBSG). Both visual and semantic information of each image is used. We train visual sub-model to separate each image from the other images of different classes. We also train semantic sub-model to separate this image from the other images described with different semantics. We concatenate the outputs of visual and semantic sub-models to represent images. Image classification model is then learned by measuring visual similarity and semantic consistency of both source and target images. We conduct zero-shot recognition experiments on four widely used datasets. Experimental results show the effectiveness of the proposed EBSG method.Superheated nanodroplet (ND) vaporization by proton radiation was recently demonstrated, opening the door to ultrasound-based in vivo proton range verification. However, at body temperature and physiological pressures, perfluorobutane nanodroplets (PFB-NDs), which offer a good compromise between stability and radiation sensitivity, are not directly sensitive to primary protons. Instead, they are vaporized by infrequent secondary particles, which limits the precision for range verification. The radiation-induced vaporization threshold (i.e., sensitization threshold) can be reduced by lowering the pressure in the droplet such that ND vaporization by primary protons can occur. Here, we propose to use an acoustic field to modulate the pressure, intermittently lowering the proton sensitization threshold of PFB-NDs during the rarefactional phase of the ultrasound wave. Simultaneous proton irradiation and sonication with a 1.1 MHz focused transducer, using increasing peak negative pressures (PNPs), were applied on a dilution of PFB-NDs flowing in a tube, while vaporization was acoustically monitored with a linear array. Sensitization to primary protons was achieved at temperatures between [Formula see text] and 40 °C using acoustic PNPs of relatively low amplitude (from 800 to 200 kPa, respectively), while sonication alone did not lead to ND vaporization at those PNPs. Sensitization was also measured at the clinically relevant body temperature (i.e., 37 °C) using a PNP of 400 kPa. These findings confirm that acoustic modulation lowers the sensitization threshold of superheated NDs, enabling a direct proton response at body temperature.
In 2020, critical care departments underwent profound changes imposed by the COVID-19 pandemic. The aim of this study was to evaluate the impact of the pandemic on the intensive care residency program in Portugal.

The Association of Critical Care Residents (AIMINT) prepared a questionnaire using the Google Forms® tool, which was applied during August 2020 to the Critical Care residents in Portugal. A descriptive analysis was performed with the data collected.

Eighty-five residents participated in the questionnaire, yieldinga response rate of 62%. Three-quarters of all participants provided care to COVID-19 patients. More than 80% of the surveyed participants were on rotations, and these were canceled in 59% of cases. Seventy-eight percent reported a workload greater than 40 hours per week.

The COVID-19 pandemic had an impact on the Critical Care Residency program in Portugal. Most residents surveyed provided care to COVID-19 patients and not only saw their rotations suspended but also experienced difficulties in rescheduling them.
The COVID-19 pandemic had an impact on the Critical Care Residency program in Portugal. Most residents surveyed provided care to COVID-19 patients and not only saw their rotations suspended but also experienced difficulties in rescheduling them.The purpose of this study was to investigate the effects of a combined robot-assisted gait training (RAGT) with standard physiotherapy (PT) on trunk control and posture in non-ambulatory children with cerebral palsy (CP). This nonrandomized, controlled study included 31 CP assigned into two groups. Study Group RAGT (three times a week, 30 min/session, for 6 weeks) + PT. Control group PT only. The patients were evaluated using gross motor function measure (GMFM)-88 (Section B, Sitting) and Trunk Impairment Scale (TIS), pre-treatment and 3rd month post-treatment. In the RAGT group, significant improvements were observed in the GMFM-B and TIS scores at the 3rd month post-treatment (p  less then  0.05). Comparison of the changes in GMFM-B and TIS scores from end to beginning of the study, the change in TIS static are significantly higher in the RAGT group than control group (p  less then  0.05). Addition of RAGT to standard physiotherapy seems to improve trunk control, sitting balance, and posture in non-ambulatory CP.
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