Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos

Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same.Comment: accepted by TPAMI202

Estimation of Crosstalk among Multiple Stripline Traces Crossing a Split by Compressed Sensing

In printed circuit board (PCB) designs, it is common to split power/ground planes into different partitions, which leads to more crosstalk among signal traces that route crossing a split. It is of general interest to develop a crosstalk model for various geometric parameters. However, the long time required to simulate the structure with any given set of geometric parameters renders general modelling approaches such as interpolation inefficient. in this paper, we develop an empirical model based upon the compressed sensing technique to characterize the crosstalk among traces as a function of geometric parameters. a good agreement between the empirical model and full-wave simulations is observed for various test examples, with an exceptionally small number of samples. © 2011 IEEE

PPNet: A Two-Stage Neural Network for End-to-end Path Planning

The classical path planners, such as sampling-based path planners, can provide probabilistic completeness guarantees in the sense that the probability that the planner fails to return a solution if one exists, decays to zero as the number of samples approaches infinity. However, finding a near-optimal feasible solution in a given period is challenging in many applications such as the autonomous vehicle. To achieve an end-to-end near-optimal path planner, we first divide the path planning problem into two subproblems, which are path space segmentation and waypoints generation in the given path's space. We further propose a two-stage neural network named Path Planning Network (PPNet) each stage solves one of the subproblems abovementioned. Moreover, we propose a novel efficient data generation method for path planning named EDaGe-PP. EDaGe-PP can generate data with continuous-curvature paths with analytical expression while satisfying the clearance requirement. The results show the total computation time of generating random 2D path planning data is less than 1/33 and the success rate of PPNet trained by the dataset that is generated by EDaGe-PP is about 2 times compared to other methods. We validate PPNet against state-of-the-art path planning methods. The results show that PPNet can find a near-optimal solution in 15.3ms, which is much shorter than the state-of-the-art path planners

Perioperative inflammatory index differences between pulmonary squamous cell carcinoma and adenocarcinoma and their prognostic implications

BackgroundPerioperative inflammatory indices reflect systemic inflammatory responses and have been linked to cancer progression and prognosis. This study aims to explore the differences in perioperative inflammatory indices between lung squamous cell carcinoma (LSCC) and adenocarcinoma (LUAD) and their association with long-term outcomes.MethodsThis study included 287 lung cancer patients who underwent curative resection between June 2016 and December 2017, comprising 61 cases of LSCC and 226 cases of LUAD. Perioperative baseline information and inflammatory cell counts were collected. Patients were followed up for a median duration of 76 months, during which disease-free survival (DFS) and overall survival (OS) were recorded. Cox regression analysis was used to evaluate the prognostic significance of inflammatory factor levels.ResultsSignificant differences were observed in white blood cell count and systemic inflammation response index (SIRI) between LSCC and LUAD (P < 0.05). Regression analysis identified age (OR=2.096, P=0.004), postoperative day 1 D-dimer level (OR=1.550, P<0.001), and Platelet-to-lymphocyte ratio (PLR) (OR=1.901, P=0.031) as independent risk factors for perioperative venous thromboembolism (VTE). Furthermore, open surgical approach (HR=2.437, P=0.016), tumor type (LSCC; HR=2.437, P=0.016), and PLR (HR=1.534, P=0.019) were independent risk factors for DFS.ConclusionInflammatory index is key predictors of perioperative VTE and DFS in lung cancer, emphasizing their critical role in prognosis

A nomogram model to predict the portal vein thrombosis risk after surgery in patients with pancreatic cancer

BackgroundPortal vein thrombosis (PVT) is a common postoperative complication in patients with pancreatic cancer (PC), significantly affecting their quality of life and long-term prognosis. Our aim is to establish a new nomogram to predict the risk of PVT after PC surgery.MethodWe collected data from 416 patients who underwent PC surgery at our hospital between January 2011 and June 2022. This includes 87 patients with PVT and 329 patients without PVT. The patients were randomly divided into a training group and a validation group at a ratio of 7:3. We constructed a nomogram model using the outcomes from both univariate and multivariate logistic regression analyses conducted on the training group. The nomogram’s predictive capacity was assessed using calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsIn the study, the prevalence of PVT was 20.9%. Age, albumin, vein reconstruction and preoperative D-dimer were independent related factors. The model achieved a C-index of 0.810 (95% confidence interval: 0.752–0.867), demonstrating excellent discrimination and calibration performance. The area under the ROC curve of the nomogram was 0.829 (95% CI: 0.750–0.909) in the validation group. DCA confirmed that the nomogram model was clinically useful when the incidence of PVT in patients was 5%–60%.ConclusionWe have established a high-performance nomogram for predicting the risk of PVT in patients undergoing PC surgery. This will assist clinical doctors in identifying individuals at high risk of PVT and taking appropriate preventive measures

New insights into the mitogenomic phylogeny and evolutionary history of Murinae (Rodentia, Muridae) with the description of a new tribe

Murinae is the largest known subfamily of Muridae and includes 15 tribes and 3 genera (incertae sedis). Although the phylogeny of Murinae has been studied, its phylogenetic relationships have not been completely elucidated. We used phylogenetic framework and molecular dating methodologies with the vast majority of available mitochondrial genomes to disentangle the phylogenetic relationships and evolutionary history of Murinae. Sixteen tribes were identified within the Murinae subfamily. Among these, fifteen tribes were found to be consistent with those currently recognized. Hapalomyini (Clade A) was located at the base of the Murinae clade with strong nodal support contrary to previous studies, which showed that Phloeomyini diverged first. The Clade B consisted of Micromyini, Rattini, and the genus Vernaya. Vernaya cannot be accommodated in any existing tribe. The origin of Murinae dates back to 17.22 Ma. The split between Micromyini and Vernayini was dated to 11.69 Ma during the Miocene, indicating that they were both early branches of Murinae. Combined with the differences between Vernaya and its sister tribes (Micromyini and Rattini) in morphology, skull and teeth, we validated a new tribe, Vernayini tribe nov. We believe that it is necessary to combine morphological and molecular perspectives (especially from a genome-wide perspective) to determine the phylogenetic position of tribes with an uncertain taxonomic position in Murinae

Prognostic and recurrent significance of SII in patients with pancreatic head cancer undergoing pancreaticoduodenectomy

BackgroundTo investigate the clinical significance of preoperative inflammatory status in patients with pancreatic head carcinoma (PHC), we performed a single-center study to assess it.MethodWe studied a total of 164 patients with PHC undergoing PD surgery (with or without allogeneic venous replacement) from January 2018 to April 2022. Systemic immune-inflammation index (SII) was the most important peripheral immune index in predicting the prognosis according to XGBoost analysis. The optimal cutoff value of SII for OS was calculated according to Youden index based on the receiver operating characteristic (ROC) curve and the cohort was divided into Low SII group and High SII group. Demographic, clinical data, laboratory data, follow-up data variables were obtained and compared between the two groups. Kaplan-Meier curves, univariable and multivariable Cox regression models were used to determine the association between preoperative inflammation index, nutritional index and TNM staging system with OS and DFS respectively.ResultsThe median follow-up time was 16 months (IQR 23), and 41.4% of recurrences occurred within 1 year. The cutoff value of SII was 563, with a sensitivity of 70.3%, and a specificity of 60.7%. Peripheral immune status was different between the two groups. Patients in High SII group had higher PAR, NLR than those in Low SII group (P <0.01, <0.01, respectively), and lower PNI (P <0.01). Kaplan–Meier analysis showed significantly poorer OS and DFS (P < 0.001, <0.001, respectively) in patients with high SII. By using the multivariable Cox regression model, high SII (HR, 2.056; 95% CI, 1.082–3.905, P=0.028) was significant predictor of OS. Of these 68 high-risk patients who recurrence within one year, patients with widespread metastasis had lower SII and worse prognosis (P <0.01).ConclusionHigh SII was significantly associated with poor prognosis in patients with PHC. However, in patients who recurrence within one year, SII was lower in patients at TNM stage III. Thus, care needs to be taken to differentiate those high-risk patients

Synaptotagmin I delays the fast inactivation of Kv1.4 channel through interaction with its N-terminus

BACKGROUND: The voltage-gated potassium channel Kv1.4 is an important A-type potassium channel and modulates the excitability of neurons in central nervous system. Analysis of the interaction between Kv1.4 and its interacting proteins is helpful to elucidate the function and mechanism of the channel. RESULTS: In the present research, synaptotagmin I was for the first time demonstrated to be an interacting protein of Kv1.4 and its interaction with Kv1.4 channel did not require the mediation of other synaptic proteins. Using patch-clamp technique, synaptotagmin I was found to delay the inactivation of Kv1.4 in HEK293T cells in a Ca(2+)-dependent manner, and this interaction was proven to have specificity. Mutagenesis experiments indicated that synaptotagmin I interacted with the N-terminus of Kv1.4 and thus delayed its N-type fast inactivation. CONCLUSION: These data suggest that synaptotagmin I is an interacting protein of Kv1.4 channel and, as a negative modulator, may play an important role in regulating neuronal excitability and synaptic efficacy

Epidemiological and clinical characteristics of COVID-19 patients in Nantong, China

Introduction: COVID-19 is a newly emerging life-threatening respiratory disease caused by a newly identified coronavirus SARS-CoV-2. Methodology: We included 28 COVID-19 patients admitted to Nantong Third Hospital from January 23 to February 26, 2020. SARS-CoV-2 infection was confirmed using real-time RT-PCR. The demographic, epidemiological, clinical, laboratory parameters were obtained from each patient. Results: The vast majority (71.4%) of confirmed COVID-19 patients were brought in from outside of the city, and all others had contact history with these confirmed cases. The median age of patients was 50 years old and half had underlying diseases. The most common symptoms at the onset of illness were fever (96.4%), cough (67.9%), and chilly (28.6%), and 75.0% patients had two or more symptoms. Increased erythrocyte sedimentation rate, serum ferritin and C-reactive protein levels, and reduced absolute counts of total lymphocytes and T lymphocyte subsets were observed among the patients. The vast majority (85.7%) of patients showed bilateral or unilateral pneumonia, and three symptomatic patients and one asymptomatic case did not show abnormalities in their CT image. Among the 28 admitted patients, 24 were discharged as of February 26, 2020, with an average hospital stay of 14.96 (±4.27) days, which was not significantly associated with the interval between the onset of symptoms and admission. Conclusions: In the absence of specific antiviral drugs or a vaccine, quarantine or isolation is the most effective intervention strategy for preventing the spread of the virus. Adequate supportive medical care is crucial for good prognosis of COVID-19 patients