Learning-Initialized Trajectory Planning in Unknown Environments

Autonomous flight in unknown environments requires precise planning for both the spatial and temporal profiles of trajectories, which generally involves nonconvex optimization, leading to high time costs and susceptibility to local optima. To address these limitations, we introduce the Learning-Initialized Trajectory Planner (LIT-Planner), a novel approach that guides optimization using a Neural Network (NN) Planner to provide initial values. We first leverage the spatial-temporal optimization with batch sampling to generate training cases, aiming to capture multimodality in trajectories. Based on these data, the NN-Planner maps visual and inertial observations to trajectory parameters for handling unknown environments. The network outputs are then optimized to enhance both reliability and explainability, ensuring robust performance. Furthermore, we propose a framework that supports robust online replanning with tolerance to planning latency. Comprehensive simulations validate the LIT-Planner's time efficiency without compromising trajectory quality compared to optimization-based methods. Real-world experiments further demonstrate its practical suitability for autonomous drone navigation

Estimation of MIMO transmit-antenna number using higher-order moments based hypothesis testing

This letter proposes a higher-order-moment based hypothesis testing algorithm to estimate the transmit-antenna number for multiple-input multiple-output (MIMO) systems. Exploiting the asymptotic normal distribution of the moments composed by noise eigenvalues, the proposed algorithm improves the estimation performance for low signal-to-noise ratios (SNRs). Moreover, since the empirical distribution of the moments converges quickly to the normal distribution when the number of samples increases, our algorithm can make a reliable estimation in a sample starved condition. Computer simulations are provided to demonstrate that the proposed algorithm outperforms the conventional algorithms

Worst-input mutation approach to web services vulnerability testing based on SOAP messages

The growing popularity and application of Web services have led to an increase in attention to the vulnerability of software based on these services. Vulnerability testing examines the trustworthiness, and reduces the security risks of software systems, however such testing of Web services has become increasing challenging due to the cross-platform and heterogeneous characteristics of their deployment. This paper proposes a worst-input mutation approach for testing Web service vulnerability based on SOAP (Simple Object Access Protocol) messages. Based on characteristics of the SOAP messages, the proposed approach uses the farthest neighbor concept to guide generation of the test suite. The test case generation algorithm is presented, and a prototype Web service vulnerability testing tool described. The tool was applied to the testing of Web services on the Internet, with experimental results indicating that the proposed approach, which found more vulnerability faults than other related approaches, is both practical and effective

Layer-Based Data Aggregation and Performance Analysis in Wireless Sensor Networks

Due to the similarity and correlation among sensed data in wireless sensor network, it is an important way to reduce the number of packets transmitted with data aggregation technology so as to prolong the network lifetime. However, data aggregation is still a challenging issue since quality-of-service, such as end-to-end delay, is generally considered as a severe criterion required in many applications. We focus on the minimum-latency data aggregation problem and proposed a new efficient scheme for it. The basic idea is that we first build an aggregation tree by ordering nodes into layers, and then we proposed a scheduling algorithm on the basis of the aggregation tree to determine the transmission time slots for all nodes in the network with collision avoiding. We have proved that the upper bound for data aggregation with our proposed scheme is bounded by (15R+Δ-15) for wireless sensor networks in two-dimensional space. Extensive simulation results have demonstrated that the proposed scheme has better practical performance compared with related works

Application of plasma metagenomic next-generation sequencing improves prognosis in hematology patients with neutropenia or hematopoietic stem cell transplantation for infection

IntroductionMetagenomic next-generation sequencing (mNGS) is a novel technique for detecting pathogens. This retrospective study evaluated the diagnostic value of mNGS using plasma for infections in hematology patients and its impact on clinical treatment and prognosis in different subgroups of hematology patients.MethodsA total of 153 hematology patients with suspected infection who underwent mNGS using plasma were enrolled in the study. Their clinical histories, conventional microbiological test (CMT) results, mNGS results, treatment and prognosis were retrospectively analyzed.ResultsIn 153 plasma samples, mNGS yielded a higher positivity rate than CMT (total: 88.24% vs. 40.52%, P<0.001; bacteria: 35.95% vs. 21.57%, P < 0.01; virus: 69.93% vs. 21.57%, P<0.001; fungi: 20.26% vs. 7.84%, P<0.01). mNGS had a higher positivity rate for bacteria and fungi in the neutropenia group than in the non-neutropenia group (bacteria: 48.61% vs. 24.69%, P<0.01; fungi: 27.78% vs. 13.58%, P<0.05). mNGS demonstrated a greater advantage in the group of patients with hematopoietic stem cell transplantation (HSCT). Both the 3-day and 7-day efficacy rates in the HSCT group were higher than those in the non-HSCT group (3-day: 82.22% vs. 58.65%, P < 0.01; 7-day: 88.89% vs. 67.31%, P < 0.01), and the 28-day mortality rate was lower in the HSCT group than in the non-HSCT group (6.67% vs. 38.89%, P < 0.000). The neutropenia group achieved similar efficacy and mortality rates to the non-neutropenia group (7-day efficiency rate: 76.39% vs. 71.43%, P > 0.05; mortality rate: 29.17% vs. 29.63%, P > 0.05) with more aggressive antibiotic adjustments (45.83% vs. 22.22%, P < 0.01).ConclusionmNGS can detect more microorganisms with higher positive rates, especially in patients with neutropenia. mNGS had better clinical value in patients with hematopoietic stem cell transplantation (HSCT) or neutropenia, which had a positive effect on treatment and prognosis

On the Diversification Effect in Solvency II for Extremely Dependent Risks

In this article, we investigate the validity of diversification effect under extreme-value copulas, when the marginal risks of the portfolio are identically distributed, which can be any one having a finite endpoint or belonging to one of the three maximum domains of attraction. We show that Value-at-Risk (V@R) under extreme-value copulas is asymptotically subadditive for marginal risks with finite mean, while it is asymptotically superadditive for risks with infinite mean. Our major findings enrich and supplement the context of the second fundamental theorem of quantitative risk management in existing literature, which states that V@R of a portfolio is typically non-subadditive for non-elliptically distributed risk vectors. In particular, we now pin down when the V@R is super or subadditive depending on the heaviness of the marginal tail risk. According to our results, one can take advantages from the diversification effect for marginal risks with finite mean. This justifies the standard formula for calculating the capital requirement under Solvency II in which imperfect correlations are used for various risk exposures

Metatranscriptomic analysis revealed Prevotella as a potential biomarker of oropharyngeal microbiomes in SARS-CoV-2 infection

Background and objectivesDisease severity and prognosis of coronavirus disease 2019 (COVID-19) disease with other viral infections can be affected by the oropharyngeal microbiome. However, limited research had been carried out to uncover how these diseases are differentially affected by the oropharyngeal microbiome of the patient. Here, we aimed to explore the characteristics of the oropharyngeal microbiota of COVID-19 patients and compare them with those of patients with similar symptoms.MethodsCOVID-19 was diagnosed in patients through the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Characterization of the oropharyngeal microbiome was performed by metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 144 COVID-19 patients, 100 patients infected with other viruses, and 40 healthy volunteers.ResultsThe oropharyngeal microbiome diversity in patients with SARS-CoV-2 infection was different from that of patients with other infections. Prevotella and Aspergillus could play a role in the differentiation between patients with SARS-CoV-2 infection and patients with other infections. Prevotella could also influence the prognosis of COVID-19 through a mechanism that potentially involved the sphingolipid metabolism regulation pathway.ConclusionThe oropharyngeal microbiome characterization was different between SARS-CoV-2 infection and infections caused by other viruses. Prevotella could act as a biomarker for COVID-19 diagnosis and of host immune response evaluation in SARS-CoV-2 infection. In addition, the cross-talk among Prevotella, SARS-CoV-2, and sphingolipid metabolism pathways could provide a basis for the precise diagnosis, prevention, control, and treatment of COVID-19

The BACH1 inhibitor ASP8731 inhibits inflammation and vaso-occlusion and induces fetal hemoglobin in sickle cell disease

In sickle cell disease (SCD), heme released during intravascular hemolysis promotes oxidative stress, inflammation, and vaso-occlusion. Conversely, free heme can also activate expression of antioxidant and globin genes. Heme binds to the transcription factor BACH1, which represses NRF2-mediated gene transcription. ASP8731, is a selective small molecule inhibitor of BACH1. We investigated the ability of ASP8731 to modulate pathways involved in SCD pathophysiology. In HepG2 liver cells, ASP8731 increased HMOX1 and FTH1 mRNA. In pulmonary endothelial cells, ASP8731 decreased VCAM1 mRNA in response to TNF-α and blocked a decrease in glutathione in response to hemin. Townes-SS mice were gavaged once per day for 4 weeks with ASP8731, hydroxyurea (HU) or vehicle. Both ASP8731 and HU inhibited heme-mediated microvascular stasis and in combination, ASP8731 significantly reduced microvascular stasis compared to HU alone. In Townes-SS mice, ASP8731 and HU markedly increased heme oxygenase-1 and decreased hepatic ICAM-1, NF-kB phospho-p65 protein expression in the liver, and white blood cell counts. In addition, ASP8731 increased gamma-globin expression and HbF+ cells (F-cells) as compared to vehicle-treated mice. In human erythroid differentiated CD34+ cells, ASP8731 increased HGB mRNA and increased the percentage of F-cells 2-fold in manner similar to HU. ASP8731 and HU when given together induced more HbF+ cells compared to either drug alone. In CD34+ cells from one donor that was non-responsive to HU, ASP8731 induced HbF+ cells ~2-fold. ASP8731 and HU also increased HBG and HBA, but not HBB mRNA in erythroid differentiated CD34+ cells derived from SCD patients. These data indicate that BACH1 may offer a new therapeutic target to treat SCD

Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response

The SARS-CoV-2 virus, the causative agent of COVID-19, is undergoing constant mutation. Here, we utilized an integrative approach combining epidemiology, virus genome sequencing, clinical phenotyping, and experimental validation to locate mutations of clinical importance. We identified 35 recurrent variants, some of which are associated with clinical phenotypes related to severity. One variant, containing a deletion in the Nsp1-coding region (D500-532), was found in more than 20% of our sequenced samples and associates with higher RT-PCR cycle thresholds and lower serum IFN-beta levels of infected patients. Deletion variants in this locus were found in 37 countries worldwide, and viruses isolated from clinical samples or engineered by reverse genetics with related deletions in Nsp1 also induce lower IFN-beta responses in infected Calu-3 cells. Taken together, our virologic surveillance characterizes recurrent genetic diversity and identified mutations in Nsp1 of biological and clinical importance, which collectively may aid molecular diagnostics and drug design.Peer reviewe

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure