Abstract-In this paper, we propose a distributed multi-object tracking algorithm through the use of multi-Bernoulli (MB) filter based on generalized Covariance Intersection (G-CI). Our analyses show that the G-CI fusion with two MB posterior distributions does not admit an accurate closed-form expression. To solve this problem, we firstly approximate the fused posterior as the unlabeled version of δ-generalized labeled multi-Bernoulli (δ-GLMB) distribution, referred to as generalized multi-Bernoulli (GMB) distribution. Then, to allow the subsequent fusion with another multi-Bernoulli posterior distribution, e.g., fusion with a third sensor node in the sensor network, or fusion in the feedback working mode, we further approximate the fused GMB posterior distribution as an MB distribution which matches its first-order statistical moment. The proposed fusion algorithm is implemented using sequential Monte Carlo technique and its performance is highlighted by numerical results.

On the basis of Covid-19-induced pulmonary pathological and vascular changes, we hypothesize that the anti-vascular endothelial growth factor (VEGF) drug bevacizumab might be beneficial for treating Covid-19 patients. From Feb 15 to April 5, 2020, we conducted a single-arm trial (NCT04275414) and recruited 26 patients from 2-centers (China and Italy) with severe Covid-19, with respiratory rate ≥30 times/min, oxygen saturation ≤93% with ambient air, or partial arterial oxygen pressure to fraction of inspiration O2 ratio (PaO2/FiO2) >100 mmHg and ≤300 mmHg, and diffuse pneumonia confirmed by chest imaging. Followed up for 28 days. Among these, bevacizumab plus standard care markedly improves the PaO2/FiO2 ratios at days 1 and 7. By day 28, 24 (92%) patients show improvement in oxygen-support status, 17 (65%) patients are discharged, and none show worsen oxygen-support status nor die. Significant reduction of lesion areas/ratios are shown in chest computed tomography (CT) or X-ray within 7 days. Of 14 patients with fever, body temperature normalizes within 72 h in 13 (93%) patients. Relative to comparable controls, bevacizumab shows clinical efficacy by improving oxygenation and shortening oxygen-support duration. Our findings suggest bevacizumab plus standard care is highly beneficial for patients with severe Covid-19. Randomized controlled trial is warranted.

Objective: Hypoxia-induced pulmonary hypertension (HPH) increases lipid peroxidation with generation of toxic aldehydes that are metabolized by detoxifying enzymes, including ALDH2 (aldehyde dehydrogenase 2). However, the role of lipid peroxidation and ALDH2 in HPH pathogenesis remain undefined. Approach and Results: To determine the role of lipid peroxidation and ALDH2 in HPH, C57BL/6 mice, ALDH2 transgenic mice, and ALDH2 knockout (ALDH2 −/− ) mice were exposed to chronic hypoxia, and recombinant tissue-specific ALDH2 overexpression adeno-associated viruses were introduced into pulmonary arteries via tail vein injection for ALDH2 overexpression. Human pulmonary artery smooth muscle cells were used to elucidate underlying mechanisms in vitro. Chronic hypoxia promoted lipid peroxidation due to the excessive production of reactive oxygen species and increased expression of lipoxygenases in lung tissues. 4-hydroxynonenal but not malondialdehyde level was increased in hypoxic lung tissues which might reflect differences in detoxifying enzymes. ALDH2 overexpression attenuated the development of HPH, whereas ALDH2 knockout aggravated it. Specific overexpression of ALDH2 using AAV1 (adeno-associated virus)-ICAM (intercellular adhesion molecule) 2p-ALDH2 and AAV2-SM22αp (smooth muscle 22 alpha)-ALDH2 viral vectors in pulmonary artery smooth muscle cells, but not endothelial cells, prevented the development of HPH. Hypoxia or 4-hydroxynonenal increased stabilization of HIF (hypoxia-inducible factor)-1α, phosphorylation of Drp1 (dynamin-related protein 1) at serine 616, mitochondrial fission, and pulmonary artery smooth muscle cells proliferation, whereas ALDH2 activation suppressed the latter 3. Conclusions: Increased 4-hydroxynonenal level plays a critical role in the development of HPH. ALDH2 attenuates the development of HPH by regulating mitochondrial fission and smooth muscle cell proliferation suggesting ALDH2 as a potential new therapeutic target for pulmonary hypertension.

Abstract-This paper considers the problem of the distributed fusion of multi-object posteriors in the labeled random finite set filtering framework, using Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI fusion with labeled multi-object densities strongly relies on label consistencies between local multi-object posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of Principle of Minimum Discrimination Information and the so called yesobject probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multi-object densities that is robust to label inconsistencies between sensors. Specifically, the labeled multi-object posteriors are firstly marginalized to their unlabeled posteriors which are then fused using GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including δ-GLMB, marginalized δ-GLMB and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.

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On the basis of Covid-19-induced pulmonary pathological and vascular changes, we hypothesized that the anti-VEGF drug bevacizumab might be beneficial for treating Covid-19 patients. We recruited 26 patients from 2-centers (China and Italy) with confirmed severe Covid-19, with respiratory rate ≥30 times/min, oxygen saturation ≤93% with ambient air, or partial arterial oxygen pressure to fraction of inspiration O2 ratio (PaO2/FiO2) >100mmHg and ≤300 mmHg, and diffuse pneumonia confirmed by chest radiological imaging. This trial was conducted from Feb 15 to April 5, 2020, and followed up for 28 days. Relative to comparable control patients with severe Covid-19 admitted in the same centers, bevacizumab showed clinical efficacy by improving oxygenation and shortening oxygen-support duration. Among 26 hospitalized patients with severe Covid-19 (median age, 62 years, 20 [77%] males), bevacizumab plus standard care markedly improved the PaO2/FiO2 ratios at days 1 and 7 (elevated values, day 1, 50.5 [4.0,119.0], p<0.001; day 7, 111.0 [85.0,165.0], p<0.001). By day 28, 24 (92%) patients showed improvement in oxygen-support status, 17 (65%) patients were discharged, and none showed worsen oxygen-support status nor died. Significant reduction of lesion areas and ratios were shown in chest CT or X-ray analysis within 7 days. Of 14 patients with fever, body temperature normalized within 72 hours in 13 (93%) patients. Lymphocyte counts in peripheral blood were significantly increased and CRP levels were markedly decreased as shown in available data. Our findings suggested bevacizumab plus standard care was highly beneficial for treating patients with severe Covid-19. Clinical efficacy of bevacizumab warrants double blind, randomized, placebo-controlled trials.

This paper proposes a computationally efficient algorithm for distributed fusion in a sensor network in which multi-Bernoulli (MB) filters are locally running in every sensor node for multi-target tracking. The generalized Covariance Intersection (GCI) fusion rule is employed to fuse multiple MB random finite set densities. The fused density comprises a set of fusion hypotheses that grow exponentially with the number of Bernoulli components. Thus, GCI fusion with MB filters can become computationally intractable in practical applications that involve tracking of even a moderate number of objects. In order to accelerate the multi-sensor fusion procedure, we derive a theoretically sound approximation to the fused density. The number of fusion hypotheses in the resulting density is significantly smaller than the original fused density. It also has a parallelizable structure that allows multiple clusters of Bernoulli components to be fused independently. By carefully clustering Bernoulli components into isolated clusters using the GCI divergence as the distance metric, we propose an alternative to build exactly the approximated density without exhaustively computing all the fusion hypotheses. The combination of the proposed approximation technique and the fast clustering algorithm can enable a novel and fast GCI-MB fusion implementation. Our analysis shows that the proposed fusion method can dramatically reduce the computational and memory requirements with small bounded L1-error. The Gaussian mixture implementation of the proposed method is also presented. In various numerical experiments, including a challenging scenario with up to forty objects, the efficacy of the proposed fusion method is demonstrated. arXiv:1906.07991v1 [eess.SY]