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To investigate the effect of hydrogen sulfide (H2S) on myocardial injury in sepsis-induced myocardial dysfunction (SIMD), male C57BL/6 mice were intraperitoneally injected with lipopolysaccharide (LPS) (10 mg/kg, i.p.) to induce cardiac dysfunction without or with the H2S donor sodium hydrosulfide (NaHS) (50 µmol/kg, i.p.) administration 3 h after LPS injection. Six hours after the LPS injection, echocardiography, cardiac hematoxylin and eosin (HE) staining, myocardial damage and inflammatory biomarkers and Western blot results were analyzed. In mice, the administration of LPS decreased left ventricular ejection fraction (LVEF) by 30 % along with lowered H2S levels (35 % reduction). It was observed that cardiac troponin I (cTnI), tumor necrosis factor-α (TNF-α), and interleukin-1β (IL-1β) levels were all increased (by 0.22-fold, 2000-fold and 0.66-fold respectively). HE staining revealed structural damage and inflammatory cell infiltration in the myocardial tissue after LPS administration. Moreover, after 6 h of LPS treatment, toll-like receptor 4 (TLR4) and nod-like receptor protein 3 (NLRP3) expressions were up-regulated 2.7-fold and 1.6-fold respectively. When compared to the septic mice, NaHS enhanced ventricular function (by 0.19-fold), decreased cTnI, TNF-α, and IL-1β levels (by 11 %, 33 %, and 16 % respectively) and downregulated TLR4 and NLRP3 expressions (by 64 % and 31 % respectively). Furthermore, NaHS did not further improve cardiac function and inflammation in TLR4-/- mice or mice in which NLRP3 activation was inhibited by MCC950, after LPS injection. In conclusion, these findings imply that decreased endogenous H2S promotes the progression of SIMD, whereas exogenous H2S alleviates SIMD by inhibiting inflammation via the TLR4-NLRP3 pathway suppression.

Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is applicable to only the vector-type random variables, while certain applications rely on set-type random variables with an unknown number of vector elements. In this paper, we first develop BP rules for set-type random variables and demonstrate that vector-type BP is a special case of set-type BP. We further propose factor graphs with set-factor and set-variable nodes by devising the set-factor nodes that can address the set-variables with random elements and cardinality, while the number of vector elements in vector-type is known. To demonstrate the validity of developed set-type BP, we apply it to the Poisson multi-Bernoulli (PMB) filter for simultaneous localization and mapping (SLAM), which naturally leads to a new set-type BP-SLAM filter. Finally, we reveal connections between the vector-type BP-SLAM filter and the proposed set-type BP-SLAM filter and show a performance gain of the proposed set-type BP-SLAM filter in comparison with the vector-type BP-SLAM filter., 17 pages, 7 figures

Background: B cell lymphoma 6 (BCL6) is an important transcription factor of T follicular helper (Tfh) cells, which regulate the humoral response by supporting the maturation of germinal center B cells and plasma cells. The aim of this study is to investigate the expansion of T follicular helper cells and the effect of the BCL6 inhibitor FX1 in acute and chronic cardiac transplant rejection models.Methods: A mouse model of acute and chronic cardiac transplant rejection was established. Splenocytes were collected at different time points after transplantation for CXCR5+PD-1+ and CXCR5+BCL6+ Tfh cells detection by flow cytometry (FCM). Next, we treated the cardiac transplant with BCL6 inhibitor FX1 and the survival of grafts was recorded. The hematoxylin and eosin, Elastica van Gieson, and Masson staining of cardiac grafts was performed for the pathological analysis. Furthermore, the proportion and number of CD4+ T cells, effector CD4+ T cells (CD44+CD62L−), proliferating CD4+ T cells (Ki67+), and Tfh cells in the spleen were detected by FCM. The cells related to humoral response (plasma cells, germinal center B cells, IgG1+ B cells) and donor-specific antibody were also detected.Results: We found that the Tfh cells were significantly increased in the recipient mice on day 14 post transplantation. During the acute cardiac transplant rejection, even the BCL6 inhibitor FX1 did not prolong the survival or attenuate the immune response of cardiac graft, the expansion of Tfh cell expansion inhibit. During the chronic cardiac transplant rejection, FX1 prolonged survival of cardiac graft, and prevented occlusion and fibrosis of vascular in cardiac grafts. FX1 also decreased the proportion and number of splenic CD4+ T cells, effector CD4+ T cells, proliferating CD4+ T cells, and Tfh cells in mice with chronic rejection. Moreover, FX1 also inhibited the proportion and number of splenic plasma cells, germinal center B cells, IgG1+ B cells, and the donor-specific antibody in recipient mice.Conclusion: We found BCL6 inhibitor FX1 protects chronic cardiac transplant rejection and inhibits the expansion of Tfh cells and the humoral response, which suggest that BCL6 is a potential therapeutic target of the treatment for chronic cardiac transplant rejection.

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, most of the camera-based perception methods transform the extracted image perspective view features into the bird's-eye view geometrically via "depth-based splatting" proposed in Lift-Splat-Shoot (LSS), and some researchers exploit other modals such as LiDARs or ordinary automotive radars for enhancement. Recently, a few works have applied the "sampling" strategy for image view transformation, showing that it outperforms "splatting" even without image depth prediction. However, the potential of "sampling" is not fully unleashed. In this paper, we investigate the "sampling" view transformation strategy on the camera and 4D imaging radar fusion-based 3D object detection. In the proposed model, LXL, predicted image depth distribution maps and radar 3D occupancy grids are utilized to aid image view transformation, called "radar occupancy-assisted depth-based sampling". Experiments on VoD and TJ4DRadSet datasets show that the proposed method outperforms existing 3D object detection methods by a significant margin without bells and whistles. Ablation studies demonstrate that our method performs the best among different enhancement settings.

Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others. In recent years, deep learning (DL) has been increasingly used in MOT for improving tracking performance, but mostly in settings where the measurements are high-dimensional and there are no available models of the measurement likelihood and the object dynamics. The model-based setting instead has not attracted as much attention, and it is still unclear if DL methods can outperform traditional model-based Bayesian methods, which are the state of the art (SOTA) in this context. In this paper, we propose a Transformer-based DL tracker and evaluate its performance in the model-based setting, comparing it to SOTA model-based Bayesian methods in a variety of different tasks. Our results show that the proposed DL method can match the performance of the model-based methods in simple tasks, while outperforming them when the task gets more complicated, either due to an increase in the data association complexity, or to stronger nonlinearities of the models of the environment.

In this paper, we demonstrate that deep learning based method can be used to fuse multi-object densities. Given a scenario with several sensors with possibly different field-of-views, tracking is performed locally in each sensor by a tracker, which produces random finite set multi-object densities. To fuse outputs from different trackers, we adapt a recently proposed transformer-based multi-object tracker, where the fusion result is a global multi-object density, describing the set of all alive objects at the current time. We compare the performance of the transformer-based fusion method with a well-performing model-based Bayesian fusion method in several simulated scenarios with different parameter settings using synthetic data. The simulation results show that the transformer-based fusion method outperforms the model-based Bayesian method in our experimental scenarios., Comment: Accepted for publication in ICASSP 2023. Python implementation is available at https://github.com/Lechili/DeepFusion

In this paper, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking (EOT), which directly estimates the set of object trajectories, using belief propagation (BP). The proposed filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the PMB mixture (PMBM) posterior after the update step is approximated as a PMB. The efficient PMB approximation relies on several important theoretical contributions. First, we present a PMBM conjugate prior on the posterior of sets of trajectories for a generalized measurement model, in which each object generates an independent set of measurements. The PMBM density is a conjugate prior in the sense that both the prediction and the update steps preserve the PMBM form of the density. Second, we present a factor graph representation of the joint posterior of the PMBM set of trajectories and association variables for the Poisson spatial measurement model. Importantly, leveraging the PMBM conjugacy and the factor graph formulation enables an elegant treatment on undetected objects via a Poisson point process and efficient inference on sets of trajectories using BP, where the approximate marginal densities in the PMB approximation can be obtained without enumeration of different data association hypotheses. To achieve this, we present a particle-based implementation of the proposed filter, where smoothed trajectory estimates, if desired, can be obtained via single-object particle smoothing methods, and its performance for EOT with ellipsoidal shapes is evaluated in a simulation study., Comment: MATLAB implementation available at https://github.com/yuhsuansia/Trajectory-PMB-EOT-BP

This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli mixture (PMBM) filter, with point-target models. The PMBM track initiation is obtained via Bayes' rule applied on the predicted PMBM density, and creates one Bernoulli component for each received measurement, representing that this measurement may be clutter or a detection from a new target. Adaptive birth mimics this procedure by creating a Bernoulli component for each measurement using a different rule to determine the probability of existence and a user-defined single-target density. This paper first provides an analysis of the differences that arise in track initiation based on isolated measurements. Then, it shows that adaptive birth underestimates the number of objects present in the surveillance area under common modelling assumptions. Finally, we provide numerical simulations to further illustrate the differences., Comment: Matlab implementations of PMBM filters can be found at https://github.com/Agarciafernandez/MTT and https://github.com/yuhsuansia

Xinwei Xu,1,* Jintu Huang,2,* Dickson Kofi Wiredu Ocansey,1,3 Yuxuan Xia,1 Zihan Zhao,1 Zhiwei Xu,1 Yongmin Yan,1 Xu Zhang,1 Fei Mao1 1Department of Clinical Laboratory Diagnostics, Key Laboratory of Medical Science and Laboratory Medicine of Jiangsu Province, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212013, People’s Republic of China; 2Clinical Laboratory Department, The People’s Hospital of Danyang, Affiliated Danyang Hospital of Nantong University, Zhenjiang, Jiangsu, 212300, People’s Republic of China; 3Department of Clinical Laboratory Diagnostics, Directorate of University Health Services, University of Cape Coast, Cape Coast, Ghana*These authors contributed equally to this workCorrespondence: Fei MaoKey Laboratory of Medical Science and Laboratory Medicine of Jiangsu Province, School of Medicine, Jiangsu University, Zhenjiang, 212013, Jiangsu, People’s Republic of ChinaTel/Fax +86 511 8503 8215Email maofei2003@ujs.edu.cnAbstract: Methylation, first proposed in DNAs, but later found in RNAs, serves as one of the most widespread epigenetic modifications in eukaryotes, where N6-methyladenosine (m6A) modification has been found to play an important role in a variety of cancers including colorectal cancer (CRC). Under the action of various enzymes and proteins, the regulatory role of m6A in RNAs and immune cells has also been gradually realized. This paper reviews the general biogenesis and effects of m6A, and its emerging crucial role in intestinal mucosal immunity via the regulation of RNAs and immune cells, and thus closely related to the occurrence and development of inflammatory bowel disease (IBD) and CRC. m6A-related genes and regulatory factors are expected to be potential predictive markers and therapeutic targets.Keywords: N6-methyladenosine, inflammatory bowel disease, colorectal cancer, non-coding RNA, intestinal mucosal immunity

For the standard point target model with Poisson birth process, the Poisson Multi-Bernoulli Mixture (PMBM) is a conjugate multi-target density. The PMBM filter for sets of targets has been shown to have state-of-the-art performance and a structure similar to the Multiple Hypothesis Tracker (MHT). In this paper we consider a recently developed formulation of multiple target tracking as a random finite set (RFS) of trajectories, and present three important and interesting results. First, we show that, for the standard point target model, the PMBM density is conjugate also for sets of trajectories. Second, based on this we develop PMBM trackers (trajectory RFS filters) that efficiently estimate the set of trajectories. Third, we establish that for the standard point target model the multi-trajectory density is PMBM for trajectories in any time window, given measurements in any (possibly non-overlapping) time window. In addition, the PMBM trackers are evaluated in a simulation study, and shown to yield state-of-the-art performance., Comment: arXiv admin note: text overlap with arXiv:1812.05131

The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture (MBM) are two multi-target distributions for which closed-form filtering recursions exist. The PMBM has a Poisson birth process, whereas the MBM has a multi-Bernoulli birth process. This paper considers a recently developed formulation of the multi-target tracking problem using a random finite set of trajectories, through which the track continuity is explicitly established. A multi-scan trajectory PMBM filter and a multi-scan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented. In addition, a multi-scan trajectory $\text{MBM}_{01}$ filter, in which the existence probabilities of all Bernoulli components are either 0 or 1, is presented. This paper proposes an efficient implementation that performs track-oriented $N$-scan pruning to limit computational complexity, and uses dual decomposition to solve the involved multi-frame assignment problem. The performance of the presented multi-target trackers, applied with an efficient fixed-lag smoothing method, are evaluated in a simulation study., Comment: Published in Journals of Advances in Information Fusion, Special issue on Multiple Hypothesis Tracking, Volume 14, Number 2, Page 213-235, December 2019. MATLAB code is available at https://github.com/yuhsuansia/Multi-scan-trajectory-PMBM-filter

Circulating tumor cells (CTCs) experience hemodynamic shear stress in circulation and play critical roles in cancer metastasis. The effect of shear on CTCs, however, remains less studied. Here, we described a protocol to circulate HCT116 human colon cancer cells in a microfluidic circulatory system mimicking physiologically relevant circulating conditions. This protocol represents a useful scaffold to mimic the transportation of CTCs in circulation and thus provides an effective means to study the effect of shear on CTCs. We anticipate that future studies using the developed system will help us to further investigate the regulatory roles of shear in molecular responses of CTCs.