Bayesian inference and neural estimation of acoustic wave propagation

In this work, we introduce a novel framework which combines physics and machine learning methods to analyse acoustic signals. Three methods are developed for this task: a Bayesian inference approach for inferring the spectral acoustics characteristics, a neural-physical model which equips a neural network with forward and backward physical losses, and the non-linear least squares approach which serves as benchmark. The inferred propagation coefficient leads to the room impulse response (RIR) quantity which can be used for relocalisation with uncertainty. The simplicity and efficiency of this framework is empirically validated on simulated data

An approach for smooth trajectory planning of high-speed pick-and-place parallel robots using quintic B-splines

This paper presents a new, highly effective approach for optimal smooth trajectory planning of high-speed pick-and-place parallel robots. The pick-and-place path is decomposed into two orthogonal coordinate axes in the Cartesian space and quintic B-spline curves are used to generate the motion profile along each axis for achieving C4-continuity. By using symmetrical properties of the geometric path defined, the proposed motion profile becomes essentially dominated by two key factors, representing the ratios of the time intervals for the end-effector to move from the initial point to the adjacent virtual and/or the via-points on the path. These two factors can then be determined by maximizing a weighted sum of two normalized single-objective functions and expressed by curve fitting as functions of the width/height ratio of the pick-and-place path, so allowing them to be stored in a look-up table to enable real-time implementation. Experimental results on a 4-DOF SCARA type parallel robot show that the residual vibration of the end-effector can be substantially reduced thanks to the very continuous and smooth joint torques obtained

Imunozaštitno djelovanje rekombinantne vanjske membrane proteina LolA od bakterije Actinobacillus pleuropneumoniae u miševa

Porcine contagious pleuropneumonia (PCP) is a serious respiratory disease in the pig industry. In the present study, candidate vaccines with broad-spectrum and disease-resistant effects were screened. The App lolA gene was amplified by PCR from the Actinobacillus pleuropneumoniae (App) serotype 1 genome. The bioinformatics analysis results revealed that the App lolA consisted of 633 bp, encoding 210 amino acid proteins. In addition, the App LolA shared a high sequence identity among the different App serotypes. In this study, the App lolA was inserted into pET32a (+). The recombinant protein LolA (rLolA) was produced in E. coli BL21 and then determined by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and a Western Blot process. Immune protective efficacy testing of the rLolA was performed in 4-week old BALA/c mice. The serum IgG levels were assayed using an enzyme-linked immune sorbent assay (ELISA) method. The results showed that the serum IgG levels of the mice in the experimental group were significantly higher than those in the Frund incompleted adjuvant (IFA) and saline immunization groups after the third immunization. In particular, significant differences in the high dose group (50 rLolA + IFA) (P<0.01). 50 μg rLolA +IFA, 30 μg rLolA + IFA, 30 μg rLolA and 10 μg rLolA+ IFA treatments had induced 40%, 20%, 20%, 10% and 10% survival rates respectively. However, no mice had survived among the adjuvant group and saline groups. The 50 μg rLolA +IFA group displayed reductions in lung lesions. Therefore, it was ascertained from these results that the rLolA had induced partial immune protection in the experimental mice against App challenge infections. This study successfully accumulated data valuable for the future exploration of new vaccines for porcine contagious pleuropneumonia.zarazna pleuropneumonija svinja (PCP) teška je respiratorna bolest u svinjogojskoj industriji. U ovom su istraživanju analizirana kandidatna cjepiva širokog spektra i njihova učinkovitost u otpornosti na tu bolest. Gen App lolA umnožen je PCr-om od genoma serotipa 1 Actinobacillus pleuropneumoniae (App). rezultati bioinformatičke analize pokazali su da se App lolA sastoji od 633 bp, kodirajući 210 aminokiselinskih proteina. Također, App LolA dijeli visoki stupanj prepoznatljivosti sekvenci među različitim serotipovima App. U ovom je istraživanju App lolA umetnut u pET32a (+) plazmid. rekombinantni protein LolA rLolA je proizveden u E. coli BL21 i zatim određen poliakrilamidnom (SDS-PAGE) gel-elektroforezom i western blot metodom. Testiranje imunozaštitnog djelovanja rLolA provedeno je u miševa BALA/c starih četiri tjedna. Serumske razine IgG analizirane su primjenom metode ELISA. rezultati su pokazali znakovito više serumske razine IgG-a u miševa u eksperimentalnoj skupini od onih u skupini s adjuvansom (IFA) i skupini sa soli nakon treće imunizacije. navedeno posebno vrijedi za znakovite razlike u skupini sa visokim dozama (50 rLolA + IFA) (P < 0,01).Stope preživljavanja po skupinama iznosile su 40 % (50 μg rLolA + IFA), 20 % (30 μg rLolA + IFA), 20 % (30 μg rLolA) and 10 % (10 μg rLolA+ IFA). U skupinama u kojima je imunizacija provedena s adjuvansom i solju nije bilo preživjelih miševa. Skupina 50 μg rLolA + IFA pokazala je smanjenje plućnih lezija. rezultati ovoga istraživanja upućuju da je rLolA izazvao djelomičnu imunosnu zaštitu od infekcija izazvanih App-om u pokusnih miševa i kao takvi daju doprinos za buduća istraživanja cjepiva protiv zarazne pleuropneumonije svinja

Decoupled Diffusion Models with Explicit Transition Probability

Recent diffusion probabilistic models (DPMs) have shown remarkable abilities of generated content, however, they often suffer from complex forward processes, resulting in inefficient solutions for the reversed process and prolonged sampling times. In this paper, we aim to address the aforementioned challenges by focusing on the diffusion process itself that we propose to decouple the intricate diffusion process into two comparatively simpler process to improve the generative efficacy and speed. In particular, we present a novel diffusion paradigm named DDM (Decoupled Diffusion Models) based on the Ito diffusion process, in which the image distribution is approximated by an explicit transition probability while the noise path is controlled by the standard Wiener process. We find that decoupling the diffusion process reduces the learning difficulty and the explicit transition probability improves the generative speed significantly. We prove a new training objective for DPM, which enables the model to learn to predict the noise and image components separately. Moreover, given the novel forward diffusion equation, we derive the reverse denoising formula of DDM that naturally supports fewer steps of generation without ordinary differential equation (ODE) based accelerators. Our experiments demonstrate that DDM outperforms previous DPMs by a large margin in fewer function evaluations setting and gets comparable performances in long function evaluations setting. We also show that our framework can be applied to image-conditioned generation and high-resolution image synthesis, and that it can generate high-quality images with only 10 function evaluations

Whole-Body Lesion Segmentation in 18F-FDG PET/CT

There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers. Recent advances in the medical image segmentation shows the nnUNET is feasible for diverse tasks. However, lesion segmentation in the PET images is not straightforward, because lesion and physiological uptake has similar distribution patterns. The Distinction of them requires extra structural information in the CT images. The present paper introduces a nnUNet based method for the lesion segmentation task. The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. It allows for automated segmentation of potential lesions. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume

Semi-Supervised and Long-Tailed Object Detection with CascadeMatch

This paper focuses on long-tailed object detection in the semi-supervised learning setting, which poses realistic challenges, but has rarely been studied in the literature. We propose a novel pseudo-labeling-based detector called CascadeMatch. Our detector features a cascade network architecture, which has multi-stage detection heads with progressive confidence thresholds. To avoid manually tuning the thresholds, we design a new adaptive pseudo-label mining mechanism to automatically identify suitable values from data. To mitigate confirmation bias, where a model is negatively reinforced by incorrect pseudo-labels produced by itself, each detection head is trained by the ensemble pseudo-labels of all detection heads. Experiments on two long-tailed datasets, i.e., LVIS and COCO-LT, demonstrate that CascadeMatch surpasses existing state-of-the-art semi-supervised approaches -- across a wide range of detection architectures -- in handling long-tailed object detection. For instance, CascadeMatch outperforms Unbiased Teacher by 1.9 AP Fix on LVIS when using a ResNet50-based Cascade R-CNN structure, and by 1.7 AP Fix when using Sparse R-CNN with a Transformer encoder. We also show that CascadeMatch can even handle the challenging sparsely annotated object detection problem.Comment: International Journal of Computer Vision (IJCV), 202

Anorectal malformation associated with a mutation in the P63 gene in a family with split hand–foot malformation

PURPOSE: The aims of this study were to identify the mutation gene of a Chinese family with anorectal malformation (ARM) associated with split hand–foot malformation and to determine the spatiotemporal expression of the mutated gene during hindgut and anorectum development in human embryos. METHOD: A Chinese family with intrafamilial clinically variable manifestation was analyzed and primers were designed for exons 3–14 of P63, DLX5, DLX6, DAC, and HOXD13 as candidate genes and direct sequence analysis of the exons was performed. Immunohistochemical study of mutated gene in the hindgut and anorectum of human embryos of 4th–10th weeks was performed. RESULT: Affected individuals were found to have an Arg227Gln P63 gene mutation. From the 4th–10th weeks of gestation of the human embryo, the P63-positive cells were mainly located on the epithelium of the apical urorectal septum, hindgut, and cloacal membrane. After the anorectum ruptured during the 8th week, the P63 remained strongly immunoreactive on the epithelium of the anal canal and urethra, but the mucous membrane of the rectum exhibited no reaction. CONCLUSIONS: The mutation identified strongly suggests a causal relationship between the ARM phenotype and P63. The expression of P63 was persistently active during the dynamic and incessant septation of the cloaca and hindgut, suggesting that P63 may play a pivotal role in the morphogenesis of the hindgut and anorectum