Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer

Recently, 3D input data based hand pose estimation methods have shown state-of-the-art performance, because 3D data capture more spatial information than the depth image. Whereas 3D voxel-based methods need a large amount of memory, PointNet based methods need tedious preprocessing steps such as K-nearest neighbour search for each point. In this paper, we present a novel deep learning hand pose estimation method for an unordered point cloud. Our method takes 1024 3D points as input and does not require additional information. We use Permutation Equivariant Layer (PEL) as the basic element, where a residual network version of PEL is proposed for the hand pose estimation task. Furthermore, we propose a voting based scheme to merge information from individual points to the final pose output. In addition to the pose estimation task, the voting-based scheme can also provide point cloud segmentation result without ground-truth for segmentation. We evaluate our method on both NYU dataset and the Hands2017Challenge dataset. Our method outperforms recent state-of-the-art methods, where our pose accuracy is currently the best for the Hands2017Challenge dataset

Hand Pose-based Task Learning from Visual Observations with Semantic Skill Extraction

Learning from Demonstrations is a promising technique to transfer task knowledge from a user to a robot. We propose a framework for task programming by observing the human hand pose and object locations solely with a depth camera. By extracting skills from the demonstrations, we are able to represent what the robot has learned, generalize to unseen object locations and optimize the robotic execution instead of replaying a non-optimal behavior. A two-staged segmentation algorithm that employs skill template matching via Hidden Markov Models has been developed to extract motion primitives from the demonstration and gives them semantic meanings. In this way, the transfer of task knowledge has been improved from a simple replay of the demonstration towards a semantically annotated, optimized and generalized execution. We evaluated the extraction of a set of skills in simulation and prove that the task execution can be optimized by such means

Whole transcriptome analysis reveals that immune infiltration- lncRNAs are related to cellular apoptosis in liver transplantation

IntroductionIn most instances, liver transplantation (LT) is the only available treatment for end‐stage liver diseases. However, LT could also induce serious liver diseases or injury, and the underlying mechanisms of LT-induced complications remain largely unknown, especially the mechanisms of the dysfunction of the immune system mediated by long noncoding RNAs (lncRNAs).MethodsIn this study, we globally analyzed the proportion of immune cells by using the transcriptome sequencing data (RNA-seq) of needle-core liver biopsies from pre- and post-transplantation recipients. Dysregulated lncRNAs were found to be correlated with the altered fractions of immune cells. We finally explored the potential targets of dysregulated lncRNAs and analyzed their functions in LT.ResultsWe found that in the samples, some immune cells changed significantly after LT, including CD4 T cells, NK cells and mast cells. The proportion of macrophages in different polarization states also changed significantly, with M0 macrophages increasing and M2 macrophages decreasing. Through weighted gene co-expression network analysis (WGCNA), 7 gene expression modules related to LT were identified. These modules were related to changes in the proportion of different immune cells. The functions of these modules represent the response modes of different functional genes after LT. Among these modules, MEtan and MEyellow modules were primarily enriched in apoptosis and inflammatory pathways. Twelve immunity-related lncRNAs were identified for the first time, and the regulatory network co-changing with immune cells was also identified. The co-expressed genes of these lncRNAs were highly enriched in apoptosis-related pathways. Many apoptosis-related genes were found to be up-regulated after LT.DiscussionIn summary, we speculated that the expression and regulation of these apoptotic genes may be related to the changes in the proportion of immune cells. Some of these lncRNAs and apoptosis-related genes have been reported to be related to cell proliferation and apoptosis. They are also potential biomarkers or therapeutic targets

Flux regulation through glycolysis and respiration is balanced by inositol pyrophosphates in yeast

Although many prokaryotes have glycolysis alternatives, it\u27s considered as the only energy-generating glucose catabolic pathway in eukaryotes. Here, we managed to create a hybrid-glycolysis yeast. Subsequently, we identified an inositol pyrophosphatase encoded by OCA5 that could regulate glycolysis and respiration by adjusting 5-diphosphoinositol 1,2,3,4,6-pentakisphosphate (5-InsP7) levels. 5-InsP7 levels could regulate the expression of genes involved in glycolysis and respiration, representing a global mechanism that could sense ATP levels and regulate central carbon metabolism. The hybrid-glycolysis yeast did not produce ethanol during growth under excess glucose and could produce 2.68 g/L free fatty acids, which is the highest reported production in shake flask of Saccharomyces cerevisiae. This study demonstrated the significance of hybrid-glycolysis yeast and determined Oca5 as an inositol pyrophosphatase controlling the balance between glycolysis and respiration, which may shed light on the role of inositol pyrophosphates in regulating eukaryotic metabolism

Comprehensive genomic profiling of cell-free circulating tumor DNA detects response to Ribociclib plus Letrozole in a patient with metastatic breast cancer

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Analysis of cell-free circulating tumor DNA obtained by liquid biopsy is a non-invasive approach that may provide clinically actionable information when conventional tissue biopsy is inaccessible or infeasible. Here, we followed a patient with hormone receptor-positive and human epidermal growth factor receptor (HER) 2-negative breast cancer who developed bone metastases seven years after mastectomy. We analyzed circulating cell-free DNA (cfDNA) extracted from plasma using high-depth massively parallel sequencing targeting 468 cancer-associated genes, and we identified a clonal hotspot missense mutation in the PIK3CA gene (3:178952085, A > G, H1047R) and amplification of the CCND1 gene. Whole-exome sequencing revealed that both alterations were present in the primary tumor. After treatment with ribociclib plus letrozole, the genetic abnormalities were no longer detected in cfDNA. These results underscore the clinical utility of combining liquid biopsy and comprehensive genomic profiling to monitor treatment response in patients with metastasized breast cancer.This work was supported by Fundação para a Ciência e Tecnologia (FCT)/Ministério da Ciência, Tecnologia e Ensino Superior, Portugal, through Fundos do Orçamento de Estado to Instituto de Medicina Molecular João Lobo Antunes (LA/P/0082/2020), and FCT/FEDER/POR Lisboa 2020, Programa Operacional Regional de Lisboa, PORTUGAL 2020 (LISBOA-01-0145-FEDER-016394), and FEDER/POR Lisboa 2020-Programa Operacional Regional de Lisboa, PORTUGAL 2020 (Infogene, 045300). C.S. was a recipient of a FCT fellowship (SFRH/BDE/110544/2015). This work was funded in part by the National Institutes of Health (NIH)/National Cancer Institute (NCI) Cancer Center Support Grant (P30 CA008748; MSK). J.S.R-F. and B.W. are funded in part by the NIH/NCI P50 CA247749 01 grant and a Breast Cancer Research Foundation grant. J.S.R.-F. is also funded in part by a Susan G Komen leadership grant, and B.W. by a Cycle for Survival grant.info:eu-repo/semantics/publishedVersio

Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development

© The Author(s) 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.This work was financed by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences - UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy - i4HB. This research was also funded by: PTDC/MED-ONC/28660/2017 from Fundação para a Ciência e Tecnologia (FCT) to A.R.G. A.R.G is recipient of Researcher Grant CEECIND/02699/2017 from FCT. The biobanking of CRC samples from Hospital Santa Maria, Lisbon, Portugal was supported by FCT research grant PIC/IC/82821/2007. This work was produced with the support of INCD funded by FCT and FEDER under the project 22153-01/SAICT/2016.info:eu-repo/semantics/publishedVersio

RGB-D SLAM in Dynamic Environments using Static Point Weighting

We propose a real-time depth edge based RGB-D SLAM system for dynamic environment. Our visual odometry method is based on frame-to-keyframe registration, where only depth edge points are used. To reduce the influence of dynamic objects, we propose a static weighting method for edge points in the keyframe. Static weight indicates the likelihood of one point being part of the static environment. This static weight is added into the Intensity Assisted Iterative Closest Point (IAICP) method to perform the registration task. Furthermore, our method is integrated into a SLAM (Simultaneous Localization and Mapping) system, where an efficient loop closure detection strategy is used. Both our visual odometry method and SLAM system are evaluated with challenging dynamic sequences from the TUM RGB-D dataset. Compared to state-of-the-art methods for dynamic environment, our method reduces the tracking error significantly

Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer

Recently, 3D input data based hand pose estimation methods have shown state-of-the-art performance, because 3D data capture more spatial information than the depth image. Whereas 3D voxel-based methods need a large amount of memory, PointNet based methods need tedious preprocessing steps such as K-nearest neighbour search for each point. In this paper, we present a novel deep learning hand pose estimation method for an unordered point cloud. Our method takes 1024 3D points as input and does not require additional information. We use Permutation Equivariant Layer (PEL) as the basic element, where a residual network version of PEL is proposed for the hand pose estimation task. Furthermore, we propose a voting-based scheme to merge information from individual points to the final pose output. In addition to the pose estimation task, the votingbased scheme can also provide point cloud segmentation result without ground-truth for segmentation. We evaluate our method on both NYU dataset and the Hands2017Challenge dataset, where our method outperforms recent state-of-theart methods