Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings

The classification of histopathological images is of great value in both cancer diagnosis and pathological studies. However, multiple reasons, such as variations caused by magnification factors and class imbalance, make it a challenging task where conventional methods that learn from image-label datasets perform unsatisfactorily in many cases. We observe that tumours of the same class often share common morphological patterns. To exploit this fact, we propose an approach that learns similarity-based multi-scale embeddings (SMSE) for magnification-independent histopathological image classification. In particular, a pair loss and a triplet loss are leveraged to learn similarity-based embeddings from image pairs or image triplets. The learned embeddings provide accurate measurements of similarities between images, which are regarded as a more effective form of representation for histopathological morphology than normal image features. Furthermore, in order to ensure the generated models are magnification-independent, images acquired at different magnification factors are simultaneously fed to networks during training for learning multi-scale embeddings. In addition to the SMSE, to eliminate the impact of class imbalance, instead of using the hard sample mining strategy that intuitively discards some easy samples, we introduce a new reinforced focal loss to simultaneously punish hard misclassified samples while suppressing easy well-classified samples. Experimental results show that the SMSE improves the performance for histopathological image classification tasks for both breast and liver cancers by a large margin compared to previous methods. In particular, the SMSE achieves the best performance on the BreakHis benchmark with an improvement ranging from 5% to 18% compared to previous methods using traditional features

E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking

In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible selfdocking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from inaccurate scoring functions and high inference cost. Recently, data-driven methods based on deep learning techniques are attracting growing interest thanks to their efficiency during inference and promising performance. These methods usually either adopt a two-stage approach by first predicting the distances between proteins and ligands and then generating the final coordinates based on the predicted distances, or directly predicting the global roto-translation of ligands. In this paper, we take a different route. Inspired by the resounding success of AlphaFold2 for protein structure prediction, we propose E3Bind, an end-to-end equivariant network that iteratively updates the ligand pose. E3Bind models the protein-ligand interaction through careful consideration of the geometric constraints in docking and the local context of the binding site. Experiments on standard benchmark datasets demonstrate the superior performance of our end-to-end trainable model compared to traditional and recently-proposed deep learning methods.Comment: International Conference on Learning Representations (ICLR 2023

Single-Cell RNA Sequencing of hESC-Derived 3D Retinal Organoids Reveals Novel Genes Regulating RPC Commitment in Early Human Retinogenesis.

The development of the mammalian retina is a complicated process involving the generation of distinct types of neurons from retinal progenitor cells (RPCs) in a spatiotemporal-specific manner. The progression of RPCs during retinogenesis includes RPC proliferation, cell-fate commitment, and specific neuronal differentiation. In this study, by performing single-cell RNA sequencing of cells isolated from human embryonic stem cell (hESC)-derived 3D retinal organoids, we successfully deconstructed the temporal progression of RPCs during early human retinogenesis. We identified two distinctive subtypes of RPCs with unique molecular profiles, namely multipotent RPCs and neurogenic RPCs. We found that genes related to the Notch and Wnt signaling pathways, as well as chromatin remodeling, were dynamically regulated during RPC commitment. Interestingly, our analysis identified that CCND1, a G1-phase cell-cycle regulator, was coexpressed with ASCL1 in a cell-cycle-independent manner. Temporally controlled overexpression of CCND1 in retinal organoids demonstrated a role for CCND1 in promoting early retinal neurogenesis. Together, our results revealed critical pathways and novel genes in early retinogenesis of humans

Dynamic characteristics of magnetorheological fluid lubricated journal bearing and its application to rotor vibration control

Application of smart lubricants like magnetorheological (MR) fluids is always considered to be a promising field of realizing smart bearings with semi-active controllable capability. For bearings lubricated with MR fluid, the dynamic characteristics i.e. the stiffness and damping coefficients are important, while few studies have focused on this field. The present work adopts the Herschel-Bulkley model to describe the rheological behavior of MR fluid. The shearing-thinning effect incorporated in this model leads to different result compared to that from the Bingham model. Stiffness and damping coefficients of bearings lubricated with Newtonian fluid, Bingham fluid are calculated. Calculations show that shear-thinning effect has great influences on both static and dynamic characteristics of the journal bearing. Simulations of rotordynamics of a turbo-expander rotor with different bearing properties are performed to investigate the possibility of MR fluid as lubricants to control the behavior of rotor. Results show that MR fluids are applicable to change performances of the rotor system. Vibration amplitude suppression and critical speed alteration can be achieved by MR fluids

Ecology of Yuqing County Carbon Sink Calculation and Ecosystem Protection Measures

Based on the remote sensing statistical data of land use of terrestrial ecosystems in Yuqing County, this paper calculates the amount of carbon sinks in the county according to the existing carbon sink carbon density index, compares the amount of different types of carbon sinks, and analyzes their respective carbon sink potential. The results show that the forest carbon sink is the largest, about 2.2 million tons, accounting for 75% of the total carbon sink in the county, showing the great potential of forest vegetation to absorb CO2 through photosynthesis, followed by the carbon sink produced by dry land (cultivated land), about 400,000 tons, accounting for 13% of the total carbon sink in the county; Although the amount of wetland aquatic carbon sink is small, its carbon density is very large, and it has the advantages of short renewal time and fast carbon sink, so it has great potential and can be artificially regulated to increase carbon sink. Based on the above research and analysis, combined with the spirit of the national carbon peak and carbon neutral policy and the natural law of ecosystem development, three measures to protect and increase carbon sinks in terrestrial ecosystems were put forward: (1) continuing to carry out forestry planting and do a good job in forestry protection; (2) stabilizing the surface water area and developing aquatic carbon sinks; (3) Establish a long-term monitoring system to ensure the contribution of carbon sinks, provide support for the protection of ecosystem and the development of carbon sink potential in Yuqing County from two aspects of science and management, and compare the amount of different types of carbon sinks, and analyze their carbon sink potential. On this basis, combined with the spirit of the national carbon peak and carbon neutral policy and the natural law of ecosystem development, three kinds of terrestrial ecosystem carbon sink protection and increase wording were put forward accordingly, which provided support for ecosystem protection and carbon sink potential development in Yuqing County from two aspects of science and management

Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information

Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.Comment: 7 pages, 5 figure