Training-Free Instance Segmentation from Semantic Image Segmentation Masks

In recent years, the development of instance segmentation has garnered significant attention in a wide range of applications. However, the training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods (i.e., with image-level class labels or point labels) struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm for instance segmentation called training-free instance segmentation (TFISeg), which achieves instance segmentation results from image masks predicted using off-the-shelf semantic segmentation models. TFISeg does not require training a semantic or/and instance segmentation model and avoids the need for instance-level image annotations. Therefore, it is highly efficient. Specifically, we first obtain a semantic segmentation mask of the input image via a trained semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, instance segmentation results are obtained after being refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets and representative semantic segmentation baselines (including CNNs and Transformers) demonstrate that TFISeg can achieve competitive results compared to the state-of-the-art fully-supervised instance segmentation methods without the need for additional human resources or increased computational costs. The code is available at: TFISegComment: 14 pages,5 figure

New insights for the design of bionic robots:adaptive motion adjustment strategies during feline landings

Felines have significant advantages in terms of sports energy efficiency and flexibility compared with other animals, especially in terms of jumping and landing. The biomechanical characteristics of a feline (cat) landing from different heights can provide new insights into bionic robot design based on research results and the needs of bionic engineering. The purpose of this work was to investigate the adaptive motion adjustment strategy of the cat landing using a machine learning algorithm and finite element analysis (FEA). In a bionic robot, there are considerations in the design of the mechanical legs. (1) The coordination mechanism of each joint should be adjusted intelligently according to the force at the bottom of each mechanical leg. Specifically, with the increase in force at the bottom of the mechanical leg, the main joint bearing the impact load gradually shifts from the distal joint to the proximal joint; (2) the hardness of the materials located around the center of each joint of the bionic mechanical leg should be strengthened to increase service life; (3) the center of gravity of the robot should be lowered and the robot posture should be kept forward as far as possible to reduce machine wear and improve robot operational accuracy

{(1R,2R)-N,N′-Bis[2-(N-methyl­anilino)benzyl­idene]cyclo­hexane-1,2-diamine-κ2 N,N′}dichloridoiron(II)

In the title compound, [FeCl2(C34H36N4)], the FeII ion is coordinated by two Cl atoms and by two N atoms from a (1R,2R)-N,N′-bis[2-(N-methyl­anilino)benzyl­idene]cyclo­hexane-1,2-diamine ligand in a distorted tetra­hedral geometry. The mol­ecule has approximate C 2 point symmetry. The dihedral angles between the phenyl and benzene rings on either side of the ligand are 64.56 (14) and 65.61 (13)°

Clinical analysis of 82 cases of acute promyelocytic leukemia with PML-RARα short isoform in children and adults

BackgroundAcute promyelocytic leukemia (APL) with PML/RARα fusion gene is a distinct variant of acute myeloid leukemia. According to the different break sites of the PML gene, there are three transcripts: Long (bcr1), Variant (bcr2) and Short (bcr3).MethodsWe retrospectively analyzed 82 APL cases with PML-RARα short isoform.ResultsA total of 384 patients with APL were seen, of which 85(22.14%) had PML/RARα short isoform (bcr3) and 82 met the inclusion criteria. The median age was 33.5 years (range, 2-72 years). The incidences of hemorrhage in the intermediate- and high-risk group were higher, but only the incidence between medium and low risk differed statistically (P=0.006), and the incidences of fever, fatigue, splenomegaly, and lymph node enlargement and differentiation syndrome (DS) in those groups were not statistically significant (P>0.05). FLT3 gene mutation rate and the mortality rate of the high-risk group were significantly higher than that of other groups (P=0.040 and P=0.004, P=0.041 and P=0.037, respectively). The mortality rate was lowest (4.26%) in the group treated with ATRA combined with arsenic and anthracycline. The 3-year OS and the 3-year DFS of the low and intermediate-risk group were better (P=0.019 and P=0.017, respectively).ConclusionsATRA combined with arsenic and anthracycline had significant impact on outcomes in APL with PML-RARα short isoform

A new method proposed to explore the feline's paw bones of contributing most to landing pattern recognition when landed under different constraints

Felines are generally acknowledged to have natural athletic ability, especially in jumping and landing. The adage “felines have nine lives” seems applicable when we consider its ability to land safely from heights. Traditional post-processing of finite element analysis (FEA) is usually based on stress distribution trend and maximum stress values, which is often related to the smoothness and morphological characteristics of the finite element model and cannot be used to comprehensively and deeply explore the mechanical mechanism of the bone. Machine learning methods that focus on feature pattern variable analysis have been gradually applied in the field of biomechanics. Therefore, this study investigated the cat forelimb biomechanical characteristics when landing from different heights using FEA and feature engineering techniques for post-processing of FEA. The results suggested that the stress distribution feature of the second, fourth metacarpal, the second, third proximal phalanx are the features that contribute most to landing pattern recognition when cats landed under different constraints. With increments in landing altitude, the variations in landing pattern differences may be a response of the cat's forelimb by adjusting the musculoskeletal structure to reduce the risk of injury with a more optimal landing strategy. The combination of feature engineering techniques can effectively identify the bone's features that contribute most to pattern recognition under different constraints, which is conducive to the grasp of the optimal feature that can reveal intrinsic properties in the field of biomechanics

Reciprocal genomic evolution in the ant-fungus agricultural symbiosis

The attine ant–fungus agricultural symbiosis evolved over tens of millions of years, producing complex societies with industrial-scale farming analogous to that of humans. Here we document reciprocal shifts in the genomes and transcriptomes of seven fungus-farming ant species and their fungal cultivars. We show that ant subsistence farming probably originated in the early Tertiary (55–60 MYA), followed by further transitions to the farming of fully domesticated cultivars and leaf-cutting, both arising earlier than previously estimated. Evolutionary modifications in the ants include unprecedented rates of genome-wide structural rearrangement, early loss of arginine biosynthesis and positive selection on chitinase pathways. Modifications of fungal cultivars include loss of a key ligninase domain, changes in chitin synthesis and a reduction in carbohydrate-degrading enzymes as the ants gradually transitioned to functional herbivory. In contrast to human farming, increasing dependence on a single cultivar lineage appears to have been essential to the origin of industrial-scale ant agriculture