See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data

Zero-shot point cloud segmentation aims to make deep models capable of recognizing novel objects in point cloud that are unseen in the training phase. Recent trends favor the pipeline which transfers knowledge from seen classes with labels to unseen classes without labels. They typically align visual features with semantic features obtained from word embedding by the supervision of seen classes' annotations. However, point cloud contains limited information to fully match with semantic features. In fact, the rich appearance information of images is a natural complement to the textureless point cloud, which is not well explored in previous literature. Motivated by this, we propose a novel multi-modal zero-shot learning method to better utilize the complementary information of point clouds and images for more accurate visual-semantic alignment. Extensive experiments are performed in two popular benchmarks, i.e., SemanticKITTI and nuScenes, and our method outperforms current SOTA methods with 52% and 49% improvement on average for unseen class mIoU, respectively.Comment: Accepted by ICCV 202

Enable Language Models to Implicitly Learn Self-Improvement From Data

Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that there is always room for improvement in the quality of model responses. To address this challenge, various approaches have been proposed to enhance the performance of LLMs. There has been a growing focus on enabling LLMs to self-improve their response quality, thereby reducing the reliance on extensive human annotation efforts for collecting diverse and high-quality training data. Recently, prompting-based methods have been widely explored among self-improvement methods owing to their effectiveness, efficiency, and convenience. However, those methods usually require explicitly and thoroughly written rubrics as inputs to LLMs. It is expensive and challenging to manually derive and provide all necessary rubrics with a real-world complex goal for improvement (e.g., being more helpful and less harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework that implicitly learns the improvement goal from human preference data. PIT only requires preference data that are used to train reward models without extra human efforts. Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response. In this way, PIT is implicitly trained with the improvement goal of better aligning with human preferences. Experiments on two real-world datasets and one synthetic dataset show that our method significantly outperforms prompting-based methods.Comment: 28 pages, 5 figures, 4 table

Association between endometrial blood and clinical outcome in frozen single blastocyst transfer cycles

Background: The success of embryo transfer cycle depends mainly on the quality of embryo and endometrial receptivity. Ultrasound examination is still the most widely used non-invasive evaluation method for its advantages of convenience, non-invasiveness and repeatability. Ultrasound-measured endometrial blood flow is one of the important evaluation indicators of morphology.Aims: To investigate the effect of the number of endometrial blood flow branches on pregnancy outcome of frozen-thawed embryo transfer cycles which have undergoing hormone replacement therapy (HRT-FET).Material and methods: A retrospective cohort study was performed looking at a total of 1390 HRT-FET cycles from our reproductive medicine center between January 2017 to December 2021, which transferred one blastocyst frozen on day 5 with good quality in morphology. Associations between endometrial blood flow branches and pregnancy outcomes were evaluated with multivariable linear regression analysis.Results: The number of endometrial blood flow branches was independently associated with clinical pregnancy (OR 1.10; 95% CI 1.02–1.20). After adjusting for potential confounders, the effect size (odds ratio) was 1.09 (95% CI 1.00–1.19), and the results showed that the clinical pregnancy rate and live birth rate of T2 and T3 groups were significantly higher than those in group T1 (p < 0.05). Subgroup analysis showed that a consistent association between the endometrial blood flow branches and clinical pregnancy in all subgroups.Conclusion: Our study provided evidence for the influence of endometrial blood flow on pregnancy outcomes. There may be an independent association between the number of endometrial blood flow branches and pregnancy outcomes in frozen-thawed single blastocyst transfer cycles

Fast transitions of X-ray variability in the black hole transient GX 339--4: comparison with MAXI J1820+070 and MAXI J1348-630

Fast transitions between different types of power density spectra (PDS) happening over timescales of several tens of seconds are rare phenomena in black hole X-ray binaries. In this paper, we report a broadband spectral-timing analysis of the fast transitions observed in the 2021 outburst of GX 339-4 using NICER and HXMT observations. We observe transitions between band-limited noise-dominated PDS and type-B quasi-periodic oscillations (QPOs), and their rapid appearance or disappearance. We also make a detailed comparison between the fast transitions in GX 339-4 with those seen in MAXI J1820+070 and MAXI J1348--630. By comparing the spectra of the periods with and without type-B QPOs, we find that the spectral ratios above 10 keV are nearly constant or slightly decreasing, and the values are different between sources. Below 10 keV, the flux change of the Comptonization component is inversely proportional to the flux change of the thermal component, suggesting that the appearance of type-B QPOs is associated with a redistribution of the accretion power between the disc and the Comptonizing emission region. The spectral ratios between the periods with type-B QPO and those with broadband noise are significantly different from that with type-B QPO and without type-B QPO, where the ratios (type-B QPO/broadband noise) show a maximum at around 4 keV and then decrease gradually towards high energies. Finally, we discuss the possible change of the geometry of the inner accretion flow and/or jet during the transitions

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Raffinose degradation-related gene GhAGAL3 was screened out responding to salinity stress through expression patterns of GhAGALs family genes

A-galactosidases (AGALs), the oligosaccharide (RFO) catabolic genes of the raffinose family, play crucial roles in plant growth and development and in adversity stress. They can break down the non-reducing terminal galactose residues of glycolipids and sugar chains. In this study, the whole genome of AGALs was analyzed. Bioinformatics analysis was conducted to analyze members of the AGAL family in Gossypium hirsutum, Gossypium arboreum, Gossypium barbadense, and Gossypium raimondii. Meanwhile, RT-qPCR was carried out to analyze the expression patterns of AGAL family members in different tissues of terrestrial cotton. It was found that a series of environmental factors stimulated the expression of the GhAGAL3 gene. The function of GhAGAL3 was verified through virus-induced gene silencing (VIGS). As a result, GhAGAL3 gene silencing resulted in milder wilting of seedlings than the controls, and a significant increase in the raffinose content in cotton, indicating that GhAGAL3 responded to NaCl stress. The increase in raffinose content improved the tolerance of cotton. Findings in this study lay an important foundation for further research on the role of the GhAGAL3 gene family in the molecular mechanism of abiotic stress resistance in cotton