OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for Object-Centric Learning

Humans possess the cognitive ability to comprehend scenes in a compositional manner. To empower AI systems with similar abilities, object-centric representation learning aims to acquire representations of individual objects from visual scenes without any supervision. Although recent advancements in object-centric representation learning have achieved remarkable progress on complex synthesis datasets, there is a huge challenge for application in complex real-world scenes. One of the essential reasons is the scarcity of real-world datasets specifically tailored to object-centric representation learning methods. To solve this problem, we propose a versatile real-world dataset of tabletop scenes for object-centric learning called OCTScenes, which is meticulously designed to serve as a benchmark for comparing, evaluating and analyzing object-centric representation learning methods. OCTScenes contains 5000 tabletop scenes with a total of 15 everyday objects. Each scene is captured in 60 frames covering a 360-degree perspective. Consequently, OCTScenes is a versatile benchmark dataset that can simultaneously satisfy the evaluation of object-centric representation learning methods across static scenes, dynamic scenes, and multi-view scenes tasks. Extensive experiments of object-centric representation learning methods for static, dynamic and multi-view scenes are conducted on OCTScenes. The results demonstrate the shortcomings of state-of-the-art methods for learning meaningful representations from real-world data, despite their impressive performance on complex synthesis datasets. Furthermore, OCTScenes can serves as a catalyst for advancing existing state-of-the-art methods, inspiring them to adapt to real-world scenes. Dataset and code are available at https://huggingface.co/datasets/Yinxuan/OCTScenes

Single-cell RNA sequencing reveals distinct tumor microenvironment of ground glass nodules and solid nodules in lung adenocarcinoma

Introduction: Lung adenocarcinoma (LUAD) is the most prevalent lung cancer. LUAD presents as ground glass nodules (GGN) and solid nodules (SN) in imaging studies. GGN is an early type of LUAD with good prognosis. However, SN exhibits a more malignant behavior than GGN, including worse pathological staging and tumor prognosis. The mechanism leading to the different malignancy levels of GGN and SN remains elusive.Methods: Three patients with GGN and three patients with SN diagnosed with early LUAD were enrolled. The tumor samples were digested to a single-cell suspension and analyzed using 10× Genomic Single-cell ribonucleic acid sequences (scRNA-seq) techniques.Results: A total of 15,902 cells were obtained and classified into nine major types. The tumor microenvironment (TME) was subsequently described in detail. ScRNA-seq revealed that ribosome-related pathways and cell adhesion played similar but distinct roles in the two groups. SN also had more active cell proliferation, enriched cell cycle regulatory pathways, and severe inflammatory responses.Conclusion: We observed changes in the cellular composition and transcriptomic profile of GGN and SN. The study improved the understanding of the underlying mechanisms of lung carcinogenesis and contributed to lung cancer prevention and treatment

Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment

Chemotherapy-related cognitive impairment (CRCI) or “chemo brain” is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro–/– female mice with doxorubicin (DOX) because Ptpro–/– female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro–/– female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro–/– female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI.</p

Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment.

Chemotherapy-related cognitive impairment (CRCI) or chemo brain is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro-/- female mice with doxorubicin (DOX) because Ptpro-/- female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro-/- female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro-/- female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI

Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment

Chemotherapy-related cognitive impairment (CRCI) or “chemo brain” is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro–/– female mice with doxorubicin (DOX) because Ptpro–/– female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro–/– female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro–/– female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI.</p

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson’s correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity

9 W average power, 150 kHz repetition rate diamond Raman laser at 1519 nm, pumped by a Yb fibre amplifier

Commercially available pulsed fibre lasers at ~1.5 μm have many uses in imaging, defense, communications and light radar (LIDAR) [1]. For 3D scanning LIDAR, higher signal-to-noise ratio requires lasers with high average power and high pulse repetition rate (ideally several MHz) for faster scanning rate, whereas to improve distance resolution requires pulse durations <10 ns [2,3]. One limitation of the pulsed fibre lasers at ~1.5 μm is scaling to high average powers [4]. Raman frequency conversion of high average power fibre master oscillator power amplifier (MOPA) systems at ~1 μm is a potential alternative. The large Raman shift and Raman gain of diamond allows two-stage Raman conversion to ~1.5 μm for ~1 μm pumping [5]. Excellent thermal properties make diamond suitable for high average powers [6]. Much work has been done on conversion of 1.064 μm lasers to 1.485 μm using diamond [7]; however, the “eye-safety” requirements for LIDAR typically call for wavelengths above 1.5 μm, due to the order of magnitude higher Maximum Permissible Exposure limit [8]. Developing such a diamond Raman laser (DRL) was the major motivation for this research

Experimental and Numerical Investigation on Seismic Performance of RC Exterior Beam-Column Joints with Slabs

The effect of a cast-in-place slab on the beam-column joint at beam ends in reinforced concrete (RC) structures under earthquake attacks has not been fully understood, and therefore, it is not manifestly addressed to some of the design criteria or specifications. Consequently, the contribution of slab to the seismic resistance of structures is often ignored or just included in an approximate manner. In this study, the experiment of two 1/2-scaled exterior beam-column joints without slabs and three 1/2-scaled joints with slabs under the combination of quasi-static repeated cyclic loading and constant axial force was carried out to investigate the effect of the cast-in-place slab on the seismic performance of exterior beam-column joints. The results show that for specimens EBCJ1 and EBCJ2 without slab, the decline was approximately 5%, while for specimens EBCSJ1 and EBCSJ3, the decline in the load is obvious and approximately more than 10%. For specimen EBCSJ2, which exhibited a slightly different behavior in the hysteresis curves, the maximum carrying capacity reached at the displacement of 70 mm during the first cycle. The cast-in-place slab has different effects on the failure mechanism and load transfer mechanism of the exterior beam-column joint, which depends on the column-beam moment strength ratio in the loading protocol. The slab has a positive effect on the energy dissipation capacity of the joint but has a negative effect on the load carrying capacity. In addition, finite element (FE) analysis of the tested specimens was conducted. The FE numerical models were established based on the construction information and loading conditions from the experiment and then validated by comparing them with the experimental observation

Characteristics and Current Harmonic Control of N* Three-Phase PMSG for HVDC Transmission Based on MMC

The permanent magnet synchronous generator (PMSG) is widely used in high voltage DC transmission technology of wind power systems due to its high-power density, high reliability and simple maintenance. To meet the increasing rated power of the generator (&ge;10 MW), the multi-converter parallel connection is usually used, which significantly increases the complexity and cost of the system. Therefore, a new modular N* three-phase PMSG is proposed in this paper. Based on the structure and working principle of the generator, the finite element model is established, and the electromagnetic properties are obtained by finite element analysis. Aiming at the unique winding structure and characteristics of modular N* three-phase PMSG, a generator side current converter harmonic control algorithm, combining a resonant controller with the proportional-integral regulator, is proposed. The suppression of stator current harmonics at different speeds could be achieved by the proposed algorithm, and the efficiency of the wind power generation system can be improved. Finally, the feasibility of the novel generator and the effectiveness of the proposed algorithm are verified by simulation and experiment