Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease

MetaLR: Layer-wise Learning Rate based on Meta-Learning for Adaptively Fine-tuning Medical Pre-trained Models

When applying transfer learning for medical image analysis, downstream tasks often have significant gaps with the pre-training tasks. Previous methods mainly focus on improving the transferabilities of the pre-trained models to bridge the gaps. In fact, model fine-tuning can also play a very important role in tackling this problem. A conventional fine-tuning method is updating all deep neural networks (DNNs) layers by a single learning rate (LR), which ignores the unique transferabilities of different layers. In this work, we explore the behaviors of different layers in the fine-tuning stage. More precisely, we first hypothesize that lower-level layers are more domain-specific while higher-level layers are more task-specific, which is verified by a simple bi-directional fine-tuning scheme. It is harder for the pre-trained specific layers to transfer to new tasks than general layers. On this basis, to make different layers better co-adapt to the downstream tasks according to their transferabilities, a meta-learning-based LR learner, namely MetaLR, is proposed to assign LRs for each layer automatically. Extensive experiments on various medical applications (i.e., POCUS, BUSI, Chest X-ray, and LiTS) well confirm our hypothesis and show the superior performance of the proposed methods to previous state-of-the-art fine-tuning methods

Aligned Metal–Organic Framework Nanoplates in Mixed‐Matrix Membranes for Highly Selective CO2/CH4 Separation

Abstract 2D metal–organic frameworks are attractive filler in mixed matrix membranes (MMMs) due to the high aspect ratio and contact opportunity at the filler–polymer interface. However, their alignment in polymer matrix remains a challenge to fully play their functions. Herein, to our best knowledge, for the first time, the facile synthesis of KAUST‐7‐NH2 (KAUST, King University of Science and Technology) nanoplate is reported with 1D channels with an aspect ratio greater than 30. The nanoplates are incorporated and aligned in the 4,4′‐(hexafluoroisopropylidene) diphthalic anhydride‐2,4‐diaminomesitylene (6FDA‐DAM) polymer matrix under the shear force with a filler loading up to 50 wt%. The large difference in adsorption abilities between CO2 and CH4 from the (001)‐oriented KAUST‐7‐NH2 nanoplate‐based MMMs and the favorable interaction at the filler–polymer interface contribute to the excellent CO2/CH4 separation performance. The resultant membranes show CO2/CH4 selectivity with 66.2% enhancement (surpassed 2008 Robeson upper bound), antiplasticization up to 17 bar, and long‐term stability up to 240 h indicating its good potential for natural gas treatment

The exceedingly strong two-dimensional ferromagnetism in bi-atomic layer SrRuO3 with a critical conduction transition

In recent years, few-layer or even monolayer ferromagnetic materials have drawn a great deal of attention due to the promising integration of two-dimensional (2D) magnets into next-generation spintronic devices. The SrRuO3 monolayer is a rare example of stable 2D magnetism under ambient conditions, but only weak ferromagnetism or antiferromagnetism has been found. The bi-atomic layer SrRuO3 as another environmentally inert 2D magnetic system has been paid less attention heretofore. Here we study both the bi-atomic layer and monolayer SrRuO3 in (SrRuO3)n/(SrTiO3)m (n = 1, 2) superlattices in which the SrTiO3 serves as a non-magnetic and insulating space layer. Although the monolayer exhibits arguably weak ferromagnetism, we find that the bi-atomic layer exhibits exceedingly strong ferromagnetism with a Tc of 125 K and a saturation magnetization of 1.2 µB/Ru, demonstrated by both superconducting quantum interference device (SQUID) magnetometry and element-specific X-ray circular dichroism. Moreover, in the bi-atomic layer SrRuO3, we demonstrate that random fluctuations and orbital reconstructions inevitably occurring in the 2D limit are critical to the electrical transport, but are much less critical to the ferromagnetism. Our study demonstrates that the bi-atomic layer SrRuO3 is an exceedingly strong 2D ferromagnetic oxide which has great potentials for applications of ultracompact spintronic devices. [Figure not available: see fulltext.]

Complex structure of the fission yeast SREBP-SCAP binding domains reveals an oligomeric organization

Sterol regulatory element-binding protein (SREBP) transcription factors are master regulators of cellular lipid homeostasis in mammals and oxygen-responsive regulators of hypoxic adaptation in fungi. SREBP C-terminus binds to the WD40 domain of SREBP cleavage-activating protein (SCAP), which confers sterol regulation by controlling the ER-to-Golgi transport of the SREBP-SCAP complex and access to the activating proteases in the Golgi. Here, we biochemically and structurally show that the carboxyl terminal domains (CTD) of Sre1 and Scp1, the fission yeast SREBP and SCAP, form a functional 4:4 oligomer and Sre1-CTD forms a dimer of dimers. The crystal structure of Sre1-CTD at 3.5 Å and cryo-EM structure of the complex at 5.4 Å together with in vitro biochemical evidence elucidate three distinct regions in Sre1-CTD required for Scp1 binding, Sre1-CTD dimerization and tetrameric formation. Finally, these structurally identified domains are validated in a cellular context, demonstrating that the proper 4:4 oligomeric complex formation is required for Sre1 activation

The evolutionary origin and domestication history of goldfish (Carassius auratus)

Goldfish have been subjected to over 1,000 y of intensive domestication and selective breeding. In this report, we describe a high-quality goldfish genome (2n = 100), anchoring 95.75% of contigs into 50 pseudochromosomes. Comparative genomics enabled us to disentangle the two subgenomes that resulted from an ancient hybridization event. Resequencing 185 representative goldfish variants and 16 wild crucian carp revealed the origin of goldfish and identified genomic regions that have been shaped by selective sweeps linked to its domestication. Our comprehensive collection of goldfish varieties enabled us to associate genetic variations with a number of well-known anatomical features, including features that distinguish traditional goldfish clades. Additionally, we identified a tyrosine-protein kinase receptor as a candidate causal gene for the first well-known case of Mendelian inheritance in goldfish-the transparent mutant. The goldfish genome and diversity data offer unique resources to make goldfish a promising model for functional genomics, as well as domestication