Microcodium in Chinese loess as a recorder for the oxygen isotopic composition of monsoonal rainwater

Records of Asia Summer Monsoon (ASM) from the Chinese loess and the speleothem display distinct features. The very different proxies that were applied to the two archives may be responsible for this discrepancy. A direct comparison between the speleothem and the loess records under the same proxy system of rainwater delta O-18 may help to resolve this puzzle. Here we show that the calcified microcodium in the loess deposits may record the oxygen isotopic composition of the summer rainwater. A microcodium based delta O-18 record covering the past 140 kyrs was generated, which shows similar magnitude of the overall variation to that of the speleothem records. However, much weaker precession variability was registered in the microcodium record during the last interglacial period. Instead, the microcodium delta O-18 record is more consistent with the widely used summer monsoon proxy of magnetic susceptibility in the loess deposits with clear glacial-interglacial pattern. This similarity may originate from the low sedimentation rate of the interglacial paleosol layer that preferentially record the peak ASM signals on the precession band. It is also possible that the orbital variability of ASM between the North China and South China is inherently different with more ice-volume related influence in the north. A longer microcodium delta O-18 record in sequences of higher sedimentation rate and a reliable record of summer rainfall may help to resolve these possibilities. (C) 2017 Elsevier Ltd and INQUA. All rights reserved

Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization

It is well-known that stochastic gradient noise (SGN) acts as implicit regularization for deep learning and is essentially important for both optimization and generalization of deep networks. Some works attempted to artificially simulate SGN by injecting random noise to improve deep learning. However, it turned out that the injected simple random noise cannot work as well as SGN, which is anisotropic and parameter-dependent. For simulating SGN at low computational costs and without changing the learning rate or batch size, we propose the Positive-Negative Momentum (PNM) approach that is a powerful alternative to conventional Momentum in classic optimizers. The introduced PNM method maintains two approximate independent momentum terms. Then, we can control the magnitude of SGN explicitly by adjusting the momentum difference. We theoretically prove the convergence guarantee and the generalization advantage of PNM over Stochastic Gradient Descent (SGD). By incorporating PNM into the two conventional optimizers, SGD with Momentum and Adam, our extensive experiments empirically verified the significant advantage of the PNM-based variants over the corresponding conventional Momentum-based optimizers.Comment: ICML 2021; 20 pages; 13 figures; Key Words: deep learning theory, optimizer, momentum, generalization, gradient nois

An Excitatory Circuit in the Perioculomotor Midbrain for Non-REM Sleep Control.

The perioculomotor (pIII) region of the midbrain was postulated as a sleep-regulating center in the 1890s but largely neglected in subsequent studies. Using activity-dependent labeling and gene expression profiling, we identified pIII neurons that promote non-rapid eye movement (NREM) sleep. Optrode recording showed that pIII glutamatergic neurons expressing calcitonin gene-related peptide alpha (CALCA) are NREM-sleep active; optogenetic and chemogenetic activation/inactivation showed that they strongly promote NREM sleep. Within the pIII region, CALCA neurons form reciprocal connections with another population of glutamatergic neurons that express the peptide cholecystokinin (CCK). Activation of CCK neurons also promoted NREM sleep. Both CALCA and CCK neurons project rostrally to the preoptic hypothalamus, whereas CALCA neurons also project caudally to the posterior ventromedial medulla. Activation of each projection increased NREM sleep. Together, these findings point to the pIII region as an excitatory sleep center where different subsets of glutamatergic neurons promote NREM sleep through both local reciprocal connections and long-range projections

HSC-GPT: A Large Language Model for Human Settlements Construction

The field of human settlement construction encompasses a range of spatial designs and management tasks, including urban planning and landscape architecture design. These tasks involve a plethora of instructions and descriptions presented in natural language, which are essential for understanding design requirements and producing effective design solutions. Recent research has sought to integrate natural language processing (NLP) and generative artificial intelligence (AI) into human settlement construction tasks. Due to the efficient processing and analysis capabilities of AI with data, significant successes have been achieved in design within this domain. However, this task still faces several fundamental challenges. The semantic information involved includes complex spatial details, diverse data source formats, high sensitivity to regional culture, and demanding requirements for innovation and rigor in work scenarios. These factors lead to limitations when applying general generative AI in this field, further exacerbated by a lack of high-quality data for model training. To address these challenges, this paper first proposes HSC-GPT, a large-scale language model framework specifically designed for tasks in human settlement construction, considering the unique characteristics of this domain

Structural Mechanism for the Specific Assembly and Activation of the Extracellular Signal Regulated Kinase 5 (ERK5) Module

Mitogen-activated protein kinase (MAPK) activation depends on a linear binding motif found in all MAPK kinases (MKK). In addition, the PB1 (Phox and Bem1) domain of MKK5 is required for extracellular signal regulated kinase 5 (ERK5) activation. We present the crystal structure of ERK5 in complex with an MKK5 construct comprised of the PB1 domain and the linear binding motif. We show that ERK5 has distinct protein-protein interaction surfaces compared with ERK2, which is the closest ERK5 paralog. The two MAPKs have characteristically different physiological functions and their distinct protein-protein interaction surface topography enables them to bind different sets of activators and substrates. Structural and biochemical characterization revealed that the MKK5 PB1 domain cooperates with the MAPK binding linear motif to achieve substrate specific binding, and it also enables co-recruitment of the upstream activating enzyme and the downstream substrate into one signaling competent complex. Studies on present day MAPKs and MKKs hint on the way protein kinase networks may evolve. In particular, they suggest how paralogous enzymes with similar catalytic properties could acquire novel signaling roles by merely changing the way they make physical links to other proteins

Shoc2 Is Targeted to Late Endosomes and Required for Erk1/2 Activation in EGF-Stimulated Cells

Shoc2 is the putative scaffold protein that interacts with RAS and RAF, and positively regulates signaling to extracellular signal-regulated protein kinases 1 and 2 (ERK1/2). To elucidate the mechanism by which Shoc2 regulates ERK1/2 activation by the epidermal growth factor (EGF) receptor (EGFR), we studied subcellular localization of Shoc2. Upon EGFR activation, endogenous Shoc2 and red fluorescent protein tagged Shoc2 were translocated from the cytosol to a subset of late endosomes containing Rab7. The endosomal recruitment of Shoc2 was blocked by overexpression of a GDP-bound H-RAS (N17S) mutant and RNAi knockdown of clathrin, suggesting the requirement of RAS activity and clathrin-dependent endocytosis. RNAi depletion of Shoc2 strongly inhibited activation of ERK1/2 by low, physiological EGF concentrations, which was rescued by expression of wild-type recombinant Shoc2. In contrast, the Shoc2 (S2G) mutant, that is myristoylated and found in patients with the Noonan-like syndrome, did not rescue ERK1/2 activation in Shoc2-depleted cells. Shoc2 (S2G) was not located in late endosomes but was present on the plasma membrane and early endosomes. These data suggest that targeting of Shoc2 to late endosomes may facilitate EGFR-induced ERK activation under physiological conditions of cell stimulation by EGF, and therefore, may be involved in the spatiotemporal regulation of signaling through the RAS-RAF module

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors

Strategies for protein synthetic biology

Proteins are the most versatile among the various biological building blocks and a mature field of protein engineering has lead to many industrial and biomedical applications. But the strength of proteins—their versatility, dynamics and interactions—also complicates and hinders systems engineering. Therefore, the design of more sophisticated, multi-component protein systems appears to lag behind, in particular, when compared to the engineering of gene regulatory networks. Yet, synthetic biologists have started to tinker with the information flow through natural signaling networks or integrated protein switches. A successful strategy common to most of these experiments is their focus on modular interactions between protein domains or domains and peptide motifs. Such modular interaction swapping has rewired signaling in yeast, put mammalian cell morphology under the control of light, or increased the flux through a synthetic metabolic pathway. Based on this experience, we outline an engineering framework for the connection of reusable protein interaction devices into self-sufficient circuits. Such a framework should help to ‘refacture’ protein complexity into well-defined exchangeable devices for predictive engineering. We review the foundations and initial success stories of protein synthetic biology and discuss the challenges and promises on the way from protein- to protein systems design