The effect of horizontal resolution on the representation of the global monsoon annual cycle in Atmospheric General Circulation Models

The sensitivity of the representation of the global monsoon annual cycle to horizontal resolution is compared in three Atmospheric General Circulation Models (AGCMs): the Met Office Unified Model-Global Atmosphere 3.0 (MetUM-GA3), the Meteorological Research Institute AGCM3 (MRI-AGCM3) and Global High Resolution AGCM from the Geophysical Fluid Dynamics Laboratory (GFDL-HiRAM). For each model, we use two horizontal resolution configurations for the period 1998–2008. Increasing resolution consistently improves simulated precipitation and low-level circulation of the annual mean and the first two annual cycle modes, as measured by pattern correlation coefficient and Equitable Threat Score. Improvements in simulating the summer monsoon onset and withdrawal are region-dependent. No consistent response to resolution is found in simulating summer monsoon retreat. Regionally, increased resolution reduces the positive bias in simulated annual mean precipitation, the two annual-cycle modes over the West African monsoon and Northwestern Pacific monsoon. An overestimation of the solstitial mode and an underestimation of the equinoctial asymmetric mode of the East Asian monsoon are reduced in all high-resolution configurations. Systematic errors exist in lower-resolution models for simulating the onset and withdrawal of the summer monsoon. Higher resolution models consistently improve the early summer monsoon onset over East Asia and West Africa, but substantial differences exist in the responses over Indian monsoon region, where biases differ across the three low-resolution AGCMs. This study demonstrates the importance of a multi-model comparison when examining the added value of resolution and the importance of model physical parameterizations for the Indian monsoon simulation

Added value of high resolution models in simulating global precipitation characteristics

Climate models tend to overestimate percentage of the contribution (to total precipitation) and frequency of light rainfall while underestimate the heavy rainfall. This article investigates the added value of high resolution of atmospheric general circulation models (AGCMs) in simulating the characteristics of global precipitation, in particular extremes. Three AGCMs, global high resolution atmospheric model from the Geophysical Fluid Dynamics Laboratory (GFDL-HiRAM), the Meteorological Research Institute-atmospheric general circulation model (MRI-AGCM) and the Met Office Unified Model (MetUM), each with one high and one low resolution configurations for the period 1998–2008 are used in this study. Some consistent improvements are found across all three AGCMs with increasing model resolution from 50–83 to 20–35 km. A reduction in global mean frequency and amount percentile of light rainfall (20 mm day−1) are shown in high resolution models of GFDL-HiRAM and MRI-AGCM, while the improvement in MetUM is not obvious. A consistent response to high resolution across the three AGCMs is seen from the increase of light rainfall frequency and amount percentile over the desert regions, particularly over the ocean desert regions. It suppresses the overestimation of CDD over ocean desert regions and makes a better performance in high resolution models of GFDL-HiRAM and MRI-AGCM, but worse in MetUM-N512. The impact of model resolution differs greatly among the three AGCMs in simulating the fraction of total precipitation exceeding the 95th percentile daily wet day precipitation. Inconsistencies among models with increased resolution mainly appear over the tropical oceans and in simulating extreme wet conditions, probably due to different reactions of dynamical and physical processes to the resolution, indicating their crucial role in high resolution modelling

Positron emission tomography in the COVID-19 pandemic era

Coronavirus disease 2019 (COVID-19) has become a major public health problem worldwide since its outbreak in 2019. Currently, the spread of COVID-19 is far from over, and various complications have roused increasing awareness of the public, calling for novel techniques to aid at diagnosis and treatment. Based on the principle of molecular imaging, positron emission tomography (PET) is expected to offer pathophysiological alternations of COVID-19 in the molecular/cellular perspectives and facilitate the clinical management of patients. A number of PET-related cases and research have been reported on COVID-19 over the past one year. This article reviews the current studies of PET in the diagnosis and treatment of COVID-19, and discusses potential applications of PET in the development of management strategy for COVID-19 patients in the pandemic era

FlyBase: genomes by the dozen

FlyBase () is the primary database of genetic and genomic data for the insect family Drosophilidae. Historically, Drosophila melanogaster has been the most extensively studied species in this family, but recent determination of the genomic sequences of an additional 11 Drosophila species opens up new avenues of research for other Drosophila species. This extensive sequence resource, encompassing species with well-defined phylogenetic relationships, provides a model system for comparative genomic analyses. FlyBase has developed tools to facilitate access to and navigation through this invaluable new data collection

Optical molecular imaging and theranostics in neurological diseases based on aggregation-induced emission luminogens

Optical molecular imaging and image-guided theranostics benefit from special and specific imaging agents, for which aggregation-induced emission luminogens (AIEgens) have been regarded as good candidates in many biomedical applications. They display a large Stokes shift, high quantum yield, good biocompatibility, and resistance to photobleaching. Neurological diseases are becoming a substantial burden on individuals and society that affect over 50 million people worldwide. It is urgently needed to explore in more detail the brain structure and function, learn more about pathological processes of neurological diseases, and develop more efficient approaches for theranostics. Many AIEgens have been successfully designed, synthesized, and further applied for molecular imaging and image-guided theranostics in neurological diseases such as cerebrovascular disease, neurodegenerative disease, and brain tumor, which help us understand more about the pathophysiological state of brain through noninvasive optical imaging approaches. Herein, we focus on representative AIEgens investigated on brain vasculature imaging and theranostics in neurological diseases including cerebrovascular disease, neurodegenerative disease, and brain tumor. Considering different imaging modalities and various therapeutic functions, AIEgens have great potential to broaden neurological research and meet urgent needs in clinical practice. It will be inspiring to develop more practical and versatile AIEgens as molecular imaging agents for preclinical and clinical use on neurological diseases

Therapeutic potential of tumor treating fields for malignant brain tumors

BACKGROUND: Malignant brain tumors are among the most threatening diseases of the central nervous system, and despite increasingly updated treatments, the prognosis has not been improved. Tumor treating fields (TTFields) are an emerging approach in cancer treatment using intermediate-frequency and low-intensity electric field and can lead to the development of novel therapeutic options. RECENT FINDINGS: A series of biological processes induced by TTFields to exert anti-cancer effects have been identified. Recent studies have shown that TTFields can alter the bioelectrical state of macromolecules and organelles involved in cancer biology. Massive alterations in cancer cell proteomics and transcriptomics caused by TTFields were related to cell biological processes as well as multiple organelle structures and activities. This review addresses the mechanisms of TTFields and recent advances in the application of TTFields therapy in malignant brain tumors, especially in glioblastoma (GBM). CONCLUSIONS: As a novel therapeutic strategy, TTFields have shown promising results in many clinical trials, especially in GBM, and continue to evolve. A growing number of patients with malignant brain tumors are being enrolled in ongoing clinical studies demonstrating that TTFields-based combination therapies can improve treatment outcomes

Efficient gene editing in adult mouse livers via adenoviral delivery of CRISPR/Cas9

AbstractWe developed an adenovirus-based CRISPR/Cas9 system for gene editing in vivo. In the liver, we demonstrated that the system could reach the level of tissue-specific gene knockout, resulting in phenotypic changes. Given the wide spectrum of cell types susceptible to adenoviral infection, and the fact that adenoviral genome rarely integrates into its host cell genome, we believe the adenovirus-based CRISPR/Cas9 system will find applications in a variety of experimental settings

The ORF7a Protein of SARS-CoV-2 Initiates Autophagy and Limits Autophagosome-Lysosome Fusion via Degradation of SNAP29 To Promote Virus Replication

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is closely related to various cellular aspects associated with autophagy. However, how SARS-CoV-2 mediates the subversion of the macroautophagy/autophagy pathway remains largely unclear. In this study, we demonstrate that overexpression of the SARS-CoV-2 ORF7a protein activates LC3-II and leads to the accumulation of autophagosomes in multiple cell lines, while knockdown of the viral ORF7a gene via shRNAs targeting ORF7a sgRNA during SARS-CoV-2 infection decreased autophagy levels. Mechanistically, the ORF7a protein initiates autophagy via the AKT-MTOR-ULK1-mediated pathway, but ORF7a limits the progression of autophagic flux by activating CASP3 (caspase 3) to cleave the SNAP29 protein at aspartic acid residue 30 (D30), ultimately impairing complete autophagy. Importantly, SARS-CoV-2 infection-induced accumulated autophagosomes promote progeny virus production, whereby ORF7a downregulates SNAP29, ultimately resulting in failure of autophagosome fusion with lysosomes to promote viral replication. Taken together, our study reveals a mechanism by which SARS-CoV-2 utilizes the autophagic machinery to facilitate its own propagation via ORF7a.Abbreviations: 3-MA: 3-methyladenine; ACE2: angiotensin converting enzyme 2; ACTB/β-actin: actin beta; ATG7: autophagy related 7; Baf A1: bafilomycin A1; BECN1: beclin 1; CASP3: caspase 3; COVID-19: coronavirus disease 2019; GFP: green fluorescent protein; hpi: hour post-infection; hpt: hour post-transfection; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MERS: Middle East respiratory syndrome; MTOR: mechanistic target of rapamycin kinase; ORF: open reading frame; PARP: poly(ADP-ribose) polymerase; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; shRNAs: short hairpin RNAs; siRNA: small interfering RNA; SNAP29: synaptosome associated protein 29; SQSTM1/p62: sequestosome 1; STX17: syntaxin 17; TCID50: tissue culture infectious dose; TEM: transmission electron microscopy; TUBB, tubulin, beta; ULK1: unc-51 like autophagy activating kinase 1

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional MRI (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain sub-networks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency