Experimental Validation of DeeP-LCC for Dissipating Stop-and-Go Waves in Mixed Traffic

We present results on the experimental validation of leading cruise control (LCC) for connected and autonomous vehicles (CAVs). In a mixed traffic situation that is dominated by human-driven vehicles, LCC strategies are promising to smooth undesirable stop-and-go waves. Our experiments are carried out on a mini-scale traffic platform. We first reproduce stop-and-go traffic waves in a miniature scale, and then show that these traffic instabilities can be dissipated by one or a few CAVs that utilize Data-EnablEd Predicted Leading Cruise Control (DeeP-LCC). Rather than identifying a parametric traffic model, DeeP-LCC relies on a data-driven non-parametric behavior representation for traffic prediction and CAV control. DeeP-LCC also incorporates input and output constraints to achieve collision-free guarantees for CAVs. We experimentally demonstrate that DeeP-LCC is able to dissipate traffic waves caused by car-following behavior and significantly improve both driving safety and travel efficiency. CAVs utilizing DeeP-LCC may bring additional societal benefits by mitigating stop-and-go waves in practical traffic.Comment: 8 pages, 6 figure

HDAC4 Inhibitors as Antivascular Senescence Therapeutics

Aging is an inevitable consequence of life, and during this process, the epigenetic landscape changes and reactive oxygen species (ROS) accumulation increases. Inevitably, these changes are common in many age-related diseases, including neurodegeneration, hypertension, and cardiovascular diseases. In the current research, histone deacetylation 4 (HDAC4) was studied as a potential therapeutic target in vascular senescence. HDAC4 is a specific class II histone deacetylation protein that participates in epigenetic modifications and deacetylation of heat shock proteins and various transcription factors. There is increasing evidence to support that HDAC4 is a potential therapeutic target, and developments in the synthesis and testing of HDAC4 inhibitors are now gaining interest from academia and the pharmaceutical industry

Signaling pathways in rheumatoid arthritis: implications for targeted therapy

Rheumatoid arthritis (RA) is an incurable systemic autoimmune disease. Disease progression leads to joint deformity and associated loss of function, which significantly impacts the quality of life for sufferers and adds to losses in the labor force. In the past few decades, RA has attracted increased attention from researchers, the abnormal signaling pathways in RA are a very important research field in the diagnosis and treatment of RA, which provides important evidence for understanding this complex disease and developing novel RA-linked intervention targets. The current review intends to provide a comprehensive overview of RA, including a general introduction to the disease, historical events, epidemiology, risk factors, and pathological process, highlight the primary research progress of the disease and various signaling pathways and molecular mechanisms, including genetic factors, epigenetic factors, summarize the most recent developments in identifying novel signaling pathways in RA and new inhibitors for treating RA. therapeutic interventions including approved drugs, clinical drugs, pre-clinical drugs, and cutting-edge therapeutic technologies. These developments will hopefully drive progress in new strategically targeted therapies and hope to provide novel ideas for RA treatment options in the future

Oncogenic state and cell identity combinatorially dictate the susceptibility of cells within glioma development hierarchy to IGF1R targeting

Glioblastoma is the most malignant cancer in the brain and currently incurable. It is urgent to identify effective targets for this lethal disease. Inhibition of such targets should suppress the growth of cancer cells and, ideally also precancerous cells for early prevention, but minimally affect their normal counterparts. Using genetic mouse models with neural stem cells (NSCs) or oligodendrocyte precursor cells (OPCs) as the cells‐of‐origin/mutation, it is shown that the susceptibility of cells within the development hierarchy of glioma to the knockout of insulin‐like growth factor I receptor (IGF1R) is determined not only by their oncogenic states, but also by their cell identities/states. Knockout of IGF1R selectively disrupts the growth of mutant and transformed, but not normal OPCs, or NSCs. The desirable outcome of IGF1R knockout on cell growth requires the mutant cells to commit to the OPC identity regardless of its development hierarchical status. At the molecular level, oncogenic mutations reprogram the cellular network of OPCs and force them to depend more on IGF1R for their growth. A new‐generation brain‐penetrable, orally available IGF1R inhibitor harnessing tumor OPCs in the brain is also developed. The findings reveal the cellular window of IGF1R targeting and establish IGF1R as an effective target for the prevention and treatment of glioblastoma

Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files

Discovery proteomics has limited quantification capabilities because of stochastic precursor-ion selection. Several data-independent acquisition (DIA) methods have been proposed to overcome this limitation1, 2, 3, 4, including the sequential-window acquisition of all theoretical mass spectra (SWATH-MS)4.the National Science Foundation (NSF) of China (grants 91429301 and 31221065), 973 Program 2015CB553800, National Major Project 2013ZX10002-002, 111 Project B12001, funding from Xiamen City (grant 3502Z20130027) and the NSF of China for Fostering Talents in Basic Research (grant J1310027)

Transformer State Assessment Method Based on Fuzzy and Evidence Theories

Accurate and reliable assessment of power equipment operation state is the premise and basis for maintenance of power system state. This article builds the transformer body state assessment model based on fuzzy and evidence theories taking 500kV oil-immersed transformer as the object of research. The representative parameters in preventive test are selected as state assessment indicators by making reference to the factory values and threshold-crossing values of which the indicator normalization is carried out to determine the degrees of membership of each indicator relative to different state assessment levels using fuzzy evaluation method. These indicators are divided into three sub-evidence bodies, i.e. gas dissolved in oil, oilation test and electrical test, and information combination of these three sub-evidence bodies is carried out using evidence theory to further assess the operation state of transformer body. The effectiveness of this assessment model applied in state assessment of transformer body is verified by example analysis of the data of a 500kV transformer. This assessment model has clear ideas and doesn’t need too much historical data, it provides a new method for transformer state assessment.

Palladium-catalyzed borylation of aryl (pseudo)halides and its applications in biaryl synthesis

Abstract A facile and efficient palladium-catalyzed borylation of aryl (pseudo)halides at room temperature has been developed. Arylboronic esters were expeditiously assembled in good yields and with a broad substrate scope and good functional group compatibility. This approach has been successfully applied to the one-pot two-step borylation/Suzuki–Miyaura cross-coupling reaction, providing a concise access to biaryl compounds from readily available aryl halides. Furthermore, a parallel synthesis of biaryl analogs is accomplished at room temperature using the strategy, which enhances the practical usefulness of this method

Brachypodium distachyon T-DNA insertion lines: a model pathosystem to study nonhost resistance to wheat stripe rust

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (PST), is one of the most destructive diseases and can cause severe yield losses in many regions of the world. Because of the large size and complexity of wheat genome, it is difficult to study the molecular mechanism of interaction between wheat and PST. Brachypodium distachyon has become a model system for temperate grasses’ functional genomics research. The phenotypic evaluation showed that the response of Brachypodium distachyon to PST was nonhost resistance (NHR), which allowed us to present this plant-pathogen system as a model to explore the immune response and the molecular mechanism underlying wheat and PST. Here we reported the generation of about 7,000 T-DNA insertion lines based on a highly efficient Agrobacterium-mediated transformation system. Hundreds of mutants either more susceptible or more resistant to PST than that of the wild type Bd21 were obtained. The three putative target genes, Bradi5g17540, BdMYB102 and Bradi5g11590, of three T-DNA insertion mutants could be involved in NHR of Brachypodium distachyon to wheat stripe rust. The systemic pathologic study of this T-DNA mutants would broaden our knowledge of NHR, and assist in breeding wheat cultivars with durable resistance

Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images

The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017–2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options