High Diversity of Cytospora Associated With Canker and Dieback of Rosaceae in China, With 10 New Species Described

Cytospora canker is a destructive disease of numerous hosts and causes serious economic losses with a worldwide distribution. Identification of Cytospora species is difficult due to insufficient phylogenetic understanding and overlapped morphological characteristics. In this study, we provide an assessment of 23 Cytospora spp., which covered nine genera of Rosaceae, and focus on 13 species associated with symptomatic branch or twig canker and dieback disease in China. Through morphological observation and multilocus phylogeny of internal transcribed spacer (ITS), large nuclear ribosomal RNA subunit (LSU), actin (act), RNA polymerase II subunit (rpb2), translation elongation factor 1-α (tef1-α), and beta-tubulin (tub2) gene regions, the results indicate 13 distinct lineages with high branch support. These include 10 new Cytospora species, i.e., C. cinnamomea, C. cotoneastricola, C. mali-spectabilis, C. ochracea, C. olivacea, C. pruni-mume, C. rosicola, C. sorbina, C. tibetensis, and C. xinjiangensis and three known taxa including Cytospora erumpens, C. leucostoma, and C. parasitica. This study provides an initial understanding of the taxonomy of Cytospora associated with canker and dieback disease of Rosaceae in China

The Theory of Dynamic Public Transit Priority with Dynamic Stochastic Park and Ride

Public transit priority is very important for relieving traffic congestion. The connotation of dynamic public transit priority and dynamic stochastic park and ride is presented. Based on the point that the travel cost of public transit is not higher than the travel cost of car, how to determine the level of dynamic public transit priority is discussed. The traffic organization method of dynamic public transit priority is introduced. For dynamic stochastic park and ride, layout principle, scale, and charging standard are discussed. Traveler acceptability is high through the analysis of questionnaire survey. Dynamic public transit priority with dynamic stochastic park and ride has application feasibility

DNA damage-induced cellular senescence is sufficient to suppress tumorigenesis: a mouse model

Tumor suppressor p53-dependent apoptosis is critical in suppressing tumorigenesis. Previously, we reported that DNA double-strand breaks (DSBs) at the V(D)J recombination loci induced genomic instability in the developing lymphocytes of nonhomologous end-joining (NHEJ)–deficient, p53-deficient mice, which led to rapid lymphomagenesis. To test the ability of p53-dependent cell cycle arrest to suppress tumorigenesis in the absence of apoptosis in vivo, we crossbred NHEJ-deficient mice into a mutant p53R172P background; these mice have defects in apoptosis induction, but not cell cycle arrest. These double-mutant mice survived longer than NHEJ/p53 double-null mice and, remarkably, were completely tumor free. We detected accumulation of aberrant V(D)J recombination–related DSBs at the T cell receptor (TCR) locus, and high expression levels of both mutant p53 and cell cycle checkpoint protein p21, but not the apoptotic protein p53-upregulated modulator of apoptosis. In addition, a substantial number of senescent cells were observed among both thymocytes and bone marrow cells. Cytogenetic studies revealed euploidy and limited chromosomal breaks in these lymphoid cells. The results indicate that precursor lymphocytes, which normally possess a high proliferation potential, are able to withdraw from the cell cycle and undergo senescence in response to the persistence of DSBs in a p53–p21–dependent pathway; this is sufficient to inhibit oncogenic chromosomal abnormality and suppress tumorigenesis

Deep Reinforcement Learning for Image-to-Image Translation

Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model. However, designing such a single-step model is always challenging, requiring a huge number of parameters and easily falling into bad global minimums and overfitting. In this work, we reformulate I2IT as a step-wise decision-making problem via deep reinforcement learning (DRL) and propose a novel framework that performs RL-based I2IT (RL-I2IT). The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image. Considering that it is challenging to handle high dimensional continuous state and action spaces in the conventional RL framework, we introduce meta policy with a new concept Plan to the standard Actor-Critic model, which is of a lower dimension than the original image and can facilitate the actor to generate a tractable high dimensional action. In the RL-I2IT framework, we also employ a task-specific auxiliary learning strategy to stabilize the training process and improve the performance of the corresponding task. Experiments on several I2IT tasks demonstrate the effectiveness and robustness of the proposed method when facing high-dimensional continuous action space problems

Design of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment

Purpose – For the purpose of reducing the incidence of hazardous materials transport accident, eliminating the potential threats and ensuring their safety, aiming at the shortcomings in the process of current hazardous materials transportation management, this paper aims to construct the framework of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment. Design/methodology/approach – The system takes the intelligent connected vehicle as the main supporter, integrating GIS, GPS, eye location, GSM, networks and database technology. Findings – By analyzing the transportation characteristics of hazardous materials, this system consists of five subsystems, which are vehicle and driver management subsystem, dangerous sources and hazardous materials management subsystem, route analysis and optimization subsystem, early warning and emergency rescue management subsystem, and basic information query subsystem. Originality/value – Hazardous materials transportation safety management system includes omnibearing real-time monitoring, timely updating of system database, real-time generation and optimization of emergency rescue route. The system can reduce the transportation cost and improve the ability of accident prevention and emergency rescue of hazardous materials

The Functions of Non-coding RNAs in rRNA Regulation

Ribosomes are ribonucleoprotein machines that decode the genetic information embedded in mRNAs into polypeptides. Ribosome biogenesis is tightly coordinated and controlled from the transcription of pre-rRNAs to the assembly of ribosomes. Defects or disorders in rRNA production result in a number of human ribosomopathy diseases. During the processes of rRNA synthesis, non-coding RNAs, especially snoRNAs, play important roles in pre-rRNA transcription, processing, and maturation. Recent research has started to reveal that other long and short non-coding RNAs, including risiRNA, LoNA, and SLERT (among others), are also involved in pre-rRNA transcription and rRNA production. Here, we summarize the current understanding of the mechanisms of non-coding RNA-mediated rRNA generation and regulation and their biological roles

CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment

Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users. However, a longstanding challenge in human alignment techniques based on reinforcement learning lies in their inherent complexity and difficulty in training. To address this challenge, we present a simple yet effective Contrastive Learning Framework for Human Alignment (CLHA) to align LLMs with human preferences directly. CLHA employs a novel rescoring strategy to evaluate the noise within the data by considering its inherent quality and dynamically adjusting the training process. Simultaneously, CLHA utilizes pairwise contrastive loss and adaptive supervised fine-tuning loss to adaptively modify the likelihood of generating responses, ensuring enhanced alignment with human preferences. Using advanced methods, CLHA surpasses other algorithms, showcasing superior performance in terms of reward model scores, automatic evaluations, and human assessments on the widely used ``Helpful and Harmless'' dataset

Impaired V(D)J Recombination and Lymphocyte Development in Core RAG1-expressing Mice

RAG1 and RAG2 are the lymphocyte-specific components of the V(D)J recombinase. In vitro analyses of RAG function have relied on soluble, highly truncated “core” RAG proteins. To identify potential functions for noncore regions and assess functionality of core RAG1 in vivo, we generated core RAG1 knockin (RAG1c/c) mice. Significant B and T cell numbers are generated in RAG1c/c mice, showing that core RAG1, despite missing ∼40% of the RAG1 sequence, retains significant in vivo function. However, lymphocyte development and the overall level of V(D)J recombination are impaired at the progenitor stage in RAG1c/c mice. Correspondingly, there are reduced numbers of peripheral RAG1c/c B and T lymphocytes. Whereas normal B lymphocytes undergo rearrangement of both JH loci, substantial levels of germline JH loci persist in mature B cells of RAG1c/c mice, demonstrating that DJH rearrangement on both IgH alleles is not required for developmental progression to the stage of VH to DJH recombination. Whereas VH to DJH rearrangements occur, albeit at reduced levels, on the nonselected alleles of RAG1c/c B cells that have undergone D to JH rearrangements, we do not detect VH to DH rearrangements in RAG1c/c B cells that retain germline JH alleles. We discuss the potential implications of these findings for noncore RAG1 functions and for the ordered assembly of VH, DH, and JH segments

A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen

Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities. Document type: Articl