Current View on Hematopoiesis and Beyond

Hematopoietic stem cells (HSCs) have the ability to self-renew and give rise to all lineages of blood cells while remain the capacity of regenerative in hematopoiesis. As the only stem cell type in routine clinical use, HSCs can be isolated from bone marrow, peripheral blood and umbilical cord blood. Stem cells transplantation is mainly used in HSCs while the trans-differentiation ability broadens the research of HSCs in regenerative medicine. Here, we focus on the current view on hematopoiesis and beyond and summarize the clinical application and the regulation of the fate of HSCs. We intend to outline recent advances in the human HSCs research area and review the characteristic of HSCs from definition through development to their clinical applications and future prospect

AVARS -- Alleviating Unexpected Urban Road Traffic Congestion using UAVs

Reducing unexpected urban traffic congestion caused by en-route events (e.g., road closures, car crashes, etc.) often requires fast and accurate reactions to choose the best-fit traffic signals. Traditional traffic light control systems, such as SCATS and SCOOT, are not efficient as their traffic data provided by induction loops has a low update frequency (i.e., longer than 1 minute). Moreover, the traffic light signal plans used by these systems are selected from a limited set of candidate plans pre-programmed prior to unexpected events' occurrence. Recent research demonstrates that camera-based traffic light systems controlled by deep reinforcement learning (DRL) algorithms are more effective in reducing traffic congestion, in which the cameras can provide high-frequency high-resolution traffic data. However, these systems are costly to deploy in big cities due to the excessive potential upgrades required to road infrastructure. In this paper, we argue that Unmanned Aerial Vehicles (UAVs) can play a crucial role in dealing with unexpected traffic congestion because UAVs with onboard cameras can be economically deployed when and where unexpected congestion occurs. Then, we propose a system called "AVARS" that explores the potential of using UAVs to reduce unexpected urban traffic congestion using DRL-based traffic light signal control. This approach is validated on a widely used open-source traffic simulator with practical UAV settings, including its traffic monitoring ranges and battery lifetime. Our simulation results show that AVARS can effectively recover the unexpected traffic congestion in Dublin, Ireland, back to its original un-congested level within the typical battery life duration of a UAV

Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection

Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of news articles based on limited fact-checks. Existing approaches typically follow a "Train-from-Scratch" paradigm, which is fundamentally bounded by the availability of large-scale annotated data. While expressive pre-trained language models (PLMs) have been adapted in a "Pre-Train-and-Fine-Tune" manner, the inconsistency between pre-training and downstream objectives also requires costly task-specific supervision. In this paper, we propose "Prompt-and-Align" (P&A), a novel prompt-based paradigm for few-shot fake news detection that jointly leverages the pre-trained knowledge in PLMs and the social context topology. Our approach mitigates label scarcity by wrapping the news article in a task-related textual prompt, which is then processed by the PLM to directly elicit task-specific knowledge. To supplement the PLM with social context without inducing additional training overheads, motivated by empirical observation on user veracity consistency (i.e., social users tend to consume news of the same veracity type), we further construct a news proximity graph among news articles to capture the veracity-consistent signals in shared readerships, and align the prompting predictions along the graph edges in a confidence-informed manner. Extensive experiments on three real-world benchmarks demonstrate that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.Comment: Accepted to CIKM 2023 (Full Paper

Wheat leaf rust fungus effector Pt13024 is avirulent to TcLr30

Wheat leaf rust, caused by Puccinia triticina Eriks. (Pt), is a global wheat disease threatening wheat production. Dissecting how Pt effector proteins interact with wheat has great significance in understanding the pathogenicity mechanisms of Pt. In the study, the cDNA of Pt 13-5-72 interacting with susceptible cultivar Thatcher was used as template to amplify Pt13024 gene. The expression pattern and structure of Pt13024 were analyzed by qRT-PCR and online softwares. The secretion function of Pt13024 signal peptide was verified by the yeast system. Subcellular localization of Pt13024 was analyzed using transient expression on Nicotiana benthamiana. The verification that Pt13024 inhibited programmed cell death (PCD) was conducted on N. benthamiana and wheat. The deletion mutation of Pt13024 was used to identify the virulence function motif. The transient transformation of wheat mediated by the type III secretion system (TTSS) was used to analyze the activity of regulating the host defense response of Pt13024. Pt13024 gene silencing was performed by host-induced gene silencing (HIGS). The results showed that Pt13024 was identified as an effector and localized in the cytoplasm and nucleus on the N. benthamiana. It can inhibit PCD induced by the Bcl-2-associated X protein (BAX) from mice and infestans 1 (INF1) from Phytophthora infestans on N. benthamiana, and it can also inhibit PCD induced by DC3000 on wheat. The amino acids 22 to 41 at N-terminal of the Pt13024 are essential for the inhibition of programmed cell death (PCD) induced by BAX. The accumulation of reactive oxygen species and deposition of callose in near-isogenic line TcLr30, which is in Thatcher background with Lr30, induced by Pt13024 was higher than that in 41 wheat leaf rust-resistant near-isogenic lines (monogenic lines) with different resistance genes and Thatcher. Silencing of Pt13024 reduced the leaf rust resistance of Lr30 during the interaction between Pt and TcLr30. We can conclude that Pt13024 is avirulent to TcLr30 when Pt interacts with TcLr30. These findings lay the foundation for further investigations into the role of Pt effector proteins in pathogenesis and their regulatory mechanisms

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Sequence Analysis of TNFRSF13b, Encoding TACI, in a Patient with Very Early Onset Inflammatory Bowel Disease: a Case Report

Very early onset inflammatory bowel disease (VEO-IBD), IBD diagnosed before 6 years of age, frequently presents with increased severity, aggressive progression, and often poor response to conventional treatments. Although the cause of IBD is generally considered to be intestinal immune dysfunction induced by polygenic mutations and environment and other factors, VEO-IBD has a stronger genetic susceptibility specifically the neonatal- or infantile-onset IBD. Herein we report compound heterozygous mutations in the tumor necrosis factor receptor superfamily member 13b (TNFRSF13B) gene in a 3-year-old male that was admitted to our hospital with lasted jaundice, repeated fever and diarrhea in May 2014 at 2-month-old. He was diagnosed with VEO-IBD based on clinical, laboratory and histopathological examination. However, he was unresponsive to the conventional therapy, including the nutritional support therapy, antibiotic and immunosuppressive treatment, and surgical release of neonatal intestinal obstruction. Novel compound heterozygous mutations, c.[365G>A];[452C>T](p.[R122Q];[P151L]), were discovered in TNFRSF13B, encoding TACI, for this patient

Communication management in distributed sensor interpretation

Distributed Sensor Interpretation (DSI) problems have been the subject of considerable research within the cooperative MAS community. In a DSI system, data is collected from different sensors and must be integrated to produce the best interpretation. Distributed approaches to DSI emphasize not only distributed collecting of data, but also distributed processing of data. However, in virtually all real-world DSI systems the agents must exchange data, local results, and/or other information to develop a solution with acceptable quality. Unless communication among the agents is appropriately limited, the cost of communication may negate much of the benefit of distributed processing. Unfortunately, the state-of-the-art in MAS is such that there are not yet formal design methods that allow one to evaluate a potential DSI domain and determine the optimal coordination strategy. I believe that this is a serious issue that will hinder the deployment of many important applications of sensor networks. My work is one of the first attempts to address this issue. I formalized the communication problem in DSI with a Distributed Bayesian Network and solved the question of what to communicate with a decentralized Markov Decision Process (DEC-MDP). With this model, one is able to generate a communication strategy for a given DSI problem such that only minimum communication cost is needed to achieve a required confidence level in the interpretation task. Though general communication can be naturally modeled with a DEC-MDP, techniques need to be developed to address the complexity issue before the system can be scaled up. I approach this problem from two perspectives. First, I proposed an algorithm to automatically generate a set of abstract communication actions such that when the abstract information is transferred between the agents, the goal of the system is more likely to be reached. By allowing only abstract communication actions in certain states, both the expected communication cost required and the time needed to solve the DEC-MDP are reduced. Second, I established that the interaction present among the agents is the cause of the high complexity of a DEC-MDP. This understanding is crucial to identifying new and more tractable models as well as developing appropriate approximations to otherwise intractable problems. I proved that deciding a distributed MDP whose interaction history contains information of a size polynomial in the number of states is NP-complete, and that deciding a non-polynomially encodable distributed MDP is harder than NP. This is the first time that a well defined condition has been identified that can distinguish between multi-agent problems in NP and those that are strictly harder than NP. It is an important step in mapping out the complexity hierarchy of multi-agent systems. The significance of this theoretical result also has a more practical side. Most multi-agent systems are provably harder than NP and solving them optimally is not possible. This work provides theoretical guidance in understanding how the approximations in a model limit the search space and reduce the complexity

Algorithms, Design

In complex distributed applications, a problem is often decomposed into a set of subproblems that are distributed to multiple agents. We formulate this class of problems with a two layer Bayesian Network. Instead of merely providing a statistical view, we propose a satisficing approach to predict the minimum expected communication needed to reach a desired solution quality. The problem is modelled with a decentralized MDP, and two approximate algorithms are developed to find the near optimal communication strategy for a given problem structure and a required solution quality

Algorithms, Design

In complex distributed applications, a problem is often decomposed into a set of subproblems that are distributed to multiple agents. We formulate this class of problems with a two layer Bayesian Network. Instead of merely providing a statistical view, we propose a satisficing approach to predict the minimum expected communication needed to reach a desired solution quality. The problem is modelled with a decentralized MDP, and two approximate algorithms are developed to find the near optimal communication strategy for a given problem structure and a required solution quality