DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Since semantic trajectories can discover more semantic meanings of a user\u27s interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method

Network-based survival analysis methods for pathway detection in cancer

We compare three penalized Cox regression methods for high-dimensional survival data in order to identify the pathways involved into cancer occurrence and pro- gression. We analyze each method with three gene expression datasets including breast, lung and ovarian cancer. More precisely, we focus on cancer survival prediction and on top signature genes. The goal of this study is to gain a deeper insight of the benefits and drawbacks of the regression techniques in order to find the pathways involved in a specific type of cancer and identify cancer biomarkers useful for prognosis, diagnosis and treatment

Immunotherapy for Type 1 Diabetes Mellitus by Adjuvant-Free Schistosoma Japonicum-Egg Tip-Loaded Asymmetric Microneedle Patch (STAMP)

BACKGROUND: Type 1 diabetes mellitus (T1DM) is an autoimmune disease mediated by autoreactive T cells and dominated by Th1 response polarization. Insulin replacement therapy faces great challenges to this autoimmune disease, requiring highly frequent daily administration. Intriguingly, the progression of T1DM has proven to be prevented or attenuated by helminth infection or worm antigens for a relatively long term. However, the inevitable problems of low safety and poor compliance arise from infection with live worms or direct injection of antigens. Microneedles would be a promising candidate for local delivery of intact antigens, thus providing an opportunity for the clinical immunotherapy of parasitic products. METHODS: We developed a Schistosoma japonicum-egg tip-loaded asymmetric microneedle patch (STAMP) system, which serves as a new strategy to combat TIDM. In order to improve retention time and reduce contamination risk, a specific imperfection was introduced on the STAMP (asymmetric structure), which allows the tip to quickly separate from the base layer, improving reaction time and patient\u27s comfort. After loading Schistosoma japonicum-egg as the immune regulator, the effects of STAMP on blood glucose control and pancreatic pathological progression improvement were evaluated in vivo. Meanwhile, the immunoregulatory mechanism and biosafety of STAMP were confirmed by histopathology, qRT-PCR, ELISA and Flow cytometric analysis. RESULTS: Here, the newly developed STAMP was able to significantly reduce blood glucose and attenuate the pancreatic injury in T1DM mice independent of the adjuvants. The isolated Schistosoma japonicum-eggs micron slowly degraded in the skin and continuously released egg antigen for at least 2 weeks, ensuring localization and safety of antigen stimulation. This phenomenon should be attributed to the shift of Th2 immune response to reduce Th1 polarization. CONCLUSION: Our results exhibited that STAMP could significantly regulate the blood glucose level and attenuate pancreatic pathological injury in T1DM mice by balancing the Th1/Th2 immune responses, which is independent of adjuvants. This technology opens a new window for the application of parasite products in clinical immunotherapy

SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process

Transformer Hawkes process models have shown to be successful in modeling event sequence data. However, most of the existing training methods rely on maximizing the likelihood of event sequences, which involves calculating some intractable integral. Moreover, the existing methods fail to provide uncertainty quantification for model predictions, e.g., confidence intervals for the predicted event's arrival time. To address these issues, we propose SMURF-THP, a score-based method for learning Transformer Hawkes process and quantifying prediction uncertainty. Specifically, SMURF-THP learns the score function of events' arrival time based on a score-matching objective that avoids the intractable computation. With such a learned score function, we can sample arrival time of events from the predictive distribution. This naturally allows for the quantification of uncertainty by computing confidence intervals over the generated samples. We conduct extensive experiments in both event type prediction and uncertainty quantification of arrival time. In all the experiments, SMURF-THP outperforms existing likelihood-based methods in confidence calibration while exhibiting comparable prediction accuracy

Microglia, TREM2, and Therapeutic Methods of Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most common causes of dementia all around the world. It is characterized by the deposition of amyloid-β protein (Aβ) and the formation of neurofibrillary tangles (NFTs), which contribute to neuronal loss and cognitive decline. Microglia, as innate immune cells in brain, plays dual roles in the pathological process of AD. Expression in different subtypes of microglia is diverse in AD genes. Triggering receptor expressed on myeloid cells 2 (TREM2) is a transmembrane glycoprotein mainly expressed on microglia in the central nervous system (CNS). Soluble TREM2 (sTREM2), a proteolytic product of TREM2, which is abundant in the cerebrospinal fluid, shows a dynamic change in different stages and ameliorates the pathological process of AD. The interplay between the different subtypes of apolipoprotein and TREM2 is closely related to the mechanism of AD and serves as important regulatory sites. Moreover, several therapeutic strategies targeting TREM2 have shown positive outcomes during clinical trials and some novel therapies at different points are in progress. In this review, we mainly talk about the interrelationships among microglia, TREM2, and AD, and hope to give an overview of the strategies of AD

A unified interaction equation for strength and global stability of solid and hollow concrete-filled steel tube columns under room and elevated temperatures

On the basis of plastic limit analysis, this paper proposes a novel, simple and unified interaction equation (N-M) for Concrete-filled Steel Tube (CFST) columns subjected to combined compression and bending. A unique feature of the new N-M equation is that the single equation is valid for a range of columns that can be solid, hollow, circular, polygonal, short or long. The single equation can also apply to columns under both room and elevated temperatures. Validations against independent laboratory test, analytical and numerical results are carried out to assess the accuracy and applicability of the equation. The new equation agrees well with most of the results used in the comparisons. It can be concluded that the simple and unified equation can be used in practical design with sufficient accuracy

Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding

In online advertising, advertisers participate in ad auctions to acquire ad opportunities, often by utilizing auto-bidding tools provided by demand-side platforms (DSPs). The current auto-bidding algorithms typically employ reinforcement learning (RL). However, due to safety concerns, most RL-based auto-bidding policies are trained in simulation, leading to a performance degradation when deployed in online environments. To narrow this gap, we can deploy multiple auto-bidding agents in parallel to collect a large interaction dataset. Offline RL algorithms can then be utilized to train a new policy. The trained policy can subsequently be deployed for further data collection, resulting in an iterative training framework, which we refer to as iterative offline RL. In this work, we identify the performance bottleneck of this iterative offline RL framework, which originates from the ineffective exploration and exploitation caused by the inherent conservatism of offline RL algorithms. To overcome this bottleneck, we propose Trajectory-wise Exploration and Exploitation (TEE), which introduces a novel data collecting and data utilization method for iterative offline RL from a trajectory perspective. Furthermore, to ensure the safety of online exploration while preserving the dataset quality for TEE, we propose Safe Exploration by Adaptive Action Selection (SEAS). Both offline experiments and real-world experiments on Alibaba display advertising platform demonstrate the effectiveness of our proposed method.Comment: Accepted by The Web Conference 2024 (WWW'24) as an oral pape

IvyGPT: InteractiVe Chinese pathwaY language model in medical domain

General large language models (LLMs) such as ChatGPT have shown remarkable success. However, such LLMs have not been widely adopted for medical purposes, due to poor accuracy and inability to provide medical advice. We propose IvyGPT, an LLM based on LLaMA that is trained and fine-tuned with high-quality medical question-answer (QA) instances and Reinforcement Learning from Human Feedback (RLHF). After supervised fine-tuning, IvyGPT has good multi-turn conversation capabilities, but it cannot perform like a doctor in other aspects, such as comprehensive diagnosis. Through RLHF, IvyGPT can output richer diagnosis and treatment answers that are closer to human. In the training, we used QLoRA to train 33 billion parameters on a small number of NVIDIA A100 (80GB) GPUs. Experimental results show that IvyGPT has outperformed other medical GPT models.Comment: 5 pages, 3 figure

Study on compressive stress-strain relationship of ultra-high performance concrete with coarse aggregates under and after high temperatures

Uniaxial compression tests are conducted on ultra-high performance concrete (UHPC) with different volume fractions of coarse aggregates (0 %, 10 %, 20 %, and 30 %) at temperatures ranging from room temperature to 900°C, both during and after high-temperature exposure. The compressive failure modes and the stress-strain curves of the ultra-high performance concrete with coarse aggregates (CA-UHPC) under and after high temperatures are obtained. A systematic analysis of the key characteristic parameters of the stress-strain curve, including axial compressive strength, elastic modulus, and peak strain, is carried out, and respective temperature-dependent calculation formulas are proposed. Experimental results show that the failure modes under and after high temperatures are similar, both exhibiting shear failure. It is found that both the temperature and the coarse aggregate contents affect the shape of the stress-strain curve. The uniaxial compressive performance of the CA-UHPC under and after high temperatures is compared. Finally, the uniaxial compressive stress-strain relationships of the CA-UHPC under and after high temperatures are established, considering the temperature, coarse aggregate content, and steel fiber content