A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval

Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e.g., products with aspects such as category and brand. Recent work has proposed two approaches to incorporate the aspect information into item representations for effective retrieval by predicting the values associated with the item aspects. Despite their efficacy, they treat the values as isolated classes (e.g., "Smart Homes", "Home, Garden & Tools", and "Beauty & Health") and ignore their fine-grained semantic relation. Furthermore, they either enforce the learning of aspects into the CLS token, which could confuse it from its designated use for representing the entire content semantics, or learn extra aspect embeddings only with the value prediction objective, which could be insufficient especially when there are no annotated values for an item aspect. Aware of these limitations, we propose a MUlti-granulaRity-aware Aspect Learning model (MURAL) for multi-aspect dense retrieval. It leverages aspect information across various granularities to capture both coarse and fine-grained semantic relations between values. Moreover, MURAL incorporates separate aspect embeddings as input to transformer encoders so that the masked language model objective can assist implicit aspect learning even without aspect-value annotations. Extensive experiments on two real-world datasets of products and mini-programs show that MURAL outperforms state-of-the-art baselines significantly.Comment: Accepted by WSDM2024, updat

A novel HIV-1-encoded microRNA enhances its viral replication by targeting the TATA box region

BACKGROUND: A lot of microRNAs (miRNAs) derived from viral genomes have been identified. Many of them play various important roles in virus replication and virus-host interaction. Cellular miRNAs have been shown to participate in the regulation of HIV-1 viral replication, while the role of viral-encoded miRNAs in this process is largely unknown. RESULTS: In this report, through a strategy combining computational prediction and deep sequencing, we identified a novel HIV-1-encoded miRNA, miR-H3. MiR-H3 locates in the mRNA region encoding the active center of reverse transcriptase (RT) and exhibits high sequence conservation among different subtypes of HIV-1 viruses. Overexpression of miR-H3 increases viral production and the mutations in miR-H3 sequence significantly impair the viral replication of wildtype HIV-1 viruses, suggesting that it is a replication-enhancing miRNA. MiR-H3 upregulates HIV-1 RNA transcription and protein expression. A serial deletion assay suggests that miR-H3 targets HIV-1 5′ LTR and upregulates the promoter activity. It interacts with the TATA box in HIV-1 5′ LTR and sequence-specifically activates the viral transcription. In addition, chemically-synthesized small RNAs targeting HIV-1 TATA box activate HIV-1 production from resting CD4(+) T cells isolated from HIV-1-infected patients on suppressive highly active antiretroviral therapy (HAART). CONCLUSIONS: We have identified a novel HIV-1-encoded miRNA which specifically enhances viral production and provide a specific method to activate HIV-1 latency

Engineering allosteric inhibition of homoserine dehydrogenase by semi-rational saturation mutagenesis screening

Allosteric regulation by pathway products plays a vital role in amino acid metabolism. Homoserine dehydrogenase (HSD), the key enzyme for the biosynthesis of various aspartate family amino acids, is subject to feedback inhibition by l-threonine and l-isoleucine. The desensitized mutants with the potential for amino acid production remain limited. Herein, a semi-rational approach was proposed to relieve the feedback inhibition. HSD from Corynebacterium glutamicum (CgHSD) was first characterized as a homotetramer, and nine conservative sites at the tetramer interface were selected for saturation mutagenesis by structural simulations and sequence analysis. Then, we established a high-throughput screening (HTS) method based on resistance to l-threonine analog and successfully acquired two dominant mutants (I397V and A384D). Compared with the best-ever reported desensitized mutant G378E, both new mutants qualified the engineered strains with higher production of CgHSD-dependent amino acids. The mutant and wild-type enzymes were purified and assessed in the presence or absence of inhibitors. Both purified mutants maintained >90% activity with 10 mM l-threonine or 25 mM l-isoleucine. Moreover, they showed >50% higher specific activities than G378E without inhibitors. This work provides two competitive alternatives for constructing cell factories of CgHSD-related amino acids and derivatives. Moreover, the proposed approach can be applied to engineering other allosteric enzymes in the amino acid synthesis pathway

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Aluminum microstructure evolution and effects on mechanical properties in quenching and aging process

High strength aluminum alloys are recently widely used in aircraft, automobile and construction industry fields. Typical T6 heat treatment process can be applied to improve the heat treatable aluminum alloy in order to facilitate the formation of prime strengthening precipitate phases. Critical steps in T6 heat treatment process include solution treatment, quenching and aging. Due to high thermal gradients in quenching process and aging process, large thermal stress will remain in the matrix and may bring unexpected deformation or distortion in further machining. Therefore, in order to predict the thermal stress effects, constitutive model and precipitate hardening model are needed to simulate the mechanical properties of alloy. In this dissertation, an optimized constitutive model, which is used to describe the mechanical behavior during quenching and intermediate period of quenching and aging process, was given based on constitutive models with Zenor-Holloman parameter. Modification for constitutive model is based on the microstructure model, which is developed for the quenching and aging processes. Quench factor analysis method was applied to describe the microstructure evolution and volume fraction of primary precipitate phases during quenching process. Some experimental phenomena are discussed and explained by precipitate distributions. Classical precipitate hardening models were reviewed and two models were selected for Al-Cu-Mn alloy aging treatment. Thermal growth model and Euler algorithm were used to improve the accuracy and the selected precipitate hardening models were validated by yield stress and microstructure observations of Al-Cu-Mn aging response experiments

Ground states for fractional nonlocal equations with logarithmic nonlinearity

In this paper, we study on the fractional nonlocal equation with the logarithmic nonlinearity formed by $$\begin{cases}\mathcal{L}_{K} u(x)+u \log |u|+|u|^{q-2} u=0, & x \in \Omega \\ u=0, & x \in \mathbb{R}^{n} \backslash \Omega\end{cases}$$ where 2 < q < 2∗s, LK is a non-local operator, Ω is an open bounded set of Rn with Lipschitz boundary. By using the fractional logarithmic Sobolev inequality and the linking theorem, we present the existence theorem of the ground state solutions for this nonlocal problem

Developmental Bisphenol A Exposure Modulates Immune-Related Diseases

Bisphenol A (BPA), used in polycarbonate plastics and epoxy resins, has a widespread exposure to humans. BPA is of concern for developmental exposure resulting in immunomodulation and disease development due to its ability to cross the placental barrier and presence in breast milk. BPA can use various mechanisms to modulate the immune system and affect diseases, including agonistic and antagonistic effects on many receptors (e.g., estrogen receptors), epigenetic modifications, acting on cell signaling pathways and, likely, the gut microbiome. Immune cell populations and function from the innate and adaptive immune system are altered by developmental BPA exposure, including decreased T regulatory (Treg) cells and upregulated pro- and anti-inflammatory cytokines and chemokines. Developmental BPA exposure can also contribute to the development of type 2 diabetes mellitus, allergy, asthma and mammary cancer disease by altering immune function. Multiple sclerosis and type 1 diabetes mellitus may also be exacerbated by BPA, although more research is needed. Additionally, BPA analogs, such as bisphenol S (BPS), have been increasing in use, and currently, little is known about their immune effects. Therefore, more studies should be conducted to determine if developmental exposure BPA and its analogs modulate immune responses and lead to immune-related diseases