Diffusion Augmentation for Sequential Recommendation

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.Comment: Under Revie

Exploration of microbiome diversity of stacked fermented grains by flow cytometry and cell sorting

Sauce-flavor baijiu is one of the twelve flavor types of Chinese distilled fermented product. Microbial composition plays a key role in the stacked fermentation of Baijiu, which uses grains as raw materials and produces flavor compounds, however, the active microbial community and its relationship remain unclear. Here, we investigated the total and active microbial communities of stacked fermented grains of sauce-flavored Baijiu using flow cytometry and high-throughput sequencing technology, respectively. By using traditional high-throughput sequencing technology, a total of 24 bacterial and 14 fungal genera were identified as the core microbiota, the core bacteria were Lactobacillus (0.08–39.05%), Acetobacter (0.25–81.92%), Weissella (0.03–29.61%), etc. The core fungi were Issatchenkia (23.11–98.21%), Monascus (0.02–26.36%), Pichia (0.33–37.56%), etc. In contrast, using flow cytometry combined with high-throughput sequencing, the active dominant bacterial genera after cell sorting were found to be Herbaspirillum, Chitinophaga, Ralstonia, Phenylobacterium, Mucilaginibacter, and Bradyrhizobium, etc., whereas the active dominant fungal genera detected were Aspergillus, Pichia, Exophiala, Candelabrochaete, Italiomyces, and Papiliotrema, etc. These results indicate that although the abundance of Acetobacter, Monascus, and Issatchenkia was high after stacked fermentation, they may have little biological activity. Flow cytometry and cell sorting techniques have been used in the study of beer and wine, but exploring the microbiome in such a complex environment as Chinese baijiu has not been reported. The results also reveal that flow cytometry and cell sorting are convenient methods for rapidly monitoring complex microbial flora and can assist in exploring complex environmental samples

Ladder-like energy-relaying exciplex enables 100% internal quantum efficiency of white TADF-based diodes in a single emissive layer.

Development of white organic light-emitting diodes based on purely thermally activated delayed fluorescence with a single-emissive-layer configuration has been a formidable challenge. Here, we report the rational design of a donor-acceptor energy-relaying exciplex and its utility in fabricating single-emissive-layer, thermally activated delayed fluorescence-based white organic light-emitting diodes that exhibit 100% internal quantum efficiency, 108.2 lm W-1 power efficiency, and 32.7% external quantum efficiency. This strategy enables thin-film fabrication of an 8 cm × 8 cm thermally activated delayed fluorescence white organic light-emitting diodes (10 inch2) prototype with 82.7 lm W-1 power efficiency and 25.0% external quantum efficiency. Introduction of a phosphine oxide-based acceptor with a steric group to the exciplex limits donor-acceptor triplet coupling, providing dual levels of high-lying and low-lying triplet energy. Transient spectroscopic characterizations confirm that a ladder-like energy relaying occurs from the high-lying triplet level of the exciplex to a blue emitter, then to the low-lying triplet level of the phosphine oxide acceptor, and ultimately to the yellow emitter. Our results demonstrate the broad applicability of energy relaying in multicomponent systems for exciton harvesting, providing opportunities for the development of third-generation white organic light-emitting diode light sources

Study on the construction technology of β-alanine synthesizing Escherichia coli based on cellulosome assembly

Introduction: β-Alanine is the only β-amino acid in nature; it is widely used in food additives, medicines, health products, and surfactants. To avoid pollution caused by traditional production methods, the synthesis of β-alanine has been gradually replaced by microbial fermentation and enzyme catalysis, which is a green, mild, and high-yield biosynthesis method.Methods: In this study, we constructed an Escherichia coli recombinant strain for efficient β-alanine production using glucose as the raw material. The microbial synthesis pathway of L-lysine-producing strain, Escherichia coli CGMCC 1.366, was modified using gene editing by knocking out the aspartate kinase gene, lysC. The catalytic efficiency and product synthesis efficiency were improved by assembling key enzymes with cellulosome.Results: By-product accumulation was reduced by blocking the L-lysine production pathway, thereby increasing the yield of β-alanine. In addition, catalytic efficiency was improved by the two-enzyme method to further increase the β-alanine content. The key cellulosome elements, dockerin (docA) and cohesin (cohA), were combined with L-aspartate-α-decarboxylase (bspanD) from Bacillus subtilis and aspartate aminotransferase (aspC) from E.coli to improve the catalytic efficiency and expression level of the enzyme. β-alanine production reached 7.439 mg/L and 25.87 mg/L in the two engineered strains. The β-alanine content reached 755.465 mg/L in a 5 L fermenter.Discussion: The content of β-alanine synthesized by constructed β-alanine engineering strains were 10.47 times and 36.42 times higher than the engineered strain without assembled cellulosomes, respectively. This research lays the foundation for the enzymatic production of β-alanine using a cellulosome multi-enzyme self-assembly system

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

How Many Conformations of Enzymes Should Be Sampled for DFT/MM Calculations? A Case Study of Fluoroacetate Dehalogenase

The quantum mechanics/molecular mechanics (QM/MM) method (e.g., density functional theory (DFT)/MM) is important in elucidating enzymatic mechanisms. It is indispensable to study “multiple” conformations of enzymes to get unbiased energetic and structural results. One challenging problem, however, is to determine the minimum number of conformations for DFT/MM calculations. Here, we propose two convergence criteria, namely the Boltzmann-weighted average barrier and the disproportionate effect, to tentatively address this issue. The criteria were tested by defluorination reaction catalyzed by fluoroacetate dehalogenase. The results suggest that at least 20 conformations of enzymatic residues are required for convergence using DFT/MM calculations. We also tested the correlation of energy barriers between small QM regions and big QM regions. A roughly positive correlation was found. This kind of correlation has not been reported in the literature. The correlation inspires us to propose a protocol for more efficient sampling. This saves 50% of the computational cost in our current case

Incorporation of Poly(Ionic Liquid) with PVDF-HFP-Based Polymer Electrolyte for All-Solid-State Lithium-Ion Batteries

A solid-state polymer electrolyte membrane is formed by blending poly(vinylidene fluoride-co-hexafluoropropylene) with the synthesized copolymer of poly(methyl methacrylate-co-1-vinyl-3-butyl-imidazolium bis(trifluoromethanesulfonyl)imide, in which lithium bis(trifluoromethane)sulfonimide molecules are applied as the source of lithium ions. The accordingly formed membrane that contains 14 wt.% of P(MMA-co-VBIm-TFSI), 56 wt.% of PVDF-HFP, and 30 wt.% of LiTFSI manifests the best electrochemical properties, achieving an ionic conductivity of 1.11 × 10−4 S·cm−1 at 30 °C and 4.26 × 10−4 S·cm−1 at 80 °C, a Li-ion transference number of 0.36, and a wide electrochemical stability window of 4.7 V (vs. Li/Li+). The thus-assembled all-solid-state lithium-ion battery of LiFePO4/SPE/Li delivers a discharge specific capacity of 148 mAh·g−1 in the initial charge–discharge cycle at 0.1 C under 60 °C. The capacity retention of the cell is 95.2% after 50 cycles at 0.1 C and the Coulombic efficiency remains close to 100% during the cycling process