Fractional Denoising for 3D Molecular Pre-training

Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples and isotropic force field. The underlying reason is that molecular distributions assumed by existing denoising methods fail to capture the anisotropic characteristic of molecules. To tackle these challenges, we propose a novel hybrid noise strategy, including noises on both dihedral angel and coordinate. However, denoising such hybrid noise in a traditional way is no more equivalent to learning the force field. Through theoretical deductions, we find that the problem is caused by the dependency of the input conformation for covariance. To this end, we propose to decouple the two types of noise and design a novel fractional denoising method (Frad), which only denoises the latter coordinate part. In this way, Frad enjoys both the merits of sampling more low-energy structures and the force field equivalence. Extensive experiments show the effectiveness of Frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of MD17

Multimodal Molecular Pretraining via Modality Blending

Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery. Current studies consider leveraging both 2D and 3D molecular structures for representation learning. However, relying on straightforward alignment strategies that treat each modality separately, these methods fail to exploit the intrinsic correlation between 2D and 3D representations that reflect the underlying structural characteristics of molecules, and only perform coarse-grained molecule-level alignment. To derive fine-grained alignment and promote structural molecule understanding, we introduce an atomic-relation level "blend-then-predict" self-supervised learning approach, MoleBLEND, which first blends atom relations represented by different modalities into one unified relation matrix for joint encoding, then recovers modality-specific information for 2D and 3D structures individually. By treating atom relationships as anchors, MoleBLEND organically aligns and integrates visually dissimilar 2D and 3D modalities of the same molecule at fine-grained atomic level, painting a more comprehensive depiction of each molecule. Extensive experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D molecular benchmarks. We further provide theoretical insights from the perspective of mutual-information maximization, demonstrating that our method unifies contrastive, generative (cross-modality prediction) and mask-then-predict (single-modality prediction) objectives into one single cohesive framework

Comparison of Actual Performance in the Flow and Fraction of Inspired O2 among Different High-Flow Nasal Cannula Devices: A Bench Study

Background. High-flow nasal cannula (HFNC) oxygen therapy has been recommended for use in coronavirus disease 2019 (COVID-19) patients with acute respiratory failure and many other clinical conditions. HFNC devices produced by different manufacturers may have varied performance. Whether there is a difference in these devices and the extent of the differences in performance remain unknown. Methods. Four HFNC devices (AIRVO 2, TNI softFlow 50, HUMID-BH, and OH-70C) and a ventilator with an HFNC module (bellavista 1000) were evaluated. The flow was set at 20, 25, 30, 35, 40, 45, 50, 60, 70, and 80 L/min, and the FiO2 was set at 21%, 26%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, 80%, and 90%. Then, one side of the cannulas was clipped to simulate the compression, bending, or blocking of the nasal cannulas. The flow and FiO2 of the delivered gas were recorded and compared among settings and devices. Results. The actual-flow and actual-FiO2 delivered by different settings and devices varied. AIRVO 2 had superior performance in flow and FiO2 accuracy. bellavista 1000 and OH-70C had good performance in the accuracy of actual-flows and actual-FiO2, respectively. bellavista 1000 and HUMID-BH had a larger flow range from 10 to 80 L/min, but only bellavista 1000 could provide a stable flow with an excessive resistance up to 60 L/min. TNI softFlow 50 had the best flow compensation and could provide sufficient flow with excessive resistance at 20–50 L/min. Conclusions. The variation in flow, FiO2 settings, and devices could influence the actual-flow and actual-FiO2 delivered. AIRVO 2 and OH-70C showed better FiO2 accuracy. TNI softFlow 50, bellavista 1000, and HUMID-BH could lower the risk of insufficient flow support due to accidental compression or blocking of the cannulas. In addition, ventilators with HFNC modules provided comparable flow and FiO2 and could be an alternative to standalone HFNC devices

Elastic Information Bottleneck

Information bottleneck is an information-theoretic principle of representation learning that aims to learn a maximally compressed representation that preserves as much information about labels as possible. Under this principle, two different methods have been proposed, i.e., information bottleneck (IB) and deterministic information bottleneck (DIB), and have gained significant progress in explaining the representation mechanisms of deep learning algorithms. However, these theoretical and empirical successes are only valid with the assumption that training and test data are drawn from the same distribution, which is clearly not satisfied in many real-world applications. In this paper, we study their generalization abilities within a transfer learning scenario, where the target error could be decomposed into three components, i.e., source empirical error, source generalization gap (SG), and representation discrepancy (RD). Comparing IB and DIB on these terms, we prove that DIB’s SG bound is tighter than IB’s while DIB’s RD is larger than IB’s. Therefore, it is difficult to tell which one is better. To balance the trade-off between SG and the RD, we propose an elastic information bottleneck (EIB) to interpolate between the IB and DIB regularizers, which guarantees a Pareto frontier within the IB framework. Additionally, simulations and real data experiments show that EIB has the ability to achieve better domain adaptation results than IB and DIB, which validates the correctness of our theories

Dietary γ-Aminobutyric Acid Supplementation Inhibits High-Fat Diet-Induced Hepatic Steatosis via Modulating Gut Microbiota in Broilers

The present study aims to investigate the effect of γ-aminobutyric acid (GABA) on liver lipid metabolism and on AA broilers. Broilers were divided into three groups and fed with low-fat diets, high-fat diets, and high-fat diets supplemented with GABA. Results showed that GABA supplementation decreased the level of triglyceride (TG) in the serum and liver of broilers fed high-fat diets, accompanied by up-regulated mRNA expression of genes related to lipolysis and β-oxidation in the liver (p < 0.05). Furthermore, GABA supplementation increased liver antioxidant capacity, accompanied by up-regulated mRNA expression of antioxidant genes (p < 0.05). 16S rRNA gene sequencing showed that GABA improved high-fat diet-induced dysbiosis of gut microbiota, increased the relative abundance of Bacteroidetes phylum and Barnesiella genus, and decreased the relative abundance of Firmicutes phylum and Ruminococcus_torques_group and Romboutsia genus (p < 0.05). Moreover, GABA supplementation promoted the production of propionic acid and butyric acid in cecal contents. Correlation analysis further suggested the ratio of Firmicutes/Bacteroidetes negatively correlated with hepatic TG content, and positively correlated with cecal short chain fatty acids content (r > 0.6, p < 0.01). Together, these data suggest that GABA supplementation can inhibit hepatic TG deposition and steatosis via regulating gut microbiota in broilers

A Leakage Model of Contact Mechanical Seals Based on the Fractal Theory of Porous Medium

A theoretical model for calculating the leakage rate of contact mechanical seals based on the fractal theory of the porous media, which can consider the real seal contact interface and objectively reflect the flow of the interfacial fluid from a microscopic perspective, is established. In order to obtain the microstructural parameters of the porous media included in the leakage model, such as the fractal dimension and the maximum pore diameter, the real seal contact interface obtained from experiments is reconstructed, a contact model between the dynamic and static rings is proposed, and then the calculation methods for the interface characteristic parameters are provided. Numerical simulation results show that as the contact pressure increases from 0.05 to 0.5 MPa, the interface porosity and the maximum pore diameter decreases gradually. Furthermore, the fractal dimension of the pore area increases and the leakage rate of the interface decreases from 0.48 to 0.33 mL/h. The proposed method provides a novel way of calculating the leakage rate of contact mechanical seals

Comparison of the Effects of Feeding Compound Probiotics and Antibiotics on Growth Performance, Gut Microbiota, and Small Intestine Morphology in Yellow-Feather Broilers

This study was devoted to the comparison of the probiotic effect of compound probiotics to antibiotics as a feed additive for chicken. Two hundred and seventy newly hatched yellow-feather broilers were randomly divided into three groups: the control group (Con), probiotics (Pb), and antibiotics group (Ab). The Pb group received compound probiotics (Bifidobacterium, Lactobacillus acidophilus, Streptococcus faecalis, and yeast) via drinking water for 24 days. The Ab group received antibiotics (zinc bacitracin and colistin sulfate) in their diet for 24 days. All broilers were slaughtered on day 42. Compared with the Con group, the body weight was significantly increased on days 13, 28, and 42 in the Pb group (p p p p p > 0.05). The feed conversion ratio of the broilers treated with antibiotics or probiotics significantly decreased compared to the Con group (p p p Bacteroides and Barnesiella were the most significantly enriched bacteria in the Ab and Pb groups, respectively (p p Barnesiella abundance, which is related to a decrease in the drug-resistant gene expression. Moreover, the probiotics treatment improved small intestinal morphology and fecal emissions, while antibiotics have no significant effect on these indicators, indicating a bright future for probiotics as an alternative to feed antibiotics in the yellow-feather broiler industry

Adjuvant Therapy of Oral Chinese Herbal Medicine for Menopausal Depression: A Systematic Review and Meta-Analysis

Objective. The aim of this meta-analysis was to evaluate the effectiveness of oral Chinese herbal medicine (OCHM) combined with pharmacotherapy for menopausal depression. Methods. The electronic databases were searched from their inception to December 25, 2016, comprising PubMed, Embase, Cochrane Central Register of Controlled Trials, Chinese National Knowledge Infrastructure (CNKI), Wanfang database, and Chinese Biomedical (CBM) database. Randomized controlled trials investigating the effectiveness of OCHM combined with pharmacotherapy for the people with menopausal depression were eligible. Risk of bias was evaluated according to the Cochrane handbook. Meta-analyses were performed to pool the effect size. Heterogeneity and publication bias were also examined. Results. Twenty-two RCTs with 1770 participants were included in the review. None of the studies used placebo as the control and the risk of bias was high in blinding the participants and personnel. Overall, the meta-analysis demonstrated that adjuvant therapy of OCHM was effective in reducing the Hamilton Rating Scale for Depression (HAMD) scores compared to pharmacotherapy (MD = −3.75; 95% CI = −5.22, −2.29; P < 0.00001). The meta-analysis also suggested that OCHM adjuvant therapy for menopausal depression was superior to pharmacotherapy in terms of response rate of reducing HAMD scores (RR = 1.17; 95% CI = 1.10, 1.25; I2 = 55%). Conclusions. OCHM may provide additional effectiveness to pharmacotherapy for the people with menopausal depression. RCTs including the placebo control were required to further determine the additional efficacy of OCHM for menopausal depression