Learning to Group Auxiliary Datasets for Molecule

The limited availability of annotations in small molecule datasets presents a challenge to machine learning models. To address this, one common strategy is to collaborate with additional auxiliary datasets. However, having more data does not always guarantee improvements. Negative transfer can occur when the knowledge in the target dataset differs or contradicts that of the auxiliary molecule datasets. In light of this, identifying the auxiliary molecule datasets that can benefit the target dataset when jointly trained remains a critical and unresolved problem. Through an empirical analysis, we observe that combining graph structure similarity and task similarity can serve as a more reliable indicator for identifying high-affinity auxiliary datasets. Motivated by this insight, we propose MolGroup, which separates the dataset affinity into task and structure affinity to predict the potential benefits of each auxiliary molecule dataset. MolGroup achieves this by utilizing a routing mechanism optimized through a bi-level optimization framework. Empowered by the meta gradient, the routing mechanism is optimized toward maximizing the target dataset's performance and quantifies the affinity as the gating score. As a result, MolGroup is capable of predicting the optimal combination of auxiliary datasets for each target dataset. Our extensive experiments demonstrate the efficiency and effectiveness of MolGroup, showing an average improvement of 4.41%/3.47% for GIN/Graphormer trained with the group of molecule datasets selected by MolGroup on 11 target molecule datasets

Propagation Characteristics Of Density Currents And Implications To Pollutant Transport In A Stratified Reservoir

With global warming, the frequency and intensity of extreme rainfall events were predicted to change more dramatically in the near future while the amount of total precipitation will change slightly. Large volume of turbid inflow will enter the source water reservoir after a heavy rainfall, and evolve in various types of density currents depending on the density difference between the inflow and background water. Density currents play an important role in the thermal structure and pollutant transport in the reservoir. Understanding the behaviors of density current is fundamental to study the changes of source water quality during the flooding season. Characteristics of density currents were first experimentally investigated in a pilot stratified reservoir with a length of 2.0m and a depth of 0.54m, in which the thermal stratification was achieved with a heating method. When the stratification stability indexes were of 0.0112～0.0197 m-1 and the buoyancy frequencies were of 0.3314～0.4393 s-1, the turbid inflow was observed to separate from the bed slope and to propagate horizontally into its equilibrium layer, namely interflow. The separation depth of density currents and the thickness of the interflow were both smaller in the strong stratification cases than those in the weak cases, which had an important impact on the pollutant transport in the reservoir. Propagation characteristics of density currents and its implications to pollutant transport were systemically explored by numerically simulating behaviors of density currents under different conditions of stratification stability index, inflow velocity and sediment content of inflow. After careful calibration of Euler-Euler model, the simulated separation depth of density currents and the thickness of the interflow agreed well with the experimental ones, which showed the propagation of inflow was closely related to the stratification level. Impacts of inflow velocity and sediment content of inflow on the propagation of density currents were different under the simulated conditions. When the volume fraction of sediment in the inflow was increased from 0.025% to 0.20%, the separation depth of density currents was decreased from 21.0cm to 18.5cm, the thickness of the interflow was slightly increased from 6.2cm to 7.8cm, but the heights of the internal hydraulic jump were almost the same. The inflow velocity mainly influenced the time of developing the interflow, the developing time decreased as the inflow velocity increased, which implied the water quality would deteriorate quickly after a heavy rainfall. Under larger inflow velocity conditions, mixing between the inflow and background water was stronger due to the higher energy carried by the inflow, and this caused the larger depth of interflow and the bigger height of internal hydraulic jump, which indicated the pollutants carried by turbid inflow would be transported more widely

In-Situ Algae Control Using Water-Lifting Aerators In A Eutrophicated Source Water Reservoir

Algal pollution is a common water quality problem in many source water reservoirs worldwide and directly threatens the safety of drinking water. Water-lifting aeration has been recently developed to improve the source water quality and to control the excessive algae growth by transporting algae from the surface water to the bottom water of the reservoir. Taking the Shibianyu Reservoir supplying 20% source water to Xi’an, China, as a study case, the effect of in-situ algae control using water-lifting aerators was numerically investigated with Fluent. Two submerged water-lifting aerators with different circulation flow rates were installed with different local water depths respectively. Accurate geometry data required for the mesh generation were obtained using a global position system based on real time kinematic technique (V8 Star) and a depth meter (HD 17). The three-dimensional flow velocities were measured with an Acoustic Doppler Profiler (WH600kHz, LAUREL). The water-lifting aerator was simplified as a cylinder and the periodic velocity at the aerator’s outlet was numerically calculated with Euler-Euler multiphase model. The temporal distribution of velocity at the domain inlet, the vertical distributions of initial water temperature and volume fraction of algae, the density and viscosity with water temperature were all imposed with user defined functions written in C programming language. The volume fraction of algae was calculated with the algae content and algae diameter. The algae transport was also simulated with Euler-Euler multiphase model and the turbulence was modeled with RNG model. Before running the aerators, the water temperature decreased from 18℃ at the surface to 16℃ at the water depth of 46m, and to 10℃ at the depth of 50m; the volume fraction of algae increased from 2.0e-3 at the surface to 3.3e-3 at the depth of 5m, decreased to 9.1e-5 at the depth of 15m and then remained constant in the deeper water. At day 10 after operating the aerators, the water temperature became nearly uniform in the reservoir, the algae content in the surface area was greatly decreased, but the algae content in the lower water was increased. Due to its floating velocity of 0.000275m/s, the microcystis aeruginosa, which is the dominant species of algae, was transported to and deposited at zones near the bottom and side wall. The distributions of simulated flow velocity, water temperature and algae content agreed well with the measured ones on the field. Based on the simulation results of algae transport under other characteristic water levels and temperature gradients, the effect of algae control using water-lifting aeration was comprehensively evaluated and the optimized operational conditions were suggested for the reservoir management

Enriched Physical Environment Attenuates Spatial and Social Memory Impairments of Aged Socially Isolated Mice

Background: Social isolation in the elderly is one of the principal health risks in an aging society. Physical environmental enrichment is shown to improve sensory, cognitive, and motor functions, but it is unknown whether environmental enrichment can protect against brain impairments caused by social isolation. Methods: Eighteen-month-old mice were housed, either grouped or isolated, in a standard or enriched environment for 2 months, respectively. Behavioral tests were performed to evaluate cognitive functional and social interaction ability. Synaptic protein levels, myelination, neuroinflammation, brain derived neurotrophic factor, and NOD-like receptor protein 3 inflammasome signaling pathways were examined in the medial prefrontal cortex and hippocampus. Results: Isolated aged mice exhibited declines in spatial memory and social memory compared with age-matched littermates living within group housing. The aforementioned memory malfunctions were mitigated in isolated aged mice that were housed in a large cage with a running wheel and novel toys. Enriched housing prevented synaptic protein loss, myelination defects, and downregulation of brain derived neurotrophic factor, while also increasing interleukin 1 beta and tumor necrosis factor alpha in the medial prefrontal cortex and hippocampus of isolated mice. In addition, activation of glial cells and NOD-like receptor protein 3 inflammasomes was partially ameliorated in the hippocampus of isolated mice treated with physical environmental enrichment. Conclusions: These results suggest that an enriched physical environment program may serve as a nonpharmacological intervention candidate to help maintain healthy brain function of elderly people living alone

A novel method of rapid detection for heavy metal copper ion via a specific copper chelator bathocuproinedisulfonic acid disodium salt

The extensive usage and production of copper may lead to toxic effects in organisms due to its accumulation in the environment. Traditional methods for copper detection are time consuming and infeasible for field usage. It is necessary to discover a real-time, rapid and economical method for detecting copper to ensure human health and environmental safety. Here we developed a colorimetric paper strip method and optimized spectrum method for rapid detection of copper ion based on the specific copper chelator bathocuproinedisulfonic acid disodium salt (BCS). Both biological assays and chemical methods verified the specificity of BCS for copper. The optimized reaction conditions were 50 mM Tris–HCl pH 7.4, 200 μM BCS, 1 mM ascorbate and less than 50 μM copper. The detection limit of the copper paper strip test was 0.5 mg/L by direct visual observation and the detection time was less than 1 min. The detection results of grape, peach, apple, spinach and cabbage by the optimized spectrum method were 0.91 μg/g, 0.87 μg/g, 0.19 μg/g, 1.37 μg/g and 0.39 μg/g, respectively. The paper strip assays showed that the copper contents of grape, peach, apple, spinach and cabbage were 0.8 mg/L, 0.9 mg/L, 0.2 mg/L, 1.3 mg/L and 0.5 mg/L, respectively. These results correlated well with those determined by inductively coupled plasma-mass spectrometry (ICP-MS). The visual detection limit of the paper strip based on Cu-BCS-AgNPs was 0.06 mg/L. Our study demonstrates the potential for on-site, rapid and cost-effective copper monitoring of foods and the environment

BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs

In-Batch contrastive learning is a state-of-the-art self-supervised method that brings semantically-similar instances close while pushing dissimilar instances apart within a mini-batch. Its key to success is the negative sharing strategy, in which every instance serves as a negative for the others within the mini-batch. Recent studies aim to improve performance by sampling hard negatives \textit{within the current mini-batch}, whose quality is bounded by the mini-batch itself. In this work, we propose to improve contrastive learning by sampling mini-batches from the input data. We present BatchSampler\footnote{The code is available at \url{https://github.com/THUDM/BatchSampler}} to sample mini-batches of hard-to-distinguish (i.e., hard and true negatives to each other) instances. To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances. To form the mini-batch, we leverage random walk with restart on the proximity graph to help sample hard-to-distinguish instances. BatchSampler is a simple and general technique that can be directly plugged into existing contrastive learning models in vision, language, and graphs. Extensive experiments on datasets of three modalities show that BatchSampler can consistently improve the performance of powerful contrastive models, as shown by significant improvements of SimCLR on ImageNet-100, SimCSE on STS (language), and GraphCL and MVGRL on graph datasets.Comment: 17 pages, 16 figure

Effects of Seasonal Thermal Stratification on the Functional Diversity and Composition of the Microbial Community in a Drinking Water Reservoir

The microbial communities within reservoir ecosystems are shaped by water quality and hydrological characteristics. However, there are few studies focused on the effects of thermal stratification on the bacterial community diversity in drinking water reservoirs. In this study, we collected water samples from the Jinpen Reservoir around the re-stratification period. To explore the functional diversity and bacterial community composition, we used the Biolog method and 16S rRNA-based 454 pyrosequencing combined with flow cytometry. The results indicated that stratification of the reservoir had great effects on temperature and oxygen profiles, and both the functional diversity and the composition of the bacterial community strongly reflected the significant vertical stratification in the reservoir. The results of the Biolog method showed a significantly higher utilization of carbon sources in the hypolimnion than in the epilimnion. The result of pyrosequencing also showed a significantly higher species diversity and richness in the hypolimnion than in the epilimnion with different dominant phylum. Redundancy analysis also indicated that the majority of environmental variables, especially pH and dissolved oxygen, played key roles in shaping bacterial community composition. Our study provides a better understanding of the functional diversity of bacterial communities, and the response of microorganisms to seasonal thermal stratification

Effect of water stratification and mixing on phytoplankton functional groups: a case study of Xikeng Reservoir, China

A shift in reservoir stratification and mixing significantly affects the water column ecosystem, which in turn leads to changes in phytoplankton abundance and community structure. To explore the effects of stratification and mixing on the phytoplankton community structure of a diversion reservoir, a 1-year survey was divided into a stratification period in 2020, a mixing period in 2020, and a stratification period in 2021, and redundancy analysis (RDA), variance partitioning analysis (VPA) and Pearson correlation analysis were used to analyse the key drivers affecting the phytoplankton functional groups, using Xikeng Reservoir as a case study. During the study period, 8 phyla, 69 genera and 9 major functional groups were observed in this reservoir. The dominant functional groups varied significantly, being X1 in the stratified period in 2020; P and D in the mixing period in 2020; and D, X1, and M in the stratified period in 2021. The phytoplankton diversity index was greater in the mixing period than in the stratification period, in agreement with the results of the aquatic ecological status evaluation (Q index, higher in the mixing period than in the stratification period). However, phytoplankton diversity of Xikeng Reservoir was of limited value in assessing the degree of water pollution, so should be considered in combination with the Q index. Water temperature (WT), mixing depth (Zmix), nitrogen–phosphorus ratio (N/P), and total nitrogen (TN) were important drivers of phytoplankton functional group dynamics in different periods. The study provides a valuable reference for assessing the relationship between environmental factors and phytoplankton communities, as well as for the evaluation and conservation of aquatic ecosystems in southern China's water diversion reservoirs

Sediment enzyme activities and microbial community diversity in an oligotrophic drinking water reservoir, eastern China.

Drinking water reservoir plays a vital role in the security of urban water supply, yet little is known about microbial community diversity harbored in the sediment of this oligotrophic freshwater environmental ecosystem. In the present study, integrating community level physiological profiles (CLPPs), nested polymerase chain reaction (PCR)-denaturing gradient gel electrophoresis (DGGE) and clone sequence technologies, we examined the sediment urease and protease activities, bacterial community functional diversity, genetic diversity of bacterial and fungal communities in sediments from six sampling sites of Zhou cun drinking water reservoir, eastern China. The results showed that sediment urease activity was markedly distinct along the sites, ranged from 2.48 to 11.81 mg NH₃-N/(g·24 h). The highest average well color development (AWCD) was found in site C, indicating the highest metabolic activity of heterotrophic bacterial community. Principal component analysis (PCA) revealed tremendous differences in the functional (metabolic) diversity patterns of the sediment bacterial communities from different sites. Meanwhile, DGGE fingerprints also indicated spatial changes of genetic diversity of sediment bacterial and fungal communities. The sequence BLAST analysis of all the sediment samples found that Comamonas sp. was the dominant bacterial species harbored in site A. Alternaria alternate, Allomyces macrogynus and Rhizophydium sp. were most commonly detected fungal species in sediments of the Zhou cun drinking water reservoir. The results from this work provide new insights about the heterogeneity of sediment microbial community metabolic activity and genetic diversity in the oligotrophic drinking water reservoir