Ground air pollutants explanation based on multiple visibility graph of complex network by temporal community division.

In air pollution studies, the correlation analysis of environmental variables has usually been challenged by parametric diversity. Such variable variations are not only from the extrinsic meteorological conditions and industrial activities but also from the interactive influences between the multiple parameters. A promising solution has been motivated by the recent development of visibility graph (VG) on multi-variable data analysis, especially for the characterization of pollutants' correlation in the temporal domain, the multiple visibility graph (MVG) for nonlinear multivariate time series analysis has been verified effectively in different realistic scenarios. To comprehensively study the correlation between pollutant data and season, in this work, we propose a multi-layer complex network with a community division strategy based on the joint analysis of the atmospheric pollutants. Compared to the single-layer-based complex networks, our proposed method can integrate multiple different atmospheric pollutants for analysis, and combine them with multivariate time series data to obtain higher temporary community division for ground air pollutants interpretation. Substantial experiments have shown that this method effectively utilizes air pollution data from multiple representative indicators. By mining community information in the data, it successfully achieves reasonable and strong interpretive analysis of air pollution data.

Aspiration-driven co-evolution of cooperation with individual behavioral diversity.

In evolutionary game, aspiration-driven updates and imitation updates are the two dominant game models, and individual behavior patterns are mainly categorized into two types: node player and link player. In more recent studies, the mixture strategy of different types of players has been proven to improve cooperation substantially. Motivated by such a co-evolution mechanism, we combine aspiration dynamics with individual behavioral diversity, where self-assessed aspirations are used to update imitation strategies. In this study, the node players and the link players are capable to transform into each other autonomously, which introduces new features to cooperation in a diverse population as well. In addition, by driving all the players to form specific behavior patterns, the proposed mechanism achieves a survival environment optimization of the cooperators. As expected, the interaction between node players and link players allows the cooperator to avoid the invasion of the defector. Based on the experimental evaluation, the proposed work has demonstrated that the co-evolution mechanism has facilitated the emergence of cooperation by featuring mutual transformation between different players. We hope to inspire a new way of thinking for a promising solution to social dilemmas.

Federated Learning via Conditional Mutual Learning for Alzheimer's Disease Classification on T1w MRI.

Data-driven deep learning has been considered a promising method for building powerful models for medical data, which often requires a large amount of diverse data to be sufficiently effective. However, the expensive cost of collecting and the privacy constraints lead to the fact that existing medical datasets are small-scale and distributed. Federated learning via model distillation is a data-private collaborative learning where the model can leverage all available data without direct sharing. The data knowledge is shared by distillation through the multi-site average prediction scores on the public dataset. However, the average consensus is suboptimal to individual client due to data domain shift in MRI data caused by acquisition protocols, recruitment criteria, etc. In this work, we propose a federated conditional mutual learning (FedCM) to improve the performance by considering the clients' local performance and the similarity between clients. This work is the first federated learning on multi-dataset Alzheimer's disease classification by 3DCNN using T1w MRI. Our method achieves the best recognition rates comparing with FedMD and other frameworks. Further visualization and relevance ranking on the region of interests (ROI) in human brains implies that the left hemisphere may have greater relevance than the right hemisphere does. Several potential regions are listed for future investigation.

One-Pot CuI/DBU-Catalyzed Carboxylative Cyclization toward Oxazolidinones Using Recyclable Molecular Sieves as Efficient Promoters for Fixation of CO2 in Water Medium.

An efficient one-pot synthesis of oxazolidinones was developed through CuI/DBU/MS joint system-catalyzed carboxylative cyclization of arylacetylene, arylaldehyde, and arylamine in water medium under a 1 atm carbon dioxide (CO2) atmosphere. The 4 Šmolecular sieves (MSs) were added to improve CO2 capture and facilitate carboxylation to give the products in high yields. The CuI/DBU/MS system is robust and highly effective for the reactions with different substrates, and some target products were obtained in an excellent yield of ∼96%, with no side products in the final step.

Behavior Score-Embedded Brain Encoder Network for Improved Classification of Alzheimer Disease Using Resting State fMRI.

The ability to accurately detect onset of dementia is important in the treatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients are based on an integrated assessment of psychological tests and brain imaging such as positron emission tomography (PET) and anatomical magnetic resonance imaging (MRI). In this work using two different datasets, we propose a behavior score-embedded encoder network (BSEN) that integrates regularly adminstrated psychological tests information into the encoding procedure of representing subject's resting-state fMRI data for automatic classification tasks. BSEN is based on a 3D convolutional autoencoder structure with contrastive loss jointly optimized using behavior scores from Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). Our proposed classification framework of using BSEN achieved an overall recognition accuracy of 59.44% (3-class classification: AD, MCI and Healthy Control), and we further extracted the most discriminative regions between healthy control (HC) and AD patients.

Automation of fizzy extraction enabled by inexpensive open-source modules.

The implementation of most instrumental analysis methods requires a considerable amount of human effort at every step, including sample preparation, detection, and data processing. Automated analytical workflows decrease the amount of required work. However, commercial automated platforms are mainly available for well-established sample processing methods. In contrast, newly developed prototypes of analytical instruments are often operated manually, what limits their performance and decreases the chance of their adoption by the broader community. Open-source electronic modules facilitate the prototyping of complex analytical instruments and enable the incorporation of automated functions at the early stage of technique development. Here, we exemplify this advantage of open-source electronics while prototyping an automated analytical device. Fizzy extraction takes advantage of the effervescence phenomenon to extract semi-volatile solutes from the liquid to the gas phase. The entire fizzy extraction process has been automated by using three Arduino-related microcontrollers. The functions of the developed autonomous fizzy extraction device include triggering the analysis by a smartphone app, control of carrier gas pressure in the headspace of the sample chamber, displaying experimental conditions on an LCD screen, acquiring mass spectrometry data in real time, filtering electronic noise, integrating peaks, calculating the analyte concentration in the extracted sample, printing the analysis report, storing the acquired data in non-volatile memory, monitoring the condition of the motor by counting the number of extraction cycles, and cleaning the elements exposed to the sample (to minimize carryover). The performance of this automated system has been evaluated using standards and real samples.

On-line coupling of fizzy extraction with gas chromatography.

Fizzy extraction (FE) is carried out by first dissolving a carrier gas (typically, carbon dioxide) in a liquid sample at a moderate pressure (typically, 150 kPa) and then rapidly depressurizing the sample. The depressurization leads to instant release of numerous microbubbles in the liquid matrix. The abruptly released gas extracts the volatile solutes and elutes them toward a detector in a short period of time. Here, we describe on-line coupling of FE with gas chromatography (GC). The two platforms are highly compatible and could be combined following several modifications of the interface and adjustments of the extraction sequence. The analytes are released within a short period of time (1.5 s). Thus, the chromatographic peaks are satisfactorily narrow. There is no need to trap the extracted analytes in a loop or on a sorbent, as it is done in standard headspace and microextraction methods. The approach requires only minor sample pretreatment. The main parameters of the FE-GC-mass spectrometry (MS) method were optimized. The results of FE were compared with those of headspace flushing (scavenging headspace vapors), and the enhancement factors were in the order of ~ 2 to 13 (for various analytes). The limits of detection for some of the tested analytes were lower in the proposed FE-GC-MS method than in FE combined with atmospheric pressure chemical ionization MS. The method was further tested in analyses of selected real samples (apple flavor milk, mixed fruit and vegetable juice drink, mango flavored drink, pineapple green tea, toothpaste, and yogurt). Graphical abstract.

Fizzy Extraction of Volatile Organic Compounds Combined with Atmospheric Pressure Chemical Ionization Quadrupole Mass Spectrometry.

Chemical analysis of volatile and semivolatile compounds dissolved in liquid samples can be challenging. The dissolved components need to be brought to the gas phase, and efficiently transferred to a detection system. Fizzy extraction takes advantage of the effervescence phenomenon. First, a carrier gas (here, carbon dioxide) is dissolved in the sample by applying overpressure and stirring the sample. Second, the sample chamber is decompressed abruptly. Decompression leads to the formation of numerous carrier gas bubbles in the sample liquid. These bubbles assist the release of the dissolved analyte species from the liquid to the gas phase. The released analytes are immediately transferred to the atmospheric pressure chemical ionization interface of a triple quadrupole mass spectrometer. The ionizable analyte species give rise to mass spectrometric signals in the time domain. Because the release of the analyte species occurs over short periods of time (a few seconds), the temporal signals have high amplitudes and high signal-to-noise ratios. The amplitudes and areas of the temporal peaks can then be correlated with concentrations of the analytes in the liquid samples subjected to fizzy extraction, which enables quantitative analysis. The advantages of fizzy extraction include: simplicity, speed, and limited use of chemicals (solvents).