Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis

Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks

Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain. While Graph Neural Networks (GNNs) offer a structured approach to represent neuroimaging data, they are limited by their need for a predefined graph structure to depict associations between brain regions, a detail not solely provided by FCs. To bridge this gap, we introduce the Gated Graph Transformer (GGT) framework, designed to predict cognitive metrics based on FCs. Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model, further accentuating its potential in identifying pivotal neural connectivities that correlate with human cognitive processes.

Magnetic mesoporous Fe3O4@nSiO2@mSiO2 nanoparticles for high-throughput mass spectrometry detection of hydrolyzed products of organophosphorus nerve agents

Rapid and accurate detection of hydrolyzed products of organophosphorus nerve agents (OPNAs) is an important method to effectively confirm the use of these agents. OPNAs are rapidly hydrolyzed to the methyl phosphonates (MPs) in the environment, which can be used as environmental traceability marker for OPNAs. Herein, magnetic mesoporous materials combined with real-time in situ mass spectrometry (MS) were used to achieve high-throughput detection of MPs. Novel magnetic mesoporous nanoparticles Fe3O4@nSiO2@mSiO2 were synthesized via co-condensation of tetraethyl orthosilicate and cetyltrimethylammonium bromide (CTAB) on the surface of nonporous silica-coated Fe3O4 under alkaline conditions. CTAB templates were removed by the reflux of ethanol (0.0375 mM ammonium nitrate) to form mesoporous SiO2, which has a large specific surface area of 549 m2 g-1 and an excellent magnetization strength of 59.6 emu g-1. A quick, cost-effective, rugged, and safe magnetic preparation method, magnetic QuEChERS, was established with magnetic mesoporous nanoparticles (Fe3O4@nSiO2@mSiO2) as adsorption materials for direct analysis in real-time and tandem MS (DART-MS/MS) of MPs in environmental samples. The method exhibits good linearity (R2 > 0.992) in the range of 20.0-4.00 μg mL-1, the limits of detection were <5.00 ng mL-1, the limits of quantification were <20.0 ng mL-1, and the extraction recoveries were 70.2-98.1%, with relative standard deviations (RSDs) in the range of 1.97-10.6%. Additionally, using this method, analysis of 70 environmental samples could be completed within 20 min. Then, the M-QuEChERS-DART-MS/MS method was applied to the 52nd Organisation for the Prohibition of Chemical Weapons (OPCW) environmental spiked samples analysis, where the accuracy was 95.2-116%, and the RSD was 1.16-7.83%. The results demonstrated that Fe3O4@nSiO2@mSiO2 based on the QuEChERS method can quickly and efficiently remove the matrix of environmental samples and when coupled with the DART-MS/MS can achieve high-throughput determination of MPs in environmental samples.

Enhancing the Performance of the p-n Heterostructure Electrolyte for Solid Oxide Fuel Cells via A-Site-Deficiency Engineering

Semiconductor ionic electrolytes are attracting growing interest for developing low-temperature solid oxide fuel cells (LT-SOFCs). Our recent study has proposed a p-n heterostructure electrolyte based on perovskite oxide BaCo0.4Fe0.4Zr0.1Y0.1O3-δ (BCFZY) and ZnO, achieving promising fuel cell performance. Herein, to further improve the performance of the heterostructure electrolyte, an A-site-deficiency strategy is used to solely modify BCFZY for regulating the ionic conduction and catalytic activity of the heterostructure. Two new electrolytes, B0.9CFZY-ZnO and B0.8CFZY-ZnO, were developed and systematically studied. The results show that the two samples gain improved ionic conductivity and auxiliary catalytic activity after A-site deficiency as a result of the increment of the surface and interface oxygen vacancies. The single cells with B0.9CFZY-ZnO and B0.8CFZY-ZnO exhibit enhanced peak power outputs at 450-550 °C compared to the cell based on B1.0CFZY-ZnO (typically, 745 and 795 vs 542 mW cm-2 at 550 °C). Particular attention is paid to the impact of A-site deficiency on the interface energy band alignment between BxCFZY and ZnO, which suggests that the p-n heterojunction effect of BxCFZY-ZnO for charge carrier regulation can be tuned by A-site deficiency to enable high proton transport while avoiding fuel cell current leakage. This study thus confirms the feasibility of A-site-deficiency engineering to optimize the performance of the heterostructure electrolyte for developing LT-SOFCs.

The axial and sagittal CT values of the 7th thoracic vertebrae in screening for osteoporosis and osteopenia

AIM

To evaluate the difference in computed tomography (CT) attenuation value of different planes of the 7th thoracic vertebra and investigate the efficacy of axial and sagittal vertebral CT measurements in predicting osteoporosis.

MATERIALS AND METHODS

Patients who underwent routine chest CT and dual-energy X-ray absorptiometry (DXA) within 1 month were included in this retrospective study. The CT attenuation values of different planes were compared. Logistic regression and receiver operating characteristic (ROC) were used to analyse the difference of each plane in the diagnosis of osteoporosis.

RESULTS

The study included 1,338 patients (mean age of 61.9±11.9; 54% female). The CT attenuation values decreased successively in the normal group, osteopenia group, and osteoporosis group. The paired t-test results showed that the mid-axial measurements were greater than mid-sagittal measurements, with a mean difference of 9 HU, the difference was statistically significant (p<0.001, 95% confidence interval [CI] = 7.8-10.1). For each one-unit reduction in mid-sagittal CT attenuation value, the risk of osteopenia or osteoporosis increased by 3.6%. To distinguish osteoporosis from non-osteoporosis (osteopenia + normal), the sensitivity was 90% and the specificity was 52.4% at the mid-sagittal threshold of 113.7 HU.

CONCLUSIONS

The CT attenuation values of mid-sagittal plane have higher diagnostic efficacy than axial planes in predicting osteoporosis. For patients with a sagittal CT attenuation value of <113.7 HU in the T7, further DXA examination is warranted.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

Rapid detection of carbamate nerve agent analogues using dually functionalized gold nanoclusters

Carbamate nerve agents (CMNAs) are a type of lethal cholinesterase inhibitor with one or more quaternary amine centres and aromatic rings. CMNAs have been recently added to the Annex on Chemicals of the Chemical Weapons Convention (CWC) and Schedules of Controlled Chemicals of China. In this study, a rapid, sensitive and selective method was developed for the fluorescence detection of ambenonium chloride (AC) through host-guest and electrostatic dual interactions between AC and cyclodextrin/11-mercaptoundecanoic acid (CD/MUA) dually functionalized gold nanoclusters (AuNCs). Through this method, AC was detected with a limit of detection of 10.0 ng/mL. Method evaluation showed high selectivity towards AC over other related compounds. The practical applicability was verified, as satisfactory recoveries were obtained for AC spiked in river water and urine, as well as Proficiency Test samples from Organisation for the Prohibition of Chemical Weapons (OPCW). In addition, a fluorescence sensing array comprising four AuNCs was designed to distinguish six carbamates and structurally similar compounds. This method provides a potential approach for the rapid, sensitive and selective recognition and detection of CMNAs.

Latent Similarity Identifies Important Functional Connections for Phenotype Prediction

OBJECTIVE

Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets.

METHODS

LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method.

RESULTS

LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence.

CONCLUSION

We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks.

SIGNIFICANCE

We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.

Angle Basis: a Generative Model and Decomposition for Functional Connectivity

Functional connectivity (FC) is one of the most common inputs to fMRI-based predictive models, due to a combination of its simplicity and robustness. However, there may be a lack of theoretical models for the generation of FC. In this work, we present a straightforward decomposition of FC into a set of basis states of sine waves with an additional jitter component. We show that the decomposition matches the predictive ability of FC after including 5-10 bases. We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%. Additionally, we find the residual can be used for subject fingerprinting, with 97.3% same-subject, different-scan identifiability, compared to 62.5% for FC. Unlike PCA or Factor Analysis methods, our method does not require knowledge of a population to perform its decomposition; a single subject is enough. Our decomposition of FC into two equally-predictive components may lead to a novel appreciation of group differences in patient populations. Additionally, we generate synthetic patient FC based on user-specified characteristics such as age, sex, and disease diagnosis. By creating synthetic datasets or augmentations we may reduce the high financial burden associated with fMRI data acquisition.

A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks

Quick and accurate detection of inside packet drop attackers is of critical importance to reduce the damage they can have on the network. Trust mechanisms have been widely used in wireless sensor networks for this purpose. However, existing trust models are not effective because they cannot distinguish between packet drops caused by an attack and those caused by normal network failure. We observe that insider packet drop attacks will cause more consecutive packet drops than a network abnormality. Therefore, we propose the use of consecutive packet drops to speed up the detection of inside packet drop attackers. In this article, we describe a new trust model based on consecutive drops and develop a hybrid trust mechanism to seamlessly integrate the new trust model with existing trust models. We perform extensive OPNET (Optimized Network Engineering Tool) simulations using a geographic greedy routing protocol to validate the effectiveness of our new model. The simulation results show that our hybrid trust model outperforms existing trust models for all types of inside packet drop attacks, not only in terms of detection speed and accuracy as it is designed for, but also in terms of other important network performance metrics, such as packet delivery rate, routing reliability, and energy efficiency.

Dynamic weighted hypergraph convolutional network for brain functional connectome analysis

The hypergraph structure has been utilized to characterize the brain functional connectome (FC) by capturing the high order relationships among multiple brain regions of interest (ROIs) compared with a simple graph. Accordingly, hypergraph neural network (HGNN) models have emerged and provided efficient tools for hypergraph embedding learning. However, most existing HGNN models can only be applied to pre-constructed hypergraphs with a static structure during model training, which might not be a sufficient representation of the complex brain networks. In this study, we propose a dynamic weighted hypergraph convolutional network (dwHGCN) framework to consider a dynamic hypergraph with learnable hyperedge weights. Specifically, we generate hyperedges based on sparse representation and calculate the hyper similarity as node features. The hypergraph and node features are fed into a neural network model, where the hyperedge weights are updated adaptively during training. The dwHGCN facilitates the learning of brain FC features by assigning larger weights to hyperedges with higher discriminative power. The weighting strategy also improves the interpretability of the model by identifying the highly active interactions among ROIs shared by a common hyperedge. We validate the performance of the proposed model on two classification tasks with three paradigms functional magnetic resonance imaging (fMRI) data from Philadelphia Neurodevelopmental Cohort. Experimental results demonstrate the superiority of our proposed method over existing hypergraph neural networks. We believe our model can be applied to other applications in neuroimaging for its strength in representation learning and interpretation.

Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies

OBJECTIVE

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework.

METHODS

We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson's correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex l2,1-2 and l1-2 terms is introduced for selecting both common and task-specific features.

RESULTS AND CONCLUSION

We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson's correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson's correlation-based FC.

SIGNIFICANCE

This paper presents a novel framework for functional connectome developmental studies, characterizing developmental gender differences in FC patterns.

Network Configuration in App Design: The Effects of Simplex and Multiplex Networks on Team Performance

Members of mobile app design teams collaborate with each other to accomplish tasks and/or to socialize. However, how network configuration of instrumental and expressive interactions affects team creativity, efficiency, and satisfaction has not yet been studied. Accounting for both simplex and multiplex social networks in teams, this study develops a research model examining the mechanisms by which the centralization of different types of networks impacts team performance. To test our research hypotheses, we collected data from 62 student teams working on an app design class project. We found that the centralization of the instrumental-expressive multiplex network reduces teams’ information elaboration and similarity perception; the centralization of the instrumental simplex network is beneficial to information elaboration; and team information elaboration positively influences team creativity, efficiency, and satisfaction. We also found that team similarity perception negatively affects team creativity and positively affects team satisfaction. To alleviate concerns about the potential simultaneity bias between network configuration and information elaboration or similarity perception, we replicated the results based on a cross-lagged analysis with additional data collected from 48 design teams at two points: at team establishment and at project completion. This paper contributes to the literature on software development by examining the mechanisms via which the configuration of multiplex and simplex networks affects team performance.

Brain Functional Connectivity Analysis via Graphical Deep Learning

OBJECTIVE

Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions.

METHOD

In this work, a graph convolutional network (GCN) based framework is proposed by exploiting the information from both region-to-region connectivities of the brain and subject-subject relationships. We first construct an affinity subject-subject graph followed by GCN analysis. A Laplacian regularization term is introduced in our model to tackle the overfitting problem. We apply and validate the proposed model to the Philadelphia Neurodevelopmental Cohort for the brain cognition study.

RESULTS

Experimental analysis shows that our proposed framework outperforms other competing models in classifying groups with low and high Wide Range Achievement Test (WRAT) scores. Moreover, to examine each brain region's contribution to cognitive function, we use the occlusion sensitivity analysis method to identify cognition-related brain functional networks. The results are consistent with previous research yet yield new findings.

CONCLUSION AND SIGNIFICANCE

Our study demonstrates that GCN incorporating prior knowledge about brain networks offers a powerful way to detect important brain networks and regions associated with cognitive functions.

AGNet: Automatic generation network for skin imaging reports

Medical imaging has been increasingly adopted in the process of medical diagnosis, especially for skin diseases, where diagnoses based on skin pathology are extremely accurate. The diagnostic reports of skin pathology images has the distinguishing features of extreme repetitiveness and rigid formatting. However, reports written by inexperienced radiologists and pathologists can have a high error rate, and even experienced clinicians can find the reporting task both tedious and time-consuming. To address this challenge, this paper studies the automatic generation of diagnostic reports based on images of skin pathologies. A novel deep learning-based image caption framework named the automatic generation network (AGNet), which is an effective network for the automatic generation of skin imaging reports, is proposed. The proposed AGNet consists of four parts: (1) the image model that extracts features and classifies images; (2) the language model that codes data and generates words using comprehensible language; (3) the attention module that connects the "tail" of the image model and the "head" of the language model, and computes the relationship between images and captions; (4) the embedding and labeling module that processes the input caption data. In case study, The AGNet is verified on a skin pathological image dataset and compared with several state-of-the-art models. The results show that the AGNet achieves the highest scores of the evaluation metrics of image caption among all comparison models, demonstrating the promising performance of the proposed method.

Age-Aware Utility Maximization in Relay-Assisted Wireless Powered Communication Networks

This article investigates a relay-assisted wireless powered communication network (WPCN), where the access point (AP) inspires the auxiliary nodes to participate together in charging the sensor, and then the sensor uses its harvested energy to send status update packets to the AP. An incentive mechanism is designed to overcome the selfishness of the auxiliary node. In order to further improve the system performance, we establish a Stackelberg game to model the efficient cooperation between the AP–sensor pair and auxiliary node. Specifically, we formulate two utility functions for the AP–sensor pair and the auxiliary node, and then formulate two maximization problems respectively. As the former problem is non-convex, we transform it into a convex problem by introducing an extra slack variable, and then by using the Lagrangian method, we obtain the optimal solution with closed-form expressions. Numerical experiments show that the larger the transmit power of the AP, the smaller the age of information (AoI) of the AP–sensor pair and the less the influence of the location of the auxiliary node on AoI. In addition, when the distance between the AP and the sensor node exceeds a certain threshold, employing the relay can achieve better AoI performance than non-relaying systems.

Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction

OBJECTIVE

Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks.

METHODS

To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating both fMRI time series and functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data. A manifold-based regularization term is enforced to consider the relationships of subjects both within and between modalities. Furthermore, we propose the gradient-weighted regression activation mapping (Grad-RAM) and the edge mask learning to interpret the model, which is then used to identify significant cognition-related biomarkers.

RESULTS

We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score. Our model obtains superior predictive performance over GCN with a single modality and other competing approaches. The identified biomarkers are cross-validated from different approaches.

CONCLUSION AND SIGNIFICANCE

This paper develops a new interpretable graph deep learning framework for cognition prediction, with the potential to overcome the limitations of several current data-fusion models. The results demonstrate the power of MGCN in analyzing multi-modal fMRI and discovering significant biomarkers for human brain studies.

Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition

The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed to capture their complex associations, resulting in improved diagnosis of diseases. However, deep learning models are often difficult to interpret, bringing about challenges for uncovering biological mechanisms using these models. In this work, we develop an interpretable multimodal fusion model to perform automated diagnosis and result interpretation simultaneously. We name it Grad-CAM guided convolutional collaborative learning (gCAM-CCL), which is achieved by combining intermediate feature maps with gradient-based weights. The gCAM-CCL model can generate interpretable activation maps to quantify pixel-level contributions of the input features. Moreover, the estimated activation maps are class-specific, which can therefore facilitate the identification of biomarkers underlying different groups. We validate the gCAM-CCL model on a brain imaging-genetic study, and demonstrate its applications to both the classification of cognitive function groups and the discovery of underlying biological mechanisms. Specifically, our analysis results suggest that during task-fMRI scans, several object recognition related regions of interests (ROIs) are activated followed by several downstream encoding ROIs. In addition, the high cognitive group may have stronger neurotransmission signaling while the low cognitive group may have problems in brain/neuron development due to genetic variations.

Functional network estimation using multigraph learning with application to brain maturation study

Although most dramatic structural changes occur in the perinatal period, a growing body of evidences demonstrates that adolescence and early adulthood are also important for substantial neurodevelopment. We were thus motivated to explore brain development during puberty by evaluating functional connectivity network (FCN) differences between childhood and young adulthood using multi-paradigm task-based functional magnetic resonance imaging (fMRI) measurements. Different from conventional multigraph based FCN construction methods where the graph network was built independently for each modality/paradigm, we proposed a multigraph learning model in this work. It promises a better fitting to FCN construction by jointly estimating brain network from multi-paradigm fMRI time series, which may share common graph structures. To investigate the hub regions of the brain, we further conducted graph Fourier transform (GFT) to divide the fMRI BOLD time series of a node within the brain network into a range of frequencies. Then we identified the hub regions characterizing brain maturity through eigen-analysis of the low frequency components, which were believed to represent the organized structures shared by a large population. The proposed method was evaluated using both synthetic and real data, which demonstrated its effectiveness in extracting informative brain connectivity patterns. We detected 14 hub regions from the child group and 12 hub regions from the young adult group. We show the significance of these findings with a discussion of their functions and activation patterns as a function of age. In summary, our proposed method can extract brain connectivity network more accurately by considering the latent common structures between different fMRI paradigms, which are significant for both understanding brain development and recognizing population groups of different ages.

Recombinant lactobacillin PlnK adjusts the gut microbiome distribution in broilers

1. The heterologous expression and biological function of the Lactobacillus bacteriocin plantaricin K (PlnK) remain largely unknown. 2. In this study, PlnK was efficiently expressed in competent E. coli BL21 (used in transformation and protein expression) after 12 h, at 37°C and in 0.4 mmol/l isopropyl β- d-1-thiogalactopyranoside (IPTG). 3. The inhibitory bacterial spectrum of recombinant PlnK was investigated and indicated that levels of PlnK above 0.10 mg/ml produced an obvious inhibitory effect on gram-positive bacteria and gram-negative bacteria in vitro. 4. The effects of PlnK on intestinal immune function and the gut microbiome distribution in broilers were studied. The results revealed that, after consuming 2.50 × 10^-3 mg/ml of PlnK in water for one week, at the phylum level, the abundance of Firmicutes was increased and the abundance of Bacleroidetes was decreased. At the family level, the abundance of Lachnospiraceae, Ruminococcaceae and Streptococcaceae were significantly improved, but the abundance of Bacteroidaceae was reduced. At the genus level, the abundances of Lachnoclostridium, Streptococcus and Ruminococcaceae-UCG-013, were significantly up-regulated, and the abundance of Bacteroides was down-regulated. 5. After oral liquid intake of PlnK for one week, levels of secretory immunoglobulin A (sIgA) in the duodenal mucus were not significantly increased, but the mRNA levels of TLR3, MDA5, IFN-α, IFN-β, IFITM3 and IFITM10 in the duodenum were significantly reduced. 6. This study demonstrated that the recombinant PlnK could adjust the intestinal microbiome distribution and downregulate the IFN pathway.

Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis

Although convolutional neural networks have achieved tremendous success on histopathology image classification, they usually require large-scale clean annotated data and are sensitive to noisy labels. Unfortunately, labeling large-scale images is laborious, expensive and lowly reliable for pathologists. To address these problems, in this paper, we propose a novel self-ensembling based deep architecture to leverage the semantic information of annotated images and explore the information hidden in unlabeled data, and meanwhile being robust to noisy labels. Specifically, the proposed architecture first creates ensemble targets for feature and label predictions of training samples, by using exponential moving average (EMA) to aggregate feature and label predictions within multiple previous training epochs. Then, the ensemble targets within the same class are mapped into a cluster so that they are further enhanced. Next, a consistency cost is utilized to form consensus predictions under different configurations. Finally, we validate the proposed method with extensive experiments on lung and breast cancer datasets that contain thousands of images. It can achieve 90.5% and 89.5% image classification accuracy using only 20% labeled patients on the two datasets, respectively. This performance is comparable to that of the baseline method with all labeled patients. Experiments also demonstrate its robustness to small percentage of noisy labels.

Association between caregivers' anxiety and depression symptoms and feeding difficulties of preschool children: A cross-sectional study in rural China

PURPOSE

To investigate the prevalence of feeding difficulties in preschool children and explore the association between caregivers' anxiety and depression symptoms and preschool children's feeding difficulties.

METHODS

This cross-sectional study was conducted between June 2017 and January 2018 in rural areas of Anhui province, China. A total of 2231 preschool children and their caregivers were interviewed. Feeding difficulties of preschool children were reported by caregivers using the adapted Identification and Management of Feeding Difficulties (IMFeD) tool. Anxiety and depression symptoms of caregivers were evaluated via the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS).

RESULTS

In total, 54.1% of preschool children were reported to have feeding difficulties by their caregivers. Among all children, when the caregivers had symptoms of anxiety or depression, the children had a higher risk of feeding difficulties. Specifically, for caregivers' anxiety symptoms, the odds ratios (ORs) of feeding difficulties in all children, left-behind children (LBC), and non-LBC were 1.91 (95% confidence interval [CI]: 1.42-2.57), 2.04 (95% CI: 1.34-3.09), and 1.86 (95% CI: 1.21-2.87), respectively; for caregivers' depression symptoms, the ORs of feeding difficulties in all children, LBC, and non-LBC were 1.86 (95% CI: 1.46-2.39), 1.76 (95% CI: 1.24-2.51), and 2.08 (95% CI: 1.45-2.97), respectively. In addition, when caregivers who were parents or grandparents had anxiety or depression symptoms, their children had a higher risk of feeding difficulties. Specifically, for parents and grandparents with anxiety symptoms, the ORs of feeding difficulties were 1.84 (95% CI: 1.14-2.98) and 2.17 (95% CI: 1.46-3.22), respectively; for parents and grandparents with depression symptoms, the ORs of feeding difficulties were 2.03 (95% CI: 1.40-2.95) and 1.93 (95% CI: 1.37-2.73), respectively.

CONCLUSION

Caregivers' anxiety or depression symptoms are positively associated with feeding difficulties in children.

Ultrastretchable and Wireless Bioelectronics Based on All-Hydrogel Microfluidics

Hydrogel bioelectronics that can interface biological tissues and flexible electronics is at the core of the growing field of healthcare monitoring, smart drug systems, and wearable and implantable devices. Here, a simple strategy is demonstrated to prototype all-hydrogel bioelectronics with embedded arbitrary conductive networks using tough hydrogels and liquid metal. Due to their excellent stretchability, the resultant all-hydrogel bioelectronics exhibits stable electrochemical properties at large tensile stretch and various modes of deformation. The potential of fabricated all-hydrogel bioelectronics is demonstrated as wearable strain sensors, cardiac patches, and near-field communication (NFC) devices for monitoring various physiological conditions wirelessly. The presented simple platform paves the way of implantable hydrogel electronics for Internet-of-Things and tissue-machine interfacing applications.

LncRNA WWOX‑AS1 inhibits the proliferation, migration and invasion of osteosarcoma cells

Recently, numerous long non-coding (lnc)RNAs have been revealed as serving important roles in human gene regulation. Previous studies have suggested that aberrant expression of lncRNAs is associated with cancer progression and metastasis. Previous studies have also demonstrated that decreased expression of WW domain-containing oxidoreductase (WWOX) is associated with poor prognosis in numerous cancer types. However, the effect of WWOX antisense RNA 1 (WWOX-AS1) in the development of cancer remains unknown. The aim of the present study was to investigate the role of WWOX-AS1 in osteosarcoma. The expression levels of WWOX-AS1 in human osteosarcoma cell lines and a normal osteoblastic cell line were investigated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The results revealed that WWOX-AS1 expression was downregulated in osteosarcoma tissues. Furthermore, the association between WWOX-AS1 and the prognosis of patients with osteosarcoma was investigated using Kaplan-Meier and log-rank tests. The results suggested that patients exhibiting high WWOX-AS1 expression demonstrated a greater overall survival compared with patients exhibiting low WWOX-AS1 expression. In addition, overexpression and knockdown of WWOX-AS1 was performed using transfection experiments and confirmed by RT-qPCR in MG63 and SAOS2 cells, respectively. The results demonstrated that WWOX-AS1 and WWOX expression were positively correlated. Furthermore, the results of the knockdown and overexpression functional experiments suggested that WWOX-AS1 overexpression inhibited the proliferation, migration and invasion of MG63 cells, and knockdown of WWOX-AS1 enhanced the proliferation, migration and invasion of MG63 cells in SAOS2 cells. In conclusion, the results of the present study suggested that WWOX-AS1 may represent a potential biomarker and therapeutic target for the treatment of osteosarcoma.

Scalable and Automated Fabrication of Conductive Tough-Hydrogel Microfibers with Ultrastretchability, 3D Printability, and Stress Sensitivity

Creating complex three-dimensional structures from soft yet durable materials enables advances in fields such as flexible electronics, regenerating tissue engineering, and soft robotics. Tough hydrogels that mimic the human skin can bear enormous mechanical loads. By employing a spider-inspired biomimetic microfluidic nozzle, we successfully achieve continuous printing of tough hydrogels into fibers, two-dimensional networks, and even three-dimensional structures without compromising their extreme mechanical properties. The resultant thin fibers demonstrate a stretch up to 21 times of their original length at a water content of 52%, and are intrinsically transparent, biocompatible, and conductive at high stretches. Moreover, the printed robust tough-hydrogel networks can sense strain that are orders of magnitude lower than stretchable conductors by percolations of conductive particles. To demonstrate their potential application, we use printed tough-hydrogel fiber networks as wearable sensors for detecting human motions. The capability to shape tough hydrogels into complex structures by scalable continuous printing opens opportunities for new areas of applications such as tissue scaffolds, large-area soft electronics, and smart textiles.

4‑Phenylbutyrate protects rat skin flaps against ischemia‑reperfusion injury and apoptosis by inhibiting endoplasmic reticulum stress

4-phenylbutyrate (4-PBA) is a low molecular weight fatty acid, which has been demonstrated to regulate endoplasmic reticulum (ER) stress. ER stress-induced cell apoptosis has an important role in skin flap ischemia; however, a pharmacological approach for treating ischemia-induced ER dysfunction has yet to be reported. In the present study, the effects of 4-PBA-induced ER stress inhibition on ischemia-reperfusion injury were investigated in the skin flap of rats, and transcriptional regulation was examined. 4-PBA attenuated ischemia-reperfusion injury and inhibited cell apoptosis in the skin flap. Furthermore, 4-PBA reversed the increased expression levels of two ER stress markers: CCAAT/enhancer-binding protein-homologous protein and glucose-regulated protein 78. These results suggested that 4-PBA was able to protect rat skin flaps against ischemia-reperfusion injury and apoptosis by inhibiting ER stress marker expression and ER stress-mediated apoptosis. The beneficial effects of 4-PBA may prove useful in the treatment of skin flap ischemia-reperfusion injury.

Role of Glycol Chitosan-incorporated Ursolic Acid Nanoparticles in the Treatment of Osteosarcoma

Purpose: To investigate the effect of ursolic acid (UA)-incorporated glycol chitosan (GC) nanoparticles on inhibition of human osteosarcoma.Methods: U2OS and Saos-2 osteosarcoma cells were transfected with ursolic acid (UA) incorporated glycol chitosan (GC) nanoparticles. Ultraviolet (UV)  spectrophotometry was used to measure drug contents in nanoparticles at 365 nm with empty GC vehicles as blank. Bicinchoninic acid assay (BCA) method was employed to determine protein concentration. Identification of apoptosis and necrosis in osteosarcoma cells was performed by propidium iodide and FITC-annexin V reagents, respectively. FAC Scan flow cytometry was used to analyse apoptotic cells.Results: Among the range of UA concentrations tested, the minimum effective concentration was 10 μM with half inhibitory concentration IC50 of 25 μM. In U2OS cells, treatment with 10 and 25 μM UAinduced apoptosis in 5.89 ± 3.90 and 60.54 ± 5.40 % cells, respectively, compared to 2.05 ± 1.01 % cells for control. In Saos-2 cells, exposure to 10 and 25 μM UA induced apoptosis in 9.86 ± 8.89 and 47.54 ± 14.5 % cells, respectively, compared to 1.79 ± 0.23 % for control cells. Western blot analysis revealed  translocation of Bax and Bcl-2 proteins from mitochondria to cell cytosol. Increase in UA concentration  from 10 μM to 25 μM led to increase in the proportion of cells in G0/G1 phase and decrease in the number of cells in S and G2/M phases. These results confirm that UA transfection arrests cell cycle in G0/G1 phase in human osteosarcoma cell lines.Conclusion: UA transfection resulted in the inhibition of cell proliferation, Ezh2 expression inhibition, and apoptosis via mitochondrial pathway due to decrease in membrane potential and release of cytochrome C, as well as cell cycle arrest in G0/G1 phase.Keywords: Osteosarcoma, Cell cycle arrest, Palliation, Glycol chitosan, Ursolic acid

Increased expression of phospholipase Dα1 in guard cells decreases water loss with improved seed production under drought in Brassica napus

The activation of phospholipase Dα1 (PLDα1) produces lipid messenger phosphatidic acid and promotes stomatal closure in Arabidopsis. To explore the use of the PLDα1-mediated signalling towards decreasing water loss in crop plants, we introduced Arabidopsis PLDα1 under the control of a guard cell-specific promoter AtKatIpro into two canola (Brassica napus) cultivars. Multiple AtKatIpro ::PLDα1 lines in each cultivar displayed decreased water loss and improved biomass accumulation under hyperosmotic stress conditions, including drought and high salinity. Moreover, AtKatIpro ::PLDα1 plants produced more seeds than did WT plants in fields under drought. The results indicate that the guard cell-specific expression of PLDα1 has the potential to improve crop yield by enhancing drought tolerance.

Global identification of peptidase specificity by multiplex substrate profiling

We developed a simple and rapid multiplex substrate-profiling method to reveal the substrate specificity of any endo- or exopeptidase using liquid chromatography-tandem mass spectrometry sequencing. We generated a physicochemically diverse library of peptides by incorporating all combinations of neighbor and near-neighbor amino acid pairs into decapeptide sequences that are flanked by unique dipeptides at each terminus. Addition of a panel of evolutionarily diverse peptidases to a mixture of these tetradecapeptides generated information on prime and nonprime sites as well as on substrate specificity that matched or expanded upon known substrate motifs. This method biochemically confirmed the activity of the klassevirus 3C protein responsible for polypeptide processing and allowed granzyme B substrates to be ranked by enzymatic turnover efficiency using label-free quantitation of precursor-ion abundance. Additionally, the proteolytic secretions from schistosome parasitic flatworm larvae and a pancreatic cancer cell line were deconvoluted in a subtractive strategy using class-specific peptidase inhibitors.

Soil quality indicators response to application of hydrophilic polymers to a soil from a sulfide mine

In soils impacted by mining activities a vegetal cover is required to protect the site from the erosive forces of water and wind. The success of this objective depends on plant establishment and canopy closure. Polyacryalate polymers aid the growth of crops and indigenous plants in soils from sulfide mines. Soil characteristics change as a consequence of polymer application, but indicators that pinpoint these changes have not been identified yet. Our objectives were to (1) identify the sensitive indicators of changes in soil quality following polymer application, (2) relate these with assessment based on plant growth and soil cover. A mine soil was left unamended or received a characterized polyacrylate, a polyacrylate removed from diapers, or shredded diapers. Biomass of Spergularia purpurea was measured and proportion of soil cover evaluated. Soil enzymes, microbial activity, and respiration were analyzed. Availability of potentially toxic trace elements was estimated by their concentration in shoots. Factor analysis identified three factors that accounted for 94% of the variation in parameters, and the scores separated the four treatments. The indicators with greatest communality were correlated with plant growth and soil cover. The best soil quality indicators were As and Zn in shoots, protease, β-glucosidase, and fructose-induced respiration. It seems that the most important indicators to be used to assess the restoration of sulfide mine soils are those related with bioavailability of trace elements and soil enzymatic activities.

Use of hydrophilic polymers from diapers to aid the establishment of Spergularia purpurea in a mine soil

We used hydrophilic polymers from diapers to aid the establishment of an indigenous plant (Spergularia purpurea (Persoon) G. Don fil.) in a soil from a pyrite mine. Lysimeters were filled with the mine soil with no amendment (control), with a polyacrylate polymer, with a polymer removed from diapers, and with shredded diapers. The establishment of a plant cover was faster in soil amended with polymer from diapers, and 85 days after sowing the soil was completely covered in all treatments except control. The concentrations of trace elements in plant shoots decreased in amended soil. The activities of soil acid phosphatase, beta-glucosidase, protease and cellulase were greatest in soil amended with the polyacrylate polymer or with polymer removed from diapers, while the application of shredded diapers leads to values that were in general intermediate between these treatments and unamended control. Basal- and substrate-induced respirations, and dehydrogenase were greatest in soil amended with polymers, but the presence of a plastic film and fibrous materials from shredded diapers prevented any improvement in these parameters compared with unamended soil. In the second experiment, we evaluated the risk of downward movement of polymers in columns of a sandy soil. Polymer from diapers, with or without Cu, was placed at a 10 cm-depth. Five leaching cycles with artificial rain took place and leachates were analyzed for organic matter and Cu. At the end of the experiment, the soil columns were sliced and each layer was analyzed separately. Some repacking of soil and polymer particles took place, but there was no indication that polymers moved to any great depth in soil columns.

Use of insoluble polyacrylate polymers to aid phytostabilization of mine soils: effects on plant growth and soil characteristics

We evaluated the use of polyacrylate polymers to aid phytostabilization of mine soils. In a pot experiment, perennial ryegrass was grown in a mine soil and in uncontaminated soil. Growth was stimulated in the polymer-amended mine soil compared with an unamended control, and water-extractable levels of soil Cu and Zn decreased after polymer application. In an experiment performed in six 60-cm-diameter cylinders filled with fertilized mine soil, polymers were applied to three cylinders, with the remainder used as unamended control. Total biomass produced by indigenous plant species sown in polymer-amended soil was 1.8 (Spring-Summer) or 2.4 times (Fall-Winter) greater than that of plants from unamended soil. The application of polymers to the mine soil led to the greatest activity of soil enzymes. Soil pH, biomass of Spergularia purpurea and Chaetopogon fasciculatus, and activities of protease and cellulase had large loadings on principal component (PC)1, whereas growth of Briza maxima and the activities of urease, acid phosphatase, and beta-glucosidase had large loadings on PC2. The treatments corresponding to controls were located on the negative side of PC1 and PC2. Amended treatments were on the positive side of PC2 (Spring-Summer) or on the positive side of PC1 (Fall-Winter), demonstrating differential responses of plants and soil parameters in the two growth cycles.

Beta-sitosterol inhibits cell growth and induces apoptosis in SGC-7901 human stomach cancer cells

Beta-sitosterol is an important phytosterol found in plant food. It has been shown to have antiproliferative effects on cancers of the colon, breast, and prostate, but its effect on stomach cancer cells in vitro is unknown. Proliferation, cytotoxicity, and apoptosis in SGC-7901 human stomach cancer cells were examined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, clone formation, lactate dehydrogenase (LDH) leakage assay, acridine orange (AO)/ethidium bromide (EB) double staining, 4',6-diamidine-2'-phenylindole dihydrochloride (DAPI) staining, comet assay, and Western blotting. The results showed that beta-sitosterol suppresses the proliferation and induces the cell cytotoxicity of SGC-7901 stomach cancer cells in a time- and dose-dependent manner. Cells treated with different concentrations of beta-sitosterol also showed changes typical of apoptosis: morphological changes, DNA damage, increased expression of pro-caspase-3 and bax (p < 0.05), and activation of pro-caspase-3 and suppression of bcl-2 expression (p < 0.05). This study therefore revealed that beta-sitosterol significantly inhibits the growth and induces the apoptosis of SGC-7901 human stomach cancer cells in vitro. The decrease of the bcl-2/bax ratio and DNA damage may be the critical mechanisms of apoptosis induced by beta-sitosterol in SGC-7901 human stomach cancer cells.

The osteogenic potential of pseudoarthrosis tissue and bone from human scaphoid non-unions

Scaphoid fractures have the highest prevalence of non-union in the human body, but little is known about the osteogenic potential of cells at the pseudoarthrosis. It was our goal to determine whether cells isolated from non-unions could be stimulated to differentiate into osteoblasts and produce bone in vitro. Fifteen human scaphoid non-unions were excised during surgery and bone from either side of the non-union and the fibrocartilagenous central regions were harvested. Osteoblastic populations were subcultured from these. The number of bone nodules (colonies of osteoblast cells that produced bone) from all three regions was similar to the number of nodules derived from iliac bone cultures from the same patients. Treatment of cells with rhBMP-2 resulted in a 3- to 10-fold increase in bone nodule formation in vitro from cells derived from the non-unions. These data demonstrate that cells at the pseudoarthrosis have osteogenic capability and can be stimulated by rhBMP-2, possibly increasing the ability to heal.

AffyProbeMiner: a web resource for computing or retrieving accurately redefined Affymetrix probe sets

MOTIVATION

Affymetrix microarrays are widely used to measure global expression of mRNA transcripts. That technology is based on the concept of a probe set. Individual probes within a probe set were originally designated by Affymetrix to hybridize with the same unique mRNA transcript. Because of increasing accuracy in knowledge of genomic sequences, however, a substantial number of the manufacturer's original probe groupings and mappings are now known to be inaccurate and must be corrected. Otherwise, analysis and interpretation of an Affymetrix microarray experiment will be in error.

RESULTS

AffyProbeMiner is a computationally efficient platform-independent tool that uses all RefSeq mature RNA protein coding transcripts and validated complete coding sequences in GenBank to (1) regroup the individual probes into consistent probe sets and (2) remap the probe sets to the correct sets of mRNA transcripts. The individual probes are grouped into probe sets that are 'transcript-consistent' in that they hybridize to the same mRNA transcript (or transcripts) and, therefore, measure the same entity (or entities). About 65.6% of the probe sets on the HG-U133A chip were affected by the remapping. Pre-computed regrouped and remapped probe sets for many Affymetrix microarrays are made freely available at the AffyProbeMiner web site. Alternatively, we provide a web service that enables the user to perform the remapping for any type of short-oligo commercial or custom array that has an Affymetrix-format Chip Definition File (CDF). Important features that differentiate AffyProbeMiner from other approaches are flexibility in the handling of splice variants, computational efficiency, extensibility, customizability and user-friendliness of the interface.

AVAILABILITY

The web interface and software (GPL open source license), are publicly-accessible at http://discover.nci.nih.gov/affyprobeminer.

Scaling-up production of recombinant AAV vectors for clinical applications

Recombinant adeno-associated virus (AAV)-based vectors capable of expressing therapeutic gene products in vivo have shown significant promise for human gene therapy. A major challenge for these applications is the development of processes to enable production of large quantities of AAV vectors and purification of material that is well characterized and appropriate for parenteral administration. Several cell culture systems have been developed for AAV vector production, and a limited number of these demonstrate the potential to generate AAV vectors at concentrations compatible with cost-effective large-scale production. Vector purification protocols, in particular those based on the use of scalable column chromatography, have concurrently been developed that demonstrate the potential to provide highly purified AAV vector preparations with high yield. These advances support the potential for AAV vectors as therapeutic agents for gene therapy.

Purification of recombinant adeno-associated virus vectors by column chromatography and its performance in vivo

Recombinant adeno-associated virus (AAV) holds much promise for human gene therapy. While evidence indicates that AAV mediates long-term gene transfer in several different tissues, difficulty in preparing and purifying this viral vector in large quantities remains a major obstacle for evaluating AAV vectors in clinical trials. The current method of purification, based on sedimentation through cesium chloride, is not scaleable and yields product of insufficient quality. In this article we report a new technique for purifying AAV, using a fully closed two-column chromatography system. Yields of AAV vectors purified by this method are high, potency is increased, and the purity of column-purified preparations is substantially improved. We previously reported a novel method to generate AAV based on an AAV Rep/Cap-containing cell line (B50) and an Ad-AAV hybrid virus, which is amenable to scale-up in bioreactors. By combining the new, fully scaleable purification process we report here with the B50/hybrid production method, it would be feasible to prepare AAV vectors to the scale and purity required for clinical and potential commercial applications.

Vibrational spectroscopic studies on ion solvation of lithium perchlorate in propylene carbonate + N,N-dimethylformamide mixtures

The infrared (IR) and Raman spectra are reported for solutions of lithium perchlorate in propylene carbonate (PC), N,N-dimethylformamide (DMF) and PC + DMF mixtures. The band splittings of symmetric ring deformation for PC and O=CN deformation for DMF suggest that there is a strong interaction between lithium cations and solvent molecules. The solvent molecules have been assigned to two types, the free and complexed molecules. By a comparison of the intensity for the corresponding bands, it has been concluded that Li+ cations are preferentially solvated by DMF molecules in the LiClO4/PC-DMF solutions. This has been explained by the difference in values of donor number.