Altered Hierarchical Gradients of Intrinsic Neural Timescales in Mild Cognitive Impairment and Alzheimer's Disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of seniors in the United States. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of neural signals from rs-fMRI, is thought to quantify the duration that neural information is stored in a local circuit. Such heterogeneity of the timescales forms a basis of the brain functional hierarchy and captures an aspect of circuit dynamics relevant to excitation/inhibition balance, which is broadly relevant for cognitive functions. Given that, we applied rs-fMRI to test whether distinct changes of INT at different hierarchies are present in people with MCI, those progressing to AD (called Converter), and AD patients of both sexes. Linear mixed-effect model was implemented to detect altered hierarchical gradients across populations followed by pairwise comparisons to identify regional differences. High similarities between AD and Converter were observed. Specifically, the inferior temporal, caudate, and pallidum areas exhibit significant alterations in both AD and Converter. Distinct INT-related pathological changes in MCI and AD were found. For AD/Converter, neural information is stored for a longer time in lower hierarchical areas, while higher levels of hierarchy seem to be preferentially impaired in MCI leading to a less pronounced hierarchical gradient. These results inform that the INT holds great potential as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.

Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.

This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.

Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model.

Functional connectivity (FC) between brain region has been widely studied and linked with cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation of fMRI blood oxygen level-dependent (BOLD) signals between two brain regions. Although FC has been effective to understand brain organization, it cannot reveal the direction of interactions. Many directed acyclic graph (DAG) based methods have been applied to study the directed interactions but their performance was limited by the small sample size while high dimensionality of the available data. By enforcing group regularization and utilizing samples from both case and control groups, we propose a joint DAG model to estimate the directed FC. We first demonstrate that the proposed model is efficient and accurate through a series of simulation studies. We then apply it to the case-control study of schizophrenia (SZ) with data collected from the MIND Clinical Imaging Consortium (MCIC). We have successfully identified decreased functional integration, disrupted hub structures and characteristic edges (CtEs) in SZ patients. Those findings have been confirmed by previous studies with some identified to be potential markers for SZ patients. A comparison of the results between the directed FC and undirected FC showed substantial differences in the selected features. In addition, we used the identified features based on directed FC for the classification of SZ patients and achieved better accuracy than using undirected FC or raw features, demonstrating the advantage of using directed FC for brain network analysis.

Risk factors and antibiotic sensitivity of aerobic bacteria in Chinese children with adenoid hypertrophy.

BACKGROUND: Bacterial infection of adenoid is currently considered to be an important cause of adenoid hypertrophy (AH) in children. Although several bacteriology studies on adenoid diseases have been reported, the aerobic bacterial study regarding risk factors and antibiotic sensitivity of AH in Chinese children is lacking. This study aims to investigate the risk factors for aerobic bacterial colonization of AH in Chinese children and to elucidate aerobic bacterial profiles and antibiotic sensitivity. METHODS: Samples were collected from the adenoid core and surface tissue of 466 children undergoing adenoidectomy. Aerobic cultures and antibiotic sensitivity were observed. The risk factors for bacterial colonization of adenoid were analyzed statistically. RESULTS: A total of 143 children could be detected opportunistic pathogens in adenoid surface and/or core tissue, with a carriage rate of 30.7%. The presence of chronic rhinosinusitis, tonsillar hypertrophy and adenoidal size were the risk factors for aerobic bacterial colonization of adenoid in univariate analysis. Multivariate analysis showed that chronic rhinosinusitis and tonsil hypertrophy were significant variables associated with the aerobic bacterial colonization. The most frequently isolated aerobic bacteria were Haemophilus influenzae, followed by Staphylococcus aureus and Streptococcus pneumoniae. There was no statistically significant difference in bacterial species between the adenoid surface and core. The above common bacteria were more sensitive to cephalosporins and quinolones antibiotics, and significantly resistant to penicillin antibiotics and non-ß-lactamase inhibitors. CONCLUSION: Our results provide recent aerobic bacterial profiles for AH among Chinese children and confirm the risk factors and antibiotic sensitivity. This study contributes to understanding the role of different risk factors in the development of AH and will be helpful to the treatment of AH among Chinese children.

Advances in Electrostatic Spinning of Polymer Fibers Functionalized with Metal-Based Nanocrystals and Biomedical Applications.

Considering the metal-based nanocrystal (NC) hierarchical structure requirements in many real applications, starting from basic synthesis principles of electrostatic spinning technology, the formation of functionalized fibrous materials with inorganic metallic and semiconductor nanocrystalline materials by electrostatic spinning synthesis technology in recent years was reviewed. Several typical electrostatic spinning synthesis methods for nanocrystalline materials in polymers are presented. Finally, the specific applications and perspectives of such electrostatic spun nanofibers in the biomedical field are reviewed in terms of antimicrobial fibers, biosensing and so on.

Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder.

Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter-subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest-rest pair). Furthermore, high-level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high-order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions.

QTL mapping for foxtail millet plant height in multi-environment using an ultra-high density bin map.

KEY MESSAGE: Using a fixed RIL population derived from a widely used foxtail millet backbone breeding line and an elite cultivar, we constructed a high-density bin map and identified six novel multi-environment effect QTLs and seven candidate genes for dwarf phenotype. Plant height is an important trait that determines tradeoffs between competition and resource allocation, which is crucial for yield potential. To improve the C4 model plant foxtail millet (Setaria italica) productivity, it is necessary to isolate plant height-related genes that contribute to ideal plant architecture in breeding. In the present study, we generated a foxtail millet population of 333 recombinant inbred lines (RILs) derived from a cross between a backbone line Ai 88 and an elite cultivar Liaogu 1. We evaluated plant height in 13 environmental conditions across 4 years, the mean plant height of the RIL population ranged from 89.5 to 149.9 cm. Using deep re-sequencing data, we constructed a high-density bin map with 3744 marker bins. Quantitative trait locus (QTL) mapping identified 26 QTLs significantly associated with plant height. Of these, 13 QTLs were repeatedly detected under multiple environments, including six novel QTLs that have not been reported before. Seita.1G242300, a gene encodes gibberellin 2-oxidase-8, which was detected in nine environments in a 1.54-Mb interval of qPH1.3, was considered as an important candidate gene. Moreover, other six genes involved in GA biosynthesis or signaling pathways, and fifteen genes encode F-box domain proteins which might function as E3 ligases, were also considered as candidate genes in different QTLs. These QTLs and candidate genes identified in this study will help to elucidate the genetic basis of foxtail millet plant height, and the linked markers will be useful for marker-assistant selection of varieties with ideal plant architecture and high yield potential.

Genetic control and phenotypic characterization of panicle architecture and grain yield-related traits in foxtail millet (Setaria italica).

KEY MESSAGE: Multi-environment QTL mapping identified 23 stable loci and 34 co-located QTL clusters for panicle architecture and grain yield-related traits, which provide a genetic basis for foxtail millet yield improvement. Panicle architecture and grain weight, both of which are influenced by genetic and environmental factors, have significant effects on grain yield potential. Here, we used a recombinant inbred line (RIL) population of 333 lines of foxtail millet, which were grown in 13 trials with varying environmental conditions, to identify quantitative trait loci (QTL) controlling nine agronomic traits related to panicle architecture and grain yield. We found that panicle weight, grain weight per panicle, panicle length, panicle diameter, and panicle exsertion length varied across different geographical locations. QTL mapping revealed 159 QTL for nine traits. Of the 159 QTL, 34 were identified in 2 to 12 environments, suggesting that the genetic control of panicle architecture in foxtail millet is sensitive to photoperiod and/or other environmental factors. Eighty-eight QTL controlling different traits formed 34 co-located QTL clusters, including the triple QTL cluster qPD9.2/qPL9.5/qPEL9.3, which was detected 23 times in 13 environments. Several candidate genes, including Seita.2G388700, Seita.3G136000, Seita.4G185300, Seita.5G241500, Seita.5G243100, Seita.9G281300, and Seita.9G342700, were identified in the genomic intervals of multi-environmental QTL or co-located QTL clusters. Using available phenotypic and genotype data, we conducted haplotype analysis for Seita.2G002300 and Seita.9G064000,which showed high correlations with panicle weight and panicle exsertion length, respectively. These results not only provided a basis for further fine mapping, functional studies and marker-assisted selection of traits related to panicle architecture in foxtail millet, but also provide information for comparative genomics analyses of cereal crops.

Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI.

Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.

The intrinsic microstructure of supramolecular hydrogels derived from α-cyclodextrin and pluronic F127: nanosheet building blocks and hierarchically self-assembled structures.

Supramolecular hydrogels derived from the self-assembly of α-cyclodextrin with pluronic F127 were found to be built up with polypseudorotaxane nanosheets with a thickness of 30-40 nm and possessed flower-like hierarchically assembled structures. The findings in this work could provide critical guidance for material design for biomedical purposes.

LC-MS/MS assay for the quantification of foretinib in rat plasma and its application to preclinical pharmacokinetic study.

A simple and sensitive ultra-high-performance liquid chromatography tandem mass spectrometric method was developed and validated for the determination of foretinib in rat plasma. The analyte and internal standard were extracted from the bio-samples with acetonitrile and then separated on an Acquity UPLC BEH C18 column (50 × 2.1 mm, 1.7 µm) using 0.1% formic acid aqueous and acetonitrile as mobile phase, at a flow rate of 0.4 ml/min. The mass detection was performed in positive selected reaction monitoring mode with precursor-to-product transitions at m/z 317.1 > 128.1 for foretinib and m/z 502.2 > 323.1 for internal standard. The assay was demonstrated to be linear in the concentration range of 0.5-1000 ng/ml, with correlation coefficient >0.999. The mean extraction recovery of foretinib from rat plasma was within the range of 84.55-88.09%, while the matrix effect was in the range of 88.56-99.21%. The intra- and inter-day precisions were <12.95% and the accuracy ranged from -7.55 to 8.57%. Foretinib was stable in rat plasma under the tested storage conditions. The validated assay was successfully applied to the pharmacokinetic study of foretinib in the rats. The results revealed that foretinib showed moderate elimination half-life, low clearance and dose-independent pharmacokinetic profiles inrats.

Refined measure of functional connectomes for improved identifiability and prediction.

Brain functional connectome analysis is commonly based on population-wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population-wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject-specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group-wise inferences and refined connectomes are indeed desirable for brain mapping.

Analysis of potential key genes in very early hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the major pathological type of primary liver cancer, one of the leading causes of cancer death worldwide. In addition, the long-term survival rates of HCC still remain low. Therefore, we attempted to identify the potential key genes in the occurrence of HCC by comparing the expression profiles of very early HCC tissue samples with that of chronic cirrhotic tissue samples by integrating the bioinformatics analysis in this study. METHODS: Gene expression profiles of 19 very early HCC and 19 cirrhotic tissue samples were selected from GSE63898. Differentially expressed genes (DEGs) were also identified by using online tool GEO2R. Furthermore, the GO and KEGG enrichment analysis of the DGEs were conducted on DAVID datasets. Then a protein-protein interaction (PPI) network was constructed and the modules were analyzed based on STRING database and Cytoscape software. The hub genes were screened by applying the cytoHubba plugin and then analyzed with the Kaplan Meier plotter. RESULTS: A total of 118 DEGs were identified between very early HCC and cirrhotic tissue samples. These DGEs were strongly associated with several biological processes, such as negative regulation of growth and p53 signaling pathway. A PPI network was constructed and top eight hub genes, including CDKN3, CDK1, CCNB1, TOP2A, CCNA2, CCNB2, PRC1, and RRM2, were determined. High expressions of CDK1, CCNB1, TOP2A, CCNA2, PRC1, RRM2, CDKN3, and CCNB2 were associated with poorer overall survivals (OS) in HCC patients. CONCLUSION: We had compared the expression profiles between the very early HCC and cirrhotic tissue samples by using bioinformatics analysis tools, which might help us better to understand the molecular mechanism of the initiation of HCC and even to find novel targets for HCC therapy.

Sonographic diagnosis of an ileal adenomyoma in a neonate.

An ileal adenomyoma leading to intussusception is very rare. We describe the case of a 4-month-old boy who presented with hematochezia. He was diagnosed with ileal adenomyoma by sonography, which was confirmed by histopathology. This case confirms that sonography is the preferred modality to diagnose an ileal adenomyoma.

How Does PHEMA Pass through the Cavity of γ-CDs to Create Mismatched Overfit Polypseudorotaxanes?

A syndiotactic-rich PHEMA oligomer ( rr = 74%, DP = 29, PDI = 1.19) was synthesized and subsequently subjected to self-assembly with a varying amount of γ-CDs in its aqueous solution to create mismatched overfit polypseudorotaxanes (PPRs). The inclusion complexation proceeded in an obvious mismatched manner between the cavity of γ-CDs and the cross-sectional area of an incoming PHEMA chain. The 2D-NOESY NMR analysis provided direct evidence indicating that two adjacent pendant hydroxyethyl groups in PHEMA preferably adopt a curled conformation to pass through the cavity of γ-CDs, giving the PPRs characteristics of a mismatched overfit instead of a matched tight-fit crystal structure. The results suggested that the mutual adaption of pendant side chains of HEMA units with the cavity geometry of γ-CDs would play a dominant role in this unfavorable overfit inclusion complexation besides the size of γ-CDs and the stereoregularity of the PHEMA chain.

Fast and non-invasive serum detection technology based on surface-enhanced Raman spectroscopy and multivariate statistical analysis for liver disease.

This study explored a rapid and nondestructive liver disease detection technique based on surface-enhanced Raman spectroscopy (SERS) to realize the early diagnosis, prevention, and treatment of liver disease. SERS signals of serum were obtained from 304 normal individuals, 333 patients with hepatopathy, and 99 patients with esophageal cancer. The Raman spectra of different diseases were compared and diagnostic models of liver disease were established using orthogonal partial least squares discriminant analysis (OPLS-DA). The classification efficiencies of the different models were comprehensively evaluated through the receiver operating characteristic (ROC) curve and ten-fold cross validation. Area under the ROC curve is of greater than 0.97, indicating excellent classification of the groups. The accuracy rate of the test set reached 95.33%, and the lowest was 81.76% using the ten-fold cross validation. Thus, OPLS-DA combined with serum SERS is a rapid and non-invasive technique for the diagnosis of liver disease.

Rapidly Recoverable Thixotropic Hydrogels from the Racemate of Chiral OFm Monosubstituted Cyclo(Glu-Glu) Derivatives.

Both chiral OFm monosubstituted cyclo(l-Glu-l-Glu) and cyclo(d-Glu-d-Glu) display a robust gelation ability in a variety of organic solvents and water. In contrast to an individual enantiomer, their racemate can form rapidly recoverable thixotropic hydrogels with a remarkably shorter thixotropic recovery time. This unexpected thixotropic behavior is induced by the random arrangement of d- and l-enantiomers in the cell units, leading to the formation of "pseudoracemate", noncrystalline self-assemblies in the resulting 3D fibrous network.

Manure and mineral fertilization change enzyme activity and bacterial community in millet rhizosphere soils.

Fertilization is a key agricultural practice for increasing millet yields and influencing soil properties, enzyme activities and rhizosphere bacterial communities. High throughput Illumina sequencing of the 16S rDNA was applied to compare the bacterial community structures and diversities among six different soil samples. The experiments involved the following: no fertilizer (CK), phosphate (P) and potassium (K) plus organic manure (M) (PKM), nitrogen (N) and K plus M (NKM), NPM, NPK and NPKM fertilization. The results showed that the NPKM fertilization of the millet field had a maximal yield of 3617 kg ha-1 among the six different treatments. The abundances of the Actinobacteria and Bacteroidetes phyla, especially the Devosia, Mycobacterium, Opitutus and Chitinophaga genera, were higher in NPKM than those in the other samples. Redundancy analysis showed that the soil organic matter (SOM), available phosphorus (AP), and urease (UR) activity were significantly correlated with bacterial communities, while SOM and AP were strongly correlated with soil enzyme activities. Pearson's correlation showed that the available nitrogen was strongly correlated with Devosia and Mycobacterium, and SOM was strongly correlated with Opitutus and Chitinophaga. Besides, catalase was significantly related to Iamia, the UR was significantly related to Devosia, phosphatase was significantly related to Luteimonas and Chitinophaga. Based on the soil quality and millet yield, NPKM treatment was a better choice for the millet field fertilization practices. These findings provide a better understanding of the importance of fertilization in influencing millet yield, soil fertility and microbial diversity, and they lead to a choice of scientific fertilization practices for sustainable development of the agroecosystem.

Low-Molecular-Weight Organo- and Hydrogelators Based on Cyclo(l-Lys-l-Glu).

Four cyclo(l-Lys-l-Glu) derivatives (3-6) were synthesized from the coupling reaction of protecting l-lysine with l-glutamic acid followed by the cyclization, deprotection, and protection reactions. They can efficiently gelate a wide variety of organic solvents or water. Interestingly, a spontaneous chemical reaction proceeded in the organogel obtained from 3 in acetone exhibiting not only visual color alteration but also increasing mechanical strength with the progress of time due to the formation of Schiff base. Moreover, 6 bearing a carboxylic acid and Fmoc group displayed a robust hydrogelation capability in PBS solution. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) revealed the characteristic gelation morphologies of 3D fibrous network structures in the resulting organo- and hydrogels. FT-IR and fluorescence analyses indicated that the hydrogen bonding and π-π stacking play as major driving forces for the self-assembly of these cyclic dipeptides as low-molecular-weight gelators. X-ray diffraction (XRD) measurements and computer modeling provided information on the molecular packing model in the hydrogelation state of 6. A spontaneous chemical reaction proceeded in the organogel obtained from 3 in acetone exhibiting visual color alteration and increasing mechanical strength. 6 bearing an optimized balance of hydrophilicity to lipophilicity gave rise to a hydrogel in PBS with MGC at 1 mg/mL.

A retrospective review of patients with urothelial cancer in 3,370 recipients after renal transplantation: a single-center experience.

OBJECTIVE: To summarize the diagnosis, surgical intervention and postoperative management of patients with urothelial cancer (UC) after renal transplants (RTx). METHODS: In our retrospective review of 3,370 RTx recipients in our transplant center from 1974 to 2012, all recipients underwent routine checkups and follow-up. Imaging was performed in all patients suspected of having malignancies, and the histological cell type of the specimen slices was reappraised by pathologists. The data of all recipients with malignancies were retrospectively reviewed to determine clinical characteristics after RTx. RESULTS: A total of 169 patients of the cohort of 3,370 had malignancies after RTx. Of 180 tumors, 106 tumors were confirmed as UC. Fifty-two patients had taken drugs containing aristolochic acid. The median time to neoplasia after RTx in the group taking aristolochic acid (30 months) was significantly less than in those not taking aristolochic acid (60.3 months). We recommended surgical intervention for RTx recipients with UC, transurethral resection of bladder tumors for patients with solitary or concomitant superficial UC, and radical cystectomy for high-risk bladder UC. We performed simultaneous bilateral or unilateral nephroureterectomy in patients with upper urinary tract UC. CONCLUSION: Our results suggest that UC is the predominant tumor in Chinese RTx recipients and that regular urinalysis and imaging are needed in all recipients after RTx, especially women with a history of taking aristolochic acid. Surgical interventions did not increase the risk beyond that in UC patients without RTx.