AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.

Data-driven decomposition and staging of flortaucipir uptake in Alzheimer's disease.

INTRODUCTION: Previous approaches pursuing in vivo staging of tau pathology in Alzheimer's disease (AD) have typically relied on neuropathologically defined criteria. In using predefined systems, these studies may miss spatial deposition patterns which are informative of disease progression. METHODS: We selected discovery (n = 418) and replication (n = 132) cohorts with flortaucipir imaging. Non-negative matrix factorization (NMF) was applied to learn tau covariance patterns and develop a tau staging system. Flortaucipir components were also validated by comparison with amyloid burden, gray matter loss, and the expression of AD-related genes. RESULTS: We found eight flortaucipir covariance patterns which were reproducible and overlapped with relevant gene expression maps. Tau stages were associated with AD severity as indexed by dementia status and neuropsychological performance. Comparisons of flortaucipir uptake with amyloid and atrophy also supported our model of tau progression. DISCUSSION: Data-driven decomposition of flortaucipir uptake provides a novel framework for tau staging which complements existing systems. HIGHLIGHTS: NMF reveals patterns of tau deposition in AD. Data-driven staging of flortaucipir tracks AD severity. Learned flortaucipir patterns overlap with AD-related gene expression.

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics.

The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.

Genome insights into the pharmaceutical and plant growth promoting features of the novel species Nocardia alni sp. nov.

BACKGROUND: Recent studies highlighted the biosynthetic potential of nocardiae to produce diverse novel natural products comparable to that of Streptomyces, thereby making them an attractive source of new drug leads. Many of the 119 Nocardia validly named species were isolated from natural habitats but little is known about the diversity and the potential of the endophytic nocardiae of root nodule of actinorhizal plants. RESULTS: The taxonomic status of an actinobacterium strain, designated ncl2T, was established in a genome-based polyphasic study. The strain was Gram-stain-positive, produced substrate and aerial hyphae that fragmented into coccoid and rod-like elements and showed chemotaxonomic properties that were also typical of the genus Nocardia. It formed a distinct branch in the Nocardia 16S rRNA gene tree and was most closely related to the type strains of Nocardia nova (98.6%), Nocardia jiangxiensis (98.4%), Nocardia miyuensis (97.8%) and Nocardia vaccinii (97.7%). A comparison of the draft genome sequence generated for the isolate with the whole genome sequences of its closest phylogenetic neighbours showed that it was most closely related to the N. jiangxiensis, N. miyuensis and N. vaccinii strains, a result underpinned by average nucleotide identity and digital DNA-DNA hybridization data. Corresponding taxogenomic data, including those from a pan-genome sequence analysis showed that strain ncl2T was most closely related to N. vaccinii DSM 43285T. A combination of genomic, genotypic and phenotypic data distinguished these strains from one another. Consequently, it is proposed that strain ncl2T (= DSM 110931T = CECT 30122T) represents a new species within the genus Nocardia, namely Nocardia alni sp. nov. The genomes of the N. alni and N. vaccinii strains contained 36 and 29 natural product-biosynthetic gene clusters, respectively, many of which were predicted to encode for a broad range of novel specialised products, notably antibiotics. Genome mining of the N. alni strain and the type strains of its closest phylogenetic neighbours revealed the presence of genes associated with direct and indirect mechanisms that promote plant growth. The core genomes of these strains mainly consisted of genes involved in amino acid transport and metabolism, energy production and conversion and transcription. CONCLUSIONS: Our genome-based taxonomic study showed that isolate ncl2T formed a new centre of evolutionary variation within the genus Nocardia. This novel endophytic strain contained natural product biosynthetic gene clusters predicted to synthesize novel specialised products, notably antibiotics and genes associated with the expression of plant growth promoting compounds.

Using a public database of Neisseria gonorrhoeae genomes to detect mutations associated with zoliflodacin resistance.

BACKGROUND: Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is an urgent global health threat. Zoliflodacin is a novel antibiotic undergoing clinical trials for the treatment of gonorrhoea. While there are limited data regarding zoliflodacin resistance in N. gonorrhoeae, three amino acid mutations have been associated with increased MICs of zoliflodacin. OBJECTIVES: To determine the prevalence of three amino acid mutations associated with zoliflodacin resistance within a large, public database of nearly 13 000 N. gonorrhoeae genomes. METHODS: PathogenWatch is an online genomic epidemiology platform with a public database of N. gonorrhoeae genomes. That database was used to extract gyrB sequence data and a Basic Local Alignment Search Tool (BLAST) search was performed to identify any of the three amino acid mutations in GyrB that are associated with increased zoliflodacin MICs: D429N, K450N or K450T. As a control for the search methodology, all GyrA sequences were also extracted and S91F mutations were identified and compared with the PathogenWatch database. RESULTS: In total, 12 493 N. gonorrhoeae genomes from the PathogenWatch database were included. Among those genomes, none was identified that harboured any of the three mutations associated with increased zoliflodacin MICs. One genome was identified to have a mutation at position 429 in GyrB (D429V). CONCLUSIONS: The findings suggest that the prevalence of the three mutations associated with zoliflodacin resistance in N. gonorrhoeae is very low. However, further research into the mechanisms of zoliflodacin resistance in N. gonorrhoeae is needed. Genomic epidemiology platforms like PathogenWatch can be used to enhance the global surveillance of AMR.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training.

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Genome-based reclassification of Paenibacillus jamilae Aguilera et al. 2001 as a later heterotypic synonym of Paenibacillus polymyxa (Prazmowski 1880) Ash et al. 1994.

Paenibacillus is one of the genera that has high species diversity and Paenibacillus polymyxa, the type species of the genus, is mainly isolated from plant-associated environments. Among the plant-associated species, Paenibacillus jamilae B.3T (=CECT 5266T=DSM 13815T=KACC 10925T=KCTC 13919T) was proposed to be a novel species according to 16S rRNA gene similarity and DNA-DNA relatedness with related species, including Paenibacillus polymyxa. Nevertheless, in the description of Paenibacillus jamilae the used strain of Paenibacillus polymyxa was not the type strain of this species. In this work we found that the type strains of both species showed 16S rRNA gene similarity of 99.6 %. Therefore, in this study, we sequenced the genome of Paenibacillus jamilae KACC 10925T and compared it with those of the type strain of Paenibacillus polymyxa ATCC 842T and other phylogenetically related species. Genome relatedness value calculated by DNA-DNA hybridization between type strains of Paenibacillus polymyxa and Paenibacillus jamilae was 73.5 %, which is higher than the threshold value (70 %). For more objective and repeatable results of genome relatedness, we analysed an average nucleotide identity (ANI) between two strains. Our results showed that ANI value between the type strains of Paenibacillus jamilae and Paenibacillus polymyxa is 98.5 %, a phylogenetic distance also higher than the threshold values (95~96 %). These values were proposed by Yoon et al. (2017). In addition, their phylogenetic distance based on 92 bacterial core genes is highly close compared to other species. These mean that Paenibacillus jamilae and Paenibacillus polymyxa should be reclassified as a single species. Based on the results from genomic level comparison as well as reexamination results of physiological and chemotaxonomic features, we propose reclassification of Paenibacillus jamilae as a later heterotypic synonym of Paenibacillus polymyxa.

Multivalent Binding of a Ligand-Coated Particle: Role of Shape, Size, and Ligand Heterogeneity.

We utilize a multiscale modeling framework to study the effect of shape, size, and ligand composition on the efficacy of binding of a ligand-coated particle to a substrate functionalized with the target receptors. First, we show how molecular dynamics along with steered molecular dynamics calculations can be used to accurately parameterize the molecular-binding free energy and the effective spring constant for a receptor-ligand pair. We demonstrate this for two ligands that bind to the α5ß1-domain of integrin. Next, we show how these effective potentials can be used to build computational models at the meso- and continuum-scales. These models incorporate the molecular nature of the receptor-ligand interactions and yet provide an inexpensive route to study the multivalent interaction of receptors and ligands through the construction of Bell potentials customized to the molecular identities. We quantify the binding efficacy of the ligand-coated-particle in terms of its multivalency, binding free-energy landscape, and the losses in the configurational entropies. We show that 1) the binding avidity for particle sizes less than 350 nm is set by the competition between the enthalpic and entropic contributions, whereas that for sizes above 350 nm is dominated by the enthalpy of binding; 2) anisotropic particles display higher levels of multivalent binding compared to those of spherical particles; and 3) variations in ligand composition can alter binding avidity without altering the average multivalency. The methods and results presented here have wide applications in the rational design of functionalized carriers and also in understanding cell adhesion.

ContEst16S: an algorithm that identifies contaminated prokaryotic genomes using 16S RNA gene sequences.

Thanks to the recent advancement of DNA sequencing technology, the cost and time of prokaryotic genome sequencing have been dramatically decreased. It has repeatedly been reported that genome sequencing using high-throughput next-generation sequencing is prone to contaminations due to its high depth of sequencing coverage. Although a few bioinformatics tools are available to detect potential contaminations, these have inherited limitations as they only use protein-coding genes. Here we introduce a new algorithm, called ContEst16S, to detect potential contaminations using 16S rRNA genes from genome assemblies. We screened 69 745 prokaryotic genomes from the NCBI Assembly Database using ContEst16S and found that 594 were contaminated by bacteria, human and plants. Of the predicted contaminated genomes, 8 % were not predicted by the existing protein-coding gene-based tool, implying that both methods can be complementary in the detection of contaminations. A web-based service of the algorithm is available at www.ezbiocloud.net/tools/contest16s.

Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies.

The recent advent of DNA sequencing technologies facilitates the use of genome sequencing data that provide means for more informative and precise classification and identification of members of the Bacteria and Archaea. Because the current species definition is based on the comparison of genome sequences between type and other strains in a given species, building a genome database with correct taxonomic information is of paramount need to enhance our efforts in exploring prokaryotic diversity and discovering novel species as well as for routine identifications. Here we introduce an integrated database, called EzBioCloud, that holds the taxonomic hierarchy of the Bacteria and Archaea, which is represented by quality-controlled 16S rRNA gene and genome sequences. Whole-genome assemblies in the NCBI Assembly Database were screened for low quality and subjected to a composite identification bioinformatics pipeline that employs gene-based searches followed by the calculation of average nucleotide identity. As a result, the database is made of 61 700 species/phylotypes, including 13 132 with validly published names, and 62 362 whole-genome assemblies that were identified taxonomically at the genus, species and subspecies levels. Genomic properties, such as genome size and DNA G+C content, and the occurrence in human microbiome data were calculated for each genus or higher taxa. This united database of taxonomy, 16S rRNA gene and genome sequences, with accompanying bioinformatics tools, should accelerate genome-based classification and identification of members of the Bacteria and Archaea. The database and related search tools are available at www.ezbiocloud.net/.

A large-scale evaluation of algorithms to calculate average nucleotide identity.

Average nucleotide identity (ANI) is a category of computational analysis that can be used to define species boundaries of Archaea and Bacteria. Calculating ANI usually involves the fragmentation of genome sequences, followed by nucleotide sequence search, alignment, and identity calculation. The original algorithm to calculate ANI used the BLAST program as its search engine. An improved ANI algorithm, called OrthoANI, was developed to accommodate the concept of orthology. Here, we compared four algorithms to compute ANI, namely ANIb (ANI algorithm using BLAST), ANIm (ANI using MUMmer), OrthoANIb (OrthoANI using BLAST) and OrthoANIu (OrthoANI using USEARCH) using >100,000 pairs of genomes with various genome sizes. By comparing values to the ANIb that is considered a standard, OrthoANIb and OrthoANIu exhibited good correlation in the whole range of ANI values. ANIm showed poor correlation for ANI of <90%. ANIm and OrthoANIu runs faster than ANIb by an order of magnitude. When genomes that are larger than 7 Mbp were analysed, the run-times of ANIm and OrthoANIu were shorter than that of ANIb by 53- and 22-fold, respectively. In conclusion, ANI calculation can be greatly sped up by the OrthoANIu method without losing accuracy. A web-service that can be used to calculate OrthoANIu between a pair of genome sequences is available at http://www.ezbiocloud.net/tools/ani . For large-scale calculation and integration in bioinformatics pipelines, a standalone JAVA program is available for download at http://www.ezbiocloud.net/tools/orthoaniu .

Caffeic acid phenethyl ester lessens disease symptoms in an experimental autoimmune uveoretinitis mouse model.

Experimental autoimmune uveoretinitis (EAU) is an autoimmune disease that models human uveitis. Caffeic acid phenethyl ester (CAPE), a phenolic compound isolated from propolis, possesses anti-inflammatory and immunomodulatory properties. CAPE demonstrates therapeutic potential in several animal disease models through its ability to inhibit NF-κB activity. To evaluate these therapeutic effects in EAU, we administered CAPE in a model of EAU that develops after immunization with interphotoreceptor retinal-binding protein (IRBP) in B10.RIII and C57BL/6 mice. Importantly, we found that CAPE lessened the severity of EAU symptoms in both mouse strains. Notably, treated mice exhibited a decrease in the ocular infiltration of immune cell populations into the retina; reduced TNF-α, IL-6, and IFN-γ serum levels: and inhibited TNF-α mRNA expression in retinal tissues. Although CAPE failed to inhibit IRBP-specific T cell proliferation, it was sufficient to suppress cytokine, chemokine, and IRBP-specific antibody production. In addition, retinal tissues isolated from CAPE-treated EAU mice revealed a decrease in NF-κB p65 and phospho-IκBα. The data identify CAPE as a potential therapeutic agent for autoimmune uveitis that acts by inhibiting cellular infiltration into the retina, reducing the levels of pro-inflammatory cytokines, chemokine, and IRBP-specific antibody and blocking NF-κB pathway activation.

Thermally conductive polyamide 6/carbon filler composites based on a hybrid filler system.

We explored the use of a hybrid filler consisting of graphite nanoplatelets (GNPs) and single walled carbon nanotubes (SWCNTs) in a polyamide 6 (PA 6) matrix. The composites containing PA 6, powdered GNP, and SWCNT were melt-processed and the effect of filler content in the single filler and hybrid filler systems on the thermal conductivity of the composites was examined. The thermal diffusivities of the composites were measured by the standard laser flash method. Composites containing the hybrid filler system showed enhanced thermal conductivity with values as high as 8.8 W (m · K)-1, which is a 35-fold increase compared to the thermal conductivity of pure PA 6. Thermographic images of heat conduction and heat release behaviors were consistent with the thermal conductivity results, and showed rapid temperature jumps and drops, respectively, for the composites. A composite model based on the Lewis-Nielsen theory was developed to treat GNP and SWCNT as two separate types of fillers. Two approaches, the additive and multiplicative approaches, give rather good quantitative agreement between the predicted values of thermal conductivity and those measured experimentally.

Reliability of the modified Thomas test using a lumbo-plevic stabilization.

[Purpose] The purpose of this study was to examine the test-retest reliability of the modified Thomas test using lumbo-pelvic stabilization. [Subjects] Thirteen subjects (male=10, female=3) with hip flexor tightness voluntarily participated in the study. [Methods] The participants underwent the modified Thomas test under three conditions: 1) the general modified Thomas test (GM), 2) active lumbo-pelvic stabilization (ALS), and 3) passive lumbo-pelvic stabilization (PLS). Intra-class correlation coefficients (ICC) were used to determine the test-retest reliability of the knee joint angle measurement under three conditions. The standard error of measurement (SEM) and minimal detectable difference (95% confidence interval) (MDD(95)) were calculated for each measurement to assess absolute consistency. [Results] The ALS (ICC = 0.99) and PLS (ICC = 0.98) methods for the modified Thomas test were more reliable than GM method (ICC = 0.97). The MDD(95) score for the ALS method, 2.35 degrees, indicated that a real difference existed between two testing sessions compared with the scores for the PLS (3.70 degrees) and GM methods (4.17 degrees) [Conclusion] Lumbo-pelvic stabilization is one of the considerations for precise measurement and may help to minimize measurement error when evaluating hip flexor tightness using the modified Thomas test.

Reliability of ultrasonography measurement for the longus colli according to inward probe pressure.

[Purpose] To investigate the intra- and inter-rater reliability of the cross-sectional area (CSA) and muscle thickness (MT) of the longus colli according to the inward pressure of an ultrasonography (US) probe (0.5 and 1 kg). [Subjects] Thirteen subjects (11 males and 2 females; age, 23.1 ± 2.9 years) were recruited via convenience sampling of university students. [Methods] Real-time US measurements of the CSA and MT of the longus colli were recorded. Repeated US measurements using a standard protocol were performed on the same day 1 hour apart to assess intra- and inter-rater reliability. Intra-class correlation coefficients (ICC; 2, 1) were used to determine the intra- and inter-rater reliability of the CSA and MT measurements. [Results] This study demonstrated that the US measurements (0.5 and 1 kg) of the CSA and MT of the longus colli give reliable and consistent results. [Conclusion] Based on these results, a consistent inward pressure of the probe is needed to ensure precise US measurement of the longus colli muscle.

EzEditor: a versatile sequence alignment editor for both rRNA- and protein-coding genes.

EzEditor is a Java-based molecular sequence editor allowing manipulation of both DNA and protein sequence alignments for phylogenetic analysis. It has multiple features optimized to connect initial computer-generated multiple alignment and subsequent phylogenetic analysis by providing manual editing with reference to biological information specific to the genes under consideration. It provides various functionalities for editing rRNA alignments using secondary structure information. In addition, it supports simultaneous editing of both DNA sequences and their translated protein sequences for protein-coding genes. EzEditor is, to our knowledge, the first sequence editing software designed for both rRNA- and protein-coding genes with the visualization of biologically relevant information and should be useful in molecular phylogenetic studies. EzEditor is based on Java, can be run on all major computer operating systems and is freely available from http://sw.ezbiocloud.net/ezeditor/.

Thermal conductivity improvement of surface-enhanced polyetherimide (PEI) composites using polyimide-coated h-BN particles.

In this study, we investigated the thermal conductivities and mechanical properties of polyetherimide (PEI) composites using polyimide (PI)-coated h-BN (PI-BN) particles. We found that PI-coated h-BN effectively increased adhesion with the PEI matrix, imparting enhanced mechanical and thermal stability and thermal conductivity with increasing BN content. The thermal conductivity of the PEI composite containing 60 wt% PI-BN was 3.3 W m(-1) K(-1), while the thermal conductivity of the PEI/BN composite without modification was 2.6 W m(-1) K(-1). The PEI/PI-BN composites show higher impact strengths than the PEI/BN composites because of less BN particle agglomeration and good wettability between PEI and h-BN. The results indicate that the PI-coated BN incorporated into the PEI matrix effectively enhances the thermal conductivity and mechanical properties of the PEI composites.

Lumbopelvic motion during seated hip flexion in subjects with low-back pain accompanying limited hip flexion.

PURPOSE: Limited hip flexion may lead to a poor lumbopelvic motion during seated active hip flexion in people with low-back pain (LBP). The purpose of this study was to compare lumbopelvic motion during seated hip flexion between subjects with and without LBP accompanying limited hip flexion. METHODS: Fifteen patients with LBP accompanying limited hip flexion and 16 healthy subjects were recruited. The subjects performed seated hip flexion with the dominant leg three times. A three-dimensional motion-analysis system was used to measure lumbopelvic motion during seated hip flexion. RESULTS: During seated active hip flexion, the angle of hip flexion was significantly lower in patients with LBP accompanying limited hip flexion (17.4 ± 4.4 in the LBP group, 20.8 ± 2.6 in the healthy group; t = 2.63, p = 0.014). The angle of the lumbar flexion (4.8 ± 2.2 in the LBP group, 2.6 ± 2.0 in the healthy group; t = -2.96, p = 0.006) and posterior pelvic tilting (5.0 ± 2.6 in the LBP group, 2.9 ± 2.0 in the healthy group; t = 2.48 p = 0.019), however, were significantly greater in patients with this condition. CONCLUSIONS: The results of this study suggest that limited hip flexion in LBP can contribute to excessive lumbar flexion and posterior pelvic tilting during hip flexion in the sitting position. Further studies are required to confirm whether improving the hip flexion range of motion can reduce excessive lumbar flexion in patients with LBP accompanying limited hip flexion.

The importance of a normal breathing pattern for an effective abdominal-hollowing maneuver in healthy people: an experimental study.

CONTEXT: A normal breathing pattern while performing the abdominal-hollowing (AH) maneuver or spinal-stabilization exercise is essential for the success of rehabilitation programs and exercises. In previous studies, subjects were given standardized instructions to control the influence of respiration during the AH maneuver. However, the effect of breathing pattern on abdominal-muscle thickness during the AH maneuver has not been investigated. OBJECTIVE: To compare abdominal-muscle thickness in subjects performing the AH maneuver under normal and abnormal breathing-pattern conditions and to investigate the effect of breathing pattern on the preferential contraction ratio (PCR) of the transverse abdominis. DESIGN: Comparative, repeated-measures experimental study. SETTING: University research laboratory. PARTICIPANTS: 16 healthy subjects (8 male, 8 female) from a university population. MEASUREMENT: A real-time ultrasound scanner was used to measure abdominal-muscle thickness during normal and abnormal breathing patterns. A paired t test was used to assess the effect of breathing pattern on abdominal-muscle thickness and PCR. RESULTS: Muscle thickness in the transverse abdominis and internal oblique muscles was significantly greater under the normal breathing pattern than under the abnormal pattern (P < .05). The PCR of the transverse abdominis was significantly higher under the normal breathing pattern compared with the abnormal pattern (P < .05). CONCLUSION: The results indicate that a normal breathing pattern is essential for performance of an effective AH maneuver. Thus, clinicians should ensure that patients adopt a normal breathing pattern before performing the AH maneuver and monitor transverse abdominis activation during the maneuver.