The Lipophilic Extract from Ginkgo biloba L. Leaves Promotes Glucose Uptake and Alleviates Palmitate-Induced Insulin Resistance in C2C12 Myotubes.

Ginkgo biloba L. (ginkgo) is a widely used medicinal plant around the world. Its leaves, which have been used as a traditional Chinese medicine, are rich in various bioactive components. However, most of the research and applications of ginkgo leaves have focused on terpene trilactones and flavonol glycosides, thereby overlooking the other active components. In this study, a lipophilic extract (GL) was isolated from ginkgo leaves. This extract is abundant in lipids and lipid-like molecules. Then, its effect and potential mechanism on glucose uptake and insulin resistance in C2C12 myotubes were investigated. The results showed that GL significantly enhanced the translocation of GLUT4 to the plasma membrane, which subsequently promoted glucose uptake. Meanwhile, it increased the phosphorylation of AMP-activated protein kinase (AMPK) and its downstream targets. Both knockdown of AMPK with siRNA and inhibition with AMPK inhibitor compound C reversed these effects. Additionally, GL ameliorated palmitate-induced insulin resistance by enhancing insulin-stimulated glucose uptake, increasing the phosphorylation of protein kinase B (PKB/AKT), and restoring the translocation of GLUT4 from the cytoplasm to the membrane. However, pretreatment with compound C abolished these beneficial effects of GL. In conclusion, GL enhances basal glucose uptake in C2C12 myotubes and improves insulin sensitivity in palmitate-induced insulin resistant myotubes through the AMPK pathway.

Optimization of Extraction and Purification of Flavonoids from Stigmaless Floral Residues of Crocus sativus L. and Their Stimulatory Effect on Glucose Uptake In Vitro.

Saffron, the dried stigma of Crocus sativus L., is a renowned spice and medicinal herb. During its production, a significant amount of floral residues, rich in bioactive compounds, are discarded as agricultural by-products. This study presents a novel approach to the sustainable utilization of these stigmaless floral residues (FRC) by optimizing the extraction and purification of their flavonoids, analyzing their chemical composition, and evaluating their effect on glucose uptake. The extraction of flavonoids from FRC was optimized using single-factor experiments and response surface methodology. The optimal conditions for extraction were an ethanol concentration of 67.7%, a temperature of 67.6 °C, a solid-to-liquid ratio of 1:30, an extraction time of 3 h, and two extractions. The crude extract obtained was then purified using macroporous resin HPD100, selected after comparing the adsorption and desorption characteristics of six different resins. The optimal purification parameters were an adsorption concentration of 40 mg/mL, a loading volume of 7 bed volumes (BV) at a flow rate of 3 BV/h, and 80% ethanol as the eluent with a volume of 4 BV. The resulting flavonoid-enriched extract (FFRC) had an experimental yield of 8.67% ± 0.01 and a flavonoid content of 128.30 ± 4.64 mg/g. The main flavonoids in FFRC were identified as kaempferol glycosides, isorhamnetin glycosides, and quercetin glycosides. Moreover, FFRC significantly stimulated glucose consumption and uptake in C2C12 myotubes, suggesting its potential utility as a natural hypoglycemic agent. This study contributes to the sustainable and value-added utilization of agricultural resources by providing data for the exploitation and application of flavonoids from saffron by-products.

Extract of Phyllanthus emblica L. fruit stimulates basal glucose uptake and ameliorates palmitate-induced insulin resistance through AMPK activation in C2C12 myotubes.

BACKGROUND: The fruit of Phyllanthus emblica L., a traditional medicine in China and India, is used to treat diabetes mellitus. Its water extract (WEPE) has demonstrated hypoglycemic effects in diabetic rats, but its mechanisms on glucose utilization and insulin resistance in skeletal muscle remain unclear. Therefore, this study aims to investigate the effects and underlying mechanisms of WEPE on glucose utilization and insulin resistance using C2C12 myotubes. METHODS: Effects of WEPE on glucose uptake, GLUT4 translocation, and AMPK and AKT phosphorylation were investigated in C2C12 myotubes and palmitate-treated myotubes. An AMPK inhibitor and siRNA were used to explore the mechanisms of WEPE. Glucose uptake was determined using a 2-(N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino)-2-deoxyglucose (2-NBDG) uptake assay, and protein expression and GLUT4 translocation were assessed via western blotting. RESULTS: In normal myotubes, WEPE significantly stimulated glucose uptake and GLUT4 translocation to the plasma membrane at concentrations of 125 and 250 µg/mL. This was accompanied by an increase in the phosphorylation of AMPK and its downstream targets. However, both compound C and AMPK siRNA blocked the WEPE-induced GLUT4 translocation and glucose uptake. Moreover, pretreatment with STO-609, a calcium/calmodulin-dependent protein kinase kinase ß (CaMKKß) inhibitor, inhibited WEPE-induced AMPK phosphorylation and attenuated the WEPE-stimulated glucose uptake and GLUT4 translocation. In myotubes treated with palmitate, WEPE prevented palmitate-induced insulin resistance by enhancing insulin-mediated glucose uptake and AKT phosphorylation. It also restored the insulin-mediated translocation of GLUT4 from cytoplasm to membrane. However, these effects of WEPE on glucose uptake and GLUT4 translocation were blocked by pretreatment with compound C. CONCLUSIONS: WEPE significantly stimulated basal glucose uptake though CaMKKß/AMPK pathway and markedly ameliorated palmitate-induced insulin resistance by activating the AMPK pathway in C2C12 myotubes.

Specimen plane orientation determination for analysis based on scanning electron microscope: Comparing top-down and side-view approaches.

Many attachments to a scanning electron microscope (SEM), such as energy dispersive x-ray spectroscopy, extend its function significantly. Typically, the application of such attachments requires that the specimen has a planar surface at a specific orientation. It is a challenge to make the plane of a microscale specimen satisfy the orientation requirement since they are visible only in an SEM. An in-situ procedure is needed to adjust specimen orientation by using stage rotation and tilting functions, in the process of which the key is to determine the initial orientation. This study proposed and tested top-down and side-view approaches to determine the orientation of a planar surface inside an SEM. In the top-down one, the projected area is monitored on SEM images as stage rotation and tilt angles are adjusted. When the surface normal is along the electron beam direction, the area has a maximum value. In the side-view approach, the stage is adjusted so that the projection appears to be a straight horizontal line on the SEM image. Once the orientation of the specimen for top-down or side-view observation is determined, the original can be calculated, and a desired orientation can be realized by manipulating the stage. The procedures have been tested by analyzing planar surfaces of spherical particles in Al-Cu-Fe alloy in the form of facets. The measured angles between two surfaces are consistent with those expected from crystallographic consideration within 2.7° and 1.7° for the top-down and side-view approaches, respectively. RESEARCH HIGHLIGHTS: Top-down and side-view approaches have been proposed and tested for in-situ determination of specimen planar surface orientation in a Scanning Electron Microscope. The measured angles between two surfaces are consistent with those expected from crystallographic consideration within 2.7° and 1.7° for the top-down and side-view approaches, respectively.

Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm.

BACKGROUND: Using multiple modalities of biomarkers, several machine leaning-based approaches have been proposed to characterize patterns of structural, functional and metabolic differences discernible from multimodal neuroimaging data for Alzheimer's disease (AD). Current investigations report several studies using binary classification often augmented with local feature selection methods, while fewer other studies address the challenging problem of multiclass classification. NEW METHOD: To assess the merits of each of these research directions, this study introduces a supervised Gaussian discriminative component analysis (GDCA) algorithm, which can effectively delineate subtle changes of early mild cognitive impairment (EMCI) group in relation to the cognitively normal control (CN) group. Using 251 CN, 297 EMCI, 196 late MCI (LMCI), and 162 AD subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and considering both structural and functional (metabolic) information from magnetic resonance imaging (MRI) and positron emission tomography (PET) modalities as input, the proposed method conducts a dimensionality reduction algorithm taking into consideration the interclass information to define an optimal eigenspace that maximizes the discriminability of selected eigenvectors. RESULTS: The proposed algorithm achieves an accuracy of 79.25 % for delineating EMCI from CN using 38.97 % of Gaussian discriminative components (i.e., dimensionality reduction). Moreover, for detecting the different stages of AD, a multiclass classification experiment attained an overall accuracy of 67.69 %, and more notably, discriminates MCI and AD groups from the CN group with an accuracy of 75.28 % using 48.90 % of the Gaussian discriminative components. COMPARISON WITH EXISTING METHOD(S): The classification results of the proposed GDCA method outperform the more recently published state-of-the-art methods in AD-related multiclass classification tasks, and seems to be the most stable and reliable in terms of relating the most relevant features to the optimal classification performance. CONCLUSION: The proposed GDCA model with its high prospects for multiclass classification has a high potential for deployment as a computer aided clinical diagnosis system for AD.

A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging.

BACKGROUND: Diagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer's disease (AD) with its delineation from the cognitively normal (CN) group remains a challenging but essential step for the planning of early treatment. Although several studies have focused on the MCI diagnosis, this study introduces the early stage of MCI to assess more thoroughly the earliest signs of disease manifestation and progression. NEW METHOD: We used random forest feature selection model with a Gaussian-based algorithm to perform method evaluation. This integrated method serves to define multivariate normal distributions in order to classify different stages of AD, with the focus placed on detecting EMCI subjects in the most challenging classification of CN vs. EMCI. RESULTS: Using 896 participants classified into the four categories of CN, EMCI, late mild cognitive impairment (LMCI) and AD, the results show that the EMCI group can be delineated from the CN group with a relatively high accuracy of 78.8% and sensitivity of 81.3%. COMPARISON WITH EXISTING METHOD(S): The feature selection model and classifier are compared with some other prominent algorithms. Although higher accuracy has been achieved using the Gaussian process (GP) model (78.8%) over the SVM classifier (75.6%) for CN vs. EMCI classification, with 0.05 being the cutoff for significance, and based on student's t-test, it was determined that the differences for accuracy, sensitivity, specificity between the GP method and support vector machine (SVM) are not statistically significant. CONCLUSION: Addressing the challenging classification of CN vs. EMCI provides useful information to help clinicians and researchers determine essential measures that can help in the early detection of AD.

Gastric Sleeve Surgery Alleviates Obesity-Associated Insulin Resistance and Suppresses Endoplasmic Reticulum Stress in Adipose Tissue of db/db Mice.

OBJECTIVE: The aims of this study were to evaluate the effects of gastric sleeve surgery on diabetes remission in db/db mice as well as to determine the underlying mechanisms. METHODS: Thirty spontaneously obese, diabetic mice (C57BL/Ksj-db/db) were randomly divided into three groups: sleeve gastrectomy group, sham-operated group, and control db/db group. Ten db/m lean mice were used as nondiabetic littermate controls. All mice were sacrificed on day 28. The fasting plasma glucose, serum insulin, lipid profile, and oral glucose tolerance were measured pre- and postoperatively. Inflammatory cytokines (TNF-α and IL-6), endoplasmic reticulum (ER) stress-related markers (GRP78, PERK, IRE-1, and ATF6), and glucose transporter 4 (GLUT4) in the adipose tissue were assayed. RESULTS: Sleeve gastrectomy significantly reduced the body weight and food intake in the db/db mice. This surgery improved glucose and lipid metabolism, as manifested by the decrease in the fasting plasma glucose level and partial restoration of lipid abnormalities. Also, the surgery improved glucose tolerance and alleviated insulin resistance in db/db mice. Sleeve gastrectomy surgery induced downregulation of the inflammatory adipocytokines TNF-α and IL-6; suppressed expression of the ER stress-related markers GRP78, PERK, IRE-1, and ATF-6; and increased the expression and distribution of GLUT4 in adipose tissue of db/db mice. CONCLUSION: The improvement in glucose tolerance following sleeve gastrectomy is associated with alleviation of insulin resistance, reduction of inflammatory adipocytokine levels, and suppression of ER stress. Further studies are needed to assess whether these effects have a causal role.

Greater Regional Cortical Thickness is Associated with Selective Vulnerability to Atrophy in Alzheimer's Disease, Independent of Amyloid Load and APOE Genotype.

BACKGROUND: Regional cortical thickness (rCTh) among cognitively normal (CN) adults (rCThCN) varies greatly between brain regions, as does the vulnerability to neurodegeneration. OBJECTIVE: The goal of this study was to: 1) rank order rCThCN for various brain regions, and 2) explore their vulnerability to neurodegeneration in Alzheimer's disease (AD) within these brain regions. METHODS: The relationship between rCTh among the CN group (rCThCN) and the percent difference in CTh (% CThDiff) in each region between the CN group and AD patients was examined. Pearson correlation analysis was performed accounting for amyloid-ß (Aß) protein and APOE genotype using 210 age, gender, and APOE matched CN (n = 105, age range: 56-90) and AD (n = 105, age range: 56-90) ADNI participants. RESULTS: Strong positive correlations were observed between rCThCN and % CThDiff accounting for Aß deposition and APOE status. Regions, such as the entorhinal cortex, which had the greatest CTh in the CN state, were also the regions which had the greatest % CThDiff. CONCLUSIONS: Regions with the greatest CTh at the CN stage are found to aggregate in disease prone regions of AD, namely in the medial temporal lobe, including the temporal pole, ERC, parahippocampal gyrus, fusiform and the middle and inferior temporal gyrus. Although rCTh has been found to vary considerably across the different regions of the brain, our results indicate that regions with the greatest CTh at the CN stage are actually regions which have been found to be most vulnerable to neurodegeneration in AD.

Optical rectification at isotropic chiral film composed of tripod-like molecules.

In the paper, the three-coupled-oscillator model presented by us is used to study the optical rectification in isotropic chiral films. The zero frequency hyperpolarizabilities of chiral molecules with a tripod-like structure are calculated. The expressions of the static-electric polarization and the relations between the optical rectification and the microscopic parameters of chiral medium are obtained by theoretical derivation. Furthermore, the relations of the dc electric-dipole polarization with the wavelength of incident light and microscopic parameters of chiral molecules have been simulated numerically.

Application of classical models of chirality to optical rectification.

Classical models of chirality are used to investigate the optical rectification effect in chiral molecular media. Calculation of the zero frequency first hyperpolarizabilities of chiral molecules with different structures is performed and applied to the derivation of a dc electric-dipole polarization. The expression of second-order nonlinear static-electric-dipole susceptibilities is obtained by theoretical derivation in the isotropic chiral thin films. The microscopic mechanism producing optical rectification is analyzed in view of this calculation. We find that optical rectification is derived from interaction between the electric field gradient (spatial dispersion) and chiral molecules in optically active liquids and solution by our calculation, which is consistent with the result given by Wozniak and Wagniere [Opt. Commun. 114, 131 (1995)]: The optical rectification depends on the fourth-order electric-dipole susceptibilities.

Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer's Disease.

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

A Neuroimaging Web Services Interface as a Cyber Physical System for Medical Imaging and Data Management in Brain Research: Design Study.

BACKGROUND: Structural and functional brain images are essential imaging modalities for medical experts to study brain anatomy. These images are typically visually inspected by experts. To analyze images without any bias, they must be first converted to numeric values. Many software packages are available to process the images, but they are complex and difficult to use. The software packages are also hardware intensive. The results obtained after processing vary depending on the native operating system used and its associated software libraries; data processed in one system cannot typically be combined with data on another system. OBJECTIVE: The aim of this study was to fulfill the neuroimaging community's need for a common platform to store, process, explore, and visualize their neuroimaging data and results using Neuroimaging Web Services Interface: a series of processing pipelines designed as a cyber physical system for neuroimaging and clinical data in brain research. METHODS: Neuroimaging Web Services Interface accepts magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and functional magnetic resonance imaging. These images are processed using existing and custom software packages. The output is then stored as image files, tabulated files, and MySQL tables. The system, made up of a series of interconnected servers, is password-protected and is securely accessible through a Web interface and allows (1) visualization of results and (2) downloading of tabulated data. RESULTS: All results were obtained using our processing servers in order to maintain data validity and consistency. The design is responsive and scalable. The processing pipeline started from a FreeSurfer reconstruction of Structural magnetic resonance imaging images. The FreeSurfer and regional standardized uptake value ratio calculations were validated using Alzheimer's Disease Neuroimaging Initiative input images, and the results were posted at the Laboratory of Neuro Imaging data archive. Notable leading researchers in the field of Alzheimer's Disease and epilepsy have used the interface to access and process the data and visualize the results. Tabulated results with unique visualization mechanisms help guide more informed diagnosis and expert rating, providing a truly unique multimodal imaging platform that combines magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and resting state functional magnetic resonance imaging. A quality control component was reinforced through expert visual rating involving at least 2 experts. CONCLUSIONS: To our knowledge, there is no validated Web-based system offering all the services that Neuroimaging Web Services Interface offers. The intent of Neuroimaging Web Services Interface is to create a tool for clinicians and researchers with keen interest on multimodal neuroimaging. More importantly, Neuroimaging Web Services Interface significantly augments the Alzheimer's Disease Neuroimaging Initiative data, especially since our data contain a large cohort of Hispanic normal controls and Alzheimer's Disease patients. The obtained results could be scrutinized visually or through the tabulated forms, informing researchers on subtle changes that characterize the different stages of the disease.

Semantic Intrusions and Failure to Recover From Semantic Interference in Mild Cognitive Impairment: Relationship to Amyloid and Cortical Thickness.

BACKGROUND: Accumulating evidence indicates that the failure to recover from the effects of proactive semantic interference [frPSI] represents an early cognitive manifestation of preclinical Alzheimer's disease. A limitation of this novel paradigm has been a singular focus on the number of targets correctly recalled, without examining co-occurring semantic intrusions [SI] that may highlight specific breakdowns in memory. OBJECTIVES: We focused on SI and their relationship to amyloid load and regional cortical thickness among persons with amnestic mild cognitive impairment (aMCI). METHODS: Thirty-three elders diagnosed with aMCI underwent F-18 florbetaben amyloid PET scanning with MRI scans of the brain. We measured the correlation of SI elicited on cued recall trials of the Loewenstein-Acevedo Scales for Semantic Interference and Learning [LASSI-L] with mean cortical amyloid load and regional cortical thickness in AD prone regions. RESULTS: SI on measures sensitive to frPSI was related to greater total amyloid load and lower overall cortical thickness [CTh]. In particular, SI were highly associated with reduced CTh in the left entorhinal cortex [r=-.71; p<.001] and left medial orbital frontal lobe [r=-.64; p<.001]; together accounting for 66% of the explained variability in regression models. CONCLUSION: Semantic intrusions on measures susceptible to frPSI related to greater brain amyloid load and lower cortical thickness. These findings further support the hypothesis that frPSI, as expressed by the percentage of intrusions, may be a cognitive marker of initial neurodegeneration and may serve as an early and distinguishing test for preclinical AD that may be used in primary care or clinical trial settings.

Utilizing semantic intrusions to identify amyloid positivity in mild cognitive impairment.

OBJECTIVE: Semantic intrusion (SI) errors may highlight specific breakdowns in memory associated with preclinical Alzheimer disease (AD); however, there have been no investigations to determine whether SI errors occur with greater frequency in persons with amnestic mild cognitive impairment (aMCI) confirmed as amyloid positive (Amy+) vs those who have clinical symptoms of aMCI-AD with negative amyloid scans (suspected non-AD pathology [SNAP]) or persons who are diagnosed with other brain disorders affecting cognition. METHODS: Eighty-eight participants with aMCI underwent brain amyloid PET and MRI scans and were classified as early AD (Amy+), SNAP (Amy-), or other neurological/psychiatric diagnosis (Amy-). We focused on SI on the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L) targeting proactive semantic interference (PSI; old semantic learning interferes with new semantic learning), failure to recover from PSI after an additional learning trial (frPSI), and retroactive semantic interference (new semantic learning interferes with memory for old semantic learning). RESULTS: SIs on measures of PSI and frPSI distinguished between Amy+ AD and SNAP and other non-AD cases. PSI and frPSI intrusions evidenced moderately high associations with reduced volumes in the entorhinal cortex, superior temporal regions, and supramarginal gyrus. No such associations were observed in cases with SNAP. CONCLUSIONS: SIs on the LASSI-L related to PSI and frPSI uniquely differentiated Amy+ and Amy- participants with aMCI and likely reflect deficits with inhibition and source memory in preclinical AD not captured by traditional cognitive measures. This may represent a specific, noninvasive test successful at distinguishing cases with true AD from those with SNAP.

Optimization of all-optical EDFA-based Sagnac-interferometer switch.

We perform optimization of all-optical EDFA-based Sagnac - interferometer switch through an analytical model and numerical simulations by solving nonlinear Schrödinger equations. The effects of the performance of EDFA on the bit rate and the switching power are investigated for all-optical switch based on self-phase or cross-phase modulation. The simulated results show that ultra-low switching power (<1mW) all-optical switch for 40 Gb/s data can be realized by properly selecting the length of highly nonlinear photonic crystal fiber and adjusting the performance of EDFA.

The Relationship of Brain Amyloid Load and APOE Status to Regional Cortical Thinning and Cognition in the ADNI Cohort.

BACKGROUND: Both amyloid (Aß) load and APOE4 allele are associated with neurodegenerative changes in Alzheimer's disease (AD) prone regions and with risk for cognitive impairment. OBJECTIVE: To evaluate the unique and independent contribution of APOE4 allele status (E4+∖E4-), Aß status (Amy+∖Amy-), and combined APOE4 and Aß status on regional cortical thickness (CoTh) and cognition among participants diagnosed as cognitively normal (CN, n = 251), early mild cognitive impairment (EMCI, n = 207), late mild cognitive impairment (LMCI, n = 196), and mild AD (n = 162) from the ADNI. METHODS: A series of two-way ANCOVAs with post-hoc Tukey HSD tests, controlling independently for Aß and APOE4 status and age were examined. RESULTS: Among LMCI and AD participants, cortical thinning was widespread in association with Amy+ status, whereas in association with E4+ status only in the inferior temporal and medial orbito-frontal regions. Among CN and EMCI participants, E4+ status, but not Amy+ status, was independently associated with increased CoTh, especially in limbic regions [e.g., in the entorhinal cortex, CoTh was 0.123 mm greater (p = 0.002) among E4+ than E4-participants]. Among CN and EMCI, both E4+ and Amy+ status were independently associated with cognitive impairment, which was greatest among those with combined E4 + and Amy+ status. CONCLUSION: Decreased CoTh is independently associated with Amy+ status in many brain regions, but with E4+ status in very restricted number of brain regions. Among CN and EMCI participants, E4 + status is associated with increased CoTh, in medial and inferior temporal regions, although cognitive impairment at this state is independently associated with Amy+ and E4 + status. These findings imply a unique pathophysiological mechanism for E4 + status in AD and its progression.

Recovery from Proactive Semantic Interference and MRI Volume: A Replication and Extension Study.

BACKGROUND: The rise in incidence of Alzheimer's disease (AD) has led to efforts to advance early detection of the disease during its preclinical stages. To achieve this, the field needs to develop more sensitive cognitive tests that relate to biological markers of disease pathology. Failure to recover from proactive interference (frPSI) is one such cognitive marker that is associated with volumetric reductions in the hippocampus, precuneus, and other AD-prone regions, and to amyloid load in the brain. OBJECTIVE: The current study attempted to replicate and extend our previous findings that frPSI is a sensitive marker of early AD, and related to a unique pattern of volumetric loss in AD prone areas. METHODS: Three different memory measures were examined relative to volumetric loss and cortical thickness among 45 participants with amnestic mild cognitive impairment. RESULTS: frPSI was uniquely associated with reduced volumes in the hippocampus (r = 0.50) precuneus (r = 0.41), and other AD prone regions, replicating previous findings. Strong associations between frPSI and lower entorhinal cortex volumes and cortical thickness (r≥0.60) and precuneus (r = 0.50) were also observed. CONCLUSION: Unique and strong associations between volumetric reductions and frPSI as observed by Loewenstein and colleagues were replicated. Together with cortical thickness findings, these results indicate that frPSI is worthy of further study as a sensitive and early cognitive marker of AD.

Application of automated crystallography for transmission electron microscopy in the study of grain-boundary segregation.

Analytical Electron Microscopy (AEM) has brought significant progress in the study of grain-boundary segregation. Using X-ray energy-dispersive spectrometry (XEDS) in the AEM, elemental segregation information can be related to the crystallographic character of the same boundary via conventional Transmission Electron Microscope (TEM) diffraction techniques. While significant efforts have been made to improve XEDS analysis of sub-nanometer segregation layers, the methods for crystallographic characterization of grain boundaries have remained the same for several decades and labor-intensive processes. Recently, a method termed Automated Crystallography for TEM (ACT) was developed, which automates crystallographic characterization of grains under TEM observation. In the present work, we combine ACT and X-ray mapping via EDS in AEM for the study of Sb grain-boundary segregation in a rapidly solidified Cu-0.08 wt % Sb alloy. In contrast with previous reports, a large degree of anisotropy in Sb segregation level between different boundaries is found. ACT results suggest that one of the several grain boundaries observed with no detectable Sb segregation is very close to a Sigma 3 coincidence-site lattice structure. The reason for the observed anisotropy in the present alloy is discussed, based upon McLean's theory of segregation.