Low-density lipoprotein receptor related proteins 5 and 6 (LRP5/6) are transmembrane receptors that initiate Wnt/beta-catenin signaling. Phosphorylation of PPPSP motifs in the LRP6 cytoplasmic domain is crucial for signal transduction. Using a kinome-wide RNAi screen, we show that PPPSP phosphorylation requires the Drosophila Cyclin-dependent kinase (CDK) L63. L63 and its vertebrate homolog PFTK are regulated by the membrane tethered G2/M Cyclin, Cyclin Y, which mediates binding to and phosphorylation of LRP6. As a consequence, LRP6 phosphorylation and Wnt/beta-catenin signaling are under cell cycle control and peak at G2/M phase; knockdown of the mitotic regulator CDC25/string, which results in G2/M arrest, enhances Wnt signaling in a Cyclin Y-dependent manner. In Xenopus embryos, Cyclin Y is required in vivo for LRP6 phosphorylation, maternal Wnt signaling, and Wnt-dependent anteroposterior embryonic patterning. G2/M priming of LRP6 by a Cyclin/CDK complex introduces an unexpected new layer of regulation of Wnt signaling.

Amyloid PET imaging is an indispensable tool widely used in the investigation, diagnosis and monitoring of Alzheimer's disease (AD). Currently, a reference region based approach is used as the mainstream quantification technique for amyloid imaging. This approach assumes the reference region is amyloid free and has the same tracer influx and washout kinetics as the regions of interest. However, this assumption may not always be valid. The goal of this work is to evaluate an amyloid imaging quantification technique that uses arterial region of interest as the reference to avoid potential bias caused by specific binding in the reference region. 21 participants, age 58 and up, underwent Pittsburgh compound B (PiB) PET imaging and MR imaging including a time-of-flight (TOF) MR angiography (MRA) scan and a structural scan. FreeSurfer based regional analysis was performed to quantify PiB PET data. Arterial input function was estimated based on coregistered TOF MRA using a modeling based technique. Regional distribution volume (VT) was calculated using Logan graphical analysis with estimated arterial input function. Kinetic modeling was also performed using the estimated arterial input function as a way to evaluate PiB binding (DVRkinetic) without a reference region. As a comparison, Logan graphical analysis was also performed with cerebellar cortex as reference to obtain DVRREF. Excellent agreement was observed between the two distribution volume ratio measurements (r>0.89, ICC>0.80). The estimated cerebellum VT was in line with literature reported values and the variability of cerebellum VT in the control group was comparable to reported variability using arterial sampling data. This study suggests that image-based arterial input function is a viable approach to quantify amyloid imaging data, without the need of arterial sampling or a reference region. This technique can be a valuable tool for amyloid imaging, particularly in population where reference normalization may not be accurate.

Genomic analysis of human hepatocellular carcinoma (HCC) is potentially confounded by the differentiation state of the hepatic cell-of-origin. Here we integrated genomic analysis of mouse HCC (with defined cell-of-origin) along with normal development. We found a major shift in expression of Wnt and RXR-α pathway genes (up and down, respectively) coincident with the transition from hepatoblasts to hepatocytes. A combined Wnt and RXR-α gene signature categorized HCCs into two subtypes (high Wnt, low RXR-α and low Wnt, high RXR-α), which matched cell-of-origin in mouse models and the differentiation state of human HCC. Suppression of RXR-α levels in hepatocytes increased Wnt signaling and enhanced tumorigenicity, whereas ligand activation of RXR-α achieved the opposite. These results corroborate that there are two main HCC subtypes that correspond to the degree of hepatocyte differentation and that RXR-α, in part via Wnt signaling, plays a key functional role in the hepatocyte-like subtype and potentially could serve as a selective therapeutic target.

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. As positron emission tomography (PET) scanners have limited spatial resolution, measured signals are distorted by partial volume effects. Various techniques have been proposed for correcting partial volume effects, but there is no consensus as to whether these techniques are necessary in amyloid imaging, and, if so, how they should be implemented. We evaluated a two-component partial volume correction technique and a regional spread function technique using both simulated and human Pittsburgh compound B (PiB) PET imaging data. Both correction techniques compensated for partial volume effects and yielded improved detection of subtle changes in PiB retention. However, the regional spread function technique was more accurate in application to simulated data. Because PiB retention estimates depend on the correction technique, standardization is necessary to compare results across groups. Partial volume correction has sometimes been avoided because it increases the sensitivity to inaccuracy in image registration and segmentation. However, our results indicate that appropriate PVC may enhance our ability to detect changes in amyloid deposition.

Indole-3-acetic acid (IAA) extraction and purification are of great importance in auxin research, which is a hot topic in the plant growth and development field. Solid-phase extraction (SPE) is frequently used for IAA extraction and purification. However, no IAA-specific SPE columns are commercially available at the moment. Therefore, the development of IAA-specific recognition materials and IAA extraction and purification methods will help researchers meet the need for more precise analytical methods for research on phytohormones.

Stargazin mutation results in absence epilepsy and cerebellar ataxia in stargazer (stg) mice. We have previously discovered defects of AMPA receptor function, failure of BDNF expression and immature morphology specifically in the cerebellar cortex of stg mice. To further characterize the nature of synaptic abnormalities, we examined the ultrastructure of cerebellar granule cell output synapses and measured the expression levels of several synaptic proteins in different brain regions of stg mutant. Electron microscopic examination revealed a number of immature features in the molecular layer of the mutant cerebellar cortex, including the presence of desmosoid plaques, concentric profiles of parallel fibers, smaller presynaptic terminal and fewer synaptic vesicles. Quantitative measurement showed a significantly lower number of synapses and smaller area of presynaptic terminals in adult stg cerebellum when compared with age-matched wildtype. Immunoblotting analysis of the SNARE proteins revealed selective reduction of the levels of synaptobrevin and synaptophysin in synaptosomes from stg cerebellum. The expression levels of synapsins were not altered in stg cerebellum, but showed a significant upregulation in stg cerebral cortex and hippocampus. Our results suggest that, despite the relatively normal gross morphology of cerebellum, stargazin mutation results in abnormal ultrastructure of cerebellar synapses, and stargazin-induced regional failure of BDNF expression may be responsible for abnormal SNARE protein distribution and partially attributes to the defects in the synaptic ultrastructure.

The objective of this study was to examine the distribution and severity of tau-PET binding in cognitively normal adults with preclinical Alzheimer's disease as determined by positive beta-amyloid PET. 18F-AV-1451 tau-PET data from 109 cognitively normal older adults were processed with 34 cortical and 9 subcortical FreeSurfer regions and averaged across both hemispheres. Individuals were classified as being beta-amyloid positive (N = 25, A+) or negative (N = 84, A-) based on a 18F-AV-45 beta-amyloid-PET standardized uptake value ratio of 1.22. We compared the tau-PET binding in the 2 groups using covariate-adjusted linear regressions. The A+ cohort had higher tau-PET binding within 8 regions: precuneus, amygdala, banks of the superior temporal sulcus, entorhinal cortex, fusiform gyrus, inferior parietal cortex, inferior temporal cortex, and middle temporal cortex. These findings, consistent with preclinical involvement of the medial temporal lobe and parietal lobe and association regions by tauopathy, emphasize that therapies targeting tauopathy in Alzheimer's disease could be considered before the onset of symptoms to prevent or ameliorate cognitive decline.

Hypertrophic cardiomyopathy (HCM) patients present altered myocardial mechanics due to the hypertrophied ventricular wall and are typically diagnosed by the increase in myocardium wall thickness. This study aimed to quantify regional left ventricular (LV) shape, wall stress and deformation from cardiac magnetic resonance (MR) images in HCM patients and controls, in order to establish superior measures to differentiate HCM from controls. A total of 19 HCM patients and 19 controls underwent cardiac MR scans. The acquired MR images were used to reconstruct 3D LV geometrical models and compute the regional parameters (i.e., wall thickness, curvedness, wall stress, area strain and ejection fraction) based on the standard 16 segment model using our in-house software. HCM patients were further classified into four quartiles based on wall thickness at end diastole (ED) to assess the impact of wall thickness on these regional parameters. There was a significant difference between the HCM patients and controls for all regional parameters (P < 0.001). Wall thickness was greater in HCM patients at the end-diastolic and end-systolic phases, and thickness was most pronounced in segments at the septal regions. A multivariate stepwise selection algorithm identified wall stress index at ED (σ i,ED ) as the single best independent predictor of HCM (AUC = 0.947). At the cutoff value σ i,ED < 1.64, both sensitivity and specificity were 94.7%. This suggests that the end-diastolic wall stress index incorporating regional wall curvature-an index based on mechanical principle-is a sensitive biomarker for HCM diagnosis with potential utility in diagnostic and therapeutic assessment.

Efficient biomass bioconversion is a promising solution to alternative energy resources and environmental issues associated with lignocellulosic wastes. The Trichoderma species of cellulolytic fungi have strong cellulose-degrading capability, and their cellulase systems have been extensively studied. Currently, a major limitation of Trichoderma strains is their low production of β-glucosidases.

Relapse is a major cause of treatment failure after allogeneic hematopoietic stem-cell transplantation (allo-HSCT) for high-risk acute myeloid leukemia (HR-AML). The aim of this study was to explore the effect of recombinant human granulocyte colony-stimulating factor (rhG-CSF) combined with minimal-dose decitabine (Dec) on the prevention of HR-AML relapse after allo-HSCT.

We identified a PSEN1 (presenilin 1) mutation carrier from the world's largest autosomal dominant Alzheimer's disease kindred, who did not develop mild cognitive impairment until her seventies, three decades after the expected age of clinical onset. The individual had two copies of the APOE3 Christchurch (R136S) mutation, unusually high brain amyloid levels and limited tau and neurodegenerative measurements. Our findings have implications for the role of APOE in the pathogenesis, treatment and prevention of Alzheimer's disease.

The pathogenesis of dental caries remains unclear, with increasing evidence suggesting that genetic susceptibility plays an essential role. Previous studies have reported the association between genetic polymorphisms in lactotransferrin (LTF) and the risk of dental caries with inconsistent results.

Genome-wide association study (GWAS) has determined the metabotropic glutamate receptor 7 (GRM7) gene as potential locus for schizophrenia risk variants; However, the relationship between the GRM7 variants and the risk of schizophrenia is still uncertain, and there are significant individual variations in response to the antipsychotic drugs. In order to identify susceptible gene and drug-response-related markers, 2413 subjects in our research were chosen for determining drug-response-related markers in schizophrenia. The rs1516569 variant (OR = 0.95, P < 3.47 × 10-4) was a significant risk factor, and a single-nucleotide polymorphism of GRM7 gene- rs9883258 (OR = 0.84, P = 2.18 × 10-3) has been determined as potential biomarkers for therapeutic responses of seven commonly used antipsychotic drugs (aripiprazole, haloperidol, olanzapine, perphenazine, quetiapine, risperidone and ziprasidone) in Chinese Han population; Significant associations with treatment response for several single-nucleotide polymorphisms in every antipsychotic drugs, such as rs779746 (OR = 1.39, P = 0.03), rs480409 (OR = 0.73, P = 0.04), rs78137319 (OR = 3.09, P = 0.04), rs1154370 (OR = 1.51, P = 0.006) have been identified in our study. Hence our research elucidates that GRM7 variants play the critical role of predicting the risk of schizophrenia and antipsychotic effect of seven common drugs.

DNA methylation (DNAm) age was found to be an indicator for all-cause mortality, cancer incidence, and longevity, but no study has involved in the associations of DNAm age with the prognosis of breast cancer.

Abscisic acid (ABA) functions as a stress phytohormone in many growth and developmental processes in plants. The ultra-sensitive determination of ABA would help to better understand its vital roles and action mechanisms.

The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have been developed to serve the purpose. With extensive methodology development efforts on neuroimaging, an emerging field is deep learning research. One great challenge facing deep learning is the limited medical imaging data available. To address the issue, researchers explore the use of transfer learning to extend the applicability of deep models on neuroimaging research for AD diagnosis and prognosis. Existing transfer learning models mostly focus on transferring the features from the pre-training into the fine-tuning stage. Recognizing the advantages of the knowledge gained during the pre-training, we propose an AD-NET (Age-adjust neural network) with the pre-training model serving two purposes: extracting and transferring features; and obtaining and transferring knowledge. Specifically, the knowledge being transferred in this research is an age-related surrogate biomarker. To evaluate the effectiveness of the proposed approach, AD-NET is compared with 8 classification models from literature using the same public neuroimaging dataset. Experimental results show that the proposed AD-NET outperforms the competing models in predicting the MCI patients at risk for conversion to the AD stage.

Neurofilament light chain (NfL) is a protein that is selectively expressed in neurons. Increased levels of NfL measured in either cerebrospinal fluid or blood is thought to be a biomarker of neuronal damage in neurodegenerative diseases. However, there have been limited investigations relating NfL to the concurrent measures of white matter (WM) decline that it should reflect. White matter damage is a common feature of Alzheimer's disease. We hypothesized that serum levels of NfL would associate with WM lesion volume and diffusion tensor imaging (DTI) metrics cross-sectionally in 117 autosomal dominant mutation carriers (MC) compared to 84 non-carrier (NC) familial controls as well as in a subset (N = 41) of MC with longitudinal NfL and MRI data. In MC, elevated cross-sectional NfL was positively associated with WM hyperintensity lesion volume, mean diffusivity, radial diffusivity, and axial diffusivity and negatively with fractional anisotropy. Greater change in NfL levels in MC was associated with larger changes in fractional anisotropy, mean diffusivity, and radial diffusivity, all indicative of reduced WM integrity. There were no relationships with NfL in NC. Our results demonstrate that blood-based NfL levels reflect WM integrity and supports the view that blood levels of NfL are predictive of WM damage in the brain. This is a critical result in improving the interpretability of NfL as a marker of brain integrity, and for validating this emerging biomarker for future use in clinical and research settings across multiple neurodegenerative diseases.

Background: Major depressive disorder (MDD) is a common psychiatric disorder associated with working memory (WM) impairment. Neuroimaging studies showed divergent results of the WM process in MDD patients. Stress could affect the occurrence and development of depression, in which childhood maltreatment played an important role. Methods: Thirty-seven MDD patients and 54 healthy control subjects were enrolled and completed a WM functional magnetic resonance imaging task with maintenance and manipulation conditions under stress and non-stress settings. We collected demographical and clinical data, using 17-item Hamilton Depression Scale (HAMD-17) and Childhood Trauma Questionnaire (CTQ) in MDD patients. In the WM task, we analyzed the main diagnosis effect and explored the correlation of impaired brain regions in MDD patients with CTQ and HAMD-17. Results: No group differences were found in the accuracy rate and reaction time between the two groups. MDD patients had lower brain activation in following regions (P FWE < 0.05). The left fusiform gyrus showed less activation in all conditions. The right supplementary motor area (SMA) exhibited decreased activation under non-stress. The anterior prefrontal cortex showed reduced activation during manipulation under stress, with the β estimations of the peak voxel showing significant group difference negatively correlated with childhood sex abuse (P Bonferroni < 0.05). Conclusions: In our pilot study, MDD patients had reduced brain activation, affecting emotional stimuli processing function, executive function, and cognitive control function. Childhood maltreatment might affect brain function in MDD. This work might provide some information for future studies on MDD.

Biomarker assisted preclinical/early detection and intervention in Alzheimer's disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near-infrared reflectance (Vis-NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580-1850 nm based on Savitzky-Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R2 between Zn contents and the soil spectral reflectance achieved the highest (R2 = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R2 = 0.97, RPD = 3.39, RMSE = 1.05 mg kg-1, MAE = 0.79 mg kg-1). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis-NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas.