New antimony fluorooxoborates with strong birefringence and unprecedented structural characterisation.

Fluorooxoborates constitute a rich source of optical crystals due to their structural diversity and excellent performance. Antimony fluorooxoborates with stereochemically active lone pairs of electrons still have not been found, although the first antimony borate was discovered several years ago. In this study, we have achieved the successful synthesis of the first antimony(III) fluorooxoborate with an unprecedented [B2O4F]∞ chain, namely SbB2O4F. Remarkably, SbB2O4F shows strong birefringence (0.171@1064 nm) and short UV cutoff edges (about 220 nm) according to calculations. The birefringence of SbB2O4F mainly originates from the highly distorted [SbO4] groups.

Non-iterative phase tilt interferometry for the tilt shift calculation and phase extraction.

This study proposed a non-iterative phase tilt interferometry (NIPTI) for the tilt shift calculation and phase extraction, which can solve the random tilt-shift problem caused by external vibration. The method approximates the higher order terms of the phase to make it suitable for linear fitting. Then according to an estimated tilt, the accurate tilt shift can be obtained by least squares method without iteration, and the phase distribution can be calculated by it. The simulation results indicated that the root mean square error of the phase calculated by the NIPTI can reach up to 0.0002λ. The experimental results showed that the calculated phase exhibited no significant ripple when using the NIPTI for cavity measurements in a time-domain phase shift Fizeau interferometer. Moreover, the root mean square repeatability of the calculated phase reached up to 0.0006λ. It indicates that the NIPTI provides an efficient and high-precision solution for random tilt-shift interferometry under vibration.

C-4 Regioselective Alkylation of Pyridines Driven by Mechanochemically Activated Magnesium Metal.

Mechanochemically activated magnesium(0) metal as a highly active mediator driving direct C-4-H alkylation of pyridines with alkyl halides has been developed. Excellent regioselectivity and substrate scope, including those containing reducible functionalities, free amines, and alcohols, as well as biologically relevant molecules were achieved to access 4-alkylpyridine products. Preliminary mechanistic studies suggested a radical-radical coupling pathway.

System design and error correction of simultaneous phase-shifting point-diffraction interferometer for dynamic wavefront detection with 400 mm aperture.

In this paper, a simultaneous phase-shifting point-diffraction interferometer (SPS-PDI)  at 632.8 nm is designed with the assistance of an off-axis parabolic mirror (OAPM), through which the dynamic wavefront with 400 mm aperture can be detected. In the system, a polarization point-diffraction plate (P-PDP) is developed to modulate the polarization states of the reference light and the test light through a simultaneous phase-shifting system based on a chessboard phase grating and a retarder array, and four phase-shifting interferograms can be acquired to realize dynamic detection. Furthermore, the circular carrier squeezing interferometry (CCSI) is proposed to suppress the phase errors generated by position mismatch, intensity distortion, and phase-shift error. The detection result of the SPS-PDI is consistent with the 4D PhaseCam6000 dynamic interferometer. The difference of the peak-to-valley (PV) and root-mean-square (RMS) values are only 0.04λ and 0.008λ. Additionally, the capacity to detect dynamic wavefront is good.

Iterative correction method of a retrace error in interferometry.

In interferometer measurements, the inconsistency of the optical range through which the reference and test lights pass introduces a retrace error in the phase measurement. In this study, we propose an iterative retrace error correction method in interferometry. A black-box model is established based on the linear and squared relationships between the retrace error and the tilt of the testing surface. The error correction phase is obtained using the least-squares method; thereafter, the global tilt is determined to iteratively correct the retrace error. The root mean square (RMS) of the residuals was > 3.2 × 10-5λ, >6.4 × 10-3λ, and >1.4 × 10-3λ in the simulation, experimentally computed retrace error correction in the planar measurement, and spherical measurement, respectively, proving that the retrace error can be effectively corrected.

Hierarchical Modulation of Optical Anisotropy Driven by Metal Cation Polyhedra in Fluorooxoborates MII B4 O6 F2 (MII =Be, Mg, Pb, Zn, Cd).

The enhancement mechanism of birefringence is very important to modulate optical anisotropy and materials design. Herein, the different cations extending from alkaline-earth to alkaline-earth, d10 electron configuration, and 6s2 lone pair cations are highlighted to explore the influence on the birefringence. A flexible fluorooxoborate framework from AEB4 O6 F2 (AE=Ca, Sr) is adopted for UV/deep-UV birefringent structures, namely, MII B4 O6 F2 (MII =Be, Mg, Pb, Zn, Cd). The maximal enhancement on birefringence can reach 46.6 % with the cation substitution from Ca, Sr to Be, Mg (route-I), Pb (route-II), and Zn, Cd (route-III). The influence of the cation size, the stereochemically active lone pair, and the binding capability of metal cation polyhedra is investigated for the hierarchical improvement on birefringence. Significantly, the BeB4 O6 F2 structure features the shortest UV cutoff edge 146 nm among the available anhydrous beryllium borates with birefringence over 0.1 at 1064 nm, and the PbB4 O6 F2 structure has the shortest UV cutoff edge 194 nm within the reported anhydrous lead borates that hold birefringence larger than 0.1 at 1064 nm. This work sheds light on how metal cation polyhedra modulate birefringence, which suggests a credible design strategy to obtain desirable birefringent structures by cation control.

Iterative deformation calibration of a transmission flat via the ring-point support on a 300-mm-aperture vertical Fizeau interferometer.

In the development of a high-precision vertical Fizeau interferometer with a 300-mm aperture, the deformation of the transmission flat because of clamping and gravity must be considered. In this paper, we proposed a ring-point support scheme for the deformation calibration of a large-diameter transmission flat. The calibration theory of the ring-point support system with elastic deformation was derived. The changes in the surface and stress field of the transmission flat were analyzed quantitatively by finite element method modeling, leading to the optimization of the support structure. To validate the proposed calibration approach, we performed an absolute test of the transmission flat using a liquid reference. The test result was compared to a measurement of the Zygo interferometer demonstrating the effectiveness of the proposed ring-point support design. Finally, with the iterative deformation calibration, the accuracy of the transmission flat reached λ/25 (Peak Valley, PV) for a 300-mm aperture.

Alpha-lipoic acid improves the reproduction performance of breeder hens during the late egg-laying period.

Alpha-lipoic acid (ALA), a multifunctional antioxidant, can promote fatty acid mobilization, energy expenditure and scavenge free radicals. The effects of dietary ALA on the reproductive performance of breeder hens were investigated in the current study. In the 5-week experiment, 180 54-week-old Qiling breeder hens were randomly divided into three treatments with five replicates and supplemented with three levels of ALA (0, 300 and 600 mg/kg) in the basic corn-soya bean meal diets. 600 mg/kg ALA treatment group (HLA) significantly improved the eggshell thickness and strength (p < .05). ALA-treated groups improved egg-laying rate compared with the CON group, but with no statistically significant difference (p > .05). The levels of HDL-C, ALB and estradiol (E2) of the serum in the HLA group were elevated compared with the CON group (p < .05). In addition, ALA (600 mg/kg) treatment exhibited a reduced level of serum AST and TG (p < .05). Dietary ALA increased the activity of hepatic lipase in liver (p < .05). Supplemental 600 mg/kg ALA also improved the SOD activity and total antioxidant capacity level, along with a decreased MDA in ovarian tissue (p < .05). Furthermore, the mRNA expressions of ESR1, ESR2, VTG2 and ApoB in the liver and FSHR in follicles were upregulated in the HLA group (p < .05). In conclusion, dietary supplementation with 600 mg/kg ALA during the late egg-laying period could improve lipid metabolism and reproductive performance of breeder hens.

System design and error correction for 300 mm aperture vertical Fizeau spatial-temporal phase-shifting interferometer.

With the development of high-power lasers for aerospace, electronics, etc., the demand for large-aperture planar optical elements has become more urgent, along with the demand for measurement methods. In this paper, the design of a 300 mm aperture vertical Fizeau spatial-temporal phase-shifting interferometer is discussed. Based on position difference between laser sources, the spatial phase-shifting technique is achieved by generating a laser source array on the focal plane of the collimation lens, and four pairs of coherent beams with different phase shifts are integrated in a vertical Fizeau interference system. Combined with a tunable laser diode, a temporal phase-shifting technique can be realized in any pair of coherent beams through wavelength tuning. The key techniques, which include laser duplication to introduce different phase shifts, conjugate imaging, and separation for interferograms, and assembly for a transmission flat, are demonstrated. The systematic error and position mismatch error of interferograms are eliminated. Comparison experiments are conducted between spatial and temporal phase-shifting techniques. A dynamic water surface is also measured to verify its capacity for detecting dynamic objects.

Oral Exposure to Genistein during Conception and Lactation Period Affects the Testicular Development of Male Offspring Mice.

Sexual hormones are essential for the process of spermatogenesis in the testis. However, the effect of maternal genistein (GEN) on the pups' testicular development remain-unclear. Our present study evaluated the effects of supplementing GEN for parental and offspring mice on the reproductive function and growth performance of the male pups. Mothers during gestation and lactation period were assigned to a control diet (CON group), low dose GEN (LGE group) diet (control diet +40 mg/kg GEN), and high dose of GEN (HGE group) diet (control diet +800 mg/kg GEN). Their male offspring underwent the same treatment of GEN after weaning. LGE treatment (40 mg/kg GEN) significantly increased body weights (p < 0.001), testes weights (p < 0.05), diameters of seminiferous tubule (p < 0.001) and heights of seminiferous epithelium (p < 0.05) of offspring mice. LGE treatment also increased serum testosterone (T) levels and spermatogenesis scoring (p < 0.05). However, HGE treatment (800mg/kg GEN) significantly decreased body weights (p < 0.001), testes weights (p < 0.05) and testis sizes (p < 0.001). Furthermore, mRNA expressions of ESR2 (p < 0.05), CYP19A1 (p < 0.001), SOX9 (p < 0.001) and BRD7 (p < 0.001) in testis of mice were increased in the LGE group. Similarly, HGE treatment increased mRNA expressions of ESR2 (p < 0.05) and CYP19A1 (p < 0.001). However, mRNA expressions of SOX9 and BRD7 were decreased significantly in the HGE group (p < 0.001). Meanwhile, higher ratio apoptotic germ cells and abnormal sperms were detected in the HGE group (p < 0.001). In conclusion, exposure to a low dose of GEN during fetal and neonatal life could improve testicular development of offspring mice, whereas, unfavorable adverse effects were induced by a high dose of GEN.

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks.

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. A total of 10 300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a ten-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We, thus, demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

Effects of High-Dose Genistein on the Hypothalamic RNA Profile and Intestinal Health of Female Chicks.

Genistein is abundant in animal feed. In this study, the side effects of high-dose genistein on intestinal health and hypothalamic RNA profile were evaluated. Chicks exposed to high-dose genistein by intraperitoneal injection (416 ± 21, 34.5 ± 2.5) and feed supplementation (308 ± 19, 27.2 ± 2.1) both showed a reduced body weight gain and feed intake in comparison with the control group (261 ± 16, 22.7 ± 1.6, P < 0.01). In comparison with the control (22.4 ± 0.5, 33.3 ± 2.4), serum levels of albumin and total protein were decreased after high-dose genistein injection (21.6 ± 0.5, 31.8 ± 1.6) and diet supplementation (20.6 ± 0.9, 29.9 ± 2.5, P < 0.001). Interestingly, the genistein diet presented the chick hypothalamus with downregulated expression of bitter receptors (TAS1R3, P < 0.05). Meanwhile, it upregulated the expressions of TAS2R1 (P < 0.05) and downstream genes (PLCB2 and IP3R3) in the ileum (P < 0.05). Accordingly, high-dose dietary genistein reduced villus height and the abundance of Lactobacillus, along with the increased abundance of pathogenic bacteria in the ileum (P < 0.05). Furthermore, transcriptomic analysis identified 348 differently expressed genes (168 upregulated and 224 downregulated) in the high-dose dietary genistein treated group in comparison with the control (P < 0.05, |log2FoldChange| > 0.585). Therefore, high-dose dietary genistein altered the hypothalamic RNA profile and signal processing. Cluster analysis further revealed that high-dose dietary genistein significantly influenced apoptosis, the immune process, and the whole synthesis of steroid hormones in the hypothalamus (P < 0.05). In conclusion, high-dose dietary genistein altered the hypothalamic RNA profile and intestinal health of female chicks.

Early Diagnosis of Alzheimer's Disease Based on Resting-State Brain Networks and Deep Learning.

Computerized healthcare has undergone rapid development thanks to the advances in medical imaging and machine learning technologies. Especially, recent progress on deep learning opens a new era for multimedia based clinical decision support. In this paper, we use deep learning with brain network and clinical relevant text information to make early diagnosis of Alzheimer's Disease (AD). The clinical relevant text information includes age, gender, and ApoE gene of the subject. The brain network is constructed by computing the functional connectivity of brain regions using resting-state functional magnetic resonance imaging (R-fMRI) data. A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD. The proposed method reveals discriminative brain network features effectively and provides a reliable classifier for AD detection. Compared to traditional classifiers based on R-fMRI time series data, about 31.21 percent improvement of the prediction accuracy is achieved by the proposed deep learning method, and the standard deviation reduces by 51.23 percent in the best case that means our prediction model is more stable and reliable compared to the traditional methods. Our work excavates deep learning's advantages of classifying high-dimensional multimedia data in medical services, and could help predict and prevent AD at an early stage.

Partial volume correction for PET quantification and its impact on brain network in Alzheimer's disease.

Amyloid positron emission tomography (PET) imaging is a valuable tool for research and diagnosis in Alzheimer's disease (AD). Partial volume effects caused by the limited spatial resolution of PET scanners degrades the quantitative accuracy of PET image. In this study, we have applied a method to evaluate the impact of a joint-entropy based partial volume correction (PVC) technique on brain networks learned from a clinical dataset of AV-45 PET image and compare network properties of both uncorrected and corrected image-based brain networks. We also analyzed the region-wise SUVRs of both uncorrected and corrected images. We further performed classification tests on different groups using the same set of algorithms with same parameter settings. PVC has sometimes been avoided due to increased noise sensitivity in image registration and segmentation, however, our results indicate that appropriate PVC may enhance the brain network structure analysis for AD progression and improve classification performance.

Matched signal detection on graphs: Theory and application to brain imaging data classification.

Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data.

Localizing Sources of Brain Disease Progression with Network Diffusion Model.

Pinpointing the sources of dementia is crucial to the effective treatment of neurodegenerative diseases. In this paper, we propose a diffusion model with impulsive sources over the brain connectivity network to model the progression of brain atrophy. To reliably estimate the atrophy sources, we impose sparse regularization on the source distribution and solve the inverse problem with an efficient gradient descent method. We localize the possible origins of Alzheimer's disease (AD) based on a large set of repeated magnetic resonance imaging (MRI) scans in Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The distribution of the sources averaged over the sample population is evaluated. We find that the dementia sources have different concentrations in the brain lobes for AD patients and mild cognitive impairment (MCI) subjects, indicating possible switch of the dementia driving mechanism. Moreover, we demonstrate that we can effectively predict changes of brain atrophy patterns with the proposed model. Our work could help understand the dynamics and origin of dementia, as well as monitor the progression of the diseases in an early stage.

A spectral graph regression model for learning brain connectivity of Alzheimer's disease.

Understanding network features of brain pathology is essential to reveal underpinnings of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) for learning structural brain connectivity of Alzheimer's disease (AD) measured by amyloid-ß deposits. The proposed GRM regards 11C-labeled Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging data as smooth signals defined on an unknown graph. This graph is then estimated through an optimization framework, which fits the graph to the data with an adjustable level of uniformity of the connection weights. Under the assumed data model, results based on simulated data illustrate that our approach can accurately reconstruct the underlying network, often with better reconstruction than those obtained by both sample correlation and ℓ1-regularized partial correlation estimation. Evaluations performed upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects demonstrate that the connectivity patterns revealed by the GRM are easy to interpret and consistent with known pathology. Moreover, the hubs of the reconstructed networks match the cortical hubs given by functional MRI. The discriminative network features including both global connectivity measurements and degree statistics of specific nodes discovered from the AD and NC amyloid-beta networks provide new potential biomarkers for preclinical and clinical AD.

Evaluating structural symmetry of weighted brain networks via graph matching.

We study the symmetry of weighted brain networks to understand the roles of individual brain areas and the redundancy of the brain connectivity. We quantify the structural symmetry of every node pair in the network by isomorphism of the residual graphs of those nodes. The efficacy of the symmetry measure is evaluated on both simulated networks and real data sets. In the resting state fMRI (rs-fMRI) data, we discover that subjects with inattentive type of Attention Deficit Hyperactivity Disorder (ADHD) demonstrate a higher level of network symmetry in contrast to the typically development group, consistent with former findings. Moreover, by comparing the average functional networks of normal subjects during resting state and activation, we obtain a higher symmetry level in the rs-fMRI network when applying median thresholds to the networks. But the symmetry levels of the networks are almost the same when larger thresholds are used, which may imply the invariance of the prominent network symmetry for ordinary people.

Sulfur and iron co-doped titanoniobate nanosheets: a novel efficient solid acid catalyst for alcoholysis of styrene epoxide at room temperature.

Sulfur and iron co-doped titanoniobate nanosheets were prepared and evaluated in alcoholysis of styrene epoxide. The resultant co-doped catalyst exhibited excellent catalytic performance (yield of 99% with methanol as the nucleophile in only 1 h at room temperature) and may act as a promising candidate in many acid-catalyzed reactions.

Matched signal detection on graphs: theory and application to brain network classification.

We develop a matched signal detection (MSD) theory for signals with an intrinsic structure described by a weighted graph. Hypothesis tests are formulated under different signal models. In the simplest scenario, we assume that the signal is deterministic with noise in a subspace spanned by a subset of eigenvectors of the graph Laplacian. The conventional matched subspace detection can be easily extended to this case. Furthermore, we study signals with certain level of smoothness. The test turns out to be a weighted energy detector, when the noise variance is negligible. More generally, we presume that the signal follows a prior distribution, which could be learnt from training data. The test statistic is then the difference of signal variations on associated graph structures, if an Ising model is adopted. Effectiveness of the MSD on graph is evaluated both by simulation and real data. We apply it to the network classification problem of Alzheimer's disease (AD) particularly. The preliminary results demonstrate that our approach is able to exploit the sub-manifold structure of the data, and therefore achieve a better performance than the traditional principle component analysis (PCA).