The Application Research of OLAP in Police Intelligence Decision System

AbstractAiming at the large amounts of data collected by the public security organs, the technologies of data warehouse and OLAP are used to realize the police intelligence decision system based on SQL Server 2008 platform. The multidimensional analysis results reveal some potential regularity between criminal's action and the cases, so as to help the policemen make correct judgments

An Analytical Model for Fatigue Crack Propagation Prediction with Overload Effect

In this paper a theoretical model was developed to predict the fatigue crack growth behavior under the constant amplitude loading with single overload. In the proposed model, crack growth retardation was accounted for by using crack closure and plastic zone. The virtual crack annealing model modified by Bauschinger effect was used to calculate the crack closure level in the outside of retardation effect region. And the Dugdale plastic zone model was employed to estimate the size of retardation effect region. A sophisticated equation was developed to calculate the crack closure variation during the retardation area. Model validation was performed in D16 aluminum alloy and 350WT steel specimens subjected to constant amplitude load with single or multiple overloads. The predictions of the proposed model were contrasted with experimental data, and fairly good agreements were observed

Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video

Synthesizing realistic videos according to a given speech is still an open challenge. Previous works have been plagued by issues such as inaccurate lip shape generation and poor image quality. The key reason is that only motions and appearances on limited facial areas (e.g., lip area) are mainly driven by the input speech. Therefore, directly learning a mapping function from speech to the entire head image is prone to ambiguity, particularly when using a short video for training. We thus propose a decomposition-synthesis-composition framework named Speech to Lip (Speech2Lip) that disentangles speech-sensitive and speech-insensitive motion/appearance to facilitate effective learning from limited training data, resulting in the generation of natural-looking videos. First, given a fixed head pose (i.e., canonical space), we present a speech-driven implicit model for lip image generation which concentrates on learning speech-sensitive motion and appearance. Next, to model the major speech-insensitive motion (i.e., head movement), we introduce a geometry-aware mutual explicit mapping (GAMEM) module that establishes geometric mappings between different head poses. This allows us to paste generated lip images at the canonical space onto head images with arbitrary poses and synthesize talking videos with natural head movements. In addition, a Blend-Net and a contrastive sync loss are introduced to enhance the overall synthesis performance. Quantitative and qualitative results on three benchmarks demonstrate that our model can be trained by a video of just a few minutes in length and achieve state-of-the-art performance in both visual quality and speech-visual synchronization. Code: https://github.com/CVMI-Lab/Speech2Lip

Pharmacological Inhibition of O-GlcNAcase (OGA) Prevents Cognitive Decline and Amyloid Plaque Formation in Bigenic Tau/APP Mutant Mice

Background Amyloid plaques and neurofibrillary tangles (NFTs) are the defining pathological hallmarks of Alzheimer’s disease (AD). Increasing the quantity of the O-linked N-acetylglucosamine (O-GlcNAc) post-translational modification of nuclear and cytoplasmic proteins slows neurodegeneration and blocks the formation of NFTs in a tauopathy mouse model. It remains unknown, however, if O-GlcNAc can influence the formation of amyloid plaques in the presence of tau pathology. Results We treated double transgenic TAPP mice, which express both mutant human tau and amyloid precursor protein (APP), with a highly selective orally bioavailable inhibitor of the enzyme responsible for removing O-GlcNAc (OGA) to increase O-GlcNAc in the brain. We find that increased O-GlcNAc levels block cognitive decline in the TAPP mice and this effect parallels decreased β-amyloid peptide levels and decreased levels of amyloid plaques. Conclusions This study indicates that increased O-GlcNAc can influence β-amyloid pathology in the presence of tau pathology. The findings provide good support for OGA as a promising therapeutic target to alter disease progression in Alzheimer disease

Selective Fluorogenic β-Glucocerebrosidase Substrates for Convenient Analysis of Enzyme Activity in Cell and Tissue Homogenates

Within mammals, there are often several functionally related glycoside hydrolases, which makes monitoring their activities problematic. This problem is particularly acute for the enzyme β-glucocerebrosidase (GCase), the malfunction of which is a key driver of Gaucher's disease (GD) and a major risk factor for Parkinson's disease (PD). Humans harbor two other functionally related β-glucosidases known as GBA2 and GBA3, and the currently used fluorogenic substrates are not selective, which has driven the use of complicated subtractive assays involving the use of detergents and inhibitors. Here we describe the preparation of fluorogenic substrates based on the widely used nonselective substrate resorufin β-d-glucopyranoside. Using recombinant enzymes, we show that these substrates are highly selective for GCase. We also demonstrate their value through the analysis of GCase activity in brain tissue homogenates from transgenic mice expressing mutant human GCase and patient fibroblasts expressing mutant GCase. This approach simplifies the analysis of cell and tissue homogenates and should facilitate the analysis of clinical and laboratory tissues and samples.

Pharmacological Inhibition of O-GlcNAcase Enhances Autophagy in Brain through an mTOR-Independent Pathway

The glycosylation of nucleocytoplasmic proteins with O-linked N-acetylglucosamine residues (O-GlcNAc) is conserved among metazoans and is particularly abundant within brain. O-GlcNAc is involved in diverse cellular processes ranging from the regulation of gene expression to stress response. Moreover, O-GlcNAc is implicated in various diseases including cancers, diabetes, cardiac dysfunction, and neurodegenerative diseases. Pharmacological inhibition of O-GlcNAcase (OGA), the sole enzyme that removes O-GlcNAc, reproducibly slows neurodegeneration in various Alzheimer’s disease (AD) mouse models manifesting either tau or amyloid pathology. These data have stimulated interest in the possibility of using OGA-selective inhibitors as pharmaceuticals to alter the progression of AD. The mechanisms mediating the neuroprotective effects of OGA inhibitors, however, remain poorly understood. Here we show, using a range of methods in neuroblastoma N2a cells, in primary rat neurons, and in mouse brain, that selective OGA inhibitors stimulate autophagy through an mTOR-independent pathway without obvious toxicity. Additionally, OGA inhibition significantly decreased the levels of toxic protein species associated with AD pathogenesis in the JNPL3 tauopathy mouse model as well as the 3×Tg-AD mouse model. These results strongly suggest that OGA inhibitors act within brain through a mechanism involving enhancement of autophagy, which aids the brain in combatting the accumulation of toxic protein species. Our study supports OGA inhibition being a feasible therapeutic strategy for hindering the progression of AD and other neurodegenerative diseases. Moreover, these data suggest more targeted strategies to stimulate autophagy in an mTOR-independent manner may be found within the O-GlcNAc pathway. These findings should aid the advancement of OGA inhibitors within the clinic

MicroRNA-181a Regulates the Proliferation and Differentiation of Hu Sheep Skeletal Muscle Satellite Cells and Targets the YAP1 Gene

MicroRNA (miRNA) is of great importance to muscle growth and development, including the regulation of the proliferation and differentiation of skeletal muscle satellite cells (SMSCs). In our research group’s previous study, we found that miR-181a is differentially expressed in the longissimus dorsi muscle of Hu sheep at different stages. We speculated that miR-181a may participate in the growth and development process of Hu sheep. To understand the mechanism of miR-181a regulating the growth and development of Hu sheep skeletal muscle, we extracted skeletal muscle satellite cells from the longissimus dorsi muscle of 3-month-old Hu sheep fetuses and performed a series of experiments. Our results showed that miR-181a suppressed SMSCs’ proliferation using QRT-PCR, Western blot, CCK-8, EDU, and Flow cytometry cycle tests. In addition, QRT-PCR, Western blot, and immunofluorescence indicated that miR-181a facilitated the differentiation of SMSCs. Then, we used dual-luciferase reporter gene detection, QRT-PCR, and Western blot to find that the Yes1-related transcription regulator (YAP1) is the target gene of miR-181a. Our study supplies a research basis for understanding the regulation mechanism of miR-181a on the growth of Hu sheep skeletal muscle

Selective Neural Deletion of the Atg7 Gene Reduces Irradiation-Induced Cerebellar White Matter Injury in the Juvenile Mouse Brain by Ameliorating Oligodendrocyte Progenitor Cell Loss

Radiotherapy is an effective tool for treating brain tumors, but irradiation-induced toxicity to the normal brain tissue remains a major problem. Here, we investigated if selective neural autophagy related gene 7 (Atg7) deletion has a persistent effect on irradiation-induced juvenile mouse brain injury. Ten-day-old Atg7 knockout under a nestin promoter (KO) mice and wild-type (WT) littermates were subjected to a single dose of 6 Gy whole-brain irradiation. Cerebellar volume, cell proliferation, microglia activation, inflammation, and myelination were evaluated in the cerebellum at 5 days after irradiation. We found that neural Atg7 deficiency partially prevented myelin disruption compared to the WT mice after irradiation, as indicated by myelin basic protein staining. Irradiation induced oligodendrocyte progenitor cell (OPC) loss in the white matter of the cerebellum, and Atg7 deficiency partly prevented this. The mRNA expression of oligodendrocyte and myelination-related genes (Olig2, Cldn11, CNP, and MBP) was higher in the cerebellum in Atg7 KO mice compared with WT littermates. The total cerebellar volume was significantly reduced after irradiation in both Atg7 KO and WT mice. Atg7-deficient cerebellums were in a regenerative state before irradiation, as judged by the increased OPC-related and neurogenesis-related transcripts and the increased numbers of microglia; however, except for the OPC parameters these were the same in both genotypes after irradiation. Finally, there was no significant change in the number of astrocytes in the cerebellum after irradiation. These results suggest that selective neural Atg7 deficiency reduces irradiation-induced cerebellar white matter injury in the juvenile mouse brain, secondary to prevention of OPC loss

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery