Detection and Analysis of Routine Blood Indexes in Guike Pig

[Objectives] In this study,routine blood indexes of Guike pig were detected to clarify quantity variation and morphology distribution of different functional cells（ red blood cells,white blood cells and platelets）,thus providing reference data for immunological evaluation and disease resistance research of Guike pig. [Methods] Blood samples were collected from forty-four 49-day-old healthy Guike pigs（ 22 males and 22 females） once a week for seven consecutive weeks to detect routine blood indexes,and the results were compared with routine blood indexes of Landrace pig and Enshi black pig. [Results]Among red blood cell indexes of Guike pig at different day ages,red blood cell count（ RBC） and hemoglobin count（ HGB） both reached the lowest levels（ 6. 85 × 10^12/L and 97. 1 g/L）at 70 days of age,which were significantly lower than the corresponding indexes at 49,56,63,84 and 91 days of age（ P 〈 0. 05,similarly hereinafter）; mean corpuscular hemoglobin（ MCH） and mean corpuscular hemoglobin concentration（ MCHC） demonstrated basically the same variation pattern,which both reached the lowest levels（ 15. 11 pg and 275. 70 g/L） at 70 days of age and reached the highest levels（ 16. 40 pg and 314. 30 g/L） at 91 days of age; hematocrit（ HCT）,mean corpuscular volume（ MCV） and red blood cell distribution width（ RDW） exhibited relatively stable changes with no significant differences among different day ages（ P 〉 0. 05,similarly hereinafter）. Among white blood cell indexes of Guike pig at different day ages,white blood cell count（ WBC） reached the lowest level（ 13. 71 × 10^9/L） at 63 days of age and reached the highest level（ 17. 52 × 10^9/L） at 84 days of age,with significant differences; neutrophil count（ NEUT）reached the lowest level（ 3. 61 × 10^9/L） at 70 days of age,which was significantly lower than that at 49,56 and 77 days of age; lymphocyte count（ LYMPH） exhibited relatively stable changes and was not significantly affected by the increase in day age. Among platelet indexes of Guike pig at different day ages,platelet count（ PLT）,thrombocytocrit（ PCT） and platelet volume distribution width（ PDW） exhibited relatively stable changes among different day ages,which were not significantly affected by the increase in day age. Compared to 35-day-old Landrace pig and 5-month-old Enshi black pig,most routine blood indexes of Guike pig were similar to or ranged between those of Landrace pig and Enshi black pig except HCT,mean platelet volume（ MPV）,PCT and PDW. [Conclusions]Guike pig has a certain uniqueness in routine blood indexes,which has developed a stable immune system at 49-91 days of age.

Detection and Analysis of Ebola Virus in Sierra Leone-China Friendship Biosafety Laboratory from March 11 to April 20, 2015

Ebola virus disease reemerged in Western Africa in 2014.Chinese Center for Disease Control and Prevention dispatched the first Ebola virus（EBOV）detection team to run newly established Sierra Leone-China Friendship Biological Safety Laboratory.The aims of study were to understand epidemiology,clinical manifestations and survival time of EBOV in patient＇s blood.A total of 913specimens were tested between March 11 and April20, 2015. EBOV positivity occurred in 7.37% of the blood and 0.53% in throat swabs.

Test analysis of detection of damage to a complicated spatial model structure

A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam鈥揷olumn connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.

Iron-Mediated Oxidative DNA Damage Detected by Fluorometric Analysis of DNA Unwinding in Isolated Rat Liver Nuclei

Studies were performed to determine the extent of nuclear DNA degradation induced by iron, iron-ascorbate, or iron-bleomycin under aerobic conditions in a model system using isolated rat liver nuclei. The effects of five antioxidants (catalase, superoxide dismutase, dimethyl sulfoxide, glutathione and diallyl sulfide) on this oxidative nuclear damage were also investigated. At the 0.05 level for statistical significance, iron induced concentration-dependent DNA degradation, and this effect was enhanced by ascorbate and bleomycin. The antioxidants catalase, dimethyl sulfoxide, and diallyl sulfide significantly reduced the iron-ascorbate-induced DNA damage, whereas superoxide dismutase and dimethyl sulfoxide significantly reduced iron-bleomycin-induced damage. Glutathione significantly increased the iron-bleomycin-induced DNA damage. These results suggest that the reactive oxygen species generated by iron, iron-ascorbate, and iron-bleomycin are responsible for the DNA strand breaks in isolated rat liver nuclei.

SELECTIVE SULFUR DIOXIDE DETERMINATION BY GAS DIFFUSION FLOW INJECTION ANALYSIS WITH CHEMILUMINESCENT DETECTION

Sulfur dioxide has been found to decrease the chemiluminescence of luminol-iodine system.A new determination method for sulfur dioxide in atmosphere is developed by applying this reaction to a flow injection gas diffusion separation system.This permits the determination of sulfur dioxide selectively and rapidly.

Detection and Diagnosis of Gear Fault By the Single Gear Tooth Analysis Technique

This paper presents a procedure of sing le gear tooth analysis for early detection and diagnosis of gear faults. The objec tive of this procedure is to develop a method for more sensitive detection of th e incipient faults and locating the faults in the gear. The main idea of the sin gle gear tooth analysis is that the vibration signals collected with a high samp ling rate are divided into a number of segments with the same time interval. The number of signal segments is equal to that of the gear teeth. The analysis of i ndividual segments reveals more sensitively the changes of the vibration signals in both time and frequency domain caused by gear faults. In addition, the locat ion of a failed tooth can be indicated in terms of the position of the segment t hat deviates from the normal segments. An experimental investigation verified th e advantages of the single gear tooth analysis.

Application of Mass Spectrometry (MS)-coupled Techniques in Pesticide Residue Detection

Pesticide residue detection is an important work to ensure the quality safety of agricultural products.In the process of agricultural production,in order to prevent and control agricultural diseases and pests,a certain amount of pesticides need to be used.However,if pesticides are used excessively,there will be certain pesticide residues in crops and related products.Therefore,it is necessary to do a good job in pesticide residue detection.The gas chromatography-mass spectrometry(GC-MS)and liquid chromatography-mass spectrometry(LC-MS)detection methods have good results and can effectively detect pesticide residues in related products.This paper reviewed and analyzed the application of GC-MS and LC-MS in pesticide residue detection,and proposed optimization measures based on practical experience,hoping to provide reference for relevant scholars.

Assessment of structural damage using natural frequency changes

The present paper develops a new method for damage localization and severity estimation based on the employment of modal strain energy. This method is able to determine the damage locations and estimate their severities, requiring only the information about the changes of a few lower natural frequencies. First, a damage quantification method is formulated and iterative approach is adopted for determining the damage extent. Then a damage localization algorithm is proposed, in which a damage indicator is formulated where unity value corresponds to the true damage scenario. Finally, numerical studies and model tests are conducted to demonstrate the effectiveness of the developed algorithm.

A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

Land Use/Land Cover (LULC) Dynamics in a Semi-Arid Watershed in Eastern Rajasthan, India Using Geospatial Tools

Land use/land cover (LULC) change analysis has become a unique approach in determining the extent of degradation of natural resources within a given period of time. Remote sensing and GIS techniques have proved to be efficient tools for mapping and analyzing LULC changes over the last few decades. LULC change analysis has been carried out in Ruparel watershed which is situated in Alwar district, Eastern Rajasthan, India, based on visual image interpretation and change detection analysis of multi-temporal satellite data pertaining to IRS-P6 LISS III data of 2004 (Path-Row 95:52), IRS-P6 LISS III of 2014 (Path-Row 95:52) and IRS-R2A LISS III data of 2021. Visual image interpretation led to the delineation of 13 LULC classes using ArcGIS 10.5 software and include categories such as cultivated land, fallow land dense forest, open forest, degraded forest, open scrub, gullied/ravenous land, settlement/built-up land, River/waterbody, dry waterbody/dry river, plantation, barren/rocky/stony waste, and stone quarry. Results of the analysis depict significant LULC changes that have taken place in the area from 2004 to 2021. LULC categories such as cultivated land and settlement/built-up land have reported major changes in terms of their increase with 56.42 km2 (4.63%) and 31.9 km2 (2.63%) respectively primarily because of an increase in population. Likewise, the dense forest has reported a decrease of 33.78 km2 (2.78%) in its area and has been converted into degraded forest i.e., 32.04 km2 (2.64%) and open forest 2.85 km2 (0.24%) due to increased human exploitation of forest resources and mining activities taking place within the forested area. The study area needs the immediate attention of policymakers and stakeholders as the study area being part of the National Capital Region (NCR) will see excessive in-migration of the population in coming years which will further deplete the precious resources in the area.

Wavefront detection performance analysis of plenoptic sensor

A numerical simulation model of plenoptic sensor aberration wavefront detection is established to simulate and analyze the detection performance of plenoptic sensor aberration wavefront for different turbulence intensities.The results show that the plenoptic sensor can achieve better distortion wavefront detection,and its wavefront detection accuracy improves with turbulence intensity.The unique optical structure design of the plenoptic sensor makes it more suitable for aberration wavefront detection in strong turbulent conditions.The wavefront detection performance of the plenoptic sensor is not only related to its wavefront reconstruction algorithm but also closely related to its structural parameter settings.The influence of structural parameters on the wavefront detection accuracy of plenoptic sensors under different turbulence intensities is simulated and analyzed.The variation law of wavefront detection accuracy and structural parameters under different turbulence intensities is summarized to provide a reference for the structural design and parameter optimization of plenoptic sensors.

Wheeze detecting method based on spectrogram entropy analysis

In order to eliminate the subjectivity of wheeze diagnosis and improve the accuracy of objective detecting methods,this paper introduces a wheeze detecting method based on spectrogram entropy analysis.This algorithm mainly comprises three steps which are preprocessing,features extracting and wheeze detecting based on support vector machine(SVM).Herein,the preprocessing consists of the short-time Fourier transform(STFT) decomposition and detrending.The features are extracted from the entropy of spectrograms.The step of detrending makes the difference of the features between wheeze and normal lung sounds more obvious.Moreover,compared with the method whose decision is based on the empirical threshold,there is no uncertain detecting result any more.Results of two testing experiments show that the detecting accuracy(AC) are 97.1%and 95.7%,respectively,which proves that the proposed method could be an efficient way to detect wheeze.

Community Detection in Disease-Gene Network Based on Principal Component Analysis

The identification of communities is imperative in the understanding of network structures and functions.Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.

MobSafe:Cloud Computing Based Forensic Analysis for Massive Mobile Applications Using Data Mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win＋Intel alliance in PC, Android＋ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app＇s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.