Range anomaly suppression based on neighborhood pixels detection in ladar range images

Research on the range anomaly suppression algorithm in laser radar （ladar） range images is significant in the application and development of ladar. But most of existing algorithms cannot protect the edge and linear target well while suppressing the range anomaly. Aiming at this problem, the differences among the edge, linear target, and range anomaly are analyzed and a novel algo- rithm based on neighborhood pixels detection is proposed. Firstly, the range differences between current pixel and its neighborhood pixels are calculated. Then, the number of neighborhood pixels is detected by the range difference threshold. Finally, whether the current pixel is a range anomaly is distinguished by the neighbor- hood pixel number threshold. Experimental results show that the new algorithm not only has a better range anomaly suppression performance and higher efficiency, but also protects the edge and linear target preferably compared with other algorithms.

An AIEgen nano-assembly for simultaneous detection of ATP and H_(2)S

A new aggregation-induced emission(AIE)-based fluorescence sensor,TPEPy-SS-C14,for simultaneous recognition of adenosine triphosphate(ATP)and hydrogen sulfide(H_(2)S)has been reported via the aggregation-disaggregation mechanism.The probe self-assembles nano-structure aggregations in aqueous solution.It shows fluorescence turn-on response toward ATP for the complexation-enhanced aggregation,but leads to fluorescence quenching of H_(2)S for cleavage the aggregations.

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

Determination of Trace Amounts of Cobalt and Copper by High Performance Liquid Chromatography with Indigo Carmine Chemiluminescence Detection

A post-column reaction chemiluminescence detection method in ion chromatography is described in this paper.The chemiluminescence system was indigo cramine-hydrogen peroxide. The chemiluminescence reaction conditions and the separation conditions of Co and Cu in chromatography were presented.The detection limits were 1.0 and 2.0×10-8 g/mL for Co and Cu respectively.

Scanning electron microscopy coupled with energydispersive X-ray spectrometry for quick detection of sulfuroxidizing bacteria in environmental water samples

Detection of sulfur-oxidizing bacteria has largely been dependent on targeted gene sequencing technology or traditional cell cultivation, which usually takes from days to months to carry out. This clearly does not meet the requirements of analysis for time-sensitive samples and/or complicated environmental samples. Since energy-dispersive X-ray spectrometry(EDS) can be used to simultaneously detect multiple elements in a sample, including sulfur, with minimal sample treatment, this technology was applied to detect sulfur-oxidizing bacteria using their high sulfur content within the cell. This article describes the application of scanning electron microscopy imaging coupled with EDS mapping for quick detection of sulfur oxidizers in contaminated environmental water samples, with minimal sample handling. Scanning electron microscopy imaging revealed the existence of dense granules within the bacterial cells, while EDS identified large amounts of sulfur within them. EDS mapping localized the sulfur to these granules. Subsequent 16S rRNA gene sequencing showed that the bacteria detected in our samples belonged to the genus Chromatium, which are sulfur oxidizers. Thus, EDS mapping made it possible to identify sulfur oxidizers in environmental samples based on localized sulfur within their cells, within a short time(within 24 h of sampling). This technique has wide ranging applications for detection of sulfur bacteria in environmental water samples.

YOLOV5s object detection based on Sim SPPF hybrid pooling

Aiming at the problem of low surface defect detection accuracy of industrial products, an object detection method based on simplified spatial pyramid pooling fast(Sim SPPF) hybrid pooling improved you only look once version 5s(YOLOV5s) model is proposed. The algorithm introduces channel attention(CA) module, simplified SPPF feature vector pyramid and efficient intersection over union(EIOU) loss function. Feature vector pyramids fuse high-dimensional and low-dimensional features, which makes semantic information richer. The CA mechanism performs maximum pooling and average pooling operations on the feature map. Hybrid pooling comprehensively improves detection computing efficiency and accurate deployment ability. The results show that the improved YOLOV5s model is better than the original YOLOV5s model. The average test accuracy(mAP) can reach 91.8%, which can be increased by 17.4%, and the detection speed can reach 108 FPS, which can be increased by 18 FPS. The improved model is practicable, and the overall performance is better than other conventional models.

Mycotoxin’s Infections and Prevention Methods: State of the Art

Fungi that attack field crops and contaminate agricultural commodities produce mycotoxins when conditions are favorable. These are specialized metabolites made of poisonous chemical compounds created by bacteria, fungi, or plants that aren’t involved in the organism’s usual growth, development, or reproduction. They have an adversarial hold on humans, wildlife, and the agricultural sector, resulting in mysterious ailments and economic disruptions. Mycotoxins-tainted food and fodder can be found all over the world, posing a global concern. Consumption of contaminated food and fodder is a typical cause of epidemic outbreaks. Other mycotoxins have been associated with esophageal cancer and neural tube defects (NTDs);the immunotoxin deoxynivalenol (DON) causes diarrhea when combined with trichothecenes, and ochratoxin A (OTA) has been linked to kidney failure. The direct market costs associated with missed trade or lower revenues owing to tainted food or feed could be viewed as the economic repercussions of mycotoxins on human society. This review describes frequent groups of mycotoxins in detail, their impact on global health, their impact on the socio-economy;the methods of detection and prevention of these mycotoxins.

Candida accommodates non-culturable Helicobacter pylori in its vacuole-Koch's postulates aren't applicable

The following are the responses to the "letter to the editor"("Helicobacter is preserved in yeast vacuoles! Does Koch's postulates confirm it?") authored by Nader Alipour and Nasrin Gaeini that rejected the methods, results, discussions and conclusions summarized in the review article authored by Siavoshi F and Saniee P. In the article, 7 papers, published between 1998 and 2013, were reviewed. The 7 papers had been reviewed and judged very carefully by the assigned expertise of the journals involved, including the reviewers of the World Journal of Gastroenterology(WJG), before publication. In the review article, 121 references were used to verify the methods, results and discussions of these 7 papers. The review article was edited by the trustworthy British editor of the(WJG), and the final version was rechecked and finally accepted by the reviewers of(WJG). None of the reviewers made comments like those in this "letter to the editor", especially the humorous comments, which seem unprofessional and nonscientific. Above all, the authors' comments show a lack of understanding of basic and advanced microbiology, e.g. bacterial endosymbiosis in eukaryotic cells. Accordingly, their comments all through the letter contain misconceptions. The comments are mostly based on personal conclusions, without any scientific support. It would have been beneficial if the letter had been reviewed by the reviewers of the article by Siavoshi and Saniee.

System Fault Detection by Generalized Likelihood Ratio Method

This Paper successfully develops the method of Generalized Likelihood Ratio(G.L.R.)in detectingfaults of the system.It has been found very imPOrtant to pay more attention to the validation of measuringdata which must contain richer information relating to the fault.The foil measuringpoints,partial measuring points, single observation and multi-observation for detection of single fault and multi-fault have allbeen considered in our algorithm which basically depends on the theory of G.L.R.,the new concept of calculating residues is also put up as the base of this detecting algorithm. It can not only accurately detect thefaulted branches of the System, but also locate the slanted measures and even determine amplitude of thefault.By simulation with the software MATLAB, the simulation results of some application examples showthat our detection is much better than that by [1]. HOwever, for our method, the precision of sensors shouldbe known and a threshold of confidence for decision should be determined by user.

Specific detection and effective inhibition of a single bacterial species in situ using peptide mineralized Au cluster probes

Increasingly serious microbial infections call for the development of new simpler methods for the precise diagnosis and specific inhibition of such pathogens. In this work, a peptide mineralized Au cluster probe was applied as a new simplified strategy to both recognize and inhibit a single bacteria species of Staphylococcus aureus(S. aureus) simultaneously. The probes are composed of peptides and Au clusters. Moreover, the peptides specifically target S. aureus cells and the Au clusters provide fluorescent imaging and have an antibacterial effect. These new probes enable the simultaneous specific detection and effective destruction S. aureus cells in situ.

Step-size selection for split-step based nonlinear compensation with coherent detection in 112-Gb/s 16-QAM transmission

Non-uniform step-size distribution is implemented for split-step based nonlinear compensation in singlechannel 112-Gb/s 16 quadrature amplitude modulation （QAM） transmission. Numerical simulations of the system including a 20 × 80 km uncompensated link are performed using logarithmic step size distribution to compensate signal distortions. 50% of reduction in number of steps with respect to using constant step sizes is observed. The performance is further improved by optimizing nonlinear calculating position （NLCP） in case of using constant step sizes while NLCP optimization becomes unnecessary when using logarithmic step sizes, which reduces the computational effort due to uniformly distributed nonlinear phase for all successive steps.

超声S-Detect分类技术在乳腺包块良恶性诊断中的应用价值

目的探讨超声S-Detect分类技术在乳腺包块良恶性鉴别诊断中的应用价值。 方法选取我院2016年1－12月间经手术或病理穿刺活检证实的47例乳腺包块患者（共61个病灶）。所有病灶分别进行二维超声成像BI-RADS分类（由3类不同年资乳腺专科超声医师进行判别）以及计算机S-Detect分类，分别计算人为BI-RADS分类及S-Detect分类对乳腺包块良恶性诊断的敏感性、特异性、准确性、阳性预测值及阴性预测值。绘制各组的ROC曲线，比较ROC曲线下面积。 结果61个乳腺病灶中，病理证实良性病灶36个，恶性病灶25个。BI-RADS分类诊断的敏感性、特异性及准确性分别为：工作2年医师，69.4%、72.0%、70.5%；工作5年医师，64.0%、92.0%、75.4%；工作7年医师，69.4%、92.0%、78.7%。计算机S-Detect分类诊断敏感性、特异性及准确性分别为80.6%、96.0%、86.9%。S-Detect分类诊断特异性、准确性及阳性预测值均高于工作2年医师BI-RADS分类，差异均具有统计学意义（P〈0.05）。各组ROC曲线下面积分别为：工作2年医师，0.729 ；工作5年医师，0.786；工作7年医师，0.801；S-Detect分类，0.917。 结论与人工BI-RADS分类诊断相比，S-Detect分类在乳腺包块良恶性诊断中具有优势，尤其对于低年资医师，S-Detect分类有助于提高其诊断准确率。

Electrospun Cu-doped In_(2)O_(3) hollow nanofibers with enhanced H2S gas sensing performance

One-dimensional nanofibers can be transformed into hollow structures with larger specific surface area, which contributes to the enhancement of gas adsorption. We firstly fabricated Cu-doped In_(2)O_(3) (Cu-In_(2)O_(3)) hollow nanofibers by electrospinning and calcination for detecting H2S. The experimental results show that the Cu doping concentration besides the operating temperature, gas concentration, and relative humidity can greatly affect the H2S sensing performance of the In_(2)O_(3)-based sensors. In particular, the responses of 6%Cu-In_(2)O_(3) hollow nanofibers are 350.7 and 4201.5 to 50 and 100 ppm H2S at 250 ℃, which are over 20 and 140 times higher than those of pristine In_(2)O_(3) hollow nanofibers, respectively. Moreover, the corresponding sensor exhibits excellent selectivity and good reproducibility towards H2S, and the response of 6%Cu-In_(2)O_(3) is still 1.5 to 1 ppm H2S. Finally, the gas sensing mechanism of Cu-In_(2)O_(3) hollow nanofibers is thoroughly discussed, along with the assistance of first-principles calculations. Both the formation of hollow structure and Cu doping contribute to provide more active sites, and meanwhile a little CuO can form p–n heterojunctions with In_(2)O_(3) and react with H2S, resulting in significant improvement of gas sensing performance. The Cu-In_(2)O_(3) hollow nanofibers can be tailored for practical application to selectively detect H2S at lower concentrations.

On-chip classification of micro-particles using laser light scattering and machine learning

The rapid detection of microparticles exhibits a broad range of applications in the field of science and technology. The proposed method differentiates and identifies the 2 μm and 5 μm sized particles using a laser light scattering. The detection method is based on measuring forward light scattering from the particles and then classifying the acquired data using support vector machines. The device is composed of a microfluidic chip linked with photosensors and a laser device using optical fiber. Connecting the photosensors and laser device using optical fibers makes the device more diminutive in size and portable. The prepared sample containing microspheres was passed through the channel, and the surrounding photosensors measured the scattered light. The time-domain features were evaluated from the acquired scattered light, and then the SVM classifier was trained to distinguish the particle’s data. The real-time detection of the particles was performed with an overall classification accuracy of 96.06%. The optimum conditions were evaluated to detect the particles with a minimum concentration of 0.2 μg/m L. The developed system is anticipated to be helpful in developing rapid testing devices for detecting pathogens ranging between 2 μm to 10 μm.

Zinc Phthalocyanine Thin Film-Based Optical Waveguide H_(2)S Gas Sensor

The detection of hydrogen sulfide(H_(2)S)is essential because of its toxicity and abundance in the environment.Hence,there is an urgent requisite to develop a highly sensitive and economical H_(2)S detection system.Herein,a zinc phthalocyanine(ZnPc)thin film-based K+-exchanged optical waveguide(OWG)gas sensor was developed for H_(2)S detection by using spin coating.The sensor showed excellent H_(2)S sensing performance at room temperature with a wide linear range(0.1 ppm 500 ppm),reproducibility,stability,and a low detection limit of 0.1ppm.The developed sensor showed a significant prospect in the development of cost-effective and highly sensitive H_(2)S gas sensors.

A visual photothermal paper sensor for H_2S recognition through rational modulation LSPR wavelength of plasmonics

It is known that the localized surface plasmon resonance(LSPR) wavelength of plasmonics is highly dependent on compositions and geometry of plasmonics as well as the surrounding environments. Here, monodispersed Au@Ag core-shell nanoparticles(Au@Ag NPs) were prepared by carefully optimizing the shell thickness of Au@Ag NPs, and the presence of hydrogen sulfide(H_2 S) would significantly alter the LSPR wavelength. On the basis of this, a photothermal paper sensor for on-site recognition of H_2 S was constructed with a visual detection limit of 12.8 ng/L.

A near-infrared fluorescent probe for monitoring fluvastatin-stimulated endogenous H_2S production

Most reported fluorescent probes have limitations in practical applications in living systems due to the strong autofluorescence background,construction of probes with near-infrared（NIR） fluorescence emission is an accessible approach for addressing this challenge.We here designed a NIR fluorescent probe for monitoring the endogenous production of H2S in living cells.The designed probe showed significant NIR fluorescence turn-on response to H2S with high selectivity,enabling the sensitive detection H2S.Importantly,the probe could be applied in monitoring the endogenous production of H2S in raw 264.7 macrophages.This study showed that fluvastatin can promote the activity of cystathionineγ-lyase（CSE） for generation H2S.

Speech Recognition for Parkinson’s Disease Based on Improved Genetic Algorithm and Data Enhancement Technology

Parkinson’s disease is one of the most destructive diseases to the nervous system.Speech disorder is one of the typical symptoms of Parkinson’s disease.Approximately 90%of Parkin-son’s patients develop some degree of speech disorder,which affects speech function faster than any other subsystem of the body.Screening Parkinson’s disease by sound is a very effective method that has attracted a growing number of researchers over the past decade.Patients with Parkinson’s disease could be identified by recording the sound signal of the pronunciation of words,extracting appropriate features and identifying the disturbance in their voices.This paper proposes an improved genetic algorithm combined with a data enhancement method for Parkinson’s speech signal recognition.Specifically,the methods first extract representative speech signal features through the L1 regularization SVM and then enhance the representative feature data by the SMOTE algorithm.Following this,both original and enhanced features are used to train an SVM classifier for speech signal recognition.An improved genetic algorithm was applied to find the optimal parameters of the SVM.The effectiveness of our proposed model is demonstrated by using Parkinson’s disease audio data set from the UCI machine learning library,and compared with the most advancedmethods,our proposed method has the best performance.