Near-Infrared Alcohol Detection Circuit Based On Multisim

Because the number of private cars has expanded, drunk driving has become more and more frequent. The detection of a driver’s alcohol concentration has become the focus of attention. Therefore, infrared alcohol detection was studied. The principle of infrared blood glucose noninvasive detection was investigated, and it was compared with infrared spectrum detection. Finally, using transmission technology and an infrared emitter and receiver, an infrared alcohol identification circuit was designed by NBohr’s Law and the Correcting Beer-Lambert Law. It was composed of an infrared acquisition circuit, an infrared electronic filter circuit, and an infrared amplifier circuit. And the infrared alcohol identification circuit was composed of multiple circuits in series and parallel. At various pins on the first AD844AN, the infrared electronic filter circuit receives an alternating current source voltage of 1000V with a basic signal frequency of 60 Hz. At the input end, the infrared amplifier circuit receives a current signal with a frequency of 1 Hz and an amplitude of 5 uA and performs the reproduction experiment using Multisim. As a result of the signal being upgraded to fulfill the objective of recognition, distinct information reappears and exhibits different waveforms

Genomic analysis of indigenous goats in Southwest Asia reveals evidence of ancient adaptive introgression related to desert climate

Understanding how evolutionary pressures related to climate change have shaped the current genetic background of domestic animals is a fundamental pursuit of biology. Here, we generated whole-genome sequencing data from native goat populations in Iraq and Pakistan. Combined with previously published data on modern, ancient (Late Neolithic to Medieval periods), and wild Capra species worldwide, we explored the genetic population structure, ancestry components, and signatures of natural positive selection in native goat populations in Southwest Asia (SWA). Results revealed that the genetic structure of SWA goats was deeply influenced by gene flow from the eastern Mediterranean during the Chalcolithic period, which may reflect adaptation to gradual warming and aridity in the region. Furthermore, comparative genomic analysis revealed adaptive introgression of the KITLG locus from the Nubian ibex (C. nubiana) into African and SWA goats. The frequency of the selected allele at this locus was significantly higher among goat populations located near northeastern Africa. These results provide new insights into the genetic composition and history of goat populations in the SWA region

Metabolic risk factors of cognitive impairment in young women with major psychiatric disorder

BackgroundCognitive performance improves clinical outcomes of patients with major psychiatric disorder (MPD), but is impaired by hyperglycemia. Psychotropic agents often induce metabolism syndrome (MetS). The identification of modifiable metabolic risk factors of cognitive impairment may enable targeted improvements of patient care.ObjectiveTo investigate the relationship between MetS and cognitive impairment in young women with MPD, and to explore risk factors.MethodsWe retrospectively studied women of 18–34 years of age receiving psychotropic medications for first-onset schizophrenia (SCH), bipolar disorder (BP), or major depressive disorder (MDD). Data were obtained at four time points: presentation but before psychotropic medication; 4–8 and 8–12 weeks of psychotropic therapy; and enrollment. MATRICS Consensus Cognitive Battery, (MCCB)—based Global Deficit Scores were used to assess cognitive impairment. Multiple logistic analysis was used to calculate risk factors. Multivariate models were used to investigate factors associated with cognitive impairment.ResultsWe evaluated 2,864 participants. Cognitive impairment was observed in 61.94% of study participants, and was most prevalent among patients with BP (69.38%). HbA1c within the 8–12 week-treatment interval was the most significant risk factor and highest in BP. Factors in SCH included pre-treatment waist circumference and elevated triglycerides during the 8–12 weeks treatment interval. Cumulative dosages of antipsychotics, antidepressants, and valproate were associated with cognitive impairment in all MPD subgroups, although lithium demonstrated a protect effect (all P < 0.001).ConclusionsCognitive impairment was associated with elevated HbA1c and cumulative medication dosages. Pre-treatment waist circumference and triglyceride level at 8–12 weeks were risk factors in SCH. Monitoring these indices may inform treatment revisions to improve clinical outcomes

ROI-based brain functional connectivity using fMRI : regional signal representation, modelling and analysis

Inferring brain functional connectivity from functional magnetic resonance imaging (fMRI) data extends our understanding of systems-level functional organization of the brain. Functional connectivity can be assessed at the individual voxel or Regions of Interest (ROI) level, with pros and cons of each approach. This thesis focuses on addressing fundamental problems associated with ROI-based brain functional connectivity inference, including regional signal representation, brain functional connectivity modelling and brain functional connectivity analysis. Functional connectivity involving brainstem ROIs has been rarely studied. We propose a novel framework for brainstem-cortical functional connectivity modelling where the regional signal of brainstem nuclei is estimated by Partial Least Squares and connections between brainstem nuclei and other cortical/subcortical brain regions are reliably estimated by partial correlation. We then apply the proposed framework to assess functional connectivity of one particular brainstem nucleus - the pedunculopontine nucleus (PPN), which is important for ambulation, and is affected in diseases putting people at risk for falls (e.g., Parkinson’s Disease). A key issue for ROI-based brain functional connectivity assessment is how to summarize the information contained in the voxels of a given ROI. Currently, the signals from the same ROI voxels are simply averaged, neglecting any inhomogeneity in each ROI and assuming that the same voxels will interact with different ROIs in a similar manner. In this thesis, we develop a novel method of representing ROI activity and estimating brain functional connectivity that takes the regionally-specific nature of brain activity, the spatial location of concentrated activity, and activity in other ROIs into account. Finally, to facilitate the interpretation of the estimated brain functional connectivity networks, we propose the use of dynamic graph theoretical measures (e.g., the newly introduced graph spectral metric, Fiedler value) as potential MRI-related biomarkers. The proposed methods were applied to real fMRI datasets, with a primary focus on Parkinson’s disease. The proposed methods demonstrated enhanced robustness of brain functional connection estimation, with potential use in disease assessment and treatment evaluation. More broadly, this thesis suggests that brain functional connectivity offers a promising avenue for non-invasive and quantitative assessment of neurological diseases.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

An Approximately Isotropic Origami Honeycomb Structure and Its Energy Absorption Behaviors

Honeycomb structures have a wide range of applications owing to their light weight and promising energy absorption features. However, a conventional honeycomb structure is designed to absorb impact energy only in the out-of-plane direction and demonstrates unsatisfactory performance when the impact energy originates from a different direction. In this study, we proposed an origami honeycomb structure with the aim of providing an approximately isotropic energy absorption performance. The structure was created by folding a conventional honeycomb structure based on the Miura origami pattern, and it was investigated using both numerical and experimental approaches. Investigations of the structural behaviors under both out-of-plane and in-plane compressions were conducted, and the results revealed significantly different deformation modes in comparison with those of a conventional honeycomb structure. To determine the influences of geometries, we conducted a series of numerical studies, considering various structural parameters, and analyzed the response surface of the mean stress in three directions. Based on the numerical and experimental results, a parameter indicating the approximate isotropy of the origami honeycomb structure was introduced. The proposed structure is promising for absorbing energy from any direction and has potential applications in future metamaterial design work

Pathogen-Derived Extracellular Vesicles: Emerging Mediators of Plant-Microbe Interactions

Extracellular vesicles (EVs) are lipid bilayer–enclosed nanoparticles that deliver bioactive proteins, nucleic acids, lipids, and other small molecules from donor to recipient cells. They have attracted significant interest recently due to their important roles in regulating plant-microbe interaction. During microbial infection, plant EVs play a prominent role in defense by delivering small regulatory RNA into pathogens, resulting in the silencing of pathogen virulence genes. Pathogens also deliver small RNAs into plant cells to silence host immunity genes. Recent evidence indicates that microbial EVs may be involved in pathogenesis and host immunity modulation by transporting RNAs and other biomolecules. However, the biogenesis and function of microbial EVs in plant-microbe interaction remain ill-defined. In this review, we discuss various aspects of microbial EVs, with a particular focus on current methods for EV isolation, composition, biogenesis, and their roles in plant-microbe interaction. We also discussed the potential role of microbial EVs in cross-kingdom RNA trafficking from pathogens to plants, as it is a highly likely possibility to explore in the future. [Graphic: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license

SSD-KD: a self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images

Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Over the last few years, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, the knowledge distillation (KD) method has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability. To bridge the gap, this study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin disease classification. Our method models an intra-instance relational feature representation and integrates it with existing KD research. A dual relational knowledge distillation architecture is self-supervised trained while the weighted softened outputs are also exploited to enable the student model to capture richer knowledge from the teacher model. To demonstrate the effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale open-accessed benchmark of skin diseases dermoscopic images. Experiments show that our distilled MobileNetV2 can achieve an accuracy as high as 85% for the classification tasks of 8 different skin diseases with minimal parameters and computing requirements. Ablation studies confirm the effectiveness of our intra- and inter-instance relational knowledge integration strategy. Compared with state-of-the-art knowledge distillation techniques, the proposed method demonstrates improved performance. To the best of our knowledge, this is the first deep knowledge distillation application for multi-disease classification on the large-scale dermoscopy database. Our codes and models are available at https://github.com/enkiwang/Portable-Skin-Lesion-Diagnosis.We acknowledge the support of the National Natural Science Foundation of China (NSFC) [funding reference number 62201357] and the Natural Sciences and Engineering Research Council of Canada (NSERC) [funding reference numbers 2017-04932 and 2022-03049]

Three-Component Covalent Organic Framework Nanosheets for the Detection of MicroRNAs

The development of new techniques for the detection of microRNAs (miRNAs) is highly desirable. Herein, a new crystalline three-component covalent organic framework (COF) termed EB-TAPB-TFP COF was synthesized under solvothermal conditions utilizing 1,3,5-triformylphloroglucinol, 1,3,5-tris(4-aminophenyl)benzene and ethidium bromide as monomers. Interestingly, EB-TAPB-TFP COF can be self-exfoliated into two-dimensional nanosheets (NSs) in an aqueous medium. The obtained EB-TAPB-TFP NSs exhibited a remarkable fluorescence intensity enhancement in the presence of a DNA-miRNA heteroduplex when compared to the presence of single-stranded DNA and other phosphate-based small molecules, making it promising in the detection of miRNA without tagging any fluorescent marker. Moreover, the EB-TAPB-TFP NSs can also be used as sensing material for the detection of a DNA-miRNA heteroduplex using the quartz crystal microbalance technique, which is in good agreement with the fluorescence sensing result. The exploration of COF-based sensors in this work demonstrates a new pathway for the selective detection of miRNAs

Three-Component Covalent Organic Framework Nanosheets for the Detection of MicroRNAs

The development of new techniques for the detection of microRNAs (miRNAs) is highly desirable. Herein, a new crystalline three-component covalent organic framework (COF) termed EB-TAPB-TFP COF was synthesized under solvothermal conditions utilizing 1,3,5-triformylphloroglucinol, 1,3,5-tris(4-aminophenyl)benzene and ethidium bromide as monomers. Interestingly, EB-TAPB-TFP COF can be self-exfoliated into two-dimensional nanosheets (NSs) in an aqueous medium. The obtained EB-TAPB-TFP NSs exhibited a remarkable fluorescence intensity enhancement in the presence of a DNA-miRNA heteroduplex when compared to the presence of single-stranded DNA and other phosphate-based small molecules, making it promising in the detection of miRNA without tagging any fluorescent marker. Moreover, the EB-TAPB-TFP NSs can also be used as sensing material for the detection of a DNA-miRNA heteroduplex using the quartz crystal microbalance technique, which is in good agreement with the fluorescence sensing result. The exploration of COF-based sensors in this work demonstrates a new pathway for the selective detection of miRNAs

Exploring the potential of Chinese GF-6 images for crop mapping in regions with complex agricultural landscapes

Accurate and timely crop mapping is crucial for environment assessment, food security and agricultural production. However, for the areas with high landscape heterogeneity and frequent cloudy and rainy weather, the insufficient high-quality satellite images limit the accuracy of crop classification. The recently launched Chinese GF-6 wide field-of-view camera (WFV) with a revisit cycle of 4-day and spatial resolution of 16-meter shows great potential for agricultural monitoring. In this study, Qianjiang City characterized by complex agricultural landscapes was selected as the research area to assess the potential of GF-6 data in identifying crop types. Firstly, the pairwise and global separability were calculated to analyze the effect of different spectral-temporal features of GF-6 images on crop classification. A total of 255 spectral-temporal features derived from 15 GF-6 tiles were then used to perform random forest classification. Furthermore, the classification results were evaluated based on 671 field samples and then compared the accuracy between GF-6 data and Sentinel-2 or Landsat-8 data. In addition, the earliest identifiable time of crop types was also determined by iteratively using all available GF-6 data during each time period. The results suggested that the overall accuracy (OA) of all available GF-6 images was 91.55%, which was significantly higher than that of Landsat-8 data (OA = 85.97%) and was slightly lower than that of Sentinel-2 data (OA = 93.10%). The newly added red-edge bands (0.69 ∼ 0.73 μm, 0.73 ∼ 0.77 μm) and their derivative vegetation indices were important spectral features, and the period from mid-March to early-April was the best temporal window for crop identification in our research area. Moreover, late July was the earliest crop identifiable time with overall accuracy of 90% for the first time of the year. These results indicated the great potential of GF-6 images for classifying crop types in the areas with complex cropping system and fragmented agricultural landscapes, particularly when integrating other satellite data with comparable spatial resolution (e.g. Chinese GF-1 data and Sentinel-2 data)