TMEM175: A lysosomal ion channel associated with neurological diseases.

Lysosomes are acidic intracellular organelles with autophagic functions that are critical for protein degradation and mitochondrial homeostasis, while abnormalities in lysosomal physiological functions are closely associated with neurological disorders. Transmembrane protein 175 (TMEM175), an ion channel in the lysosomal membrane that is essential for maintaining lysosomal acidity, has been proven to coordinate with V-ATPase to modulate the luminal pH of the lysosome to assist the digestion of abnormal proteins and organelles. However, there is considerable controversy about the characteristics of TMEM175. In this review, we introduce the research progress on the structural, modulatory, and functional properties of TMEM175, followed by evidence of its relevance for neurological disorders. Finally, we discuss the potential value of TMEM175 as a therapeutic target in the hope of providing new directions for the treatment of neurodegenerative diseases.

Dual roles of interleukin-33 in cognitive function by regulating central nervous system inflammation.

With the advent of an aging society, the incidence of dementia is increasing, resulting in a vast burden on society. It is increasingly acknowledged that neuroinflammation is implicated in various neurological diseases with cognitive dysfunction such as Alzheimer's disease, multiple sclerosis, ischemic stroke, traumatic brain injury, and central nervous system infections. As an important neuroinflammatory factor, interleukin-33 (IL-33) is highly expressed in various tissues and cells in the mammalian brain, where it plays a role in the pathogenesis of a number of central nervous system conditions. Reams of previous studies have shown that IL-33 has both pro- and anti-inflammatory effects, playing dual roles in the progression of diseases linked to cognitive impairment by regulating the activation and polarization of immune cells, apoptosis, and synaptic plasticity. This article will summarize the current findings on the effects IL-33 exerts on cognitive function by regulating neuroinflammation, and attempt to explore possible therapeutic strategies for cognitive disorders based on the adverse and protective mechanisms of IL-33.

Fast estimation of optical properties of pear using a single snapshot technique combined with a least-squares support vector regression model based on spatial frequency domain imaging.

Spatial frequency domain imaging has great potential in agricultural produce quality control due to its advantage of wide-field mapping of absorption (µa) and reduced scattering (µs') parameters. However, it is not widely adopted in real applications due to the large time cost during image acquisition and inversion calculation processes. In this study, a single snapshot technique was used to obtain ac and dc components (Rd_ac, Rd_dc) of diffuse reflectance of turbid media (phantoms and pears). The validation results for the snapshot method indicate that at the spatial frequency of 1000/3 m-1, it achieved the optimal demodulation, by comparison with the results obtained by the commonly used time-domain amplitude demodulation method. Diffusion approximation, artificial neural network, least-squares support vector machine regression (LSSVR), and LSSVR combined with a genetic algorithm (LSSVR+GA) were then used to predict µa and µs' from the obtained Rd_ac, Rd_dc at the fx of 1000/3 m-1. Validation results indicated that the LSSVR method took the least time to calculate µa and µs' with high performance. The proposed imaging system and algorithm were implemented for the inspection of a pear bruise. Results indicated that the bruise, which is not obviously distinguishable in original gray maps, can show obvious contrast in calculated µa and µs' maps, especially in µa maps. Further, the contrast becomes more obvious with the passage of time. In summary, this study developed a low-cost spatial frequency imaging system and matching software that could realize fast detection of optical properties for a pear with the proposed snapshot and LSSVR algorithms.

Nondestructive determination of optical properties of a pear using spatial frequency domain imaging combined with phase-measuring profilometry.

Spatial frequency domain imaging (SFDI), as a rapid, noncontact, and scan-free method, can realize wide-field, quantitative optical property mapping and tomographic imaging for a biological sample. Phase-measuring profilometry (PMP) is a surface profile characterization method. Since the projection of structured light onto an object is the basis for PMP and SFDI, the SFDI system is capable of performing both techniques. In this work, we present the results of a feasibility study with the developed SFDI system to realize acquisition of the optical property information and the surface profile information. The surface profile information was used to correct the absorption (µa) maps and reduced scattering (µs') maps. The evaluation of correction effect of the PMP and the calibration and calculation of detection accuracy of the SFDI system were realized by using a series of self-made hemispheric and homogeneous solid phantoms covering a wide range of absorption and reduced scattering coefficients. The results show that the µa and µs' maps become more uniform after using profilometry correction. The maximum relative errors of the system after profilometry correction and calibration were 8.74% for µa and 4.97% for µs' at the wavelength of 527 nm, respectively. A case study was carried out on a pear to verify the application prospect of the method in the field of agricultural products quality inspection. Results indicate that µa and µs' maps of a pear after profilometry correction and calibration were more uniform and more comparable with the reported values.

Effectiveness of a smartphone app on improving immunization of children in rural Sichuan Province, China: a cluster randomized controlled trial.

BACKGROUND: The aim of this study was to assess the effectiveness of an EPI smartphone application (EPI app) on improving vaccination coverage in rural Sichuan Province, China. METHODS: This matched-pair cluster randomized controlled study included 32 village doctors, matched in 16 pairs, and took place from 2013 to 2015. Village doctors in the intervention group used the EPI app and reminder text messages while village doctors in the control group used their usual procedures and text messages. The primary outcome was full vaccination coverage with all five vaccines (1 dose of BCG, 3 doses of hepatitis B, 3 doses of OPV, 3 doses of DPT and 1 dose of measles vaccine), and the secondary outcome was coverage with each dose of the five individual vaccines. We also conducted qualitative interviews with village doctors to understand perceptions on using the EPI app and how this changed their vaccination work. RESULTS: The full vaccination coverage increased statistically significant from baseline to end-line in both the intervention (67 % [95 % CI:58-75 %] to 84 % [95 % CI:76-90 %], P = 0.028) and control group (71 % [95 % CI:62-79 %] to 82 % [95 % CI:74-88 %], P = 0.014). The intervention group had higher increase in full vaccination coverage from baseline to end-line compared to the control group (17 % vs 10 %), but this was not statistically significant (P = 0.164). Village doctors found it more convenient to use the EPI app to manage child vaccination and also reported saving time by looking up information of caregivers and contacting caregivers for overdue vaccinations quicker. However, village doctors found it hard to manage children who migrated out of the counties. CONCLUSIONS: This study showed that an app and text messages can be used by village doctors to improve full vaccination coverage, though no significant increase in vaccination coverage was found when assessing the effect of the app on its own. Village doctors using EPI app reported having improved their working efficiency of managing childhood vaccination. Future studies should be conducted to evaluate the impact of more integrated approach of mHealth intervention on child immunization. TRIAL REGISTRATION: Chinese Clinical Trials Registry (ChiCTR): ChiCTR-TRC- 13003960 , registered on December 6, 2013.

CCD-Based Skinning Injury Recognition on Potato Tubers (Solanum tuberosum L.): A Comparison between Visible and Biospeckle Imaging.

Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests). The features extracted from visible images were pixel-wise color and texture features, while region-wise BA (Biospeckle Activity) was calculated from biospeckle imaging. In addition, the calculation of BA using varied numbers of speckle patterns were compared. Finally, extracted features were implemented into classifiers of LS-SVM (Least Square Support Vector Machine) and BLR (Binary Logistic Regression), respectively. Results showed that color features performed better than texture features in classifying sound skin and injured skin, especially for injured skin stored no less than 1 day, with the average classification accuracy of 90%. Image capturing and processing efficiency can be speeded up in biospeckle imaging, with captured 512 frames reduced to 125 frames. Classification results obtained based on the feature of BA were acceptable for early skinning injury stored within 1 day, with the accuracy of 88.10%. It is concluded that skinning injury can be recognized by visible and biospeckle imaging during different stages. Visible imaging has the aptitude in recognizing stale skinning injury, while fresh injury can be discriminated by biospeckle imaging.

Effectiveness of a smart phone app on improving immunization of children in rural Sichuan Province, China: study protocol for a paired cluster randomized controlled trial.

BACKGROUND: Although good progress has been achieved in expanding immunization of children in China, disparities exist across different provinces. Information gaps both from the service supply and demand sides hinder timely vaccination of children in rural areas. The rapid development of mobile health technology (mHealth) provides unprecedented opportunities for improving health services and reaching underserved populations. However, there is a lack of literature that rigorously evaluates the impact of mHealth interventions on immunization coverage as well as the usability and feasibility of smart phone applications (apps). This study aims to assess the effectiveness of a smart phone-based app (Expanded Program on Immunization app, or EPI app) on improving the coverage of children's immunization. METHODS/DESIGN: This cluster randomized trial will take place in Xuanhan County, Sichuan Province, China. Functionalities of the app include the following: to make appointments automatically, record and update children's immunization information, generate a list of children who missed their vaccination appointments, and send health education information to village doctors. After pairing, 36 villages will be randomly allocated to the intervention arm (n=18) and control arm (n=18). The village doctors in the intervention arm will use the app while the village doctors in the control arm will record and manage immunization in the usual way in their catchment areas. A household survey will be used at baseline and at endline (8 months of implementation). The primary outcome is full-dose coverage and the secondary outcome is immunization coverage of the five vaccines that are included in the national Expanded Program on Immunization program as well as Hib vaccine, Rotavirus vaccine and Pneumococcal conjugate vaccine. Multidimensional evaluation of the app will also be conducted to assess usability and feasibility. DISCUSSION: This study is the first to evaluate the effectiveness of a smart phone app for child immunization in rural China. This study will contribute to the knowledge about the usability and feasibility of a smart phone app for managing immunization in rural China and to similar populations in different settings. TRIAL REGISTRATION: Chinese Clinical Trials Registry (ChiCTR): ChiCTR-TRC-13003960.

Evolution Pattern in Bruised Tissue of 'Red Delicious' Apple.

The study of apple damage mechanisms is key to improving post-harvest apple treatment techniques, and the evolution pattern of damaged tissue is fundamental to the study of apple damage mechanisms. In the study, 'Red Delicious' apples were used to explore the relationship between damage and time. A cell death zone was found in the pulp of the damaged tissue after the apple had been bruised. The tissue damage was centered in the cell death zone and developed laterally, with the width of the damage increasing with injury time. The extent of tissue damage in the core and pericarpal directions varied. About 60% of the damaged tissue developed in the core direction and 40% in the pericarpal direction, and the damage ratios in both directions remained consistent throughout the injury. The depth of damage and the rate of damage were influenced by the impact force size and the difference in the size of the damaged part of the apple, but the damage development pattern was independent of the impact force size and the difference in the damaged part. The maximum damage rate was reached at about 30 min, and the depth of damage was stabilized at about 72 min. By studying the evolution pattern of the damaged tissue of the bruised 'Red Delicious' apple, it provides the research idea and theoretical basis for enhancing the prediction accuracy and robustness of early stage damage in apples.

C/EBPß: A transcription factor associated with the irreversible progression of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder distinguished by a swift cognitive deterioration accompanied by distinctive pathological hallmarks such as extracellular Aß (ß-amyloid) peptides, neuronal neurofibrillary tangles (NFTs), sustained neuroinflammation, and synaptic degeneration. The elevated frequency of AD cases and its proclivity to manifest at a younger age present a pressing challenge in the quest for novel therapeutic interventions. Numerous investigations have substantiated the involvement of C/EBPß in the progression of AD pathology, thus indicating its potential as a therapeutic target for AD treatment. AIMS: Several studies have demonstrated an elevation in the expression level of C/EBPß among individuals afflicted with AD. Consequently, this review predominantly delves into the association between C/EBPß expression and the pathological progression of Alzheimer's disease, elucidating its underlying molecular mechanism, and pointing out the possibility that C/EBPß can be a new therapeutic target for AD. METHODS: A systematic literature search was performed across multiple databases, including PubMed, Google Scholar, and so on, utilizing predetermined keywords and MeSH terms, without temporal constraints. The inclusion criteria encompassed diverse study designs, such as experimental, case-control, and cohort studies, restricted to publications in the English language, while conference abstracts and unpublished sources were excluded. RESULTS: Overexpression of C/EBPß exacerbates the pathological features of AD, primarily by promoting neuroinflammation and mediating the transcriptional regulation of key molecular pathways, including δ-secretase, apolipoprotein E4 (APOE4), acidic leucine-rich nuclear phosphoprotein-32A (ANP32A), transient receptor potential channel 1 (TRPC1), and Forkhead BoxO (FOXO). DISCUSSION: The correlation between overexpression of C/EBPß and the pathological development of AD, along with its molecular mechanisms, is evident. Investigating the pathways through which C/EBPß regulates the development of AD reveals numerous multiple vicious cycle pathways exacerbating the pathological progression of the disease. Furthermore, the exacerbation of pathological progression due to C/EBPß overexpression and its molecular mechanism is not limited to AD but also extends to other neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and multiple sclerosis (MS). CONCLUSION: The overexpression of C/EBPß accelerates the irreversible progression of AD pathophysiology. Additionally, C/EBPß plays a crucial role in mediating multiple pathways linked to AD pathology, some of which engender vicious cycles, leading to the establishment of feedback mechanisms. To sum up, targeting C/EBPß could hold promise as a therapeutic strategy not only for AD but also for other degenerative diseases.

The interaction of lipocalin-2 and astrocytes in neuroinflammation: mechanisms and therapeutic application.

Neuroinflammation is a common pathological process in various neurological disorders, including stroke, Alzheimer's disease, Parkinson's disease, and others. It involves the activation of glial cells, particularly astrocytes, and the release of inflammatory mediators. Lipocalin-2 (Lcn-2) is a secretory protein mainly secreted by activated astrocytes, which can affect neuroinflammation through various pathways. It can also act as a pro-inflammatory factor by modulating astrocyte activation and polarization through different signaling pathways, such as NF-κB, and JAK-STAT, amplifying the inflammatory response and aggravating neural injury. Consequently, Lcn-2 and astrocytes may be potential therapeutic targets for neuroinflammation and related diseases. This review summarizes the current knowledge on the role mechanisms, interactions, and therapeutic implications of Lcn-2 and astrocytes in neuroinflammation.

Dynamic Prediction Model for Initial Apple Damage.

Prediction models of damage severity are crucial for the damage expression of fruit. In light of issues such as the mismatch of existing models in actual damage scenarios and the failure of static models to meet research needs, this article proposes a dynamic prediction model for damage severity throughout the entire process of apple damage and studies the relationship between the initial bruise form and impact energy distribution of apple damage. From the experiments, it was found that after impact a "cell death zone" appeared in the internal pulp of the damaged part of Red Delicious apples. The reason for the appearance of the cell death zone was that the impact force propagated in the direction of the fruit kernel in the form of stress waves; the continuous action of which continuously compressed the pulp's cell tissue. When the energy absorbed via elastic deformation reached the limit value, intercellular disadhesion of parenchyma cells at the location of the stress wave peak occurred to form cell rupture. The increase in intercellular space for the parenchyma cells near the rupture site caused a large amount of necrocytosis and, ultimately, formed the cell death zone. The depth of the cell death zone was closely related to the impact energy. The correlation coefficient r between the depth of the cell death zone and the distribution of impact energy was slightly lower at the impact height of 50 mm. As the impact height increased, the correlation coefficient r increased, approaching of value of 1. When the impact height was lower (50 mm), the correlation coefficient r had a large distribution range (from 0.421 to 0.983). As the impact height increased, the distribution range significantly decreased. The width of the cell death zone had a poor correlation with the pressure distribution on the impact surface of the apples that was not related to the impact height. In this article, the corresponding relationship between the form and impact energy distribution of the internal damaged tissues in the initial damage of Red Delicious apples was analyzed. This analysis aimed to provide a research concept and theoretical basis for more reliable research on the morphological changes in the damaged tissues of apples in the future, further improving the prediction accuracy of damage severity.

Eggshell biometrics for individual egg identification based on convolutional neural networks.

Individual egg identification technology has potential applications in breeding, product tracking/tracing, and anti-counterfeit. This study developed a novel method for individual egg identification based on eggshell images. A convolutional neural network-based model, named Eggshell Biometric Identification (EBI) model, was proposed and evaluated. The main workflow included eggshell biometric feature extraction, egg information registration, and egg identification. The image dataset of individual eggshell was collected from the blunt-end region of 770 chicken eggs using an image acquisition platform. The ResNeXt network was then trained as a texture feature extraction module to obtain sufficient eggshell texture features. The EBI model was applied to a test set of 1,540 images. The testing results showed that when an appropriate Euclidean distance threshold for classification was set (17.18), the correct recognition rate and the equal error rate reached 99.96% and 0.02%. This new method provides an efficient and accurate solution for individual chicken egg identification, and can be extended to eggs of other poultry species for product tracking/tracing and anti-counterfeit.

Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging.

BACKGROUND: Automated discrimination of fruits with canker from other fruit with normal surface and different type of peel defects has become a helpful task to enhance the competitiveness and profitability of the citrus industry. Over the last several years, hyperspectral imaging technology has received increasing attention in the agricultural products inspection field. This paper studied the feasibility of classification of citrus canker from other peel conditions including normal surface and nine peel defects by hyperspectal imaging. RESULTS: A combination algorithm based on principal component analysis and the two-band ratio (Q(687/630)) method was proposed. Since fewer wavelengths were desired in order to develop a rapid multispectral imaging system, the canker classification performance of the two-band ratio (Q(687/630)) method alone was also evaluated. The proposed combination approach and two-band ratio method alone resulted in overall classification accuracy for training set samples and test set samples of 99.5%, 84.5% and 98.2%, 82.9%, respectively. CONCLUSION: The proposed combination approach was more efficient for classifying canker against various conditions under reflectance hyperspectral imagery. However, the two-band ratio (Q(687/630)) method alone also demonstrated effectiveness in discriminating citrus canker from normal fruit and other peel diseases except for copper burn and anthracnose.

Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging.

Hyperspectral fluorescence imaging (HSFI) is potentially useful for assessing food and agricultural products, because it combines the merits of both hyperspectral imaging and fluorescence spectroscopy. This paper provides an introduction to HSFI: the principle and components of HSFI, calibration and image processing are described. In addition, recent advances in the application of HSFI to food and agricultural product assessment are reviewed, such as contaminant detection, constituent analysis and quality evaluation. Finally, current limitations and likely future development trends are discussed.

[Application of hyperspectral fluorescence image technology in detection of early rotten oranges].

Rottenness is most prevalent and devastating disease that threats citrus fruit. Automatic detection of early rottenness can enhance the competitiveness and profitability of the citrus industry. However, there is no efficient automatic detection technology at this time that could detect this disease. The navel orange was selected as research objective. Hyperspectral fluorescence imaging was used to detect early rottenness in orange. Optimum index factor (OIF) method was applied to identify the optimal band combination. 100% detection rate was achieved based on the optimal bands ratio image and two threshold values. The research showed that the proposed method can effectively overcome the affect from florescence effect because stem damage area and stem also can produce florescence under ultraviolet light. This study will lay a foundation for developing multispectral detection system used in on-line detection of early rottenness fruit.

Feather Damage Monitoring System Using RGB-Depth-Thermal Model for Chickens.

Feather damage is a continuous health and welfare challenge among laying hens. Infrared thermography is a tool that can evaluate the changes in the surface temperature, derived from an inflammatory process that would make it possible to objectively determine the depth of the damage to the dermis. Therefore, the objective of this article was to develop an approach to feather damage assessment based on visible light and infrared thermography. Fusing information obtained from these two bands can highlight their strengths, which is more evident in the assessment of feather damage. A novel pipeline was proposed to reconstruct the RGB-Depth-Thermal maps of the chicken using binocular color cameras and a thermal infrared camera. The process of stereo matching based on binocular color images allowed for a depth image to be obtained. Then, a heterogeneous image registration method was presented to achieve image alignment between thermal infrared and color images so that the thermal infrared image was also aligned with the depth image. The chicken image was segmented from the background using a deep learning-based network based on the color and depth images. Four kinds of images, namely, color, depth, thermal and mask, were utilized as inputs to reconstruct the 3D model of a chicken with RGB-Depth-Thermal maps. The depth of feather damage can be better assessed with the proposed model compared to the 2D thermal infrared image or color image during both day and night, which provided a reference for further research in poultry farming.

[Advance on application of hyperspectral imaging to nondestructive detection of agricultural products external quality].

Hyperspectral imaging is an emerging technique that integrates conventional imaging and spectroscopy to obtain both spatial and spectral information from a studied object simultaneously. The images data can reflect the external features, surface defects and contamination. The spectra data can analyze physical structure and chemical composition in studied object. Therefore, hyperspectral imaging technology causes more and more attention, and has become a hot research topic recently. In order to track the latest research developments at home and abroad, application of hyperspectral reflectance and fluorescence imaging techniques to nondestructive detection of agricultural products external quality was reviewed, which would provide reference for application of hyperspectral imaging to agriculture.

[Research on the trash content measurement and classification of ginned cotton by using NIR spectroscopy technique].

Near infrared (NIR) spectroscopy was investigated to predict trash content and classify types of ginned cotton by using a fiberoptic in diffuse reflectance mode. Different spectra preprocessing methods were compared, and partial least-squares (PLS) regression was established to predict the trash content of ginned cotton. Discriminant analysis (DA) was used to classify various types of lint and content level of trash. The correlation coefficient r was 0.906 for optimal PLS model using three factors based on first-order derivative spectra, and RMSEC and RMSEP was 0.440 and 0.823 respectively. To classify ginned cotton with and without plant trash, the accuracy rate reached 95.4% using 15 principal components (PCs) via DA, whereas the prediction accuracy rate was only 80.9% for the classification of sample types due to containing foreign fiber, and the classification result for the content level of trash in lint was not good for the samples without any preprocessing. The result indicated that the NIR spectra of sample can be used to predict trash content in ginned cotton, which is often disturbed by type, content and distribution of foreign matters, and the accuracy of some prediction model is unsatisfactory. In order to improve the prediction accuracy, some methods would be applied in future research, such as pretreatment according to acquisition request of solid sample, or using transmission mode.

[Qualitative analysis of fragrant pear class based on near infrared diffuse reflectance spectroscopy].

A method was developed to automatically discriminate the persistent calyx fruit and fruit without calyx of fragrant pear by means of near infrared spectroscopy (NIRS). The prediction performance of different band regions range, different principal component numbers and different preprocessing methods of the spectra (multiplicative signal correction, standard normal variate, and derivative spectra) together with discriminant analysis (DA) was also investigated, and The calibration model was established to classify the different kinds of fragrant pear. The research results for the fragrant pear classification showed that DA calibration models using these parameters with band regions between 9 091 and 4 000 cm(-1) and original spectra are optimal, with the percentage of correct sample classification being 100% and 95% for the calibration and validation set, respectively.

[Identification of tea from different regions using X-ray fluorescence].

The existence of fake tea from non-origin seriously impacts on the credibility of the famous tea. A method was developed to identify tea from difference regions on the basis of the fact that the content of heavy metals in different origin tea is varied by using X-ray fluorescence technique and pattern recognition technique. Samples from different origins were grouped respectively, and their X-ray fluorescence spectra were acquired, and then the principal components of these spectral data were calculated, and the average of the principal components of each group was used as the center of each group. The Mahalanobis distance value between a sample and the center of a group were calculated, when the Mahalanobis distance value reached minimum, the sample was classed to current group, and in this way, a sample was identified. A Niton 792 portable X-ray spectrometer was used to class 120 tea samples from Anji, Jinhua, Hangzhou and Taizhou, in zhejiang province of China. It was found that the spectra between 3 and 13 KeV and the first 4 principal components give enough information for the identification of tea from different regions,and the rate of error was 4.2%.