Quantifying morphologies of developing neuronal cells using deep learning with imperfect annotations.

The functionality of human intelligence relies on the interaction and health of neurons, hence, quantifying neuronal morphologies can be crucial for investigating the functionality of the human brain. This paper proposes a deep learning (DL) based method for segmenting and quantifying neuronal structures in fluorescence microscopy images of developing neuronal cells cultured in vitro. Compared to the majority of supervised DL-based segmentation methods that heavily rely on creating exact corresponding masks of neuronal structures for the preparation of training samples, the proposed approach allows for imperfect annotation of neurons, as it only requires tracing the centrelines of the neurites. This ability accelerates the preparation of training data by several folds. Our proposed framework is built on a modified version of PSPNet with an EfficientNet backbone pre-trained on the CityScapes dataset. To handle the imperfectness of training samples, we incorporated a weighted combination of two loss functions, namely the Dice loss and Lovász loss functions, into our network. We evaluated the proposed framework and several other state-of-the-art methods on a published dataset of approximately 900 manually quantified cultured mouse neurons. Our results indicate a close correlation between the proposed method and manual quantification in terms of neuron length and the number of branches while demonstrating improved analysis speed. Furthermore, the proposed method achieved high accuracy in neuron segmentation, as evidenced by the evaluation of the neurons' length and number of branches.

Post-harvest ultraviolet irradiation induces changes in physical-chemical properties and levels of polycyclic aromatic hydrocarbons and gene expression in mulberry fruit.

BACKGROUND: Earlier studies reported that post-harvest ultraviolet (UV) irradiation could increase the health-promoting compounds in fruit but the effects of UV irradiation on the reduction of the polycyclic aromatic hydrocarbon (PAH) content in mulberries remain less known. Black mulberry fruit were exposed to two UV illumination dosages (3.5 and 7 kJ m-2 ) and were stored for 4, 8, and 12 days. RESULTS: Mulberries treated in this way displayed higher antioxidant enzyme activity and phenolic compound content in comparison with a control condition. The transcription factors (TFs) MdoMYB121, MdoMYB155, MdbZIP2, and MdbZIP48 were strongly expressed in two UV illumination dosages (about 45-95% higher than the control). The fluorine (Flu) and naphthalene (Nap) content in treated fruit decreased by 21-85% in comparison with the control condition. CONCLUSION: The findings of this study indicate that UV irradiation can be considered as a promising technique to remove some PAHs in black mulberries, to increase their health-promoting potential, and indirectly to improve their aesthetic quality due to the resulting desirable color parameters. © 2023 Society of Chemical Industry.

Evaluation of hackberry (Celtis australis L.) fruits as sources of bioactive compounds.

Hackberry (Celtis australis L.) is native to the Mediterranean region and is distributed in Europe, Turkey, North Africa, and Iran. To the best of our knowledge, no study has been conducted on C. australis L. in the Arasbaran region, Iran. In the present study, total phenol (TP), flavonoid (TF), antioxidant capacity based on DPPH and FRAP assays and phenolic compounds and sugars profiles were investigated. According to the results, the range of antioxidant capacity based on DPPH and FRAP assays was 14.12-88.24% and 44.35-117.87 mg Fe2+/100 g, respectively. Also, the range of gallic acid, caffeic acid, chlorogenic acid, rutin, p-coumaric acid, rosmaric acid, cinnamic acid, and apigenin content was 2.59-26.32, 2.03-9.32, 0.94-11.35, 1.80-4.857, 2.32-9.52, 4.74-51.38, 0.18-2.10 and 0.27-1.37 mg/g, respectively. The results of factor analysis showed that the C12, C14, C15, C20, C8, C16, C3, and C20 genotypes are positively characterized by the first principal component (PCA1) that have a higher caffeic acid, chlorogenic acid, rutin, p-coumaric acid, rosmaric acid, quercetin, cinnamic acid, and apigenin phenolic compounds. Based on cluster analysis, the twenty genotypes were located in 2 main clusters. In general, the obtained results can be useful for breeding programs and the introduction of cultivars in Celtis australis L.

Using a Blur Metric to Estimate Linear Motion Blur Parameters.

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.

A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images.

Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.

Identification of Fasciola Species Using Tegumental Spines in Tissue Sections.

BACKGROUND: Efforts to find a reliable non-molecular means of identification has been the main purpose of the current work that always is persuaded by researchers interested in the field of parasitology. METHODS: Adult fasciolids were obtained from the slaughterhouses in different parts of Iran in 2017, and investigated using the classical old fashion morphological appearances of the worms implementing a camera lucida equipped microscope. Histological procedure was subsequently performed for almost the entire collected adult worms followed by Hematoxylin and Eosin (H&E) staining technique. DNA extraction and RFLP-PCR technique were carried out for the entire fasciolid liver flukes. To attain more comparable morphological conclusions, Scanning Electron Micrographs were also implemented for two molecularly identified fasciolids. RESULTS: Based on spine morphology observed in worm's tissue sections two types of tegumental spines, "pointed" and "molar" shapes have been identified addressing to distinguish F. hepatica and F. gigantica species respectively. The present identification has been also supported by Molecular analysis using RFLP-PCR technique. CONCLUSION: There are some hidden morphological characters implemented in species identification for certain helminths. Meanwhile, the emergence of computer image analysis system (CIAS) on the scene of taxonomy, has revolutionized the accuracy of measurement in morphology by employing detailed parameters that have not been regarded before. The current study has illustrated the tegumental spines of two Fasciola species in tissue sections which has not been enough considered in helminthological publications so far.

Physiological, biochemical and gene-expressional responses to water deficit in apple subjected to partial root-zone drying (PRD).

Water scarcity is one of the major factors limiting apple production. Partial root-zone drying (PRD) is a water-saving irrigation technique necessary to improve the efficiency of irrigation techniques to optimize the amount of fruit produced with the volume of water used. The apple trees cv. Red Delicious were exposed to four treatments, including (1) control with 100% of the crop evapotranspiration (ETc) needs; (2) alternate partial root-zone drying with 75% of the ETc needs (APRD75); (3) fixed partial root-zone drying with 75% of the ETc needs (FPRD75); (4) fixed partial root-zone irrigation with 50% of the ETc needs (FPRD50) in a semiarid region of Iran. Results showed that leaf water potential (Ψ leaf), and chlorophyll were significantly decreased in FPRD50 compared to control and other PRD treatments. APRD75 and FPRD75 treatments significantly enhanced (+) -catechin (+C), epicatechin (EC), chlorogenic acid (CGA), caffeic acid (CA) as well as increased water use efficiency (WUE) (by 30-40% compared to control) without significant reduction of yield. PRD reduced gibberellic acid (GA3) and kinetin, while, increased the abscisic acid (ABA) and salicylic acid (SA) levels. The abiotic stress-responsive transcription factors (TFs) MdoMYB121, MdoMYB155, MdbZIP2, and MdbZIP48 were highly expressed in all PRD treatments. Our results demonstrated that APRD75 and FPRD75 have the potential to stimulate antioxidant defense mechanisms, hormonal signaling pathways, and expression of drought-tolerance TFs to improve WUE while maintaining crop yield. Therefore, APRD75andFPRD75 with water savings as compared to full irrigation might be a suitable strategy for irrigation apple trees under water scarcity.

Treatment Failure in Cutaneous Leishmaniasis Patients Referred to the School of Public Health, Tehran University of Medical Sciences during 2008-2017.

BACKGROUND: Cutaneous leishmaniasis (CL) is a vector borne disease predominantly found in tropical and subtropical countries, including Iran. For more than 6 decades, pentavalent antimonials have been used successfully worldwide for the treatment of leishmaniasis, but over the past few years, clinical resistance to these medications has increased. In this study, we evaluated CL patients who did not show any desirable responses to the anti-leishmanial treatment within a 10-year period (2008 to 2017). METHODS: All patients from different parts of Iran suspected of having cutaneous leishmaniasis, who were referred to the laboratory of leishmaniosis in Tehran University of Medical Sciences from 2008-2017 were parasitological examined. RESULTS: During this period, a total of 1480 suspected CL patients were referred to the laboratory of leishmaniosis. Samples from 655 patients (70.8%) suspected of having CL were positive microscopically. The failure rate in patients treated with anti-leishmaniasis medications for a minimum of three complete treatment periods was 1.83% (12 cases). There was no association between the number and size of skin lesions and patient characteristics. Also, the route of drug administration had no significant effect on the number and size of lesions. CONCLUSION: In the present study, treatment failure was found in some confirmed CL patients treated with meglumine antimoniate. Over the past few years, it seems that had been increased in resistance to these medications. So, a review of the correct implementation of the treatment protocol and/or a combination therapy may be helpful in preventing an increase in the rate of treatment failure.

Wild Rodent Ectoparasites Collected from Northwestern Iran.

BACKGROUND: Rodents play an important role as reservoir of some pathogens, and the host of some ectoparasites as well. These ectoparasites can transmit rodents' pathogens to human or animals. The aim of this study was to assess the distribution and infestation load of ectoparasites on rodents in Meshkin-Shahr District, northwestern Iran. METHODS: Rodents were captured using baited live traps in spring 2014 from Meshkin-Shahr District and were transferred to the laboratory for identification to the species level. Their ectoparasites were collected, mounted and identified. RESULTS: Three rodent species including Meriones persicus (74%), Mus musculus (16.9%) and Cricetulus migratorius (9%) were identified. Among all rodents, 185 specimens (90.69%) were infested with a total of 521 ectoparasites. Overall, 10 arthropods species were collected, including fleas (97.6%), one mite (1.6%) and one louse species (0.6%) as follows: Xenopsylla nubica, X. astia, X. buxtoni, X. cheopis, Nosopsyllus fasciatus, N. iranus, Ctenocephalides felis, Ctenophthalmus rettigismiti, Ornithonyssus sp and one species of genus Polyplax. The most prevalent ectoparasites species was X. nubica (89%). CONCLUSION: Nearly all rodent species were infested with Xenopsylla species. Monitoring of ectoparasites on infested rodents is very important for awareness and early warning towards control of arthropod-borne diseases.

Employing image processing techniques for cancer detection using microarray images.

Microarray technology is a powerful genomic tool for simultaneously studying and analyzing the behavior of thousands of genes. The analysis of images obtained from this technology plays a critical role in the detection and treatment of diseases. The aim of the current study is to develop an automated system for analyzing data from microarray images in order to detect cancerous cases. The proposed system consists of three main phases, namely image processing, data mining, and the detection of the disease. The image processing phase performs operations such as refining image rotation, gridding (locating genes) and extracting raw data from images the data mining includes normalizing the extracted data and selecting the more effective genes. Finally, via the extracted data, cancerous cell is recognized. To evaluate the performance of the proposed system, microarray database is employed which includes Breast cancer, Myeloid Leukemia and Lymphomas from the Stanford Microarray Database. The results indicate that the proposed system is able to identify the type of cancer from the data set with an accuracy of 95.45%, 94.11%, and 100%, respectively.

An intelligent approach for variable size segmentation of non-stationary signals.

In numerous signal processing applications, non-stationary signals should be segmented to piece-wise stationary epochs before being further analyzed. In this article, an enhanced segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals, such as electroencephalogram (EEG), magnetoencephalogram (MEG) and electromyogram (EMG), is proposed. In the proposed approach, discrete wavelet transform (DWT) decomposes the signal into orthonormal time series with different frequency bands. Then, the FD of the decomposed signal is calculated within two sliding windows. The accuracy of the segmentation method depends on these parameters of FD. In this study, four EAs are used to increase the accuracy of segmentation method and choose acceptable parameters of the FD. These include particle swarm optimization (PSO), new PSO (NPSO), PSO with mutation, and bee colony optimization (BCO). The suggested methods are compared with other most popular approaches (improved nonlinear energy operator (INLEO), wavelet generalized likelihood ratio (WGLR), and Varri's method) using synthetic signals, real EEG data, and the difference in the received photons of galactic objects. The results demonstrate the absolute superiority of the suggested approach.

Time-frequency based newborn EEG seizure detection using low and high frequency signatures.

The nonstationary and multicomponent nature of newborn EEG seizures tend to increase the complexity of the seizure detection problem. In dealing with this type of problem, time-frequency based techniques were shown to outperform classical techniques. Neonatal EEG seizures have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas. Seizure detection techniques have been proposed that concentrate on either low frequency or high frequency signatures of seizures. They, however, tend to miss seizures that reveal themselves only in one of the frequency areas. To overcome this problem, we propose a detection method that uses time-frequency seizure features extracted from both low and high frequency areas. Results of applying the proposed method on five newborn EEGs are very encouraging.