Search results for: crop disease detection

Authors: Chih-Yi Ho, Tzu-Fang Chang, Chih-Chia Huang, Chia-Yen Lee

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In dental clinics, the dentists use the periapical radiography image to assess the effectiveness of endodontic treatment of teeth with chronic apical periodontitis. Periapical radiography images are taken at different times to assess alveolar bone variation before and after the root canal treatment, and furthermore to judge whether the treatment was successful. Current clinical assessment of apical tissue recovery relies only on dentist personal experience. It is difficult to have the same standard and objective interpretations due to the dentist or radiologist personal background and knowledge. If periapical radiography images at the different time could be registered well, the endodontic treatment could be evaluated. In the image registration area, it is necessary to assign representative control points to the transformation model for good performances of registration results. However, detection of representative control points (feature points) on periapical radiography images is generally very difficult. Regardless of which traditional detection methods are practiced, sufficient feature points may not be detected due to the low-contrast characteristics of the x-ray image. Barnard detector is an algorithm for feature point detection based on grayscale value gradients, which can obtain sufficient feature points in the case of gray-scale contrast is not obvious. However, the Barnard detector would detect too many feature points, and they would be too clustered. This study uses the local extrema of clustering feature points and the suppression radius to overcome the problem, and compared different feature point detection methods. In the preliminary result, the feature points could be detected as representative control points by the proposed method.

Keywords: feature detection, Barnard detector, registration, periapical radiography image, endodontic treatment

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Authors: Andhiani Sharfina Arnellya, Mochammad Indra Permana, Dika Pramita Destiani, Ellin Febrina

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Health Research Data, Ministry of Health of Indonesia in 2007, showed coronary heart disease (CHD) or coronary artery disease (CAD) was the third leading cause of death in Indonesia after hypertension and stroke with 7.2% incidence rate. Antiplatelet is one of the important therapy in management of patients with CHD. In addition to therapeutic effect on patients, quality of life is one aspect of another assessment to see the success of antiplatelet therapy. The purpose of this study was to determine the quality of life of patients on oral antiplatelet therapy in outpatient cardiac department Dr. Hasan Sadikin central general hospital, Bandung, Indonesia. This research is a cross sectional by collecting data through quality of life questionnaire of patients which performed prospectively as primary data and secondary data from medical record of patients. The results of this study showed that 54.3% of patients had a good quality of life, 45% had a moderate quality of life, and 0.7% had a poor quality of life. There are no significant differences in quality of life-based on age, gender, diagnosis, and duration of drug use.

Keywords: antiplatelet, quality of life, coronary artery disease, coronary heart disease

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Authors: Hossam M. Moftah, Aboul Ella Hassanien

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In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. Traffic counting as a kind of traffic control is important to know the road traffic density in real time. This paper presents a fast and robust traffic counting system using different image processing techniques. The proposed system is composed of the following four fundamental building phases: image acquisition, pre-processing, object detection, and finally counting the connected objects. The object detection phase is comprised of the following five steps: subtracting the background, converting the image to binary, closing gaps and connecting nearby blobs, image smoothing to remove noises and very small objects, and detecting the connected objects. Experimental results show the great success of the proposed approach.

Keywords: traffic counting, traffic management, image processing, object detection, computer vision

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Authors: Seyed Mehrzad Pahlavani, Mozaffar Haji Jafari Anaraki, Sayma Mohammadi

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A total of 34700 day-old broilers were exposed to green, blue and yellow light using a light-emitting diode system for 6 weeks to investigate the effects of light wave length on antibody responses to Newcastle disease by HI test and the correlation with ELISA. 3 poultry house broiler farms with the same conditions was selected and the lightening system of each was set according to the requirement. Blood samples were taken from 20 chicks on days 1, 24 and 46 and the Newcastle virus specific antibody was titered in serum using HI an ELISA test. On day 24, the probability value of more than 0/05 was observed in HI and ELISA tests of all groups while at the end of breeding period, the average HI serum antibody titer was more in the green light than the yellow one while the blue light was not significantly different from both. At the last titration, the green light has got the highest titer of Newcastle antibodies. There were no significant differences of Newcastle antibody titers between all groups and ages in broiler pullets in ELISA. According to the sampling and analysis of HI and ELISA serum tests, there were no significant relationships between all broiler pullets breeding in green, blue and yellow light on days 24 and 46 and the P-value was more than 0/05. It is suggested that the monochromatic light is effective on broilers immunity against Newcastle disease.

Keywords: monochromatic light, Newcastle disease, HI test, ELISA test

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Authors: Mohammad Emran, Colton Wayne, Shannon M Koehler, P. Stephen Almond, Haroon Patel

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Purpose: Assessment of anastomotic perfusion in Hirschsprung disease using Indocyanine Green (ICG)-near-infrared (NIR) fluorescence angiography. Introduction: Anastomotic stricture and leak are well-known complications of Hirschsprung pull-through procedures. Complications are due to tension, infection, and/or poor perfusion. While a surgeon can visually determine and control the amount of tension and contamination, assessment of perfusion is subject to surgeon determination. Intraoperative use of ICG-NIR enhances this decision-making process by illustrating perfusion intensity and adequacy in the pulled-through bowel segment. This technique, proven to reduce anastomotic stricture and leak in adults, has not been studied in children to our knowledge. ICG, an FDA approved, nontoxic, non-immunogenic, intravascular (IV) dye, has been used in adults and children for over 60 years, with few side effects. ICG-NIR was used in this report to demonstrate the adequacy of perfusion during transanal pullthrough for Hirschsprung’s disease. Method: 8 patients with Hirschsprung disease were evaluated with ICG-NIR technology. Levels of affected area ranged from sigmoid to total colonic Hirschsprung disease. After leveling, but prior to anastomosis, ICG was administered at 1.25 mg (< 2 mg/kg) and perfusion visualized using an NIR camera, before and during anastomosis. Video and photo imaging was performed and perfusion of the bowel was compared to surrounding tissues. This showed the degree of perfusion and demarcation of perfused and non-perfused bowel. The anastomosis was completed uneventfully and the patients all did well. Results: There were no complications of stricture or leak. 5 of 8 patients (62.5%) had modification of the plan based on ICG-NIR imaging. Conclusion: Technologies that enhance surgeons’ ability to visualize bowel perfusion prior to anastomosis in Hirschsprung’s patients may help reduce post-operative complications. Further studies are needed to assess the potential benefits.

Keywords: colonic anastomosis, fluorescence angiography, Hirschsprung disease, pediatric surgery, SPY

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Authors: Catarina Isabel Ramos Pereira, Jorge Machado, Begona Alonso Criado, Maria João Santos

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Introduction: Gait disorders are one of the symptoms that have severe implications on the quality of life in Parkinson's disease (PD). Currently, there is no therapy to reverse or treat this condition. None of the drugs used in conventional medical treatment is entirely efficient, and all have a high incidence of side effects. Acupuncture treatment is believed to improve motor ability, but there is still little scientific evidence in individuals with PD. Aim: The aim of the study is to investigate the acute effect of acupuncture on gait parameters in Parkinson's disease. Methods: This is a randomized and controlled crossover study. The same individual patient was part of both the experimental (real acupuncture) and control group (false acupuncture/sham), and the sequence was randomized. Gait parameters were measured at two different moments, before and after treatment, using four force platforms as well as the collection of 3D markers positions taken by 11 cameras. Images were quantitatively analyzed using Qualisys Track Manager software that let us extract data related to the quality of gait and balance. Seven patients with the diagnosis of Parkinson's disease were included in the study. Results: Statistically significant differences were found in gait speed (p = 0.016), gait cadence (p = 0.006), support base width (p = 0.0001), medio-lateral oscillation (p = 0.017), left-right step length (p = 0.0002), and stride length: right-right (p = 0.0000) and left-left (p = 0.0018), time of left support phase (p = 0.029), right support phase (p = 0.025) and double support phase (p = 0.015), between the initial and final moments for the experimental group. Differences in right-left stride length were found for both groups. Conclusion: Our results show that acupuncture could enhance gait in Parkinson's disease patients. Deep research involving a larger number of volunteers should be accomplished to validate these encouraging findings.

Keywords: acupuncture, traditional Chinese medicine, Parkinson's disease, gait

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Authors: Dachuan Shi, M. Hecht, Y. Ye

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With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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Authors: Bilal Ahmad

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Alzheimer’s disease (AD) is one of the most common neurodegenerative illnesses. However, how Gut-microbiota plays a role in the pathogenesis of AD is not well elucidated. The purpose of this literature review is to summarize and understand the current findings that may elucidate the gut microbiota's role in the development of AD. Methods: A literature review of all the relevant papers known to the author was conducted. Relevant articles, abstracts and research papers were collected from well-accepted web sources like PubMed, PMC, and Google Scholar. Results: Recent studies have shown that Gut-microbiota has an important role in the progression of AD via Gut-Microbiota-Brain Axis. The onset of AD supports the ‘Hygiene Hypothesis’, which shows that AD might begin in the Gut, causing dysbiosis, which interferes with the intestinal barrier by releasing pro-inflammatory cytokines and making its way up to the brain via the blood-brain barrier (BBB). Molecular mechanisms lipopolysaccharides and serotonin kynurenine (tryptophan) pathways have a direct association with inflammation, the immune system, neurodegeneration, and AD. Conclusion: The studies helped to analyze the molecular basis of AD, other neurological conditions like depression, autism, and Parkinson's disease and how they are linked to Gut-microbiota. Further, studies to explore the therapeutic effects of probiotics in AD and cognitive enhancement should be warranted to provide significant clinical and practical value.

Keywords: gut-microbiota, Alzheimer’s disease, second brain aging, lipopolysaccharides, short-chain fatty acids

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Authors: Sanjaya Humagain, Rajendra Koju

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Background: Rheumatic heart disease (RHD) is the most common cardiovascular disease in children and young adults. Though declined and almost non-existent in developed nations, RHD is still one of the leading cause for premature death and disability in developing countries. Prevalence of RHD is high in both rural as well as urban area of Nepal. Present study is designed to look at the pattern of valvular involvement and demographic features in RHD. Methods: 326 patients indicated for inj. Benzathine penicillin were selected and echocardiograph performed to see the pattern of vavular involvement. Data analysis was done using SPSS 17. Result: The most common type of lesion was mixed type with mitral valve involvement. MR was the most common isolated lesion. MS was more commonly seen in females whereas AS was more common in males. Secondary prophylaxis was more common than primary prophylaxis. Conclusion: RHD still being a major problem and a preventable disease so extensive screening program is required to identify them early and prevent the complication.

Keywords: acute rheumatic fever, RHD, MS, MR, AS, AR, Inj benzathine penicillin

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Authors: Lilia M. Fernando, Matthew K. Vasher, Evangelyn C. Alocilja

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This paper describes the development of a DNA-based nanobiosensor to detect the dengue virus in mosquito using electrically active magnetic (EAM) nanoparticles as the concentrator and electrochemical transducer. The biosensor detection encompasses two sets of oligonucleotide probes that are specific to the dengue virus: the detector probe labeled with the EAM nanoparticles and the biotinylated capture probe. The DNA targets are double hybridized to the detector and the capture probes and concentrated from nonspecific DNA fragments by applying a magnetic field. Subsequently, the DNA sandwiched targets (EAM-detector probe–DNA target–capture probe-biotin) are captured on streptavidin modified screen printed carbon electrodes through the biotinylated capture probes. Detection is achieved electrochemically by measuring the oxidation–reduction signal of the EAM nanoparticles. Results indicate that the biosensor is able to detect the redox signal of the EAM nanoparticles at dengue DNA concentrations as low as 10 ng/ul.

Keywords: dengue, magnetic nanoparticles, mosquito, nanobiosensor

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Authors: Tareq AlNuaim, Mubashir Alam, Abdulrazaq Aldowesh

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The usage of micro-Unmanned Ariel Vehicles (UAVs) has witnessed an enormous increase recently. Detection of such drones became a necessity nowadays to prevent any harmful activities. Typically, such targets have low velocity and low Radar Cross Section (RCS), making them indistinguishable from clutter and phase noise. Multiple-Input Multiple-Output (MIMO) Radars have many potentials; it increases the degrees of freedom on both transmit and receive ends. Such architecture allows for flexibility in operation, through utilizing the direct access to every element in the transmit/ receive array. MIMO systems allow for several array processing techniques, permitting the system to stare at targets for longer times, which improves the Doppler resolution. In this paper, a 2×2 MIMO radar prototype is developed using Software Defined Radio (SDR) technology, and its performance is evaluated against a slow-moving low radar cross section micro-UAV used by hobbyists. Radar cross section simulations were carried out using FEKO simulator, achieving an average of -14.42 dBsm at S-band. The developed prototype was experimentally evaluated achieving more than 300 meters of detection range for a DJI Mavic pro-drone

Keywords: digital beamforming, drone detection, micro-UAV, MIMO, phased array

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Authors: S. M. Dissanayake, I. R. Palihakkara , K. G. Premathilaka

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Oil palm (Elaeisguineensis) is the world’s leading vegetable oil-producing plant and is well established as a perennial plantation crop in tropical countries. Oil palm in Sri Lanka has spread over 10,000 hectares in the wet zone of the Island. In immature plantations, land productivity can be increased with some selected intercrops. At the immature stage of the plantations (age up to 3-5 years), there is a large amount of free space available inside the plantations. This study attempts to determine the suitability of different intercrops during the immature phase of the oil palm. A field experiment is being conducted at Thalgaswella estate (WL2a) in Galle district, Sri Lanka. The objectives of the study are to evaluate and recommend a suitable immature oil palm-based intercropping system/s. This experiment was established with randomized complete block design (RCBD) with four treatments, including control in three replicates. Banana, ginger, and turmeric were selected as intercrops. Growth parameters of intercrops (plant height, length, width of D-leaf, and yield of intercrops) and girth, length, and number of leaflets of 17th frond in oil palms were taken at two months intervals. In addition to this, chlorophyll content was also measured in both intercrops and oil palm trees. Soil chemical parameters were measured annually. Results were statistically analyzed with SAS software. Results revealed that intercropped banana, turmeric, and ginger had given yields of 7.61Mt/ha, 4.92Mt/ha, and 4.53Mt/ha, respectively. When comparing these yields with mono-crop, banana, turmeric, and ginger intercrop yields as percentages of 16.9%, 24.6%, and 30.2%, respectively. The results of this study could be used to make appropriate policies to increase the unit land productivity in oil palm plantations in a low country wet zone (WL2a) of Sri Lanka.

Keywords: inter-cropping, oil palm, policies, mono-crop, land productivity

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Authors: Bhuvanesh Baniya

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Alzheimer disease is illnesses that are responsible for neuronal cell death and resulting in lifelong cognitive problems. Due to their unclear mechanism, there are no effective drugs available for the treatment. For a long time, herbal drugs have been used as a role model in the field of the drug discovery process. Holy basil in the Indian medicinal system (Ayurveda) is used for several neuronal disorders like insomnia and memory loss for decades. This study aims to identify active components of holy basil as potential inhibitors for the treatment of Alzheimer disease. To fulfill this objective, the Network pharmacology approach, gene ontology, pharmacokinetics analysis, molecular docking, and molecular dynamics simulation (MDS) studies were performed. A total of 7 active components in holy basil, 12 predicted neurodegenerative targets of holy basil, and 8063 Alzheimer-related targets were identified from different databases. The network analysis showed that the top ten targets APP, EGFR, MAPK1, ESR1, HSPA4, PRKCD, MAPK3, ABL1, JUN, and GSK3B were found as significant target related to Alzheimer disease. On the basis of gene ontology and topology analysis results, APP was found as a significant target related to Alzheimer’s disease pathways. Further, the molecular docking results to found that various compounds showed the best binding affinities. Further, MDS top results suggested could be used as potential inhibitors against APP protein and could be useful for the treatment of Alzheimer’s disease.

Keywords: holy basil, network pharmacology, neurodegeneration, active phytochemicals, molecular docking and simulation

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Authors: Joanna Cabaj, Sylwia Baluta, Karol Malecha

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Catecholamines are vital neurotransmitters that mediate a variety of central nervous system functions, such as motor control, cognition, emotion, memory processing, and endocrine modulation. Dysfunctions in catecholamine neurotransmission are induced in some neurologic and neuropsychiatric diseases. Changeable neurotransmitters level in biological fluids can be a marker of several neurological disorders. Because of its significance in analytical techniques and diagnostics, sensitive and selective detection of neurotransmitters is increasingly attracting a lot of attention in different areas of bio-analysis or biomedical research. Recently, optical techniques for the detection of catecholamines have attracted interests due to their reasonable cost, convenient control, as well as maneuverability in biological environments. Nevertheless, with the observed need for a sensitive and selective catecholamines sensor, the development of a convenient method for this neurotransmitter is still at its basic level. The manipulation of nanostructured materials in conjunction with biological molecules has led to the development of a new class of hybrid-modified enzymatic sensors in which both enhancement of charge transport and biological activity preservation may be obtained. Immobilization of biomaterials on electrode surfaces is the crucial step in fabricating electrochemical as well as optical biosensors and bioelectronic devices. Continuing systematic investigation in manufacturing of enzyme–conducting sensitive systems, here is presented a convenient fluorescence as well as electrochemical sensing strategy for catecholamines detection.

Keywords: biosensors, catecholamines, fluorescence, enzymes

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Authors: Po-Hui Yang, Jing-Min Chen, Po-Yu Kuo, Chia-Chun Wu

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This paper proposed a high resolution Vernier Delay Line (VDL) measurement circuit with coarse and fine detection mechanism, which improved the trade-off problem between high resolution and less delay cells in traditional VDL circuits. And the measuring time of proposed measurement circuit is also under the high resolution requests. At first, the testing range of input signal which proposed high resolution delay line is detected by coarse detection VDL. Moreover, the delayed input signal is transmitted to fine detection VDL for measuring value with better accuracy. This paper is implemented at 0.18μm process, operating frequency is 100 MHz, and the resolution achieved 2.0 ps with only 16-stage delay cells. The test range is 170ps wide, and 17% stages saved compare with traditional single delay line circuit.

Keywords: vernier delay line, D-type flip-flop, DFF, metastable phenomenon

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: Emil Sakharov, Alex Zotov, Ilkin Osmanov, Oleg Shelest, Aleksander Troitskiy, Robert Khabazov

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Objective: Since 2015, our center has been actively implementing methods of surgical correction of atrial fibrillation, in particular, in patients with coronary heart disease. The study presents a comparative analysis of the late postoperative period in patients with coronary artery bypass grafting and atrial fibrillation. Methods: The study included 150 patients with ischemic heart disease and atrial fibrillation for the period from 2015 to 2021. Patients were divided into 2 groups. The first group is represented by patients with ischemic heart disease and atrial fibrillation who underwent coronary bypass surgery and surgical correction of atrial fibrillation (N=50). The second group is represented by patients with ischemic heart disease and atrial fibrillation who underwent only myocardial revascularization (N=100). Patients were comparable in age, gender, and initial severity of the condition. Among the patients in group 1 there were 82% were men, while in the second group, their number was 75%. Among the patients of the first group, there were 36% with persistent atrial fibrillation, 20% with long-term persistent atrial fibrillation. In the second group, 10% with persistent atrial fibrillation and 17% with long-term persistent atrial fibrillation. Results: Average follow-up for groups 1 and 2 amounted to 47 months. There were no complications in group 1, such as bleeding and stroke. There was only 1 patient in group 1, who had died from cardiovascular disease. Freedom of atrial fibrillation was in 82% without AADs therapy. In group 2 there were 8 patients who had died from cardiovascular diseases and total freedom of atrial fibrillation was in 35% of patients, among which 42.8% had additional AADs therapy. Follow-up data are presented in Table 2. Progression of heart failure was observed in 3% in group 1 and 7% in group 2. Combined endpoints (recurrence of AF, stroke, progression of heart failure, myocardial infarction) were achieved in 16% in group 1 and 34% in group 2, respectively. Freedom from atrial fibrillation without antiarrhythmic therapy was 82% for group 1 and 35% for group 2. In the first group, there is a more pronounced decrease in heart failure rates. Deaths from cardiovascular causes were recorded in 2% for group 1 and 7% for group 2. Conclusion: Surgical treatment of atrial fibrillation helps to reduce adverse complications in the late postoperative period and contributes to the regression of heart failure.

Keywords: atrial fibrillation, coronary artery bypass grafting, ischaemic heart disease, heart failure

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Authors: Naseem Fatima, A. N. Srivastava, Tasleem Raza, Vijay Kumar

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Introduction: Carcinoma gallbladder is third most common gastrointestinal lethal disease with the highest incidence and mortality rate among women in Northern India. Scientists have found several risk factors that make a person more likely to develop gallbladder cancer; among these risk factors, deregulation of miRNAs has been demonstrated to be one of the most crucial factors. The changes in the expression of specific miRNA genes result in the control of inflammation, cell cycle regulation, stress response, proliferation, differentiation, apoptosis and invasion thus mediate the process in tumorgenesis. The aim of this study was to investigate the role of MiRNA-335 and may as a molecular marker in early detection of gallbladder cancer in suspected cases. Material and Methods: A total of 20 consecutive patients with gallbladder cancer aged between 30-75 years were registered for the study. Total RNA was extracted from tissue by using the mirVANA MiRNA isolation Kit according to the manufacturer’s protocol. The MiRNA- 335 and U6 snRNA-specific cDNA were reverse-transcribed from total RNA using Taqman microRNA reverse-transcription kit according to the manufacturer’s protocol. TaqMan MiRNA probes hsa-miR-335 and Taqman Master Mix without AmpEase UNG, Individual real-time PCR assays were performed in a 20 μL reaction volume on a Real-Time PCR system (Applied Biosystems StepOnePlus™) to detect MiRNA-335 expression in tissue. Relative quantification of target MiRNA expression was evaluated using the comparative cycle threshold (CT) method. The correlation was done in between cycle threshold (CT Value) of target MiRNA in gallbladder cancer with respect to non-cancerous Cholelithiasis gallbladder. Each sample was examined in triplicate. The Newman-Keuls Multiple Comparison Test was used to determine the expression of miR-335. Results: MiRNA335 was found to be significantly downregulated in the gallbladder cancer tissue (P<0.001), when compared with non-cancerous Cholelithiasis gallbladder cases. Out of 20 cases, 75% showed reduced expression of MiRNA335, were at last stage of disease with low overall survival rate and remaining 25% were showed up-regulated expression of MiRNA335 with high survival rate. Conclusion: The present study showed that reduced expression of MiRNA335 is associated with the advancement of the disease, and its deregulation may provide important clues to understanding it as a prognostic marker and opportunities for future research.

Keywords: carcinoma gallbladder, downregulation, MiRNA-335, RT-PCR assay

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Authors: T. Heikkinen, J. Oksman, T. Bragge, A. Nurmi, O. Kontkanen, T. Ahtoniemi

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Motor impairment is an inherent phenotypic feature of several chronic neurodegenerative diseases, and pharmacological therapies aimed to counterbalance the motor disability have a great market potential. Animal models of chronic neurodegenerative diseases display a number deteriorating motor phenotype during the disease progression. There is a wide array of behavioral tools to evaluate motor functions in rodents. However, currently existing methods to study motor functions in rodents are often limited to evaluate gross motor functions only at advanced stages of the disease phenotype. The most commonly applied traditional motor assays used in CNS rodent models, lack the sensitivity to capture fine motor impairments or improvements. Fine motor skill characterization in rodents provides a more sensitive tool to capture more subtle motor dysfunctions and therapeutic effects. Importantly, similar approach, kinematic movement analysis, is also used in clinic, and applied both in diagnosis and determination of therapeutic response to pharmacological interventions. The aim of this study was to apply kinematic gait analysis, a novel and automated high precision movement analysis system, to characterize phenotypic deficits in three different chronic neurodegenerative animal models, a transgenic mouse model (SOD1 G93A) for amyotrophic lateral sclerosis (ALS), and R6/2 and Q175KI mouse models for Huntington’s disease (HD). The readouts from walking behavior included gait properties with kinematic data, and body movement trajectories including analysis of various points of interest such as movement and position of landmarks in the torso, tail and joints. Mice (transgenic and wild-type) from each model were analyzed for the fine motor kinematic properties at young ages, prior to the age when gross motor deficits are clearly pronounced. Fine motor kinematic Evaluation was continued in the same animals until clear motor dysfunction with conventional motor assays was evident. Time course analysis revealed clear fine motor skill impairments in each transgenic model earlier than what is seen with conventional gross motor tests. Motor changes were quantitatively analyzed for up to ~80 parameters, and the largest data sets of HD models were further processed with principal component analysis (PCA) to transform the pool of individual parameters into a smaller and focused set of mutually uncorrelated gait parameters showing strong genotype difference. Kinematic fine motor analysis of transgenic animal models described in this presentation show that this method isa sensitive, objective and fully automated tool that allows earlier and more sensitive detection of progressive neuromuscular and CNS disease phenotypes. As a result of the analysis a comprehensive set of fine motor parameters for each model is created, and these parameters provide better understanding of the disease progression and enhanced sensitivity of this assay for therapeutic testing compared to classical motor behavior tests. In SOD1 G93A, R6/2, and Q175KI mice, the alterations in gait were evident already several weeks earlier than with traditional gross motor assays. Kinematic testing can be applied to a wider set of motor readouts beyond gait in order to study whole body movement patterns such as with relation to joints and various body parts longitudinally, providing a sophisticated and translatable method for disseminating motor components in rodent disease models and evaluating therapeutic interventions.

Keywords: Gait analysis, kinematic, motor impairment, inherent feature

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Authors: Andrew Kirabira

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This study is an investigation into the effectiveness of climate-smart agriculture (CSA) technologies in improving productivity through managing biotic and abiotic stresses in the coffee agroecological zones of Uganda. The motive is to enhance farmer livelihoods. The study was initiated as a result of the decreasing productivity of the crop in Uganda caused by the increasing prevalence of pests, diseases and abiotic stresses. Despite 9 years of farmers’ application of CSA, productivity has stagnated between 700kg -800kg/ha/yr which is only 26% of the 3-5tn/ha/yr that CSA is capable of delivering if properly applied. This has negatively affected the incomes of the 10.6 million people along the crop value chain which has in essence affected the country’s national income. In 2019/20 FY for example, Uganda suffered a deficit of $40m out of singularly the increasing incidence of one pest; BCTB. The amalgamation of such trends cripples the realization of SDG #1 and #13 which are the eradication of poverty and mitigation of climate change, respectively. In probing CSA’s effectiveness in curbing such a trend, this study is guided by the objectives of; determining the existing farmers’ knowledge and perceptions of CSA amongst the coffee farmers in the diverse coffee agro-ecological zones of Uganda; examining the relationship between the use of CSA and prevalence of selected coffee pests, diseases and abiotic stresses; ascertaining the difference in the market organization and pricing between conventionally and CSA produced coffee; and analyzing the prevailing policy environment concerning the use of CSA in coffee production. The data collection research design is descriptive in nature; collecting data from farmers and agricultural extension workers in the districts of Ntungamo, Iganga and Luweero; each of these districts representing a distinct coffee agroecological zone. Policy custodian officers at district, cooperatives and at the crop’s overseeing national authority were also interviewed.

Keywords: climate change, food security, field stresses, Productivity

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Authors: Tugba Sevinc Gamsiz, Emine Koroglu, Ozcan Keskin

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Aim: Nonalcoholic fatty liver disease (NAFLD) is considered the most common chronic liver disease in the general population. The higher mortality and morbidity among NAFLD patients and lack of symptoms makes early detection and management important. In our study, we aimed to evaluate the relationship between noninvasive imaging and biochemical markers in diabetic and nondiabetic patients diagnosed with NAFLD. Materials and Methods: The study was conducted from (September 2017) to (December 2017) on adults admitted to Internal Medicine and Gastroenterology outpatient clinics with hepatic steatosis reported on ultrasound or transient elastography within the last six months that exclude patients with other liver diseases or alcohol abuse. The data were collected and analyzed retrospectively. Number cruncher statistical system (NCSS) 2007 program was used for statistical analysis. Results: 116 patients were included in this study. Diabetic patients compared to nondiabetics had significantly higher Controlled Attenuation Parameter (CAP), Liver Stiffness Measurement (LSM) and fibrosis values. Also, hypertension, hepatomegaly, high BMI, hypertriglyceridemia, hyperglycemia, high A1c, and hyperuricemia were found to be risk factors for NAFLD progression to fibrosis. Advanced fibrosis (F3, F4) was present in 18,6 % of all our patients; 35,8 % of diabetic and 5,7 % of nondiabetic patients diagnosed with hepatic steatosis. Conclusion: Transient elastography is now used in daily clinical practice as an accurate noninvasive tool during follow-up of patients with fatty liver. Early diagnosis of the stage of liver fibrosis improves the monitoring and management of patients, especially in those with metabolic syndrome criteria.

Keywords: diabetes, elastography, fatty liver, fibrosis, metabolic syndrome

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Authors: Sina Mahdavi

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Background and Objective: Multiple sclerosis (MS) is the most common inflammatory autoimmune disease of the central nervous system (CNS) that affects the myelination process in the CNS. Complex interactions of various "environmental or infectious" factors may act as triggers in autoimmunity and disease progression. The association between viral infections, especially the John Cunningham virus (JCV) and MS is one potential cause that is not well understood. This study aims to summarize the available data on JCV infection in MS disease progression. Materials and Methods: For this study, the keywords "Multiple sclerosis", " John Cunningham virus ", and "central nervous system" in the databases PubMed, Google Scholar, Sid, and MagIran between 2019 and 2022 were searched, and 12 articles were chosen, studied, and analyzed. Results: MS patients are candidates for natalizumab therapy, which inhibits lymphocyte migration and increases the risk of progressive multifocal leukoencephalopathy (PML), a rare lytic infection of glial cells caused by JCV. Oligodendrocytes may be the target of JCV infection in the central nervous system (CNS). Conclusion: There is a high expression of JCV during the natalizumab treatment period for MS patients, suggesting that the virus may play a role in the development of MS by inducing an inflammatory state. Therefore, it is necessary to evaluate anti-JCV antibody serum as an important risk factor for the development of PML before deciding on the treatment course for these patients.

Keywords: multiple sclerosis, John Cunningham virus, central nervous system, autoimmunity

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Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

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Authors: Afia Naheed, Manmohan Singh, David Lucy

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This work is based on a mathematical as well as statistical study of an SEIJTR deterministic model for the interpretation of transmission of severe acute respiratory syndrome (SARS). Based on the SARS epidemic in 2003, the parameters are estimated using Runge-Kutta (Dormand-Prince pairs) and least squares methods. Possible graphical and numerical techniques are used to validate the estimates. Then effect of the model parameters on the dynamics of the disease is examined using sensitivity and uncertainty analysis. Sensitivity and uncertainty analytical techniques are used in order to analyze the affect of the uncertainty in the obtained parameter estimates and to determine which parameters have the largest impact on controlling the disease dynamics.

Keywords: infectious disease, severe acute respiratory syndrome (SARS), parameter estimation, sensitivity analysis, uncertainty analysis, Runge-Kutta methods, Levenberg-Marquardt method

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Authors: Fahui Jiang, Xinwei Xue, Liyan Zhang, Yanyan Zuo, Hao Zhang, Wei Zheng, Limei Bian, Lingling Hu, Chunlei Hao, Jianghong Du, Yanhua Ci, Ruibao Cheng, Ciren Dawa, Mithun Biswas, Mahbub Ul Islam, Fansheng Meng, Xinhua Peng

Abstract:

Context: Soil erosion is a global issue that poses a significant threat to agricultural sustainability, particular in northern of China, which experiences the most severe wind erosion worldwide. Conservation tillage is vital in arid regions for preserving soil, enhancing water retention, and sustaining agricultural productivity in the face of limited rainfall. However, the long-term impacts of conservation tillage in semi-arid regions, especially its effects on soil health, wind erosion, and crop productivity, are poorly understood. Objective: Assess the impacts of conservation tillage on soil hydrothermal properties, wind erosion rates, nutrient dynamics, and crop yield, as well as elucidating the underlying mechanisms driving these impacts. Methods: A 9-year in-situ study was conducted in Chifeng, Inner Mongolia Province, comparing conventional rotary tillage (CK) with two conservation tillage methods: no-tillage with straw mulching (CT-1) and no-tillage with standing straw (CT-2). Results: Soil bulk density increased significantly under CT-1 and CT-2 in the topsoil layer (0–20 cm) compared with CK. Soil moisture content exhibited a significant increase pattern under CT-1 and CT-2, while soil temperature decreased under CT-1 but increased under CT-2, relative to CK. These variations in soil hydrothermal properties were more pronounced during the early (critical) crop growth stages and higher temperature conditions (afternoon). Soil loss due to wind erosion, accumulated from a height of 0–50 cm on the land surface, was reduced by 31.3 % and 25.5 % under CT-1 and by 51.5 % and 38.2 % under CT-2 in 2021 and 2022, respectively, compared to CK. Furthermore, the proportion of soil finer particles (clay and silt) increased under CT due to reduced wind erosion. Soil organic carbon significantly increased throughout the soil profile (0–60 cm), particularly in the deeper layers (20–40 cm and 40–60 cm), compared to the surface layer (0–20 cm), with corresponding increases of +57.0 % and +0.18 %, +66.2 % and +80.3 %, and +27.1 % and +14.2 % under CT-1 and CT-2, respectively, relative to CK in 2021. The concentrations of soil nutrients such as total nitrogen, available nitrogen, and available phosphorus and potassium, consistently increased under CT-1 and CT-2 compared to CK, with notable enhancements observed in the topsoil layer (0–20 cm) before seedling time, albeit declining after crop harvest. Generally, CT treatments significantly increased dry matter accumulation (+4.8 % to +30.8 %) and grain yield (+2.22 % to +0.44 %) of maize compared to CK in the semi-arid region over the 9-year study period, particularly notable in dry years and with long-term application. Conclusions and implications: Conservation tillage in semi-arid regions enhanced soil properties, reduced soil erosion, and increased soil nutrient dynamics and crop yield, promising sustainable agricultural practices with environmental benefits. Furthermore, our findings suggest that no-tillage with straw mulching is more suitable for dry and wind erosion sensitive regions.

Keywords: no tillage, conventional tillage, soil water, soil temperature, soil physics

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: Mulugeta K. Tefera, Xiaolong Yang, Jian Liu

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Detection and tracking of moving objects are very important in many application contexts such as detection and recognition of people, visual surveillance and automatic generation of video effect and so on. However, the task of detecting a real shape of an object in motion becomes tricky due to various challenges like dynamic scene changes, presence of shadow, and illumination variations due to light switch. For such systems, once the moving object is detected, tracking is also a crucial step for those applications that used in military defense, video surveillance, human computer interaction, and medical diagnostics as well as in commercial fields such as video games. In this paper, an object presents in dynamic background is detected using adaptive mixture of Gaussian based analysis of the video sequences. Then the detected moving object is tracked using the region based moving object tracking and inter-frame differential mechanisms to address the partial overlapping and occlusion problems. Firstly, the detection algorithm effectively detects and extracts the moving object target by enhancing and post processing morphological operations. Secondly, the extracted object uses region based moving object tracking and inter-frame difference to improve the tracking speed of real-time moving objects in different video frames. Finally, the plotting method was applied to detect the moving objects effectively and describes the object’s motion being tracked. The experiment has been performed on image sequences acquired both indoor and outdoor environments and one stationary and web camera has been used.

Keywords: background modeling, Gaussian mixture model, inter-frame difference, object detection and tracking, video surveillance

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Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer

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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).

Keywords: diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography (VOG)

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Authors: YingWei Tan, XueFeng Ding

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Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.

Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding

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Authors: R. A. Eman Elsheikh, Bart Schultz, Abraham Mehari Haile, Hussein S. Adam

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A field experiment was conducted at Gezira research station farm during the winter season in the third week of November 2012, in WadMedani, Sudan (Lat 14.23 W, Long 33.39 E and altitude 405 m above sea level, in deep cracking alkaline heavy clay Vertisols). The objective of this study was to determine the effect of three different irrigation for 10 days (W1), 15 days (W2) and 20 days (W3) and for two rows of 30 cm (S1) and 40 cm (S2), respectively. The experimental design was split plot with three replicates. The sunflower test variety was Hysun 33 cultivar. The seasonal water applied during the study was 6898, 6647, 5256, 5435, 5214, 5416 m3/ha for W1S1, W1S2, W2S1, W2S2, W3S1 and W3S2 respectively. The seed yield obtained for the above treatment in that sequence was 4208, 5542, 5167, 4579, 2931, 2936 kg/ha. The corresponding computed water productivity was 0.61, 0.82, 0.87, 0.95, 0.54, 0.56 kg/m3. The study clearly indicated that the highest seed yield was obtained when the crop was sown at 40 cm row spacing and was irrigated every 10 days (W1S2), followed by W2S1.

Keywords: water productivity, water deficit, sunflower, plant spacing

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