Limnoecology and carp fish species peak spawning timing in haor basin of Bangladesh

The present study was conducted to investigate the peak spawning timing based on the reproductive performance of carps species related to limnoecology variables, emphasizing to assess within fish population breeding variability and monthly effects over a period from April to July 2016 in the haors basin of Bangladesh. The reproductive performances were evaluated through artificial breeding technique by hormone injection in matured brood fish. The reproductive performance in terms of mean fecundity, egg weight, fertilization, hatchling and larval survival were significantly (P<0.05) highest in the month of May to June, and April compared to the months of spawning timing for the three Indian major carps, and the three exotic carp respectively. The ovulation and hatching time were significantly (p<0.05) highest in the month of July and April for the Labeo rohita, in the month of Aril for Catla catla and Cirrhinus cirrhosus and for the three exotic carp in the month of June to July compared with the others months of the breeding season. However, total length, weight and spawning response were not significantly (P>0.05) different among the months of the breeding time for the six carp species and a trend similar to the spawning success was also observed and numerically the mean value of the spawning response rate was between 88 to 93% and 83 to 90% for the three Indian major carps and the three exotic carp respectively among the months of the study. Furthermore, mean monthly values of limnoecological variables such as temperature, pH, and dissolved oxygen were not significantly (P>0.05) different among the months of the breeding season of the carp species in the three haor study sites. This study may serve as an update on carp fish species peak spawning timing related to limnoecological variables in the haor basin for carps species broodstock sustainable management to get quality seeds in the aquaculture hatchery industry

A Solar Panel-Integrated Modified Planner Inverted F Antenna for Low Earth Orbit Remote Sensing Nanosatellite Communication System

One of the most efficient methods to observe the impact of geographical, environmental, and geological changes is remote sensing. Nowadays, nanosatellites are being used to observe climate change using remote sensing technology. Communication between a remote sensing nanosatellite and Earth significantly depends upon antenna systems. Body-mounted solar panels are the main source of satellite operating power unless deployable solar panels are used. Lower ultra-high frequency (UHF) nanosatellite antenna design is a crucial challenge due to the physical size constraint and the need for solar panel integration. Moreover, nanosatellite space missions are vulnerable because of antenna and solar panel deployment complexity. This paper proposes a solar panel-integrated modified planner inverted F antenna (PIFA) to mitigate these crucial limitations. The antenna consists of a slotted rectangular radiating patch with coaxial probe feeding and a rectangular ground plane. The proposed antenna has achieved a −10 dB impedance bandwidth of 6.0 MHz (447.5 MHz–453.5 MHz) with a small-sized (80 mm× 90 mm× 0.5 mm) radiating element. In addition, the antenna achieved a maximum realized gain of 0.6 dB and a total efficiency of 67.45% with the nanosatellite structure and a solar panel. The challenges addressed by the proposed antenna are to ensure solar panel placement between the radiating element and the ground plane, and provide approximately 55% open space to allow solar irradiance into the solar panel

Antioxidant Potential and Brine Shrimp Lethality bioassay of Spilanthes acmella Flower Extract

The current research study has been carried out to explore the antioxidant activity and brine shrimp lethality bioassay of different fractions from the flower extract of Spilanthes acmella. Besides, this experiment was also assessed to find out the proximate analysis and phytochemical screening by following the perfect protocol. To fractionate by soxhletion using sequential extraction techniques powdered flower of the plant were treated with different solvents including n-hexane, chloroform, ethanol and water. For the evaluation of antioxidant activity, total antioxidant capacity determination, determination of total phenolic content and total Flvonoids contents by aluminium tricholoride method were used. In addition, ascorbic acid and gallic acid was used as a standard antioxidant compound in these studies. Concerning the proximate analysis, moisture content, total ash value, acid insoluble ash and water soluble ash value were found 8.6%, 3.76%, 3.30%, 3.20% respectively. To evaluate cytotoxicity, the brine shrimp lethality bioassay was used. For phytochemical screening different extract of those solvents were utilized that disclosed the presence of alkaloids, flavonoids, phenolic compounds, Tannins, amino acids on different fractions but the absence of reducing sugar and saponins. The results of all assay showed that all the extracts of Spilanthes acmella flower possess significant antioxidant activity. In brine shrimp lethality bioassay, ethanol extract of flower effect to brine shrimp nauplii and exhibiting highest toxicity having LC50 value 1.20 μg/ml as compared to standard dimethyl sulfoxide (LC50 1.31 µg/ml). These evaluations suggest that Spilanthes acmella flowers might be a better source of antioxidants and possess important cytotoxic effect

Development of microwave brain stroke imaging system using multiple antipodal vivaldi antennas based on Raspberry Pi technology

This paper proposes a Microwave Imaging System (MIS) for brain stroke detection. In the MIS, the primary challenge is to improve in terms of cost, size, and stroke image quality. Thus, the main contribution of this work is the economy and the compact rotation platform integrated with an array of nine antipodal Vivaldi antenna in circular arrangement and single computer board, Raspberry Pi Module (RPM) as microcontroller developed. The design and fabrication of wideband antenna based on Computer Simulation Technology (CST) software and Rogers RO4350B substrate, which operated from 2.06 GHz to 2.61 GHz. In the RPM, the Python programming language used for regulating the angle of rotation and antenna switching process. The process of receiving reflection signals from the head phantom for each antenna supervised by Single-Pole 8-Throw (SP8T) Radio Frequency (RF) switch. The fabricated head phantom based on the primary tissues of the brain, white matter using inexpensive materials, and located in the middle of the platform. Platform rotation is a combination of wood-based platform with the size 0.36m2 and material Perspex. Then, through an interfacing process between Python script and Vector Network Analyzer (VNA), the raw data in S-Parameters transferred to the MATLAB software for analysis. The fabricated antenna able to realize high directivity, 86.92% efficiency, and 2.45 dBi gain. Overall, the proposed system offers the cost-effective, compact, and able to collect the data effectively around the head phantom that consist of a target clot and without a target clot at 50 different positions. It successfully tracked the presence of stroke clots through color differences in color plots

Inductively tuned modified split ring resonator based quad band epsilon negative (ENG) with near zero index (NZI) metamaterial for multiband antenna performance enhancement

An inductively tuned modified split-ring resonator-based metamaterial (MTM) is presented in this article that provides multiple resonances covering S, C, X, and Ku-bands. The MTM is designed on an FR-4 substrate with a thickness of 1.5 mm and an electrical dimension of 0.063λ × 0.063λ where wavelength, λ is calculated at 2.38 GHz. The resonator part is a combination of three squared copper rings and one circular ring in which all the square rings are modified shaped, and the inner two rings are interconnected. The resonance frequency is tuned by adding inductive metal strips in parallel two vertical splits of the outer ring that causes a significant shift of resonances towards the lower frequencies and a highly effective medium ratio (EMR) of 15.75. Numerical simulation software CST microwave studio is used for the simulation and performance analysis of the proposed unit cell. The MTM unit cell exhibits six resonances of transmission coefficient (S21) at 2.38, 4.24, 5.98, 9.55, 12.1, and 14.34 GHz covering S, C, X, and Ku-bands with epsilon negative (ENG), near-zero permeability, and near-zero refractive index (NZI). The simulated result is validated by experiment with good agreement between them. The performance of the array of the unit cells is also investigated in both simulation and measurement. The equivalent circuit modeling has been accomplished using Advanced Design Software (ADS) that shows a similar S21 response compared to CST simulation. Noteworthy to mention that with the copper backplane, the same unit cell provides multiband absorption properties with four major absorption peaks of 99.6%, 95.7%, 99.9%, 92.7% with quality factors(Q-factor) of 28.4, 34.4, 23, and 32 at 3.98, 5.5, 11.73 and 13.47 GHz, respectively which can be applied for sensing and detecting purposes. The application of an array of the unit cells is investigated using it as a superstrate of an antenna that provides a 73% (average) increase of antenna gain. Due to its simple design, compact dimension with high EMR, ENG property with near-zero permeability, this multiband NZI metamaterial can be used for microwave applications, especially for multiband antenna gain enhancement.This work was supported by the Ministry of Higher Education (MoHE) project grant code: FRGS/1/2019/TK04/ UKM/01/1.This work was also supported by Grant NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar, and the claims made herein are solely the responsibility of the authors.Scopu

Development Of Microwave Brain Stroke Imaging System Using Multiple Antipodal Vivaldi Antennas Based On Raspberry Pi Technology

This paper proposes a Microwave Imaging System (MIS) for brain stroke detection. In the MIS, the primary challenge is to improve in terms of cost, size, and stroke image quality. Thus, the main contribution of this work is the economy and the compact rotation platform integrated with an array of nine antipodal Vivaldi antenna in circular arrangement and single computer board, Raspberry Pi Module (RPM) as microcontroller developed. The design and fabrication of wideband antenna based on Computer Simulation Technology (CST) software and Rogers RO4350B substrate, which operated from 2.06 GHz to 2.61 GHz. In the RPM, the Python programming language used for regulating the angle of rotation and antenna switching process. The process of receiving reflection signals from the head phantom for each antenna supervised by Single-Pole 8-Throw (SP8T) Radio Frequency (RF) switch. The fabricated head phantom based on the primary tissues of the brain, white matter using inexpensive materials, and located in the middle of the platform. Platform rotation is a combination of wood-based platform with the size 0.36m2 and material Perspex. Then, through an interfacing process between Python script and Vector Network Analyzer (VNA), the raw data in S-Parameters transferred to the MATLAB software for analysis. The fabricated antenna able to realize high directivity, 86.92% efficiency, and 2.45 dBi gain. Overall, the proposed system offers the cost-effective, compact, and able to collect the data effectively around the head phantom that consist of a target clot and without a target clot at 50 different positions. It successfully tracked the presence of stroke clots through color differences in color plots

Microwave Imaging Sensor Using Low Profile Modified Stacked Type Planar Inverted F Antenna

Microwave imaging is the technique to identify hidden objects from structures using electromagnetic waves that can be applied in medical diagnosis. The change of dielectric property can be detected using microwave antenna sensor, which can lead to localization of abnormality in the human body. This paper presents a stacked type modified Planar Inverted F Antenna (PIFA) as microwave imaging sensor. Design and performance analysis of the sensor antenna along with computational and experimental analysis to identify concealed object has been investigated in this study. The dimension of the modified PIFA radiating patch is 40 × 20 × 10 mm3. The reflector walls used, are 45 mm in length and 0.2-mm-thick inexpensive copper sheet is considered for the simulation and fabrication which addresses the problems of high expenses in conventional patch antenna. The proposed antenna sensor operates at 1.55–1.68 GHz where the maximum realized gain is 4.5 dB with consistent unidirectional radiation characteristics. The proposed sensor antenna is used to identify tumor in a computational human tissue phantom based on reflection and transmission coefficient. Finally, an experiment has been performed to verify the antenna’s potentiality of detecting abnormality in realistic breast phantom

Artificial intelligence-enabled rapid and symptom-based medication recommendation system (COV-MED) for the COVID-19 patients

In a general COVID-19 population in Cox’s Bazar, Bangladesh, we developed a medication recommendation system based on clinical information from the electronic medical record (EMR). Our goal was also to enable deep learning (DL) strategies to quickly assist physicians and COVID-19 patients by recommending necessary medications. The general demographic data, clinical symptoms, basic clinical tests, and drug information of 8953 patients were used to create a dataset. The learning model in this COVID-MED model was created using Keras (an open-source artificial neural network library) to solve regression problems. In this study, a sequential model was adopted. In order to improve the prediction capability and achieve global minima quickly and smoothly, the COVID-MED model incorporates an adaptive optimizer dubbed Adam. The model calculated a mean absolute error of 0.0037, a mean squared error of 0.000035, and a root mean squared error of 0.0059. The model predicts the output medications, such as injections or other oral medications, with around 99% accuracy. These findings show that medication can be predicted using information from the EMR. Similar models allow for patient-specific decision support to help prevent medication errors in diseases other than COVID-19

QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model

The reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20–25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative. This study used seven different publicly available datasets, where the largest one consisting of fifteen CBC biomarkers collected from 1624 patients (52% COVID-19 positive) admitted at San Raphael Hospital, Italy from February to May 2020 was used to train and validate the proposed model. White blood cell count, monocytes (%), lymphocyte (%), and age parameters collected from the patients during hospital admission were found to be important biomarkers for COVID-19 disease prediction using five different feature selection techniques. Our stacking model produced the best performance with weighted precision, sensitivity, specificity, overall accuracy, and F1-score of 91.44%, 91.44%, 91.44%, 91.45%, and 91.45%, respectively. The stacking machine learning model improved the performance in comparison to other state-of-the-art machine learning classifiers. Finally, a nomogram-based scoring system (QCovSML) was constructed using this stacking approach to predict the COVID-19 patients. The cut-off value of the QCovSML system for classifying COVID-19 and Non-COVID patients was 4.8. Six datasets from three different countries were used to externally validate the proposed model to evaluate its generalizability and robustness. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.961 for the internal cohort and average AUC of 0.967 for all external validation cohort, respectively. The external validation shows an average weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 92.02%, 95.59%, 93.73%, 90.54%, and 93.34%, respectively

Genome-Wide Association Study Identifies Chromosome 10q24.32 Variants Associated with Arsenic Metabolism and Toxicity Phenotypes in Bangladesh

Arsenic contamination of drinking water is a major public health issue in many countries, increasing risk for a wide array of diseases, including cancer. There is inter-individual variation in arsenic metabolism efficiency and susceptibility to arsenic toxicity; however, the basis of this variation is not well understood. Here, we have performed the first genome-wide association study (GWAS) of arsenic-related metabolism and toxicity phenotypes to improve our understanding of the mechanisms by which arsenic affects health. Using data on urinary arsenic metabolite concentrations and approximately 300,000 genome-wide single nucleotide polymorphisms (SNPs) for 1,313 arsenic-exposed Bangladeshi individuals, we identified genome-wide significant association signals (P<5×10−8) for percentages of both monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) near the AS3MT gene (arsenite methyltransferase; 10q24.32), with five genetic variants showing independent associations. In a follow-up analysis of 1,085 individuals with arsenic-induced premalignant skin lesions (the classical sign of arsenic toxicity) and 1,794 controls, we show that one of these five variants (rs9527) is also associated with skin lesion risk (P = 0.0005). Using a subset of individuals with prospectively measured arsenic (n = 769), we show that rs9527 interacts with arsenic to influence incident skin lesion risk (P = 0.01). Expression quantitative trait locus (eQTL) analyses of genome-wide expression data from 950 individual's lymphocyte RNA suggest that several of our lead SNPs represent cis-eQTLs for AS3MT (P = 10−12) and neighboring gene C10orf32 (P = 10−44), which are involved in C10orf32-AS3MT read-through transcription. This is the largest and most comprehensive genomic investigation of arsenic metabolism and toxicity to date, the only GWAS of any arsenic-related trait, and the first study to implicate 10q24.32 variants in both arsenic metabolism and arsenical skin lesion risk. The observed patterns of associations suggest that MMA% and DMA% have distinct genetic determinants and support the hypothesis that DMA is the less toxic of these two methylated arsenic species. These results have potential translational implications for the prevention and treatment of arsenic-associated toxicities worldwide