Cervical ectopic pregnancy: a rare complication

Cervical ectopic gestation is a rare site and frequently confused with neoplastic process. Profuse bleeding can occur if the placenta is mistaken for a tumor and a biopsy is taken which may lead to hysterectomy. Cervical gestation also can be mistaken for an incomplete abortion where Products Of Conception (POC) are retained within the cervical canal may lead to diagnostic dilemma. We are presenting a case of cervical ectopic which was successfully managed conservatively

Selection of Optimal Processing Condition during Removal of Methylene Blue Dye Using Treated Betel Nut Fibre Implementing Desirability Based RSM Approach

Adsorption of Methylene Blue onto chemically (Na2CO3) treated ripe betel nut fibre (TRBNF) was studied using batch adsorption process for different concentrations of dye solutions (50, 100, 150 and 200 mg/L). Experiments were carried out as a function of contact time, initial solution pH (3 to11), adsorbent dose (10 gm/L – 18 gm/L) and temperature (293, 303 and 313 K). The adsorption was favoured at neutral pH and lower temperatures. Adsorption data were well described by the Langmuir isotherm and subsequently optimised using a second-order regression model by implementing face-centred CCD of Response Surface Methodology (RSM). The adsorption process followed the pseudo-second-order kinetic model. The maximum sorption capacity (qmax) was found to be 31.25 mg/g. Thermodynamic parameters suggest that the adsorption is a typical physical process, spontaneous, enthalpy driven and exothermic in nature. The maximum adsorption occurred at pH 7.0. The effect of adsorption was studied and optimum adsorption was obtained at a TRBNF dose of15 gm/L

IRON-MAN: An Approach To Perform Temporal Motionless Analysis of Video using CNN in MPSoC

This paper proposes a novel human-inspired methodology called IRON-MAN ( Integrated RatiONal prediction and Motionless ANalysis ) for mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video ( TMAV ). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image ( ECTI ) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption and lifespan reliability of the device

Status of subclinical mastitis in lactating cows of selected dairy farms in Bangladesh and farmers’ understanding of mastitis

Background: Subclinical mastitis, a disease that is economically important to dairy cows, affects milk production. Management of udder health is an essential element in the process of safe milk production. Consumption of milk with a high somatic cell count (SCC) may also pose a health risk to humans. Objectives. We aimed to evaluate the SCC method for the detection of SCM in dairy cows and to assess farmers’ knowledge, attitudes, and practices towards mastitis detection and control. Methods. A cross-sectional study was conducted to evaluate the current status of subclinical mastitis (SCM) in lactating dairy cows. The research work was also designed to assess farmers’ knowledge, attitudes, and practices about bovine mastitis. A total of 320 milk samples from 80 lactating cows were collected for physical examination and tested for subclinical mastitis using SCC. Results. Out of 80 lactating cows examined, 53 (66%, 95% CI: 55-76%) cows were diagnosed with subclinical mastitis. The average cow level SCC per ml of milk was 503101. According to the farmers’ self-report, 100% of farms practiced hand milking and 100% of milkmen washed their hands before milking. The majority of farms (90%) had brick floors. Most of the farmers (70%) knew about screening for subclinical mastitis. However, they never performed screening to detect subclinical mastitis. Conclusion. The high prevalence of SCM in dairy raised significant concerns about farm management, personal hygiene and biosecurity practices. Further study is needed to identify the etiologies of SCM and its associated risk factors

Reference Interval of Muscle Damage Indices and Cortisol in Young Athletes of Various Sports Discipline

Creatine kinase (CK), lactate dehydrogenase (LDH) and cortisol are widely accepted as biological markers. The purpose of the study was to frame the reference interval for muscle damage indices (CK, LDH) and cortisol in the young athletic population of various sports disciplines. 260 young male players [i.e., football (n=62), hockey (n=60), gymnastics (n=36), swimming (n=28), table tennis (n=25), sprint-jump-throw (n=36) and middle-long distance running (n=13)] were recruited for the study (mean age = 15.6±1.59 yrs). Assay of LDH, CK and cortisol was done using the standard enzymatic protocol. The reference interval was calculated by following the Clinical and Laboratory Standard Institute (CLSI) C28-A3 guideline and “MedCalc” software (version 19) with a 90% confidence interval. Serum LDH range was from 148.00-324.00 IU/L with a mean of 233.2±34.74 and a median around 236.25. Serum CK ranged from 17.00-43.50 IU/L with a mean of 28.93±5.23 IU/L and a median around 28.00. Cortisol ranged from 4.99-15.78 µg/dl with a mean of 9.31±2.09 µg/dl and a median around 8.90. The present study confers 165.63 - 303.43 IU/L, 19.00 – 40.09 IU/L and 6.07-14.15 µg/dl as the reference interval values for LDH, CK and cortisol, respectively. The present finding will guide the researchers to avoid misinterpretation of muscle damage indices values during any phase of competitive training of sports person

TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC

Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs

DeadPool: Performance Deadline Based Frequency Pooling and Thermal Management Agent in DVFS Enabled MPSoCs

High operating temperature and frequent thermal cycles in a multi-processor system-on-chip, which is now popularly utilized in mobile/Edge devices, harm the overall lifespan and reliability of such devices. In this paper, we propose an intelligent software agent that works alongside other resource mapping and partitioning mechanism in order to monitor and reduce the operating temperature of the system by regulating the operating frequency of the CPU cores while catering for performance constraint at the same time. Our proposed approach?, DeadPool thermal management agent, is able to reduce the overall operating temperature of the system by 24.21% and reduce thermal cycle by 67.42% at the most when compared to the state-of-the-art methods

TEEM: Online Thermal- and Energy-Efficiency Management on CPU-GPU MPSoCs

Heterogeneous Multiprocessor System-on-Chip (MPSoC) are progressively becoming predominant in most modern mobile devices. These devices are required to perform processing of applications within thermal, energy and performance constraints. However, most stock power and thermal management mechanisms either neglect some of these constraints or rely on frequency scaling to achieve energy-efficiency and temperature reduction on the device. Although this inefficient technique can reduce temporal thermal gradient, but at the same time hurts the performance of the executing task. In this paper, we propose a thermal and energy management mechanism which achieves reduction in thermal gradient as well as energy-efficiency through resource mapping and thread-partitioning of applications with online optimization in heterogeneous MPSoCs. The efficacy of the proposed approach is experimentally appraised using different applications from Polybench benchmark suite on Odroid-XU4 developmental platform. Results show 28% performance improvement, 28.32% energy saving and reduced thermal variance of over 76% when compared to the existing approaches. Additionally, the method is able to free more than 90% in memory storage on the MPSoC, which would have been previously utilized to store several task-to-thread mapping configurations