Induced spawning and larval rearing of the sea cucumber Holothuria nobilis

Sea cucumber Holothuria nobilis is an economically important species for livelihoods in many countries. However, an increase in demand for this species has led to the depletion of wild stocks. The introduction of this species in aquaculture is necessary to reduce fishing pressure. This study was taken to establish breeding and larval rearing techniques for the development of H. nobilis aquaculture. The broodstock collected from the wild were induced to spawn by using thermal stimulation and the combination of thermal and algal stimulation methods. The larvae obtained from induced spawning were reared using different diets (mixed microalgae and mixed microalgae with artificial feeds) at different stocking densities (300, 600, and 1000 larvae/l). Thermal stimulation is the best method of inducing spawning to H. nobilis yielding up to 1,300,000 fertilized eggs. The highest survival rate (27.5%) of doliolaria larvae was achieved using a mixed microalgae diet. The highest survival rate of doliolaria larvae (41.5 %), growth, and development were in the stocking density of 600 larvae/l

Effects of ASE noise and dispersion chromatic on performance of DWDM networks using distributed Raman amplifiers

We investigate effects of amplified spontaneous emission noise (ASE), noise figure (NF) and dispersion chromatic on the performance of DWDM networks using distributed optical fiber Raman amplifiers (DRAs) in two different pump configurations, i.e., forward and backward pumping. We found that the pumping configurations, ASE noise, and dispersion play an important role in network performance improving since it reduces noise figure and bit error rate (BER) of the system. Simulation results show that the lowest bit error rate and noise figure when using forward pumping configuration. Moreover, we have also compared ASE noise powers of the simulation with these of the experiment, they are match

Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation

The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to improve detection performance. Our method is evaluated on a real-world medical imaging dataset and outperforms relevant baselines that do not consider disagreements among annotators.Comment: This is a short version submitted to the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, US

A Multiwell-Based Detection Platform with Integrated PDMS Concentrators for Rapid Multiplexed Enzymatic Assays

We report an integrated system for accelerating assays with concentrators in a standard 12-well plate (ISAAC-12) and demonstrate its versatility for rapid detection of matrix metalloproteinase (MMP)-9 expression in the cell culture supernatant of breast cancer cell line MDA-MB-231 by accelerating the enzymatic reaction and end-point signal intensity via electrokinetic preconcentration. Using direct printing of a conductive ion-permselective polymer on a polydimethylsiloxane (PDMS) channel, the new microfluidic concentrator chip can be built without modifying the underlying substrate. Through this decoupling fabrication strategy, our microfluidic concentrator chip can easily be integrated with a standard multiwell plate, the de facto laboratory standard platform for high-throughput assays, simply by reversible bonding on the bottom of each well. It increases the reaction rate of enzymatic assays by concentrating the enzyme and the reaction product inside each well simultaneously for rapid multiplexed detection.publishersversionpublishe

BIOLOGICAL AMMONIA REMOVAL BY SUBMERGED AERATED FILTER FROM HANOI GROUND WATER

Joint Research on Environmental Science and Technology for the Eart

The Distribution of Dengue Virus Serotype in Quang Nam Province (Vietnam) during the Outbreak in 2018

Objectives: Quang Nam province in the Centre of Vietnam has faced an outbreak of dengue hemorrhagic fever (DHF) in 2018. Although DHF is a recurrent disease in this area, no epidemiological and microbiological reports on dengue virus serotypes have been conducted mainly due to lack of facilities for such a kind of advanced surveillance. The aim of this study was to detect different dengue virus serotypes in patients’ blood samples. Design and Methods: Suspected cases living in Quang Nam province (Vietnam) and presenting clinical and hematological signs of dengue hemorrhagic fever were included in the study. The screening was performed, and the results were compared by using two methodologies: RT real-time PCR (RT-rPCR) and the Dengue NS1 rapid test. Results: From December 2018 to February 2019, looking both at RT-rPCR [+] and NS1 [+] methodologies, a total of 488 patients were screened and 336 were positive for dengue virus detection (74 children and 262 adults); 273 of these patients (81.3%) underwent viral serotype identification as follows: 12.82% (35/273) D1 serotype, 17.95% (49/273) D2, 0.37% (1/273) D3, 68.50 (187/283) D4, and 0.37% (1/273) D2+D4 serotypes. The RT-rPCR outcomes showed higher sensitivity during the first three days of infection compared to NS1 (92.3% vs. 89.7%). The NS1 increased sensitivity after the first 3 days whilst the RT-rPCR decreased. Conclusions: Advanced surveillance with dengue virus serotypes identification, if performed routinely, may help to predict and prevent further DHF epidemics based on the exposure of the different serotypes during different periods that lead to the intensification of disease severity as a consequence of antibody-dependent enhancement (ADE)