scholarly journals Zebrafish Models for Human Skeletal Disorders

2021 ◽  
Vol 12 ◽  
Author(s):  
Manuel Marí-Beffa ◽  
Ana B. Mesa-Román ◽  
Ivan Duran
Keyword(s):  
Skeletal Dysplasia ◽  
Low Cost ◽  
Scientific Research ◽  
Reference List ◽  
Chemical Treatments ◽  
Causal Genes ◽  
Skeletal Disorders ◽  
Interesting Species ◽  
State Of Art

In 2019, the Nosology Committee of the International Skeletal Dysplasia Society provided an updated version of the Nosology and Classification of Genetic Skeletal Disorders. This is a reference list of recognized diseases in humans and their causal genes published to help clinician diagnosis and scientific research advances. Complementary to mammalian models, zebrafish has emerged as an interesting species to evaluate chemical treatments against these human skeletal disorders. Due to its versatility and the low cost of experiments, more than 80 models are currently available. In this article, we review the state-of-art of this “aquarium to bedside” approach describing the models according to the list provided by the Nosology Committee. With this, we intend to stimulate research in the appropriate direction to efficiently meet the actual needs of clinicians under the scope of the Nosology Committee.

scholarly journals Quantification and Classification of Diclofenac Sodium Content in Dispersed Commercially Available Tablets by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

2021 ◽  
Vol 14 (5) ◽  
pp. 440
Author(s):  
Eirini Siozou ◽  
Vasilios Sakkas ◽  
Nikolaos Kourkoumelis
Keyword(s):  
Infrared Spectroscopy ◽  
Low Cost ◽  
Diclofenac Sodium ◽  
Reference Method ◽  
Sodium Content ◽  
Attenuated Total Reflection ◽  
Accuracy And Precision ◽  
Significant Difference ◽  
Ft Ir

A new methodology, based on Fourier transform infrared spectroscopy equipped with an attenuated total reflectance accessory (ATR FT-IR), was developed for the determination of diclofenac sodium (DS) in dispersed commercially available tablets using chemometric tools such as partial least squares (PLS) coupled with discriminant analysis (PLS-DA). The results of PLS-DA depicted a perfect classification of the tablets into three different groups based on their DS concentrations, while the developed model with PLS had a sufficiently low root mean square error (RMSE) for the prediction of the samples’ concentration (~5%) and therefore can be practically used for any tablet with an unknown concentration of DS. Comparison with ultraviolet/visible (UV/Vis) spectrophotometry as the reference method revealed no significant difference between the two methods. The proposed methodology exhibited satisfactory results in terms of both accuracy and precision while being rapid, simple and of low cost.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals FIWARE based low-cost wireless acoustic sensor network for monitoring and classification of urban soundscape

2021 ◽  
pp. 108199
Author(s):  
Pau Arce ◽  
David Salvo ◽  
Gema Piñero ◽  
Alberto Gonzalez
Keyword(s):  
Sensor Network ◽  
Low Cost ◽  
Acoustic Sensor

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Antibacterial Activity of Polymer Nanocomposites Incorporating Graphene and Its Derivatives: A State of Art

Polymers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 2105
Author(s):  
Ana M. Díez-Pascual ◽  
José A. Luceño-Sánchez
Keyword(s):  
Graphene Oxide ◽  
Polymeric Nanocomposites ◽  
Natural Polymers ◽  
Vast Number ◽  
Antimicrobial Materials ◽  
Reduced Graphene ◽  
Different Shapes ◽  
Different Characteristics ◽  
State Of Art

The incorporation of carbon-based nanostructures into polymer matrices is a relevant strategy for producing novel antimicrobial materials. By using nanofillers of different shapes and sizes, and polymers with different characteristics, novel antimicrobial nanocomposites with synergistic properties can be obtained. This article describes the state of art in the field of antimicrobial polymeric nanocomposites reinforced with graphene and its derivatives such as graphene oxide and reduced graphene oxide. Taking into account the vast number of articles published, only some representative examples are provided. A classification of the different nanocomposites is carried out, dividing them into acrylic and methacrylic matrices, biodegradable synthetic polymers and natural polymers. The mechanisms of antimicrobial activity of graphene and its derivatives are also reviewed. Finally, some applications of these antimicrobial nanocomposites are discussed. We aim to enhance understanding in the field and promote further work on the development of polymer-based antimicrobial nanocomposites incorporating graphene-based nanomaterials.

Deep Learning based Tomato’s Ripe and Unripe Classification System

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Ccd Camera ◽  
Agricultural Products ◽  
Training Data ◽  
Maturity Level ◽  
Agriculture Sector ◽  
The Past ◽  
State Of Art

Effective productivity estimates of fresh produced crops are very essential for efficient farming, commercial planning, and logistical support. In the past ten years, machine learning (ML) algorithms have been widely used for grading and classification of agricultural products in agriculture sector. However, the precise and accurate assessment of the maturity level of tomatoes using ML algorithms is still a quite challenging to achieve due to these algorithms being reliant on hand crafted features. Hence, in this paper we propose a deep learning based tomato maturity grading system that helps to increase the accuracy and adaptability of maturity grading tasks with less amount of training data. The performance of proposed system is assessed on the real tomato datasets collected from the open fields using Nikon D3500 CCD camera. The proposed approach achieved an average maturity classification accuracy of 99.8 % which seems to be quite promising in comparison to the other state of art methods.

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