Effect of neck cut position on time to collapse in halal slaughtered cattle without stunning

This study examined the effect of neck cut position on the time to physical collapse in upright restrained halal slaughtered cattle (n = 644). Time to collapse was used as an indirect indicator of the early stages of onset of unconsciousness. Cattle were slaughtered with either a conventional low (LNC) (n = 561) or a high neck cut (HNC) (n = 83). Mean time to final collapse was higher in the LNC compared to HNC group (18.9 ± 1.1 s and 13.5 ± 1.3 s respectively (P 20 s to final collapse had larger false aneurysms. In summary, the HNC reduced the mean time to final collapse and the frequency of animals that took longer than 20 s to collapse

Developing a Functional Food Systems Literacy for Interdisciplinary Dynamic Learning Networks

The impact of human activity on the planet cannot be understated. Food systems are at the centre of a tangled web of interactions affecting all life. They are a complex nexus that directly and indirectly affects, and is affected by, a diverse set of social, environmental and technological phenomena. The complexity and often intractability of these interactions have created a variety of food-related problems that people seek to address in a collaborative and interdisciplinary manner through the adoption of a holistic food systems perspective. However, operationalising a systemic approach to address food system challenges is not a guarantee of success or positive outcomes. This is largely due to the partiality inherent in taking a systems perspective, and the difficulty in communicating these different perspectives among stakeholders. A functional food systems literacy is therefore required to aid people in communicating and collaborating on food system problems within dynamic learning networks. The Interdisciplinary Food Systems Teaching and Learning (IFSTAL) programme has been operating since 2015 as a social learning system to develop a food systems pedagogy with a range of multi-sectoral partners. The findings in this paper arise out of iterative reflexive practice into our teaching approach and delivery methods by former and current staff. In order to foster integrative engagement on food system challenges, we propose and define a functional food systems literacy—a theoretical minimum that can aid diverse stakeholders to explore and intervene in food systems through more effective communication and collaboration. Derived from a reflective analysis of instruments and methods in delivering the IFSTAL programme, we provide a framework that disaggregates functional food systems literacy according to four knowledge types, and includes examples of skills and activities utilised in the IFSTAL programme to support learning in these different domains. We argue that claims to comprehensive food systems knowledge are unrealistic and therefore propose that a functional food systems literacy should focus on providing a means of navigating partial claims to knowledge and uncertainty as well as fostering effective collaboration. We believe that this will enhance the capabilities of stakeholders to work effectively within dynamic learning networks

Computer vision-based breast self-examination palpation pressure level classification using artificial neural networks and wavelet transforms

Breast cancer is the leading cause of cancer mortality among women and early diagnosis with proper treatment is the key to survival. Women who practice regular breast self-examination are the ones most likely to detect early abnormalities in their breast. However, studies have shown that most women performing BSE do not carry out the procedure efficiently. This paper presents a method for BSE procedure guidance through the classification of palpation pressure levels, i.e. superficial, medium, and deep, based on computer vision. In particular, we utilize an artificial neural network (ANN) to classify the pressure levels of the image frames extracted from an actual BSE video yielding an accuracy of 91 % respectively. © 2012 IEEE

How to help Bartonella quintana grow more and better... and what for

Resumen del trabajo presentado a la VII Biennial Congress of Sociedad Española de Biología Evolutiva (SEBE), celebrada en Sevilla (España) del 5 al 7 de febrero de 2020.Obligatory endosymbiotic organisms, whether parasitic or mutualistic, tend to have reduced genomes compared to their free-living relatives, as a result of the evolutionary process called ‘genomic reduction syndrome’. Although many genes get lost, these streamlined genomes maintain those genes involved in essential functions, getting close to the definition of a minimal genome. Therefore, their characterization, as well as the possibility of optimizing them, by eliminating superfluous genes or by adding genes to complete impaired metabolic pathways, is highly relevant in synthetic biology. However, most endosymbionts cannot be cultured in the laboratory, making it difficult to manipulate them. Bartonella quintana is a parasitic endosymbiont of humans that can grow in vitro, but it has a very slow growth rate due to its complex nutritional requirements. In our research group, we have generated a metabolic model of B. quintana from genomic data and, through flux balance analysis (FBA), we have determined which compounds are limiting factors for its growth and are deficient in commercial media. We have established a protocol for culturing this bacterium using media supplemented with these compounds in different concentrations, to define the ideal medium composition that improves its growth efficiency. This has also an impact on the ease of performing genomic manipulation experiments for a better characterization of the model prior of its use as an endosymbiont chassis.Peer reviewe

Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms

This paper focuses on breast self-examination (BSE) stroke position and palpation level classification for the development of a computer vision-based BSE training and guidance system. In this study, image frames are extracted from a BSE video and processed considering the color information, shape, and texture by wavelet transform and first order color moment. The new approach using artificial neural network and wavelet transform can identify BSE stroke positions and palpation levels, i.e. light, medium, and deep, at 97.8 % and 87.5 % accuracy respectively. © 2012 IEEE

Detecting and tracking female breasts using neural network in real-time

The general aim of this research is helping women to perform breast self-examination (BSE) for finding out any abnormality, change, or lump in the breasts. BSE involves checking the breasts for finding abnormalities, lumps, or changes. This paper reports about our initial efforts to detect and track the left and right breasts in real-time imaging. Image frames were processed considering the color information, and integral image processing to segment regions of interest (ROI) according to common colors of breast features. After getting the preliminary candidate regions, the vector of features were used as the inputs of neural network. The algorithm applies each ROI into the artificial neural network (ANN) for detection of the right and left breasts. Results of the study show that the proposed ANN successfully identifies the position and location of the breasts. © 2013 IEEE

Implementation of GA-KSOM and ANFIS in the classification of colonic histopathological images

The WHO reports that colon cancer is one of the leading causes of cancer mortality in the world with the majority of people with this type of cancer belonging to those who are 60 years or older. Similar to other types of cancer, early detection is very important for a successful treatment. This paper reports on the implementation of Kohonen Self-Organizing Map (KSOM) with genetic algorithms (GA), and neuro-fuzzy classifier to classify colonic histopathological images into normal, adenomatous polyp, and cancerous. KSOM with GA, or GA-KSOM for short, was used in the feature selection stage while a neuro-fuzzy algorithm was used in the classification stage. ANFIS or Adaptive Neuro-Fuzzy Inference System was chosen as the structure/architecture of the neuro-fuzzy algorithm. The classification accuracies obtained were very promising with 86.7% and 87.8% for the training and testing sets, respectively. © 2012 IEEE