Miracle-dz/UniFAR-FTUs huggingface dataset:
Aflatoxins in Poultry

Aflatoxins (AF) are mycotoxins that are produced by various Aspergillus species including A. flavus, A.parasiticus and A. nominus. As secondary metabolites of these fungi, AF may contaminate a variety of food and feedstuffs, especially corn, peanuts and cottonseed. Chemically, AF are difuranocoumarin compounds and include aflatoxin B1, B2, G1, G2, M1 and M2 depending on their structures (Figure 1). Aflatoxin M1 and M2, however, mainly occur in milk (small quantities of AFM1 have been reported in eggs) as metabolites of AFB1 and AFB2, respectively. Among the known AF, AFB1 is most commonly encountered and considered the most toxic (classified as a human carcinogen; Yunus et al, 2011). AF have been given considerable attention because of their demonstrated carcinogenic potential and hepatotoxic effects in both humans and animals. In animals, adverse effects of AF also include reduction in growth rate and feed efficiency, decreased egg production and hatchability, and increased susceptibility to disease. In addition, residues of AF from animals can appear in edible animal products for human consumption, which raises public health concerns. X. Chen, B. Grenier, and T.J. Applegate Purdue University Department of Animal Sciences
Evaluation of pleasant and unpleasant emotions evoked by visual stimuli using NIRS

A relation between a level of brain activity and pleasant and unpleasant emotions has been studied using Near-infrared Spectroscopy (NIRS). In this study, the relation between the brain activity and the pleasant and unpleasant emotions was evaluated using International Affective Picture System (IAPS) and NIRS. Based on the NIRS recording for 21 participants, the effect of pleasant and unpleasant emotion on the brain activity was measured. The detection of pleasant and unpleasant emotion from NIRS signal was conducted using Neural Network. It was shown that the pleasant and unpleasant emotion can be detected with the accuracy of 96% (the highest) and 75% (average).
A deep learning approach for automatically generating descriptions of images containing people

Generating image descriptions is a challenging Artificial Intelligence problem with many interesting applications such as robots’ communication or helping visually impaired people. However, it is a complex task for computers: it requires Computer Vision algorithms, to understand what the image depicts, and Natural Language Processing algorithms, to generate a well-formed sentence. Nowadays, deep neural networks are the state-of-the-art in these two Artificial Intelligence fields. Furthermore, we believe that images that contain people are described in a slightly different manner and that restricting an image description generator model to these images may produce better descriptions. Therefore, the main objective of this project is to develop a Deep Learning model that automatically produces descriptions of images containing people and to conclude if it is a good practice the restriction to this kind of images. For this purpose, we have reviewed and studied the literature in the field and we have built, trained and compared four different models using Deep Learning techniques and a GPU to speed-up the computation, as well as a big and complete dataset.
A Python framework for efficient use of pre-computed Green's functions in seismological and other physical forward and inverse source problems

Abstract. The finite physical source problem is usually studied with the concept of volume and time integrals over Green's functions (GFs), representing delta-impulse solutions to the governing partial differential field equations. In seismology, the use of realistic Earth models requires the calculation of numerical or synthetic GFs, as analytical solutions are rarely available. The computation of such synthetic GFs is computationally and operationally demanding. As a consequence, the on-the-fly recalculation of synthetic GFs in each iteration of an optimisation is time-consuming and impractical. Therefore, the pre-calculation and efficient storage of synthetic GFs on a dense grid of source to receiver combinations enables the efficient lookup and utilisation of GFs in time-critical scenarios. We present a Python-based framework and toolkit – Pyrocko-GF – that enables the pre-calculation of synthetic GF stores, which are independent of their numerical calculation method and GF transfer function. The framework aids in the creation of such GF stores by interfacing a suite of established numerical forward modelling codes in seismology (computational back ends). So far, interfaces to back ends for layered Earth model cases have been provided; however, the architecture of Pyrocko-GF is designed to cover back ends for other geometries (e.g. full 3-D heterogeneous media) and other physical quantities (e.g. gravity, pressure, tilt). Therefore, Pyrocko-GF defines an extensible GF storage format suitable for a wide range of GF types, especially handling elasticity and wave propagation problems. The framework assists with visualisations, quality control, and the exchange of GF stores, which is supported through an online platform that provides many pre-calculated GF stores for local, regional, and global studies. The Pyrocko-GF toolkit comes with a well-documented application programming interface (API) for the Python programming language to efficiently facilitate forward modelling of geophysical processes, e.g. synthetic waveforms or static displacements for a wide range of source models.
Characterisation of the expression of adenosine and estrogen receptors in myofibroblast differentiation in Peyronie's disease

Introduction: Peyronie’s disease (PD) is a fibroproliferative disease of the penis in which myofibroblast differentiation has a central role. Adenosine and estrogen signalling mediate their effect via interaction with their respective receptors which have been associated with the pathophysiology of a variety of fibroproliferative diseases, but this has yet to be studied in PD. Hypothesis: Adenosine and/or estrogen receptor expression is involved in myofibroblast differentiation in PD and may, therefore, be novel potential targets for anti-fibrotic therapies in this disease. Aim: The aim of this project was to characterise the myofibroblast differentiation process in human tunica albuginea-derived fibroblasts and to investigate the role of adenosine and estrogen receptors in this process. Methods: Tissue samples from non-PD TA tissue and PD plaque tissue were obtained and fibroblast cell lines were established from each sample. Cells were exposed to transforming growth factor (TGF)-β1. The mRNA levels and the protein levels of several targets of interest, alpha-smooth muscle actin (α-SMA), four adenosine receptors and two estrogen receptors (ERs), were evaluated using real-time RT-PCR (RT-qPCR), immunocytochemistry (ICC), immunohistochemistry (IHC), Western blot and In-Cell Western (ICW). The effect of compound modulators of two of the receptors on TGF-β1-induced myofibroblast transformation was assessed. Results: Both RT-qPCR and ICW methods were successfully developed and/or validated. The α-SMA mRNA and protein levels increased in cells isolated from non-PD TA tissue and PD plaque tissue when exposed to TGF-β1. The two cell groups expressed ADORA1 and ADORA2B and there was a differential effect to TGF-β1 in these two receptors. An ADORA2B agonist (BAY 60-6583) significantly inhibited TGF-β1-induced myofibroblast differentiation in the two cell types investigated. The two cell groups expressed ERβ but did not express ERα. Two selective estrogen receptor modulators (SERMs), tamoxifen and raloxifene, significantly inhibited TGF-β1-induced myofibroblast differentiation, suggesting that these SERMs interact with ERβ. Conclusions: Cells isolated from non-PD TA tissue and PD plaque tissue expressed ADORA1, ADORA2B and ERβ. TGF-β1 had differential effects on the receptors investigated depending on the cell type. An adenosine receptor agonist and two SERMs significantly inhibited TGF-β1-induced myofibroblast differentiation in a concentration-dependent manner suggesting that these receptors and consequently the adenosine and estrogen pathways may be involved in the differentiation of myofibroblasts and may be potential novel therapeutic targets in PD.
Neural Network Reconstruction via Graph Locality-Driven Machine Learning

A ubiquitous problem within the field of computational neuroscience is the determination of biological neural network structure and connectivity from imaging of stochastic, large-scale network activity. We propose an algorithm inspired by convolutional approaches to image processing, adapted to the graph structure of neural networks. To achieve this, we redefine locality in terms of graph adjacency, and create a scale-independent algorithm facilitated by modern machine learning techniques to incorporate this locality data into individual connection prediction.
The Newsletter of the Society for the Study of Artificial Intelligence and Simulation of Behaviour the Neural Engineering Framework

The Neural Engineering Framework (NEF) is a general methodology that allows the building of large-scale, biologically plausible, neural models of cognition [1]. The NEF acts as a neural compiler: once the properties of the neurons, the values to be represented, and the functions to be computed are specified, it solves for the connection weights between components that will perform the desired functions. Importantly , this works not only for feed-forward computations, but also for recurrent connections, allowing for complex dynamical systems including integrators, oscillators, Kalman filters, etc. [2]. The NEF also incorporates realistic local error-driven learning rules, allowing for the on-line adaptation and optimisation of responses [3]. The NEF has been used to model visual attention [4], inductive reasoning [5], reinforcement learning [6] and many other tasks. Recently, we used it to build Spaun, the world's largest functional brain model, using 2.5 million neu-rons to perform eight different cog-nitive tasks by interpreting visual input and producing handwritten output via a simulated 6-muscle arm [7,8]. Our open-source software Nengo was used for all of these, and is available at http://nengo.ca, along with tutorials, demos, and down-loadable models.
[The protective effect of bicyclol on ischemia-reperfusion induced kidney injury in rats].

AIM To investigate the protective effect of bicyclol on kidney injury induced by ischemia-reperfusion in rats. METHODS Bicyclol was orally administered to rats at doses of 50 and 200 mg.kg-1 before ischemia-reperfusion injury to evaluate the influence of bicyclol on the formation of MDA and BUN in serum, the content of GSH and the activity of GST in kidney, as well as kidney mitochondria membrane fluidity in ischaemia-reperfusion rats. RESULTS Bicyclol given orally at doses of 50 and 200 mg.kg-1 was shown to significantly decrease the increment of MDA and BUN in serum and protect the GSH depletion in kidney. Bicyclol was also shown to induce kidney GST and ameliorate the decrease of mitochondria membrane fluidity. The protective effects of bicyclol on kidney injury induced by ischemia-reperfusion are dose-dependent. CONCLUSION The protective action of bicyclol on kidney injury induced by ischemia-reperfusion may be attributed to its induction of kidney GSH and the GST, stabilization on mitochondria membrane and the inhibition on lipid peroxidation.
A Mapping Language for IoT Device Descriptions

Component models for IoT devices regain popularity. As more and more devices must be semantically connected within IoT platforms, digital abstractions for these devices are needed. For this purpose, textual device descriptions which encapsulate device-specific characteristics are a suitable candidate. Such component descriptions formally describe a device’s information model as well as the offered functionality in a standardized way. However, smart IoT platforms mainly solve user goals by composing various IoT devices in a suitable manner. Current IoT descriptions, such as Eclipse Vorto do not address this need at all. In this paper, we introduce a formal mapping language that allows to capture functional interaction semantics already during device integration time. Our evaluation shows that only few mapping elements are needed to define functional mappings between operations as well as to capture the underlying communication pattern.
Improving Minority Access to Genetic Counseling for Cancer Risk

The Second Annual Robert H. Lurie Comprehensive Cancer Center Health Policy Symposium, held last year in Chicago, was entitled “Cancer, Reaching Medically Underserved Populations: Low Literacy and Culturally Specific Barriers. ” This is the fourth in a series of reports on the conference presentations, prepared for ONI by researchers at Northwestern Medical School, that will put the discussions into a broader context. This month’s article reviews a presentation by Chanita Hughes, PhD, Georgetown University Medical Center, Lombardi Cancer Center, Washington, DC.
Structural analysis of offshore wind turbine blades using finite element method

Wind energy is one among the most promising renewable energy sources, and hence there is fast growth of wind energy farm implantation over the last decade, which is expected to be even faster in the coming years. Wind turbine blades are complex structures considering the different scientific fields involved in their study. Indeed, the study of blade performance involves fluid mechanics (aerodynamic study), solids mechanics (the nature of materials, the type of solicitations …), and the fluid coupling structure (IFS). The scope of the present work is to investigate the mechanical performances and structural integrity of a large offshore wind turbine blade under critical loads using blade element momentum. The resulting pressure was applied to the blade by the use of a user subroutine “DLOAD” implemented in ABAQUS finite element analysis software. The main objective is to identify and predict the zones which are sensitive to damage and failure as well as to evaluate the potential of composite materials (carbon fiber and glass fiber) and their effect on reduction of rotor’s weight, as well as the increase of resistance to wear, and stiffness.
Structural changes in white matter lesion patients and their correlation with cognitive impairment

Background White matter lesions (WMLs) play a role in cognitive decline and dementia. Little is known about gray matter (GM) changes in WMLs. This study aimed to investigate GM changes in WML patients. Materials and methods Correlations between altered structural volume and cognitive assessment scores were investigated. GM and white matter (WM) changes in 23 WML-vascular dementia (VaD) patients, 22 WML-non-dementia vascular cognitive impairment (VCIND) patients, and 23 healthy control (HC) subjects were examined. Gray matter density (GMD) was calculated by measuring local proportions of GM at thousands of homologous cortical locations. WM volume was obtained by fully automated software using voxel-based morphometry (VBM). Results Widespread GMD was significantly lower in WML patients compared to control subjects in cortical and subcortical regions (p<0.05). Greatest differences were found in the bilateral anterior cingulate cortex, inferior frontal gyrus, insula, angular gyrus, caudate, precentral gyrus, and right middle temporal gyrus, right thalamus. Secondary region of interest (ROI) analysis indicated significantly greater GMD in the bilateral caudate among WML-VCIND patients (n=22) compared to HCs (p<0.05). There was a significant difference in WM volume between WML patients and control subjects (p<0.05). Greatest differences were located in the genu/body/splenium of the corpus callosum and superior corona radiata L, and posterior corona radiata L. There was a significant association between structural changes and cognitive scores (Montreal Cognitive Assessment [MoCA] score) (p<0.05). There was no significant correlation between structural changes and Mini Mental State Examination (MMSE) scores (p>0.05). Conclusion GMD and WM volume were changed in WMLs, and the changes were detectable. Correlation between structural changes and cognitive function was promising in understanding the pathological and physiological mechanisms of WMLs.
Predicting Pavement Performance Utilizing Artificial Neural Network (ANN)

Pavement management systems (PMS) play a significant role in cost-effective management of highway networks to optimize pavement performance over predicted service life of the pavements. Although, the Iowa DOT manages three primary highway systems (i.e., Interstate, US, and Iowa highways) that represent 8% (approximately 9,000 miles) of the total roadway system in the state (114,000 miles), these systems carry around 62% of the total vehicle miles traveled (VMT) and 92% of the total large truck VMT. In this research, historical climate data was acquired from relevant agencies and integrated with pavement condition data to include all related variables in prediction modeling. An artificial neural network (ANN) model was used to predict the performance of ride, cracking, rutting, and faulting indices on different pavement types. The goodness of fit of the ANN prediction models was compared with the conventional multiple linear regression (MLR) models. The results show that ANN models are more accurate in predicting future conditions than MLR models. The contribution of input variables in prediction models were also estimated. The results indicated that climate factors directly influenced the pavement conditions, and ANN model results can be used by the decision makers to establish appropriate management actions to withstand harsh weather over the years. Keywords— Pavements, Pavement management system, Artificial Neural Networks, Asphalt concrete pavements, Pavement prediction modeling.
ON THE NATURE OF THE L-TYPE Ca2+ CURRENT INACTIVATION IN RAT VENTRICULAR CARDIOMYOCYTES

Facilitation of cardiac L-type Ca2+ current (ICaL) by high stimulation rates or by depolarizing prepulses is characterized by an increase in the fast inactivation time constant (τ f ast) of ICaL. To explain this phenomenon, mechanisms related to calcium-dependent inactivation (CDI) have been considered. Here we studied in more detail the mechanism of ICaL facilitation by depolarizing prepulses using isolated rat ventricular cardiomyocytes. Increases in τ f ast at a test pulse to 0 mV by low voltage (not activating ICaL) prepulses were associated with hyperpolarizing shifts in ICaL kinetics. Experimental conditions aimed to decrease CDI, by blocking the Ca2+ release from or its reload to the sarcoplasmic reticulum, did not abolish the increase in τ f ast by prepulses. Our results suggest that this kind of ICaL facilitation is also associated to a strong voltage-dependent mechanism. A model is proposed in which depolarizing prepulses allow the Ca2+ channel to dwell longer times in the open state. La facilitación de la corriente de Ca2+ tipo L cardı́aca (ICaL) por alta frecuencia de estimulación o prepulsos despolarizantes (PP), consiste en un aumento en la constante de tiempo de inactivación rápida (τ f ast) de ICaL. Para explicar este fenómeno se han considerado mecanismos relacionados a la inactivación dependiente de calcio (IDC). Nosotros estudiamos más detalladamente la facilitación de ICaL por PP utilizando cardiomiocitos ventriculares de rata. El incremento en τ f ast en un pulso test a 0 mV por PP que no activan ICaL se asoció a un desplazamiento hiperpolarizante de las cinéticas de ICaL. Diferentes condiciones experimentales en las que la IDC estaba deprimida, por bloqueo de la liberación de Ca2+ desde o su recaptación hacia el retı́culo sarcoplasmático, no previnieron el incremento en τ f ast por PP. Nuestros resultados sugieren que este tipo de facilitación de ICaL está también asociado a un mecanismo dependiente de voltaje. Se propone un modelo en el que los PP permiten al canal de Ca2+ estar más tiempo en el estado abierto.
Exploring the value proposition of digital technologies in smart farming: an approach co-designed by agricultural advisors.

: The promise of technology development in agriculture is well publicised with some claiming that ‘digital disruption’ will transform the way farming and food production is done (Australian Farm Institute, 2017; Hall, 2007). However despite the recognised potential and proliferation of digital technologies for agriculture, there has been an unexpectedly slow uptake of some tools and services by farmers and agricultural advisors worldwide. In Australia, a pluralistic Agricultural Innovation System (AIS) is characterised by a changing mix of private and public interests with government investment in extension being gradually withdrawn over the past two decades. In this context, private agricultural advisors increasingly work with farmers and others to support farm management using digital technologies. To better understand the role of these advisors in supporting the capacity of producers to gain benefits from digital technologies in smart farming contexts, we undertook collaborative research to investigate the opportunities and constraints for advisors to engage with smart farming, as well as the kinds of interventions and practices that might enable or strengthen this engagement. In this paper we report on a particular research-practice intervention co-designed with agricultural advisors from the Australian sugar, cotton and horticultural industries. Through a facilitated, action research process inspired by participatory technology assessment approaches, the advisors in this intervention co-created a Digital Value Assessment Tool (DVA Tool) for assessing the costs and benefits of a smart farming tool or service. Findings from this research demonstrate that the DVA Tool is strategic decision support tool (DST) for agricultural advisors and their clients to reduce risk and enhance benefits related to engaging with digital agricultural tools and services. This decision support tool is enacted through self-directed inquiry by professional advisors and has the potential to be integrated into their routine business practices to maximise the opportunities from engaging with digital technologies. There is a role for agricultural industry bodies to support advisors with such decision support by hosting and promoting the DVA Tool (and other DST like it) and continually engaging with their constitutents, including advisors, on issues of how to harnass and mobilise diverse skills and knowledge for agricultural innovation in a digitally disrupted world.