Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating the intake and nitrogen excretion from cows grazing forages fertilised with increasing rates of nitrogen

The dry matter intake (DMI), production, and urinary nitrogen (N) excretion of dairy cows grazing forage grown under increasing N fertiliser rates were explored using a mechanistic model of a grazing ruminant. The MINDY cow model was initialised to represent a multiparous Holstein-Friesian dairy cow in late lactation (age 3.5 years, 212 days in milk). Simulated cows were offered a common allowance (30 kg DM/cow/d) of chicory, plantain, lucerne, or perennial ryegrass, fertilised at rates of 0, 100, 200, 350 and 500 kg N/ha/y (0N, 100N etc.) for 20 days in March. Forage physical and chemical composition data were obtained from a Waikato experimental field site. When N rate increased from 0–500N, predicted DMI of cows grazing plantain and ryegrass increased by 5.2 and 2.7 kg DM/cow/day (43 and 20% increase), respectively, facilitated by increased sward height and mass, and reduced herbage strength. Predicted DMI of cows grazing chicory peaked at 200N and, for cows grazing lucerne, declined with N fertilisation. Intake of highly fertilised (>200N) chicory and lucerne diets were potentially affected by high herbage soluble N content resulting in high predicted rumen ammonia concentrations (>18 mmol/L), reducing incentive for grazing. While predicted milk production from cows grazing chicory, plantain and ryegrass increased, the efficiency of dietary N used for milk production declined, with increasing N applied. Increasing N rate also increased predicted urine N concentration, and urinary N excretion from simulated cows fed chicory, plantain, and ryegrass. This study suggests that N fertiliser rates of 200N for chicory, plantain and ryegrass provides a balance between cow production and N excretion.
Filtering Reordering Table Using a Novel Recursive Autoencoder Model for Statistical Machine Translation

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (
Title PET-MR and SPECT-MR multimodality probes : development and challenges

Positron emission tomography (PET)-magnetic resonance (MR) or single photon emission computed tomography (SPECT)-MR hybrid imaging is being used in daily clinical practice. Due to its advantages over stand-alone PET, SPECT or MR imaging, in many areas such as oncology, the demand for hybrid imaging techniques is increasing dramatically. The use of multimodal imaging probes or biomarkers in a single molecule or particle to characterize the imaging subjects such as disease tissues certainly provides us with more accurate diagnosis and promotes therapeutic accuracy. A limited number of multimodal imaging probes are being used in preclinical and potential clinical investigations. The further development of multimodal PET-MR and SPECT-MR imaging probes includes several key elements: novel synthetic strategies, high sensitivity for accurate quantification and high anatomic resolution, favourable pharmacokinetic profile and target-specific binding of a new probe. This review thoroughly summarizes all recently available and noteworthy PET-MR and SPECT-MR multimodal imaging probes including small molecule bimodal probes, nano-sized bimodal probes, small molecular trimodal probes and nano-sized trimodal probes. To the best of our knowledge, this is the first comprehensive overview of all PET-MR and SPECT-MR multimodal probes. Since the development of multimodal PET-MR and SPECT-MR imaging probes is an emerging research field, a selection of 139 papers were recognized following the literature review. The challenges for designing multimodal probes have also been addressed in order to offer some future research directions for this novel interdisciplinary research field.
1-21-2019 CCNE 1 amplification is associated with poor prognosis in patients with triple negative breast cancer

Background: Triple negative breast cancer (TNBC) is aggressive with limited treatment options upon recurrence. Molecular discordance between primary and metastatic TNBC has been observed, but the degree of biological heterogeneity has not been fully explored. Furthermore, genomic evolution through treatment is poorly understood. In this study, we aim to characterize the genomic changes between paired primary and metastatic TNBCs through transcriptomic and genomic profiling, and to identify genomic alterations which may contribute to chemotherapy resistance. Methods: Genomic alterations and mRNA expression of 10 paired primary and metastatic TNBCs were determined through targeted sequencing, microarray analysis, and RNA sequencing. Commonly mutated genes, as well as differentially expressed and co-expressed genes were identified. We further explored the clinical relevance of differentially expressed genes between primary and metastatic tumors to patient survival using large public datasets. Results: Through gene expression profiling, we observed a shift in TNBC subtype classifications between primary and metastatic TNBCs. A panel of eight cancer driver genes (CCNE1, TPX2, ELF3, FANCL, JAK2, GSK3B, CEP76, and SYK) were differentially expressed in recurrent TNBCs, and were also overexpressed in TCGA and METABRIC. CCNE1 and TPX2 were co-overexpressed in TNBCs. DNA mutation profiling showed that multiple mutations occurred in genes comprising a number of potentially targetable pathways including PI3K/AKT/mTOR, RAS/MAPK, cell cycle, and growth factor receptor signaling, reaffirming the wide heterogeneity of mechanisms driving TNBC. CCNE1 amplification was associated with poor overall survival in patients with metastatic TNBC. Conclusions: CCNE1 amplification may confer resistance to chemotherapy and is associated with poor overall survival in TNBC.
Trigraph latency as a method to infer fatigue during typing

TRIGRAPH LATENCY AS A METHOD TO INFER FATIGUE DURING TYPING by Bhagirathi Nagarajir University of New Hampshire, September, 2006 Due to the use of computers for long durations and prolonged typing on keyboards many computer users are suffering from Repetitive Strain Injuries. In this thesis keystroke dynamics, specifically the changes in the trigraph latencies of some words are analyzed to infer fatigue during typing. These dynamics are collected using a software application and are represented as bar-graphs for data analysis. The technology developed is completely seamless and does not require any hardware modifications. The investigation here is one aspect of the integrated effort to infer fatigue from typing dynamics. The thesis is divided into three phases. In the first phase, a software application is developed to collect the keystroke dynamics under consideration. In the second phase the collected data is displayed as bar-charts for analysis purposes. In the final phase to validate the software, data from three volunteers, who typed on the keyboard for four hours is used to predict their onset fatigue.
Disease Prediction Using Decision Tree Based Weighed Voting System in Data Mining

The accessibility of the enormous size of medical dataset hints towards the requirement of a tool which analyses data to extract valuable information. Data scientists have attempted several methods in order to improvise the examination of large data sets. Previously, various data mining techniques have been implemented in the healthcare systems. The proposed approach can adjust each classifier’s weight based on their ability and history of making correct predictions. A rule that mixes majority voting and weights of classifiers was proposed and applied for the final diagnosis decision.
Numerical Simulation of Fluid Flow and Lubricant Powder Behavior in Steel Continuous Casting Mold for Improving of Produced Ingot Properties

Many studies have been performed to improve the quality and cleanliness of steel ingots produced by continuous casting process. One of the important defects is entrapping of mold lubricant powder in the ingot. In the paper, the mathematical modeling methods and computational fluid dynamic (CFD) were used to investigate the powder fluid behavior during continuous casting of steel. For this purpose, the factors of powder entrapping and the behavior of the powder in the melt were investigated and some solutions were proposed to prevent these defects. Therefore, the boundary and initial conditions were studied in developed model and the effect of each one were investigated. The results showed that it can be possible to prevent the powder entrapping in the steel ingot by decreasing the casting speed from 0.9 to 0.76 m/min, increasing the nozzle dip depth from 130 to 140 mm, decreasing the nuzzle diameter from 42 to 36mm, decreasing the powder particles size from 500 to 63 micrometers and by pouring of clean steel melt without the large inclusions.
A LASER PHOTO ACOUSTIC IMAGING TECHNIQUE FOR THE DETECTION OF SMALL OBJECTS EMBEDDED IN TISSUE 1

We have applied a non-invasive photoacoustic technique to produce two-dimensional images of objects visually hidden within biological tissue. The technique has a foreseeable application for diagnosis of skin and subcutaneous diseases. We used a forward looking photoacoustic probe that integrated an optical-fibre laser pulse delivery system with a polymer transducer (PVDF) as a sensing element. The system’s frequency response was in the few MHz regions. The probe was designed to transmit laser pulses and detect returning ultrasonic waves from features within tissue. Tissue sample were prepared using fresh chicken breast. They contained a strand of human hair or hairs threaded into the chicken breast near the surface, serving as a foreign body. B-scan studies under normal saline solution were performed by scanning the PVDF probe across the chicken breast. Results revealed an image of the hair or hairs with a lateral resolution down to ~ 500(im and depth resolution of about ~80|im. Some images not only showed the presence of a foreign body within the tissue, but they also contained photoacoustic features that may indicate the cross-sectional dimension of a human hair.
stimation of force of infection based on different epidemiological roxies : 2009 / 2010 Influenza epidemic in Malta

Information about infectious disease outbreaks is often gathered indirectly, from doctor’s reports and health board records. It also typically underestimates the actual number of cases, but the relationship between the observed proxies and the numbers that drive the diseases is complicated, nonlinear and potentially timeand state-dependent. We use a combination of data collection from the 2009–2010 H1N1 outbreak in Malta, compartmental modelling and Bayesian inference to explore the effect of using various sources of information (consultations, doctor’s diagnose, swabbing and molecular testing) on estimation of the effective basic reproduction ratio, Rt. Different proxies and different sampling rates ompartmental modelling ayesian inference arkov chain methods eproduction ratio (daily and weekly) lead to similar behaviour of Rt as the epidemic unfolds, although individual parameters (force of infection, length of latent and infectious period) vary. We also demonstrate that the relationship between different proxies varies as epidemic progresses, with the first period characterised by high ratio of consultations and influenza diagnoses to actual confirmed cases of H1N1. This has important consequences for modelling that is based on reconstructing influenza cases from doctor’s reports. © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND
NUTRITIVE BUSINESS MODELS OF CONSUMER BEHAVIOR WHEN PURCHASING FOOD STUFFS ( Preliminary communication )

The aim is to develop a nutrition based business model to obtain information on the extent of the impact of nutritional properties when buying food products and information related to significant new elements of the nutrition determinant, which ought to be included in food products. The paper discusses the application of an original concept of modeling for building a model of consumer behavior, when purchasing food products. The model is built on the basis of several principles by using modern technologies such as Geographic Information System (GIS) and data mining, and one of the most important principles is to provide greater clarity and full understanding of the process implemented in the model, and its integration with the feature of self-explanation. To determine the level of the impact of the nutrition determinant on the behavior in a separate group of consumers of food products a total of 350 students from Faculty of Technological and Technical Sciences in Veles in all four years of the study program “Nutrition and food technology and biotechnology” were surveyed. The survey consists 12 questions. The output of the model is comprised from survey responses from consumers. Building a good nutrition model of consumer behavior when purchasing food will allow obtaining information on the impact and role of the nutrition determinant on consumer behavior and the need for improvement and production of healthy food products. Certaincly, this kind of production is of a particular importance in satisfying personal needs of consumers in all social spheres, and accordingly, for improving kinesiologic, that is physical, functional and health capcities of athletes, children and students treated in the physical education processes.
Innovative approaches in nursing education

In the entire world, higher education programs are trying to broaden their educational capacity by applying innovative strategies to meet their future needs of labor. Educators in the field of nursing also state that there is a need for deep improvements in the current curriculum for innovation. The use of innovative practices in education allows educators to prepare nursing students for current clinical settings. The use of simulations in nursing training has become increasingly prevalent and important in order to achieve adaptation to clinical practices and overcome difficulties that may be encountered in clinics. With increased educational technologies, in order to meet the changing expectations of the generation x that has grown up in a digital era, “simulations that do not contain high technology” are increasingly being replaced by “simulations that contain advanced technology”. In this context, augmented reality, virtual reality, games and haptic systems are becoming prevalent in training nurses. While the effectiveness of technological education practices has been determined by some studies, in this study, it was planned to explain current innovative examples of this in line with types of simulations.
Leaf gas exchange and fluorescence of two teosinte species : Zea mays ssp

This study compared the leaf blade characteristics, pigment composition, photosystem II (PSII) photochemistry, and photosynthetic rates (P) of a multi-stemmed annual (Zea mays ssp. parviglumis Iltis & Doebley) and a perennial (Z. diploperennis Iltis, Doebley & Guzman) teosinte species growing in a glasshouse under analogous nutritional and environmental conditions. Similar chlorophyll contents and ratios implied comparable efficiencies of photon energy capture and electron flow through PSII in both species. Maximum photochemical efficiency (Fv/Fm) was about 0.75 at predawn and declined linearly to a minimum at midday of about 0.65, implying down-regulation of photosynthesis. By contrast, maximum P (Pmax) was 11.5% higher in the annual compared to the perennial teosinte, signifying that wild maize germplasm could potentially be used for improving the photosynthetic efficiency of cultivated maize. Future studies, however, should assess a larger number of teosinte species for their differences in leaf area per plant, growth habit, grain yield, and leaf photosynthesis.
UvA-DARE ( Digital Academic Repository ) Lipidomic Response to Coffee Consumption

Coffee is widely consumed and contains many bioactive compounds, any of which may impact pathways related to disease development. Our objective was to identify individual lipid changes in response to coffee drinking. We profiled the lipidome of fasting serum samples collected from a previously reported single blinded, three-stage clinical trial. Forty-seven habitual coffee consumers refrained from drinking coffee for 1 month, consumed 4 cups of coffee/day in the second month and 8 cups/day in the third month. Samples collected after each coffee stage were subject to quantitative lipidomic profiling using ion-mobility spectrometry–mass spectrometry. A total of 853 lipid species mapping to 14 lipid classes were included for univariate analysis. Three lysophosphatidylcholine (LPC) species including LPC (20:4), LPC (22:1) and LPC (22:2), significantly decreased after coffee intake (p < 0.05 and q < 0.05). An additional 72 species mapping to the LPC, free fatty acid, phosphatidylcholine, cholesteryl ester and triacylglycerol classes of lipids were nominally associated with coffee intake (p < 0.05 and q > 0.05); 58 of these decreased after coffee intake. In conclusion, coffee intake leads to lower levels of specific LPC species with potential impacts on glycerophospholipid metabolism more generally.
Fifty Years of Candidate Pulsar Selection-What next ?

For fifty years astronomers have been searching for pulsar signals in observational data. Throughout this time the process of choosing detections worthy of investigation, so called candidate selection, has been effective, yielding thousands of pulsar discoveries. Yet in recent years technological advances have permitted the proliferation of pulsar-like candidates, straining our candidate selection capabilities, and ultimately reducing selection accuracy. To overcome such problems, we now apply intelligent machine learning tools. Whilst these have achieved success, candidate volumes continue to increase, and our methods have to evolve to keep pace with the change. This talk considers how to meet this challenge as a community.
Educational intervention on preterm infants’ behaviours for the promotion of parental confidence

Due to their immaturity, preterm infants communicate differently from term infants, with distinctive signs and a unique ‘language’. Their stress responses can be interpreted by parents as negative replies to their attempts to interact with them and even be perceived as a rejection. As preterm infants’ behaviours are less comprehensible for parents, it may complicate the establishment of the parent-infant relationship and thus undermine their parental confidence. Inspired by Zahr, Raines and Brustad define parental confidence as: “The perception of capability in caring for and understanding the infant and the infant’s needs”. The level of parental confidence in parents of preterm infants is lower than those of term infants. Parents report a lack of knowledge and skills about the observation and interpretation of their preterm infant’s specific behaviours, which could affect their parental confidence. Moreover, a lack of knowledge on how to interact with their preterm infant, coupled with an inability to fully exploit their parental role, can cause high stress in such parents. This high level of stress would also have a negative impact on parental confidence. Therefore, the promotion of parental confidence could improve the interactions between parents and their preterm infant, reduce the level of parental stress and facilitate the establishment of the parent-infant relationship. Educational intervention on preterm infants’ behaviours for the promotion of parental confidence
Low Resolution Brain Source Localization Using EEG Signals

Each mental or physical task gives rise to generate electromagnetic activity in the brain. These electrical signals are analyzed by using various neuroimaging techniques which include electroencephalography (EEG), magnetoencephalogy (MEG), positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). However, when the brain sources which are responsible for such electrical activity are localized, then it’s called brain source localization or source estimation. This information is utilized to comprehend brain’s physiological, pathological, mental, functional abnormalities. Also, the information is used to diagnose cognitive behaviour of the brain. Various methodologies based upon EEG signals are adopted to localize the active sources such as minimum norm estimation (MNE), low resolution brain electromagnetic tomography (LORETA), standardized LORETA, exact LORETA, multiple signal classification (MUSIC), focal underdetermined system solution (FOCUSS) etc. This research discusses localizing ability of low resolution techniques (LORETA and sLORETA) for various head models (finite difference model and concentric model). The simulations are carried out by using NETSTATION software. The results are compared in terms of activations for same EEG data with the same stimulus provided to subjects. However, it is observed that the combination of finite difference method (FDM) with sLORETA produced best results in terms of source intensity level (nA). Hence, the combination of inverse method sLORETA with FDM produces better source localization.
Quantitative assessment of speci fi c defects in roasted ground co ff ee via infrared-photoacoustic spectroscopy

Chemical analyses and sensory evaluation are the most applied methods for quality control of roasted and ground coffee (RG). However, faster alternatives would be highly valuable. Here, we applied infrared-photoacoustic spectroscopy (FTIR-PAS) on RG powder. Mixtures of specific defective beans were blended with healthy (defect-free) Coffea arabica and Coffea canephora bases in specific ratios, forming different classes of blends. Principal Component Analysis allowed predicting the amount/fraction and nature of the defects in blends while partial Least Squares Discriminant Analysis revealed similarities between blends (=samples). A successful predictive model was obtained using six classes of blends. The model could classify 100% of the samples into four classes. The specificities were higher than 0.9. Application of FTIR-PAS on RG coffee to characterize and classify blends has shown to be an accurate, easy, quick and “green” alternative to current methods.