Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting the Infant Gut Microbiota Through a Perinatal Educational Dietary Intervention: Protocol for a Randomized Controlled Trial

Background The early life gut microbiota are an important regulator of the biological pathways contributing toward the pathogenesis of noncommunicable disease. It is unclear whether improvements to perinatal diet quality could alter the infant gut microbiota. Objective The aim of this study is to assess the efficacy of a perinatal educational dietary intervention in influencing gut microbiota in mothers and infants 4 weeks after birth. Methods The Healthy Parents, Healthy Kids randomized controlled trial aimed to recruit 90 pregnant women from Melbourne, Victoria, Australia. At week 26 of gestation, women were randomized to receive dietary advice from their doctor (n=45), or additionally receive a dietary intervention (n=45). The intervention included an educational workshop and 2 support calls aiming to align participants’ diets with the Australian Dietary Guidelines and increase intakes of prebiotic and probiotic foods. The educational design focused on active learning and self-assessment. Behavior change techniques were used to support dietary adherence, and the target behavior was eating for the gut microbiota. Exclusion criteria were age under 18 years, diagnosed mental illnesses, obesity, diabetes mellitus, diagnosed bowel conditions, exclusion diets, illicit drug use, antibiotic use, prebiotic or probiotic supplementation, and those lacking dietary autonomy. The primary outcome measure is a between-group difference in alpha diversity in infant stool collected 4 weeks after birth. Secondary outcomes include evaluating the efficacy of the intervention in influencing infant and maternal stool microbial composition and short chain fatty acid concentrations, epigenetic profile, and markers of inflammation and stress, as well as changes in maternal dietary intake and well-being. The study and intervention feasibility and acceptance will also be evaluated as secondary outcomes. Results The study results are yet to be written. The first participant was enrolled on July 28, 2016, and the final follow-up assessment was completed on October 11, 2017. Conclusions Data from this study will provide new insights regarding the ability of interventions targeting the perinatal diet to alter the maternal and infant gut microbiota. If this intervention is proven, our findings will support larger studies aiming to guide the assembly of gut microbiota in early life. Trial Registration Australian Clinical Trials Registration Number ACTRN12616000936426; https://www.anzctr. org.au/Trial/Registration/TrialReview.aspx? id=370939 International Registered Report Identifier (IRRID) DERR1-10.2196/14771
Effect of aqueous leaf extracts of Leptadenia hastata Pers (Decne) on spermiogram and testicular histology in albino rats (Wistar strain)

This study was designed to determine the effects of prolonged oral administration of aqueous leaf extract of Leptadenia hastata Pers (Decne) on sperm concentration, sperm motility and testicular histology in Wistar rats. Rats were given graded doses (200, 400, 600 and 800 mg kg-1) of aqueous leaf extract of L. hastata orally for 28 days. Weekly changes in sperm concentration and motility as well as testicular weight and histology were monitored. Sperm concentration, motility and testicular weights did not show significant change (p>0.05) among the groups, but a significant difference (p<0.05) exist between control group and the group that had the highest dose (800 mg/kg) of L. hastata. Histopathological studies revealed marked depletion of spermatozoa in the testes and epididymis of rats that received 800 mg/kg of L. hastata. Prolonged administration of aqueous leaf extract of L. hastata at 800 mg/kg caused a reduction in total sperm concentration, sperm motility and a reduction in testicular weight in rats. Key words: Fertility, Leptadenia hastata, spermiogram, rats, testis.
Bayesian Update For Descriptive Statistics In Fisheries Science

Abstract In the present paper we have examined Bayesian update for descriptive statistics for a sample of 730 Por’s Goatfish (Upeneus pori) (Ben-Tuvia and Golani, 1989), collected from Iskenderun Bay, in the northeast Mediterranean Sea. The computational approach uses the Markov Chain Monte Carlo simulation to draw samples from the posterior distributions of model parameters implementing the simulation in OpenBUGS software. We assigned the results of previous studies as a prior distribution. The posterior distribution for mean length and variance were found to be 11.1 cm and 0.003, while for weight, they were 15.7 g and 0.026. The 95% confidence limits of length and weight were 10.99-11.21 and 15.42-16.05 respectively. The key aspect of this research is that when previous studies are included in the estimation, this significantly reduces the variance and uncertainty, leading to a more sufficient and reliable estimation.
Extensions of k-Nearest Neighbor Algorithm

The aim of this study is to review various extensions of the nearest neighbor algorithm and discuss their approach along with limitations of the method. In nonparametric classification, no prior information is required for predicting the class label. k-Nearest Neighbor is the simplest and well known algorithm used in data mining The various extensions of k-nearest neighbor algorithm which have been studied are weighted nearest neighbor, feature selection methods, fuzzy nearest neighbor, genetic algorithm based classifiers and nearest neighbor algorithm using ensembling techniques.
Comparison of Piezoelectric and Optical Projection Imaging for Three-Dimensional In Vivo Photoacoustic Tomography

Ultrasound sensor arrays for photoacoustic tomography (PAT) are investigated that create line projections of the pressure generated in an object by pulsed light illumination. Projections over a range of viewing angles enable the reconstruction of a three-dimensional image. Two line-integrating arrays are compared in this study for the in vivo imaging of vasculature, a piezoelectric array, and a camera-based setup that captures snapshots of the acoustic field emanating from the sample. An array consisting of 64 line-shaped sensors made of piezoelectric polymer film, which was arranged on a half-cylindrical area, was used to acquire spatiotemporal data from a human finger. The optical setup used phase contrast to visualize the acoustic field generated in the leg of a mouse after a selected delay time. Time-domain back projection and frequency-domain back propagation were used for image reconstruction from the piezoelectric and optical data, respectively. The comparison yielded an about threefold higher resolution for the optical setup and an about 13-fold higher sensitivity of the piezoelectric array. Due to the high density of data in the camera images, the optical technique gave images without streak artifacts, which were visible in the piezo array images due to the discrete detector positions. Overall, both detection concepts are suited for almost real-time projection imaging and three-dimensional imaging with a data acquisition time of less than a minute without averaging, which was limited by the repetition rate of the laser.
Pain in Neonates: A Concept Analysis

Context The term pain in neonates is emerged in recent decades. However, studies on pain in neonates are still limited and have various indicators. In addition, the term pain still often overlaps with stress in various studies and clinical circumstances. The concepts of pain and stress in the neonates need to be clarified to be better understood and then applied to improve neonates’ quality of life. Therefore, the current study aimed at clarifying the concept of pain in neonates. Evidence Acquisition The current study employed the concept analysis approach developed by Walker and Avant. The authors reviewed articles from JSTOR, ScienceDirect, Proquest, Sage, Cochrane, and Springer databases from 1980 to 2016 using keywords pain, stress, neonatal, neonates, and quality of life. Results Pain had attributes such as tissue damage, physiological changes, metabolic changes, and behavioral changes. Stress had attributes such as physiological changes, metabolic changes, and behavioral changes. Conclusions Attributes of pain and stress have similarities and differences. The main difference is the stimulus that induces a response. Pain has an attribute of tissue damage, whereas stress is not always due to tissue damage. The attributes of physical, metabolic, and behavioral changes between pain and stress are similar.
Acute but Not Permanent Effects of Propranolol on Fear Memory Expression in Humans

Experimental evidence in humans and non-human animals suggests that the administration of propranolol shortly after the retrieval of an emotional memory can lead to an attenuation of its later expression, a phenomenon known as post-reactivation amnesia. Using more potent amnestic drugs, post-reactivation amnesia has been shown in animals to be reversible by re-administration of the drug prior to memory retention testing. The latter finding suggests that, at least under some circumstances, post-reactivation amnesia may not reflect a disruption of reconsolidation (i.e., a memory storage deficit) but an acquired state-dependency of memory expression (i.e., a memory retrieval deficit that is relieved when the drug state is recreated during testing). We conducted a double-blind, placebo-controlled study to investigate whether the previously established amnestic effects of post-reactivation propranolol administration on memory retention in humans may similarly reflect a retrieval deficit. In four groups of participants, fear memories were first established through differential fear conditioning. One day later, a single presentation of the CS+ without shock was used to reactivate the memory in three of the four groups, followed by the administration of 40 mg Propranolol HCl (Groups PrPl and PrPr) or placebo (Group PlPl). Memory was not reactivated in the fourth group (Group NR). Another 24 h later, Propranolol HCl (Group PrPr) or placebo (Groups PrPl, PlPl, and NR) was again administered, followed by a test of memory retention (extinction testing) and recovery (reinstatement testing). We did not observe any effects of post-reactivation propranolol on memory retention; conditioned responding was similar for all groups at the start of retention testing and similarly sensitive to recovery through reinstatement. We did observe an acute effect of propranolol administration on fear-potentiated startle responding during retention testing in Group PrPr, where participants exhibited attenuated startle responses during extinction testing but similar sensitivity to reinstatement as participants in the other groups. While our findings fail to corroborate previous reports of propranolol-induced post-reactivation amnesia in humans, they do point to acute effects of propranolol administration on extinction performance.
Optimized Memory Allocation and Power Minimization for FPGA-Based Image Processing

Memory is the biggest limiting factor to the widespread use of FPGAs for high-level image processing, which require complete frame(s) to be stored in situ. Since FPGAs have limited on-chip memory capabilities, efficient use of such resources is essential to meet performance, size and power constraints. In this paper, we investigate allocation of on-chip memory resources in order to minimize resource usage and power consumption, contributing to the realization of power-efficient high-level image processing fully contained on FPGAs. We propose methods for generating memory architectures, from both Hardware Description Languages and High Level Synthesis designs, which minimize memory usage and power consumption. Based on a formalization of on-chip memory configuration options and a power model, we demonstrate how our partitioning algorithms can outperform traditional strategies. Compared to commercial FPGA synthesis and High Level Synthesis tools, our results show that the proposed algorithms can result in up to 60% higher utilization efficiency, increasing the sizes and/or number of frames that can be accommodated, and reduce frame buffers’ dynamic power consumption by up to approximately 70%. In our experiments using Optical Flow and MeanShift Tracking, representative high-level algorithms, data show that partitioning algorithms can reduce total power by up to 25% and 30%, respectively, without impacting performance.
Food Security for Whom? The Effectiveness of Food Reserves in Poor Developing Countries

During the Global Food Price Crisis of 2007-2011, millions of people suffered from hunger because food had become expensive. To cope with this problem, the governments of several countries decided to establish public food reserves in order to stabilize domestic prices. In this paper we develop a model to evaluate the optimal grain storage policy for a poor grain-importing country. Households are heterogeneous in their income endowment, and those who cannot afford enough food suffer from hunger. The international price of grain follows a Markov process with two states (tranquil periods and food crises), and households are unable to self-insure against changes in this price. The objective of the reserve operation is to reduce hunger rates. The model captures the trade-off in implementing the policy: raising a stock to prevent hunger tomorrow requires resources that could be used to reduce hunger today. Parameters are calibrated to reflect food supply and demand in Haiti. Our results suggest that rather than storing food, a better approach for a poor country is to focus on fighting poverty directly, since the modest social protection provided by a storage policy could be also be obtained through relatively small improvements in income per capita and income distribution.
Opposing Roles of apolipoprotein E in aging and neurodegeneration

This study investigates how APOE modulates neuronal function integrity during normal aging and in the context of amyloidosis. This work demonstrates that APOE is a necessary partner of Aβ-dependent neuronal dysfunction and synaptotoxicity but also preserves neuronal network during aging. Apolipoprotein E (APOE) effects on brain function remain controversial. Removal of APOE not only impairs cognitive functions but also reduces neuritic amyloid plaques in mouse models of Alzheimer’s disease (AD). Can APOE simultaneously protect and impair neural circuits? Here, we dissociated the role of APOE in AD versus aging to determine its effects on neuronal function and synaptic integrity. Using two-photon calcium imaging in awake mice to record visually evoked responses, we found that genetic removal of APOE improved neuronal responses in adult APP/PSEN1 mice (8–10 mo). These animals also exhibited fewer neuritic plaques with less surrounding synapse loss, fewer neuritic dystrophies, and reactive glia. Surprisingly, the lack of APOE in aged mice (18–20 mo), even in the absence of amyloid, disrupted visually evoked responses. These results suggest a dissociation in APOE’s role in AD versus aging: APOE may be neurotoxic during early stages of amyloid deposition, although being neuroprotective in latter stages of aging.
Extracellular vesicles derived from cancer-associated fibroblasts induce the migration and invasion of oral squamous cell carcinoma

ABSTRACT As one of the most abundant constituents of the tumour microenvironment (TME), cancer-associated fibroblasts (CAF) display critical roles during tumour progression and metastasis. Multiple classes of molecules including growth factors, cytokines, proteases and extracellular matrix proteins, are produced by CAF to act as mediators of the stroma-tumour interactions. One of the main channels for this communication is associated with extracellular vesicles (EV), which are secreted particles loaded with protein and genetic information. In this study, we evaluated the effects of EV derived from CAF primary human cell lines (n = 5) on proliferation, survival, migration, and invasion of oral squamous cell carcinoma (OSCC) cells. As controls, EV from human primary-established normal oral fibroblasts (NOF, n = 5) were used. Our in vitro assays showed that CAF-EV significantly induces migration and invasion of OSCC cells and promote a disseminated pattern of HSC-3 cell invasion in the 3D organotypic assay. Furthermore, gene expression analysis of EV-treated cancer cells revealed changes in the pathways associated with tumour metabolism and up-regulation of tumour invasion genes. Our findings suggest a significant role of CAF-EV in promoting the migration and invasion of OSCC cells, which are related to the activation of cancer-related pathways.
Modeling crop area allocations in Australian broadacre agriculture

A model for forecasting changes in crop areas in response to changes in output prices in Australian broadacre agriculture is outlined in this paper. The crop–livestock interactions and substitution and complementary relationships among crops are modeled as a set of land allocation decisions made simultaneously but at a number of hierarchical stages. The model developed here is broader in scope than previous models of crop area response in Australian broadacre agriculture in terms of crop coverage. The method employed takes specific account of lagged relationships and producer expectations for prices. In the model, area allocation decisions at the aggregate level are also affected by rainfall. The specified equations are estimated employing the seemingly unrelated regression procedure, over the period 1974-75 to 1995-96 using annual data at the national level. The model is then validated by simulating it over historical and forecast periods. The preliminary results suggest that the model is capable of generating plausible estimates of crop area response.
Personalized recommendation strategies for eLearning: An AHP approach

eLearning has become a key adjunct to both, education in general and the business world; it is becoming an important tool to allow the flexibility and quality requested by such a kind of learning process. One of recent challenges in eLearning industry is personalized learning (PL), aimed on meeting the needs and aspirations of each individual learner. A PL can be considered as a facility for an individual to access, combine, configure and manage digital resources (knowledge assets and services) related to their present learning needs and interests. The role of teachers in PL is also enhanced, since they should monitor learners‟ progress, make dynamic coherence between educational goals and students‟ achievements, and provide all needed recourses accordingly. The variety of PL systems are already developed, the most attempts of learner personalization are focused on the level of knowledge, background and hyperspace experience, preferences and interests, or even learning styles and achievements. It still does not fully address the issue of intelligent personalized recommendations stimulated by the huge wealth of opportunities for collaboration and communication offered by semantic technologies and intelligent reasoning techniques. In this paper, focusing on the well-known Analytical Hierarchical Process (AHP) method, we propose a framework for addressing different kinds of learners‟ preferences in PL, integration with historical data and experiences, and making recommendations and personalization accordingly. Firstly, we are focused on making analyses of relevant kinds of preferences defined by both, learners and teachers over learning process in general (including indicators of progress, learning styles, pedagogic approach, etc.), learning resources and learners‟ interests and goals. Also, relevant historical data should be recognized with appropriate retrieving methods and potential web resources if applicable. Finally, semantic structure should be proposed as conceptual framework enabling integration of all gathered data and application of AHP algorithm for processing. The final output of this paper is integrated approach for representing and reasoning over preferences in PL, with effective order decision outcomes in a way that it makes personalized recommendations over available resources, services and dynamic actions.
N I ] 1 8 Ja n 20 18 WiLAD : Wireless Localisation through Anomaly Detection

We propose a new approach towards RSS (Received Signal Strength) based wireless localisation for scenarios where, instead of absolute positioning of an object, only the information whether an object is inside or outside of a specific area is required. This is motivated through a number of applications including, but not limited to, a) security: detecting whether an object is removed from a secure location, b) wireless sensor networks: detecting sensor movements outside of a network area, and c) computational behaviour analytics: detecting customers leaving a retail store. The result of such detection systems can naturally be utilised in building a higher level contextual understanding of a system or user behaviours. We use a supervised learning method to overcome issues related to RSS based localisation systems including multipath fading, shadowing, and incorrect model parameters (as in unsupervised methods). Moreover, to reduce the cost of collecting training data, we employ a detection method called One-Class SVM (OC-SVM) which requires only one class of data (positive data, or target class data) for training. We derive a mathematical approximation of accuracy which utilises the characteristics of wireless signals as well as OC-SVM. Based on this we then propose a novel mathematical formula to find optimal placement of devices. This enables us to optimize the placement without performing any costly experiments or simulations. We validate our proposed mathematical framework based on simulated and real experiments.
Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing

Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context.
Analyzing user's task-driven interaction in mixed reality

Mixed reality (MR) provides exciting interaction approaches in several applications. The user experience of interacting in these visually rich environments depends highly on the way the user perceives, processes, and comprehends visual information. In this work we are investigating the differences between Field Dependent - Field Independent users towards their interaction behavior in a MR environment when they were asked to perform a specific task. A study was conducted using Microsoft HoloLens device in which participants interacted with a popular HoloLens application, modified by the authors to log user interaction data in real time. Analysis of the results demonstrates the differences in the visual processing of information, especially in visually complex environments and the impact on the user's interaction behavior.