Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing preference by white-footed mice (Peromyscus leucopus) for coarse woody debris

Research to expand current knowledge of habitat use by white-footed mice (Peromyscus leucopus) at fine spatial scales is needed given the species’ widespread distribution in the United States and its prevalence as a reservoir for zoonotic pathogens like Lyme disease. Downed coarse woody debris, a structural component of forest floor habitat important for many small mammals, may be preferred by woodland dwelling P. leucopus because of its multiplicity of uses from cover to landmarks that aid in spatial memory for navigation. To assess the role that downed woody debris plays in facilitating habitat preference of P. leucopus, Sherman box traps were deployed for live trapping and tagging of P. leucopus and coarse woody debris was surveyed in grids across various types of forest at Brookhaven National Laboratory during the summer of 2017. A Pearson correlation analysis produced no significant correlation between abundance estimates of P. leucopus population size and total volumes of coarse woody debris. In order to inform wildlife and forest management practices of the importance of woody debris in facilitating P. leucopus populations, more long-term studies are needed that can account for the interacting effects of other habitat characteristics like patch size and understory vegetation density. Over the course of the study I became proficient and confident in handling and processing P. leucopus, a useful skill to have for future studies in wildlife ecology.
Key Knowledge Generation

This paper proposed a method to recognize Iraqi currency by computing the average of each color (RGB) for each currency paper. The average of the color will compare with a database that already has been stored in the system. This database includes the average color of each currency. The comparison will use the correlation to find the minimum error. To improve the system and get more accurate results, the color system will convert to HSV system and use the same steps that applied to the RGB color system. Compare the result that we got from two color system to take the last decision. Matlab environment 2011a has been used in this system.
Polymorphic microsatellites loci in the black Aphid, Aphis fabae Scopoli, 1763 (Hemiptera, Aphididae)

Aphis fabae is a widespread pest in Europe. This species includes four morphological cryptic subspecies which are mostly identified by their partially distinct secondary host range. To determine the extent of gene flow and isolation between these four taxa, we isolated and characterized eight microsatellite loci from A. fabae fabae and tested them on the three other subspecies. Polymorphism ranged from five to 35 alleles on the 138 individuals collected. When analysing each taxon separately, significant deviations from Hardy-Weinberg (HW) equilibrum were observed. This could have different explanations, including the presence of null alleles, or the Wahlund effect or the mating system.
INFLUENCE OF GENDER AND LEARNED HELPLESSNESS ON SOME MATHEMATICS-RELATED AFFECTIVE BEHAVIOUR OF LESOTHO SENIOR SECONDARY SCHOOL STUDENTS

Given that mathematics is the core subject for the development of science and technology, the problem of poor and deteriorating performance in the subject among African children demands more effort by researchers than is currently the case. Several factors combine to adversely influence mathematics-related affective behaviour of and hence the performance by students in the subject. This study examines the hypotheses that gender and level of learned helplessness (LLH) significantly influences such behaviour among senior secondary school students in the Kingdom of Lesotho. Three hundred and ten students from 12 randomly selected senior secondary schools in Lesotho completed questionnaires assessing their LLH in mathematics and some other mathematics-related affective variables. Data analysis using two-way ANOVA indicated that gender and the LLH in mathematics significantly influences students’ attitude towards mathematics. Students with high LLH showed poor attitude towards mathematics, attach less value to mathematics and have high tendency to cheat in mathematics exam. However, students with low LLH showed more positive attitude and value towards mathematics and a low tendency to cheat during mathematics examination. These findings were discussed and recommendations were made.
GENDER DIFFERENTIALS IN MARKET PARTICIPATION AMONG SMALL-HOLDER CASSAVA FARMERS IN SOUTH-EASTERN NIGERIA: A DOUBLE HURDLE MODEL APPROACH

The study analysed gender differentials in market participation among small-holder cassava farmers in south-eastern Nigeria by the use of a multistage randomised sampling procedure from 360 farmers. The results show that important variables directly related to participating and level of sales in cassava markets were extension contact at 5% level, access to communication facilities, personal means of transportation, and yield at 1% level each. The coefficient for age had an indirect relationship with participation and level of sales at 10% level as well as distance from the farm to the market at 1% level. The results showed that the female farmers participated and sold more cassava in the market than their male counterparts. The results call for policies aimed at improving rural infrastructure like access roads that would facilitate faster delivery of farm produce especially perishable commodities such as cassava to urban consumers.
Counting Vehicles in Static Images

This paper addresses the problem of counting vehicles in static images with no geometric information of the scene. Four convolutional neural network architectures were studied, implemented and trained as a main part of this work. Also, a dataset that consists of 19 310 images in total from 12 views that captures 7 different scenes were taken as part of this work. The trained networks map the appearance of the input sample to its corresponding vehicles density map, which can be easily translated to the vehicle count with keeping the localization of the vehicles in the input image. The main contribution of this work is in an application and a comparison of the state-of-the-art solutions to the problem of object counting. Most of them were mainly designed to count pedestrians in crowded scenes or for medicine images, so the major goal was to adapt these solutions for vehicle counting task. The implemented models were trained on TRANCOS dataset which is a popular benchmark for counting vehicles on annotated low quality highway pictures. Their performance is compared and the results are discussed.
Bioavailable zinc in grains of bread wheat varieties of Pakistan

Estimation of zinc and its bioavailability in wheat grains is a prerequisite for an effective biofortification program. The selected 65 bread wheat varieties are representative of indigenous and foreign genetic pools being used for genotype development at the Wheat Research Institute, Faisalabad (Pakistan). The main objectives of the study were: (i) to determine the variation in phytate and zinc concentrations in whole grains; (ii) to estimate the bioavailable zinc in wheat grains by using trivariate model of zinc absorption and phytate to zinc molar ratios ([phytate]:[zinc]); and (iii) to examine the interrelationship of bioavailable zinc with year of variety release. Average zinc in grains of wheat varieties was 29 μg g−1 and ranged from 24 to 36 μg g−1. Phytate in grains ranged from 7.1 to 11.1 mg g−1 resulting in a variation in [phytate]:[zinc] of 24 to 41. The estimated bioavailable zinc in grains ranged from 1.52 to 2.15 mg zinc for 300 g of wheat flour, indicating that only 21 ± 3% of grain zinc wa.
Preliminary grouping of mutacins

All the mutacins described so far have been shown to be different substances. To classify 86 mutacinogenic strains, we determined their activity spectra towards 12 standard oral streptococci and towards themselves, using a deferred antagonism test. The mutacinogenic strains could be grouped into 15 mutacinotypes according to their activity spectra against the 12 standard oral streptococci. By combining the results of their activity spectra and their sensitivity patterns amongst themselves, we defined 24 mutacin groups. Using the 24 type strains corresponding to these groups as producers and as indicators, it will be easier to classify new mutacin-producing isolates.Key words: bacteriocin, mutacin, Streptococcus mutans, inhibitory substance.
Predicting Recessions: A New Measure of Output Gap as Predictor

In this paper, we merge two streams of literature: nonparametric methods to estimate frontier efficiency of an economy, which allows us to develop a new measure of output gap, and nonparametric methods to estimate probability of an economic recession. To illustrate the new framework we use quarterly data for Italy from 1995 to 2019, and find that our model, using either nonparametric or the linear probit model is able to provide useful insights (This abstract was borrowed from another version of this item.)
Identifying Major Power Support Signaled for Protégés: A Latent Measure Approach∗

We introduce a new latent measure of the support that major powers signal for their protégés to enhance protégé security. Major powers have an incentive to signal their support for protégés in order to reassure them and deter harm against them. Major powers have a variety of complementary signals to choose among for this purpose, including alliances, troop deployments, arms transfers, joint military exercises, and leadership visits, making it difficult to weigh the importance of individual signals and accurately specify the degree of major power support for a protégé. We address this challenge by using a measurement model for mixed multivariate responses to construct a latent variable of major power-protégé support, which provides a theoretically coherent means of identifying the overall level of support signaled by a major power for a protégé. Our approach yields a cross-sectional-time-series dataset, providing continuous measures of the degree of support signaled by major powers for all minor powers from 1950–2007. Our model also provides insights regarding which signals of support are most meaningful. In applications, we show that our latent variable is useful for predicting whether major powers intervene in minor power conflicts and exploring the degree of major power competition for protégés. ∗The latent variable data described in this paper and code for using the data will be posted online at the time of publication. †Assistant Professor. Department of Political Science. Baruch College, City University of New York. Email: Roseanne.McManus@baruch.cuny. edu ‡Assistant Professor. Department of Political Science. Iowa State University. Email: mdnieman@iastate.edu.
Nitrogen assimilation by three size fractions of the winter phytoplankton off Westland, New Zealand

Abstract The assimilation of ammonium, nitrate, and urea by the phytoplankton community off the Westland coast was measured in a detailed series of 15N‐assays in mid winter, 1987. The average nitrogen (N) uptake rates for the three substrates for three size classes of phytoplankton (< 2, < 20, < 200 μm) followed the distribution of biomass as measured by chlorophyll a, with higher values inshore. Nitrate accounted for 56% of the total N utilised by the entire community (< 200 μm), whereas ammonium and urea made up c. 19 and 25%, respectively. Picoplankton (< 2 μm) made up 33–44% of the total chlorophyll a and contributed about one‐third of the N uptake by the entire community. The relative preference index (RPI) for all three size classes consistently indicated a strong preference for regenerated N (NH4 +). NH4 + and urea became relatively more important sources of N for picoplankton in offshore oceanic waters, and to a lesser extent for nanoplankton (2–20 μm). However, the ratio of NO3 ‐ to total N assim.
Improving robustness of neural networks against adversarial examples

The main goal of this work is to design and implement the framework that yields robust neural network model against whatever adversarial attack, while result models accuracy is not signiﬁcantly lower comparing to naturally trained model. Our approach is to minimize maximization the loss function of the target model. Related work and our experiments lead us to the usage of Projected gradient descent method as a reference attack, therefore, we train against data generated by PGD. As a result, using the framework we can reach accuracy more than 90% against sophisticated adversarial attacks on MNIST dataset. The greatest contribution of this work is an implementation of adversarial attacks and defences against them because there misses any public implementation.
Small RNA signatures of the anterior cruciate ligament from patients with knee joint osteoarthritis

The anterior cruciate ligaments are susceptible to degeneration, resulting in pain, reduced mobility and development of the degenerative joint disease osteoarthritis. There is currently a paucity of knowledge on how anterior cruciate ligament degeneration and disease can lead to osteoarthritis. Small non-coding RNAs (sncRNAs), such as microRNAs, and small nucleolar RNA, are important regulators of gene expression. We aimed to identify sncRNA profiles of human anterior cruciate ligaments to provide novel insights into their roles in osteoarthritis. RNA was extracted from the anterior cruciate ligaments of non-osteoarthritic knee joints (control) and end-stage osteoarthritis knee joints, used for small RNA sequencing and significantly differentially expressed sncRNAs defined. Bioinformatic analysis was undertaken on the differentially expressed miRNAs and their putative target mRNAs to investigate pathways and biological processes affected. Our analysis identified 184 sncRNA that were differentially expressed between control ACLs derived from osteoarthritic joints with a false discovery adjusted p value<0.05; 68 small nucleolar RNAs, 26 small nuclear RNAs and 90 microRNAs. We identified both novel and previously identified (miR-206, -101, -365 and -29b and -29c) osteoarthritis-related microRNAs and other sncRNAs (including SNORD74, SNORD114, SNORD72) differentially expressed in ligaments derived from osteoarthritic joints. Significant cellular functions deduced by the differentially expressed miRNAs included differentiation of muscle (P<0.001), inflammation (P<1.42E-10), proliferation of chondrocytes (P<0.03), fibrosis (P<0.001) and cell viability (P<0.03). Putative mRNAs were associated with the canonical pathways Hepatic Fibrosis Signalling (P<3.7E-32), and Osteoarthritis (P<2.2E-23). Biological processes included apoptosis (P<1.7E-85), fibrosis (P<1.2E-79), inflammation (P<3.4E-88), necrosis (P<7.2E-88) and angiogenesis (P<5.7E-101). SncRNAs are important regulators of anterior cruciate disease during osteoarthritis and may be used as therapeutic targets to prevent and manage anterior cruciate ligament disease and the resultant osteoarthritis.
Internet-based tool for visualizing county and state level COVID-19 trends in the United States.

The novel COVID-19 outbreak started in 2019 in Wuhan China and quickly spread to at least 185 countries. We developed an interactive web application that allows users to visualize the spread of COVID-19 in the Unites States at state and county levels. This tool allows visualization of how the virus spreads over time and how state-wide efforts to reduce transmissions have affected the curve in local areas. The downloadable application data allows users to conduct additional analyses. We demonstrate exemplars of trend analyses comparing the daily infection and death rates before and after safer at home orders were implemented per state. The goal was to develop a COVID-19 tracking tool that informs users about the spread of the virus to enable them to make informed decisions after better understanding the presented data.
Effect of breed, protein genetic variants and feeding on cows' milk coagulation properties

Summary One hundred and thirty-seven (Holstein (41), Montbéliarde (42) and Tarentaise (54)) dairy cows in first or second lactation received during winter one of two levels (high, H; low, L) of energy intake, and were later fed identically at pasture. Thrice in winter and twice at pasture, the chemical composition and coagulation properties of individual milks were measured. Milk from Holstein cows had lower casein and calcium contents, and poorer coagulation properties (curd firming time and curd firmness) than that from Montbéliardes and Tarentaises (P < 0·01). These differences practically disappeared when taking into account the distribution of the different κ-casein variants and milk casein content. κ-BB milks had coagulation properties 20–50% superior, according to characteristic, to those of AA milks. In the three breeds, animals from the H group had casein contents higher by 1·4 g/l than those of the L group, which induced a significant improvement in curd firming time, curd firmness and cheese yield. Turning out to pasture induced an increase of 0·02 in milk pH, and improved milk coagulation properties. These changes did not appear to result entirely from the parallel increase in milk casein content.
Induction of Cross-Reactive Hemagglutination Inhibiting Antibody and Polyfunctional CD4+ T-Cell Responses by a Recombinant Matrix-M–Adjuvanted Hemagglutinin Nanoparticle Influenza Vaccine

Background. Recurrent reports of suboptimal influenza vaccine effectiveness have renewed calls to develop improved, broadly cross-protective influenza vaccines. Here, we evaluated the safety and immunogenicity of a novel, saponin (Matrix-M)-adjuvanted, recombinant hemagglutinin (HA) quadrivalent nanoparticle influenza vaccine (qNIV). Methods. We conducted a randomized, observer-blind, comparator-controlled (trivalent high-dose inactivated influenza vaccine [IIV3-HD], or quadrivalent recombinant influenza vaccine [RIV4]), safety and immunogenicity trial of qNIV (in 5 different doses/formulations) in healthy adults aged [≥]65 years. Vaccine immunogenicity was measured by hemagglutination-inhibition assays using reagents expressing wild-type HA sequences (wt-HAI) and cell-mediated immune (CMI) responses. Results. A total of 1375 participants were randomized, immunized, and followed for safety and immunogenicity. Matrix-M-adjuvanted qNIV induced superior wt-HAI antibody responses against 5 of 6 homologous or drifted strains evaluated compared to unadjuvanted qNIV. Adjuvanted qNIV induced post-vaccination wt-HAI antibody responses at Day 28 that were: statistically higher than IIV3-HD against a panel of homologous or drifted A/H3N2 strains; similar to IIV3-HD against homologous A/H1N1 and B (Victoria) strains; and similar to RIV4 against all homologous and drifted strains evaluated. The qNIV formulation with 75 g Matrix-M adjuvant induced substantially higher post-vaccination geometric mean fold-increases of influenza HA-specific polyfunctional CD4+ T-cells compared to IIV3-HD or RIV4. Overall, similar frequencies of solicited and unsolicited adverse events (AEs) were reported in all treatment groups. Conclusions. qNIV with 75 g Matrix-M adjuvant was well tolerated and induced robust antibody and cellular responses, notably against both homologous and drifted A/H3N2 viruses. Further investigation in a pivotal phase 3 trial is underway.
How well can we forecast the COVID-19 pandemic with curve fitting and recurrent neural networks?

Predictions of the COVID-19 pandemic in USA are compared using curve fitting and various recurrent neural networks (RNNs) including the standard long short-term memory (LSTM) RNN and 10 types of slim LSTM RNNs. The curve fitting method predicts the pandemic would end in early summer but the exact date and scale vary with the evolving data used for fitting. All LSTM RNNs result in short-term (8 to 10 days) predictions with comparable accuracies (smaller than 10 %) to curve fitting---they do not show advantage over curve fitting.
Covid-19 Detection using CNN Transfer Learning from X-ray Images

The Covid-19 first occurs in Wuhan, China in December 2019. After that the virus spread all around the world and at the time of writing this paper the total number of confirmed cases are above 4 million with over 297000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is twofold. First, a quantitative analysis where we evaluate 12 off-the-shelf convolutional neural networks (CNNs) and proposed a simple CNN architecture with less parameters and computational power that can perform as good as Xception and DenseNet architectures if trained on small dataset of chest X-ray images. Secondly, a qualitative investigation to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed most to the decision of CNNs back to the original image to visualize the most discriminating regions in the input image. Chest X-ray images used in this work are coming from multiple sources which comprises of 154 confirmed COVID-19 images and over 5000 X-rays of normal, bacterial and other viral (non-COVID-19) infections. We conclude that CNN decisions should not be taken into consideration until radiologist/clinicians can visually inspect the region(s) of the input image used by CNNs that lead to its prediction. This work also reports the necessity of segmenting the region of interest (ROI) to prevent CNNs building their decision from features outside ROI.