Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Bacillus thuringiensis corn containing pyramided traits for management of sugarcane borer, Diatraea saccharalis (F.)

The sugarcane borer, Diatraea saccharalis (F.), is a major target of Bacillus thuringiensis (Bt) corn in the U.S. mid southern region. Corn expressing pyramided Bt proteins has recently become commercially available in the U.S. The objectives of this study were 1) to determine survival and plant injury of Cry1Ab-susceptible (Cry1Ab-SS), -resistant (Cry1Ab-RR), and heterozygous (Cry1Ab-RS) genotypes of D. saccharalis on Bt corn containing single and pyramided Bt genes and 2) to assess larval movement of D. saccharalis in different planting patterns of non-Bt and Bt corn. One laboratory leaf tissue bioassay and two independent greenhouse trials were conducted to evaluate larval survival and plant injury on five corn hybrids. On intact plants of non-Bt corn, 43-62% larvae survived after 21 days. Larval survivorship on Cry1Ab corn was 4.7- 5.6% for Cry1Ab-SS, 29.4-32.5 % for Cry1Ab-RS, and 36.6- 45.6% for Cry1Ab-RR. In contrast, the 21-day survivorship on the two pyramided Bt corn hybrids was <5% for the three insect genotypes. Results of the leaf tissue bioassays were consistent with the greenhouse tests. Larval movement of D. saccharalis was evaluated in four planting patterns of non-Bt and Bt plants containing Genuity® SmartStaxTM traits. The four planting patterns were: 1) pure stand of Bt corn, 2) a non-Bt corn plant surrounded by 26 Bt corn plants, 3) pure stand of non-Bt corn, and 4) a Bt corn plant surrounded by 26 non-Bt corn plants. Studies were conducted in three conditions: 1) greenhouse; 2) open field with artificial infestations of 50 eggs on the center plants; and 3) open field study with artificial infestations of 10 neonates on every plant. Larvae of D. saccharalis showed the ability to move from infested plants to at least four plants away and from the infested rows to adjacent rows. The results showed that the pyramided Bt corn can overcome the Cry1Ab resistance and thus should offer a means for Cry1Ab resistance management in D. saccharalis. Together with previous data, the results indicate that the seed mixture strategy might be able to provide a similar refuge population of D. saccharalis as the structured refuge planting.
Food Insecurity and SNAP Use Among Immigrant Families With Children During the Economic Downturn

Immigrant workers are overrepresented in industries that have been the hardest hit in the recent economic downturn, such as the service and construction industries. As a result, many immigrant families have experienced increased economic insecurity. Federal policies restrict immigrants’ eligibility for safety net programs, like the Supplemental Nutrition Assistance Program (SNAP). Some States have filled this gap by providing SNAP-like benefits to immigrants who are otherwise ineligible. We analyze immigrant families’ SNAP participation and food insecurity, using the Food Security Supplement of the Current Population Survey, 2003-10. Results show that immigrant families’ food insecurity has risen significantly more than that of other families during the downturn. We find that immigrant families are more likely to participate in SNAP and receive higher benefits in states that expand eligibility. Being eligible for SNAP was associated with lower food insecurity among immigrant families in the sample; however, this result was not statistically significant.
Electrical spin injection, transport, and detection in graphene-hexagonal boron nitride van der Waals heterostructures: progress and perspectives

The current research in graphene spintronics strives for achieving a long spin lifetime, and efficient spin injection and detection in graphene. In this article, we review how hexagonal boron nitride (hBN) has evolved as a crucial substrate, as an encapsulation layer, and as a tunnel barrier for manipulation and control of spin lifetimes and spin injection/detection polarizations in graphene spin valve devices. First, we give an overview of the challenges due to conventional SiO2/Si substrate for spin transport in graphene followed by the progress made in hBN based graphene heterostructures. Then we discuss in detail the shortcomings and developments in using conventional oxide tunnel barriers for spin injection into graphene followed by introducing the recent advancements in using the crystalline single/bi/tri-layer hBN tunnel barriers for an improved spin injection and detection which also can facilitate two-terminal spin valve and Hanle measurements at room temperature, and are of technological importance. A special case of bias induced spin polarization of contacts with exfoliated and chemical vapour deposition (CVD) grown hBN tunnel barriers is also discussed. Further, we give our perspectives on utilizing graphene-hBN heterostructures for future developments in graphene spintronics.
Analysis and improvement of surface representativeness of high resolution Pléiades DEMs Examples from glaciers and rock glaciers in two areas of the Andes

High spatial resolution Digital Elevation Models (DEMs) are fundamental datasets to understand the dynamics of Earth surface, such as recent change of the cryosphere, and especially in remote mountainous areas, like the Andes. Here we describe a methodological framework to improve the quality in terms of representativeness of terrain surface for high spatial resolution stereoscopic DEM obtained from Pléiades images. Using as example two DEMs from highly different regions from the Andes, we analyzed the different types of errors present in both DEMs. In order to improve the output DEM a post-processing scheme was designed, following those steps: (1) filling the gaps with a spline interpolation method; (2) elimination of the granular noise with a multidirectional Lee filter; and (3) elimination of the spikes with a slope-based DTM filter. Besides improving the representativeness of both DEMs in terms of terrain feature, the post-processing also improves the accuracy of DEMs in terms of absolute accuracy of elevations.
Using Wavelet to Analyze Periodicities in Hydrologic Variables

The trend and shift in the seasonal temperature, precipitation and streamflow time series across the Midwest have been analyzed, for the period 1960-2013, using the statistical analyses (MannKendall test with and without considering short term persistence (MK2 and MK1, respectively) and Pettitt test). The paper also utilizes a relatively new approach, wavelet analysis, for testing the existence of trend and shift in the time series. The method has the ability to decompose a time series in to lower (trend) and higher frequency components (noise). Discrete wavelet transform (DWT) has been employed in the present study with an aim to find which periodicities are mainly responsible for trend in the original data. The combination of MK1, MK2 and DWT along with Pettitt test hasn’t been extensively used up to this time, especially in detecting trend and shift in the Midwest. The analysis of climate division temperature and precipitation data and USGS naturalized streamflow data revealed the presence of periodicity in the time series data. All the incorporated time series data were seasonal to analyze the trends and shifts for four seasons-winter, spring, summer and fall independently. D3 component of DWT were observed to be influential in detecting real trend in Temperature, precipitation and streamflow data, however unlike temperature, precipitation and streamflow showed decreasing trend as well. Shift was relatively observed more than trend in the region with dominance of D3 component in the data. The result indicate the significant warming trend which agrees with the “increasing temperature” observations in the past two decades, however a clear explanation for precipitation and streamflow is not obvious.
On the SINR Distribution of SWIPT MU-MIMO with Antenna Selection

In this paper, we provide a closed-form approximation of the full-duplex simultaneous wireless information and power transfer (SWIPT) multi-user MIMO (MU-MIMO) system signal-to-interference-and-noise ratio (SINR) distributions for the received signal at the sensor nodes (SNs) with perfect and imperfect channel state information (CSI) and transmit antenna selection scheme at the transmitter. We also studied the SINR distributions of the uplink with Zero-Forcing beamforming (ZFBF) and antenna selection at the aggregator (AGG). The downlink SINR with perfect and imperfect CSI are modeled by multivariate Beta type II distributions and the uplink with perfect and imperfect CSI are modeled with multivariate Wishart distributions. We compared the proposed analytical results to the Monte-Carlo simulations and we obtained a perfect match.
Partition-wise Recurrent Neural Networks for Point-based AIS Trajectory Classification

We present Partition-wise Recurrent Neural Networks (pRNNs) for point-based trajectory classiﬁcation to detect ﬁshing activities in the ocean. This method partitions each feature and uses region-speciﬁc parameters for distinct partitions, which can greatly improve the expressive power of deep recurrent neural networks on low-dimensional yet heterogeneous trajectory data. We show that our approach outperforms the state-of-the-art systems.
Risk-aware distribution of SMT outputs for translation of documents targeting many anonymous readers

Web documents and news articles are typically written for many anonymous readers. Thus, when translating such documents, the total quality of translations distributed to the entire readers should be considered. Previous statistical machine translation studies have focused on selecting the best translation from N -best candidates. However, when dealing with many readers, it is not necessary to identify the best translation. Our key idea is to distribute all good candidate translations to the readers and improve the total quality of the translations. We simulated a case with 1, 000 news document readers and showed statistically significant gain in sentencelevel BLEU scores averaged over those readers.
An Application of Category Theory to the Study of Complex Networks

We propose a new data analytical tool for directed networks by using category theory. We develop a category theoretical treatment of directed networks in order to obtain functional networks for real networks. By applying our method to concrete data on real information processing biological networks, we find a distinguishing global structure of functional networks. A possibility of a new hypothesis on network motifs is also indicated based on our theory and data analysis.
Shape of a large drop on a rough hydrophobic surface

Large drops on solid surfaces tend to flatten due to gravitational effect. Their shapes can be predicted by solving the Young-Laplace equation when their apparent contact angles are precisely given. However, for large drops sitting on rough surfaces, the apparent contact angles are often unavailable a priori and hard to define. Here we develop a model to predict the shape of a given volume of large drop placed on a rough hydrophobic surface using an overlapping geometry of double spheroids and the free energy minimization principle. The drop shape depends on the wetting state, thus our model can be used not only to predict the shape of a drop but also to infer the wetting state of a large drop through the comparison of theory and experiment. The experimental measurements of the shape of large water drops on various micropillar arrays agree well with the model predictions. Our theoretical model is particularly useful in predicting and controlling shapes of large drops on surfaces artificially patterned in .
Role of Direct Antiviral Agents in Treatment of Chronic Hepatitis C Infection in Renal Transplant Recipients

Background Since the introduction of direct antiviral agents (DAAs), morbidity of HCV has considerably decreased but still no guidelines have been formulated in renal transplant recipients (RTRs). We studied efficacy and tolerability of direct antiviral agents in RTRs. Methods This prospective observational study was conducted at Army Hospital Research & Referral, Delhi, from June 2016 to May 2017. Forty-five HCV infected RTRs with stable graft function were included. Results Median time between renal transplantation and the start of anti-HCV therapy was 36 months (1–120 months). The majority (66.7%) were infected with genotype 3. Baseline median HCV RNA level was 542648 IU/ml (1189–55028534 IU/ml). Sofosbuvir-Ribavirin combination (24 weeks) was given to 30 patients including 3 cirrhotics, Ledipasvir-Sofosbuvir combination to 8 patients, and Daclatasvir-Sofosbuvir combination to 7 patients, including 2 cirrhotics. Rapid virological response was observed in 29 patients treated with Sofosbuvir/Ribavirin, all 8 patients on Sofosbuvir/Ledipasvir, and all 7 patients on Sofosbuvir/Daclatasvir. End treatment response and sustained virological response (12 weeks) were achieved in all patients irrespective of genotype or treatment regimen. Decrease in mean HCV RNA level and transaminase level was statistically significant (p < 0.01). Ribavirin was significantly associated with anaemia (p = 0.032). Conclusions DAA regimens are well tolerated and highly efficacious. Response to DAA is good irrespective of genotype, drug combination, initial HCV RNA level, age or sex of patient, or graft age. However, Sofosbuvir/Ledipasvir and Sofosbuvir/Daclatasvir combination is preferable.
1 Disagreement in Crowdsourcing and Active Learning for Better Distant Supervision Quality

Distant supervision (DS) [Mintz et al. 2009; Welty et al. 2010] is a well-established semi-supervised method for performing relation extraction from text. It is based on the assumption that, given a knowledge base (KB) contains a relation between a pair of terms, then any sentence that contains that pair is likely to express the relation. This approach can generate false positives, as not every mention of a term pair in a sentence means a relation is also present [Angeli et al. 2014]. Crowdsourcing is a possible solution to correcting the mistakes of DS with human annotation, but the cost of employing annotators for large amounts of data is a bottleneck. [Angeli et al. 2014] present an active learning approach to select the most useful sentences that need human re-labeling using a query by committee – training a set of classifiers on partitions of DS data, and in the evaluation phase selecting the sentences where the models disagree on the relation classification and sending those to the crowd. However, as our experiments show, the error rate in the DS data can be so high that classifier agreement is not a reliable indication of good training data. Moreover, this approach does not take into account the disagreement that usually occurs between annotators, instead labeling data with the most commonly picked relation. We have proposed the CrowdTruth [Aroyo and Welty 2014] method for crowdsourcing a ground truth for relation extraction by capturing inter-annotator disagreement. Our previous work in medical relation extraction[Dumitrache et al. 2015] has shown that harnessing disagreement in crowd annotations can be used to model the ambiguity inherent in natural language. In this paper, we present ongoing work on combining active learning with the CrowdTruth methodology for further improving the quality of DS training data. We report the results of a crowdsourcing experiment ran on 2,500 sentences from the open domain. We show that modeling disagreement can be used to identify interesting types of errors caused by ambiguity in the TAC-KBP knowledge base, and we discuss how an active learning approach can incorporate these observations to utilize the crowd more efficiently.
Impaired sleep predicts onset of low back pain and burnout symptoms: evidence from a three-wave study

ABSTRACT In a three-wave questionnaire study of 405 working participants, who were initially free of low back pain (LBP) and emotional exhaustion, sleep problems were evaluated as a potential risk factor for the development of LBP and burnout up to three years later. Prospective risk paths were compared between the sexes and between two age-groups (18 to 45 years and older than 45 years). A longitudinal structural equation model showed a good fit with empirical data (RMSEA = .04, SRMR = .06, CFI = .97). Prospective risk paths between the latent constructs showed sleep problems to significantly predict self-reported LBP (β = .15, p = .011) and burnout (β = .24, p < .001) two years later. Sleep problems also predicted the occurrence of burnout three years later (β = .18, p = .002). Sleep-related risk of burnout after two years was greater in older than younger participants (βolder = .42 vs. βyounger = .13, p < .001). Sleep problems seem to precede LBP and burnout in working individuals. Health promotion initiatives should use sleep quality as an important early risk indicator, and interventions should focus on promoting better quality sleep, in an attempt to reduce the incidence of LBP and burnout.
Association between Haptoglobin Polymorphism and DNA Damage in Type 2 Diabetes

In the present study we aimed to estimate the Oxidative DNA damage in Type 2 diabetic patients in different haptoglobin polymorphism. Type 2 diabetes is associated with imbalance of antioxidant defense mechanism, causes alteration in various biomolecules, including DNA. The oxidative DNA damage and its repair is very crucial in diabetes; the defect in the repair process may lead to cancer among diabetics. Haptoglobin (Hp) is a polymorphic glycoprotein provides antioxidant function against heam- driven oxidative stress and has strong association with diabetes and its complication. We investigated the DNA damage quantification of lymphocytes by AP site (Apurine/Apyrimidine site) counting and that correlated with Hp polymorphism among type 2 diabetics. The Hp1-1 phenotype had the least DNA damage than HP 2-1 & HP 2-2; this indicates that Hp alleles differ in their protective effects against oxidative damage, and HP 1-1 was the most protective.
LitViz: Visualizing Literary Data by Means of text2voronoi

We present a LitViz, a webbased tool for visualizing literary data which utilizes the text2voronoi algorithm Mehler et al. (2016b) to map natural language texts onto voronoi diagrams. These diagrams can be used, for example, to visually differentiate between (groups of) authors. Text2voronoi utilizes the paradigm of text visualization to reconstruct text classification (e.g., authorship attribution) as a task of image classification. This means that, in contrast to conventional approaches to text classifiction, we do not directly use linguistic features, but explore visual features derived from the texts’ visualizations to perform operations on texts. We illustrate LitViz by means of 18 authors, each of whom is represented by 5 literary works.
COAXIAL TURBULENT JET MIXER FOR CONTROLLED SYNTHESIS OF NANOPARTICLES

Development of methods for high-throughput production of nanoparticles (NPs) in a reproducible and controlled manner is important for the translation of NPs to the clinic. We report a simple and versatile coaxial turbulent jet mixer platform for high-throughput preparation of various functional NPs in a reproducible and controllable manner. With a production rate sufficiently high for in vivo studies, clinical trials, and industrial scale production, this approach has the potential to improve the homogeneity and provide better quality control in the translation of nanomedicines to the clinic.
Neural Pre-Translation for Hybrid Machine Translation

Hybrid machine translation (HMT) takes advantage of different types of machine translation (MT) systems to improve translation performance. Neural machine translation (NMT) can produce more fluent translations while phrase-based statistical machine translation (PB-SMT) can produce adequate results primarily due to the contribution of the translation model. In this paper, we propose a cascaded hybrid framework to combine NMT and PB-SMT to improve translation quality. Specifically, we first use the trained NMT system to pre-translate the training data, and then employ the pre-translated training data to build an SMT system and tune parameters using the pre-translated development set. Finally, the SMT system is utilised as a post-processing step to re-decode the pre-translated test set and produce the final result. Experiments conducted on Japanese! English and Chinese! English show that the proposed cascaded hybrid framework can significantly improve performance by 2.38 BLEU points and 4.22 BLEU points, respectively, compared to the baseline NMT system.
Defect‐Minimized PEDOT:PSS/Planar‐Si Solar Cell with Very High Efficiency

Hybrid solar cells made of a p‐type conducting polymer, poly(3,4‐ethyl thiophene):polystyrenesulfonate (PEDOT:PSS), on Si have gained considerable interest in the fabrication of cost‐effective high‐efficiency devices. However, most of the high power conversion efficiency (PCE) performances have been obtained from solar cells fabricated on surface‐structured Si substrates. High‐performance planar single‐junction solar cells have considerable advantages in terms of processing and cost, because they do not require the complex surface texturing processes. The interface of single‐junction solar cells can critically influence the performance. Here, we demonstrate the effect of adding different surfactants in a co‐solvent‐optimized PEDOT:PSS polymer, which, in addition to acting as a p‐layer and as an anti‐reflective coating, also enhances the device performance of a hybrid planar‐Si solar cell. Using time‐of‐flight secondary ion mass spectrometry, we conduct three‐dimensional chemical imaging of the interface, which enables us to characterize the micropore defects found to limit the PCE. Upon minimizing these micropore defects with the addition of optimized amounts of fluorosurfactant and co‐solvent, we achieve a PEDOT:PSS/planar‐Si cell with a record high PCE of 13.3% for the first time. Our present approach of micropore defect reduction can also be used to improve the performance of other organic electronic devices based on PEDOT:PSS.
Subspace clustering by ( k , k )-sparse matrix factorization

High-dimensional data often lie in low-dimensional subspaces instead of the whole space. Subspace clustering is a problem to analyze data that are from multiple low-dimensional subspaces and cluster them into the corresponding subspaces. In this work, we propose a \begin{document}$(k,k)$\end{document} -sparse matrix factorization method for subspace clustering. In this method, data itself is considered as the "dictionary", and each data point is represented as a linear combination of the basis of its cluster in the dictionary. Thus, the coefficient matrix is low-rank and sparse. With an appropriate permutation, it is also blockwise with each block corresponding to a cluster. With an assumption that each block is no more than \begin{document}$k$\end{document} -by- \begin{document}$k$\end{document} in matrix recovery, we seek a low-rank and \begin{document}$(k,k)$\end{document} -sparse coefficient matrix, which will be used for the construction of affinity matrix in spectral clustering. The advantage of our proposed method is that we recover a coefficient matrix with \begin{document}$(k,k)$\end{document} -sparse and low-rank simultaneously, which is better fit for subspace clustering. Numerical results illustrate the effectiveness that it is better than SSC and LRR in real-world classification problems such as face clustering and motion segmentation.