Miracle-dz/UniFAR-FTUs huggingface dataset:
Using an epidemiological framework and bovine spongiform encephalopathy investigation questionnaire to investigate suspect bovine spongiform encephalopathy cases: an example from a bovine spongiform encephalopathy case in Ireland in 2015

In several EU member states, bovine spongiform encephalopathy (BSE) cases have been identified in cattle born after the reinforced ban (BARB cases), for reasons that are not entirely clear. Epidemiological investigation of these cases has proved challenging. The European Food Safety Authority recently recommended the collection of a predefined set of epidemiological data from BSE suspects and confirmed BSE cases to aid future investigations. In this study, we present an epidemiological framework and BSE investigation questionnaire to aid the investigation of suspect BSE cases, and illustrate its application during the investigation of a BSE case in Ireland in 2015. It is recommended that the framework and questionnaire are used concurrently: the framework provides structure and focus, whereas the questionnaire (with 135 questions) aids data collection. The framework focuses on confirmation and discrimination, estimating the date and location of exposure, and determining the method/source of exposure. The BSE case in Ireland in 2015 was a BARB case born in 2010. It was identified with classical BSE at an authorised knackery as part of Ireland’s targeted active surveillance programme for BSE. No definitive source of infection with the BSE agent could be attributed in this case.
Context-aware cascade network for semantic labeling in VHR image

Semantic labeling for the very high resolution (VHR) image of urban areas is challenging, because of many complex manmade objects with different materials and fine-structured objects located together. Under the framework of convolutional neural networks (CNNs), this paper proposes a novel end-to-end network for semantic labeling. Specifically, our network not only improves the labeling accuracy of complex manmade objects by aggregating multiple context semantics with a cascaded architecture, but also refines fine-structured objects by utilizing the low-level detail in shallow layers of CNNs with a hierarchical pyramid structure. Throughout the network, a dedicated residual correction scheme is employed to amend the latent fitting residual. As a result of these specific components, the whole model works in a global-to-local and coarse-to-fine manner. Experimental results show that our network outperforms the state-of-the-art methods on the large-scale ISPRS Vaihingen 2D Semantic Labeling Challenge dataset.
Improved Robust Vehicle Detection and Identification Based on Single Magnetic Sensor

Vehicle detection and identification techniques have been widely applied in traffic scene to acquire traffic information depending on various sensors, such as video camera, induction loop, and magnetic sensor. The quantity and category of vehicles are the key components of the intelligent transportation systems as they provide original data for further analysis. Compared with the inductive loop and video camera, magnetic sensor can measure magnetic field distortion caused by the movement of vehicles. The precise amount and category of a vehicle can be obtained through reasonable data analysis. A novel vehicle detection algorithm is proposed based on the short-term variance sequence transformed from raw magnetic signal. The parking-sensitive module is introduced to enhance the robustness and adaptability of a detection method. With abundant signal data, 42-D features are extracted from every vehicle signal comprising statistical features of whole waveform and short-term features of fragment signal. The Gradient Tree Boosting algorithm is employed to identify four vehicle categories. The effectiveness of the proposed approach is validated by the data collected at a freeway exit of Beijing. According to the experiential results based on 4507 vehicles, the vehicle detection algorithm proves to have 99.8% accurate rate and can be highly practical in site traffic environment. The 80.5% accuracy rate on vehicle identification approves the effectiveness of the proposed features on recognizing vehicles.
Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL

We present a minimum description length based algorithm for finding the regular correspondences between related languages and show how it can be used to quantify the similarity between not only pairs, but whole groups of languages directly from cognate sets. We employ a two-part code, which allows to use the data and model complexity of the discovered correspondences as information-theoretic quantifications of the degree of regularity of cognate realizations in these languages. Unlike previous work, our approach is not limited to pairs of languages, does not limit the size of discovered correspondences, does not make assumptions about the shape or distribution of correspondences, and requires no expert knowledge or fine-tuning of parameters. We here test our approach on the Slavic languages. In a pairwise analysis of 13 Slavic languages, we show that our algorithm replicates their linguistic classification exactly. In a four-language experiment, we demonstrate how our algorithm efficiently quantifies similarity between all subsets of the analyzed four languages and find that it is excellently suited to quantifying the orthographic regularity of closely-related languages.
Fabrication and Characterization of 3-D Photonic Crystals of Various Microspheres by Electrophoretic Self-Assembly

Fabrication of high sphericity, monodispersed microspheres (100~600 nm) of various oxides (SiO2, TiO2, ZnO, In2O3, SnO2) via sol-gel process and polystyrene (PS) microspheres (200~400 nm) via emulsion polymerization is presented. A high colloidal stability suspension was obtained by adjusting the zeta potential of such spheres and pH of the colloid. The 3-D photonic crystal (PhC) templates of opaline structure on ITO-coated glasses and silicon wafers were easily formed under electrophoretic self-assembly (EPSA) of microspheres under the influence of exerting electrical forces. Different setups of counter-electrode were attempted to establish an electrical field. The lattice constant of an ordered opal structure by EPSA can also be tuned by the electrical field gradient. Interestingly various self-assembled 3-D structures of silica microspheres in either symmetrical curvilinear profile or triangular ridges can be produced through EPSA route using specific counter-electrode setups. The measured optic properties of such 3-D PhC templates manifest photonic bandgap (PBG) based on planar-wave expansion (PWE) simulation to verify the existence of real PBG in PhC samples with tunable nanostructures. The PS PhC templates are currently used to easily transform into inverse opal structure (IOS) by infiltrating sol of other oxides with high dielectric constant (e.g. ZnO or TiO2) and filled with metallic nanoparticles (Ni or Cu) by electrochemical deposition or chemical bath deposition (CBD).
On Area and Yield Considerations for Fault-Tolerant VLSI Processor Arrays

Fault-tolerance is undoubtedly a desirable property of any processor array. However, increased design and implementation costs should be expected when fault-tolerance is being introduced into the architecture of a processor array. When the processor array is implemented within a single VLSI chip, these cost increases are directly related to the chip silicon area. Thus, the increase in area should be weighed against the improved performance of the gracefully degrading fault-tolerant processor array. In addition, a larger chip area might reduce the wafer yield to an unaceptable level making the use of fault-tolerant VLSI processor arrays impractical. The objective of this paper is to devise performance measures for the evaluation of the effectiveness and area utilization of various fault-tolerant techniques. Another goal is to analyze the reduction in wafer yield and investigate the possibility of yield enhancement through redundancy.
The role of molecular analysis of immunoglobulin and T cell receptor gene rearrangements in the diagnosis of lymphoproliferative disorders

Aims—To investigate whether the analysis of immunoglobulin (Ig)/T cell receptor (TCR) rearrangements is useful in the diagnosis of lymphoproliferative disorders. Methods—In a series of 107 consecutive cases with initial suspicion of non-Hodgkin's lymphoma (NHL), Southern blot (SB) analysis of Ig/TCR rearrangements was performed. Results—In 98 of 100 histopathologically conclusive cases, Ig/TCR gene results were concordant. In one presumed diffuse large B cell lymphoma (DLCL) and one follicular lymphoma (FL) case no clonality could be detected by SB analysis, or by polymerase chain reaction (PCR) at second stage. In the DLCL, sampling error might have occurred; the FL was revised after an initial diagnosis of reactivity. In many of the histopathologically inconclusive cases Ig/TCR gene SB analysis was helpful, giving support for the histopathological suspicion. However, because of a lack of (clinical) follow up data this could not be confirmed in a few cases. Conclusions—Experienced haematopathologists or a pathologist panel can diagnose malignant versus reactive lesions in most cases without the need for Ig/TCR gene analysis and can select the 5–10% of cases that might benefit from molecular clonality studies.
Ground2sky label transfer for fine-grained aerial car recognition

Overhead images captured by helicopters, unmanned aerial vehicles and satellites are widely available. Prior aerial target recognition methods mainly deal with generic object categories such as cars, roads, and boats. We go beyond this and aim for fine-grained recognition, e.g., distinguishing between a Toyota and a Honda sedan. This task is so challenging for human annotators that labeling images directly is no longer an option: annotators are often unable to identify the object from such an extreme viewpoint and at such a low resolution. We propose a novel solution to collect fine-grained annotations of aerial images and develop the first ground-to-sky cross-view car dataset with instance-level correspondences. We compare the performance of human experts and deep learning approaches on fine-grained car recognition from aerial imagery. Noting that intraclass variation in aerial images is limited, we further show that with simple data augmentation, a classifier can be trained from fewer instances yet achieves comparable or even significantly better performance than human experts. Our experimental evidence demonstrates that fine-grained object recognition from overhead images is not only feasible but also well suited for deep learning methods. Our dataset is available at: http://ai.bu. edu/Ground2Sky/
Simple and sophisticated inning summary generation based on encoder-decoder model and transfer learning

This paper describes an inning summarization method for a baseball game by using an encoder-decoder model. Each inning in a baseball game contains some events, such as hits, strikeouts, homeruns and scoring. Simplified description of the events leads to the improvement of readability of the inning information. Our method learns a relation between play-by-play data in each inning and inning reports. We also incorporate sophisticated expressions acquired from game summaries with the model. We call them Game-changing Phrase, GP. One problem in our task is the size of training data for the learning. To solve this problem, we apply a transfer learning approach into our method. In the experiment, we evaluate the effectiveness of our method with the transfer learning.
Period 2 is essential to maintain early endothelial progenitor cell function in vitro and angiogenesis after myocardial infarction in mice

Cellular therapeutic neovascularization has been successfully performed in clinical trials for patients with ischaemia diseases. Despite the vast knowledge of cardiovascular disease and circadian biology, the role of the circadian clock in regulating angiogenesis in myocardial infarction (MI) remains poorly understood. In this study, we aimed to investigate the role and underlying mechanisms of Period 2 (Per2) in endothelial progenitor cell (EPC) function. Flow cytometry revealed lower circulating EPC proportion in per2−/− than in wild‐type (WT) mice. PER2 was abundantly expressed in early EPCs in mice. In vitro, EPCs from per2−/− mice showed impaired proliferation, migration, tube formation and adhesion. Western blot analysis demonstrated inhibited PI3k/Akt/FoxO signalling and reduced C‐X‐C chemokine receptor type 4 (CXCR4) protein level in EPCs of per2−/− mice. The impaired proliferation was blocked by activated PI3K/Akt/FoxO signalling. Direct interaction of CXCR4 and PER2 was detected in WT EPCs. To further study the effect of per2 on in vivo EPC survival and angiogenesis, we injected saline or DiI‐labelled WT or per2−/− EPC intramyocardially into mice with induced MI. Per2−/− reduced the retention of transplanted EPCs in the myocardium, which was associated with significantly reduced DiI expression in the myocardium of MI mice. Decreased angiogenesis in the myocardium of per2−/− EPC‐treated mice coincided with decreased LV function and increased infarct size in the myocardium. Per2 may be a key factor in maintaining EPC function in vitro and in therapeutic angiogenesis in vivo.
Reconstruction of transcriptional dynamics from gene reporter data using differential equations

Motivation: Promoter-driven reporter genes, notably luciferase and green fluorescent protein, provide a tool for the generation of a vast array of time-course data sets from living cells and organisms. The aim of this study is to introduce a modeling framework based on stochastic differential equations (SDEs) and ordinary differential equations (ODEs) that addresses the problem of reconstructing transcription time-course profiles and associated degradation rates. The dynamical model is embedded into a Bayesian framework and inference is performed using Markov chain Monte Carlo algorithms. Results: We present three case studies where the methodology is used to reconstruct unobserved transcription profiles and to estimate associated degradation rates. We discuss advantages and limits of fitting either SDEs ODEs and address the problem of parameter identifiability when model variables are unobserved. We also suggest functional forms, such as on/off switches and stimulus response functions to model transcriptional dynamics and present results of fitting these to experimental data. Contact: b.f. finkenstadt@warwick.ac. uk Supplementary Information: Supplementary data are available at Bioinformatics online.
Lipid Nanoparticle–Mediated Delivery of Anti-miR-17 Family Oligonucleotide Suppresses Hepatocellular Carcinoma Growth

Hepatocellular carcinoma (HCC) is one of the most common human malignancies with poor prognosis and urgent unmet medical need. Aberrant expression of multiple members of the miR-17 family are frequently observed in HCC, and their overexpression promotes tumorigenic properties of HCC cells. However, whether pharmacologic inhibition of the miR-17 family inhibits HCC growth remains unknown. In this study, we validated that the miR-17 family was upregulated in a subset of HCC tumors and cell lines and its inhibition by a tough decoy inhibitor suppressed the growth of Hep3B and HepG2 cells, which overexpress the miR-17 family. Furthermore, inhibition of the miR-17 family led to a global derepression of direct targets of the family in all three HCC cell lines tested. Pathway analysis of the deregulated genes indicated that the genes associated with TGFβ signaling pathway were highly enriched in Hep3B and HepG2 cells. A miR-17 family target gene signature was established and used to identify RL01-17(5), a lipid nanoparticle encapsulating a potent anti-miR-17 family oligonucleotide. To address whether pharmacologic modulation of the miR-17 family can inhibit HCC growth, RL01-17(5) was systemically administrated to orthotopic Hep3B xenografts. Suppression of Hep3B tumor growth in vivo was observed and tumor growth inhibition correlated with induction of miR-17 family target genes. Together, this study provides proof-of-concept for targeting the miR-17 family in HCC therapy. Mol Cancer Ther; 16(5); 905–13. ©2017 AACR.
Pediatric pulmonology year in review 2016: Part 2

Pediatric Pulmonology continues to publish research and clinical topics related to the entire range of children's respiratory disorders. As we have done annually in recent years, we here summarize some of the past year's publications in our major topic areas, as well as selected literature in these areas from other core journals relevant to our discipline. This review (Part 2) covers selected articles on neonatology, asthma, physiology and lung function testing, and infectious diseases.
Sideband Harmonic Instability of Paralleled Inverters With Asynchronous Carriers

Paralleled inverters with asynchronous carriers are increasingly emerging into renewable power plants and other power-electronic-based power systems. The sideband harmonics resulting from the pulse width modulation tend to be different from one inverter to another, which may trigger sideband harmonic resonances among the paralleled inverters. To address the sideband harmonic instability, the nonlinearity of the digital pulse width modulator is analyzed first in this paper based on the harmonic balance principle. It is revealed that the dynamic sideband components corresponding to a given small-signal perturbation can be far below the Nyquist frequency, and they may not be effectively attenuated by antialiasing filters. A multifrequency impedance model is then developed for the inverter, which characterizes the dynamic coupling between the perturbed frequency component and the sideband frequency component. The impact of asynchronous carriers on the sideband harmonic instability of paralleled inverters can, thus, be analyzed, and the frequency and phase sequence of the resulting oscillation components are predicted. Experimental results of two paralleled inverters confirm the effectiveness of the developed model and the stability analysis
Structure/function correlations among coupled binuclear copper proteins through spectroscopic and reactivity studies of NspF

The terminal step of 4-hydroxy-3-nitrosobenzamide biosynthesis in Streptomyces murayamaensis is performed by NspF, a mono-oxygenase that converts o-aminophenols to the corresponding nitroso product (hydroxyanilinase activity). Previous biochemical characterization of the resting form of NspF suggested that this enzyme belonged to the coupled binuclear copper enzyme (CBC) family. Another member of this enzyme family, tyrosinase, is able to mono-oxygenate monophenols (monophenolase activity) but not o-aminophenols. To gain insight into the unique reactivity of NspF, we have generated and characterized the oxy form of its active site. The observation of spectral features identical to those of oxy-tyrosinase indicates that oxy-NspF contains a Cu2O2 core where peroxide is coordinated in a μ-η2∶ η2 mode, confirming that NspF is a CBC enzyme. This oxy form is found to react with monophenols, indicating that, like tyrosinase, NspF also possesses monophenolase activity. A comparison of the two electrophilic mechanisms for the monophenolase and hydroxyanilinase activity indicates a large geometric change between their respective transition states. The potential for specific interactions between the protein pocket and the substrate in each transition state is discussed within the context of the differential reactivity of this family of enzymes with equivalent μ-η2∶η2 peroxy bridged coupled binuclear copper active sites.
Rhythm and Speech Rate: A Variation Coefficient for deltaC

The percentage of vocalic intervals (%V) and the standard deviation of consonantal intervals (deltaC) in a speech signal are two dimensions according to which languages of different rhythm classes (e.g. stress-timed, syllable-timed) seem to be differentiable on an acoustic level (Ramus et al., 1999). In this context it has been found that especially deltaC varies considerably as a function of speech rate (Barry et al., 2003 and Dellwo & Wagner, 2003). The present paper argues that if deltaC was determined by speech rate it would describe speech rate rather than rhythm. For this reason a variation coefficient (varcoDeltaC) will be calculated in order to monitor relative deltaC variation across speech rates. Results for varcoDeltaC support the views that a) according to varcoDeltaC rhythm classes seem to be better differentiable and b) some languages tend to vary in rhythm as a function of speech rate (German, English), while the rhythm of other languages seems to be unaffected by changes in speech rate (French).
The Development of Composites with Negative Thermal Expansion Properties Using High Performance Fibers

Almost all solid materials exhibit positive thermal expansivity. However, in many engineering designs, materials with negative thermal expansivity are desirable. The characteristics of high performance fibers, such as high strength, exceptional thermal conductivity and electrical insulation may also include negative thermal expansivity. Therefore, it should be possible to develop fiber reinforced composites with negative thermal expansivity by using an optimum combination of particular fiber reinforcements and a polymer matrix. This paper describes the development of such high performance fiber reinforced composites with negative or low thermal expansivity using four high strength fibers — polyethylene fiber (Dyneema), poly-p-phenylenebenzobisoxazole fiber (Zylon), aramid fiber (Technora) and carbon fiber (Torayca). The results showed that the newly developed Dyneema fiber and Zylon fiber composites exhibited significant negative thermal expansivity. Structural products of a stable size over a wide temperature range can be obtained by this method of controlling the coefficient of thermal expansion.
Oral micronized progesterone combined with vaginal progesterone gel for luteal support

The aim of the present study was to compare the efficacy and satisfaction rate of combined therapy of oral micronized progesterone capsules and vaginal progesterone gel versus monotherapy with vaginal progesterone gel in luteal support. A case–control study was performed on a total number of 370 women aged <45 years undergoing IVF-ET treatment. The patients received either combination of Crinone 8% vaginal gel, 90 mg daily dose and Utrogestan oral capsules 3×100 mg, or Crinone 8% vaginal gel, 90 mg daily. Progesterone supplementation begun on the day of oocyte retrieval and continued until pregnancy was tested and in the case of pregnancy until week 8. The comparable rates of ongoing pregnancies were noted with use of combined-progesterone therapy (39.5%) and progesterone-monotherapy (33.5%). Abortion rate (6.4% vs. 15.6%) was significantly lower with the use of combined therapy. Tolerability and satisfaction of both supplements was almost equal but bleeding occurred more frequently in the progesterone-monotherapy group. In conclusion, the efficacy, satisfaction and tolerability of combined and vaginal progesterone supplements were comparable, but bleeding in early pregnancy and abortion rate presented more frequently with the use of vaginal progesterone.
Exchange Rates Impacts on Agricultural Inputs Prices using VAR

The effects of the U.S. dollar exchange rate versus the Mexican peso are evaluated for four traded nonfarm-produced inputs (fertilizer, chemicals, farm machinery, and feed) in the U.S. Unit root tests suggest that the exchange rate and the four input price ratios support the presence of unit roots with a trend model but the presence unit roots can be rejected in the first difference model. This result is consistent with a fixed price/flex price conceptual framework, with industrial prices more likely to be unresponsive to the exchange rate than farm commodity prices.