Miracle-dz/UniFAR-FTUs huggingface dataset:
Lack of relationship between simulated fish population responses and their life history traits: inadequate models, incorrect analysis, or site-specific factors?

Relationships between fish population responses to changes in their vital rates and commonly available life history traits would be a powerful screening tool to guide management about species vulnerability, to focus future data collection on species and life stages of concern, and to aid in designing effective habitat enhancements. As an extension of previous analyses by others, I analyzed the responses to changes in fecundity and yearling survival of age-structured matrix and individual-based population models of 17 populations comprising 10 species. Simulations of the matrix models showed that the magnitude of population responses, but not the relative order of species sensitivity, depended on the state (sustainable or undergoing excessive removals) of the population. Matrix and individual-based models predicted population responses that appeared to be unrelated to their species-level life history traits when responses were plotted on a three-end-point life history surface. Density-dependent adult growt.
GlobalFungi: Global database of fungal records from high-throughput-sequencing metabarcoding studies

Fungi are key players in vital ecosystem services, spanning carbon cycling, decomposition, symbiotic associations with cultural and wild plants and pathogenicity. The high importance of fungi in the ecosystem processes contrasts with the incompleteness of understanding of the patterns of fungal biogeography and the environmental factors that drive it. To close this gap of knowledge, we have here collected and validated data published on the composition of soil fungal communities in terrestrial environments including soil and plant-associated habitats and made them publicly accessible through a user interface at http://globalfungi.com. The GlobalFungi database contains over 650 million observations of fungal sequences across >20 000 samples with geographical locations and additional metadata contained in 207 original studies with millions of unique sequence variants of the fungal internal transcribed spacers (ITS) 1 and 2 representing fungal species and genera. As it is, the study represents the most comprehensive atlas of fungal distribution on the global scale open to further additions.
Predicting the presence and titer of rabies virus neutralizing antibodies from low-volume serum samples in low-containment facilities

Serology is a core component of the surveillance and management of viral zoonoses. Virus neutralization tests are a gold standard serological diagnostic, but requirements for large volumes of serum and high biosafety containment can limit widespread use. Here, focusing on Rabies lyssavirus, a globally important zoonosis, we developed a pseudotype micro-neutralization rapid fluorescent focus inhibition test (pmRFFIT) that overcomes these limitations. Specifically, we adapted an existing micro-neutralization test to use a green fluorescent protein–tagged murine leukemia virus pseudotype in lieu of pathogenic rabies virus, reducing the need for specialized reagents for antigen detection and enabling use in low-containment laboratories. We further used statistical analysis to generate rapid, quantitative predictions of the probability and titer of rabies virus neutralizing antibodies from microscopic imaging of neutralization outcomes. Using 47 serum samples from domestic dogs with neutralizing antibody titers estimated using the fluorescent antibody virus neutralization test (FAVN), pmRFFIT showed moderate sensitivity (78.79%) and high specificity (84.62%). Despite small conflicts, titer predictions were correlated across tests repeated on different dates both for dog samples (r = 0.93), and for a second dataset of sera from wild common vampire bats (r = 0.72, N = 41), indicating repeatability. Our test uses a starting volume of 3.5 μL of serum, estimates titers from a single dilution of serum rather than requiring multiple dilutions and end point titration, and may be adapted to target neutralizing antibodies against alternative lyssavirus species. The pmRFFIT enables high-throughput detection of rabies virus neutralizing antibodies in low-biocontainment settings and is suited to studies in wild or captive animals where large serum volumes cannot be obtained.
The Parasexual Cycle in Ustilago scabiosae (Ustilaginales)

The parasexual cycle has been demonstrated in Ustilago scabiosae, a fungal pathogen for species in the Dipsaceae. Wild strains of this species were auxotrophic, and the colonies were pink (+), orange (o) or yellow (y). Conjugated sporidia of a-1 y and a-2 o strains gave pink, smooth colonies on a complex medium and had diploid auxotrophic sporidia. The colonies from diploid sporidia on a complex medium were smaller by one-third to one-fourth than colonies from haploid sporidia, but the diploid sporidia were conspicuously longer and wider than the haploid sporidia. Diploid sporidia are stable, not yielding y or o segregants. After incubation on a complex medium containing thiabendazole, diploid sporidia gave uniformly yellow and orange colonies and colonies with yellow or orange sectors. Of nine yellow haploid segregants on this medium, eight were a-2 and one a-1, and of the 40 orange segregants, 39 were a-2 and one a-1. Segregants from UV-irradiated diploid sporidia gave large, uniformly yellow or orange colonies and had the a-2 mating type allele. Numerous pink colonies with one segregant color sector were also observed. The majority of the small to very small (< 1 mm) colony segregants were uniformly pink and stable or had a yellow or orange sector. The uniformly pink colonies had a-2 sporidia.
Image Recognition Approach for Expediting Chinese Cafeteria Checkout Process

One of the common running themes in modern-day Chinese cafeterias is the hold up in foot traffic in queueing due to checkout. We find out that this bottleneck is caused by the staff requiring extra time to look up the prices of those miscellaneous entrees and calculating the total due amount during checkout. In this paper, this issue is addressed by introducing real-time image recognition techniques into this process. By using a webcam taking live video feed at the checkout desk with the image recognition model outputs the total due amount simultaneously, we are able to eliminate the need to perform manual price calculations. Additionally, the nutrition facts of the meal can also be calculated and displayed to the customers based on the detected entrees. The image recognition model is based on YOLOv3 with 575 entree-catered plate images involved in model training, validation, and testing. The transfer learning technique is also incorporated to speed up the training process. Experimental results show that the recognition accuracy of individual entree is around 70% and that of the entire plate is roughly 63%. With the advanced training with a larger dataset, we believe that the accuracy can be increased, and applying the approach during the checkout will become more practicable.
MacroBase

As data volumes continue to rise, manual inspection is becoming increasingly untenable. In response, we present MacroBase, a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. MacroBase is able to deliver order-of-magnitude speedups over alternatives by optimizing the combination of explanation (i.e., feature selection) and classification tasks and by leveraging a new reservoir sampler and heavy-hitters sketch specialized for fast data streams. As a result, MacroBase delivers accurate results at speeds of up to 2M events per second per query on a single core. The system has delivered meaningful results in production, including at a telematics company monitoring hundreds of thousands of vehicles.
A Bridgeless Isolated Half Bridge Converter Based EV Charger with Power Factor Pre-regulation

Cconventional full bridge and half bridge converters are quite popular for front-end power factor pre-regulation (PFP) in EV (Electric Vehicle) battery chargers. DCM operation at high power causes increased oscillation in the circuit. These oscillation increase with parasitic components of circuit. During deep DCM operation, these oscillations are seen to be significant and contribute to harmonic content in the input current. Therefore, a power factor pre-regulation bridgeless isolated half bridge (HB) converter based electric vehicle (EV) charger with reduced LC oscillation in discontinuous conduction mode (DCM), is presented in this paper. To reduce LC oscillation, two diodes are used in series with minimum number of magnetic components. The design of proposed charger in DCM is illustrated to facilitate uniform battery current during constant current/constant voltage (CC-CV) charging. The inherent advantage of reduced conduction loss with bridgeless topology are achieved. The performance of this isolated HB converter based charger is shown for improved oscillation characteristics and power quality (PQ) operation at steady state and over a range of varying input voltages.
Genetic Variability within U.S. Maize Germplasm. II. Widely-Used Inbred Lines 1970 to 1979 1

Isozyme data for 21 loci from lines previously found to be of major importance in the production of U.S. hybrid maize (Zea mays L.) between 1970 and 1979 were analyzed by principal component analysis. The objectives were to provide an objective appraisal of the extent of genetic diversity among lines, to reveal evidence of genetic diversity in time, and to compare genetic diversity among public lines with theft found among commercial hybrids. Overall genetic variability among public lines increased gradually from 1970 to 1979. Although there was a reduction in the use of genetically diverse ‘Reid Yellow Dent’ lines, continued selections from ‘Iowa Stiff Stalk Synthetic’ produced genotypically different lines. Although there was a decline in variation among widely used ‘Lancaster Sure Crop’ lines, changes in the usage of genotypically different Lancaster Sure Crop and related lines provided genetic diversity in time. No major differences were seen in allelic frequencies between public lines and commercial hybrids. Continued use of genetically different elite lines contributes to genetic conservation by maximizing the use of currently deployed genetic res.ources. However, the longterm goal should be to increase the genetic base available to breeders and producers.
Greater Sage-Grouse Nesting Habitat: The Importance of Managing at Multiple Scales

Abstract Considering habitat selection at multiple scales is essential to fully understand habitat requirements and management needs for wildlife species of concern. We used a hierarchical information-theoretic approach and variance decomposition techniques to analyze habitat selection using local-scale habitat variables measured in the field and landscape-scale variables derived with a Geographic Information System (GIS) for nesting greater sage-grouse (Centrocercus urophasianus) in the Powder River Basin (PRB), Montana and Wyoming, USA, 2003–2007. We investigated relationships between habitat features that can and cannot be mapped in a GIS to provide insights into interpretation of landscape-scale–only GIS models. We produced models of habitat selection at both local and landscape scales and across scales, yet multiscale models had overwhelming statistical and biological support. Variance decomposition showed that local-scale measures explained the most pure variation (50%) in sage-grouse nesting-habitat selection. Landscape-scale features explained 20% of pure variation and shared 30% with local-scale features. Both local- and landscape-scale habitat features are important in sage-grouse nesting-habitat selection because each scale explained both pure and shared variation. Our landscape-scale model was accurate in predicting priority landscapes where sage-grouse nests would occur and is, therefore, useful in providing landscape context for management decisions. It accurately predicted locations of independent sage-grouse nests (validation R2 = 0.99) and showed good discriminatory ability with >90% of nests located within only 40% of the study area. Our landscape-scale model also accurately predicted independent lek locations. We estimated twice the amount of predicted nesting habitat within 3 km of leks compared to random locations in the PRB. Likewise we estimated 1.8 times more predicted nesting habitat within 10 km of leks compared to random locations. These results support predictions of the hotspot theory of lek placement. Local-scale habitat variables that cannot currently be mapped in a GIS strongly influence sage-grouse nest-site selection, but only within priority nesting habitats defined at the landscape scale. Our results indicate that habitat treatments for nesting sage-grouse applied in areas with an unsuitable landscape context are unlikely to achieve desired conservation results.
Emotional Voice Conversion Using a Hybrid Framework With Speaker-Adaptive DNN and Particle-Swarm-Optimized Neural Network

We propose a hybrid network-based learning framework for speaker-adaptive vocal emotion conversion, tested on three different datasets (languages), namely, EmoDB (German), IITKGP (Telugu), and SAVEE (English). The optimized learning model introduced is unique because of its ability to synthesize emotional speech with an acceptable perceptive quality while preserving speaker characteristics. The multilingual model is extremely beneficial in scenarios wherein emotional training data from a specific target speaker are sparsely available. The proposed model uses speaker-normalized mel-generalized cepstral coefficients for spectral training with data adaptation using the seed data from the target speaker. The fundamental frequency (F0) is transformed using a wavelet synchrosqueezed transform prior to mapping to obtain a sharpened time–frequency representation. Moreover, a feedforward artificial neural network, together with particle swarm optimization, was used for F0 training. Additionally, static-intensity modification was also performed for each test utterance. Using the framework, we were able to capture the spectral and pitch contour variabilities of emotional expression better than with other state-of-the-art methods used in this study. Considering the overall performance scores across datasets, an average melcepstral distortion (MCD) of 4.98 and root mean square error (RMSE-F0) of 10.67 were obtained in objective evaluations, and an average comparative mean opinion score (CMOS) of 3.57 and speaker similarity score of 3.70 were obtained for the proposed framework. Particularly, the best MCD of 4.09 (EmoDB-happiness) and RMSE-F0 of 9.00 (EmoDB-anger) were obtained, along with the maximum CMOS of 3.7 and speaker similarity of 4.6, thereby highlighting the effectiveness of the hybrid network model.
Towards a Research Model of Post-adoption of Open Government Data in Malaysia’s Public Sector

Open Government Data (OGD) is regarded as an organization-level innovation that works ideally in a data openness ecosystem where a government publishes data for free use and re-use by anyone without any restrictions. These days, the extant study on OGD adoption largely focused on finding the factors that influence the OGD adopter in the adoption phase. Although these studies help to clarify the adopter’s decision, the stance of the adopter after accepting the OGD is very much important. Knowing the limited availability of the literature about OGD in the post-adoption phase, this study seeks to propose a research model for OGD implementation in the post-adoption phase in Malaysia’s public sector. The research model is drawn from the Technology-Organization-Environment framework and innovation adoption process as the theoretical foundation, while the OGD principles are integrated as an added construct to the research model. With this research model, researchers would be able to explain the OGD adoption continuity in the post-adoption phase from the perspective of the data provider.
Recommendation based on Emotional Preference

This paper describes an outline and a design of a recommender system that gives recommendations of contents based on the estimation of user’s emotional preferences. Recommender systems are essential tools in the age of information overload, and they are helpful to increase comfortability and quality of life by filtering out annoying, unnecessary and troublesome information. The standard recommender system uses personal database, which stores the profile and preferences of a user. The key point of this study is a use of emotions in the personal preferences. Emotions are subjective quantities so that are difficult to deal with. This paper explains how our system identify emotions and how to deal with them in the recommender system. We also consider risk avoidance in information presentation of recommendations.
Role of elementary Ca2+ puffs in generating repetitive Ca2+ oscillations

Inositol (1,4,5)‐trisphosphate (IP3) liberates intracellular Ca2+ both as localized ‘puffs’ and as repetitive waves that encode information in a frequency‐dependent manner. Using video‐rate confocal imaging, together with photorelease of IP3 in Xenopus oocytes, we investigated the roles of puffs in determining the periodicity of global Ca2+ waves. Wave frequency is not delimited solely by cyclical recovery of the cell's ability to support wave propagation, but further involves sensitization of Ca2+‐induced Ca2+ release by progressive increases in puff frequency and amplitude at numerous sites during the interwave period, and accumulation of pacemaker Ca2+, allowing a puff at a ’focal‘ site to trigger a subsequent wave. These specific ’focal' sites, distinguished by their higher sensitivity to IP3 and close apposition to neighboring puff sites, preferentially entrain both the temporal frequency and spatial directionality of Ca2+ waves. Although summation of activity from many stochastic puff sites promotes the generation of regularly periodic global Ca2+ signals, the properties of individual Ca2+ puffs control the kinetics of Ca2+ spiking and the (higher) frequency of subcellular spikes in their local microdomain.
On the Performance of Holographic Extended Kalman Filters for Localization in Industrial Automation

Microwave localization systems are increasingly popular for the proceeding automation of logistics and modern factories, allowing for robust tracking of freely moving objects and workers. To enable reliable live-tracking, accurate positioning algorithms with high update rates are desirable. For this purpose, the holographic extended Kalman filter (HEKF) was presented in [1], combining low computational effort and an outstanding localization accuracy in the centimeter range. In this paper, the performance of the HEKF in a 24-GHz frequency modulated continuous wave system is evaluated. The impact on the accuracy of the evaluation frequency of the phase, reduced oversampling, and non-uniform movements are also investigated.
Machine Learning Application in Network Resiliency based on Capacity Regulations

There is a considerable interest in air transportation resilience as a mechanism to cope with the consequences of disruptions to local authorities. Although the identification of metrics and baselines for measuring resilience are still regarded as challenges, we believe that the meaning of disruptions is no longer driven solely by safety threats but also by emergent performance issues. In this paper, resilience of the European Air Traffic Management Network (EATMN) is studied from a performance perspective. In fact, improved predictability and reliability of planning data across the EATMN, allow reduction of reserved Air Traffic Management (ATM) capacity. Consequently, the management of emergent demand-capacity imbalances, regarded as disruptions, is added to tactical phase of Air Fraffic Flow and Capacity Management (ATFCM). In this phase of operations (i.e. day-of-operations) a limited number of variables are available to form aggregated indicators for network resilience. We consider that available ATFCM regulations data reveal restorative mechanisms for tactical Demand-Capacity Balancing (DCB). Aggregated indicators are regarded as enablers to monitor the resilient management of Area Control Centers and to observe spatial distribution of network resiliency. This paper presents an exploratory effort of the needed situational awareness by exploring supervised learning techniques in the context of ATFCM regulations to predict Air Traffic Flow Management (ATFM) delay. In particular, it focuses on the application of machine learning algorithms and comparison of different architecture variants to a regression study on tactical DCB disruptions.
Spin-labelling and Differential Scanning Calorimetry Study of the Denaturation of Aspartic Proteinases from the Fungi Endothia parasitica and Mucor miehei

Proteinases which successfully clot milk belong to the class ofenzymes known as aspartic proteinases (EC 3.4.23). A world-wide shortage of the aspartic proteinase chymosin, which is traditionally used in the production of cheese, has prompted a search for adequate substitutes. Two alternatives are the aspartic proteinases from fungi Endothia parasitica and Mucor miehei. Althogh M. miehei proteinase prossesses high milk-clotting potential relative to its capacity for general proteolysis,1'2) high thermo-
Analysis and Design of Fourth-order Quasi-Z-Source Equivalent DC-DC Boost Converter

A fourth-order quasi-Z-source equivalent DC-DC boost converter (FOEBC) exhibiting the voltage gain conversion ratio identical to Z-source dc-dc boost converter (ZSC) is proposed in this paper. It provides voltage gain identical to ZSC but with smooth source current waveform and thus leads to reduced EMI. On the basis of steady-state analysis, the principle of operation, exchange of energy among various inductive and capacitive elements is explained. Subsequently, design guidelines for inductors and capacitors is established. To highlight the controller design issues, small-signal analysis of the proposed converter is presented. A voltage-mode controller is designed to demonstrate its feasibility both in simulation and experiments. A 42 V to 100 V, 50 W laboratory prototype verifies the converter regulation capability against source and load disturbances. The comparison of proposed converters with other conventional power converters is also described.