Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed under Creative Commons Cc-by 4.0 the Inheritance of Female Colour Polymorphism in Ischnura Genei (zygoptera: Coenagrionidae), with Observations on Melanism under Laboratory Conditions

Current research on female colour polymorphism in Ischnura damselflies suggests that a balanced fitness trade-off between morphotypes contributes to the maintenance of polymorphism inside populations. The genetic inheritance system constitutes a key factor to understand morph fluctuation and fitness. Ischnura genei, an endemic species of some Mediterranean islands, has three female colour morphs, including one androchrome (male-coloured) and two gynochromes. In this study, we reared two generations of I. genei under laboratory conditions and tested male behavioural responses to female colour morphs in the field. We recorded ontogenetic colour changes and studied morph frequency in three populations from Sardinia (Italy). Morph frequencies of laboratory crosses can be explained by a model based on an autosomal locus with three alleles and sex-restricted expression, except for one crossing of 42 families with unexpected offspring. The allelic dominance relationship was androchrome > infuscans > aurantiaca. Old individuals reared in the laboratory exhibited different levels of melanism in variable extent depending on sex and morph. Results of model presentations indicate a male preference for gynochrome females and the lack of recognition of androchromes as potential mates. Aurantiaca females were the most frequent morph in the field (63–87%). Further studies in other populations and islands are needed to understand the maintenance of this polymorphism.
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture

The rapid growth of IoT era is shaping the future of mobile services. Advanced communication technology enables a heterogeneous connectivity where mobile devices broadcast information to everything. Mobile applications such as robotics and vehicles connecting to cloud and surroundings transfer the short-range on-board sensor perception system to long-range mobile-sensing perception system. However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services. In this article, we model the challenges as latency, packet loss and measurement noise which severely deteriorate the reliability and quality of IoT data. We integrate the artificial intelligence into IoT to tackle these challenges. We propose a novel architecture that leverages recurrent neural networks (RNN) and Kalman filtering to anticipate motions and interac- tions between objects. The basic idea is to learn environment dynamics by recurrent networks. To improve the robustness of IoT communication, we use the idea of Kalman filtering and deploy a prediction and correction step. In this way, the architecture learns to develop a biased belief between prediction and measurement in the different situation. We demonstrate our approach with synthetic and real-world datasets with noise that mimics the challenges of IoT communications. Our method brings a new level of IoT intelligence. It is also lightweight compared to other state-of-the-art convolutional recurrent architecture and is ideally suitable for the resource-limited mobile applications.
Local Activation or Implantation of Cardiac Progenitor Cells Rescues Scarred Infarcted Myocardium Improving Cardiac Function

Ischemic heart disease is characterized chronically by a healed infarct, foci of myocardial scarring, cavitary dilation, and impaired ventricular performance. These alterations can only be reversed by replacement of scarred tissue with functionally competent myocardium. We tested whether cardiac progenitor cells (CPCs) implanted in proximity of healed infarcts or resident CPCs stimulated locally by hepatocyte growth factor and insulin-like growth factor-1 invade the scarred myocardium and generate myocytes and coronary vessels improving the hemodynamics of the infarcted heart. Hepatocyte growth factor is a powerful chemoattractant of CPCs, and insulin-like growth factor-1 promotes their proliferation and survival. Injection of CPCs or growth factors led to the replacement of ≈42% of the scar with newly formed myocardium, attenuated ventricular dilation and prevented the chronic decline in function of the infarcted heart. Cardiac repair was mediated by the ability of CPCs to synthesize matrix metalloproteinases that degraded collagen proteins, forming tunnels within the fibrotic tissue during their migration across the scarred myocardium. New myocytes had a 2n karyotype and possessed 2 sex chromosomes, excluding cell fusion. Clinically, CPCs represent an ideal candidate cell for cardiac repair in patients with chronic heart failure. CPCs may be isolated from myocardial biopsies and, following their expansion in vitro, administered back to the same patients avoiding the adverse effects associated with the use of nonautologous cells. Alternatively, growth factors may be delivered locally to stimulate resident CPCs and promote myocardial regeneration. These forms of treatments could be repeated over time to reduce progressively tissue scarring and expand the working myocardium.
Statistical modeling of relations for 3-D object recognition

A new Bayesian framework for 3-D object classification and localization is introduced. Objects are represented as probability density functions, and observed features are treated as random variables. These probability density functions turn out a non geometric nature of models and characterize the statistical behavior of local object features like points or lines. The parameterization of model densities covers several terms of object recognition: locations and instabilities of features, rotation and translation, projection, the assignment of image and model features, as well as relations. This paper treats especially the probabilistic modeling of relational dependencies between single features. The mathematical framework, the training algorithms, as well as the localization and classification modules are discussed in detail. The experimental evaluation shows the usefulness of the introduced concepts on real image data.
Microwave emission from dry snow: a comparison of experimental and model results

Field measurements of microwave emission from snow-covered soil were carried out in 1996, 1997, and 1999 on the Italian Alps using a three-frequency dual polarized microwave system. At the same time, nivological time measurements were carried out using standard methods and an electromagnetic contact probe. Collected data confirmed the possibility of separating wet from dry snow and of estimating the water equivalent of dry snow. Simulations performed by means of a model based on the dense medium radiative theory (DMRT) were able to reproduce experimental data very well.
Approaches for Improving Field Soil Identification

Use of soil survey information by non-soil-scientists is often limited by their inability to select the correct soil map unit component (COMP). Here, we developed two approaches that can be deployed to smartphones for non-soilscientists to identify COMP from the location alone or location together with easily observed field data (i.e., slope, depth to the restrictive layer, and soil texture by depth). In addition, we also compared the two newly developed approaches with a traditional approach identifying COMP based on the dominant COMP (DC-based approach). All three approaches were tested with the Rapid Assessment of US Soil Carbon database and the combined USDANRCS Soil Survey Geographic database and the USDA-NRCS State Soil Geographic Database. The results indicated that the observation-based approach performed significantly better than the other two approaches, suggesting that a small set of easy-to-measure site-specific observations could significantly improve COMP identification. The locationand DC-based approaches had similar low performance overall. However, the location-based approach slightly improved identifications over the DC-based approach for cases where (i) there were multiple possible components within the soil map unit and (ii) the components were located in close proximity to a boundary of a different soil map unit polygon. The benefit of using the location-based approach may be greater in specific soil survey areas where topography was the major factor leading to the creation of the map unit legend.
Spectral- and energy-efficient antenna tilting in a HetNet using reinforcement learning

In cellular networks, balancing the throughput among users is important to achieve a uniform Quality-of-Service (QoS). This can be accomplished using a variety of cross-layer techniques. In this paper, the authors investigate how the down-tilt of base-station (BS) antennas can be adjusted to maximize the user throughput fairness in a heterogeneous network, considering the impact of both a dynamic user distribution and capacity saturation of different transmission techniques. Finding the optimal down-tilt in a multi-cell interference-limited network is a complex problem, where stochastic channel effects and irregular antenna patterns has yielded no explicit solutions and is computationally expensive. The investigation first demonstrates that a fixed tilt strategy yields good performances for homogeneous networks, but the introduction of HetNet elements adds a high level of sensitivity to the tilt dependent performance. This means that a HetNet must have network-wide knowledge of where BSs, access-points and users are. The paper also demonstrates that transmission techniques that can achieve a higher level of capacity saturation increases the optimal down-tilt angle. A distributed reinforcement learning algorithm is proposed, where BSs do not need knowledge of location data. The algorithm can achieve convergence to a near-optimal solution rapidly (6-15 iterations) and improve the throughput fairness by 45-56% and the energy efficiency by 21-47%, as compared to fixed strategies. Furthermore, the paper shows that a tradeoff between the optimal solution convergence rate and asymptotic performance exists for the self-learning algorithm.
Automated Architecture-Aware Mapping of Streaming Applications Onto GPUs

Graphic Processing Units (GPUs) are made up of many streaming multiprocessors, each consisting of processing cores that interleave the execution of a large number of threads. Groups of threads - called {\em warps} and {\em wave fronts}, respectively, in nVidia and AMD literature - are selected by the hardware scheduler and executed in lockstep on the available cores. If threads in such a group access the slow off-chip global memory, the entire group has to be stalled, and another group is scheduled instead. The utilization of a given multiprocessor will remain high if there is a sufficient number of alternative thread groups to select from. Many parallel general purpose applications have been efficiently mapped to GPUs. Unfortunately, many stream processing applications exhibit unfavorable data movement patterns and low computation-to-communication ratio that may lead to poor performance. In this paper, we describe an automated compilation flow that maps most stream processing applications onto GPUs by taking into consideration two important architectural features of nVidia GPUs, namely interleaved execution as well as the small amount of shared memory available in each streaming multiprocessors. In particular, we show that using a small number of compute threads such that the memory footprint is reduced, we can achieve high utilization of the GPU cores. Our scheme goes against the conventional wisdom of GPU programming which is to use a large number of homogeneous threads. Instead, it uses a mix of {\em compute} and {\em memory access} threads, together with a carefully crafted schedule that exploits parallelism in the streaming application, while maximizing the effectiveness of the unique memory hierarchy. \% small on-chip memory located within each streaming multiprocessor. We have implemented our scheme in the compiler of the Stream It programming language, and our results show a significant speedup compared to the state-of-the-art solutions.
Gavel: Software-defined network control with graph databases

Writing network policies for Software Defined Networks (SDNs) is not a straightforward task. Abstractions play a major role in easing this task, but they are typically provided for a particular use case. As a consequence, emerging applications will require the development and implementation of new abstractions, and combining the policies of different abstractions becomes difficult. To offer a simpler framework, plain data representations of the network and its control infrastructure have been proposed recently to offer programmable ad-hoc abstractions to administrators. However, these representations still induce unnecessary complexity and are additionally inferior to use function-specific abstractions from a performance point-of-view. In this work, we propose Gavel, an SDN controller based on a graph database. By exploiting the native graph support of the database engine, Gavel significantly eases application and policy writing. Additionally, we show by experimental evaluation of several typical applications that Gavel can significantly increase the performance when compared with previous works.
Left dorsal speech stream components and their contribution to phonological processing

Models propose an auditory-motor mapping via a left-hemispheric dorsal speech-processing stream, yet its detailed contributions to speech perception and production are unclear. Using fMRI-navigated repetitive transcranial magnetic stimulation (rTMS), we virtually lesioned left dorsal stream components in healthy human subjects and probed the consequences on speech-related facilitation of articulatory motor cortex (M1) excitability, as indexed by increases in motor-evoked potential (MEP) amplitude of a lip muscle, and on speech processing performance in phonological tests. Speech-related MEP facilitation was disrupted by rTMS of the posterior superior temporal sulcus (pSTS), the sylvian parieto-temporal region (SPT), and by double-knock-out but not individual lesioning of pars opercularis of the inferior frontal gyrus (pIFG) and the dorsal premotor cortex (dPMC), and not by rTMS of the ventral speech-processing stream or an occipital control site. RTMS of the dorsal stream but not of the ventral stream or the occipital control site caused deficits specifically in the processing of fast transients of the acoustic speech signal. Performance of syllable and pseudoword repetition correlated with speech-related MEP facilitation, and this relation was abolished with rTMS of pSTS, SPT, and pIFG. Findings provide direct evidence that auditory-motor mapping in the left dorsal stream causes reliable and specific speech-related MEP facilitation in left articulatory M1. The left dorsal stream targets the articulatory M1 through pSTS and SPT constituting essential posterior input regions and parallel via frontal pathways through pIFG and dPMC. Finally, engagement of the left dorsal stream is necessary for processing of fast transients in the auditory signal.
A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts

We propose a unified computational framework to build a statistical atlas of the cardiac fiber architecture from diffusion tensor magnetic resonance images (DT-MRIs). We apply this framework to a small database of nine ex vivo canine hearts. An average cardiac fiber architecture and a measure of its variability are computed using most recent advances in diffusion tensor statistics. This statistical analysis confirms the already established good stability of the fiber orientations and a higher variability of the laminar sheet orientations within a given species. The statistical comparison between the canine atlas and a standard human cardiac DT-MRI shows a better stability of the fiber orientations than their laminar sheet orientations between the two species. The proposed computational framework can be applied to larger databases of cardiac DT-MRIs from various species to better establish intraspecies and interspecies statistics on the anatomical structure of cardiac fibers. This information will be useful to guide the adjustment of average fiber models onto specific patients from in vivo anatomical imaging modalities.
A SCIENTOMETRIC ANALYSIS OF ENVIRONMENTAL MANAGEMENT RESEARCH OUTPUT DURING 1989 TO 2014

The main objective of this article is to find out most prolific authors and journals in the environmental management research output during 1989 to 2014. This area is an interdisciplinary subject and developed dramatically over the last few decades. Scientometrics offers assess the quantitative analytical techniques with the development and growth of research in Environmental management. The total sample data retrieved from the database of Web of Knowledge, includes, SCI, SSCI, A&HCI. Total records of 61877 research articles retrieved from 22 types of different sources. Applicability of Bradford’s law and Lotka’s law methods was tested.
EVOLUTIONARY RESPONSES OF A COCCOLITHOPHORID GEPHYROCAPSA OCEANICA TO OCEAN ACIDIFICATION

The ongoing ocean acidification associated with a changing carbonate system may impose profound effects on marine planktonic calcifiers. Here, we show that a coccolithophore, Gephyrocapsa oceanica, evolved in response to an elevated CO2 concentration of 1000 μatm (pH reduced to 7.8) in a long‐term (∼670 generations) selection experiment. The high CO2‐selected cells showed increases in photosynthetic carbon fixation, growth rate, cellular particulate organic carbon (POC) or nitrogen (PON) production, and a decrease in C:N elemental ratio, indicating a greater upregulation of PON than of POC production under the ocean acidification condition. Cells from the low CO2 selection process shifted to high CO2 exposure showed an enhanced cellular POC and PON production rates. Our data suggest that the coccolithophorid could adapt to ocean acidification with enhanced assimilations of carbon and nitrogen but decreased C:N ratios.
Semiparametric regression models for detecting effect modification in matched case‐crossover studies

In matched case‐crossover studies, it is generally accepted that covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of a case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. In addition, the matching covariates may be effect modification and the methods for assessing and characterizing effect modification by matching covariates are quite limited. In this article, we propose a unified approach in its ability to detect both parametric and nonparametric relationships between the predictor and the relative risk of disease or binary outcome, as well as potential effect modifications by matching covariates. Two methods are developed using two semiparametric models: (1) the regression spline varying coefficients model and (2) the regression spline interaction model. Simulation results show that the two approaches are comparable. These methods can be used in any matched case‐control study and extend to multilevel effect modification studies. We demonstrate the advantage of our approach using an epidemiological example of a 1–4 bi‐directional case‐crossover study of childhood aseptic meningitis associated with drinking water turbidity. Copyright © 2011 John Wiley & Sons, Ltd.
Low complexity cut detection in the presence of flicker

This paper deals with techniques to detect abrupt scene transitions when random brightness variations (flicker) are present. This is normally the case when trying to restore or index old films. The application of conventional techniques in this situation tends to produce a large number of false positive detection of cuts. The paper is intently restricted to techniques which require low computation (no motion estimation).
Patient-Derived Xenograft Models to Improve Targeted Therapy in Epithelial Ovarian Cancer Treatment

Despite increasing evidence that precision therapy targeted to the molecular drivers of a cancer has the potential to improve clinical outcomes, high-grade epithelial ovarian cancer (OC) patients are currently treated without consideration of molecular phenotype, and predictive biomarkers that could better inform treatment remain unknown. Delivery of precision therapy requires improved integration of laboratory-based models and cutting-edge clinical research, with pre-clinical models predicting patient subsets that will benefit from a particular targeted therapeutic. Patient-derived xenografts (PDXs) are renewable tumor models engrafted in mice, generated from fresh human tumors without prior in vitro exposure. PDX models allow an invaluable assessment of tumor evolution and adaptive response to therapy. PDX models have been applied to pre-clinical drug testing and biomarker identification in a number of cancers including ovarian, pancreatic, breast, and prostate cancers. These models have been shown to be biologically stable and accurately reflect the patient tumor with regards to histopathology, gene expression, genetic mutations, and therapeutic response. However, pre-clinical analyses of molecularly annotated PDX models derived from high-grade serous ovarian cancer (HG-SOC) remain limited. In vivo response to conventional and/or targeted therapeutics has only been described for very small numbers of individual HG-SOC PDX in conjunction with sparse molecular annotation and patient outcome data. Recently, two consecutive panels of epithelial OC PDX correlate in vivo platinum response with molecular aberrations and source patient clinical outcomes. These studies underpin the value of PDX models to better direct chemotherapy and predict response to targeted therapy. Tumor heterogeneity, before and following treatment, as well as the importance of multiple molecular aberrations per individual tumor underscore some of the important issues addressed in PDX models.
Application of explanation-based learning for efficient processing of constraint-based grammars

Describes the application of explanation-based learning (EBL) for efficient processing of constraint-based grammars. The idea is to generalize the derivations of training instances created by normal parsing automatically and to use these generalized derivations (called templates) during the run-time mode of the system. In the case when a template can be instantiated for a new input, no further grammatical analysis is necessary. The approach is not restricted to sentential level but can be applied also to arbitrary phrases. Therefore, the EBL method can be interleaved straightforwardly with normal processing to get back flexibility that otherwise would be lost. <<ETX>>