Miracle-dz/UniFAR-FTUs huggingface dataset:
The ecology and evolution of temperature‐dependent reaction norms for sex determination in reptiles: a mechanistic conceptual model

Sex‐determining mechanisms are broadly categorised as being based on either genetic or environmental factors. Vertebrate sex determination exhibits remarkable diversity but displays distinct phylogenetic patterns. While all eutherian mammals possess XY male heterogamety and female heterogamety (ZW) is ubiquitous in birds, poikilothermic vertebrates (fish, amphibians and reptiles) exhibit multiple genetic sex‐determination (GSD) systems as well as environmental sex determination (ESD). Temperature is the factor controlling ESD in reptiles and temperature‐dependent sex determination (TSD) in reptiles has become a focal point in the study of this phenomenon. Current patterns of climate change may cause detrimental skews in the population sex ratios of reptiles exhibiting TSD. Understanding the patterns of variation, both within and among populations and linking such patterns with the selection processes they are associated with, is the central challenge of research aimed at predicting the capacity of populations to adapt to novel conditions. Here we present a conceptual model that innovates by defining an individual reaction norm for sex determination as a range of incubation temperatures. By deconstructing individual reaction norms for TSD and revealing their underlying interacting elements, we offer a conceptual solution that explains how variation among individual reaction norms can be inferred from the pattern of population reaction norms. The model also links environmental variation with the different patterns of TSD and describes the processes from which they may arise. Specific climate scenarios are singled out as eco‐evolutionary traps that may lead to demographic extinction or a transition to either male or female heterogametic GSD. We describe how the conceptual principles can be applied to interpret TSD data and to explain the adaptive capacity of TSD to climate change as well as its limits and the potential applications for conservation and management programs.
Segmentation of Cervical Cell Images

The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image by a non-parametric hierarchical segmentation algorithm that uses spectral and shape information as well as the gradient information. The second phase aims to obtain nucleus regions and cytoplasm areas by classifying the segments resulting from the first phase based on their spectral and shape features. Experiments using two data sets show that our method performs well for images containing both a single cell and many overlapping cells.
The role of IL‐5 in bleomycin‐induced pulmonary fibrosis

Eosinophils are known to express cytokines capable of promoting fibrosis. Interleukin‐5 (IL‐5) is important in regulating eosinophilopoiesis, eosinophil recruitment and activation. Lung IL‐5 expression is elevated in pulmonary fibrosis, wherein the eosinophil is a primary source of fibrogenic cytokines. To determine the role of IL‐5 in pulmonary fibrosis, the effects of anti‐IL‐5 antibody were investigated in a model of bleomycin‐induced pulmonary fibrosis. Fibrosis was induced in mice by endotracheal bleomycin treatment. Animals were also treated with either anti‐IL‐5 antibody or control IgG. Lungs were then analyzed for fibrosis, eosinophil influx, chemotactic activity, and cytokine expression. The results show that a primary chemotactic activity at the height of eosinophil recruitment is IL‐5. Furthermore, anti‐IL‐5 antibody caused significant reduction in lung eosinophilia, cytokine expression, and fibrosis. These findings taken together suggest an important role for IL‐5 in pulmonary fibrosis via its ability to regulate eosinophilic inflammation, and thus eosinophil‐dependent fibrogenic cytokine production. J. Leukoc. Biol. 64: 657–666; 1998.
Beautifying Sketching-based Design Tool Content: Issues and Experiences

With the advent of the Tablet PC and stylus-based PDAs, sketching-based user interfaces for design tools have become popular. However, a major challenge with such interfaces is the need for appropriate "beautification" of the sketches. This includes both interactive beautification as content is sketched and post-design conversion of sketches to formalised, computer-drawn diagrams. We discuss a number of beautification issues and requirements for sketching-based design tools, illustrating these with examples from two quite different sketching-based applications. We illustrate ways of supporting beautification, user interface design and implementation challenges, and results from preliminary evaluations of such interfaces.
Tracking the Tracker from its Passive Sonar ML-PDA Estimates

Target motion analysis (TMA) with wideband passive sonar has received much attention. Maximum likelihood probabilistic data association (ML-PDA) represents an asymptotically efficient estimator for deterministic target motion, and is especially well suited for low-observable targets; the results presented here apply to situations with higher signal-to-noise ratio as well, including of course the situation of a deterministic target observed via “clean” measurements without false alarms or missed detections. Here we study the inverse problem, namely, how to identify the observing platform (following a “two-leg” motion model) from the results of the target estimation process, i.e., the estimated target state and the Fisher information matrix (FIM), quantities we assume an eavesdropper might intercept. We tackle the problem and we present observability properties, with supporting simulation results.
The diversity of the influenza-specific primary B-cell repertoire in BALB/c mice

The primary immune response of BALB/c mice to influenza (PR8) hemagglutinin (HA), a complex protein antigen, has been examined by the splenic focus assay, and the resulting monoclonal anti-HA antibodies have been characterized by their reactivity with heterologous viruses. The analysis of the primary B-cell response to HA revealed marked differences from responses previously defined for haptenic determinants. There were following differences: (a) the frequency of HA- specific B cells in both conventional and germ-free BALB/c mice was 1 in 1.0-1.5 X 10(5) splenic B cells, which is substantially lower than the frequency of B cells responsive to various simple haptenic determinants; (b) monoclonal anti-HA antibodies were predominantly of the IgA or IgM isotypes instead of IgG, which dominates antihapten responses; and (c) after immunization, the frequency of anti-HA- specific B cells increases by 10- to 50-fold, which is much greater increase than that observed after immunization with haptenic determinants. Fine specificity analysis of primary monoclonal HA- specific antibodies revealed extensive diversity and a considerable overlap with the specificities obtained from immune mice. Given the low overall frequency of HA-specific B cells, it could be calculated that the representation of most HA-specific clonotypes within the B-cell repertoire could not exceed 1 in 10(7) B cells. These findings indicate that the primary B-cell clonotype repertoire is extremely diverse and largely antigen independent in its generation.
Regulation of Nuclear Transport: Central Role in Development and Transformation?

Transport of macromolecules into and out of the nucleus is generally effected by targeting signals that are recognized by specific members of the importin/exportin transport receptor family. The latter mediate passage through the nuclear envelope‐embedded nuclear pore complexes (NPCs) by conferring interaction with NPC constituents, as well as with other components of the nuclear transport machinery, including the guanine nucleotide‐binding protein Ran. Importantly, nuclear transport is regulated at multiple levels via a diverse range of mechanisms, such as the modulation of the accessibility and affinity of target signal recognition by importins/exportins, with phosphorylation/dephosphorylation as a major mechanism. Alteration of the level of the expression of components of the nuclear transport machinery also appears to be a key determinant of transport efficiency, having central importance in development, differentiation and transformation.
A hybrid adaptation protocol for TCP-friendly layered multicast and its optimal rate allocation

Layered transmission has been proposed as a solution to video multicast over the Internet. Existing protocols usually perform adaptation at the receiver and use static rate allocation techniques at the sender. As a result, significant mismatches between the fixed transmission rates and the heterogeneous and dynamic rate requirements for the receivers can occur. We show that such mismatches can be minimized by employing dynamic layer rate allocation at the sender by taking advantage of the recent development in layered video coding. Specifically, we study the optimization criteria for layer rate allocation, and propose a metric, called fairness index, which fairly reflects the degree of a receiver's satisfaction. We then formulate this into an optimization problem with the objective of maximizing the expected fairness index, and derive an efficient and scalable algorithm to solve it. We further demonstrate that such sender rate adaptation can be seamlessly integrated into an end-to-end adaptation protocol called HALM (hybrid adaptation layered multicast). This protocol is designed for the current best-effort Internet and is TCP-friendly. Its control overhead is also kept at a low level. Simulation results show that HALM improves the degree of fairness for receivers with heterogeneous bandwidth requirements, and interacts with TCP flows substantially better than static allocation based protocols. In addition, increasing the number of layers in HALM always leads to a higher degree of fairness and 3 to 5 layers are usually sufficient. However, this is not true for the static allocation based protocols.
Boolean matching for LUT-based logic blocks with applications toarchitecture evaluation and technology mapping

In this paper, we present new Boolean matching methods for lookup table (LUT)-based programmable logic blocks (PLBs) and their applications to PLB architecture evaluations and field programmable gate array (FPGA) technology mapping. Our Boolean matching methods, which are based on functional decomposition operations, can characterize functions for complex PLBs consisting of multiple LUTs (possibly of different sizes) such as Xilinx XC4K CLBs. With these techniques, we conducted quantitative evaluation of four PLB architectures on their functional capabilities. Architecture evaluation results show that the XC4K CLB can implement 98% of six-input and 88% of seven-input functions extracted from MCNC benchmarks, while a simplified PLB architecture is more cost effective in terms of function implementation per LUT bit. Finally, we proposed new technology mapping algorithms that integrate Boolean matching and functional decomposition operations for depth minimization. Technology mapping results show that our PLB mapping approach achieves 12% smaller depth or 15% smaller area in XC5200 FPGAs and 18% smaller depth in XC4K FPGAs, compared to conventional LUT mapping approaches.
Examining the role of the temporo-parietal network in memory, imagery, and viewpoint transformations

The traditional view of the medial temporal lobe (MTL) focuses on its role in episodic memory. However, some of the underlying functions of the MTL can be ascertained from its wider role in supporting spatial cognition in concert with parietal and prefrontal regions. The MTL is strongly implicated in the formation of enduring allocentric representations (e.g., O'Keefe, 1976; King et al., 2002; Ekstrom et al., 2003). According to our BBB model (Byrne et al., 2007), these representations must interact with head-centered and body-centered representations in posterior parietal cortex via a transformation circuit involving retrosplenial areas. Egocentric sensory representations in parietal areas can then cue the recall of allocentric spatial representations in long-term memory and, conversely, the products of retrieval in MTL can generate mental imagery within a parietal “window. ” Such imagery is necessarily egocentric and forms part of visuospatial working memory, in which it can be manipulated for the purpose of planning/imagining the future. Recent fMRI evidence (Lambrey et al., 2012; Zhang et al., 2012) supports the BBB model. To further test the model, we had participants learn the locations of objects in a virtual scene and tested their spatial memory under conditions that impose varying demands on the transformation circuit. We analyzed how brain activity correlated with accuracy in judging the direction of an object (1) from visuospatial working memory (we assume transient working memory due to the order of tasks and the absence of change in viewpoint, but long-term memory retrieval is also possible), (2) after a rotation of viewpoint, or (3) after a rotation and translation of viewpoint (judgment of relative direction). We found performance-related activity in both tasks requiring viewpoint rotation (ROT and JRD, i.e., conditions 2 and 3) in the core medial temporal to medial parietal circuit identified by the BBB model. These results are consistent with the predictions of the BBB model, and shed further light on the neural mechanisms underlying spatial memory, mental imagery and viewpoint transformations.
Low-Profile Frequency-Scanned Antenna Based on Substrate Integrated Waveguide

A beam scanning flat antenna array with the frequency that covers about the whole X-/Ku-band is proposed in this communication. The radiation element is the continuous transverse stub (CTS) constituted by the substrate integrated waveguide (SIW). The feed structure is an SIW parabolic reflector fed by a linear source with a metallic post, which exhibits a good impedance matching characteristics within a broad bandwidth. The beam steering direction is tunable with the frequency increasing within the operation band. The design principles of the SIW-based CTS array and the feed structure are explained in detail. One 16-element array is simulated, designed, and fabricated. The measurement results show that the antenna has a scanning angle range from 52.2° to −16.8° with the 3-dB gain from 8.5 to 14.1 GHz. At the center frequency of 12 GHz, it achieves a maximum gain of 18.1 dBi with an array size of $11.7\lambda _{0}\times 1.28\lambda _{0}$ , while the first sidelobe level is −11.4 dB and the 3-dB beamwidth is 6.5°.
Temporal dynamics of time series leaf area index and the correlations with meteorological factors over China

In this study, we present a detailed inter-annual analysis of four remotely sensed leaf area index (LAI) products: GLASS, GLOBALBNU, GLOBMAP, and MODIS LAI, and conduct correlation analysis between LAI and three meteorological variables over China. The results manifest that the four products agree well in most of northern regions of China, with higher correlation coefficient and the same response to meteorological factors. The phenology matches well for different biome types, reaching the peak in July or August. The changing trend of LAI from 2001-2011 is almost same for four products, with approximately 56% greening and 44% browning. However, abrupt changes occur at different time among four products. LAI is positively correlated with meteorological station annual average precipitation and temperature, while negatively correlated with annual average sunshine duration in spatial scale.
Phonological vocoding using artificial neural networks

We investigate a vocoder based on artificial neural networks using a phonological speech representation. Speech decomposition is based on the phonological encoders, realised as neural network classifiers, that are trained for a particular language. The speech reconstruction process involves using a Deep Neural Network (DNN) to map phonological features posteriors to speech parameters - line spectra and glottal signal parameters - followed by LPC resynthesis. This DNN is trained on a target voice without transcriptions, in a semi-supervised manner. Both encoder and decoder are based on neural networks and thus the vocoding is achieved using a simple fast forward pass. An experiment with French vocoding and a target male voice trained on 21 hour long audio book is presented. An application of the phonological vocoder to low bit rate speech coding is shown, where transmitted phonological posteriors are pruned and quantized. The vocoder with scalar quantization operates at 1 kbps, with potential for lower bit-rate.
Feature reduction of hyperspectral data using Autoassociative neural networks algorithms

In this paper Autoassociative Neural Networks (AANN) are used to implement Nonlinear Principal Component Analysis (NLPCA) for dimension reduction of hyperspectral data. The nonlinear components are then considered as inputs for a Multi-Layer Perceptron (MLP) network to perform pixel-based classification. The methodology has been applied considering the test area of Tor Vergata — Frascati, Italy, and the hyperspectral data provided by the CHRIS-PROBA mission. Comparative analysis with a similar procedure considering a more standard dimensionality reduction technique such as Principal Component Analysis (PCA) has been carried out.
Jumping across biomedical contexts using compressive data fusion

Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects—such as a gene and a disease—can be related in different ways, for example, directly via gene–disease associations or indirectly via functional annotations, chemicals and pathways. Different ways of relating these objects carry different semantic meanings. However, traditional methods disregard these semantics and thus cannot fully exploit their value in data modeling. Results: We present Medusa, an approach to detect size-k modules of objects that, taken together, appear most significant to another set of objects. Medusa operates on large-scale collections of heterogeneous datasets and explicitly distinguishes between diverse data semantics. It advances research along two dimensions: it builds on collective matrix factorization to derive different semantics, and it formulates the growing of the modules as a submodular optimization program. Medusa is flexible in choosing or combining semantic meanings and provides theoretical guarantees about detection quality. In a systematic study on 310 complex diseases, we show the effectiveness of Medusa in associating genes with diseases and detecting disease modules. We demonstrate that in predicting gene–disease associations Medusa compares favorably to methods that ignore diverse semantic meanings. We find that the utility of different semantics depends on disease categories and that, overall, Medusa recovers disease modules more accurately when combining different semantics. Availability and implementation: Source code is at http://github.com/marinkaz/medusa Contact: marinka@cs.stanford. edu, blaz.zupan@fri.uni-lj.si
An Energy-Based Model for the Image Edge-Histogram Specification Problem

In this correspondence, we present an original energy-based model that achieves the edge-histogram specification of a real input image and thus extends the exact specification method of the image luminance (or gray level) distribution recently proposed by Coltuc et al. Our edge-histogram specification approach is stated as an optimization problem in which each edge of a real input image will tend iteratively toward some specified gradient magnitude values given by a target edge distribution (or a normalized edge histogram possibly estimated from a target image). To this end, a hybrid optimization scheme combining a global and deterministic conjugate-gradient-based procedure and a local stochastic search using the Metropolis criterion is proposed herein to find a reliable solution to our energy-based model. Experimental results are presented, and several applications follow from this procedure.
Swarm ant robotics for a dynamic cleaning problem — upper bounds

Several recent works considered multi a(ge)nt robotics in static environments. In this work we examine ways of operating in dynamic environments, in which changes take place independently of the agents' activity. The work focuses on a dynamic variant of the known Cooperative Cleaners problem (described and analyzed by Wagner and Bruckstein in [12]). This problem assumes a grid, having “dirty” pixels or tiles, that form a connected region of the grid. Several agents move in this dirty region, each having the ability to “clean” the place it is located in. The dynamic variant of the problem involves a deterministic expansion of dirt in the environment, simulating a spreading of contamination, or fire. A cleaning protocol for the problem is presented, as well as a variety of analytic upper bounds on its performance.
Prognostic Impact of Indocyanine Green Plasma Disappearance Rate in Hepatocellular Carcinoma Patients after Radiofrequency Ablation: A Prognostic Nomogram Study

Objective Radiofrequency ablation has been used widely for the local ablation of hepatocellular carcinoma, particularly in its early stages. The study aim was to identify significant prognostic factors and develop a predictive nomogram for patients with hepatocellular carcinoma who have undergone radiofrequency ablation. We also developed the formula to predict the probability of 3- and 5-year overall survival based on clinical variables. Methods We retrospectively studied 96 consecutive patients with hepatocellular carcinoma who had undergone radiofrequency ablation as a first-line treatment. Independent and significant factors affecting the overall survival were selected using a Cox proportional hazards model, and a prognostic nomogram was developed based on these factors. The predictive accuracy of the nomogram was determined by Harrell's concordance index and compared with the Cancer of the Liver Italian Program score and Japan Integrated Staging score. Results A multivariate analysis revealed that age, indocyanine green plasma disappearance rate, and log(des-gamma-carboxy prothrombin) level were independent and significant factors influencing the overall survival. The nomogram was based on these three factors. The mean concordance index of the nomogram was 0.74±0.08, which was significantly better than that of conventional staging systems using the Cancer of the Liver Italian Program score (0.54±0.03) and Japan Integrated Staging score (0.59±0.07). Conclusion This study suggested that the indocyanine green plasma disappearance rate and age at radiofrequency ablation (RFA) and des-gamma-carboxy-prothrombin (DCP) are good predictors of the prognosis in hepatocellular carcinoma patients after radiofrequency ablation. We successfully developed a nomogram using obtainable variables before treatment.