Miracle-dz/UniFAR-FTUs huggingface dataset:
Pollinator-Mediated Selection on Flower Color Allele Drives Reinforcement

The Constant Pollinator In ecology, reinforcement is the process by which species prevent hybridization and maintain species boundaries, but the underlying genetic mechanisms are unclear. Hopkins and Rausher (p. 1090, published online 2 February) examined reinforcement between two species of a wild flowering plant called Phlox that show incomplete hybrid sterility. Down-regulation of a flavonoid gene produces red flowers and operates in concert with a color intensity locus to adjust flower color and tone. A distinct geography of flower color has emerged in which it appears that dark coloration causes less hybridization between the species because the butterfly pollinators tend to favor light-blue flower color variants. If pollinators visit flowers with similar phenotypes more frequently than flowers with dissimilar phenotypes, this will decrease gene flow between the unlike flowers. Butterfly behavior reduces hybridization and reinforces speciation among Phlox plants. Reinforcement is the process by which reduced hybrid fitness generates selection favoring the evolution of stronger prezygotic reproductive barriers between emerging species. Using common-garden field experiments, we quantified the strength of reinforcing selection in nature by demonstrating strong selection favoring an allele conferring increased pigment intensity in the plant Phlox drummondii in areas of sympatry with the closely related species Phlox cuspidata. Incomplete hybrid sterility between the two species generates selection for traits that decrease interspecies hybridization. In contrast, selection on this locus is undetectable in the absence of P. cuspidata. We demonstrate that reinforcing selection is generated by nonrandom pollinator movement, in which pollinators move less frequently between intensely pigmented P. drummondii and P. cuspidata than between lightly pigmented P. drummondii and P. cuspidata.
Representation and Simulation of Biochemical Processes Using the pi-Calculus Process Algebra

Despite the rapidly accumulating body of knowledge about protein networks, there is currently no convenient way of sharing and manipulation of such information. We suggest that a formal computer language for describing the biomolecular processes underlying protein networks is essential for rapid advancement in this field. We propose to model biomolecular processes by using the pi-Calculus, a process algebra, originally developed for describing computer processes. Our model for biochemical processes is mathematically well-defined, while remaining biologically faithful and transparent. It is amenable to computer simulation, analysis and formal verification. We have developed a computer simulation system, the PiFCP, for execution and analysis of pi-calculus programs. The system allows us to trace, debug and monitor the behavior of biochemical networks under various manipulations. We present a pi-calculus model for the RTK-MAPK signal transduction pathway, formally represent detailed molecular and biochemical information, and study it by various PiFCP simulations.
Mutation of somatostatin receptor type 5 in an acromegalic patient resistant to somatostatin analog treatment.

Introduction of somatostatin analogs has greatly contributed to improving the prognosis of acromegaly. Although the majority of patients are effectively treated by these agents, resistance occurs in a subset of patients. So far, resistance to somatostatin has never been associated with mutations of the somatostatin receptor subtypes (sst2 and sst5) that inhibit GH secretion. Molecular analysis of genomic DNA from pituitary tumor and peripheral blood obtained from an acromegalic resistant to octreotide showed a somatic activating mutation of Gsalpha (Arg201Cys), no mutation in sst2, and one polymorphism (Pro109Ser) and one germ line mutation (Arg240Trp) in sst5. Wild-type (WT) and mutant sst5 PCR products were cloned and transfected into Chinese hamster ovary K1 cells. In Chinese hamster ovary K1 cells stably expressing mutant sst5, somatostatin-28 was less potent in inhibiting cyclic AMP levels than in WT cells. Proliferation of mutant cells exceeded that of WT by 50%. Moreover, somatostatin reduced cell growth and MAPK activity in WT but not in mutant cells in which the peptide even increased MAPK activity. We suggest that this mutation that abrogates the antiproliferative action of somatostatin and activates mitogenic pathways may be involved in the resistance to somatostatin treatment.
High-speed modulation and switching characteristics of In(Ga)As-Al(Ga)As self-organized quantum-dot lasers

The dynamic characteristics, and in particular the modulation bandwidth, of high-speed semiconductor lasers are determined by intrinsic factors and extrinsic parameters. In particular, carrier transport through the heterostructure and thermalization, or quantum capture in the gain region, tend to play an important role. We have made a detailed study of carrier relaxation and quantum capture phenomena in In(Ga)As-Al(Ga)As self-organized quantum dots (QD's) and single-mode lasers incorporating such dots in the gain region through a variety of measurements. The modulation bandwidth of QD lasers is limited to 5-6 GHz at room temperature and increases to /spl sim/30 GHz only upon lowering the temperature to 100 K. This behavior is explained by considering electron-hole scattering as the dominant mechanisms of electron relaxation in QD's and the scattering rate seems to decrease with increase of temperature. The switching of the emission wavelength, from the ground state to an excited state, has been studied in coupled cavity devices. It is found that the switching speed is determined intrinsically by the relaxation time of carriers into the QD states. Fast switching from the ground to the excited state transition is observed. The electrooptic coefficients in the dots have been measured and linear coefficient /spl tau/=2.58/spl times/10/sup -11/ m/V. The characteristics of electrooptic modulators (EOM's) are also described.
Optimising the aircraft lateral stabilization system in landing mode

Controlling position of an aircraft in its trajectory is a matter of high precision. The requirement on precision is becoming a priority primarily in the phase of landing, when the aircraft is in automated mode of landing controlling/maintaining lateral deviation along the trajectory by autopilot. The contribution is describing shaping and modeling control signals in the stage of final approach for landing. It proposes a system that can eliminate the interfering high-frequency defects of lateral control system stabilizing aircraft deviation during the landing maneuver and simulating the effects of filters onto the feedback dynamics.
Strategy of Recycling Waste Resource with Governmental Incentives and Funds

The collection of used products and subsequent remanufacturing has been increasingly encouraged by government incentives and funds. Assuming new products stochastic demands and end-of-products stochastic returns, a reverse supply chain recycling system led by government is established. Recycling fund from manufacturers, government subsidies and a target rebate-punish contract between government and recyclers are designed. Based on games theory and optimization theory, the optimal decisions are obtained in both decentralized and centralized conditions respectively. Finally, the performance of the proposed solution method has been demonstrated by a numerical simulation. By the economic consequences of these decisions, managerial insights and economic implications relevant to policy makers are obtained.
Multi-View Human Body Pose Estimation with CUDA-PSO

The authors formulate the body pose estimation as a multi-dimensional nonlinear optimization problem, suitable to be approximately solved by a meta-heuristic, specifically, the particle swarm optimization (PSO). Starting from multi-view video sequences acquired in a studio environment, a full skeletal configuration of the human body is retrieved. They use a generic subdivision-surface body model in 3-D to generate solutions for the optimization problem. PSO then looks for the best match between the silhouettes generated by the projection of the model in a candidate pose and the silhouettes extracted from the original video sequence. The optimization method, in this case PSO, is run in parallel on the Graphics Processing Unit (GPU) and is implemented in Cuda-Câ„¢ on the nVidia CUDAâ„¢ architecture. The authors compare the results obtained by different configurations of the camera setup, fitness function, and PSO neighborhood topologies.
Stakeholder Barriers and Benefits Associated With Improving Livestock Husbandry to Prevent Jaguar and Puma Depredation

ABSTRACT Motivating ranchers to adopt preventive husbandry practices that limit livestock depredation by large carnivores, such as jaguars (Panthera onca) and pumas (Puma concolor), requires reducing perceived barriers and increasing benefits associated with coexistence. We assessed stakeholder perspectives on preventive practices by conducting eight focus groups consisting of ranchers, researchers, and government wildlife officers in Costa Rica using a nominal group technique to identify and rank benefits, barriers, and motivations. We identified 29 benefits, 27 positive motivations, 33 negative motivations, and 20 barriers. Common responses among stakeholders highlight the importance of economic issues, contextual factors, and external support. However, social interactions, a reactive approach to management, and personal motivations also influence rancher decision making, but tend to be ignored by researchers and wildlife officers. Nominal group rankings reveal misunderstandings and misalignment of priorities among stakeholders that should be targeted by collaborative problem-solving processes. Motivations behind prevention expose nuances of human–wildlife conflict.
Three Handover Methods in Esteem Etiquettes Using Dual Arms and Hands of Home-Service Robot

For home-service robots one of the most common interactions with humans is the handing over of objects using arms and hands of anthropomorphic robots. We propose advanced methods of determination of grasp sites for handover operations that incorporate especially etiquettes which factors include object shapes, object functions, and safety. We also address different operations for handover: one-handed handover with one grasp, two-step handover with midair re-grasp, and two-handed handover. We show the effectiveness of our algorithm for interaction between humans and robots with graphical simulations.
Probabilistic Modelling of Performance Parameters of Carbon Nanotube Transistors

CMOS technology has come to a standstill and not much scope remains for further advancements. With a motivation to find alternatives, we explore the domain of Carbon Nanotube based Field Effect Transistors (CNTFETs). A comparison of the delay and power consumption of basic gates in both technologies shows that CNTFETs have better delay and power characteristics which provides us a strong inspiration to look out further. Process variations cause faults such as presence of metallic Carbon Nanotubes (CNTs) and special techniques are required to remove them. These faults affect performance by causing excessive leakage currents, changing delay and degrading noise margin. The number of these CNTs can be modelled as binomial function. We work on a mathematical analysis of these faults and present some directions that might be helpful when working on CNT based circuits.
Evolving agent-based simulations in the clouds

Evolving agent-based simulations enables one to automate the difficult iterative process of modeling complex adaptive systems to exhibit pre-specified/desired behaviors. Nevertheless this emerging technology, combining research advances in agent-based modeling/simulation and evolutionary computation, requires significant computing resources (i.e., high performance computing facilities) to evaluate simulation models across a large search space. Moreover, such experiments are typically conducted in an infrequent fashion and may occur when the computing facilities are not fully available. The user may thus be confronted with a computing budget limiting the use of these “evolvable simulation” techniques. We propose the use of the cloud computing paradigm to address these budget and flexibility issues. To assist this research, we utilize a modular evolutionary framework coined CASE (for complex adaptive system evolver) which is capable of evolving agent-based models using nature-inspired search algorithms. In this paper, we present an adaptation of this framework which supports the cloud computing paradigm. An example evolutionary experiment, which examines a simplified military scenario modeled with the agent-based simulation platform MANA, is presented. This experiment refers to Automated Red Teaming: a vulnerability assessment tool employed by defense analysts to study combat operations (which are regarded here as complex adaptive systems). The experimental results suggest promising research potential in exploiting the cloud computing paradigm to support computing intensive evolvable simulation experiments. Finally, we discuss an additional extension to our cloud computing compliant CASE in which we propose to incorporate a distributed evolutionary approach, e.g., the island-based model to further optimize the evolutionary search.
Annotation extension through protein family annotation coherence metrics

Protein functional annotation consists in associating proteins with textual descriptors elucidating their biological roles. The bulk of annotation is done via automated procedures that ultimately rely on annotation transfer. Despite a large number of existing protein annotation procedures the ever growing protein space is never completely annotated. One of the facets of annotation incompleteness derives from annotation uncertainty. Often when protein function cannot be predicted with enough specificity it is instead conservatively annotated with more generic terms. In a scenario of protein families or functionally related (or even dissimilar) sets this leads to a more difficult task of using annotations to compare the extent of functional relatedness among all family or set members. However, we postulate that identifying sub-sets of functionally coherent proteins annotated at a very specific level, can help the annotation extension of other incompletely annotated proteins within the same family or functionally related set. As an example we analyse the status of annotation of a set of CAZy families belonging to the Polysaccharide Lyase class. We show that through the use of visualization methods and semantic similarity based metrics it is possible to identify families and respective annotation terms within them that are suitable for possible annotation extension. Based on our analysis we then propose a semi-automatic methodology leading to the extension of single annotation terms within these partially annotated protein sets or families.
Detection of filter-like cellular automata spectra

The Fourier spectra of one-dimensional cellular automata give a quantitative and qualitative characterisation of the average final configurations obtained out of their rules, as they are applied to sets of random initial configurations. The elementary cellular automata rule space presents spectra that bring to mind those of digital filters, and the same happens to some of the cellular automata rules obtained through composition of particular elementary cellular automata. As such, one might be willing to discover other filter type rules that might exist in larger spaces. In order to explore the possibility of detecting these cellular automata in a larger space, two methods are applied: a Multilayer Perceptron and the k-Nearest Neighbours classification algorithm. Both algorithms presented considerably high accuracies, with the Multilayer Perceptron showing an overall lower false negative rate, thus indicating that the methods may be generalised to other rule spaces and to the detection of other features, providing an automatic method to detect features in cellular automata spectra.
Complementary Feeding: A Commentary by the ESPGHAN Committee on Nutrition

This position paper on complementary feeding summarizes evidence for health effects of complementary foods. It focuses on healthy infants in Europe. After reviewing current knowledge and practices, we have formulated these conclusions: Exclusive or full breast-feeding for about 6 months is a desirable goal. Complementary feeding (ie, solid foods and liquids other than breast milk or infant formula and follow-on formula) should not be introduced before 17 weeks and not later than 26 weeks. There is no convincing scientific evidence that avoidance or delayed introduction of potentially allergenic foods, such as fish and eggs, reduces allergies, either in infants considered at increased risk for the development of allergy or in those not considered to be at increased risk. During the complementary feeding period, >90% of the iron requirements of a breast-fed infant must be met by complementary foods, which should provide sufficient bioavailable iron. Cow's milk is a poor source of iron and should not be used as the main drink before 12 months, although small volumes may be added to complementary foods. It is prudent to avoid both early (<4 months) and late (≥7 months) introduction of gluten, and to introduce gluten gradually while the infant is still breast-fed, inasmuch as this may reduce the risk of celiac disease, type 1 diabetes mellitus, and wheat allergy. Infants and young children receiving a vegetarian diet should receive a sufficient amount (∼500 mL) of breast milk or formula and dairy products. Infants and young children should not be fed a vegan diet.
A COMPETITION BASED ROOF DETECTION ALGORITHM FROM AIRBORNE LIDAR DATA

3D city model reconstruction is one of the main application of airborne laser scanning system. Roof detection is a primary step which needs to find each roof from irregular point clouds with noise points involved. Another problem existed in roof detection is that the detected sequence of roofs will affect the size and shape of detected roofs even though a lot fancy algorithm are applied. In this paper, we first analyzed the reason of roof size changing caused by detected sequence, then an weighted points normal voting based algorithm is proposed to detect roofs with multiple threshold applied. After than a boundary points competition approach is investigated to optimize the gained roof which can re-adjust the size and shape of detected roof and reduce the influence of roof detection sequence. Experiments is given to prove the algorithm proposed.
Gender Inference for Arabic Language in Social Media

The widespread usage of social media has attracted a new group of researchers seeking information on who, what and, where the users are. Some of the information retrieval researchers are interested in identifying the gender, age group, and the educational level of the users. The objective of this work is to identify the gender in the Arabic posts in the social media. Most of the works related to gender classification has been for English based content in the social media. Work for other languages, such as Arabic, is almost next to none. Typically people express themselves in the social media using colloquial, so this study is geared towards the identification of genders using the Saudi dialect of the Arabic language. To solve the gender identification problem the authors, a novel method called k-Top Vector k-TV, which is based on the k-top words based on the words occurrences and the frequency of the stems, was introduced. Part of this work required compiling a dataset of Saudi dialect words. For this, a well-known widely used social site was relied on. To test the system, we compiled 1200 samples equally split between both genders. The authors trained Support Vector Machine SVM and k-NN classifiers using different number of samples for training and testing. SVM did a better job and achieved an accuracy of 95% for gender classification.
Dysfunction of Affective Network in Post Ischemic Stroke Depression: A Resting-State Functional Magnetic Resonance Imaging Study

Objective. Previous studies have demonstrated that stroke characteristics and social and psychological factors jointly contribute to the development of poststroke depression (PSD). The purpose of this study was to identify altered functional connectivity (FC) of the affective network (AN) in patients with PSD and to explore the correlation between FC and the severity of PSD. Materials and Methods. 26 PSD patients, 24 stroke patients without depression, and 24 age-matched normal controls underwent the resting-state functional MRI (fMRI) scanning. The bilateral anterior cingulated cortices (ACCs) were selected as regions of interest (ROIs). FC was calculated and compared among the three groups. The association between FC and Hamilton Depression Rate Scale (HDRS) scores of PSD group was investigated. Results. The FC of the AN was disrupted in PSD patients compared to stroke patients without depression and normal controls. Moreover, the left orbital part of inferior frontal gyrus which indicated altered FC was significantly correlated with HDRS scores in PSD patients. Conclusions. Dysfunction of the affective network may be one of the reasons of the development of PSD.
Real-time assessment of pulmonary vein disconnection during cryoablation of atrial fibrillation: can it be 'achieved' in almost all cases?

AIM Real-time assessment of pulmonary vein (PV) disconnection is possible using an inner circular mapping catheter during cryoablation of atrial fibrillation (AF). However, it has been recently demonstrated that such continuous monitoring may only be possible in <50% of PVs. We hypothesized that a stepwise mapping approach, including pacing manoeuvres, could optimize monitoring of real-time PV disconnection during ablation. METHODS AND RESULTS Single-centre, prospective observational study (NCT01843465) including 34 consecutive eligible patients (128 PVs) undergoing a first procedure of cryoballoon ablation of AF using the Artic Front Advance(®) 28 mm catheter and a 20 mm diameter Achieve(®) catheter (AC) in all cases. Monitoring of real-time entrance block was possible, when AC was maintained in the standard position (distal to the tip of the Artic Front Advance(®) catheter) in 47 (36.7%) PVs. In an additional 63 cases (49.2%), such monitoring was possible after moving AC to a more proximal position and using different torqueing movements. Finally, using supplemental systematic pacing manoeuvres to test exit block, real-time assessment of PV disconnection was possible in 15 (11.7%) more PVs. Overall, real-time assessment of PV disconnection was possible in 97.7% of cases, after a mean duration of 48.6 ± 33.0 s. CONCLUSION Our results suggest that optimal use of the AC, with a systematic stepwise mapping approach, may dramatically improve the real-time monitoring of PV disconnection during AF cryoablation.
Learning object models for whole body manipulation

We present a successful implementation of rigid grasp manipulation for large objects moved along specified trajectories by a humanoid robot. HRP-2 manipulates tables on casters with a range of loads up to its own mass. The robot maintains dynamic balance by controlling its center of gravity to compensate for reflected forces. To achieve high performance for large objects with unspecified dynamics the robot learns a friction model for each object and applies it to torso trajectory generation. We empirically compare this method to a purely reactive strategy and show a significant increase in predictive power and stability.