Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Patterns of Song Structure Variation in a Suboscine Passerine

Abstract Studies of song and its function in suboscine passerines are rare. We examined spatial and temporal variation in song structure in a wild population of Olive-sided Flycatchers (Contopus cooperi) and tested among hypotheses explaining this variation. Repeatable variation in song type was observed both within and among individuals. More than 10% of territorial males expressed atypical song types, i.e., permutations of sequential missing and repeated elements of the typical adult song. Atypical songs were predominantly expressed by unpaired males independent of habitat type. A small fraction of males sang atypical song through the middle of the breeding season, but all males sang only stereotypical adult song by the end of the season. These results suggest the expression of atypical songs reflect protracted vocal development rather than evolution of new song types, geographic variation in song structure, or an extensive song repertoire in Olive-sided Flycatchers.
GeneShelf: A Web-based Visual Interface for Large Gene Expression Time-Series Data Repositories

A widespread use of high-throughput gene expression analysis techniques enabled the biomedical research community to share a huge body of gene expression datasets in many public databases on the web. However, current gene expression data repositories provide static representations of the data and support limited interactions. This hinders biologists from effectively exploring shared gene expression datasets. Responding to the growing need for better interfaces to improve the utility of the public datasets, we have designed and developed a new web-based visual interface entitled GeneShelf (http://bioinformatics.cnmcresearch.org/GeneShelf). It builds upon a zoomable grid display to represent two categorical dimensions. It also incorporates an augmented timeline with expandable time points that better shows multiple data values for the focused time point by embedding bar charts. We applied GeneShelf to one of the largest microarray datasets generated to study the progression and recovery process of injuries at the spinal cord of mice and rats. We present a case study and a preliminary qualitative user study with biologists to show the utility and usability of GeneShelf.
Question Answering over Unstructured Data without Domain Restrictions

Information needs are naturally represented as questions. Automatic Natural-Language Question Answering (NLQA) has only recently become a practical task on a larger scale and without domain constraints. This paper gives a brief introduction to the field, its history and the impact of systematic evaluation competitions. It is then demonstrated that an NLQA system for English can be built and evaluated in a very short time using off-the-shelf parsers and thesauri. The system is based on Robust Minimal Recursion Semantics (RMRS) and is portable with respect to the parser used as a frontend. It applies atomic term unification supported by question classification and WordNet lookup for semantic similarity matching of parsed question representation and free text.
Assessing the suitability of surrogate models in evolutionary optimization

The paper deals with the application of evolutionary algorithms to black-box optimization, frequently encountered in biology, chemistry and engineering. In those areas, however, the evaluation of the black-box fitness is often costly and time-consuming. Such a situation is usually tackled by evaluating the original fitness only sometimes, and evaluating its appropriate response-surface model otherwise, called surrogate model of the fitness. Several kinds of models have been successful in surrogate modelling, and a variety of models of each kind can be obtained through parametrization. Therefore, real-world applications of surrogate modelling entail the problem of assessing the suitability of different models for the optimization task being solved. The present paper attempts to systematically investigate this problem. It surveys available methods to assess model suitability and reports the incorporation of several such methods in our recently proposed approach to surrogate modelling based on radial basis function networks. In addition to the commonly used global suitability of a model, it pays much attention also to its local suitability for a given input. Finally, it shows some results of testing several of the surveyed methods in two real-world applications.
Notice of RetractionCash flow forecasting based on GA-BP model in valuation

Forecasting cash flow objectively is very important in discounted cash flow analysis. In order to predict cash flow after mergers, this paper puts forward a model based on genetic-algorithm and BP neural network, designs the steps of algorithm, build the structure of BP neural network. By simulation of data, the result indicates the model has good performance of prediction. It can overcome the flaws of statistics linear models.
Energy&#8211;delay tradeoffs in combinational logic using gate sizing and supply voltage optimization

This paper relates the potential energy savings to the energy profile of a circuit. These savings are obtained by using gate sizing and supply voltage optimization to minimize energy consumption subject to a delay constraint. The sensitivity of energy to delay is derived from a linear delay model extended to multiple supplies. The optimizations are applied to a range of examples that span typical circuit topologies including inverter chains, SRAM decoders and adders. At a delay of 20% larger than the minimum, energy savings of 40% to 70% are possible, indicating that achieving peak performance is expensive in terms of energy.
New linear codes over 𝔽9

Let dg (n, k) be the maximum possible minimum Hamming distance of a linear [n, k] code over lFg• In this paper, twenty-four new linear codes over lFg are constructed which improve the table of dg(n, k) by Brouwer. Four of these codes meet the upper bound on dg (n, k) and so are optimal. A geometric interpretation of the small dimensional codes is also given.
Quasi-fixed points and charge and colour breaking in low scale models

We show that the current LEP2 lower bound upon the minimal supersymmetric standard model (MSSM) lightest Higgs mass rules out quasi-fixed scenarios for string scales between 106 and 1011 GeV unless the heaviest stop mass is more than 2 TeV. We consider the implications of the low string scale for charge and colour breaking (CCB) bounds in the MSSM, and demonstrate that CCB bounds from F and D-flat directions are significantly weakened. For scales less than 1010 GeV these bounds become merely that degenerate scalar mass squared values are positive at the string scale.
Prediction of frost episodes based in agrometeorological information and machine learning techniques

Frosts are one of the main risks faced by farmers during the winter and spring seasons. These events can cause significant damage to cultivations and crops. In Chile, these frost generates significant losses in the agricultural production sector, causing crop losses of an entire year and compromising the income of the following year, especially fruit and wine growers. In this work we developed a prediction model based on historical agrometeorological information able to predict efficiently and up to 12 hours earlier than the occurrence of a frost event. With a special focus on the central Chilean zone. Various algorithms and machine learning methods were evaluated, we found that the method “Random Forest” exhibits the best results overall. The results obtained in the frost prediction reach over (90%) of efficiency in most of the evaluated scenarios.
The Craniomaxillofacial Skeleton: New Approaches in Computational Biomechanics and Fracture Stabilization

The Craniomaxillofacial Skeleton: New Approaches in Computational Biomechanics and Fracture Stabilization Amir Reza Pakdel Sefidgar Doctor of Philosophy Graduate Department of the Institute of Biomaterials and Biomedical Engineering University of Toronto 2014 The mechanical behaviour of the craniomaxillofacial skeleton (CMFS) under physiological loads is among the least understood in the field of musculoskeletal biomechanics. The highly complex structure of the CMFS defies reduction to analogous or simpler components. Its modelling demands a holistic and integrated approach in order to gain insight into its structural function. As such, the biomechanical foundations upon which the practises for open-reduction internal-fixation are developed are controversial. A systematic and quantitative biomechanical framework is needed to guide the treatment of CMFS injuries and pathologies. This research aims to address these issues on two fronts: through characterization of the loading patterns of the CMFS derived from computational modelling, as well as the development of an innovative internal stabilization device for the CMFS. This thesis first addresses the challenges through a practical and novel approach whereby a deblurring algorithm is applied to clinical CT images which restores the geometry and intensity of the thin cortical bone structures. Using these restored images a biomechanical framework was further developed to produce high fidelity subject-specific finite element models of the CMFS. Validation of this approach was demonstrated through strong correlation with multi-subject in vitro experiments (r = 0.93, regression slope of unity). These models elucidated the patterns of stress and strain on the CMFS subject to a simulated physiological masticatory bite load. The observed patterns invite a critical re-analysis of the classic CMFS buttress hypothesis. Finally, a pilot experimental investigation into the mechanical efficiency of“Bone Tape”, a bioresorbable polymerceramic composite device for internal fixation of CMFS fractures, was conducted. In vitro experiments demonstrated an initial flexibility which conferred an ability to conform to complex 3-D bone topologies and a potential to equate the strength of commercially available bioresorbable systems. A prototype concept for its use was deployed in an in vivo rabbit model. The structural biomechanics knowledge gained based on the validated computational biomechanical framework will ultimately provide a robust platform which will enable the rational design and optimization of new treatment hardware and practises, such as Bone Tape, toward improved outcomes in reconstruction of the CMFS.
Endosymbiont evolution: predictions from theory and surprises from genomes

Genome data have created new opportunities to untangle evolutionary processes shaping microbial variation. Among bacteria, long‐term mutualists of insects represent the smallest and (typically) most AT‐rich genomes. Evolutionary theory provides a context to predict how an endosymbiotic lifestyle may alter fundamental evolutionary processes—mutation, selection, genetic drift, and recombination—and thus contribute to extreme genomic outcomes. These predictions can then be explored by comparing evolutionary rates, genome size and stability, and base compositional biases across endosymbiotic and free‐living bacteria. Recent surprises from such comparisons include genome reduction among uncultured, free‐living species. Some studies suggest that selection generally drives this streamlining, while drift drives genome reduction in endosymbionts; however, this remains an hypothesis requiring additional data. Unexpected evidence of selection acting on endosymbiont GC content hints that even weak selection may be effective in some long‐term mutualists. Moving forward, intraspecific analysis offers a promising approach to distinguish underlying mechanisms, by testing the null hypothesis of neutrality and by quantifying mutational spectra. Such analyses may clarify whether endosymbionts and free‐living bacteria occupy distinct evolutionary trajectories or, alternatively, represent varied outcomes of similar underlying forces.
Distal Attribution and Presence

out ever suspecting that contact with the physical world is mediate; moreover, the functionality of perception impedes many reflective individuals from appreciating the insights about perception that derive from philosophical inquiry. Oddly enough, the newly developing technology of teleoperator and virtual displays is having the unexpected effect ofpromoting such insight, for the impression of being in the remote or simulated environ-
An evaluation of computers for reminiscing

This paper presents a case study on card-based versus device-based (Apple iPad) reminiscing using photographic images. Nineteen older people participated in this study and results show that there is no significant difference between traditional and device based reminiscing of photographic images. The authors’ position on reminiscing focuses primarily on helping to understand people's current practices around reminiscing, supported by empirical studies and theories of memory that might inform technology designs.
Insight into renal Mg2+ transporters

Purpose of reviewThis review aims to describe the recent findings concerning novel Mg2+ transporters as putative interesting players in renal transepithelial Mg2+ transport. Recent findingsSo far, the best characterized Mg2+ transport proteins are found in prokaryotes and yeast cells. In recent years, phylogenetic analysis and differential gene expression studies have led to the identification of numerous genes associated with Mg2+ transport in eukaryotes. In addition to the well known transient receptor potential channel melastatin (TRPM), members 6 and 7, and the mitochondrial transporter Mrs2, additional Mg2+-transporting protein families can be acknowledged including the magnesium (Mag) transporters, the solute carrier (SLC) family 41 members, ancient conserved domain proteins (ACDP), nonimprinted in Prader-Willi/Angelman syndrome (NIPA) proteins, membrane Mg2+ transporters (MMgT) and huntingtin-interacting protein 14 (HIP14). SummaryThe identification of several mammalian proteins involved in Mg2+ transport highlights the physiological importance of this cation and its tight regulation in numerous tissues. Further investigation of these transporters might represent a key tool to complement our current knowledge about renal Mg2+ handling.
Free Abrasive Finishing with Dynamic Shear Jamming Fluid

Abrasive finishing is a machining process which alters the surface of a workpiece to achieve a specific property. Typical abrasive finishing processes focus on geometric tolerances and surface topography. Abrasive finishing is primarily dependent on finishing forces, relative velocities, and abrasive size. The material removal rate in finishing is inversely related to the surface finish. Magnetic and electric fields have been used to control the force applied to abrasives which finish the workpiece. These processes show an increase in performance when the field is used to control the process. Field assisted finishing processes can be energy intensive and expensive. A novel finishing media is proposed which does not require a field to achieve a similar force response. This media has inherent thickening mechanisms driven by shear jamming. This shear jamming mechanism can deliver forces an order of magnitude higher than shear thickening mechanisms. This novel slurry is demonstrated as a viable finishing media with performance similar to magnetic abrasive finishing.
Theoretical Study on Electronic Structures and Spectroscopic Properties of Indene-C60 Monoadduct (ICMA)

ICMA and its derivatives, high performance acceptor of polymer solar cells, have received most attention in experimental and theoretical studies. In this work, we calculated electronic structures and spectroscopic properties of the ICMA by density functional theory (DFT) using B3LYP method. The geometrical structure data show that indene has little influence on C60. In comparison with [6,6]-phenyl C61-butyric acid methyl ester (PCBM), It was proved theoretically that ICMA possesses a better photovoltaic performance and higher LUMO energy level. ICMA and its derivatives could be a promising new acceptor which further improve the power conversion efficiency of the high performance polymer solar cells. The research indicates that the frontier molecular orbital are mainly localized on the C60 sphere, which shows carbon number of cage is an important factor leading to the change of optical and electrical properties. The present study provides theoretical supporting for further application of the ICMA and its derivatives in molecular design and structure-activity relationships research.
The Differential Flow of Epidural Local Anaesthetic via Needle or Catheter: A Prospective Randomized Double-blind Study

The extent of epidural anaesthesia and pattern of spread of contrast medium, using different injection techniques, has not been well documented. Therefore, in this prospective, randomized, double-blind study, the extent of anaesthesia and pattern of spread of contrast medium following an epidural bolus injection, via either a Tuohy needle or an epidural catheter, were compared. The study had two parts. In the first, 59 of 79 patients scheduled for a lower extremity operation under epidural anaesthesia were randomly allocated to one of the two groups. Anaesthesia was achieved with an epidural injection of 10 to 15 ml (including a 3 ml test dose) of 0.75% ropivacaine and fentanyl 25 μg via either a Tuohy needle (Group N, n=31) or a catheter (Group C, n=28). The level of sensory anaesthesia was recorded. In the second part, the remaining 20 patients were randomized to initially receive 5 ml of contrast medium via either a Tuohy needle (Group NE, n=10) or a catheter (Group CE, n=10). The extent of spread was recorded radiologically. Unilateral or missed blocks and additional dose requirement were absent in Groups N and C. No differences were found in the extent of sensory anaesthesia or the spread of contrast medium. Twenty per cent of catheter tips lay outside the lateral margins of the vertebral bodies. We found that an epidural bolus injection, via either a Tuohy needle or a catheter, made no difference in regard to spread of local anaesthetic or contrast medium in the epidural space.
Bug Prediction Metrics Based Decision Support for Preventive Software Maintenance

There exist a number of large legacy systems that still undergo continuous maintenance and enhancement. Due to the sheer size and complexity of the software systems and limited resources, managers are confronted with crucial decisions regarding allocation and training of new engineers, intelligent allocation of testing personnel, assessment of release readiness of the software and so on. While the area of bug prediction by mining software repositories holds promise, and is a worthwhile endeavor, the current state of the art techniques are not accurate enough in predicting bugs and hence are of limited usefulness to managers. So instead of predicting files as buggy or not we take a different viewpoint and focus on providing decision support for managers. In this paper we present a set of metrics to guide the managers in taking these decisions. These metrics are evaluated using 4 open source systems and 2 proprietary systems.
Phone set construction based on context-sensitive articulatory attributes for code-switching speech recognition

Bilingual speakers are known for their ability to code-switch or mix their languages during communication. This phenomenon occurs when bilinguals substitute a word or phrase from one language with a phrase or word from another language. For code-switching speech recognition, it is essential to collect a large-scale code-switching speech database for model training. In order to ease the negative effect caused by the data sparseness problem in training code-switching speech recognizers, this study proposes a data-driven approach to phone set construction by integrating acoustic features and cross-lingual context-sensitive articulatory features into distance measure between phone units. KL-divergence and a hierarchical phone unit clustering algorithm are used in this study to cluster similar phone units to reduce the need of the training data for model construction. The experimental results show that the proposed method outperforms other traditional phone set construction methods.
Diagnosis and treatment of tuberculosis in undocumented migrants in low- or intermediate-incidence countries.

SETTING Countries with low or intermediate tuberculosis (TB) incidence. OBJECTIVES 1) To gather information on individuals and TB patients who are undocumented migrants and their access to TB diagnostic and treatment services; 2)to discuss interventions to strengthen diagnosis and treatment and 3) to formulate recommendations on how to ensure adequate TB prevention and control. DESIGN Questionnaires sent to members of the Working Group (WG) on Transborder Migration and TB, managers of national TB programmes and EuroTB correspondents; literature research and development of a paper by a writing committee through consultation. RESULTS Undocumented migrants represent 5-30% of immigrants and 5-10% of TB cases. Most countries reported full access to diagnosis and treatment, but in practice there were limitations. Most countries also reported that they could and did deport cases who were on TB treatment. A variety of activities to ensure access were reported from different countries. CONCLUSION The WG recommends that 1) health authorities and/or health staff should ensure easy access to low-threshold facilities where undocumented migrants who are TB suspects can be diagnosed and treated without giving their names and without fear of being reported to the police or migration officials. Health authorities should remind health staff that they have an obligation of confidentiality; 2) each country should ensure that undocumented migrants with TB are not deported until completion of treatment; and 3) authorities and non-governmental sectors should raise awareness among undocumented migrants about TB, emphasising that diagnosis and treatment should be free of charge and wholly independent of migratory status.
Cross-cultural adaptation of the Readiness for Interprofessional Learning Scale in Brazil

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Multispectral scene recognition based on dual convolutional neural networks

Multispectral sensors are becoming more accessible which draws additional attention to the problem of processing and classification of multispectral images. In this research we addressed the problem of automatic scene recognition of multispectral images using convolutional neural networks with tailored architecture. More precisely, we propose and describe a special dual network architecture which is able to efficiently process multispectral images and, at the same time, use the possibilities of networks pretrained on feature-rich image dataset. Experiments showed that dual network can efficiently recognize multispectral scenes, even though a small amount of training images had been available. Comparing to the best accuracy of descriptor based method previously reported, our method made an improvement of nearly 5%, achieving the classification accuracy over 92% on benchmark multispectral scene dataset.