Miracle-dz/UniFAR-FTUs huggingface dataset:
Association study of fibroblast growth factor 10 (FGF10) polymorphisms with susceptibility to extreme myopia in a Japanese population

Purpose The fibroblast growth factor 10 (FGF10) gene polymorphism rs339501 was previously reported to be associated with high myopia in a Chinese population. In the present study, we investigated whether FGF10 polymorphisms are associated with extreme myopia in a Japanese population as well. Methods A total of 433 Japanese patients with extreme myopia (≤ −10.00 diopters) and 542 Japanese healthy controls (+1.50 to −1.50 diopters) were recruited. We genotyped seven tagging single-nucleotide polymorphisms (SNPs), including rs339501, in FGF10. We also performed an imputation analysis to evaluate the potential association of ungenotyped FGF10 SNPs, and 34 SNPs were imputed. Results It was found that rs339501 and rs12517396 exhibited the strongest association with extreme myopia (p=3.9 × 10−4, corrected p [Pc]=0.0030). A significant association was also observed for rs10462070 (p=6.5 × 10−4, Pc=0.0059). These three SNPs were in strong linkage disequilibrium (D’ ≥0.99, r2 ≥0.96). However, the frequency of the A allele of rs339501 was increased in cases compared to controls, which differs from the increased frequency of the G allele in cases in the previous Chinese population. Conclusions Three FGF10 SNPs in complete linkage disequilibrium—rs339501, rs12517396, and rs10462070—were associated with extreme myopia in the Japanese population, and the risk allele of rs339501 differed from the previous Chinese population. Therefore, these three SNPs may not be an important risk factor for susceptibility to extreme myopia. Further studies are needed to elucidate the possible contribution of the FGF10 region in the development of extreme myopia.
A statistical text-to-phone function using ngrams and rules

Adopting concepts from statistical language modeling and rule-based transformations can lead to effective and efficient text-to-phone (TTP) functions. We present here the methods and results of one such effort, resulting in a relatively compact and fast set of TTP rules that achieves 94.5% segmental phonemic accuracy.
Meaningful conversation with mobile robots

We describe an implementation integrating a complete spoken dialogue system with a mobile robot, which a human can direct to specific locations, ask for information about its status and supply information about its environment. The robot uses an internal map for navigation, and communicates its current orientation and accessible locations to the dialogue system using a topological map as interface. We focus on linguistic and inferential aspects of the human–robot communication process. The result is a novel approach using a principled semantic theory combined with techniques from automated deduction applied to a mobile robot platform. Due to the abstract level of the dialogue system, it is easily portable to other environments or applications.
A dedicated visual pathway for prey detection in larval zebrafish

Zebrafish larvae show characteristic prey capture behavior in response to small moving objects. The neural mechanism used to recognize objects as prey remains largely unknown. We devised a machine learning behavior classification system to quantify hunting kinematics in semi-restrained animals exposed to a range of virtual stimuli. Two-photon calcium imaging revealed a small visual area, AF7, that was activated specifically by the optimal prey stimulus. This pretectal region is innervated by two types of retinal ganglion cells, which also send collaterals to the optic tectum. Laser ablation of AF7 markedly reduced prey capture behavior. We identified neurons with arbors in AF7 and found that they projected to multiple sensory and premotor areas: the optic tectum, the nucleus of the medial longitudinal fasciculus (nMLF) and the hindbrain. These findings indicate that computations in the retina give rise to a visual stream which transforms sensory information into a directed prey capture response. DOI: http://dx.doi. org/10.7554/eLife.04878. 001
Using Twitter to Measure Public Discussion of Diseases: A Case Study

Background Twitter is increasingly used to estimate disease prevalence, but such measurements can be biased, due to both biased sampling and inherent ambiguity of natural language. Objective We characterized the extent of these biases and how they vary with disease. Methods We correlated self-reported prevalence rates for 22 diseases from Experian’s Simmons National Consumer Study (n=12,305) with the number of times these diseases were mentioned on Twitter during the same period (2012). We also identified and corrected for two types of bias present in Twitter data: (1) demographic variance between US Twitter users and the general US population; and (2) natural language ambiguity, which creates the possibility that mention of a disease name may not actually refer to the disease (eg, “heart attack” on Twitter often does not refer to myocardial infarction). We measured the correlation between disease prevalence and Twitter disease mentions both with and without bias correction. This allowed us to quantify each disease’s overrepresentation or underrepresentation on Twitter, relative to its prevalence. Results Our sample included 80,680,449 tweets. Adjusting disease prevalence to correct for Twitter demographics more than doubles the correlation between Twitter disease mentions and disease prevalence in the general population (from .113 to .258, P <.001). In addition, diseases varied widely in how often mentions of their names on Twitter actually referred to the diseases, from 14.89% (3827/25,704) of instances (for stroke) to 99.92% (5044/5048) of instances (for arthritis). Applying ambiguity correction to our Twitter corpus achieves a correlation between disease mentions and prevalence of . 208 ( P <.001). Simultaneously applying correction for both demographics and ambiguity more than triples the baseline correlation to . 366 ( P <.001). Compared with prevalence rates, cancer appeared most overrepresented in Twitter, whereas high cholesterol appeared most underrepresented. Conclusions Twitter is a potentially useful tool to measure public interest in and concerns about different diseases, but when comparing diseases, improvements can be made by adjusting for population demographics and word ambiguity.
Ethanol and Caffeine Effects on Social Interaction and Recognition in Mice: Involvement of Adenosine A2A and A1 Receptors

Ethanol and caffeine are frequently consumed in combination and have opposite effects on the adenosine system: ethanol metabolism leads to an increase in adenosine levels, while caffeine is a non-selective adenosine A1/A2A receptor antagonist. These receptors are highly expressed in striatum and olfactory tubercle, brain areas involved in exploration and social interaction in rodents. Ethanol modulates social interaction processes, but the role of adenosine in social behavior is still poorly understood. The present work was undertaken to study the impact of ethanol, caffeine and their combination on social behavior, and to explore the involvement of A1 and A2A receptors on those actions. Male CD1 mice were evaluated in a social interaction three-chamber paradigm, for preference of conspecific vs. object, and also for long-term recognition memory of familiar vs. novel conspecific. Ethanol showed a biphasic effect, with low doses (0.25 g/kg) increasing social contact and higher doses (1.0–1.5 g/kg) reducing social interaction. However, no dose changed social preference; mice always spent more time sniffing the conspecific than the object, independently of the ethanol dose. Ethanol, even at doses that did not change social exploration, produced amnestic effects on social recognition the following day. Caffeine reduced social contact (15.0–60.0 mg/kg), and even blocked social preference at higher doses (30.0–60.0 mg/kg). The A1 antagonist Cyclopentyltheophylline (CPT; 3–9 mg/kg) did not modify social contact or preference on its own, and the A2A antagonist MSX-3 (1.5–6 mg/kg) increased social interaction at all doses. Ethanol at intermediate doses (0.5–1.0 g/kg) was able to reverse the reduction in social exploration induced by caffeine (15.0–30.0 mg/kg). Although there was no interaction between ethanol and CPT or MSX-3 on social exploration in the first day, MSX-3 blocked the amnestic effects of ethanol observed on the following day. Thus, ethanol impairs the formation of social memories, and A2A adenosine antagonists can prevent the amnestic effects of ethanol, so that animals can recognize familiar conspecifics. On the other hand, ethanol can counteract the social withdrawal induced by caffeine, a non-selective adenosine A1/A2A receptor antagonist. These results show the complex set of interactions between ethanol and caffeine, some of which could be the result of the opposing effects they have in modulating the adenosine system.
Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification

Logistic regression is well known to the data mining research community as a tool for modeling and classification. The presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge discovery from the underlying databases. Most of the existing outlier detection methods in regression analysis are based on the single case deletion approach that is inefficient in the presence of multiple outliers because of the well known masking and swamping effects. To avoid these effects the multiple case deletion approach has been introduced. We propose a group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression. At the same time we introduce a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations. This paper has two objectives. First, it investigates the problems of outlier detection in logistic regression, proposes a new method that can find multiple influential observations, and classifies the types of outlier. Secondly, it shows the necessity for proper identification of outliers and influential observations as a prelude for reliable knowledge discovery from modeling and classification via logistic regression. We demonstrate the efficiency of our method, compare the performance with the existing popular diagnostic methods, and explore the necessity of outlier detection for reliability and robustness in modeling and classification by using real datasets.
The potential neural mechanisms of acute indirect vibration.

There is strong evidence to suggest that acute indirect vibration acts on muscle to enhance force, power, flexibility, balance and proprioception suggesting neural enhancement. Nevertheless, the neural mechanism(s) of vibration and its potentiating effect have received little attention. One proposal suggests that spinal reflexes enhance muscle contraction through a reflex activity known as tonic vibration stretch reflex (TVR), which increases muscle activation. However, TVR is based on direct, brief, and high frequency vibration (>100 Hz) which differs to indirect vibration, which is applied to the whole body or body parts at lower vibration frequency (5-45 Hz). Likewise, muscle tuning and neuromuscular aspects are other candidate mechanisms used to explain the vibration phenomenon. But there is much debate in terms of identifying which neural mechanism(s) are responsible for acute vibration; due to a number of studies using various vibration testing protocols. These protocols include: different methods of application, vibration variables, training duration, exercise types and a range of population groups. Therefore, the neural mechanism of acute vibration remain equivocal, but spinal reflexes, muscle tuning and neuromuscular aspects are all viable factors that may contribute in different ways to increasing muscular performance. Additional research is encouraged to determine which neural mechanism(s) and their contributions are responsible for acute vibration. Testing variables and vibration applications need to be standardised before reaching a consensus on which neural mechanism(s) occur during and post-vibration. Key pointsThere is strong evidence to suggest that acute indirect vibration acts on muscle to enhance force, power, flexibility, balance and proprioception, but little attention has been given to the neural mechanism(s) of acute indirect vibration.Current findings suggest that acute vibration exposure may cause a neural response, but there is little consensus on identifying which neural mechanism(s) are specifically responsible. This is due to a number of studies using various vibration testing protocols (i.e.varying frequencies, amplitudes, durations, and methods of application). Spinal reflexes, muscle tuning and neuromuscular aspects and central motor command are all viable neuromechanical factors that may contribute at different stages to transiently increasing muscular performance.Additional research is encouraged to determine when (pre, during and post) the different neural mechanism(s) respond to direct and indirect vibration stimuli.
The temporal interpretation of Dutch children's root infinitivals: the effect of eventivity

This study concerns the presence and status of Tense in young children's nonfinite main clauses, or root infinitivals (RIs). Verb- containing utterances produced by four Dutch-speaking boys (age range 1;9-3;2) were classified along the following dimensions: finite-infinitive; temporal reference, as indicated by linguistic and non-linguistic context; and type of verb - eventive vs. non- eventive. The data show that (a) the proportion of non-eventive verbs in RIs is close to zero, whereas they make up approximately half of the finite predicates, and (b) RIs are used to refer to present, past as well as (predominantly) future eventualities, while the temporal reference of finite sentences is restricted to 'present'. It is argued that these findings support the assumption that RIs do not contain Tense, and that their temporal reference comes about as a result of the contextual interpretation of an event variable, if available. The results of a pilot experiment suggest that the pattern in production is mirrored in comprehension.
ProofCheck: Writing and checking complete proofs in L A TEX

ProofCheck is a system for writing and checking mathematical proofs. Theorems and proofs are contained in a plain TEX or L A TEX document. Parsing and proof checking are accomplished through Python programs which read the source ﬁle. A general explanation of the use and structure of the system and programs is provided and a sample proof is shown in detail. The work done by the authors has been based on standard sentence logic, a non-standard predicate logic and set theory with proper classes. Theorems and proofs based on other foundations may be checked if external data ﬁles are modiﬁed. Four such data ﬁles and their possible modiﬁcations are described. In addition, the extent to which the formal language can be shaped to accommodate an author’s preferences is discussed.
Performance Implications of Task Scheduling by Predicting Network Throughput on the Internet

A meta-scheduler is used to efficiently assign tasks to distributed resources. Additionally, the Internet is often used to share the data of tasks on the resources. Throughput prediction will play a crucial role in overcoming the instability of network resource on the Internet for the meta-scheduler. However, it is unclear that higher prediction accuracy should always guarantee better scheduling. In this paper, we focus on how predictors can affect the overall processing time of given tasks through meta-scheduler simulation. Real traces of throughput on the Internet are used to build CDF-based and SVR-based predictors, and these are adopted by the meta-scheduler. Through the simulation, the meta-scheduler using the predictor clearly reduces the processing time. Moreover, the meta-scheduler using the SVR-based, which performed better than CDF-based did in terms of throughput prediction, was observed to result in a reduction of up to 13.3% in the processing time compared to the meta-scheduler without any predictions. The expected value of performance improvement with the SVR-based predictor was calculated as 3.73%, while the value of CDF-based was 2.53%. On the other hand, the meta-scheduler using the predictor can seldom assign tasks to inappropriate sites due to only a few of inaccurate prediction results. As a result, the processing time is drastically increased in comparison with the meta-scheduler without any predictions.
Exploring the mechanism of tonal contraction in taiwan Mandarin

This study investigates the mechanism of tonal contraction when a disyllabic unit is merged into a monosyllable at fast speech rate in Taiwan Mandarin. Various degrees of contraction of bi-tonal sequences were elicited by manipulating speech rates. Functional Data Analysis was performed to compare trajectories of F0 and velocity in the contracted and non-contracted syllables. Preliminary results show that speakers always make an effort to produce the original tones, even in cases of extreme degrees of reduction. This finding militates against phonology-based accounts like the Edge-in model, according to which contraction is a process of deleting adjacent tonemes while leaving the non-adjacent tonemes intact.
Device inherent IMD reproducibility issues in GaN HEMTs

This paper stresses on critical requirements for characterizing device inherent intermodulation distortion (IMD) products in power HEMTs for 3G wideband applications. An accurate broadband RF testbench is essential for reliable nonlinearity measurements. With the optimized IMD characterization testbench, the reproducibility of sweet-spot behavior in GaAs and GaN HEMT technologies has been investigated. This involved statistical process consistency evaluation of GaAs and GaN HEMT semiconductor technologies in terms of their respective pinch-off voltages characterizing sufficient device samples. Corresponding influence on the nature of sweet-spot repeatability under two-tone and multi-carrier WCDMA stimuli revealed the importance of IF load termination.
Microprocessor Software-Based Self-Testing

This article discusses the potential role of software-based self-testing in the microprocessor test and validation process, as well as its supplementary role in other classic functional- and structural-test methods. In addition, the article proposes a taxonomy for different SBST methodologies according to their test program development philosophy, and summarizes research approaches based on SBST techniques for optimizing other key aspects.
Spacetime dynamics and baryogenesis in the braneworld

We point out that the effective theory for the Randall–Sundrum braneworld models with bulk fields contains the baryon number violation process depending on the spacetime dynamics. By combining with the curvature–current interaction, the net baryon number observed today may be explained. The resultant baryon to entropy ratio is determined by the ratio of the Planck scales in four-dimensional and five-dimensional spacetime except for the parameter for CP violation.
Interactive Shape Design Using Metaballs and Splines

Implicit surfaces have received increased attention in the design and animation of 3D objects, since they posses some useful advantages over the traditional parametric surfaces. However, interactive design of such surfaces generally suffers from the relatively slow display speed and lack of efficient control facilities. This paper experiments the combination of the implicit surface technique and spline technique, in order to enhance the modeling capability of the implicit surface in practice, particularly in the design and animation of articulated characters.
A 60-GHz 2-bit Switched-Line Phase Shifter Using SP4T RF-MEMS Switches

This paper presents a V -band 2-bit switched-line phase shifter using dc-contact single-pole four-throw (SP4T) RF microelectromechanical systems (RF-MEMS) switches for 60-GHz applications. The design and measurements of the SP4T RF-MEMS switches and the phase shifter are presented. The phase shifter demonstrates an average insertion loss of 2.5 dB in the 55-65-GHz band with a return loss better than 12 dB for each state. The phase error for each state of the switched-line phase shifter is less than 1° at 60 GHz.
Combining hierarchy encoding and pre-grouping: intelligent grouping in star join processing

Efficient star query processing is crucial for a performant data warehouse (DW) implementation and much work is available on physical optimization (e.g., indexing and schema design) and logical optimization (e.g., pre-aggregated materialized views with query rewriting). One important step in the query processing phase is, however, still a bottleneck: the residual join of results from the fact table with the dimension tables in combination with grouping and aggregation. This phase typically consumes between 50% and 80% of the overall processing time. In typical DW scenarios pre-grouping methods only have a limited effect as the grouping is usually specified on the hierarchy levels of the dimension tables and not on the fact table itself. We suggest a combination of hierarchical clustering and pre-grouping as we have implemented in the relational DBMS Transbase. Exploiting hierarchy semantics for the pre-grouping of fact table result tuples is several times faster than conventional query processing. The reason for this is that hierarchical pre-grouping reduces the number of join operations significantly. With this method even queries covering a large part of the fact table can be executed within a time span acceptable for interactive query processing.
LMI relaxations for nonquadratic stabilization of discrete-time Takagi-Sugeno systems based on polynomial fuzzy Lyapunov functions

The problem of state-feedback control design for discrete-time Takagi-Sugeno fuzzy systems is investigated in this paper. The strategy relies on the use of a quadratic in the state Lyapunov function that presents a homogeneous polynomial dependence of arbitrary degree g on the first instant of time of the premise variables and a multi-affine dependence on the successive instants of time of the premise variables until a maximum instant of time M. The tests cast in the form of LMI relaxations parametrized on both g and M and a feasible solution yields a non-PDC controller based on homogeneous polynomial matrices. Numerical examples show that the approach can be less conservative and more efficient than other methods available in the literature.