Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Nucleotide Polymorphism Discovery in Bovine Pituitary Gland Using RNA-Seq Technology

Examination of bovine pituitary gland transcriptome by strand-specific RNA-seq allows detection of putative single nucleotide polymorphisms (SNPs) within potential candidate genes (CGs) or QTLs regions as well as to understand the genomics variations that contribute to economic trait. Here we report a breed-specific model to successfully perform the detection of SNPs in the pituitary gland of young growing bulls representing Polish Holstein-Friesian (HF), Polish Red, and Hereford breeds at three developmental ages viz., six months, nine months, and twelve months. A total of 18 bovine pituitary gland polyA transcriptome libraries were prepared and sequenced using the Illumina NextSeq 500 platform. Sequenced FastQ databases of all 18 young bulls were submitted to NCBI-SRA database with NCBI-SRA accession numbers SRS1296732. For the investigated young bulls, a total of 113,882,3098 raw paired-end reads with a length of 156 bases were obtained, resulting in an approximately 63 million paired-end reads per library. Breed-wise, a total of 515.38, 215.39, and 408.04 million paired-end reads were obtained for Polish HF, Polish Red, and Hereford breeds, respectively. Burrows-Wheeler Aligner (BWA) read alignments showed 93.04%, 94.39%, and 83.46% of the mapped sequencing reads were properly paired to the Polish HF, Polish Red, and Hereford breeds, respectively. Constructed breed-specific SNP-db of three cattle breeds yielded at 13,775,885 SNPs. On an average 765,326 breed-specific SNPs per young bull were identified. Using two stringent filtering parameters, i.e., a minimum 10 SNP reads per base with an accuracy ≥ 90% and a minimum 10 SNP reads per base with an accuracy = 100%, SNP-db records were trimmed to construct a highly reliable SNP-db. This resulted in a reduction of 95,7% and 96,4% cut-off mark of constructed raw SNP-db. Finally, SNP discoveries using RNA-Seq data were validated by KASP™ SNP genotyping assay. The comprehensive QTLs/CGs analysis of 76 QTLs/CGs with RNA-seq data identified KCNIP4, CCSER1, DPP6, MAP3K5 and GHR CGs with highest SNPs hit loci in all three breeds and developmental ages. However, CAST CG with more than 100 SNPs hits were observed only in Polish HF and Hereford breeds.These findings are important for identification and construction of novel tissue specific SNP-db and breed specific SNP-db dataset by screening of putative SNPs according to QTL db and candidate genes for bovine growth and reproduction traits, one can develop genomic selection strategies for growth and reproductive traits.
Comparable modulation of the late positive potential by emotion regulation in younger and older adults

It has been suggested that emotion regulation improves with aging. Here, we investigated age differences in emotion regulation by studying modulation of the late positive potential (LPP) by emotion regulation instructions. The electroencephalogram of younger (18-26 years) and older (60-77 years) adults was recorded while they viewed neutral, unpleasant, and pleasant pictures and while they were instructed to increase or decrease the feelings that the emotional pictures elicited. The LPP was enhanced when participants were instructed to increase their emotions. No age differences were observed in this emotion regulation effect, suggesting that emotion regu- lation abilities are unaffected by aging. This contradicts studies that measured emotion regulation by self-report, yet accords with studies that measured emotion regulation by means of facial expressions or psychophysiological responses. More research is needed to resolve the apparent discrepancy between subjective self-report and objective psychophysiological measures.
An NLOS-capable 60 GHz MIMO demonstrator: System concept & performance

In this paper we present a 60 GHz 2 × 2 MIMO demonstrator which has been developed to accomplish robust line-of-sight as well as non-line-of-sight indoor transmission. The physical layer concept is based on Code-Spread Orthogonal Frequency Division Multiplexing (CS-OFDM) and Alamouti Space-Time Coding (STC) to make maximum use of both frequency and space diversity. The highly modular, reconfigurable hardware implementation comprises commercial FPGA platforms and self-developed extension modules up to fully integrated TX/RX 60 GHz frontends in III-V technology. We present results of transmission experiments conducted in a small office environment, which exemplary illustrate the capabilities and the performance of the presented approach.
Cost-sensitive Dynamic Feature Selection

We present an instance-specific test-time dynamic feature selection algorithm. Our algorithm sequentially chooses features given previously selected features and their values. It stops the selection process to make a prediction according to a user-specified accuracy-cost trade-off. We cast the sequential decision-making problem as a Markov Decision Process and apply imitation learning techniques. We address the problem of learning and inference jointly in a simple multiclass classification setting. Experimental results on UCI datasets show that our approach achieves the same or higher accuracy using only a small fraction of features than static feature selection methods.
Smart photonic networks and computer security for image data

Work reported here is part of a larger project on 'Smart Photonic Networks and Computer Security for Image Data', studying the interactions of coding and security, switching architecture simulations, and basic technologies. Coding and security: coding methods that are appropriate for data security in data fusion networks were investigated. These networks have several characteristics that distinguish them form other currently employed networks, such as Ethernet LANs or the Internet. The most significant characteristics are very high maximum data rates; predominance of image data; narrowcasting - transmission of data form one source to a designated set of receivers; data fusion - combining related data from several sources; simple sensor nodes with limited buffering. These characteristics affect both the lower level network design and the higher level coding methods.Data security encompasses privacy, integrity, reliability, and availability. Privacy, integrity, and reliability can be provided through encryption and coding for error detection and correction. Availability is primarily a network issue; network nodes must be protected against failure or routed around in the case of failure. One of the more promising techniques is the use of 'secret sharing'. We consider this method as a special case of our new space-time code diversity based algorithms for secure communication. These algorithms enable us to exploit parallelism and scalable multiplexing schemes to build photonic network architectures. A number of very high-speed switching and routing architectures and their relationships with very high performance processor architectures were studied. Indications are that routers for very high speed photonic networks can be designed using the very robust and distributed TCP/IP protocol, if suitable processor architecture support is available.
Realistic sampling of amino acid geometries for a multipolar polarizable force field

The Quantum Chemical Topological Force Field (QCTFF) uses the machine learning method kriging to map atomic multipole moments to the coordinates of all atoms in the molecular system. It is important that kriging operates on relevant and realistic training sets of molecular geometries. Therefore, we sampled single amino acid geometries directly from protein crystal structures stored in the Protein Databank (PDB). This sampling enhances the conformational realism (in terms of dihedral angles) of the training geometries. However, these geometries can be fraught with inaccurate bond lengths and valence angles due to artefacts of the refinement process of the X‐ray diffraction patterns, combined with experimentally invisible hydrogen atoms. This is why we developed a hybrid PDB/nonstationary normal modes (NM) sampling approach called PDB/NM. This method is superior over standard NM sampling, which captures only geometries optimized from the stationary points of single amino acids in the gas phase. Indeed, PDB/NM combines the sampling of relevant dihedral angles with chemically correct local geometries. Geometries sampled using PDB/NM were used to build kriging models for alanine and lysine, and their prediction accuracy was compared to models built from geometries sampled from three other sampling approaches. Bond length variation, as opposed to variation in dihedral angles, puts pressure on prediction accuracy, potentially lowering it. Hence, the larger coverage of dihedral angles of the PDB/NM method does not deteriorate the predictive accuracy of kriging models, compared to the NM sampling around local energetic minima used so far in the development of QCTFF. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.
Algorithms and the Practical World

This article is both a comment on Neyland’s ‘On organizing algorithms’ and a supplementary note to our ‘The concept of algorithm as an interpretative key of modern rationality’. In the first part we discuss the concepts of algorithm and recursive function from a different perspective from that of our previous article. Our cultural reference for these concepts is once again computability theory. We give additional arguments in support of the idea that a culture informed by an algorithmic logic has promoted modern rationality both in science and in society. We stress again the importance of distinguishing between algorithms applied to quantifiable entities such as space, time and value and those applied to ontological entities such as human actions. In the second case, the algorithm is applied outside its domain of definition and leads to social disaggregation.
Exploiting temporal and spatial variation for WiFi interference avoidance in ZigBee networks

Specifically, ZigBee networks share the 2.4 GHz ISM band with WiFi but have much lower transmission power. Therefore, the ZigBee networks inevitably suffer the interference from WiFi networks. In this paper, we focus on utilising the temporal and spatial feature of WiFi interference to adjust Zigbee channel over the whole networks. We experimentally examine the spatiotemporal variation of interference and then present a novel interference assessing method which jointly considers the intensity and density of WiFi interference in order to better characterise the relation between interference and link quality. Further considering the interference locality, we propose MuZi Multi-channel ZigBee as an interference avoiding approach for ZigBee networks. It aims to determine a better working channel for each node while taking network connectivity into consideration. Our experiments show that, under the existence of WiFi interference, MuZi can achieve 3.3 times throughput than the traditional single-channel method.
Recognizing Perceived Task Difficulty from Speech and Pause Histograms

Currently, a lot of research in the field of intelligent tutoring systems is concerned with recognising student’s emotions and affects. The recognition is done by extracting features from information sources like speech, typing and mouse clicking behaviour or physiological sensors. In former work we proposed some low-level speech features for perceived task difficulty recognition in intelligent tutoring systems. However, by extracting these features some information hidden in the speech input is loosed. Hence, in this paper we propose and investigate speech and pause histograms as features, which preserve some of the loosed information. The approach of using speech and pause histograms for perceived task difficulty recognition is evaluated by experiments on data collected in a study with German students solving mathematical tasks.
The occupational burden of chronic obstructive pulmonary disease

Although chronic obstructive pulmonary disease (COPD) is attributed predominantly to tobacco smoke, occupational exposures are also suspected risk factors for COPD. Estimating the proportion of COPD attributable to occupation is thus an important public health need. A randomly selected sample of 2,061 US residents aged 55–75 yrs completed telephone interviews covering respiratory health, general health status and occupational history. Occupational exposure during the longest-held job was determined by self-reported exposure to vapours, gas, dust or fumes and through a job exposure matrix. COPD was defined by self-reported physician's diagnosis. After adjusting for smoking status and demography, the odds ratio for COPD related to self-reported occupational exposure was 2.0 (95% confidence interval (CI) 1.6–2.5), resulting in an adjusted population attributable risk (PAR) of 20% (95% CI 13–27%). The adjusted odds ratio based on the job exposure matrix was 1.6 (95% CI 1.1–2.5) for high and 1.4 (95% CI 1.1–1.9) for intermediate probability of occupational dust exposure; the associated PAR was 9% (95% CI 3–15%). A narrower definition of COPD, excluding chronic bronchitis, was associated with a PAR based on reported occupational exposure of 31% (95% CI 19–41%). Past occupational exposures significantly increased the likelihood of chronic obstructive pulmonary disease, independent of the effects of smoking. Given that one in five cases of chronic obstructive pulmonary disease may be attributable to occupational exposures, clinicians and health policy-makers should address this potential avenue of chronic obstructive pulmonary disease causation and its prevention.
Catheter steering using a Magnetic Resonance Imaging system

A catheter is successfully bent and steered by applying magnetic gradients inside a Magnetic Resonance Imaging system (MRI). One to three soft ferromagnetic spheres are attached at the distal tip of the catheter with different spacing between the spheres. Depending on the interactions between the spheres, progressive or discontinuous/jumping displacement was observed for increasing magnetic load. This phenomenon is accurately predicted by a simple theoretical dipole interaction model.
Using Argumentation Strategies in Automated Argument Generation

During argumentation, people persuade their audience using a variety of strategies, e.g., hypothetical reasoning, reasoning by cases and ordinary premise-to-goal arguments. In this paper, we offer an operational definition of the conditions for pursuing these strategies, and incorporate into a Bayesian argument-generation system a mechanism for proposing applicable argumentation strategies, generating specific arguments based on these strategies, and selecting a final argument.
Speculation without regret: reducing deoptimization meta-data in the Graal compiler

Speculative optimizations are used in most Just In Time (JIT) compilers in order to take advantage of dynamic runtime feedback. These speculative optimizations usually require the compiler to produce meta-data that the Virtual Machine (VM) can use as fallback when a speculation fails. This meta-data can be large and incurs a significant memory overhead since it needs to be stored alongside the machine code for as long as the machine code lives. The design of the Graal compiler leads to many speculations falling back to a similar state and location. In this paper we present deoptimization grouping, an optimization using this property of the Graal compiler to reduce the amount of meta-data that must be stored by the VM without having to modify the VM. We compare our technique with existing meta-data compression techniques from the HotSpot Virtual Machine and study how well they combine. In order to make informed decisions about speculation meta-data, we present an empirical analysis of the origin, impact and usages of this meta-data.
A Framework to Support Multiple Levels of Interaction

The human world is more and more inter-connected and, as consequence, we need to communicate in a faster and smarted way. Many software applications do that, but they usually work in a single context at time. In this paper we present a framework that aims at making the interaction easier and more natural; involved actors are connected in a graph, sending and receiving signals by adjacent nodes. The framework has been developed to work in dierent contexts, especially in domotic and cultural heritage fruition; the goal is to connect dierent spaces, such as homes, museums, etc, focusing the attention on interaction perspec- tive, maintaining a natural interface for the user and hiding low-level communication issues. This work will be framed in the context of Natu- ral User Interfaces, so it is centered on the user, exchanging signals with external sensors or devices, such as a smartphone and PC, using gestures, dialogues and augmented reality.
Evaluation of integrated active filter auxiliary power modules in electrified vehicle applications

In electrified vehicles, distinct harmonic currents and corresponding ripple voltages exist mainly on the DC-links of the traction inverter and single-phase high voltage (HV) battery charger. Those harmonic currents are normally filtered by a bulk capacitor or an additional active filter (AF) circuit. This presents an obstacle for improving the power density as well as reducing the cost. This paper explores and evaluates a new method for reducing the bulk film capacitors in electrified vehicles. It applies a proposed integrated active filter auxiliary power module (AFAPM) to fulfill both the low voltage (LV) battery charging mode and the active filtering mode functions. Hence, the integrated AFAPM can achieve the AF function without extra power switches, heat sinks, and corresponding gate drivers. Therefore, the proposed method can reduce the cost and volume of the power electronics system in electrified vehicle applications. The evaluation results show that using the AFAPM to mitigate the single-phase HV battery charger's second-order harmonic current is promising and easy to achieve.
A sampling-based approach to accelerating queries in log management systems

Log management systems are common in industry and an essential part of a system administrator’s toolkit. Examples include Splunk, elk, Log Insight, Sexilog, and more. Logs in these systems are characterized by a small number of predefined fields such as timestamp and host, with the bulk of an entry being unstructured text. System administrators query these logs using a combination of range constraints over predefined fields and patterns or regular expressions over the text portion of the message. These queries are both complex and diverse. We propose a method for maintaining a subset of these logs in a much smaller database known as a sublog. Because queries are issued against a much smaller data set they run to completion quickly and avoid common scaling bottlenecks. However, the improvement in performance comes at a price. Because we only consider a subset of the original data, we are only able to provide approximate responses. Nonetheless, the reduction in accuracy is minimal and we are able to produce high-quality, high-performance results.
Working With Gamers: Implications for Counselors

Counseling Association by Dr. Garry R. Walz and Dr. Jeanne C. Bleuer of Counseling Outfitters, LLC. Its purpose is to provide a means of capturing the ideas, information and experiences generated by the annual ACA Conference and selected ACA Division Conferences. Papers on a program or practice that has been validated through research or experience may also be submitted. This digital collection of peer-reviewed articles is authored by counselors, for counselors. VISTAS Online contains the full text of over 500 proprietary counseling articles published from 2004 to present.
Restriction endonuclease analysis of the vp7 genes of human and animal rotaviruses

The vp7 genes of 194 strains of group A rotaviruses representing all known G types were analyzed with three restriction enzymes by direct digestion of amplified cDNA copies or by deduction of the restriction patterns from known sequences. Mammalian rotavirus strains were classified into 28 restriction patterns consisting of combinations of the 6 profiles (s1 to s6) obtained by digestion with Sau96I endonuclease, 9 profiles (h1 to h9) obtained with HaeIII, and 15 profiles (b1 to b15) obtained with BstYI. Digestion with Sau96I and HaeIII identified restriction sites common to all, or almost all, rotavirus strains studied, whereas BstYI was the most discriminating among rotavirus strains. A clear correlation between some restriction patterns or individual profiles and G type and/or host species of origin was found. Several discriminatory restriction sites consisted of type-specific nucleic acid sequences that encoded conserved amino acid residues. Although not directly involved in antigenic diversity, these sites appear to indicate the G type of the isolate. The technique permits rapid comparison of a large number of virus isolates directly from fecal specimens and provides useful markers for investigating the evolution of rotavirus vp7 genes and tracing vaccine virus and interspecies transmission.
Towards effective research-paper recommender systems and user modeling based on mind maps

While user-modeling and recommender systems successfully utilize items like emails, news, and movies, they widely neglect mind-maps as a source for user modeling. We consider this a serious shortcoming since we assume user modeling based on mind maps to be equally effective as user modeling based on other items. Hence, millions of mind-mapping users could benefit from user-modeling applications such as recommender systems. The objective of this doctoral thesis is to develop an effective user-modeling approach based on mind maps. To achieve this objective, we integrate a recommender system in our mind-mapping and reference-management software Docear. The recommender system builds user models based on the mind maps, and recommends research papers based on the user models. As part of our research, we identify several variables relating to mind-map-based user modeling, and evaluate the variables' impact on user-modeling effectiveness with an offline evaluation, a user study, and an online evaluation based on 430,893 recommendations displayed to 4,700 users. We find, among others, that the number of analyzed nodes, modification time, visibility of nodes, relations between nodes, and number of children and siblings of a node affect the effectiveness of user modeling. When all variables are combined in a favorable way, this novel approach achieves click-through rates of 7.20%, which is nearly twice as effective as the best baseline. In addition, we show that user modeling based on mind maps performs about as well as user modeling based on other items, namely the research articles users downloaded or cited. Our findings let us to conclude that user modeling based on mind maps is a promising research field, and that developers of mind-mapping applications should integrate recommender systems into their applications. Such systems could create additional value for millions of mind-mapping users.
Optimal Super-peer Selection for Large-scale P2P System

The peer-to-peer (P2P) systems have grown significantly over the last few years due to their potential for sharing various resources. Unstructured hybrid P2P system can improve the performance of the entire network and system using SP (Super-peer), which has the responsibility for query processing instead of OPs (Ordinary-Peer). In these systems, selecting the best SP to join is an important problem, but it is difficult to choose the optimal SP by the various reasons such as heterogeneous capacity, content similarity and dynamic capacity change. In this paper, we present the SP selection¿s problem and SP selection strategy based on dynamic capacity of the SP and content similarity. Also we measure the SP¿s score with weight to the factors distance cost, processing power and content similarity. Through the simulation, we show query processing performance is improved when OPs use our strategy to choose the best SP