Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Human Muscle Spindle Sensitivity during a Proprioceptive Attention Task

The aim of the present study was to test whether fusimotor control of human muscle spindle sensitivity changed when attention was selectively directed to the recognition of an imposed two-dimensional movement in the form of a written symbol. The unitary activities of 32 muscle spindle afferents (26 Ia, 6 II) were recorded by microneurography at the level of the common peroneal nerve. The patterns of firing rate in response to passive movements of the ankle, forming different letters or numbers, were compared in two conditions: control and recognition. No visual cues were given in either condition, but subjects had to recognize and name the character in one condition compared with not paying attention in the control condition. The results showed that 58% of the tested Ia afferents presented modified responses to movements when these had to be recognized. Changes in Ia afferent responses included decreased depth of modulation, increased variability of discharge, and changes in spontaneous activity. Not all changes were evident in the same afferent. Furthermore, the percentage of correctly recognized movements amounted to 63% when changes were observed, but it was only 48% when the primary ending sensitivity was unaltered. The responses of group II afferents were only weakly changed or unchanged. It is suggested that the altered muscle spindle sensitivity is because of selective changes in fusimotor control, the consequence of which might be to feed the brain movement trajectory information that is more accurate.
Identification and Properties of 1,119 Candidate LincRNA Loci in the Drosophila melanogaster Genome

The functional repertoire of long intergenic noncoding RNA (lincRNA) molecules has begun to be elucidated in mammals. Determining the biological relevance and potential gene regulatory mechanisms of these enigmatic molecules would be expedited in a more tractable model organism, such as Drosophila melanogaster. To this end, we defined a set of 1,119 putative lincRNA genes in D. melanogaster using modENCODE whole transcriptome (RNA-seq) data. A large majority (1.1 of 1.3 Mb; 85%) of these bases were not previously reported by modENCODE as being transcribed. Significant selective constraint on the sequences of these loci predicts that virtually all have sustained functionality across the Drosophila clade. We observe biases in lincRNA genomic locations and expression profiles that are consistent with some of these lincRNAs being involved in the regulation of neighboring protein-coding genes with developmental functions. We identify lincRNAs that may be important in the developing nervous system and in male-specific organs, such as the testes. LincRNA loci were also identified whose positions, relative to nearby protein-coding loci, are equivalent between D. melanogaster and mouse. This study predicts that the genomes of not only vertebrates, such as mammals, but also an invertebrate (fruit fly) harbor large numbers of lincRNA loci. Our findings now permit exploitation of Drosophila genetics for the investigation of lincRNA mechanisms, including lincRNAs with potential functional analogues in mammals.
Spatial and behavioral correlates of striatal neurons in rats performing a self-initiated navigation task

To investigate the spatial and behavioral correlates of striatal neurons during displacement movements, the rostromedial dorsal striata (AP 1.0–2.2, ML 1.5–2.0) of five rats were surgically implanted with advanceable bundles of fine wire electrodes. After recovery, the rats were deprived of water and trained in a square-walled open field in a dark room. The behavioral task required alternating visits to water reservoirs in the center and in the four corners. A certain corner contained the first reward for each trial; after this reward, a cue card appeared in this corner for the rest of the trial. The firing rates of striatal units were compared as the rat moved between the center and the four corners of the arena. Analyses were made of 30 units. Eight of these had firing rates that significantly increased or decreased by 62–480% while the rat was in one or more quadrants of the arena. Six of these manifested such firing rate changes only as the rat performed certain behavioral sequences in the quadrant. Three other units fired as the rat's head was in a certain orientation relative to the arena walls, in all parts of the arena. To determine the principal controlling cues and hence the frame of reference of spatial selectivity of these units, the arena, while the rat was still inside, was rotated in total darkness. The first water reward was then presented at the same position relative to the outside room as before the rotation. The cue card was then illuminated in this corner as a visual cue for the extra-arena reference frame. All 11 neurons demonstrated spatial selectivity that rotated with the arena; thus, this activity was in the frame of reference of the arena and was not controlled by the visual cue. Six other units fired at rates up to six times their resting discharge or stopped firing completely in synchrony with initiation or execution of displacement movements, and two of these were also location selective. Four other units were silent as the rat performed the task, but fired tonically following arena rotations or other interruptions of the session, independent of the rat's location or movements. Nine analyzed units had very low firing rates (< 1 impulse/sec) and showed no discernible changes in activity as the rat performed the task. These patterns of unit activity indicate that fundamental informational components required for navigation are coded in the striatum. (ABSTRACT TRUNCATED AT 400 WORDS)
Utilizing performance monitor counters to effectively guide windows and SQL server tuning efforts

Window and the SQL Server database management system provide many performance counters. However, the vast number of counters and the fact that numerous explanations provided via the Windows Performance Monitor simply restate the name of the counter make resolving performance problems in this environment a daunting task. This paper discusses methodologies, metrics, and techniques that can be utilized during a performance crisis to ascertain the cause(s) of the problem and determine an appropriate solution.
Frontotemporal lobar degeneration without lobar atrophy.

BACKGROUND Frontotemporal lobar degeneration with ubiquitin-only-immunoreactive neuronal inclusions (FTLD-U) is the most common form of frontotemporal dementia. Neuronal loss and gliosis in cornu ammonis 1 and the subiculum of the hippocampus are features of hippocampal sclerosis (HpScl), which occurs in many cases of FTLD-U. OBJECTIVE To determine if there were any clinical or magnetic resonance imaging correlates of HpScl in FTLD-U. DESIGN We reviewed demographics and clinical features of 24 cases of FTLD-U and subjectively assessed the severity of neuronal loss and frequency of ubiquitin-positive neuronal lesions in the frontal and temporal cortices and the dentate gyrus of the hippocampus. SETTING Mayo Clinic, Rochester, Minn. Patients Twenty-six cases were identified from the medical records linkage system query that met clinical criteria and had autopsy material available for additional studies. Two cases were excluded from further analysis after pathologic studies revealed coexisting Alzheimer disease, leaving 24 cases included in the study. Cases were subdivided based on the presence or absence of HpScl. MAIN OUTCOME MEASURES Patterns of gray matter atrophy were assessed in cases of FTLD-U with and without HpScl using voxel-based morphometry. RESULTS Six of the 24 cases of FTLD-U did not have HpScl. No differences were found in demographic or clinical features, including disease duration, between cases with and without HpScl; however, voxel-based morphometry analysis revealed minimal cortical atrophy in cases without HpScl, which was significantly different from the pattern of moderate to severe frontal and temporal lobe atrophy in FTLD-U with HpScl. This finding was in keeping with histopathologic observations. CONCLUSIONS Despite similar clinical features, cases of FTLD-U with HpScl differ from those without HpScl with respect to pathologic findings and structural imaging. Specifically, FTLD-U without HpScl showed on average minimal or no cortical atrophy, even at end-stage disease. Consequently, FTLD-U without HpScl does not conform to the proposed FTLD staging scheme, is underrecognized, and may have different genetic and environmental underpinnings.
What Is Personalization? Perspectives on the Design and Implementation of Personalization in Information Systems

In e-commerce and mobile commerce, personalization has been recognized as an important approach element in customer relationships and Web strategies. However, there are wide differences in how this concept is defined, characterized, and implemented in the literature. In this article we present a high-level framework for classifying approaches to personalization that delineates fundamental assumptions about personalization in the literature and relates them to strategies for developing personalization systems. The framework consists of 2 parts: (a) a set of perspectives on personalization that guide the design of personalization systems at a general level and (b) a scheme for classifying how personalization can be implemented. The personalization perspectives represent 4 distinct schools of thought on the nature of personalization distilled from the literature of several fields. These perspectives are ideal types and we discuss them in terms of the motivation they supply for personalization, the goals and means of personalization, and the ways in which they conceptualize and model users. The implementation classification scheme is constructed on 3 dimensions of implementation choices. These 3 dimensions pertain to what to personalize (content, interface, functionality, channel), to whom to personalize (individuals or categories of individuals) as well as who does the personalization (implicit or explicit personalization). The personalization perspectives represent particular concepts of personalization that guide general design choices; these choices are implemented via the options described in the implementation classification scheme. The framework contributes to the development of a common theoretical basis for the study of personalization. We discuss implications of the framework for design of personalization systems and future research directions.
Mining from open answers in questionnaire data

Surveys are an important part of marketing and customer relationship management, and open answers (i.e., answers to open questions) in particular may contain valuable information and provide an important basis for making business decisions. We have developed a text mining system that provides a new way for analyzing open answers in questionnaire data. The product is able to perform the following two functions: (A) accurate extraction of characteristics for individual analysis targets, (B) accurate extraction of the relationships among characteristics of analysis targets. In this paper, we describe the working of our text mining system. It employs two statistical learning techniques: rule analysis and Correspondence Analysis for performing the two functions. Our text mining system has already been put into use by a number of large corporations in Japan in the performance of text mining on various types of survey data, including open answers about brand images, open answers about company images, complaints about products, comments written on home pages, business reports, and help desk records. In this it has been found to be useful in forming a basis for effective business decisions.
Fast rates for online learning in Linearly Solvable Markov Decision Processes

We study the problem of online learning in a class of Markov decision processes known as linearly solvable MDPs. In the stationary version of this problem, a learner interacts with its environment by directly controlling the state transitions, attempting to balance a fixed state-dependent cost and a certain smooth cost penalizing extreme control inputs. In the current paper, we consider an online setting where the state costs may change arbitrarily between consecutive rounds, and the learner only observes the costs at the end of each respective round. We are interested in constructing algorithms for the learner that guarantee small regret against the best stationary control policy chosen in full knowledge of the cost sequence. Our main result is showing that the smoothness of the control cost enables the simple algorithm of following the leader to achieve a regret of order $\log^2 T$ after $T$ rounds, vastly improving on the best known regret bound of order $T^{3/4}$ for this setting.
Risk factors of infant and child mortality in rural Burkina Faso.

OBJECTIVE The aim of the study was to quantify the effect of risk factors for childhood mortality in a typical rural setting in sub-Saharan Africa. METHODS We performed a survival analysis of births within a population under demographic surveillance from 1992 to 1999 based on data from a demographic surveillance system in 39 villages around Nouna, western Burkina Faso, with a total population of about 30000. All children born alive in the period 1 January 1993 to 31 December 1999 in the study area (n = 10 122) followed-up until 31 December 1999 were included. All-cause childhood mortality was used as outcome variable. FINDINGS Within the observation time, 1340 deaths were recorded. In a Cox regression model a simultaneous estimation of hazard rate ratios showed death of the mother and being a twin as the strongest risk factors for mortality. For both, the risk was most pronounced in infancy. Further factors associated with mortality include age of the mother, birth spacing, season of birth, village, ethnic group, and distance to the nearest health centre. Finally, there was an overall decrease in childhood mortality over the years 1993-99. CONCLUSION The study supports the multi-causation of childhood deaths in rural West Africa during the 1990s and supports the overall trend, as observed in other studies, of decreasing childhood mortality in these populations. The observed correlation between the factors highlights the need for multivariate analysis to disentangle the separate effects. These findings illustrate the need for more comprehensive improvement of prenatal and postnatal care in rural sub-Saharan Africa.
Trade-offs in decision-theoretic planning

Decision making under uncertainty can be viewed as a planning task, because it basically amounts to determining a sequence of actions that is optimal for a problem under study. Many di erent formalisms are used in decision making under uncertainty, with many di erent restrictions enforced. In this paper, a novel, general decision-theoretic planning framework is proposed, and used to analyse various representation formalisms for decision making. Several features of these formalisms are highlighted in terms of the framework, using examples from the domain of cancertreatment planning in medicine.
AGRICULTURAL COOPERATIVES: A UNIFIED THEORY OF PRICING, FINANCE, AND INVESTMENT

Agricultural cooperatives are a significant form of business enterprise. In many respects, they are similar to the investor-owned, profit-maximizing firms that, along with other organizations such as households and government agencies, form the institutional framework for western economic theory. Yet as so many authors have pointed out, cooperatives also are distinctly different from investor-owned firms (IOFs).
Titanium nanostructures for biomedical applications

Titanium and titanium alloys exhibit a unique combination of strength and biocompatibility, which enables their use in medical applications and accounts for their extensive use as implant materials in the last 50 years. Currently, a large amount of research is being carried out in order to determine the optimal surface topography for use in bioapplications, and thus the emphasis is on nanotechnology for biomedical applications. It was recently shown that titanium implants with rough surface topography and free energy increase osteoblast adhesion, maturation and subsequent bone formation. Furthermore, the adhesion of different cell lines to the surface of titanium implants is influenced by the surface characteristics of titanium; namely topography, charge distribution and chemistry. The present review article focuses on the specific nanotopography of titanium, i.e. titanium dioxide (TiO2) nanotubes, using a simple electrochemical anodisation method of the metallic substrate and other processes such as the hydrothermal or sol-gel template. One key advantage of using TiO2 nanotubes in cell interactions is based on the fact that TiO2 nanotube morphology is correlated with cell adhesion, spreading, growth and differentiation of mesenchymal stem cells, which were shown to be maximally induced on smaller diameter nanotubes (15 nm), but hindered on larger diameter (100 nm) tubes, leading to cell death and apoptosis. Research has supported the significance of nanotopography (TiO2 nanotube diameter) in cell adhesion and cell growth, and suggests that the mechanics of focal adhesion formation are similar among different cell types. As such, the present review will focus on perhaps the most spectacular and surprising one-dimensional structures and their unique biomedical applications for increased osseointegration, protein interaction and antibacterial properties.
Salient Region Detection Based on Automatic Feature Selection

Salient regions detection in the image is very useful for image processing. It can reduce the computational complexity of the process and save computation resource. Most of the methods compute the saliency based on feature integration theory. But different features have different contribution to the saliency in different images. A new feature integration method is proposed in this paper. This method can select features and compute the weights of the features dynamically by analyzing the effect of different features on the saliency. Some experiment results and qualitative and quantitative analysis are presented in this paper. The experiment results show that the proposed feature integration strategy can select and weight the appropriate features correctly and the detection results are consistent with human visual system.
Dietary polyunsaturated fatty acids and regulation of gene transcription

Dietary polyunsaturated fatty acids (PUFAs) are a source of energy and structural components for cells. PUFAs also have dramatic effects on gene expression by regulating the activity or abundance of four families of transcription factor, including peroxisome proliferator activated receptor (PPAR) (α, β and γ), liver X receptors (LXRs) (α and β), hepatic nuclear factor-4 (HNF-4)α and sterol regulatory element binding proteins (SREBPs) 1 and 2. These transcription factors play a major role in hepatic carbohydrate, fatty acid, triglyceride, cholesterol and bile acid metabolism. Non-esterified fatty acids or fatty acid metabolites bind to and regulate the activity of PPARs, LXRs and HNF-4. In contrast, PUFAs regulate the nuclear abundance of SREBPs by controlling the proteolytic processing of SREBP precursors, or regulating transcription of the SREBP-1c gene or turnover of mRNASREBP-1c. The n3 and n6 PUFAs are feed-forward activators of PPARs, while these same fatty acids are feedback inhibitors of LXRs and SREBPs. Saturated fatty acyl coenzyme A thioesters activate HNF-4α, while coenzyme A thioesters of PUFAs antagonize HNF-4α action. Understanding how fatty acids regulate the activity and abundance of these and other transcription factors will likely provide insight into the development of novel therapeutic strategies for better management of whole body lipid and cholesterol metabolism.
Adding Semantics and Rigor to Association Rule Learning: the GenTree Approach

Learning semantically useful association rules across all attributes of a relational table requires: (1) more rigorous mining than afforded by traditional approaches; and, (2) the invention of knowledge ratings for learned rules, not just statistical ratings. Traditional algorithms began by learning rules over one attribute expressed in the domain values of that attribute (Srikant and Agrawal, 1995). “People who buy diapers tend to buy beer” is an example from grocery purchases. In the second generation, Srikant and Agrawal, (1996) introduced a hierarchy whose base values are those originally represented in the data, and values appearing at higher levels in the hierarchy represent increasingly more general concepts of base values. Rules learned over the attribute using the hierarchy are termed generalized association rules (or crosslevel rules). “People who buy baby products tend to buy controlled substances” is an example of a generalized association rule. Rules that mix the level of expressions for the same item were still not able to be learned. “People who buy beverages tend to buy beer” is an example of a rule that could not be properly learned if most beverage purchases were of beer. The work reported herein continues the evolution in the expressiveness of association rules to its broadest application – semantically rated rules learned from a large relational table having many attributes. Associated with each attribute is a hierarchy. We find the rules that are formed by combining mixed levels of generalizations across all attributes and that convey the maximum expression of information supported by attribute hierarchies, parameter settings and data tuples. We term these robust rules. An example of a robust rule is “People who buy baby products tend to buy controlled substances, use credit cards and make purchases on Saturdays. ” Multiple attributes from the transaction are included and cross-level and mix-level rules are expressed. Because of the improved expressiveness, many more statistically relevant rules are learned. To identify semantically useful rules among them, we compare rule features appearing on the left side (before “tend to”) to those appearing on the right side (after “tend to”) to identify learning in breadth (more attributes appearing on the right-side than on the left) and learning in depth (values on the right side appearing at lower levels in the corresponding hierarchies than those on the left). For a set of learned rules surpassing statistical thresholds of relevance, the most semantically relevant rules are those expressing more depth and/or breadth in the terms of their expression. The other rules are merely subset expressions. We also introduce a GenTree algorithm as an efficient means to learn robust rules from a table. Experiments using GenTree with two real-world datasets, containing 10,000 six-attributed tuples and over 4,000 eightattributed tuples each, show that learned rules convey more comprehensive information than possible with traditional association rule mining algorithms.