Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of muscle bioenergetic gene expression: implications for allometric scaling relationships of glycolytic and oxidative enzymes

SUMMARY Muscle metabolic properties vary with body size, with larger animals relying relatively less on oxidative metabolism as a result of lower specific activities of mitochondrial enzymes and greater specific activities of glycolytic enzymes. While many have argued reasons why such relationships might be grounded in physical relationships, an explanation for the regulatory basis of the differences in enzyme levels remains unexplored. Focusing on skeletal muscle, we review potential cellular and genetic explanations for the relationship between bioenergetic enzymes and body mass. Differences in myonuclear domain (the ratio of fiber volume to nuclei number) in conjunction with constitutive expression may explain part of the variation in mitochondrial content among fiber types and species. Superimposed on such constitutive determinants are (1) extrinsic signalling pathways that control the muscle contractile and metabolic phenotype and (2) intrinsic signalling pathways that translate changes in cellular milieu (ions, metabolites, oxygen, redox) arising through the contractile phenotype into changes in enzyme synthesis. These signalling pathways work through transcriptional regulation, as well as post-transcriptional, translational and post-translational regulation, acting via synthesis and degradation.
Jacobi Iteration for Spatially Constrained Independent Component Analysis

Electroencephalography (EEG) measures the potentials generated by the brain. Unfortunately, eye artifacts often contaminate EEG recordings. Independent Component Analysis (ICA) has been proposed to remove these artifacts. Standard implementations of ICA extract independent components from a set of measured signals in a fully blind way. In the case of eye artifacts, there is strong prior information about the mixing vector of this artifact component. We provide an algorithm which incorporates this knowledge into the calculation of the independent sources and use this constrained ICA technique to remove eye artifacts from the EEG. The advantage of this method is that the components do not have to be inspected afterwards in order to detect the artifactual ones.
Fading countermeasures with cognitive topology management for aerial mesh networks

Coverage and connectivity are key figures for evaluating the proposed communication aware steering algorithms. Beyond this measurable quantitative characteristics a major focus in this section lies on the qualitative analysis, e.g. scalability, stabilization durations and impact of dynamic hysteresis. In order to obtain results that can be compared to real world situations the communication channel signal is enriched with a standard deviation. In Fig. 2 the influence on coverage and fragmentation is investigated using standard deviations of 0 dBm, 5 dBm, 8 dBm and 10 dBm. Additionally the number of MUAVs is altered to demonstrate the flexibility and scalability of our algorithms. Fig. 2(a) shows the decline of the covered area while the standard deviation increases as well as the covered area depending on the number of MUAVs used. The results show that for an average standard deviation on a wireless connection between 6 dBm and 10 dBm, here specifically 8 dBm are investigated, the covered area ranges from about 1.5 km2 for 2 vehicles and up to 3.5 km2 for 24 MUAVs. Increasing the number of MUAVs naturally increases the covered space. However, due to overlapping coverage regions the gained spatial coverage does not match the factor of the additional MUAVs, e.g. doubling the number of UAVs does not necessarily double the spatial coverage. As the standard deviation of the channel increases, the RSSI values exceed the RSSIloss threshold causing an increased amount of clusters as shown in Fig. 2(b). Besides the channel quality the swarm size additionally influences the probability for fragmentations. Fig. 3(a) shows the effect of a degrading channel on coverage and average best RSSIs without this dynamic hysteresis. At first glance it seems that the algorithm is perfectly able to handle the degraded channel. However, it can be noticed that a standard deviation of 8 leads to RSSI values below the RSSIloss. Hence, packet losses would occur very frequently, leading to costly packet retransmissions. In Fig. 3(b) the dynamic hysteresis reacts to the bad channel by contracting the swarm and leading to a lower coverage while increasing at the same time the average best RSSI. This leads to an overall better connectivity. In particular the addition of the fading effect of the lognormal channel yields in most cases an RSSI value that is above the sensitivity threshold RSSIloss. Fig. 3(c) visualizes the contraction that occurs during this connectivity sustaining process. In order to maintain connectivity, which is the primary optimization goal, our algorithms adjusts the spatial coverage as the channel quality declines. The results clearly show the positive effect of the proposed algorithms with respect to connectivity on a lognormal distributed channel.
Online disease management of diabetes: Engaging and Motivating Patients Online With Enhanced Resources-Diabetes (EMPOWER-D), a randomized controlled trial

OBJECTIVE To evaluate an online disease management system supporting patients with uncontrolled type 2 diabetes. MATERIALS AND METHODS Engaging and Motivating Patients Online With Enhanced Resources for Diabetes was a 12-month parallel randomized controlled trial of 415 patients with type 2 diabetes with baseline glycosylated hemoglobin (A1C) values ≥7.5% from primary care sites sharing an electronic health record. The intervention included: (1) wirelessly uploaded home glucometer readings with graphical feedback; (2) comprehensive patient-specific diabetes summary status report; (3) nutrition and exercise logs; (4) insulin record; (5) online messaging with the patient's health team; (6) nurse care manager and dietitian providing advice and medication management; and (7) personalized text and video educational 'nuggets' dispensed electronically by the care team. A1C was the primary outcome variable. RESULTS Compared with usual care (UC, n=189), patients in the intervention (INT, n=193) group had significantly reduced A1C at 6 months (-1.32% INT vs -0.66% UC; p<0.001). At 12 months, the differences were not significant (-1.14% INT vs -0.95% UC; p=0.133). In post hoc analysis, significantly more INT patients had improved diabetes control (>0.5% reduction in A1C) than UC patients at 12 months (69.9 (95% CI 63.2 to 76.5) vs 55.4 (95% CI 48.4 to 62.5); p=0.006). CONCLUSIONS A nurse-led, multidisciplinary health team can manage a population of diabetic patients in an online disease management program. INT patients achieved greater decreases in A1C at 6 months than UC patients, but the differences were not sustained at 12 months. More INT than UC patients achieved improvement in A1C (>0.5% decrease). Trial registered in clinical trials.gov: #NCT00542204.
Cloning and Functional Characterization of Cycloartenol Synthase from the Red Seaweed Laurencia dendroidea

The red seaweed Laurencia dendroidea belongs to the Rhodophyta, a phylum of eukaryotic algae that is widely distributed across the oceans and that constitute an important source of bioactive specialized metabolites. Laurencia species have been studied since 1950 and were found to contain a plethora of specialized metabolites, mainly halogenated sesquiterpenes, diterpenes and triterpenes that possess a broad spectrum of pharmacological and ecological activities. The first committed step in the biosynthesis of triterpenes is the cyclization of 2,3-oxidosqualene, an enzymatic reaction carried out by oxidosqualene cyclases (OSCs), giving rise to a broad range of different compounds, such as the sterol precursors cycloartenol and lanosterol, or triterpene precursors such as cucurbitadienol and β-amyrin. Here, we cloned and characterized the first OSC from a red seaweed. The OSC gene was identified through mining of a L. dendroidea transcriptome dataset and subsequently cloned and heterologously expressed in yeast for functional characterization, which indicated that the corresponding enzyme cyclizes 2,3-oxidosqualene to the sterol precursor cycloartenol. Accordingly, the gene was named L. dendroidea cycloartenol synthase (LdCAS). A phylogenetic analysis using OSCs genes from plants, fungi and algae revealed that LdCAS grouped together with OSCs from other red algae, suggesting that cycloartenol could be the common product of the OSC in red seaweeds. Furthermore, profiling of L. dendroidea revealed cholesterol as the major sterol accumulating in this species, implicating red seaweeds contain a ‘hybrid’ sterol synthesis pathway in which the phytosterol precursor cycloartenol is converted into the major animal sterol cholesterol.
CTIP2 Expression in Human Head and Neck Squamous Cell Carcinoma Is Linked to Poorly Differentiated Tumor Status

Background We have demonstrated earlier that CTIP2 is highly expressed in mouse skin during embryogenesis and in adulthood. CTIP2 mutant mice die at birth with epidermal differentiation defects and a compromised epidermal permeability barrier suggesting its role in skin development and/or homeostasis. CTIP2 has also been suggested to function as tumor suppressor in cells, and several reports have described a link between chromosomal rearrangements of CTIP2 and human T cell acute lymphoblast leukemia (T-ALL). The aim of the present study was to look into the pattern of CTIP2 expression in Head and Neck Squamous Cell Carcinoma (HNSCC). Methodology/Principal Findings In the present study, we analyzed CTIP2 expression in human HNSCC cell lines by western blotting, in paraffin embedded archival specimens by immunohistochemistry (IHC), and in cDNA samples of human HNSCC by qRT-PCR. Elevated levels of CTIP2 protein was detected in several HNSCC cell lines. CTIP2 staining was mainly detected in the basal layer of the head and neck normal epithelium. CTIP2 expression was found to be significantly elevated in HNSCC (p<0.01), and increase in CTIP2 expression was associated with poorly differentiated tumor status. Nuclear co-localization of CTIP2 protein and cancer stem cell (CSC) marker BMI1 was observed in most, if not all of the cells expressing BMI1 in moderately and poorly differentiated tumors. Conclusions/Significance We report for the first time expression of transcriptional regulator CTIP2 in normal human head and neck epithelia. A statistically significant increase in the expression of CTIP2 was detected in the poorly differentiated samples of the human head and neck tumors. Actual CTIP2, rather than the long form of CTIP2 (CTIP2L) was found to be more relevant to the differentiation state of the tumors. Results demonstrated existence of distinct subsets of cancer cells, which express CTIP2 and underscores the use of CTIP2 and BMI1 co-labeling to distinguish tumor initiating cells or cancer stem cells (CSCs) from surrounding cancer cells.
AdaBoost Face Detection

Viola and Jones [1] introduced a new and effective face detection algorithm based on simple features trained by the AdaBoost Algorithm, Integral Images and Cascaded Feature sets. This paper attempts to replicate their results. The Feret Face data set is used as the training set. The AdaBoost Algorithm, simple feature set and Integral Images are briefly explained and implemented in our Matlab based program. A series of ten best features were identified out of a set of close to fifty thousand. These best features were used to produce probability of error plots. Finally our face detection Algorithm is implemented on a series or random Images taken from the internet. More than just ten best features are needed to have a face detector comparable to the two hundred best features of Viola and Jones [1] but the face detector still performs well and anyone can use our program included in the Appendix to implement this effective face detection algorithm and train as many best features as suited for their application.
The visibility of gamma-ray burst afterglows in dusty star-forming regions

ABSTRACT Recent observations of the environments of gamma-ray bursts (GRBs) favour massivestars as their progenitors, which are likely to be surrounded by gas and dust. Thevisibility of the optical and UV emission of a GRB are expected to depend on thecharacteristics of both the dust and the GRB emission itself. A reasonable distributionof surrounding dust is capable of absorbing all the optical and UV emission of theoptical ﬂash and afterglow of a GRB, unless the optical ﬂash has a peak isotropicluminosity L peak & 10 49 ergs −1 . This means that dark bursts should exist and thesebursts will have to be studied at infrared rather than optical wavelengths. In thispaper details will be given about the infrared GRB dust emission. The reprocesseddust emission peaks at a rest-frame wavelength of about 8µm. Forthcoming spacetelescopes,in particularthe IRAC cameraaboardthe SpaceInfrared Telescope Facility,could detect this emission out to a redshift of about 2. However, an accurate positionof the GRB afterglow must be provided for this emission to be identiﬁed, because thelight curve of the reprocessed dust emission does not vary on time-scales less thanseveral years.Key words: radiative transfer – methods: observational – ISM: clouds – dust, ex-tinction – gamma-rays: burst – infrared: galaxies
A click model for time-sensitive queries

User behavior on search results pages provides a clue about the query intent and the relevance of documents. To incorporate this information into search rankings, a variety of click modeling techniques have been proposed so far and now they are widely used in commercial search engines. For time-sensitive queries, however, applying click models can degrade the search relevance because the best document in the past may not be the current best answer. To address this problem, it is required to detect a time point, a turning point, where the search intent for a given query changes and to reflect it in click models. In this work, we devised a method to detect the turning point of a query from its search volume history. The proposed click model is designed to take only user behavior observed after the turning points. We applied our model in a commercial search engine and evaluated its relevance.
Application of SVM in Citation Information Extraction

Support Vector Machines are an effective form of binary-class classification algorithm. To enhance the utilization of text structural features for information extraction, which are greatly restricted by the Hidden Markov Model (HMM), this paper proposes a support vector machine multi-class classification based on Markov properties to extract the information from a citation database. The proposed model extracts symbol characteristics as features and composes a binary tree of the transition probabilities. Experiments show that the proposed method outperforms HMM and basic SVM methods.
APPLICATION OF COMPONENT-BASED ANALYSIS PATTERNS FOR PATTERN-BASED REVERSE ENGINEERING OF MOBILE ROBOT SOFTWARE

Developing software for Autonomous Mobile Robot (AMR) is difficult and requires knowledge in embedded systems, real-time software issues, control theories and artificial intelligence aspects. To tackle the difficulty in developing software for AMR, many researchers have proposed the approach of reusable software component for mobile robot systems. Software pattern provides a way to reuse knowledge of expert across domain at all level of software development. In this paper component-based analysis patterns applicable to AMR software at high-level software development is proposed. Some important AMR component-based analysis patterns on AMR embedded software requirements are presented. How the analysis patterns can help in documenting two existing AMR software through pattern-based reverse engineering process is also illustrated.
Robust Feature Extraction to Utterance Fluctuation of Articulation Disorders Based on Random Projection

We investigated the speech recognition of a person with an articulation disorder resulting from the athetoid type of cerebral palsy. The articulation of the first speech tends to become unstable due to strain on speech-related muscles, and that causes degradation of speech recognition. In this paper, we introduce a robust feature extraction method based on PCA (Principal Component Analysis) and RP (Random Projection) for dysarthric speech recognition. PCA-based feature extraction performs reducing the influence of the unstable speaking style caused by the athetoid symptoms. Moreover, we investigate the feasibility of random projection for feature transformation in order to gain more performance in dysarthric speech recognition task. Its effectiveness is confirmed by word recognition experiments. Index Terms: articulation disorders, speech recognition, PCA, random projection, ROVER
Intersectional Identities, Identity Dimensions, and Academic Contingencies of Self-Worth

ABSTRACT Many people rely on academic performance as an important part of their self-concept, or have academic contingencies of self-worth. We compared academic contingencies of self-worth in three groups of participants, who varied in their feelings about two minority identities: sexual minority Asian/Pacific Islanders, straight Asian/Pacific Islanders, and sexual minority Whites. Comparing pairs of groups that shared one marginalized social identity, we confirmed our hypothesis that participants’ feelings about their racial identity related to contingent self-worth differently based on their sexuality; in contrast, participants’ feelings about their sexual identity related to contingent self-worth in the same way regardless of race. The group defined by the intersection of two minority social identities (Asian/Pacific Islander and sexual minority) is of particular interest.
PHYSICS LEARNING USING REAL AND VIRTUAL LABORATORY ACTIVITES TO IMPROVE STUDENT COMPETENCIES IN BOYLE-GAY LUSSACS LAW AND IDEAL GASES EQUATION

The aim of this research is to describe the implementation of learning, improvement of competence, and students response toward physics learning using real and virtual laboratory in Boyle-Gay Lussacs Law and ideal gases equation. Improved competencies include knowledge and skills. The type of research was used pre-experimental with one-group pretest-posttest design by using three experimental classes. The subjects of the study were students of class XI IPA 1, XI IPA 2, and XI IPA 3 in SMAN 4 Sidoarjo. Instruments used are observation sheet of learning implementation, pretest posttest of knowledge and skill, process skill assessment sheets, and student response questionnaire. Increased student competence was analyzed by paired t-test and gain score analysis. The results showed that (1) the implementation of learning was done very well; (2) students knowledge and skill competitiveness increased significantly in high category; (3) students response to learning through real and virtual laboratory activities got good category. Key words: real laboratory, virtual laboratory, Boyle-Gay Lussacs Law and ideal gases equation.
A Strategy for Addressing Ambiguity in Regulatory Requirements

Ambiguities in legal texts can make the difference between regulatory compliance and non-compliance in software systems. Ambiguities are prevalent in laws and regulations. Policy analysts who write laws and regulations and software engineers who build software systems that must comply with laws and regulations approach ambiguity differently. In our prior work, we surfaced differences between the approach taken by policy analysts and technologists in identifying and classifying ambiguities in legal texts. Understanding the rationale behind the identification and classification of legal ambiguities is essential to disambiguating them for requirements engineering. Herein, we discuss a case study in which we seek to understand the rationale used to make determinations about ambiguities in legal texts. Our 48 case study participants identified 373 ambiguities, 99.1% of which were classified using our ambiguity taxonomy. The results of our qualitative analysis suggest participants are consistently able to identify words and phrases they believe to be ambiguous, but are unable to express and agree on a consistent rationale defending their classification. This result supports a strategy for addressing ambiguity in regulatory requirements—software engineers are likely to be successful at identifying components of legal texts that then require supplemental expertise to resolve.
Ontology-Based Task Simulation

Information services assist people in their decision-making during the performance of certain tasks. In order to determine if a data source, which commits to a given ontology, can be employed for a service, the service provider needs to evaluate its usability and utility for the decision-making process. We propose to do this by simulating with the ontologies the tasks to be supported by the service. Such a simulation needs to access data about entities based on the actions they afford and the events they participate in. This requires that ontologies include information about these affordances and events. The paper demonstrates a formalized framework, which satisfies this requirement by including functions in the ontologies and making the specifications executable. A real-world scenario for a navigation service—instructions for crossing a river by car—demonstrates the applicability and benefits of the approach in a dynamic scenario.
Analytic bias and phonological typology *

Two factors have been proposed as the main determinants of phonological typology: channel bias, phonetically systematic errors in transmission, and analytic bias, cognitive predispositions making learners more receptive to some patterns than others. Much of typology can be explained equally well by either factor, making them hard to distinguish empirically. This study presents evidence that analytic bias is strong enough to create typological asymmetries in a case where channel bias is controlled. I show that (i) phonological dependencies between the height of two vowels are typologically more common than dependencies between vowel height and consonant voicing, (ii) the phonetic precursors of the heightheight and height-voice patterns are equally robust and (iii) in two experiments, English speakers learned a height-height pattern and a voice-voice pattern better than a height-voice pattern. I conclude that both factors contribute to typology, and discuss hypotheses about their interaction.
What Grain Alignment Can Tell About Circumstellar Discs and

Grain alignment theory suggests that grains should be aligned in circumstellar regions and the observational data available supports this conclusion. We discuss the alignment of grains via (1) magnetic relaxation, (2) mechanical processes, and (3) radiative torques. We show that ferromagnetic relaxation is likely to be more important than superparamagnetic relaxation if the dust in circumstellar regions is similar to species recently captured in Earth atmosphere. Outflows and stellar winds provide grain streaming along magnetic field lines and therefore mechanical alignment competes with the ferromagnetic and radiative alignments. We estimate measures of grain alignment in circumstellar regions, comets and interplanetary space and conclude that in many circumstellar regions and in the interplanetary space radiative torques may constitue the major alignment mechanism which aligns grain longer axes perpendicular to the direction of magnetic field. Observations in submillimeter and microwave ranges are suggested as a means of disentangling effects of multiple scattering from those related to aligned grains.