Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of group-based outpatient physiotherapy with usual care after total knee replacement: a feasibility study for a randomized controlled trial

Objective: To evaluate the feasibility of conducting a randomized controlled trial comparing group-based outpatient physiotherapy with usual care in patients following total knee replacement. Design: A feasibility study for a randomized controlled trial. Setting: One secondary-care hospital orthopaedic centre, Bristol, UK. Participants: A total of 46 participants undergoing primary total knee replacement. Interventions: The intervention group were offered six group-based exercise sessions after surgery. The usual care group received standard postoperative care. Participants were not blinded to group allocation. Outcome measures: Feasibility was assessed by recruitment, reasons for non-participation, attendance, and completion rates of study questionnaires that included the Lower Extremity Functional Scale and Knee Injury and Osteoarthritis Outcome Score. Results: Recruitment rate was 37%. Five patients withdrew or were no longer eligible to participate. Intervention attendance was high (73%) and 84% of group participants reported they were ‘very satisfied’ with the exercises. Return of study questionnaires at six months was lower in the usual care (75%) than in the intervention group (100%). Mean (standard deviation) Lower Extremity Functional Scale scores at six months were 45.0 (20.8) in the usual care and 57.8 (15.2) in the intervention groups. Conclusion: Recruitment and retention of participants in this feasibility study was good. Group-based physiotherapy was acceptable to participants. Questionnaire return rates were lower in the usual care group, but might be enhanced by telephone follow-up. The Lower Extremity Functional Scale had high responsiveness and completion rates. Using this outcome measure, 256 participants would be required in a full-scale randomized controlled trial.
Free theorems involving type constructor classes: functional pearl

Free theorems are a charm, allowing the derivation of useful statements about programs from their (polymorphic) types alone. We show how to reap such theorems not only from polymorphism over ordinary types, but also from polymorphism over type constructors restricted by class constraints. Our prime application area is that of monads, which form the probably most popular type constructor class of Haskell. To demonstrate the broader scope, we also deal with a transparent way of introducing difference lists into a program, endowed with a neat and general correctness proof.
Clustering-based dome detection in lunar images using DTM data

ABSTRACT As the quest for lunar exploration is increasing, large numbers of lunar images are being acquired by many satellites. These images provide more information about topographic features on the lunar surface. Detecting and mapping these features using satellite images are of great scientific interest to understand them in detail. This article presents the development and implementation of an approach for automatic lunar domes detection from digital terrain model using clustering techniques. This approach consists of pre-processing, denoising, clustering, segmentation, post-processing, and boundary detection. This approach also examines domes morphometric property such as diameter, circularity index, and aspect ratio. The proposed method is experimented on nine test sites and evaluated by manual analysis for accurate detection with the detailed qualitative assessment. The manual analysis depicts that the results are in agreement with the automatic detection, while the overall statistical results reveal the detection performance as the true detection rate and false detection rate, which is achieved as 83.75% and 16.25%. In addition, the evaluated results also depict the morphometric parameters diameter, circularity index, and aspect ratio from the detected dome.
Wavelet Based Face Recognition for LowQuality Images

The appearance of a face image is severely affected by illumination conditions that hinder the automatic face recognition process. Face recognition (FR) under invariant conditions is challenging, and exacting illumination invariant features is an effective approach to solve this problem. In this work, a multi-resolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. Wavelet coefficients corresponding to each local region residing inside those horizontal bands are selected as features. In the selection of the dominant coefficients, a threshold criterion is proposed, which drastically reduces the feature dimension . The contribution of this paper is threefold: 1) an objective measure of illumination quality of a given face image is used to decide if the image should be pre processed to normalize its illumination; 2) the global quality-based normalization scheme is extended to a regional quality-based approach to adaptive illumination normalization; 3) the illumination quality measure is used as a means to adaptively select the weighting parameters of the fused wavelet-based multi stream face recognition scheme.
Lossless preprocessing algorithms for better compression

In this paper we present an approach which is an alternative to compression algorithms in vogue such as Huffman encoding, arithmetic encoding, the Lempel-Ziv family, Dynamic Markov Compression (DMC), Prediction by Partial Matching (PPM), and Burrows-Wheeler Transform (BWT) based algorithms. This is aimed at developing generic, reversible transformations that can be applied to a source text that improve an existing, or backend, algorithm's ability to compress. In this connection, we present two lossless, reversible transformations namely Enhanced Intelligent Dictionary Based Encoding (EIDBE) and Improved Intelligent Dictionary Based Encoding (IIDBE). We then provide experimental results over the files chosen from the classical text corpus.
A Corpus Search System Utilizing Lexical Dependency Structure

This paper presents a corpus search system utilizing lexical dependency structure. The user's query consists of lexical dependency structure. The user's query consists of a sequence of keywords. For a given query, the system automatically generates the dependency structure patterns which consist of keywords in the query, and returns the sentences whose dependency structures match the generated patterns. The dependency structure patterns are generated by using two operations: combining and interpolation, which utilize dependency structures in the searched corpus. The operations enable the system to generate only the dependency structure patterns that occur in the corpus. The system achieves simple and intuitive corpus search and it is enough linguistically sophisticated to utilize structural information.
Reinforcement learning for optimized trade execution

We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments are based on 1.5 years of millisecond time-scale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning methods to market microstructure problems. Our learning algorithm introduces and exploits a natural "low-impact" factorization of the state space.
An Improved Collaborative Filtering Similarity Model Based on Neural Networks

At present, collaborative filtering has already become one of the most widely used means to provide personalized recommendation service. The crux of collaborative filtering is to utilize user-rating-data matrix for the sake of figuring out similar neighbor users or items with similar ratings, which can be realized mainly by virtue of similarity algorithms. Recommendation consequences which are gained though the traditional algorithms, like Pearson correlation coefficient, cosine and so forth, might not be satisfactory, especially in the context of comparatively sparse user-rating-data matrix. The thesis based on the improved similarity algorithms proposed by relevant researchers, analyses the shortcomings of the pre-existing algorithms, redefines the similarity algorithm formulas including the concept of weight, and furthermore makes use of the characteristics of neural network to practice in order to the optimal weight. This thesis designs an experiment on the foundation of two real data sets, comparing the recommendation effects between the newly-built similarity algorithm models and the pre-existing ones. The results indicate that the superiority of the new method in different parameter circumstances.
Self-Serving Biases in Bargaining : Explaining Impasse

There is strong evidence that in bargaining situations with asymmetric outside options people exhibit self-serving biases concerning their fairness judgements. Moreover psychological literature suggests that this can be a driving force of bargaining impasse. This paper extends the notion of inequity aversion to incorporate self-serving biases due to asymmetric outside options and analyses whether this leads to bargaining breakdown. I distinguish between sophisticated and naive agents, that is, those agents who understand their bias and those who do not. I find that breakdown in ultimatum bargaining results from naiveté of the proposers. JEL classification: A13, C7, D63
Basing Decisions on Sentences in Decision Diagrams

The Sentential Decision Diagram (SDD) is a recently proposed representation of Boolean functions, containing Ordered Binary Decision Diagrams (OBDDs) as a distinguished subclass. While OBDDs are characterized by total variable orders, SDDs are characterized by dissections of variable orders, known as vtrees. Despite this generality, SDDs retain a number of properties, such as canonicity and a polytime apply operator, that have been critical to the practical success of OBDDs. Moreover, upper bounds on the size of SDDs were also given, which are tighter than comparable upper bounds on the size of OBDDs. In this paper, we analyze more closely some of the theoretical properties of SDDs and their size. In particular, we consider the impact of basing decisions on sentences (using dissections as in SDDs), in comparison to basing decisions on variables (using total variable orders as in OBDDs). Here, we identify a class of Boolean functions where basing decisions on sentences using dissections of a variable order can lead to exponentially more compact SDDs, compared to OBDDs based on the same variable order. Moreover, we identify a fundamental property of the decompositions that underlie SDDs and use it to show how certain changes to a vtree can also lead to exponential differences in the size of an SDD.
Effects of Adjective Orientation and Gradability on Sentence Subjectivity

Subjectivity is a pragmatic, sentence-level feature that has important implications for text processing applications such as information extraction and information retrieval. We study the effects of dynamic adjectives, semantically oriented adjectives, and gradable adjectives on a simple subjectivity classifier, and establish that they are strong predictors of subjectivity. A novel trainable method that statistically combines two indicators of gradability is presented and evaluated, complementing existing automatic techniques for assigning orientation labels.
Serum Anti-BPAG1 Auto-Antibody Is a Novel Marker for Human Melanoma

Malignant melanoma is one of the most aggressive types of tumor. Because malignant melanoma is difficult to treat once it has metastasized, early detection and treatment are essential. The search for reliable biomarkers of early-stage melanoma, therefore, has received much attention. By using a novel method of screening tumor antigens and their auto-antibodies, we identified bullous pemphigoid antigen 1 (BPAG1) as a melanoma antigen recognized by its auto-antibody. BPAG1 is an auto-antigen in the skin disease bullous pemphigoid (BP) and anti-BPAG1 auto-antibodies are detectable in sera from BP patients and are used for BP diagnosis. However, BPAG1 has been viewed as predominantly a keratinocyte-associated protein and a relationship between BPAG1 expression and melanoma has not been previously reported. In the present study, we show that bpag1 is expressed in the mouse F10 melanoma cell line in vitro and F10 melanoma tumors in vivo and that BPAG1 is expressed in human melanoma cell lines (A375 and G361) and normal human melanocytes. Moreover, the levels of anti-BPAG1 auto-antibodies in the sera of melanoma patients were significantly higher than in the sera of healthy volunteers (p<0.01). Furthermore, anti-BPAG1 auto-antibodies were detected in melanoma patients at both early and advanced stages of disease. Here, we report anti-BPAG1 auto-antibodies as a promising marker for the diagnosis of melanoma, and we discuss the significance of the detection of such auto-antibodies in cancer biology and patients.
Localization of the PE methylation pathway and SR-BI to the canalicular membrane Published, JLR Papers in Press, June 16, 2003. DOI 10.1194/jlr.M200488-JLR200

To better understand the regulation of biliary phospholipid and cholesterol excretion, canalicular membranes were isolated from the livers of C57BL/6J mice and abundant proteins separated by SDS-PAGE and identified by matrix-assisted laser desorption/ionization mass spectrometry. A prominent protein revealed by this analysis was betaine homocysteine methyltransferase (BHMT). This enzyme catalyzes the first step in a three-enzyme pathway that promotes the methylation of phosphatidylethanolamine (PE) to phosphatidylcholine (PC). Immunoblotting confirmed the presence of BHMT on the canalicular membrane, failed to reveal the presence of the second enzyme in this pathway, methionine adenosyltransferase, and localized the third enzyme of the pathway, PE N-methyltransferase (PEMT). Furthermore, immunfluorescence microscopy unambiguously confirmed the localization of PEMT to the canalicular membrane. These findings indicate that a local mechanism exists in or around hepatocyte canalicular membranes to promote phosphatidylethnolamine methylation and PC biosynthesis. Finally, immunoblotting revealed the presence and immunofluorescence microscopy unambiguously localized the scavenger receptor class B type I (SR-BI) to the canalicular membrane. Therefore, SR-BI, which is known to play a role in cholesterol uptake at the hepatocyte basolateral membrane, may also be involved in biliary cholesterol excretion. Based on these findings, a model is proposed in which local canalicular membrane PC biosynthesis in concert with the phospholipid transporter mdr2 and SR-BI, promotes the excretion of phospholipid and cholesterol into the bile.
A distributed MPC framework for road-following formation control of car-like vehicles

This work presents a novel framework for the formation control of multiple autonomous ground vehicles in an on-road environment. Unique challenges of this problem lie in 1) the design of collision avoidance strategies with obstacles and with other vehicles in a highly structured environment, 2) dynamic reconfiguration of the formation to handle different task specifications. In this paper, we design a local MPC-based trajectory planner for each individual vehicle to follow a reference trajectory while satisfying the various kinematic and dynamic constraints of the vehicles as well as collision avoidance and formation-keeping requirements. The reference trajectory of a vehicle is computed from its leader's trajectory, based on a predefined formation tree. We use logic rules to organize the collision avoidance behaviors of member vehicles. Moreover, we propose a methodology to safely reconfigure the formation on-the-fly. The proposed framework has been validated using high-fidelity simulations.
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures

The natural habitat of most Bayesian methods is data represented by exchangeable sequences of observations, for which de Finetti’s theorem provides the theoretical foundation. Dirichlet process clustering, Gaussian process regression, and many other parametric and nonparametric Bayesian models fall within the remit of this framework; many problems arising in modern data analysis do not. This article provides an introduction to Bayesian models of graphs, matrices, and other data that can be modeled by random structures. We describe results in probability theory that generalize de Finetti’s theorem to such data and discuss their relevance to nonparametric Bayesian modeling. With the basic ideas in place, we survey example models available in the literature; applications of such models include collaborative filtering, link prediction, and graph and network analysis. We also highlight connections to recent developments in graph theory and probability, and sketch the more general mathematical foundation of Bayesian methods for other types of data beyond sequences and arrays.
Transcription Factor Fli1 Regulates Collagen Fibrillogenesis in Mouse Skin

ABSTRACT Biosynthesis of fibrillar collagen in the skin is precisely regulated to maintain proper tissue homeostasis; however, the molecular mechanisms involved in this process remain largely unknown. Transcription factor Fli1 has been shown to repress collagen synthesis in cultured dermal fibroblasts. This study investigated the role of Fli1 in regulation of collagen biosynthesis in mice skin in vivo using mice with the homozygous deletion of the C-terminal transcriptional activation (CTA) domain of the Fli1 gene (Fli1ΔCTA/ΔCTA). Skin analyses of the Fli1 mutant mice revealed a significant upregulation of fibrillar collagen genes at mRNA level, as well as increased collagen content as measured by acetic acid extraction and hydroxyproline assays. In addition, collagen fibrils contained ultrastructural abnormalities including immature thin fibrils and very thick irregularly shaped fibrils, which correlated with the reduced levels of decorin, fibromodulin, and lumican. Fibroblasts cultured from the skin of Fli1ΔCTA/ΔCTA mice maintained elevated synthesis of collagen mRNA and protein. Additional experiments in cultured fibroblasts have revealed that although Fli1 ΔCTA retains the ability to bind to the collagen promoter in vitro and in vivo, it no longer functions as transcriptional repressor. Together, these results establish Fli1 as a key regulator of the collagen homeostasis in the skin in vivo.
Ultrafast Optical Switching in Quantum Dot-metallic Nanoparticle Hybrid Systems References and Links

We study ultrafast excitonic population inversion resulting from the interaction of a semiconductor quantum dot (SQD) with localized surface plasmons. The plasmonic enhanced fields are generated when a metallic nanoparticle (MNP) is subject to a nonlinear chirped few-cycle pulse train. By numerically solving the time-dependent Bloch equations beyond the rotating-wave approximation, we show that the complete population inversion can be achieved for small interparticle distance and the dynamic in population inversion exhibits a steplike transition between absorption and amplifying. This phenomenon can be exploited as an all-optical ultrafast switching device. Moreover, the final state of population inversion is shown to be modified significantly with the interparticle distances, which is not only robust against the variation of probe pulse parameters but also suggests a straightforward method for measuring the interparticle distances via probing the final populations. Semiconductor-metal nanoparticle molecules: hybrid excitons and the nonlinear Fano effect, " Phys. Coherent exciton-plasmon interaction in the hybrid semiconductor quantum dot and metal nanoparticle complex, " Opt. Optical properties of coupled metal-semiconductor and metal-molecule nanocrystal complexes: Role of multipole effects, " Phys. Optical response of strongly coupled quantum dot-metal nanoparticle systems: double peaked Fano structure and bistability, Coherent exciton-surface-plasmon-polariton interaction in hybrid metal-semiconductor nanostructures, Phys. Quantum plasmonics with quantum dot-metal nanoparticle molecules: influence of the Fano effect on photon statistics, " Phys.
Reward Facilitates Tactile Judgments and Modulates Hemodynamic Responses in Human Primary Somatosensory Cortex

Reinforcing effects of reward on action are well established, but possible effects on sensory function are less well explored. Here, using functional magnetic resonance imaging, we assessed whether reward can influence somatosensory judgments and modulate activity in human somatosensory cortex. Participants discriminated electrical somatosensory stimuli on an index finger with correct performance rewarded financially at trial end, at one of four different anticipated levels. Higher rewards improved tactile performance and led to increased hemodynamic signals from ventral striatum on rewarded trials. Remarkably, primary somatosensory cortex contralateral to the judged hand was reactivated at the point of reward delivery, despite the absence of concurrent somatosensory input at that time point. This side-specific reactivation of primary somatosensory cortex increased monotonically with level of reward. Moreover, the level of reward received on a particular trial influenced somatosensory performance and neural activity on the subsequent trial, with better discrimination and enhanced hemodynamic response in contralateral primary somatosensory cortex for trials that followed higher rewards. These results indicate that rewards can influence not only classical reward-related regions, but also early somatosensory cortex when a decision is required for that modality.
Cytoplasmic retention of Xenopus nuclear factor 7 before the mid blastula transition uses a unique anchoring mechanism involving a retention domain and several phosphorylation sites

Xenopus nuclear factor 7 (xnf7) is a maternally expressed protein that belongs to the B-box zinc finger gene family consisting of transcription factors, protooncogenes, and ribonucleoproteins. Its function is regulated by retention in the cytoplasm from oocyte maturation until the mid blastula transition (MBT) when it reenters the nucleus. We defined a 22-amino acid cytoplasmic retention domain (CRD) in xnf7 that functioned cooperatively with two phosphorylation sites within the xnf7 molecule to retain the protein in the cytoplasm until the MBT. Deletion of this region or mutations in the phosphorylation sites resulted in the early entry of xnf7 into the nucleus. A mutation changing one of the phosphorylation sites to a glutamic acid resulted in the prolonged retention of the xnf7 protein in the cytoplasm until stages 9-10, well past the MBT. Additionally, a mutant form of xnf7 possessing a second nuclear localization signal at the COOH terminus was retained in the cytoplasm. This suggests that retention of xnf7 was not due to the masking of its NLS as is the case with NFkB and dorsal but was due to a novel anchoring mechanism in which the CRD interacts with an anchor protein. The CRD sequence is also found in another B-box zinc finger protein that is also retained in the cytoplasm until the MBT in the newt. Therefore, we believe that this may be an important mechanism whereby the function of a number of nuclear proteins is regulated during development.