Miracle-dz/UniFAR-FTUs huggingface dataset:
Substrate‐assisted catalysis: Implications for biotechnology and drug design

In enzyme catalysis, the convention is that the enzyme supplies all the functional groups that are needed to convert a substrate into a product. This convention, however, is now recognized to have some exceptions. In a growing number of cases it is evident that the substrate also provides one or more functional groups that actively participate in the catalytic process. These cases are grouped together under the title “substrate‐assisted catalysis” (SAC). Examples of SAC have been described both for native and for engineered enzymes that were rendered inactive by mutations. Such mutations eliminate amino acid sidechains that participate in the catalytic process and thereby cause partial or total loss of enzymatic activity. For several of these mutant enzymes, a modified substrate bearing functional groups similar to those that had been eliminated by the mutation was found to rescue enzyme activity. A notable example is the mutual specificity found between mutant serine proteases and their modified substrates. This creates a highly specific site for proteolytic cleavage, a desirable property in the processing of recombinant fusion products. An attractive target for SAC is the G‐protein family. It was applied to two of its members—Gsα and p21‐Ras. In both cases it was possible to restore the GTPase activity of the mutants back to the level of the wild‐type proteins. Beyond restoring activity by SAC, further modifications of the substrate were introduced to support or refute particular roles of the functional groups in the GTPase reaction. This approach was also applied as a molecular tool to discriminate between specific enzymatic mechanisms and as a guideline to incorporate particular functional groups into the substrate. Taken together, these studies pave the way to novel therapeutic and biotechnological approaches aimed at restoring the activity of mutant inactive enzymes. Drug Dev. Res. 50:250–257, 2000. © 2000 Wiley‐Liss, Inc.
Flex-N-Feel: Emotive Gloves for Physical Touch Over Distance

Many couples live apart due to work or study reasons, experiencing the challenge of maintaining a long-distance relationship. These couples rely heavily on existing communication technologies, which are limited to textual, verbal, and visual mediums. Our approach was to provide couples with a haptic experience of affective touch through vibrotactile sensations. We designed and built a vibrotactile glove called Flex-N-Feel, that allows couples to feel the flex actions of their remote partners' fingers through vibrotactile sensations on their skin. We describe our design and its rationale along with a discussion of the design space.
Remodeling the Proteostasis Network to Rescue Glucocerebrosidase Variants by Inhibiting ER-Associated Degradation and Enhancing ER Folding

Gaucher’s disease (GD) is characterized by loss of lysosomal glucocerebrosidase (GC) activity. Mutations in the gene encoding GC destabilize the protein’s native folding leading to ER-associated degradation (ERAD) of the misfolded enzyme. Enhancing the cellular folding capacity by remodeling the proteostasis network promotes native folding and lysosomal activity of mutated GC variants. However, proteostasis modulators reported so far, including ERAD inhibitors, trigger cellular stress and lead to induction of apoptosis. We show herein that lacidipine, an L-type Ca2+ channel blocker that also inhibits ryanodine receptors on the ER membrane, enhances folding, trafficking and lysosomal activity of the most severely destabilized GC variant achieved via ERAD inhibition in fibroblasts derived from patients with GD. Interestingly, reprogramming the proteostasis network by combining modulation of Ca2+ homeostasis and ERAD inhibition remodels the unfolded protein response and dramatically lowers apoptosis induction typically associated with ERAD inhibition.
Contribution to the Prediction of Performances of the Hough Transform

Exact predictions of the performances of the Hough detection of straight lines in two-dimensional images are presented for rectangular and circular retinas, in Cartesian and normal parameterization, in the case of noisy signals. Detection of circles, under the same assumptions, is discussed. The limits of adaptive quantization to reduce intrinsic noise are presented, and it is shown that a signal processing approach is especially convenient to measure the performances of a detector based on the Hough transform.
Cortisol, Testosterone, and Coronary Heart Disease: Prospective Evidence From the Caerphilly Study

Background—There is a popular belief that chronic stress causes heart disease through psychoneuroendocrine mechanisms. We have examined whether an elevated circulating cortisol-to-testosterone ratio increases the risk of ischemic heart disease. Methods and Results—We undertook a prospective cohort study of 2512 men aged 45 to 59 years between 1979 and 1983 from Caerphilly, South Wales, with a mean follow-up of 16.5 years. Subjects underwent a clinical examination, and morning fasting blood samples were taken for analysis of cortisol levels, testosterone levels, and other cardiovascular risk factors. The ratio of cortisol to testosterone showed weak associations with potential confounding factors but strong positive associations with components of the insulin resistance syndrome (P<0.001). A positive linear trend was seen across quintiles of cortisol:testosterone ratio for incident ischemic heart disease (age-adjusted OR per z score change in ratio 1.22, 95% CI 1.07 to 1.38, P=0.003). This was markedly attenuated after adjustment for components of the insulin resistance syndrome (age-adjusted OR per z score change in ratio 1.10, 95% CI 0.96 to 1.25, P=0.18). There was no association between the cortisol:testosterone ratio and other causes of death (age-adjusted hazard ratio 0.99, 95% CI 0.88 to 1.11, P=0.81). Conclusions—This is the first population-based prospective study that has found a specific association between cortisol:testosterone ratio and incident ischemic heart disease, apparently mediated through the insulin resistance syndrome. Whether this reflects the effects of chronic stress, behavioral factors, or genetic influences remains to be determined.
Efficien t Processing Distributed Joins with Bloomfilter using MapReduce y

The MapReduce framework has been widely used to process and analyze largescale datasets over large clusters. As an essential problem, join operation among large clusters attracts more and more attention in recent years due to the utilization of MapReduce. Many strategies have been proposed to improve the efficiency of distributed join, among which bloomfilter is a successful one. However, the bloomfilter’s potential has not yet been fully exploited, especially in the MapReduce environment. In this paper, three strategies are presented to build the bloomfilter for the large datasets using MapReduce. Based on these strategies, we design two algorithms for two-way join and one algorithm for multi-way join. The experimental results show that our algorithms can significantly improve the efficiency of current join algorithm. Moreover, cost models of these algorithms are characterized in order to find out the way of improving the performance of two-way and multi-way joins.
Kernel analysis for architecture design trade off in convolution-based image filtering

Intending to improve design trade offs in image processing architectures this work presents some kernel analysis for convolution-based image filtering. Some well-known filter kernels have been analyzed in order to identify symmetries and to allow the development of alternative architectures that can contribute to reduce power consumption and/or FPGA resources, maintaining or improving the overall system throughput. The separable kernel technique is also analyzed, and two architectures were developed and tested. Additionally, a technique based on overlapping kernels have been developed, analyzed and tested. All architectures were implemented and synthesized using Altera Quartus II EDA software and prototyped in four real-time image processing platforms. These platforms are composed by a CMOS camera, four Terasic FPGA development kits (with Altera devices) and an LCD. Synthesis, simulations and real-time results show the suitability of such architectures for this kind of design trade off.
Nephrology Dialysis Transplantation the Effect of Uraemia, Acidosis, and Dialysis Treatment on Protein Metabolism: a Longitudinal Leucine Kinetic Study Three Patients Had Chronic Glomerulonephritis, Two Focal

intake by reducing amino-acid oxidation and protein degradation, and maintained protein synthesis at Background. Uraemia and dialysis are viewed as cata-bolic processes resulting in malnutrition in chronic normal levels. Metabolic acidosis impaired the downre-gulation of amino-acid oxidation. Maintenance dialysis renal failure (CRF) patients. To sort out the effects of uraemia, acidosis, and dialysis on protein metabolism, treatment longitudinally restored protein flux to normal and increased protein synthesis. The general we measured leucine flux in CRF patients before and after initiation of maintenance dialysis. notion that uraemia and dialysis are protein catabolic is not supported by this work. Subjects and methods. Whole-body leucine flux was measured by primed-constant infusion of L[1-13C ] Key words: leucine kinetics; protein turnover; uraemia leucine in nine CRF patients longitudinally; twice before and once after initiation of maintenance dialysis (D). Before dialysis, one leucine flux was measured when the patients were acidotic (A), and the other, Introduction when acidosis was corrected with NaHCO, (NA). Five normal subjects underwent one single leucine flux Protein malnutrition, prevalent in chronic renal failure measurement to serve as control (N). Both patients (CRF) patients [1–6 ], is generally attributed to the and normal subjects consumed a constant diet for 6 protein catabolic nature of uraemia and dialysis treat-days and leucine flux was measured on the 7th day ment [7]. There is a voluminous literature demonstrat-12 h post-absorption. Diet for the CRF patients was ing marked protein catabolism in uraemic rat models identical during the three periods. Plasma L[1-13C ] [8–13]. Dialysis, with its obligatory protein and leucine and L[1-13C ]KIC were measured by gas amino acid loss, is inherently a catabolic stress [14–16 ]. chromatography/mass spectrometry and expired 13CO 2 Furthermore, during haemodialysis, protein degrada-by isotope ratio spectrometry. Leucine kinetics were tion may be enhanced by bioincompatible membranes calculated using standard equations. [17]. To sort out the effects of uraemia, acidosis, and Results. Plasma CO 2 levels were 19, 26 and 31 mmol/l maintenance dialysis on protein metabolism, we meas-in A, NA and D periods respectively. All kinetic results ured in vivo whole body protein turnover in end-stage (mmol/kg/h) are presented as means±SD in the order renal disease patients shortly before and 8–10 weeks of A, NA, D, and N, and CRF values that are following the initiation of maintenance dialysis treat-statistically different from N are identified (*). The ment. The results from these patients were compared amounts of leucine release …
Mapping Sentinel Lymph Node Metastasis by Dual-probe Optical Imaging

Purpose: Sentinel lymph node biopsy (SLNB) has emerged as the preferred standard procedure in patients with breast cancer, melanoma and other types of cancer. Herein, we developed a method to intra-operatively map SLNs and differentiate tumor metastases within SLNs at the same time, with the aim to provide more accurate and real-time intraoperative guidance. Experimental Design: Hyaluronic acid (HA), a ligand of lymphatic vessel endothelial hyaluronan receptor (LYVE)-1, is employed as a SLN mapping agent after being conjugated with a near-infrared fluorophore (Cy5.5). Different sized HAs (5, 10 and 20K) were tested in normal mice and mice with localized inflammation to optimize LN retention time and signal to background ratio. Cetuximab, an antibody against epidermal growth factor receptor (EGFR), and trastuzumab, an antibody against human epidermal growth factor receptor 2 (HER2), were labeled with near-infrared fluorophore (IRDye800) for detecting metastatic tumors. LN metastasis model was developed by hock injection of firefly luciferase engineered human head neck squamous carcinoma cancer UM-SCC-22B cells or human ovarian cancer SKOV-3 cells. The metastases within LNs were confirmed by bioluminescence imaging (BLI). IRDye800-Antibodies were intravenously administered 24 h before local administration of Cy5.5-HA. Optical imaging was then performed to identify nodal metastases. Results: Binding of HA with LYVE-1 was confirmed by ELISA and fluorescence staining. HA with a size of 10K was chosen based on the favorable migration and retention profile. After sequential administration of IRDye800-antibodies intravenously and Cy5.5-HA locally to a mouse model with LN metastases and fluorescence optical imaging, partially metastasized LNs were successfully distinguished from un-metastasized LNs and fully tumor occupied LNs, based on the different signal patterns. Conclusions: Fluorophore conjugated HA is a potential lymphatic mapping agent for SLNB. Dual-tracer imaging with the combination of lymphatic mapping agents and tumor targeting agents can identify tumor metastases within SLNs, thus may provide accurate and real-time intra-operative guidance to spare the time spent waiting for a biopsy result.
Synergistic Pleiotropy Overrides the Costs of Complexity in Viral Adaptation

Adaptive evolution progresses as a series of steps toward a multidimensional phenotypic optimum, and organismal or environmental complexity determines the number of phenotypic dimensions, or traits, under selection. Populations evolving in complex environments may experience costs of complexity such that improvement in one or more traits is impeded by selection on others. We compared the fitness effects of the first fixed mutations for populations of single-stranded DNA bacteriophage evolving under simple selection for growth rate to those of populations evolving under more complex selection for growth rate as well as capsid stability. We detected a cost of complexity manifested as a smaller growth rate improvement for mutations fixed under complex conditions. We found that, despite imposing a cost for growth rate improvement, strong complex selection resulted in the greatest overall fitness improvement, even for single mutations. Under weaker secondary selective pressures, tradeoffs between growth rate and stability were pervasive, but strong selection on the secondary trait resulted largely in mutations beneficial to both traits. Strength of selection therefore determined the nature of pleiotropy governing observed trait evolution, and strong positive selection forced populations to find mutations that improved multiple traits, thereby overriding costs incurred as a result of a more complex selective environment. The costs of complexity, however, remained substantial when considering the effects on a single trait in the context of selection on multiple traits.
Understanding and personalising smart city services using machine learning, The Internet-of-Things and Big Data

This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work.
NF-κB as a therapeutic target in chronic lymphocytic leukemia

Importance of the field: NF-κB includes a family of transcription factors that play a critical role in the biology of normal lymphocytes and it is aberrantly activated in chronic lymphocytic leukemia (CLL) cells. Here, we review the role of constitutive NF-κB activation in CLL pathogenesis and its potential as a therapeutic target for CLL treatment. Areas covered in this review: This review highlights the different strategies reported to inhibit NF-κB signaling in CLL cells. They include both IκB kinase inhibitors and several natural compounds that act at different steps of the pathway. What the reader will gain: Targeting NF-κB leads to apoptosis of CLL cells, corroborating the role of NF-κB in the survival and clonal expansion of these tumoral cells. Moreover, several studies confirmed a synergistic effect between NF-κB inhibitors and other antitumoral agents and that inhibition of NF-κB could overcome the microenvironmental protection of CLL cells. Take home message: NF-κB is a relevant target in CLL and inhibitors of this prosurvival pathway, alone or in combination, represent a novel therapeutic strategy for the treatment of CLL patients.
The Inductive Approach to Verifying Cryptographic Protocols

Informal arguments that cryptographic protocols are secure can be made rigorous using inductive definitions. The approach is based on ordinary predicate calculus and copes with infinite-state systems. Proofs are generated using Isabelle/HOL. The human effort required to analyze a protocol can be as little as a week or two, yielding a proof script that takes a few minutes to run. Protocols are inductively defined as sets of traces. A trace is a list of communication events, perhaps comprising many interleaved protocol runs. Protocol descriptions incorporate attacks and accidental losses. The model spy knows some private keys and can forge messages using components decrypted from previous traffic. Three protocols are analyzed below: Otway-Rees (which uses shared-key encryption), Needham-Schroeder (which uses public-key encryption), and a recursive protocol (Bull and Otway, 1997) (which is of variable length). One can prove that event ev always precedes event ev' or that property P holds provided X remains secret. Properties can be proved from the viewpoint of the various principals: say, if A receives a final message from B then the session key it conveys is good.
A Soft Version of Predicate Invention Based on Structured Sparsity

In predicate invention (PI), new predicates are introduced into a logical theory, usually by rewriting a group of closely-related rules to use a common invented predicate as a "subroutine". PI is difficult, since a poorly-chosen invented predicate may lead to error cascades. Here we suggest a "soft" version of predicate invention: instead of explicitly creating new predicates, we implicitly group closely-related rules by using structured sparsity to regularize their parameters together. We show that soft PI, unlike hard PI, consistently improves over previous strong baselines for structure-learning on two large-scale tasks.
Music as a manifestation of life: exploring enactivism and the ‘eastern perspective’ for music education

The enactive approach to cognition is developed in the context of music and music education. I discuss how this embodied point of view affords a relational and bio-cultural perspective on music that decentres the Western focus on language, symbol and representation as the fundamental arbiters of meaning. I then explore how this ‘life-based’ approach to cognition and meaning-making offers a welcome alternative to standard Western academic approaches to music education. More specifically, I consider how the enactive perspective may aid in developing deeper ecological understandings of the transformative, extended and interpenetrative nature of the embodied musical mind; and thus help (re)connect students and teachers to the lived experience of their own learning and teaching. Following this, I examine related concepts associated with Buddhist psychology in order to develop possibilities for a contemplative music pedagogy. To conclude, I consider how an enactive-contemplative perspective may help students and teachers awaken to the possibilities of music education as ‘ontological education. ’ That is, through a deeper understanding of ‘music as a manifestation of life’ rediscover their primordial nature as autopoietic and world-making creatures and thus engage more deeply with musicality as a means of forming richer and more compassionate relationships with their peers, their communities and the ‘natural’ and cultural worlds they inhabit.
Employee Morale: Driving Performance in Challenging Times

Introduction Chapter 1: What is Morale? Chapter 2: How Do Organizations Measure Morale? Chapter 3: Why Morale Is So Important: Introduction Why Morale Is So Important 1: Group Morale, Financial Performance and Organization Effectiveness Why Morale Is So Important 2: Individual Morale and Performance Why Morale Is So Important 3: Morale and Customer Satisfaction Why Morale Is So Important 4: Morale, Individual and Organizational Health Chapter 4: Case Study: Culture, Morale and Customer Satisfaction. Hilti Group, Liechtenstein Chapter 5: Creating& Maintaining the High Morale Organization Chapter 6: Current Trends, Issues and Myths in Employee Morale Chapter 7: Summary and Conclusions
Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model

As a particular case of the finite mixture model, Rayleigh mixture model (RMM) is considered as a useful tool for medical ultrasound (US) image segmentation. However, conventional RMM relies on intensity distribution only and does not take any spatial information into account that leads to misclassification on boundaries and inhomogeneous regions. The authors proposed an improved RMM with neighbour (RMMN) information to solve this problem by introducing neighbourhood information through a mean template. The incorporation of the spatial information made RMMN more robust to noise on the boundaries. The size of the window which incorporates neighbour information was resized adaptively according to the local gradient distribution. They evaluated their model on experiments on synthetic data and real US images used by high-intensity focused ultrasound therapy. On this data, they demonstrated that the proposed model outperforms several state-of-the-art methods in terms of both segmentation accuracy and computation time.