Miracle-dz/UniFAR-FTUs huggingface dataset:
How to promote healthy behaviours in patients? An overview of evidence for behaviour change techniques

To identify the evidence for the effectiveness of behaviour change techniques, when used by health-care professionals, in accomplishing health-promoting behaviours in patients. Reviews were used to extract data at a study level. A taxonomy was used to classify behaviour change techniques. We included 23 systematic reviews: 14 on smoking cessation, 6 on physical exercise, and 2 on healthy diets and 1 on both exercise and diets. None of the behaviour change techniques demonstrated clear effects in a convincing majority of the studies in which they were evaluated. Techniques targeting knowledge (n = 210 studies) and facilitation of behaviour (n = 172) were evaluated most frequently. However, self-monitoring of behaviour (positive effects in 56% of the studies), risk communication (52%) and use of social support (50%) were most often identified as effective. Insufficient insight into appropriateness of technique choice and quality of technique delivery hinder precise conclusions. Relatively, however, self-monitoring of behaviour, risk communication and use of social support are most effective. Health professionals should avoid thinking that providing knowledge, materials and professional support will be sufficient for patients to accomplish change and consider alternative strategies which may be more effective.
JCB_200812034 779..786

Regulation of cell polarity and orientation is essential during symmetric and asymmetric cell division and cell differentiation, as well as in a large variety of cellular functions such as T cell interaction with their target cell, synaptic transmission, or cell migration (Krummel and Macara, 2006; Arimura and Kaibuchi, 2007; Etienne-Manneville, 2008). Directed as well as random cell migration is associated with polarization of the cellular machinery in order to define a leading protrusive front and a retracting rear. During cell migration as well as during cell differentiation or polarized cell function, the relative position of the centrosome and the nucleus is generally a good indicator of the orientation of the cell polarity axis (Schliwa et al., 1999; de Anda et al., 2005; Siegrist and Doe, 2006). Integrin engagement with the extracellular matrix plays a key role in controlling nucleus and centrosome positioning in migrating and immobile cells (Etienne-Manneville and Hall, 2001; Thery et al., 2005, 2006; Peng et al., 2008). In contrast, the role of cell–cell contacts in controlling orientation of the nucleus–centrosome axis remains unclear. Tight junctions are key to the baso-apical polarity of epithelial cells, and are likely to be involved in centrosome apical positioning (Musch, 2004; Shin et al., 2006). Herein, we investigated whether intercellular junctions can promote polarization of nonepithelial cells, such as astrocytes, that do not form tight junctions. We show that anisotropic cell–cell interactions provide extracellular cues that are sufficient to promote cell polarization and orientation toward the free cell edge. In the absence of any other polarizing cues, calcium-dependent classical cadherins, including N-cadherin as well as E-cadherin, control centrosome and nucleus positioning and cell polarization via the regulation of cell interactions with the extracellular matrix and of the actin and microtubule cytoskeletons.
Identification of energy, environmental and economical optimal refurbishment scenarios for hotels in the Alps

It is a challenge to achieve an affordable and energy efficient building renovation that leads to a decrease of the environmental impacts, as many objectives and various renovation actions should be considered. In this study, the many-objective optimisation algorithm NSGA-III is applied, for the first time in building energy. The solutions search process of NGSA-III makes it more efficient when dealing with more than three objectives. It was included in a simulation platform consisting in a dynamic building energy simulation (DBES) tool, a building life cycle assessment (LCA) tool and a cost database, in order to guide the decision towards good energy-environment-cost renovation solutions for a hotel in the Alps. Its application helps identifying good renovation strategies for the hotel. The results robustness to the climate data is then assessed.
A Generalized ideal observer model for decoding sensory neural responses

We show that many ideal observer models used to decode neural activity can be generalized to a conceptually and analytically simple form. This enables us to study the statistical properties of this class of ideal observer models in a unified manner. We consider in detail the problem of estimating the performance of this class of models. We formulate the problem de novo by deriving two equivalent expressions for the performance and introducing the corresponding estimators. We obtain a lower bound on the number of observations (N) required for the estimate of the model performance to lie within a specified confidence interval at a specified confidence level. We show that these estimators are unbiased and consistent, with variance approaching zero at the rate of 1/N. We find that the maximum likelihood estimator for the model performance is not guaranteed to be the minimum variance estimator even for some simple parametric forms (e.g., exponential) of the underlying probability distributions. We discuss the application of these results for designing and interpreting neurophysiological experiments that employ specific instances of this ideal observer model.
MRX protects fork integrity at protein–DNA barriers, and its absence causes checkpoint activation dependent on chromatin context

To address how eukaryotic replication forks respond to fork stalling caused by strong non-covalent protein–DNA barriers, we engineered the controllable Fob-block system in Saccharomyces cerevisiae. This system allows us to strongly induce and control replication fork barriers (RFB) at their natural location within the rDNA. We discover a pivotal role for the MRX (Mre11, Rad50, Xrs2) complex for fork integrity at RFBs, which differs from its acknowledged function in double-strand break processing. Consequently, in the absence of the MRX complex, single-stranded DNA (ssDNA) accumulates at the rDNA. Based on this, we propose a model where the MRX complex specifically protects stalled forks at protein–DNA barriers, and its absence leads to processing resulting in ssDNA. To our surprise, this ssDNA does not trigger a checkpoint response. Intriguingly, however, placing RFBs ectopically on chromosome VI provokes a strong Rad53 checkpoint activation in the absence of Mre11. We demonstrate that proper checkpoint signalling within the rDNA is restored on deletion of SIR2. This suggests the surprising and novel concept that chromatin is an important player in checkpoint signalling.
Subjective and Measured Evidence for Residential Lighting Metrics in the Tropics

This paper presents a post-occupancy daylighting study within 17 residences in Singapore consisting of 35 participants. Each residence was visited, and a calibrated daylighting model was constructed and validated based upon 100’s of individual illuminance measurements. A survey on subjective lighting quality was administered to each participant. The authors find that simulated annual climate-based daylight measures can be used to predict subjective lighting evaluations such as satisfaction with access to daylight, whether a space is perceived as dim or bright, and when overall daylighting levels are too low. Using the mean annual daylight level per sensor, at a spatial median illuminance threshold of 175 lx, more than 80% of people are predicted to be satisfied with access to daylight in kitchens; however, in bedrooms and living rooms, satisfaction with daylight was high even at low lighting levels. Kitchens can be identified as dim or bright and all residential space types can be identified as often underlit or not based upon median spatial values of annual sensor mean illuminance. Finally, the authors share reported reasons for dynamic window shading use in the residences studied—desire for privacy was found to be of similar importance to the direct sunlight, a difference from dynamic shading use in commercial spaces.
MEASURING AND IDENTIFYING BIAS IN YOUR SOCIAL MEDIA COMMUNITY

Brands invest in and cultivate social media communities in an effort to promote to and engage with consumers. This allows marketers both to facilitate word-of-mouth effects and to extract consumer insights. However, research has shown that online word-of-mouth appearing on social media is often subject to bias. Typically, this bias is negative and, if word-of-mouth affects brand performance, has the potential to damage the brand. In this paper, we analyze the behavior of 170 million unique users pertaining to the Facebook fan pages of more than 3000 brands to measure bias and identify factors associated with the presence of bias. We present methodology that measures latent brand favorability based on observed likes and comments on the brand’s Facebook page, adjusting for any positivity (or negativity) bias exhibited by individual users based on their behavior across brands. Research has shown that users vary in their tendencies to express positive opinions. This variation in users’ positivity can be a source of bias in the brand’s social media community. We validate our brand favorability measure against Millward Brown’s BrandZ rankings, which is based on both the financial performance of brands and traditional brand tracking surveys. We then measure bias as the difference between observed social media sentiment and our proposed brand favorability measure and examine how bias differs across brand pages. We specifically consider the effects of various factors related to the quality of the brand community (e.g., number of followers, number of comments and likes, variance in sentiment), brand traits (e.g., industry sector, size of firm, general popularity), and brand activity (e.g., posting behavior, news mentions). We find that smaller brand communities with limited opinion variance are positively biased. This poses challenges for brands in terms of how they can leverage their brand communities.
Cross-cutting Models of Distributional Lexical Semantics

In order to respond to increasing demand for natural language interfaces—and provide meaningful insight into user query intent—fast, scalable lexical semantic models with flexible representations are needed. Human concept organization is a rich epiphenomenon that has yet to be accounted for by a single coherent psychological framework: Concept generalization is captured by a mixture of prototype and exemplar models, and local taxonomic information is available through multiple overlapping organizational systems. Previous work in computational linguistics on extracting lexical semantic information from the Web does not provide adequate representational flexibility and hence fails to capture the full extent of human conceptual knowledge. In this proposal I will outline a family of probabilistic models capable of accounting for the rich organizational structure found in human language that can predict contextual variation, selectional preference and feature-saliency norms to a much higher degree of accuracy than previous approaches. These models account for cross-cutting structure of concept organization—i.e. the notion that humans make use of different categorization systems for different kinds of generalization tasks—and can be applied to Web-scale corpora. Using these models, natural language systems will be able to infer a more comprehensive semantic relations, in turn improving question answering, text classification, machine translation, and information retrieval. This work was supported by an NSF Graduate Research Fellowship and a Google Research Award. Experiments were run on the Mastodon Cluster, provided by NSF Grant EIA-0303609.
Choosing the structure of convolutional neural networks for face recognition

Evaluating the number of hidden neurons and hidden layers necessary for solving of face recognition, pattern recognition and classi(cid:28)cation tasks is one of the key problems in arti(cid:28)cial neural networks. In this note, we show that arti(cid:28)cial neural network with a two hidden layer feed forward neural network with d inputs, d neurons in the (cid:28)rst hidden layer, 2d+2 neurons in the second hidden layer, k outputs and with a sigmoidal in(cid:28)nitely di(cid:27)erentiable function can solve face recognition tasks. This result can be applied to design pattern recognition and classi(cid:28)cation models with optimal structure in the number of hidden neurons and hidden layers. In addition, we propose a new type of convolutional neural network, which is capable to extract most powerful features. The experimental results over well-known benchmark datasets shows that the convergence and the accuracy of the proposed model of arti(cid:28)cial neural network is acceptable. Findings in this paper are experimentally analyzed on (cid:28)ve di(cid:27)erent face datasets from machine learning repository.
A Simple and Efficient Protocol for Rapid Regeneration and Propagation of Taro (Colocasia esculenta (L.) Schott.) in Vitro from Apical Meristems

An efficient and simple protocol was developed for the in vitro regeneration and propagation of taro (Colocasia esculenta) cv. ‘Muktakeshi’. Apical meristems (~1 cm) excised from leaf blight-resistant taro cultivar cv. ‘Muktakeshi’ grown in a net house were used as explants. Most multiple shoots (i.e., 3.6/explant), which formed on Murashige and Skoog (MS) medium supplemented with 5 mg L 6benzyladenine and 1 mg L -naphthaleneacetic acid, could be rooted by transferring the 4-week-old shoots to MS basal medium without plant growth regulators. After 2 weeks, well developed plantlets were hardened in plastic cups in potting mixture (vermiculite + sand, 1:1, v/v). Acclimatized plants were transferred to 30-cm plastic pots containing top soil and vermicompost (3:1, v/v) where they grew well. _____________________________________________________________________________________________________________
PUBLISHED ON BEHALF OF THE ULSTER MEDICAL SOCIETY

I FEEL that no greater honour could have been conferred uponl mlie by mily colleagues than that I should have been asked to deliver this year's Robert Camlipbell Oratioln. Yet it was with more than usual diffidence that I accepted their invitation. In tlle past when we have drawni orators from outside our own school we have been able to honiour a Colebrook or a Fleming, wN7hilst inside the school scrutiny has beenl severe, and the inames of Sinclair, Houston, Fullerton, Sinton, and Purce attest to its severity. For myself, I feel that my time of attainment wTas not yet come, aind my vish would have been that I should have been more worthy to follow in the footsteps of those wlho have done service to this appointment. "WVhat you have inherited from your fathers, win for yourself so that it miay, b)e vour own. So wrote Goethe, and nio better motto could be written for the past lhistory, and I hope the future history, of this medical school. For in the past hulndre(d years each generation has produced 'ani outstan(ling, tealchcr and lea(ler. Thlle roll includes Aindrews, Gordoon, Redferni, Cuming, Svningtoni, anl
Void: A fast and light voice liveness detection system

Due to the open nature of voice assistants’ input channels, adversaries could easily record people’s use of voice commands, and replay them to spoof voice assistants. To mitigate such spoofing attacks, we present a highly efficient voice liveness detection solution called “Void. ” Void detects voice spoofing attacks using the differences in spectral power between live-human voices and voices replayed through speakers. In contrast to existing approaches that use multiple deep learning models, and thousands of features, Void uses a single classification model with just 97 features. We used two datasets to evaluate its performance: (1) 255,173 voice samples generated with 120 participants, 15 playback devices and 12 recording devices, and (2) 18,030 publicly available voice samples generated with 42 participants, 26 playback devices and 25 recording devices. Void achieves equal error rate of 0.3% and 11.6% in detecting voice replay attacks for each dataset, respectively. Compared to a state of the art, deep learning-based solution that achieves 7.4% error rate in that public dataset, Void uses 153 times less memory and is about 8 times faster in detection. When combined with a Gaussian Mixture Model that uses Melfrequency cepstral coefficients (MFCC) as classification features – MFCC is already being extracted and used as the main feature in speech recognition services – Void achieves 8.7% error rate on the public dataset. Moreover, Void is resilient against hidden voice command, inaudible voice command, voice synthesis, equalization manipulation attacks, and combining replay attacks with live-human voices achieving about 99.7%, 100%, 90.2%, 86.3%, and 98.2% detection rates for those attacks, respectively.
ACO Volume 7 Number 2 July 1998, p53

ACO Volume 7 • Number 2 • July 1998 Abstract: The pathogenesis, pathology, natural course, and in particular the treatment of lateral epicondylitis (tennis elbow) remains controversial. An extensive review of the scientific literature with respect to the conservative treatment of tennis elbow, revealed that acceptable epidemiological techniques of the prospective randomised control trials or case controlled studies are scarce. While administration of steroid compounds has traditionally been the mainstay of conservative treatment of tennis elbow, the high recurrence rate of side effects and structural tissue changes associated with steroid therapy, leaves this modality as the most controversial approach in the treatment of tennis elbow. In contrast, in some studies evidence in favour of a role for classical acupuncture, ultrasound and low level laser as effective therapeutic means in the treatment of tennis elbow has been provided. As these modalities have not been associated with any side effects, in view of the ease of application, low cost and good curative effects, their increased application has been suggested by some authors. This proposition, however, remains rather conjectural until it could be convincingly substantiated by future studies with appropriate epidemiological design.
The polyamidoamine-mediated inhibition of bcl-2 by small hairpin RNA to induce apoptosis in human lens epithelial cells

Purpose To investigate whether apoptosis of human lens epithelial cells (HLECs) can be induced with the polyamidoamine (PAMAM)-mediated inhibition of bcl-2 (b-cell lymphoma 2) by small hairpin RNA (shRNA). Methods HLECs (SRA01/04) were transfected with the fifth generation of PAMAM (PAMAM G5) by bcl-2 shRNA. At 24, 48, and 72 h after transfection, the transfection rate was measured by flow cytometry. The transfection rates mediated by PAMAM and liposome were compared. The bcl-2 mRNA level was detected by real-time PCR. Whole cell protein was extracted and the bcl-2 protein level was detected by western blotting. The percentage of HLECs undergoing apoptosis was measured by Annexin V-FITC/PI staining. The nuclear morphology of HLECs was observed by staining with Hoechst 33258. The expression of cytochrome c and the activity of cleaved caspase-3 were analyzed by western blotting. Results At 24, 48, and 72 h after transfection, the rate of transfection of bcl-2 shRNA mediated by PAMAM was higher than in the liposome-mediated group (p<0.05). The mRNA and protein levels of bcl-2 were greatly downregulated. The percentage of HLECs undergoing apoptosis was greatly improved. Hoechst staining showed that bcl-2 shRNA transfected cells had a lower growth status with nuclear fragmentation. The expression of cytochrome c and the activity of cleaved caspase-3 was greatly improved (p<0.05). Conclusions PAMAM-mediated bcl-2 shRNA can downregulate the expression of bcl-2 and induce the apoptosis of HLECs by engaging the mitochondrial pathway, including catalytic activation of the caspases.
Baby Face Generator

The first portion of the project involves implementing an algorithm for detecting human faces and localizing the key facial features. The implementation will build upon several face detection and facial feature extraction algorithms including Turk and Pentland’s work using eigenfaces to locate faces [1], Huang and Chen’s work using active contour models for facial feature extraction [2], and Saber and Tekalp’s work using color, shape, and symmetry-based cost functions for facial detection and feature extraction [3]. Additionally, the input faces’ ethnicities will be classified using eigenimages or fisher images and combined to select the baby’s ethnicity from a predefined database. The second portion of the project involves combining the identified facial features of the two individuals to form a composite image. This algorithm incorporates facial morphing techniques including Beier’s fieldmorphing algorithm [4] to properly weight and combine the input faces. A randomized weighting method will be used for selecting which features from the input images will appear in the output image so that a different baby image is generated each time the program runs. Finally, the composite image will include color correction to increase the natural appearance of the image. The final implementation of the baby face generator will include a user-friendly interface implemented in MATLAB. This project does not require a DROID camera phone.
Clustering as a simplification tool for the decision-making process on building stock renovation

Representing one of the largest energy consumer, the building sector is an important issue in terms of energy consumption and climate change mitigation. In this sense, many different actions of building energy savings have been started. Among them, retrofitting measures have been taken for different building scales from the single housing units up to the district or building stock. Finding retrofitting solutions for a single house is relatively easy, whereas the building stock dimension remains a complex issue. To facilitate the work of building stock owners and local public authorities, the developed method allows identifying groups of buildings and also at the scale of the building stock to be used as input for the retrofitting actions optimization. The proposed approach consists in clustering the buildings of a given stock according to their characteristics before refurbishment intrinsic variables as the geometry or the thermal properties – and the resulting variables as the total or heating energy consumption. Consequently, a methodology based on a clustering analysis has been implemented. It consists of successive clustering steps on a “decision space” containing the intrinsic building features and an “objective space” enclosing the energy performance. One of the main results is knowledge that the usual building classification based on climate, year of construction and type is not always sufficient and justified: compactness and height of should also be taken
Cloud big data application for transport

This paper presents a cloud service oriented approach for managing and analysing big data required by transport applications. Big data analytics brings new insights and useful correlations of large data collections providing undiscovered knowledge. Applying it to transport systems brings better understanding to the transport networks revealing unexpected choking points in cities. This facility is still largely inaccessible to small companies due to their limited access to computational resources. A cloud-oriented architecture opens new perspectives for providing efficient and personalised big data management and analytics services to (small) companies.