Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface treated Pteris vittata L. pinnae powder used as an efficient biosorbent of Pb(II), Cd(II), and Cr(VI) from aqueous solution

ABSTRACT Biosorption is a surface-dependent phenomenon. Surface modifications by chemical treatment methods could either improve or reduce the biosorption capacity of potential biosorbents. In the present work, pristine Pteris vittata L. pinnae (PPV) powder was treated separately with sodium hydroxide (NaOH), calcium chloride (CaCl2), and nitric acid (HNO3). The pristine and treated biosorbents were used to assess the biosorption of Pb(II), Cd(II), and Cr(VI) as a function of pH. Kinetics and adsorption isotherms were studied. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and scanning electron microscope combined with energy dispersive x-ray (SEM-EDX) spectroscopic techniques were used to characterize the biosorbents before and after chemical treatments. The possible functional groups contributing to the metal sorption were identified. Results revealed favorable biosorption of Pb(II), Cd(II), and Cr(VI) described by pseudo-second order kinetics. NaOH-treated P. vittata (NPV) showed higher biosorption capacity for Pb(II) and Cd(II) compared to that of PPV. ATR-FTIR studies indicated that -OH, -COOH, and -NH2 groups were mainly involved in Cr(VI) and -OH in Pb(II) and Cd(II) biosorption. The enhanced efficiency of NPV and CaCl2 treated P. vittata (CPV) in the uptake of Pb(II) and Cd(II) compared to PPV can be associated with their altered physicochemical characters.
Explainable Neural Networks based on Additive Index Models

Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to interpret the results and explain them without additional tools. This has led to much research in developing various approaches to understand the model behavior. In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features. Unlike fully connected neural networks, the features engineered by the xNN can be extracted from the network in a relatively straightforward manner and the results displayed. With appropriate regularization, the xNN provides a parsimonious explanation of the relationship between the features and the output. We illustrate this interpretable feature--engineering property on simulated examples.
Fitting 3-D data using superquadrics and free-form deformations

Recovery of 3D data with simple parametric models has been the subject of many studies over the last ten years. Many have used the notion of superquadrics, introduced for graphics in Barr (1994). It appears however that whilst superquadrics could describe a wide variety of forms, they are too simple to recover and describe complex shapes. This paper describes a two-step method to fit a parametric deformable surface to 3D points. We suppose that a 3D image has been segmented to get a set of 3D points. The first step consists in our version of a superquadric fit with global tapering. We then make use of the technique of free-form deformations, as in computer graphics. We present experimental results with synthetic and real 3D medical images where the original points are laid on an iso-surface.
Core components of Communication Skills Training in oncology: A synthesis of the literature contrasted with consensual recommendations

This systematic review synthesises the literature on Communication Skills Training (CST) programmes for oncology professionals to identify their core components and compare them with the recommendations formulated in a position paper based on a European expert consensus meeting. A systematic literature search was conducted using MEDLINE (OVID and PUBMED), CINAHL, EMBASE, PSYCHINFO, Web of Science and the Cochrane Library. The analytic approach relied on an a priori framework based on the position paper's recommendations, generating several themes. Forty-nine articles were included. The CST programmes reported between 2010 and 2016 were heterogeneous. Some recommendations, especially those regarding content and pedagogic tools, were followed by most providers, while others, such as setting, objectives and participants, were not. This synthesis raises questions on how CST programmes are conceived and how they could or should be conceived in future. While medicine, especially clinical communication, is socially and culturally embedded, some recommendations regarding CST programmes seem to be universally valuable, contributing to ensure quality and enhanced credibility, and thus endorsement and sustained implementation, of CST programmes in the oncology setting.
Contribution of social relationships to self-rated health among Japanese community-dwelling elderly

Objective: Participation in social activities is associated with physical and psychological health in the community-dwelling elderly population. We examined the two factors of social relations and community health resources, associated with higher self-rated health levels in the community-dwelling elderly. Methods: A total of 145 community-dwelling elderly people ≥ 65 years old from two neighborhood associations in Nagasaki City were recruited for this study, representing 85% of the officially registered ≥ 65-year-old population in the target area. Face-to-face interviews using a structured questionnaire were conducted by trained interviewers in August 2009, with questions related to sociodemographic characteristics, social relationships, and self-rated health (SRH). Community health resources (type and walking distance from home) were evaluated by one of the authors as a community assessment. Results: Seventy-eight community-dwelling elderly people (25 men and 53 women) participated in the study. Elderly people who reported going out every day were more likely to show higher SRH scores (excellent/good) than those going out less often (OR: 3.7; 95% confidence interval [CI]: 1.0, 14.2; P = 0.056). The numbers of interactions with friends in higher and lower SRH groups were 6.5 ± 8.4 (mean ± standard deviation) and 2.4 ± 1.1 (P = 0.01, Mann-Whitney U test), respectively. The numbers of relatives talking on the phone in higher and lower SRH groups were 2.9 ± 1.3 and 2.2 ± 1.2 (P = 0.031, Mann-Whitney U test), respectively. Meeting scores with friends in higher and lower SRH groups were 7.8 ± 5.8 and 4.5 ± 3.6 (P = 0.068), respectively. The scores of community health resources among higher and lower SRH groups were 21.2 ± 1.5 and 20.9 ± 1.4 (P = 0.547), respectively. The scores of community association/activities in higher and lower SRH groups were 3.9 ± 1.0 and 3.6 ± 0.9 (P = 0.227), respectively. Conclusion: This study indicated the importance of interaction with friends and relatives for maintaining higher SRH among community-dwelling elderly people.
An interesting case of recurrent shortness of breath and pleuritic chest pain

A 58-year-old male presented to the emergency department in August 2015 with sudden-onset shortness of breath associated with sharp right-sided pleuritic chest pain. His only past medical history was of hypertension. Prior to this presentation he had been clinically well and was fully independent. He was a smoker with a 40 pack-year history. He did not drink alcohol and had no recent travel history. Can you diagnose this case of a 58-year-old male with sudden-onset shortness of breath associated with sharp right-sided pleuritic chest pain? http://ow.ly/HLmk30j4DEH
Supplemental information for “Connecting the dots between mechanosen- sitive channel abundance, osmotic shock, and survival at single-cell resolution”

To quantitatively compare the functionality between the wild-type MscL and MscL-sfGFP, patchclamp electrophysiology experiments were conducted on each channel. Patch-clamp recordings were performed on membrane patches derived from giant protoplasts which were prepared as previously described (1). In brief, cells were grown in Luria-Bertani (LB) medium with 0.06 mg/ml cephalexin for 2.5 hours. The elongated cells were then collected by centrifugation and digested by 0.2 mg/ml lysozyme to form giant protoplasts.
Novice clinical nurse educator’s experience of a self-directed learning, education and mentoring program: a qualitative study

Background: Transition from a registered nurse to a clinical nurse educator (CNE) poses several challenges. Providing professional development opportunities to ease the transition from a registered nurse to a CNE is considered critical to a successful career and to effectively teach. A self-directed educational program and mentoring (SEM) program was designed and implemented to support nurse’s transition from a novice to a confident CNE. Aims: The aim of this study was to explore novice CNE’s experience of learning and being mentored. Design: Qualitative methodology was undertaken to conduct focus groups. Methods: All CNEs who completed the SEM program were invited to participate in the study. Willing participants provided informed consent to complete an in-depth semi-structured focus group and to record the focus group interview. Focus groups were facilitated by an independent researcher. A second researcher attended the focus groups to collect detailed notes. Data were transcribed verbatim and participants were de-identified. Simple thematic analyses were undertaken. Results: A total of 11 (58%) CNEs participated in the focus groups. Overall participants described their experience of the SEM program as positive. Three themes were identified: (1) perceived transformation of CNE practice, (2) beneficial relationships and (3) feeling connected. Mentoring relationships for some participants have continued beyond the self-directed learning, education and mentoring program. Barriers to the mentoring program included a theme of lack of time, role ambiguity and insufficient face to face education. Conclusions: Study findings highlight the benefits of providing professional development opportunities and mentoring programs for novice CNEs. Programs, such as the SEM enable transformation of a novice educator’s practice, and the consolidation of new knowledge, skills and confidence to effectively educate less experienced nurses.
Design and implementation of a WLAN/cdma2000 interworking architecture

The combination of 3G and WLAN wireless technologies offers the possibility of achieving anywhere, anytime Internet access, bringing benefits to both end users and service providers. We discuss interworking architectures for providing integrated service capability across widely deployed 3G cdma2000-based and IEEE 802.11-based networks. Specifically, we present two design choices for integration: tightly coupled and loosely coupled, and recommend the latter as a preferred option. We describe in detail the implementation of a loosely coupled integrated network which provides two kinds of roaming services, a SimpleIP service and a Mobile-IP service. We present, in detail, two new components used to build these services: a network element called a WLAN integration gateway deployed in WLAN networks; a client software on the mobile device. For a mobile device with interfaces to both technologies, our system supports seamless handoff in the presence of overlapping radio coverage.
SPRENO: a BioC module for identifying organism terms in figure captions

Abstract Recent advances in biological research reveal that the majority of the experiments strive for comprehensive exploration of the biological system rather than targeting specific biological entities. The qualitative and quantitative findings of the investigations are often exclusively available in the form of figures in published papers. There is no denying that such findings have been instrumental in intensive understanding of biological processes and pathways. However, data as such is unacknowledged by machines as the descriptions in the figure captions comprise of sumptuous information in an ambiguous manner. The abbreviated term ‘SIN’ exemplifies such issue as it may stand for Sindbis virus or the sex-lethal interactor gene (Drosophila melanogaster). To overcome this ambiguity, entities should be identified by linking them to the respective entries in notable biological databases. Among all entity types, the task of identifying species plays a pivotal role in disambiguating related entities in the text. In this study, we present our species identification tool SPRENO (Species Recognition and Normalization), which is established for recognizing organism terms mentioned in figure captions and linking them to the NCBI taxonomy database by exploiting the contextual information from both the figure caption and the corresponding full text. To determine the ID of ambiguous organism mentions, two disambiguation methods have been developed. One is based on the majority rule to select the ID that has been successfully linked to previously mentioned organism terms. The other is a convolutional neural network (CNN) model trained by learning both the context and the distance information of the target organism mention. As a system based on the majority rule, SPRENO was one of the top-ranked systems in the BioCreative VI BioID track and achieved micro F-scores of 0.776 (entity recognition) and 0.755 (entity normalization) on the official test set, respectively. Additionally, the SPRENO-CNN exhibited better precisions with lower recalls and F-scores (0.720/0.711 for entity recognition/normalization). SPRENO is freely available at https://bigodatamining.github. io/software/201801/. Database URL: https://bigodatamining.github. io/software/201801/
Convolutional Sequence to Sequence Non-intrusive Load Monitoring

A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partially overlapped output sequences of the network are averaged to produce the final output of the model. We apply the proposed model to the REDD dataset and compare it with the convolutional sequence to point model in the literature. Results show that the proposed model is able to give satisfactory disaggregation performance for appliances with varied characteristics.
Understanding Cancer Worry Among Patients in a Community Clinic-Based Colorectal Cancer Screening Intervention Study

Background To reduce colorectal cancer (CRC) screening disparities, it is important to understand correlates of different types of cancer worry among ethnically diverse individuals. Objectives The current study examined the prevalence of three types of cancer worry (i.e., general cancer worry, CRC-specific worry, and worry about CRC test results) as well as sociodemographic and health-related predictors for each type of cancer worry. Methods Participants were aged 50–75, at average CRC risk, nonadherent to CRC screening guidelines, and enrolled in a randomized controlled trial to increase CRC screening. Participants completed a baseline questionnaire assessing sociodemographics, health beliefs, healthcare experiences, and three cancer worry measures. Associations between study variables were examined with separate univariate and multivariable logistic regression models. Results Responses from a total of 416 participants were used. Of these, 47% reported experiencing moderate-to-high levels of general cancer worry. Predictors of general cancer worry were salience and coherence (aOR = 1.1, 95% CI [1.0, 1.3]), perceived susceptibility (aOR = 1.2, 95% CI [1.1, 1.3), and social influence (aOR = 1.1, 95% CI [1.0, 0.1]). Fewer (23%) reported moderate-to-high levels of CRC-specific worry or CRC test worry (35%). Predictors of CRC worry were perceived susceptibility (aOR = 1.4, 95% CI [1.3, 1.6]) and social influence (aOR = 1.1, 95% CI [1.0, 1.2]); predictors of CRC test result worry were perceived susceptibility (aOR = 1.2, 95% CI [1.1, 1.3) and marital status (aOR = 2.0, 95% CI [1.1, 3.7] for married/partnered vs. single and aOR = 2.3, 95% CI [1.3, 4.1] for divorced/widowed vs. single). Discussion Perceived susceptibility consistently predicted the three types of cancer worry, whereas other predictors varied between cancer worry types and in magnitude of association. The three types of cancer worry were generally predicted by health beliefs, suggesting potential malleability. Future research should include multiple measures of cancer worry and clear definitions of how cancer worry is measured.
Reverse iterative volume sampling for linear regression

We study the following basic machine learning task: Given a fixed set of $d$-dimensional input points for a linear regression problem, we wish to predict a hidden response value for each of the points. We can only afford to attain the responses for a small subset of the points that are then used to construct linear predictions for all points in the dataset. The performance of the predictions is evaluated by the total square loss on all responses (the attained as well as the hidden ones). We show that a good approximate solution to this least squares problem can be obtained from just dimension $d$ many responses by using a joint sampling technique called volume sampling. Moreover, the least squares solution obtained for the volume sampled subproblem is an unbiased estimator of optimal solution based on all n responses. This unbiasedness is a desirable property that is not shared by other common subset selection techniques. Motivated by these basic properties, we develop a theoretical framework for studying volume sampling, resulting in a number of new matrix expectation equalities and statistical guarantees which are of importance not only to least squares regression but also to numerical linear algebra in general. Our methods also lead to a regularized variant of volume sampling, and we propose the first efficient algorithms for volume sampling which make this technique a practical tool in the machine learning toolbox. Finally, we provide experimental evidence which confirms our theoretical findings.
Understanding the Determinants of Cloud Computing Adoption for High Performance Computing

Within the complex context of high performance computing (HPC), the factors influencing technology adoption decisions remain largely unexplored. This study extends Diffusion of Innovation (DOI) and Human-Organization-Technology fit (HOT-fit) theories into an integrated model, to explore the impact of ten factors on cloud computing adoption decisions in the HPC context. The results suggest that adopters and non-adopters have different perceptions of the indirect benefits, adequacy of resources, top management support, and compatibility of adopting cloud computing for HPC. In addition, perceptions of the indirect benefits and HPC competences can be used to predict the cloud computing adoption decision for HPC. This is one of the first studies in the information systems (IS) literature exploring the factors impacting the cloud computing adoption decision in the important context of HPC. It integrates two influential technology adoption theories and enhances understanding of the key factors influencing organizations’ cloud computing adoption decisions in this context.
Asymptotic predictions of the finite-sample risk of the k-nearest-neighbor classifier

The finite-sample risk of the k-nearest-neighbor classifier is analyzed for a family of two-class problems in which patterns are randomly generated from smooth probability distributions in an n-dimensional Euclidean feature space. First, an exact integral expression for the m-sample risk is obtained for a k-nearest-neighbor classifier that uses a reference sample of m labeled feature vectors. Using a multidimensional application of Laplace's method of integration, this integral can be represented as an asymptotic expansion in negative rational powers of m. The leading terms of this asymptotic expansion elucidate the curse of dimensionality and other properties of the finite-sample risk.
Security Circumvention: To Educate or To Enforce?

Deliberate circumvention of information systems security is a common behavioral pattern among users. It not only defeats the purpose of having the security controls in place, but can also go far beyond in terms of the total damage it can cause. An organization grappling with circumvention can try to (i) train its users, or (ii) take on enforcement measures, or adopt a combination of the two. In this work, we look at the trade-off between these two very different approaches towards circumvention and try to gain some insights about how an organization might wish to tackle this menace.
Pathwise Derivatives Beyond the Reparameterization Trick

We observe that gradients computed via the reparameterization trick are in direct correspondence with solutions of the transport equation in the formalism of optimal transport. We use this perspective to compute (approximate) pathwise gradients for probability distributions not directly amenable to the reparameterization trick: Gamma, Beta, and Dirichlet. We further observe that when the reparameterization trick is applied to the Cholesky-factorized multivariate Normal distribution, the resulting gradients are suboptimal in the sense of optimal transport. We derive the optimal gradients and show that they have reduced variance in a Gaussian Process regression task. We demonstrate with a variety of synthetic experiments and stochastic variational inference tasks that our pathwise gradients are competitive with other methods.
Seed and pollen gene dispersal in Taxus baccata, a dioecious conifer in the face of strong population fragmentation

Background and Aims Dispersal is crucial due to its direct impact on dynamics of a species' distribution as well as having a role in shaping adaptive potential through gene flow. In plants forming scarce and small populations, knowledge about the dispersal process is required to assess the potential for colonizing new habitats and connectivity of present and future populations. This study aimed to assess dispersal potential in Taxus baccata, a dioecious gymnosperm tree with a wide but highly fragmented distribution. Methods Seed and pollen dispersal kernels were estimated directly in the framework of the spatially explicit mating model, where genealogies of naturally established seedlings were reconstructed with the help of microsatellite markers. In this way, six differently shaped dispersal functions were compared. Key Results Seed dispersal followed a leptokurtic distribution, with the Exponential-Power, the Power-law and Weibull being almost equally best-fitting models. The pollen dispersal kernel appeared to be more fat-tailed than the seed dispersal kernel, and the Lognormal and the Exponential-Power function showed the best fit. The rate of seed immigration from the background sources was not significantly different from the rate of pollen immigration (13.1 % vs. 19.7 %) and immigration rates were in agreement with or below maximum predictions based on the estimated dispersal kernels. Based on the multimodel approach, 95 % of seeds travel <109 m, while 95 % of pollen travels <704 m from the source. Conclusions The results showed that, at a local spatial scale, yew seeds travel shorter distances than pollen, facilitating a rapid development of a kinship structure. At the landscape level, however, although yew exhibits some potential to colonize new habitats through seed dispersal, genetic connectivity between different yew remnants is strongly limited. Taking into account strong population fragmentation, the study suggests that gene dispersal may be a limiting factor of the adaptability of the species.