Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-cultural adaptation of children´s environmental health questionnaires for English nursing students

Objectives : Children are the most vulnerable population group with regard to environmental risks. Nursing students must be fully educated in children's environmental health as they are in a key position to prevent and reduce the effects of environmental hazards. The main objective of this study was to adapt and validate an English version of two questionnaires about children’s health and the environment to assess the knowledge and skills of English student nurses. Design : Observational cross-sectional study. Setting : An English university. Method : To translate, adapt and validate the Children's Environmental Health Knowledge Questionnaire (ChEHK-Q) and the Children's Environmental Health Skills Questionnaire (ChEHS-Q) for English nursing students ( N = 232). Subsequently, descriptive values were calculated with the data collected from English nursing students. Results : The psychometric characteristics found in both questionnaires were strong. Infit and outfit values were near one. The reliability values for the items and people were 0.96 and 0.79 for ChEHK-Q and 0.98 and 0.89 for ChEHS-Q. Only 52 (22.41%) and 77 (33.62%) participants had at least good knowledge and skills, respectively. Higher knowledge and skills were found with respect to the vulnerability of children and identification of environmental risks in the home, respectively. Lower levels of knowledge and skills were found with respect to the effects of pesticides and assessment of neoplastic pollutants, respectively. Conclusion : Findings demonstrate deficiencies in competencies related to children’s environmental health. These questionnaires will facilitate improvement in both the knowledge and skills related to children´s environmental health among future nurses.
Unsupervised Post-Time Fake Social Message Detection with Recommendation-aware Representation Learning

This paper deals with a more realistic scenario of fake message detection on social media, i.e., unsupervised post-time detection. Given a source message, our goal is to determine whether it is fake without using labeled data and without requiring user interacted with the given message. We present a novel learning framework, Recommendation-aware Message Representation (RecMR), to achieve the goal. The key idea is to learn user preferences and have them encoded into the representation of the source message through jointly training the tasks of user recommendation and binary detection. Experiments conducted on two real Twitter datasets exhibit the promising performance of RecMR, and show the effectiveness of recommended users in unsupervised detection.
Multi-touch Attribution for Complex B2B Customer Journeys using Temporal Convolutional Networks

Customer journeys in Business-to-Business (B2B) transactions contain long and complex sequences of interactions between different stakeholders from the buyer and seller companies. On the seller side, there is significant interest in the multi-touch attribution (MTA) problem, which aims to identify the most influential stage transitions (in the B2B customer funnel), channels, and touchpoints. We design a novel deep learning-based framework, which solves these attribution problems by modeling the conversion of journeys as functions of stage transitions that occur in them. Each stage transition is modeled as a Temporal Convolutional Network (TCN) on the touchpoints that precede it. Further, a global conversion model Stage-TCN is built by combining these individual stage transition models in a non-linear fashion. We apply Layer-wise Relevance Propagation (LRP) based techniques to compute the relevance of all nodes and inputs in our network and use these to compute the required attribution scores. We run extensive experiments on two real-world B2B datasets and demonstrate superior accuracy of the conversion model compared to prior works. We validate the attribution scores using perturbation-based techniques that measure the change in model output when parts of the input having high attribution scores are deleted.
Stochastic subgradient for composite optimization with functional constraints

In this paper we consider convex optimization problems with stochastic composite objective function subject to (possibly) infinite intersection of constraints. The objective function is expressed in terms of expectation operator over a sum of two terms satisfying a stochastic bounded gradient condition, with or without strong convexity type properties. In contrast to the classical approach, where the constraints are usually represented as intersection of simple sets, in this paper we consider that each constraint set is given as the level set of a convex but not necessarily differentiable function. Based on the flexibility offered by our general optimization model we consider a stochastic subgradient method with random feasibility updates. At each iteration, our algorithm takes a stochastic proximal (sub)gradient step aimed at minimizing the objective function and then a subsequent subgradient step minimizing the feasibility violation of the observed random constraint. We analyze the convergence behavior of the proposed algorithm for diminishing stepsizes and for the case when the objective function is convex or strongly convex, unifying the nonsmooth and smooth cases. We prove sublinear convergence rates for this stochastic subgradient algorithm, which are known to be optimal for subgradient methods on this class of problems. When the objective function has a linear least-square form and the constraints are polyhedral, it is shown that the algorithm converges linearly. Numerical evidence supports the effectiveness of our method in real problems.
Immunotherapy for Liver Metastatic Disease Activation of Immature Hepatic NK Cells As

NK cells can identify and eliminate emerging tumors due to altered expression of activating and inhibitory ligands on aberrant cells, a process that is greatly enhanced following NK cell activation. As a principal site of both tumor metastases and immature NK cells, the liver represents a unique anatomic location in which activation of the innate immune system could provide sub-stantial therapeutic beneﬁt. We describe here the NK cell-dependent destruction of a primary hepatic tumor following infection with an attenuated intracellular bacterium derived from Listeria monocytogenes . NK cell-mediated immunity correlated with the ordered migration and maturation of NK cells within the liver. Cytolytic activity was partially dependent on NKG2D-mediated tumor cell recognition, but surprisingly was still effective in the absence of type I IFN. Signiﬁcantly, NK cell-mediated destruction of a primary hepatic tumor in infected mice led to long-lived CD4- and CD8 T cell-dependent tumor-speciﬁc adaptive immunity. These ﬁndings establish that activation and differentiation of immature NK cells using complex microbial stimuli can elicit potent anti-tumor activity within the liver, promote cross-presentation of tumor-derived Ags leading to long-lived systemic anti-tumor immunity, and suggests a paradigm for clinical intervention of liver metastatic carcinoma. The Journal of Immunology, 2007, 179: 7376–7384. the liver and spleen following Lm (cid:2) actA (cid:2) inlB infection, we show that both liver-resident immature and peripheral mature NK cells com-prise the hepatic NK cell pool following infection. We demonstrate that this Lm (cid:2) actA (cid:2) inlB -induced hepatic NK cell pool led to cross-presentation of relevant tumor rejection Ags, priming of a systemic tumor-speciﬁc adaptive T cell response, and long-term survival. Our ﬁndings establish the rationale for harnessing liver-resident immature NK cells to treat metastatic disease within the liver.
Induced In Vivo with CTLA-4 Blockade in Diversity of Antigen-Specific Responses

CTLA-4 is a surface receptor on activated T cells that delivers an inhibitory signal, serving as an immune checkpoint. Treatment with anti–CTLA-4 Abs can induce clinical responses to different malignancies, but the nature of the induced Ag-speciﬁc recognition is largely unknown. Using microarrays spotted with >8000 human proteins, we assessed the diversity of Ab responses modulated by treatment with CTLA-4 blockade and GM-CSF. We ﬁnd that advanced prostate cancer patients who clinically respond to treatment also develop enhanced Ab responses to a higher number of Ags than nonresponders. These induced Ab responses targeted Ags to which preexisting Abs are more likely to be present in the clinical responders compared with nonresponders. The majority of Ab responses are patient-speciﬁc, but immune responses against Ags shared among clinical responders are also detected. One of these shared Ags is PAK6, which is expressed in prostate cancer and to which CD4 + T cell responses were also induced. Moreover, immunization with PAK6 can be both immunogenic and protective in mouse tumor models. These results demonstrate that immune checkpoint blockade modulates Ag-speciﬁc responses to both individualized and shared Ags, some of which can mediate anti-tumor responses. The Journal of Immunology , 2012, 189: 3759–3766. responses. In all of our patients, we any modulation of Ab responses to the control Ag Inﬂuenza A H3N2, supporting the that the treatment-induced modulation of Abs reﬂects the Ag milieu in the host. Ab responses were in fact induced to Ags both with and without detectable preexisting Abs prior to treatment. This is exempliﬁed with PAK6, to which patient 19 had low levels of Ab prior to treatment, whereas patient 20 had undetectable levels prior to treatment. Notably, clinical responders were more likely than nonresponders to generate Abs against Ags to which preexisting Abs could be detected. These results suggest that induction of preexisting rather than de novo (non-preexisting) immune responses may be important in generating anti-tumor activity in CTLA-4 blockade therapy. With one of the clinical responders (patient 19), detectable preexisting IgG to PAK6 could be detected, but no T cell response to PAK6 could be detected at baseline. After treatment, a CD4 T cell response to PAK6 was induced coinciding with an enhancement of IgG responses. These results would indicate that an immune response to PAK6 was generated spontaneously in the patient but was subsequently dampened perhaps by tumor-induced immunosuppression. Nevertheless, relieving a crucial immunologic checkpoint with CTLA4 blockade may be sufﬁcient to recover immune responses to such tumor-associated Ags.
Fast RFM Model for Customer Segmentation

With booming e-commerce and World Wide Web (WWW), a powerful tool in customer relationship management (CRM), called the RFM analysis model, has been used to ensure that major enterprises make more profit. Combined with data mining technologies, the CRM system can automatically predict the future behavior of customers to raise customer retention rate. However, a key issue is that the existing RFM analysis models are not efficient enough. Thus, in this study, a fast algorithm based on a compact list-based data structure is proposed along with several efficient pruning strategies to address this issue. The new algorithm considers recency (R), frequency (F), and monetary/utility (M) as three different thresholds to discover interesting patterns where the R, F, and M thresholds combined are no less than the user-specified minimum values. More significantly, the downward-closure property of frequency and monetary metrics are utilized to discover super-itemsets. Then, an extensive experimental study demonstrated that the algorithm outperforms state-of-the-art algorithms on various datasets. It is also demonstrated that the proposed algorithm performs well when considering the frequency metric alone.
Application of MTF in remote sensing image restoration

In order to solve the quality problem of remote sensing image degradation which is caused by fuzzy and noise in the process of push-broom full-color TDICCD space camera acquisition, transmission, and storage image, a new remote sensing image restoration method based on modulation transfer function (MTF) was proposed. The method in this paper restored point-spread function PSF by MTF, and took it as degradation model for image restoration. The ability of noise resisting of MTF extraction was raised in this paper via enhancing the local image by homomorphic filter algorithm before MTF extraction. Then by using the two times restoration method, MTF error which is caused by adding enhancement filter directly was avoided. Finally, the effect of remote sensing image restoration was evaluated by mean, contrast and edge strength. In experiments, the recovered image grayscale average increases 45%, average gradient is 3.5 times and the edge strength is 1.75 times greater than the values before restoration. And the area which is contained by MTF curves increases. The experimental results show that all above evaluation indexes are better than the original image. In conclusion, the image restoration algorithm of space TDICCD camera has certain reference significance in improving image quality.
A Human-Centered Design Approach for the Development of a Digital Care Platform in a Smart City Environment: Implications for Business Models

Digital solutions are being sought increasingly in the care sector for making services more efficient and to be prepared for demographic change and the future care service and staff shortage. One possibility here is the implementation of a digital care platform that is target group-oriented and built according to the needs of the individual stakeholders. To build this platform successfully, it is also necessary to take a closer look at the business model. This paper examines the mentioned points by applying a human-centered design approach that focuses on all perspectives and allows a deep understanding of the opportunities and challenges of a digital care platform in a smart city environment. A digital care platform was found to be promising for various involved stakeholders due to the value proposition. Consequently, the stakeholders benefit from e.g. the simplification of processes and data management, less bureaucratic effort and intensified care services for the elderly.
A Large-scale Temporal Analysis of User Lifespan Durability on the Reddit Social Media Platform

Social media platforms thrive upon the intertwined combination of user-created content and social interaction between these users. In this paper, we aim to understand what early user activity patterns fuel an ultimately durable user lifespan. We do so by analyzing what behavior causes potentially durable contributors to abandon their “social career” at an early stage, despite a strong start. We use a uniquely processed temporal dataset of over 6 billion Reddit user interactions on covering over 14 years, which we make available together with this paper. The temporal data allows us to assess both user content creation activity and the way in which this content is perceived. We do so in three dimensions, being a user’s content a) engagement and perception, b) diversification, and c) contribution. Our experiments reveal that users who leave the platform quickly may initially receive good feedback on their posts, but in time experience a decrease in the perceived quality of their content. Concerning diversification, we find that early departing users focus on fewer content categories in total, but do “jump” between those content categories more frequently, perhaps in an (unsuccessful) search for recognition or a sense of belonging. Third, we see that users who stay with the platform for a more extended period gradually start contributing, whereas early departing users post their first comments relatively quickly. The findings from this paper may prove crucial for better understanding how social media platforms can in an early stage improve the overall user experience and feeling of belonging within the social ecosystem of the platform.
DivisionAvoidance Are Quantitatively Linked to Cell T Cell Effector Function and Anergy

2 We have shown previously that T cells activated by optimal TCR and CD28 ligation exhibit marked proliferative heterogeneity, and ; 40% of these activated cells fail entirely to participate in clonal expansion. To address how prior cell division inﬂuences the subsequent function of primary T cells at the single cell level, primary CD4 1 T cells were subjected to polyclonal stimulation, sorted based on the number of cell divisions they had undergone, and restimulated by ligation of TCR/CD28. We ﬁnd that individual CD4 1 T cells exhibit distinct secondary response patterns that depend upon their prior division history, such that cells that undergo more rounds of division show incrementally greater IL-2 production and proliferation in response to restimulation. CD4 1 T cells that fail to divide after activation exist in a profoundly hyporesponsive state that is refractory to both TCR/CD28-mediated and IL-2R-mediated proliferative signals. We ﬁnd that this anergic state is associated with defects in both TCR-coupled activation of the p42/44 mitogen-activated protein kinase (extracellular signal-related kinase 1/2) and IL-2-mediated down-reg-ulation of the cell cycle inhibitor p27 kip1 . However, these defects are selective, as TCR-mediated intracellular calcium ﬂux and IL-2R-coupled STAT5 activation remain intact in these cells. Therefore, the process of cell division or cell cycle progression plays an integral role in anergy avoidance in primary T cells, and may represent a driving force in the formation of the effector/memory T cell pool. The Journal of Immunology, 2000, 165: 2432–2443.
”I’m always in so much pain and no one will understand” - Detecting Patterns in Suicidal Ideation on Reddit

Social media has become another venue for those struggling with thoughts of suicide. Many turn to social media to express suicidal ideation and look for peer support. In our study we seek to better understand patterns in the behaviors of these users particularly on the social media platform Reddit. This study will explore how Reddit users move or progress between subreddits until they express active suicidal ideation. We also look at these users’ posting pattern in the time leading up to expressing suicidal ideation and the time after. We examined a large dataset of posts from users who created at least one thread on SuicideWatch during January 2019 - August 2019 and collected their posts starting in July 2018 to create a look back period of 6 months. This generated a total of 5,892,310 posts. We defined what it means to progress between subreddits and generated a graph of progressions of all users in our dataset. We found that these users mostly progressed to or from 8 different subreddits and each of these subreddits could point to a particular emotional difficulty that a user was having such as self harm or relationship problems. Furthermore, we examined the volume of posts and the proportion of posts with negative sentiment leading up to the first incident of active suicidal ideation and found that there is an increase in both negative sentiment and volume of posts leading up to the day of the first incident of suicidal ideation on Reddit. However, during the day of first incident of suicidal ideation, there is a precipitous drop in the number of posts which goes back up on the following day. Using this insight, we can better understand these users. This will allow for developing intervention for suicide prevention in social media platforms in the future.
Detection of Infectious Disease Outbreaks in Search Engine Time Series Using Non-Specific Syndromic Surveillance with Effect-Size Filtering

Novel infectious disease outbreaks, including most recently that of the COVID-19 pandemic, could be detected by non-specific syndromic surveillance systems. Such systems, utilizing a variety of data sources ranging from Electronic Health Records to internet data such as aggregated search engine queries, create alerts when unusually high rates of symptom reports occur. This is especially important for the detection of novel diseases, where their manifested symptoms are unknown. Here we improve upon a set of previously-proposed non-specific syndromic surveillance methods by taking into account both how unusual a preponderance of symptoms is and their effect size. We demonstrate that our method is as accurate as previously-proposed methods for low dimensional data and show its effectiveness for high-dimensional aggregated data by applying it to aggregated time-series health-related search engine queries. We find that in 2019 the method would have raised alerts related to several disease outbreaks earlier than health authorities did. During the COVID-19 pandemic the system identified the beginning of pandemic waves quickly, through combinations of symptoms which varied from wave to wave. Thus, the proposed method could be used as a practical tool for decision makers to detect new disease outbreaks using time series derived from search engine data even in the absence of specific information on the diseases of interest and their symptoms.