Miracle-dz/UniFAR-FTUs huggingface dataset:
Reversible gelation in hydrophobic polyelectrolyte/protein mixtures : An example of cross-links between soft and hard colloids

Measurements were conducted on the linear viscoelastic properties of hydrophobically modified poly(sodium acrylate) reversibly cross-linked with proteins to form gels. The native structure of proteins was not markedly affected upon association with the amphiphilic polyelectrolyte. Hydrophobic interaction was shown to overcome Coulombic effects on interpolymer complexation. Because of their relative hydrophobicity, neutral papain and positively charged lysozyme were found to be less efficient cross-linking agents than negatively charged bovine serum albumin. These reversible gels exhibited similarities with chemically cross-linked macromolecules such as a plateau modulus at high frequency and characteristic scaling at the transition point between sol and gel. The sol−gel transition diagrams were determined. A semiquantitative approach, based on scaling laws, was able to capture the essential rheological properties of these mixtures, namely sol−gel transition and variations of storage modulus.
Fast and statistically robust cell extraction from large-scale neural calcium imaging datasets

State-of-the-art Ca2+ imaging studies that monitor large-scale neural dynamics can produce video datasets ~10 terabytes or more in total size, roughly comparable to ~10,000 Hollywood films. Processing such data volumes requires automated, general-purpose and fast computational methods for cell identification that are robust to a wide variety of noise sources. We introduce EXTRACT, an algorithm that is based on robust estimation theory and uses graphical processing units (GPUs) to extract neural dynamics in computing times up to 10-times faster than imaging durations. We validated EXTRACT on simulated and experimental data and processed 94 public datasets from the Allen Institute Brain Observatory in one day. Showcasing its superiority over past cell-sorting methods at removing noise contaminants, neural activity traces from EXTRACT allow more accurate decoding of animal behavior. Overall, EXTRACT provides neuroscientists with a powerful computational tool matched to the present challenges of neural Ca2+ imaging studies in behaving animals.
Chemical Nature and Composition of Compost During Mushroom Growth

Improvement of the process of mushroom composting is of importance in order to provide an optimal growing medium needed for the mushroom. Since composting produces a food source for the mushroom, the compositional changes of mushroom compost occurring during the growth cycle of the button mushroom, Agaricus bisporus, were studied. Samples were collected during spawning, casing and the following four flushes. Samples were analyzed for their elemental composition, carbohydrates, FTIR, 13C-NMR, pyrolysis-GC/MS and tetramethylammonium hydroxide (TMAH) thermochemolysis-GC/MS. The carbohydrate analysis revealed a decrease in the total amount of about 40% of the identified monosaccharides during mushroom growth. The 13C-NMR data also confirm that polysaccharides are the main fraction degraded (or utilized by the mushrooms) during the cropping period. These findings were also confirmed by changes in the absorbance of functional groups observed in the FTIR spectra. Pyrolysis data revealed chromatographic peaks related to polysaccharides-, proteins- and lignin-derived compounds. Both pyrolysis and TMAH thermochemolysis data show degradation of lignin structures during the growth period. Lignin alteration primarily involved a preferential degradation of syringyl units and oxidation of side chains of guaiacyl moieties. This study suggests that the polysaccharide fraction of the mushroom substrate is the main organic fraction utilized during the process. In addition, alterations of lignin structures have occurred although its relative content usually increases during mushroom cropping.
In the Eye of the Reviewer: An Application of Unsupervised Clustering to User Generated Imagery in Online Reviews

Mining opinions from online reviews has been shown to be extremely valuable in the past decades. There has been a surge of research focused on understanding consumer brand perceptions from the textual content of online reviews using text mining methods. With the increase in smartphone usage and ease of posting images, these reviews now often contain visual content. We propose an unsupervised cluster method to understand the user-generated imagery (UGI) of online reviews in the travel industry. Using the deep embedded clustering model we group together similar UGI and examine the average review ratings of these clusters to identify imagery associated with positive and negative reviews. After training the method on the entire dataset, we map out individual hotels and their corresponding UGI to show how hotel managers can use the method to understand their performance in particular areas of customer service based on UGI. The performance in a cluster relative to the population can be a clear indicator of areas that need improvement or areas that should be highlighted in the hotel’s marketing efforts. Overall, we present a useful application using visual analytics for mining consumer opinions and perceptions directly from image data.
Overcoming Diffusion Barriers of Digital Health Innovations Conception of an Assessment Method

Digital health innovations (DHIs) contribute to improving the health sector by revitalizing availability and continuity of care as well as mitigating rising costs. DHIs getting increasing support from health insurance companies and governmental institutions, but still struggle on their way to standard care in national healthcare systems. One of the central challenges is the multitude of diffusion barriers, which are either little known or difficult to handle in complexity and therefore pose a high risk for the translation into the healthcare practice. This paper steps into this discourse with a design-oriented research approach. A systematic literature review identified DHI barriers that are further evolved to a concept for assessing barrier resilience. On that basis, a framework to systematically administer diffusion barriers to DHI in Germany was developed. Innovators may use the proposed framework to assess the likelihood of a successful implementation and to ensure smooth scaling up process of their DHI.
Inferring the Relationship between Anxiety and Extraversion from Tweets during COVID19 - A Linguistic Analytics Approach

We investigate the longitudinal relationship between extraversion and experienced state anxiety in a cohort of Twitter users in New York using a linguistic analytics approach. We find that before COVID-19 was declared a pandemic, highly extraverted individuals experienced lower state anxiety compared to more introverted individuals. This is in line with previous literature. However, there seem to be no significant differences between individuals after the pandemic announcement, which provides evidence that COVID-19 is affecting individuals regardless of their extraversion trait disposition. Finally, a longitudinal examination of the present data shows that extraversion seems to matter more greatly in the early days of the crisis and towards the end of our examined time range. Throughout the crisis, state anxiety did not seem to vary much between individuals with different extraversion dispositions. © 2021 IEEE Computer Society. All rights reserved.
Eluto‐frontal chromatography to simulate chemical weathering of COx by low‐molecular‐weight organic compounds and early pedogenesis processes

Excavation of geological materials by civil engineering operations generates fresh parent rocks that are transferred from the deep layers to the surface in a very short time. Thus they are exposed to pedogenetic factors. Prediction of the characteristics and functioning (e.g. plant support and water filter) and impacts of the newly formed soils requires the knowledge of the pedogenetic processes affecting the parent materials (i.e. weathering, transport and neoformation). Reactive transport is here used to assess weathering processes of geological materials produced from the excavation of an underground laboratory in the Callovo‐Oxfordian (COx) layer, subsequently disposed of on the soil surface and covered with plants. The COx layer (c. 150–100 million years) is mainly composed of quartz, calcite, dolomite and argillite (complex mixtures of phyllosilicates). COx samples collected between −456.6 and −482.5 m were submitted to eluto‐frontal chromatography experiments in the presence of simple organic molecules exhibiting a range of chemical reactivity (i.e. acetate, oxalate, histidine and EDTA). Breakthrough curves of compounds contained in the COx material and mathematical modelling allowed the identification of the major chemical reactions taking place during weathering. Results showed that acidic and complexing organic compounds induced the partial solubilization of minerals and in some cases secondary precipitations. The early pedogenesis processes induced a strong release of minerals (Na, Ca, Mg) and transformation of clay minerals. Further evolution of COx material is dependent on the presence of carbonates, leading to carbonated soils such as Calcosols. Reactive transport experiments showed that early pedogenetic processes are of most importance as they produced strong changes in the parent material composition and released significant amounts of solutes into the soil solution.
Collaboration for Big Data Analytics: Investigating the (Troubled) Relationship between Data Science Experts and Functional Managers

The utilization of insights from big data analytics (BDA) in business operations has been identified as a major driver to unlock value from big data. This emphasizes the importance of the involvement of functional business managers in BDA projects and draws attention to their collaboration with BDA experts, such as data scientists. Scholars have identified several challenges that explain why the success rates of BDA projects remain low. However, the relationship between managers and data science experts has not yet been examined as a potential reason for failure. By applying a social capital perspective on the relationship between these groups, we employ a multiple case study to investigate possible obstacles. We find that the relationship is largely troubled due to incongruent cognitive interpretations of BDA applications in the business context, and the absence of structural network ties. These findings suggest a previously under-researched reason why BDA projects still frequently fail.
Predicting Unplanned Hospital Readmissions using Patient Level Data

The rate of unplanned hospital readmissions in the US is likely to face a steady rise after 2020. Hence, this issue has received considerable critical attention with the policy makers. Majority of hospitals in the US pay millions of dollars as penalty for readmitting patients within 30 days due to strict norms imposed by the Hospital Readmission Reduction Program. In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days. Both these models are hybrid sequence models that leverage both sequential events (history of events) and static features (like gender, blood pressure) of the patients to mine patterns in the data. Our results are promising, and they benchmark previous results in predicting hospital readmissions. The contributions of this study add to existing literature on healthcare analytics.
Snake Detection and Classification using Deep Learning

Object detection is a major task in computer vision. With the rapid development of machine learning in the past few decades, and more recently deep learning, it is now possible to utilise complex machine learning models to automatically detect and classify objects from potentially complex images. In this paper we consider machine (deep) learning networks suitable for detection and classification of (Australian) snakes and their deployment and performance in a mobile environment. We explore state of the art Convolutional Neural Networks (CNNs) and their use for transfer learning. We develop an iOS application supporting an offline (model-embedded on the device) approach and an online version where images are sent to a Cloud-based server for classification. We present the results and discuss the performance differences as well as the impact on the accuracy and time for classification for the two environments.
SkillsIdentifier: A Tool to Promote Career Identity and Self-efficacy Among Underrepresented Job Seekers

Today’s employment applications enable job seekers to improve their skill sets and build social networks with potential employers and colleagues. However, many of these tools cater to higher-educated and relatively afﬂuent job seekers. Research suggests that underrepresented job seekers face challenges associated with articulating their skill sets and understanding those skills’ transferability across jobs and might prefer employment tools to address these types of challenges over others. Because such articulation is vital in today’s job market, we designed, developed, and evaluated SkillsIdentiﬁer, a tool to assist job seekers in identifying their current skill set. We evaluated the tool with 20 U.S. job seekers and found that it helped to enhance their career identity and self-efﬁcacy. We contribute the empirical results of our evaluation and design implications for supporting these constructs among underrepresented job seekers.
Emotion-based IS support for Customer-Salesperson Interaction

While e-commerce is experiencing increasing sales, stationary retail is losing customers. One way to address this problem is to digitally support the advantages of stationary retail and offer new services to the customer. Following a Design Science Research approach, we present two consecutive design cycles in which we propose an emotion-based information system (IS) to support the interaction between customers and sales personnel. Design cycle 1 focuses on generating design knowledge in form of design guidelines, while design cycle 2 aims to exert them into a specific expository instantiation in order to evaluate them. In an increasingly digital world with changing customers, this contribution shows that emotions might be a key element in the design of valuable IS-supported interactions. Hereby we address the identified lack of literature on emotion-based ISs in the stationary retail sector.
The Emergence and Dynamics of Electronic Health Records - A Longitudinal Case Analysis of Multi-Sided Platforms from an Interoperability Perspective

Emerging health record platforms are interesting examples of the ongoing process of digitalization and the great opportunities they provide for innovation and additional services. Incumbent players are under increasing pressure from new entrants to offer their customers a user experience they have become familiar with through platforms such as Apple and Google. The emergence of the digital German health record is shown as a case-study, harnessing a longitudinal database and adopting a processsensitive perspective. Important events are structured into individual episodes and phases and discussed indepth. The study shows how platform owners of health records respond to changes in the highly regulated healthcare system and its digitalization in Germany. Contrasting with extant knowledge about interoperability as a relevant precondition for platforms, our study shows the important role played by interoperability as a design parameter for emerging platforms, which results in seven interoperability challenges for respective stakeholders.
Are Review Helpfulness Score and Review Unhelpfulness Score Two Sides of The Same Coin or Different Coins?

Online review platforms have increasingly incorporated the review evaluating system (i.e., a system that allows users to evaluate whether reviews are helpful/unhelpful) to assist review readers and encourage review contributors. However, although we have extensive knowledge about the review helpfulness score, our insights regarding its counterpart, the review unhelpfulness score, are lacking. Addressing this limitation is important because many researchers have adopted the review unhelpfulness score assuming that it is driven by intrinsic review characteristics while practitioners also implicitly assume that the unhelpfulness score can identify low-quality reviews. The primary objective of this work is to verify whether the review unhelpfulness score is influenced by intrinsic review characteristics that drive review helpfulness score. We find that unlike review helpfulness score, unhelpfulness score is not driven by intrinsic review characteristics, and that helpfulness voters behave significantly different than unhelpfulness voters. Further implications and future directions are also discussed.
Privacy in a Digitized Workplace: Towards an Understanding of Employee Privacy Concerns

When employees are required to work remotely, the digitization of the workplace becomes imperative to organizations. The introduction of digital workplaces leads to challenges and potentially negative consequences for employee privacy. Research did not yet shed light on the issue of employee privacy concerns. Therefore, the goal of this study is to evaluate the concept of privacy concerns in the context of the digitized workplace. Within the scope of this study, we conducted 33 semi-structured interviews with employees in order to gain insights into their Workplace Privacy Concerns (WPCs). Based on an iterative thematic analysis approach, we identified eight dimensions of WPCs: Six of these dimensions are adapted from the consumer context, two further dimensions represent concerns exclusive to the workplace context. This study serves as a starting point towards an understanding of WPCs and future research on the digitized workplace.
Drug targeting using OX7-immunoliposomes: Correlation between Thy1.1 antigen expression and tissue distribution in the rat

OX7 monoclonal antibody F(ab′)2 fragments directed against Thy1.1 antigen can be used for drug targeting by coupling to the surface of drug-loaded liposomes. Such OX7-conjugated immunoliposomes (OX7-IL) were used recently for drug delivery to rat glomerular mesangial cells, which are characterized by a high level of Thy1.1 antigen expression. In the present study, the relationship between OX7-IL tissue distribution and target Thy1.1 antigen localization in different organs in rat was investigated. Western blot and immunohistofluorescence analysis revealed a very high Thy1.1 expression in brain cortex and striatum, thymus and renal glomeruli. Moderate Thy1.1 levels were observed in the collecting ducts of kidney, lung tissue and spleen. Thy1.1 was not detected in liver and heart. There was a poor correlation between Thy1.1 expression levels and organ distribution of fluorescence- or 14C-labeled OX7-IL. The highest overall organ density of OX7-IL was observed in the spleen, followed by lung, liver and kidney. Heart and brain remained negative. With respect to intra-organ distribution, a localized and distinct signal was observed in renal glomerular mesangial cells only. As a consequence, acute pharmacological (i.e. toxic) effects of doxorubicin-loaded OX7-IL were limited to renal glomeruli. The competition with unbound OX7 monoclonal antibody F(ab′)2 fragments demonstrated that the observed tissue distribution and acute pharmacological effects of OX7-IL were mediated specifically by the conjugated OX7 antibody. It is concluded that both the high target antigen density and the absence of endothelial barriers are needed to allow for tissue-specific accumulation and pharmacological effects of OX7-IL. The liposomal drug delivery strategy used is therefore specific toward renal glomeruli and can be expected to reduce the risk of unwanted side effects in other tissues.
A Group Recommendation Model Using Diversification Techniques

In daily life groups are formed naturally, such as watching a movie with friends, or going out for dinner. In all these scenarios, using Recommendation Systems can be helpful by suggesting pieces of information (e.g. movies or restaurants) that satisﬁes all rather than a single member in the group. To do so, it is crucial to aggregate individual preferences of the group members aiming at satisfying all. Although there are consensus techniques to create the group proﬁle, the recommendations still may be repetitive and overspecialized. This drawback sets precedent for adopting diversiﬁcation techniques to group recommendations. In this paper, we propose a group recommendation model using diversiﬁcation techniques that exploits different aggregation techniques over group preferences matrix. The experiments evaluate accuracy and diversity goals for the group recommendations. Results from the experiments point out that our approach achieved 1.8% of diversity increase and 3.8% of precision improvement over compared methods.
Internal Branding in the Gig and Sharing Economy: Enhancing Service Providers' Engagement to Achieve Higher Customer Satisfaction

Traditional service industries rely on internal branding and employee engagement to ensure their employees deliver their brand promises. However, they are evolving into more on-demand work than ever before. Both gig and sharing economy (GE&SE) platforms enable individuals to provide on-demand services. Although those service providers (SPs) are not employed by GE&SE platforms, consistent delivery of brand promises by the SPs is crucial in GE&SE too. Thus, we aim to investigate the importance of internal branding in GE&SE as a key factor in achieving higher customer satisfaction by analyzing the SP’s engagement to the brand. Accordingly, we develop a comparison framework and conduct nine interviews with SPs, which we then analyze by means of a cross-case analysis. The results show that internal branding practices can play an important role in enhancing the SPs’ engagement levels. Moreover, highly engaged SPs will go the extra mile to satisfy the customers’ needs.
Emotional Response to Extended Realities: The Effects of Augmented and Virtual Technologies in a Shopping Context

Extended reality (XR) technologies such as Augmented Reality (AR) and Virtual Reality (VR) are facing mixed expectations. XR is often touted to offer deeply engrossing experiences but it can also lead to cybersickness, disappointment, and frustration. Moreover, research has not kept pace with how these technologies may affect users’ emotions. Therefore, to understand emotions in XR, this study employs a 2 (virtual: yes vs. no) ×2 (augmented: yes vs. no) between-subject experiment (N = 162) in the shopping context. Effects on emotions are assessed by measuring changes in emotional valence and examining them using Median Tests and exploratory data analysis. Results suggest that emotional responses in XR are similar to those in a physical store. However, there is an unexpected effect of the augmented experiences where negative emotions markedly vary. Implications are presented both for retail businesses and simulations, and emotionally engaging experiences such as immersive journalism and psychotherapy.