Miracle-dz/UniFAR-FTUs huggingface dataset:
Development And In Vitro Characterization Of Bladder Tumor Cell Targeted Lipid-Coated Polyplex For Dual Delivery Of Plasmids And Small Molecules

Background Bladder cancer is the fourth most common cancer in men and eleventh most common in women. Combination therapy using a gene and chemotherapeutic drug is a potentially useful strategy for treating bladder cancer in cases where a synergistic benefit can be achieved successfully. This approach relies on developing drug combinations using carrier systems that can load both hydrophilic genes and hydrophobic drugs. Ideally, the formulation for carrier system should be free of traditional high shear techniques such as sonication and extrusion to reduce shear-induced nucleic acid strand breakage. Moreover, the system should be able to protect the nucleic acid from enzymatic attack and deliver it specifically to the tumor site. Materials and methods A dual payload carrier system that was formulated using a simple flow mixing technique to complex anionic plasmid (EGFP-NLS) using a cationic polymer (CD-PEI2.5kD) followed by coating of the polyplex using lipid membranes. The resulting lipid-coated polyplex (LCP) formulations are targeted to bladder cancer cells by employing a bacterial adhesive peptide sequence, RWFV, that targets the LCP to the tumor stroma for efficiently delivering reporter plasmid, EGFP-NLS and a model small molecule drug, pyrene, to the cancer cells. Results Encapsulation efficiency of the peptide targeted carrier for the plasmid was 50% ± 0.4% and for pyrene it was 16% ± 0.4%. The ability of the targeted LCP to transfect murine bladder cancer cells was 4-fold higher than LCP bearing a scrambled peptide sequence. Fluorescence of cells due to pyrene delivery was highest after 4 hrs using targeted LCP. Finally, we loaded the peptide targeted LCP with anti-cancer agent, curcumin. The targeted formulation of curcumin resulted in only 45% viable cancer cells at a concentration of 5 µg/mL, whereas the empty and non-targeted formulations did not result any significant cell death. Conclusion These results demonstrate the specificity of the targeting peptide sequence in engaging tumor cells and the utility of the developed carrier platform to deliver a dual payload to bladder tumor cells.
Generative Mechanisms of Technology Enabled Transformation: A Critical Realist Evaluation of a Hospital Laboratory Unit Transformation

Digital transformation success remains elusive and therefore a domain worthy of developing better evidence. Advancing understanding of generative mechanisms – the underlying causal powers that explain digital transformation – would be extremely valuable insight for practice. Substantial opportunity continues to exist to explore how exactly a transformation process unfolds and how organizational structural objects that inscribe social and technological agency, intertwine with each other and with the action of actors to cause transformation. Focusing on generative mechanisms, this research used grounded theory to study a hospital laboratory unit involved in transformation efforts. Using critical realism ontology, preliminary analysis uncovered five plausible key generative mechanisms: Framing of need; Forming the need; Materializing critical thinking; Validating; and Actualizing affordances. Follow-up data and analysis will formalize a generative mechanism model of transformation. The article concludes with expected contributions and suggested future work of this study.
Switching between standard coral reef benthic monitoring protocols is complicated: proof of concept

Monitoring the state of coral reefs is necessary to identify drivers of change and assess effectiveness of management actions. There are several widely-used survey methods, each of which is likely to exhibit different biases that should be quantified if the purpose is to combine datasets obtained via different survey methods. The latter is a particularly important consideration when switching methodologies in long-term monitoring programs and is highly relevant to the Caribbean today. This is because of the continuing need for regionally comparable coral reef monitoring datasets and the fact that the Global Coral Reef Monitoring Network (GCRMN)-Caribbean node is now recommending a photoquadrat (PQ) method over the chain intercept transect method widely adopted by the members of the first truly regional monitoring network, Caribbean Coastal Marine Productivity Program (CARICOMP), in the early-1990s. Barbados, a member of the CARICOMP network, has been using a variation of the chain intercept method in its long-term coral reef monitoring program for more than two decades. Now a member of GCRMN-Caribbean, Barbados is considering switching to the PQ method in conformity with other regional members. Since we expect differences between methods, this study seeks to quantify the nature of those differences to inform Barbados and others considering switching methods. In 2017, both methods were concurrently implemented at 21 permanent monitoring plots across three major reef types in Barbados. Differences in % cover estimates for the six major benthic components, that is, hard corals, sponges, gorgonians, macroalgae, turf algae and crustose coralline algae, were examined within and among reef types. Overall, we found a complex pattern of differences between methods that depended on the benthic component, its relative abundance, and the reef type. We conclude that most benthic components would require a different conversion procedure depending on the reef type, and we provide an example of these procedures for Barbados. The factors that likely contribute to the complex pattern of between-method differences are discussed. Overall, our findings highlight that switching methods will be complicated, but not impossible. Finally, our study fills an important gap by underscoring a promising analytical framework to guide the comparison of ecological survey methods on coral reefs.
Individual Risk Perception and Choice using Cryptocurrency for Transactions

Cryptocurrency and blockchain technology have attracted interest in a variety of applications across many industries. Understanding end-user perception of risk will assist in understanding user behavior and help to facilitate the adoption of blockchain services such as cryptocurrency. This understanding becomes more timely with the recent announcement of the cryptocurrencies Facebook Libra and Telegram Gram. Based on theories from behavioral decision science and previous studies in e-Commerce, we developed measures for risk perception, proposed hypotheses to compare risk perception using cryptocurrency with those in other domains, and designed experimental scenarios to relate risk perception to choices of using cryptocurrency. Our preliminary empirical study suggests that risk perception is of the same magnitude as online risk in e-Commerce but different from those in social and financial situations. Upon further empirical validation, we expect to gain a better understanding of user risk perception and attitude towards using cryptocurrency.
A Survey On Secure Container Isolation Approaches for Multi-Tenant Container Workloads and Serverless Computing

Due to their flexibility and scalability, containers have gained popularity in recent years. Despite their name, containers are not completely closed. The guest system of each container uses the same host operating system and its services. This reduces overhead and improves performance, but can cause potential security or interoperability problems. The degree of isolation provided by the Linux kernel combines process isolation with namespaces. This concept works well, but does not close all possible security gaps due to construction, so malware can break out and gain access to the host directly or other containers sharing the same host system kernel. This is particularly critical in multi-tenant scenarios where multiple clients run containerized workloads in a shared cloud environment and the containers can run as isolated processes belonging to different clients on the same shared host. The isolation of runtime environments is also becoming increasingly important for Serverless Computing where the cloud provider provides a runtime environment for executing server-side logic which can be potentially harmful to other runtime environments running on a shared host system. The Linux kernel itself provides a number of mechanisms to isolate processes from each other and restrict system calls. Although a high level of isolation can be achieved with these mechanisms, the shared kernel remains a security risk [MITRE Corporation 2019] [Heise Online 2019]. The rise of container virtualization led to the discussion whether and how containers could inherit classic hard-
Meta-Analysis of Clipping versus Coiling for the Treatment of Unruptured Middle Cerebral Artery Aneurysms: Direct Comparison of Procedure-Related Complications

Background Endovascular coiling and surgical clipping are routinely used to treat unruptured middle cerebral artery aneurysms (MCAAs). However, the optimal treatment for unruptured MCAAs is controversial. We aimed to systematically and comprehensively compare the clinical outcomes between endovascular coiling and surgical clipping for the treatment of MCAAs. Method This meta-analysis retrieved academic articles comparing the clinical outcomes between endovascular coiling and surgical clipping for unruptured MCAAs from the Cochrane Library, Medline, PubMed, and EMBASE databases. The reference articles of the identified studies were carefully reviewed to ensure that all available articles were represented in the study. The meta-analysis was conducted in accordance with the acknowledged the prioritized reported items for systematic review and meta-analysis (PRISMA) guidelines. Results A total of 6 studies, which enrolled a total of 789 participants, were included in our analysis. Of these 789 patients with MCAAs, 144 were assigned to an endovascular coiling group, and 645 were assigned to a surgical clipping group. Our results demonstrated that endovascular coiling was associated with a higher rate of retreatment (OR = 104.926; 95% CI: 12.931 to 851.379; P<0.001) and postoperative complications (OR = 3.157; 95% CI: 1.239 to 8.048; P= 0.016) than surgical clipping, especially for postoperative thrombus without infarction (OR = 4.905, 95% CI: 1.097 to 21.933; P = 0.037). Furthermore, surgical clipping was related to a higher rate of complete occlusion (OR = 0.349, 95% CI: 0.140 to 0.872; P = 0.024) and Glasgow Outcome Scale (GOS) ≥4 (OR = 0.250; 95% CI: 0.072 to 0.867; P= 0.029) than endovascular coiling after the operation. However, there was no significant difference in the rate of death, the proportion of patients with modified Rankin Scale (mRS)>2, infarction, or bleeding. Conclusion Although this study has inherent limitations, surgical clipping of unruptured MCAAs resulted in significantly higher complete aneurysm occlusion and GOS≥4 rates and was associated with a lower incidence of retreatment and complication, especially for postoperative thrombus without infarction. Therefore, the effect induced by surgical clipping of unruptured MCAAs remains superior to that induced by endovascular coiling; surgical clipping should be regarded as the first choice of treatment for unruptured MCAAs.
From Copy to Practice: Follower's Learning Behavior in Forex Social Trading

Forex social trading platforms endows novice investors with opportunities to trade on foreign exchange markets by mimicking the investment strategies of sophisticated traders. But concurrently, the copy-trading mechanism underlying these platforms foster a conducive learning environment whereby inexperience followers could evolve into independent traders by observing and learning from the trading behaviors of prominent traders. Drawing on observational learning theory, we advance learning efficiency and effectiveness as focal yardsticks to encapsulate followers’ learning performance and explore their effects on the profitability of followers’ first independent trades. Preliminary analysis conducted on a leading forex social trading platform reveals that traders’ trading consistency amplifies followers’ learning efficiency whereas traders’ profitability bolsters followers’ learning effectiveness. Furthermore, while our empirical findings attest to the criticality of learning effectiveness on followers’ ability to profit from their initial independent trades, speeding up the learning process may not guarantee better performance.
Enabling Sustainable Public Transport in Smart Cities through Real-time Decision Support

The growing consumption of fossil fuels and its negative environmental consequences have been a major concern during the last few decades. In line with that, public transport operators face global pressure to replace diesel buses by battery-electric buses (BEBs) in many countries. However, BEBs need to be recharged several times throughout the day to avoid running out of energy due to their limited driving range and slow charging rate. Accordingly, operating BEBs is substantially more sensitive to unanticipated delays and excess energy consumption, which raises serious challenges with respect to charging schedules. Moreover, BEBs are only a truly sustainable alternative if they are powered by renewable energy generators (REGs), which have intermittent and uncertain generation. Thus, we design and propose a real-time decision support system to overcome these uncertainties and maximize the utilization of REGs and minimize the impact on the grid while guaranteeing a feasible operation for the BEBs.
Atherectomy-assisted versus percutaneous angioplasty interventions for treatment of symptomatic infra-inguinal peripheral arterial disease

Introduction The role of atherectomy (ATHERO) for the treatment of symptomatic infra-inguinal arterial lesions remains controversial. We evaluated the effectiveness and safety of atherectomy-assisted endovascular interventions in comparison with percutaneous angioplasty (PTA). Material and methods A systematic search utilizing MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials was conducted for studies comparing ATHERO with PTA from February 1995 to May 2018. Only studies comparing ATHERO to PTA for symptomatic infra-inguinal disease were included. Random-effects meta-analysis was used to pool the data and endpoints across studies. Study endpoints included vessel dissection, distal embolization, residual stenosis (> 30%), vessel patency at 6 months, target lesion revascularization (TLR) at 12 months and major amputation rates at 1, 6, and 12 months. Results A total of 2923 patients were included from 8 studies. PTA was associated with higher vessel dissection (OR = 4.00, 95% CI: 1.15–13.86) and lower 12-month major amputation rates (OR = 0.73, 95% CI: 0.59–0.90). There was no significant difference between ATHERO and PTA groups in terms of distal embolization (OR = 0.45, 95% CI: 0.04–4.63), residual stenosis (OR = 1.28, 95% CI: 0.58–2.80), vessel patency at 6 months (OR = 1.27, 95% CI: 0.50–3.22), TLR at 12 months (OR = 1.07, 95% CI: 0.46–2.51), or limb amputation at 1 month (OR = 0.69, 95% CI: 0.44–1.07) or 6 months (OR = 1.54, 95% CI: 0.38–6.15). Conclusions In patients undergoing infra-inguinal endovascular interventions, PTA was associated with higher peri-procedural vessel dissection and lower 12-month major amputation rates. Both modalities were associated with similar distal embolization, residual stenosis, and 6-month vessel patency and amputation rates.
Towards Deep Learning Interpretability: A Topic Modeling Approach

The recent development of deep learning has achieved the state-of-the-art performance in various machine learning tasks. The IS research community has started to leveraged deep learning-based text mining for analyzing textual documents. The lack of interpretability is endemic among the state-of-the-art deep learning models, constraining model improvement, limiting additional insights, and prohibiting adoption. In this study, we propose a novel text mining research framework, Neural Topic Embedding, capable of extracting useful and interpretable representations of texts through deep neural networks. Specifically, we leverage topic modeling to enrich deep learning data representations with meaning. To demonstrate the effectiveness of our proposed framework, we conducted a preliminary evaluation experiment on a testbed of fake review detection and our interpretable representations improves the state-of-the-art by almost 8 percent as measured by F1 score. Our study contributes to the IS community by opening the gate for future adoption of the state-of-the-art deep learning methods.
Hey Alexa, Please Help Us Solve This Problem! How Interactions with Smart Personal Assistants Improve Group Performance

Despite a growing body of research about the design and use of Smart Personal Assistants, such as Google’s Assistant or Amazon’s Alexa, existing work has mainly focused on their use as task support for individual users in rather simple problem scenarios. Little is known about their ability to improve collaboration among multiple users in more complex problem settings. Using a mixed-method approach, we investigate the value of Smart Personal Assistants as facilitators by directly comparing 40 groups of three who either interact with a Smart Personal Assistant or a scripted human facilitator in a complex problem-solving scenario. The results indicate that groups interacting with Smart Personal Assistant facilitators show significantly better task outcomes and higher degrees of collaboration quality. A conversation analysis approach further analyzes differences in human SPA communication. Our work contributes to computer-supported cooperative work research and emphasizes the value of conversation analysis approaches in HCI research.
Detecting Senior Executives' Personalities for Predicting Corporate Behaviors: An Attention-based Deep Learning Approach

Previous empirical studies reveal that the personalities of senior executives may influence corporate decisions. Grounded in the big-five personality model established in the field of psychology and guided by the design science methodology, the work reported in this paper examines the predictive power of executives' personalities on corporate behaviors. Our main contributions are twofold. First, we design a novel deep learning-based artifact, namely pAttCLSTM which analyzes the senior executives’ personalities on a large scale through social media data. In our experiment, pAttCLSTM outperforms other state-of-the-art detectors over two benchmark datasets. Second, empowered by pAttCLSTM, we empirically verify the predictive power of executives' personalities on firms' policy variables by using a dataset including Tweets of 507 executives worked in S&P 1500 companies. To our best knowledge, this is the first successful design of an automatic detector for senior executives’ personalities, and hence to examine their impact on firms’ behaviors.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

Sustainable mobility behavior is increasingly relevant due to the vast environmental impact of current transportation systems. With the growing variety of transportation modes, individual decisions for or against specific mobility options become more and more important and salient beliefs regarding the environmental impact of different modes influence this decision process. While information systems have been recognized for their potential to shape individual beliefs and behavior, design-oriented studies that explore their impact, in particular on environmental beliefs, remain scarce. In this study, we contribute to closing this research gap by designing and evaluating a new type of artifact, a persuasive and human-like conversational agent, in a 2x2 experiment with 225 participants. Drawing on the Theory of Planned Behavior and Social Response Theory, we find empirical support for the influence of persuasive design elements on individual environmental beliefs and discover that anthropomorphic design can contribute to increasing the persuasiveness of artifacts.
Toward Affordable and Practical Home Context Recognition: -Framework and Implementation with Image-based Cognitive API-

To provide affordable context recognition for general households, this paper presents a novel technique that integrate image-based cognitive Application Program Interface (API) and light-weight machine learning. Our key idea is to regard every image as a document by exploiting “tags” derived by the API. We first present a framework that specifies a common workflow of the machine-learning-based home context recognition. We then propose a pragmatic method that implements the framework using the “image-as-a-document” approach.
Towards a Framework for Evaluation of Blockchain Implementations

Organizations appear to implement blockchain solutions based on fear of missing out instead of a clear understanding of blockchain usefulness. Actually, ninety percent of current blockchain projects do not need a blockchain to meet their requirements. Therefore, we employ a Design Science Research approach to develop a framework for evaluation of blockchain implementations. The framework incorporates common factors of blockchain decisions, including blockchain innovation, blockchain design, inter-organizational integration, and implementation environment. We contribute to the scientific literature by structuring previous research efforts in a four-step framework, which provides a fruitful ground for future conceptual and empirical studies. For practitioners, the framework is useful to identify blockchain projects that facilitate purposeful blockchain adoption.
NEDD4 Negatively Regulates GITR via Ubiquitination in Immune Microenvironment of Melanoma

Introduction Melanoma is a common skin cancer that is usually associated with poor clinical outcomes. Recently, the immune checkpoint GITR has been identified as a promising target for immunotherapy of melanoma. In this study, we aimed to investigate the post-translational regulation mechanism of GITR in melanoma. Methods Western blotting was used to evaluate the protein expression of NEDD4, GITR and Foxp3. Real-time PCR (RT-PCR) was performed to determine expression levels of NEDD4, GITR, Foxp3 and IL-2. Cell viability was detected by MTT assay. The ubiquitination of GITR was evaluated by immunoprecipitation. NEDD4 expression data and melanoma survival data were obtained from The Cancer Genome Atlas (TCGA) and cBioPortal databases. Results We demonstrate that E3 ligase NEDD4 binds to GITR and mediates ubiquitination and degradation of GITR. Overexpression of NEDD4 inhibits anti-tumor immunity mediated by T cells against melanoma cells. We also found that the expression of NEDD4 is increased in metastatic melanoma. High NEDD4 expression level is correlated with the poor prognosis of melanoma patients. Discussion In summary, our findings demonstrated that E3 ligase NEDD4 mediates ubiquitination and degradation of GITR and suppresses T-cell-mediated-killings on melanoma cells. Our work highlighted the E3 ligase NEDD4 as a novel prognosis biomarker and therapeutic target for melanoma.
Digital Nomadism and the Market Economy: Resistance and Compliance

Digital nomadism is a growing phenomenon wherein technology enables new forms of resistance against the norms of the market economy. However, digital nomads inevitably also comply with the market economy. In this paper, we synthesise literature about the complicating role of technology in the market economy throughout history, to develop a research framework for an empirical study of digital nomadism and the market economy. Based on this research framework, we have conducted some preliminary fieldwork, observing the emergence of five trends in how digital nomads participate in the market economy: collaborative creative consumption; self-driven and self-disciplined work; reimagination of work materials; interjurisdictional prospecting; and unregulated de-facto citizenship. These trends have a range of theoretical and practical implications that we will continue to uncover in our ongoing research.