Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping the Evolving Intellectual Structure of Digital Innovation Research on the Public Sector: A Document Co-citation Analysis

This paper uses document co-citation analysis (DCA) to explore the underlying and evolving structure of research on digital innovation (DI) in the public sector. As such, the DCA examines (1) what streams of scientific literature have been used in scholarly practices of citation in the study of innovation in the domain of e-government; (2) which are the central documents in the identified research streams and; (3) whether the emerging academic contributions around DI has had an impact on this field of research. Through the DCA of 1082 peer-reviewed papers three clusters of citation are identified, mapped, and categorized as: E-government diffusion and effects; Technology acceptance and adoption; and Digital innovation and infrastructures. The first two clusters are found to be tightly coupled while the last is found to currently be infrequently connected to either clusters. Implications for research and practice are presented and discussed
From Handpress to Handheld: Rare Book and Special Collections Libraries Forming the Libraries of Instagram

This paper addresses how Instagram serves as a platform to engage a community of librarians, researchers, and bibliophiles. There is a relative lack of research on Instagram and communities of this kind. Focusing on the development of the Libraries of Instagram community, we consider the importance of hashtag campaigns in generating a community space. The paper also comments on the way the emblematic nature of Instagram posts resonates with the rare book and special collections community. The case of @Jordan_Library, the Instagram account of a Canadian rare book and special collections library, exemplifies the Libraries of Instagram community and the use of Instagram to build community across the cultural heritage sector and beyond.
In silico search of triple mutant T790M/C797S allosteric inhibitors to conquer acquired resistance problem in non-small cell lung cancer (NSCLC): a combined approach of structure-based virtual screening and molecular dynamics simulation

Abstract Third generation EGFR inhibitor osimertinib was approved as the first-line treatment for EGFR T790M mutation-positive Non-Small Cell Lung Cancer (NSCLC) patients in 2017. However, EGFR tertiary Cys797 to Ser797 (C797S) point mutation emanate rapidly after treatment of osimertinib, which is undruggable mutation to the all existing drugs. Recently, EAI045 fourth-generation allosteric EGFR inhibitor has been reported, which binds away from the ATP-binding site and not rely on Cys 797 binding. Here, we are reporting compound ZINC20531199 by virtual based screening studies as allosteric inhibitor to overcome the EGFR T790M/C797S Tyrosine Kinase (TK) mutation problem. Molecular Dynamics simulation for 10 ns further suggested that docked compound ZINC20531199 was stable into the allosteric pocket of the C797S EGFR tyrosine kinase. In silico pharmacokinetic predictions of the virtually screened compounds are within the defined range described for human use. Results indicate that the virtual screened compounds could be potential leads for the further development of new allosteric EGFR T790M/C797S inhibitors to overcome the problem of drug resistance. Communicated by Ramaswamy H. Sarma
Pharmacotherapeutic management of co-morbid alcohol and opioid use

ABSTRACT Opioid use disorder (OUD) and alcohol use disorder (AUD) are two highly prevalent substance-related disorders worldwide. Co-use of the substances is also quite prevalent, yet there are no pharmacological treatment approaches specifically designed to treat co-morbid OUD and AUD. Here, the authors critically summarize OUD, AUD and opioid/alcohol co-use and their current pharmacotherapies for treatment. They also review the mechanisms of action of opioids and alcohol within the brain reward circuitry and discuss potential combined mechanisms of action and resulting neuroadaptations. Pharmacotherapies that aim to treat AUD or OUD that may be beneficial in the treatment of co-use are also highlighted. Preclinical models assessing alcohol and opioid co-use remain sparse. Lasting neuroadaptations in brain reward circuits caused by co-use of alcohol and opioids remains largely understudied. In order to fully understand the neurobiological underpinnings of alcohol and opioid co-use and develop efficacious pharmacotherapies, the preclinical field must expand its current experimental paradigms of ‘single drug’ use to encompass polysubstance use. Such studies will provide insights on the neural alterations induced by opioid and alcohol co-use, and may help develop novel pharmacotherapies for individuals with co-occurring alcohol and opioid use disorders.
Fractional Flow Reserve Derived from Coronary Computed Tomography Angiography Safely Defers Invasive Coronary Angiography in Patients with Stable Coronary Artery Disease

Objectives: In the United States, the real-world feasibility and outcome of using fractional flow reserve from coronary computed tomography angiography (FFRCT) is unknown. We sought to determine whether a strategy that combined coronary computed tomography angiography (CTA) and FFRCT could safely reduce the need for invasive coronary angiography (ICA), as compared to coronary CTA alone. Methods: The study included 387 consecutive patients with suspected CAD referred for coronary CTA with selective FFRCT and 44 control patients who underwent CTA alone. Lesions with 30–90% diameter stenoses were considered of indeterminate hemodynamic significance and underwent FFRCT. Nadir FFRCT ≤ 0.80 was positive. The rate of patients having ICA, revascularization and major adverse cardiac events were recorded. Results: Using coronary CTA and selective FFRCT, 121 patients (32%) had at least one vessel with ≥50% diameter stenosis; 67/121 (55%) patients had at least one vessel with FFRCT ≤ 0.80; 55/121 (45%) underwent ICA; and 34 were revascularized. The proportion of ICA patients undergoing revascularization was 62% (34 of 55). The number of patients with vessels with 30–50% diameter of stenosis was 90 (23%); 28/90 (31%) patients had at least one vessel with FFRCT ≤ 0.80; 8/90 (9%) underwent ICA; and five were revascularized. In our institutional practice, compared to coronary CTA alone, coronary CTA with selective FFRCT reduced the rates of ICA (45% vs. 80%) for those with obstructive CAD. Using coronary CTA with selective FFRCT, no major adverse cardiac events occurred over a mean follow-up of 440 days. Conclusion: FFRCT safely deferred ICA in patients with CAD of indeterminate hemodynamic significance. A high proportion of those who underwent ICA were revascularized.
Genetic variants in class II transactivator are associated with chronic hepatitis B virus infection in the Han Chinese population

Class II transactivator (CIITA) is a master regulator of MHC gene expression and plays a role in inducing the expression of other immune system genes, including IL‐4, IL‐10 and Fas ligand, as well as more than 60 other immunologically significant genes. We used CIITA as a candidate gene to analyse whether any single‐nucleotide polymorphisms (SNPs) are associated with chronic hepatitis B virus (HBV) infection. In total, 773 patients with chronic HBV infection were enrolled in this hospital‐based case–control study. The patients were divided into groups according to their clinical characteristics: 596 patients had chronic hepatitis B (CHB), and 177 patients had hepatocellular carcinoma (HCC). A total of 313 patients with self‐limited HBV infection were selected as the control group. CIITA gene variants were screened using Haploview 4.2 software; improved multiplex ligation detection reaction technology was then used for genotype detection, and HaploReg v4.1 was employed to predict the functions of 15 variants. The results showed that SNPs in introns in the CIITA gene, namely, rs13333382 (TT + TA vs. AA: p = .003, odds ratio (OR) = 0.65, 95% confidence interval (CI) = 0.49–0.87) and rs4780335 (CC + CG vs. GG: p = 9.40 × 10−5, OR = 0.55, 95% CI = 0.41–0.74), were positively associated with self‐limited HBV infection in the dominant genetic model. Additionally, SNP rs1139564 (TT + TC vs. CC: p = .002, OR = 1.61, 95% CI = 1.19–2.16) in the 3' untranslated region may increase the risk of CHB. According to in silico analysis, all three statistically significant variants act as transcription factor binding motifs. However, we did not find that these 15 mutations are associated with HCC risk. Therefore, we believe that CIITA is a susceptibility gene for CHB rather than for HCC.
Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder.

In this article, we develop a computational model of obsessive-compulsive disorder (OCD). We propose that OCD is characterized by a difficulty in relying on past events to predict the consequences of patients' own actions and the unfolding of possible events. Clinically, this corresponds both to patients' difficulty in trusting their own actions (and therefore repeating them), and to their common preoccupation with unlikely chains of events. Critically, we develop this idea on the basis of the well-developed framework of the Bayesian brain, where this impairment is formalized as excessive uncertainty regarding state transitions. We illustrate the validity of this idea using quantitative simulations and use these to form specific empirical predictions. These predictions are evaluated in relation to existing evidence, and are used to delineate directions for future research. We show how seemingly unrelated findings and phenomena in OCD can be explained by the model, including a persistent experience that actions were not adequately performed and a tendency to repeat actions; excessive information gathering (i.e., checking); indecisiveness and pathological doubt; overreliance on habits at the expense of goal-directed behavior; and overresponsiveness to sensory stimuli, thoughts, and feedback. We discuss the relationship and interaction between our model and other prominent models of OCD, including models focusing on harm-avoidance, not-just-right experiences, or impairments in goal-directed behavior. Finally, we outline potential clinical implications and suggest lines for future research. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Three lessons for gender equity in biodiversity conservation

Amid a growing global agenda, biodiversity conservation has embraced gender equity as a pillar of equitable and effective practice. Gender equity has become enshrined in the global environment and development agenda through global commitments, policy and funding. However, for various reasons, conservation biodiversity often takes a simplistic view of gender as synonymous with women or as a dualism between women and men. This narrow view risks promoting inequitable processes and ineffective outcomes. Deeper engagement with feminist theory, and feminist political ecology in particular, could help advance biodiversity conservation's approach to how gender is understood, framed and integrated. Engaging with lessons from feminist political ecology can help advance gender equity in conservation through attention to power dynamics, intersectionality, and subjectivity.
Moisture-Resilient Graphene-dyed Wool Fabric for Strain Sensing.

E-textile consisting of natural fabrics has become a promising material to construct wearable sensors due to its comfortability and breathability on human body. However, the reported fabric-based e-textile materials, such as graphene-treated cotton, silk and flax, etc., generally suffer from the electrical and mechanical instability in long-term wearing. In particular, fabrics on human body have to endure heat variation, moisture evaporation from metabolic activities and even the immersion with body sweat. To face above challenges, here we report a wool-knitted fabric sensor treated with graphene oxide (GO) dyeing followed by L-ascorbic acid (L-AA) reduction (rGO). This rGO-based strain sensor is highly stretchable, washable, and durable with rapid sensing response. It exhibits excellent linearity with more than 20% elongation, and most importantly, withstand moisture from 30% to 90% (or even immersed with water) and still maintains good electrical and mechanical properties. We further demonstrate that by integrating this proposed material with the Near Field Communication (NFC) system, a battery-less, wireless wearable body movement sensor can be constructed. This material can find a wide use in smart garment applications.
Spatio-temporal control of post-Golgi exocytic trafficking in plants

ABSTRACT A complex and dynamic endomembrane system is a hallmark of eukaryotic cells and underpins the evolution of specialised cell types in multicellular organisms. Endomembrane system function critically depends on the ability of the cell to (1) define compartment and pathway identity, and (2) organise compartments and pathways dynamically in space and time. Eukaryotes possess a complex molecular machinery to control these processes, including small GTPases and their regulators, SNAREs, tethering factors, motor proteins, and cytoskeletal elements. Whereas many of the core components of the eukaryotic endomembrane system are broadly conserved, there have been substantial diversifications within different lineages, possibly reflecting lineage-specific requirements of endomembrane trafficking. This Review focusses on the spatio-temporal regulation of post-Golgi exocytic transport in plants. It highlights recent advances in our understanding of the elaborate network of pathways transporting different cargoes to different domains of the cell surface, and the molecular machinery underpinning them (with a focus on Rab GTPases, their interactors and the cytoskeleton). We primarily focus on transport in the context of growth, but also highlight how these pathways are co-opted during plant immunity responses and at the plant–pathogen interface. Summary: A review of the elaborate plant trafficking pathways transporting different cargoes to different domains of the cell surface and the molecular machinery underpinning them, with a focus on Rab GTPases.
Latent class analysis to classify injury severity in pediatric TBI.

Heterogeneity of injury severity among children with traumatic brain injury (TBI) classified by the Glasgow Coma Scale (GCS) makes comparisons across research cohorts, enrollment in clinical trials, and clinical predictions of outcomes difficult. The present study uses latent class analysis (LCA) to distinguish severity subgroups from a prospective cohort of 433 children aged 2.5 to 15 years with TBI who were recruited from two level 1 pediatric trauma centers. Indicator variables available within 48 hours post-injury including ED GCS, hospital motor GCS, Abbreviated Injury Score (AIS), Rotterdam Score, hypotension in the ED, and prehospital loss of consciousness, intubation, seizures and sedation were evaluated to define subgroups. To understand whether latent class subgroups were predictive of clinically meaningful outcomes, the Pediatric Injury Functional Outcome Scale (PIFOS) at 6 and 12 months, and the Behavior Rating Inventory of Executive Function at 12 months, were compared across subgroups. Then, outcomes were examined by GCS (primary) and AIS (secondary) classification alone to assess whether LCA provided improved outcome prediction. LCA identified four distinct increasing severity subgroups (1 to 4). Unlike GCS classification, mean outcome differences on PIFOS at 6 months showed decreasing function across classes. PIFOS differences relative to the lowest latent class (LC1) were: LC2 2.27 (0.83, 3.72); LC3 3.99 (1.88, 6.10); LC4 11.2 (7.04, 15.4). Differences in 12-month outcomes were seen between the most and least severely injured groups. Differences in outcomes in relation to AIS were restricted to the most and less severely injured at both timepoints. This study distinguished four latent classes that are clinically meaningful, distinguish a more homogenous severe injury group, and separate children by six-month functional outcomes better than GCS, alone. Systematic reporting of these variables would allow comparisons across research cohorts, potentially improve clinical predictions and increase sensitivity to treatment effects in clinical trials.
Nuclear Activation Function 2 Estrogen Receptor α Attenuates Arterial and Renal Alterations Due to Aging and Hypertension in Female Mice

Background The cardiovascular protective effects of estrogens in premenopausal women depend mainly on estrogen receptor α (ERα). ERα activates nuclear gene transcription regulation and membrane‐initiated signaling. The latter plays a key role in estrogen‐dependent activation of endothelial NO synthase. The goal of the present work was to determine the respective roles of the 2 ERα activities in endothelial function and cardiac and kidney damage in young and old female mice with hypertension, which is a major risk factor in postmenopausal women. Methods and Results Five‐ and 18‐month‐old female mice lacking either ERα (ERα−/−), the nuclear activating function AF2 of ERα (AF2°), or membrane‐located ERα (C451A) were treated with angiotensin II (0.5 mg/kg per day) for 1 month. Systolic blood pressure, left ventricle weight, vascular reactivity, and kidney function were then assessed. Angiotensin II increased systolic blood pressure, ventricle weight, and vascular contractility in ERα−/− and AF2° mice more than in wild‐type and C451A mice, independent of age. In both the aorta and mesenteric resistance arteries, angiotensin II and aging reduced endothelium‐dependent relaxation in all groups, but this effect was more pronounced in ERα−/− and AF2° than in the wild‐type and C451A mice. Kidney inflammation and oxidative stress, as well as blood urea and creatinine levels, were also more pronounced in old hypertensive ERα−/− and AF2° than in old hypertensive wild‐type and C451A mice. Conclusions The nuclear ERα‐AF2 dependent function attenuates angiotensin II–dependent hypertension and protects target organs in aging mice, whereas membrane ERα signaling does not seem to play a role.
Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion

Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support accurate fine-grained short-term prediction, since they mainly focus on coarse-grained long-term prediction using historical data. Recently, cross-domain open data provides possibilities to conduct fine-grained price forecasting, since they can be leveraged to extract various direct and indirect factors of the price. In this article, we predict the price trend over upcoming days, by leveraging cross-domain open data fusion. More specifically, we formulate the price trend into three classes (rise, slight-change, and fall), and then we predict the specific class in which the price trend of the future day lies. We take three factors into consideration: (1) supply factor considering sources providing bulk commodities, (2) demand factor focusing on vessel transportation with reflection of short time needs, and (3) expectation factor encompassing indirect features (e.g., air quality) with latent influences. A hybrid classification framework is proposed for the price trend forecasting. Evaluation conducted on nine real-world cross-domain open datasets shows that our framework can forecast the price trend accurately, outperforming multiple state-of-the-art baselines.
Hospital Infection Control: Clostridioides difficile

Abstract Clostridioides difficile remains a leading cause of healthcare-associated infection. Efforts at C. difficile prevention have been hampered by an increasingly complex understanding of transmission patterns and a high degree of heterogeneity among existing studies. Effective prevention of C. difficile infection requires multimodal interventions, including contact precautions, hand hygiene with soap and water, effective environmental cleaning, use of sporicidal cleaning agents, and antimicrobial stewardship. Roles for probiotics, avoidance of proton pump inhibitors, and isolation of asymptomatic carriers remain poorly defined.
Sugar intake among German adolescents: trends from 1990 to 2016 based on biomarker excretion in 24-h urine samples

Abstract Trend analyses based on dietary records suggest decreases in the intakes of total sugar (TS), added and free sugar since 2005 among children and adolescents in Germany. In terms of age trends, TS intake decreased with increasing age. However, self-reported sugar intake in epidemiological studies is criticised, as it may be prone to bias due to selective underreporting. Furthermore, adolescents are more susceptible to underreporting than children. We thus analysed time and age trends in urinary fructose excretion (FE), sucrose excretion (SE) and the sum of both (FE + SE) as biomarkers for sugar intake among 8·5–16·5-year-old adolescents. Urinary sugar excretion was measured by UPLC-MS/MS in 997 24-h urine samples collected from 239 boys and 253 girls participating in the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study cohort between 1990 and 2016. Time and age trends of log-transformed FE, SE and FE + SE were analysed using polynomial mixed-effects regression models. Between 1990 and 2016, FE as well as FE + SE decreased (linear time trend: P = 0·0272 and P < 0·0001, respectively). A minor increase in excretion during adolescence was confined to FE (linear age trend: P = 0·0017). The present 24-h excretion measurements support a previously reported dietary record-based decline in sugar intake since 2005. However, the previously seen dietary record-based decrease in TS from childhood to late adolescence was not confirmed by our biomarker analysis, suggesting a constant sugar intake for the period of adolescence.
Defining Open-Ended Problem Solving Through Problem Typology Framework

Problem solving is central to engineering education. Yet, there little agreement regarding what constitutes an exemplary design problem or case analysis problem for modeling undergraduate instruction after. There is even less agreement in engineering education literature regarding the best way to measure students ability or progress in learning to be better problem solvers in these discrete problem categories. We describe the development of a research method toward accessing how students think about design is described, what constitutes a measurable response, and how to compare through qualitative research methods pre and post student performance. The discussion draws from Jonassen’s (2000) framework for problem typology, as well as cognitive learning frameworks of design thinking, and metacognition as a theoretical basis that informs the problem formulation and planned approach for analysis.