Common Attributes Research Articles

Current Quest in Natural Bioactive Compounds for Alzheimer's Disease: Multi-Targeted-Designed-Ligand Based Approach with Preclinical and Clinical Based Evidence.

Alzheimer's disease is a common and most chronic neurological disorder (NDs) associated with cognitive dysfunction. Pathologically, Alzheimer's disease (AD) is characterized by the presence of β-amyloid (Aβ) plaques, hyper-phosphorylated tau proteins, and neurofibrillary tangles, however, persistence oxidative-nitrative stress, endoplasmic reticulum stress, mitochondrial dysfunction, inflammatory cytokines, pro-apoptotic proteins along with altered neurotransmitters level are common etiological attributes in its pathogenesis. Rivastigmine, memantine, galantamine, and donepezil are FDA approved drugs for symptomatic management of AD, whereas tacrine has been withdrawn because of hepatotoxic profile. These approved drugs only exert symptomatic relief and exhibit poor patient compliance. In the current scenario, the number of published evidence shows the neuroprotective potential of naturally occurring bioactive molecules via their antioxidant, anti-inflammatory, antiapoptotic and neurotransmitter modulatory properties. Despite their potent therapeutic implications, concerns have arisen in context to their efficacy and probable clinical outcome. Thus, to overcome these glitches, many heterocyclic and cyclic hydrocarbon compounds inspired by natural sources have been synthesized and showed improved therapeutic activity. Computational studies (molecular docking) have been used to predict the binding affinity of these natural bioactive as well as synthetic compounds derived from natural sources for the acetylcholine esterase, α/β secretase Nuclear Factor kappa- light-chain-enhancer of activated B cells (NF-kB), Nuclear factor erythroid 2-related factor 2(Nrf2) and other neurological targets. Thus, in this review, we have discussed the molecular etiology of AD, focused on the pharmacotherapeutics of natural products, chemical and pharmacological aspects and multi-targeted designed ligands (MTDLs) of synthetic and semisynthetic molecules derived from the natural sources along with some important on-going clinical trials.

Fast Searching Density Peak Clustering Algorithm Based on Shared Nearest Neighbor and Adaptive Clustering Center

The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms always need prior knowledge, we proposed a fast searching density peak clustering algorithm based on the shared nearest neighbor and adaptive clustering center (DPC-SNNACC) algorithm. It can automatically ascertain the number of knee points in the decision graph according to the characteristics of different datasets, and further determine the number of clustering centers without human intervention. First, an improved calculation method of local density based on the symmetric distance matrix was proposed. Then, the position of knee point was obtained by calculating the change in the difference between decision values. Finally, the experimental and comparative evaluation of several datasets from diverse domains established the viability of the DPC-SNNACC algorithm.

Ecosystem restoration is risky … but we can change that

The forthcoming UN Decade of Ecosystem Restoration brings the challenge of massively increasing restoration efforts to halt and reverse environmental degradation. This challenge must move beyond a focus on the size and number of projects to a focus on how those projects are designed and implemented. Approaches based on restoring the fundamental ecological processes that lead to self-sustaining ecosystems are essential.

Climate change worsens global inequity in maternal nutrition

Climate change worsens global inequity in maternal nutrition

Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data

The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.

Examining the efficacy of constancy and frequency as political strategies for assisted suicide legalization: Does the introduction of many bills over time or multiple bills at once increase the likelihood of assisted suicide bills passing in U.S. state legislatures?

Attempts to legalize assisted suicide has historically presented a unique set of political and procedural challenges in state legislatures, the result of which has been the overwhelming defeat of nearly 98% such attempts in the last 25 years. Only in recent years is this trend beginning to change. Vermont was the first state to adopt legislation in 2013, followed by California, and Washington DC in 2015. In 2018, Hawaii was added to the list and most recently, New Jersey, and Maine in 2019. Other trends have become evident as well. Whereas between 1995 and 2013 there was a median of nine bills introduced by six states in a given biennium, this has increased to 31 bills in 24 states between 2015 and 2019, And while until 2013, only two or three states at most would have more than a single bill in play, the median has now doubled to five states and Washington DC since 2015. This paper examines whether or not these recent trends are in fact, political strategies that could explain and potentially predict success in future sessions. We examine the efficacy of constancy (the perseverance of introducing bills over multiple sessions) and frequency (introducing multiple bills in the same session) using both the six successful attempts as well as 264 failed attempts since 1994. Comparing the common attributes of successful legalization as well as failures, perseverance cannot be ruled out as a beneficial variable, but can only be vindicated with more data in the future. There was no evidence across all the data to suggest that multiple bills contributed to success. The data also shows that ideological liberalism is currently a necessary but not sufficient condition to pass assisted suicide bills, although it is expected to become more significant as the issue settles down party lines. Increases in the number of bills, the number states with bills, and perhaps the number of bills introduced in a given state appears more as a response to environmental stimuli that makes bills appear more viable: namely previous state victories and shifts in public opinion.

Time-Varying Gene Network Analysis of Human Prefrontal Cortex Development.

The prefrontal cortex (PFC) constitutes a large part of the human central nervous system and is essential for the normal social affection and executive function of humans and other primates. Despite ongoing research in this region, the development of interactions between PFC genes over the lifespan is still unknown. To investigate the conversion of PFC gene interaction networks and further identify hub genes, we obtained time-series gene expression data of human PFC tissues from the Gene Expression Omnibus (GEO) database. A statistical model, loggle, was used to construct time-varying networks and several common network attributes were used to explore the development of PFC gene networks with age. Network similarity analysis showed that the development of human PFC is divided into three stages, namely, fast development period, deceleration to stationary period, and recession period. We identified some genes related to PFC development at these different stages, including genes involved in neuronal differentiation or synapse formation, genes involved in nerve impulse transmission, and genes involved in the development of myelin around neurons. Some of these genes are consistent with findings in previous reports. At the same time, we explored the development of several known KEGG pathways in PFC and corresponding hub genes. This study clarified the development trajectory of the interaction between PFC genes, and proposed a set of candidate genes related to PFC development, which helps further study of human brain development at the genomic level supplemental to regular anatomical analyses. The analytical process used in this study, involving the loggle model, similarity analysis, and central analysis, provides a comprehensive strategy to gain novel insights into the evolution and development of brain networks in other organisms.

HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding

Vertices in the same group tend to connect densely, and usually share common attributes. Groups of different sizes reflect the relations of vertices in different ranges, and also reflect the features of different orders of the network. In this work, we propose a novel network representation learning algorithm by introducing group features of vertices of different orders to learn more discriminative network representations, named as network representation learning algorithm using Hierarchical structure embedding (HSNR). HSNR algorithm firstly constructs hierarchical relations of network structures of different orders based on greedy algorithm and modularity. In order to introduce hierarchical features into the network representation learning model, HSNR algorithm then introduces the idea of multi-relational modeling from knowledge representation, and converts the hierarchical relations into the triplet form between vertices. Finally, HSNR proposes a joint learning model embedding vertex triplets into the network representations. The experimental results show that the HSRN algorithm presented has an excellent performance in network vertex classification task on three real-world datasets.

Tracking Knowledge Structures and Proficiencies of Students With Learning Transfer

In online intelligent education systems, to offer proactive studying services to students (e.g., learning path recommendation), a crucial demand is to track students’ knowledge mastery levels over time. However, existing works ignore the impact of learning transfer on knowledge tracing and only track knowledge proficiency. Knowledge proficiency alone cannot fully reflect students’ knowledge mastery levels. A student’s knowledge structure (the similarities and differences within knowledge concepts) and abstract principle mastery level (common attributes among knowledge concepts, such as learning methods) also need to be tracked. To this end, we propose a novel multilevel Knowledge Tracing model with Learning Transfer (KTLT) to track students’ knowledge mastery levels. First, we clarify the relationships among abstract principles, knowledge structure, and knowledge proficiency by utilizing the learning transfer theory in educational psychology. Then, we associate each problem with a knowledge vector in which each element represents an explicit knowledge concept by leveraging educational priors (i.e., a Q-matrix). Correspondingly, each student is represented as a knowledge vector (knowledge proficiency) and an abstract principle vector (abstract principle mastery). Given a student’s knowledge and abstract principle vector over time, we use the learning and forgetting curve as priors to capture the student’s knowledge proficiency and abstract principle mastery level over time. Furthermore, we embed the knowledge concept by using a student’s abstract principle mastery level and obtain a personalized knowledge relevance matrix (a student’s knowledge structure) by calculating the cosine similarity among the knowledge embedding results. Finally, we design a probabilistic matrix factorization framework by combining student and problem priors for tracking a student’s knowledge mastery. Extensive experiments on two real-world datasets demonstrate both the effectiveness and explanatory ability of the KTLT.

Investigation of Factors Considered by Health Technology Assessment Agencies in Eight Countries.

BackgroundHealth technology assessment (HTA) organizations play a crucial role in optimizing healthcare resources, but the factors influencing decision making vary by country.ObjectiveHTAs of cancer and hepatitis C drugs were evaluated across developed countries to understand differences in decision processes and criteria.MethodsThe HTA organizations evaluated are from France, Germany, Italy, Spain, the United Kingdom (UK), Australia, Canada and Japan. Economic evaluation types and 28 factors in the following categories were evaluated: clinical uncertainties/issues; disease/population/treatment consideration factors including National Institute for Health and Care Excellence’s (NICE) special circumstances factors (e.g. end-of-life and innovation); and International Society for Pharmacoeconomics and Outcomes Research (ISPOR) additional value elements. Qualitative and correspondence analyses were conducted to assess the differences across organizations.ResultsIncremental cost-effectiveness ratio (ICER) using quality-adjusted life-year (QALY) was evaluated in Canada, the UK, Australia and Japan. The highest observed clinical uncertainties were clinical benefits and comparator. For cancer drugs, correspondence analysis showed France, Australia, Canada and the UK to have common attributes observed, such as unmet needs and stakeholder persuasion. In addition, the UK reported end-of-life, issues around current treatment and innovation, whereas Germany reported manageable/insignificant adverse events more frequently. Finally, fear of contagion, equity and scientific spillover value elements were only observed in Australia.ConclusionAlthough clinical factors play a predominant role in the decision to reimburse medicine, HTA organizations consider additional aspects as well. If the methodology of HTA was clearly outlined, there would be more transparency in HTA systems leading to better understanding amongst stakeholders about decision making.Electronic supplementary materialThe online version of this article (10.1007/s41669-020-00235-6) contains supplementary material, which is available to authorized users.

Location Proof Systems for Smart Internet of Things: Requirements, Taxonomy, and Comparative Analysis

In the current hyper-connected, data-driven era, smart devices are providing access to geolocation information, enabling a paradigm shift in diverse domains. Location proof systems utilize smart devices to provide witnessed proof of location to enable secure location-based services (LBS). Applications of location proof systems include safety, asset management and operations monitoring in health care, supply chain tracking, and Internet-of-Things (IoT)-based location intelligence in businesses. In this paper, we investigate the state of the art in location proof systems, examining design challenges and implementation considerations for application in the real world. To frame the analysis, we have developed a taxonomy of location proof systems and performed a comparative analysis over the common attributes, highlighting their strength and weaknesses. Furthermore, we have identified future trends for this increasingly important area of investigation and development.

Reference dataset for rate of penetration benchmarking

In recent years, there were multiple papers published related to rate of penetration prediction using machine learning vastly outperforming analytical methods. There are models proposed reportedly achieving R2 values as high as 0.996. Unfortunately, it is most often impossible to independently verify these claims as the input data is rarely accessible to others. To solve this problem, this paper presents a database derived from Equinor's public Volve dataset that will serve as a benchmark for rate of penetration prediction methods. By providing a partially processed dataset with unambiguous testing scenarios, scientists can perform machine learning research on a level playing field. This in turn will both discourage publication of methods tested in a substandard manner as well as promote exploration of truly superior solutions. A set of seven wells with nearly 200–000 samples and twelve common attributes is proposed together with reference results from common machine learning algorithms. Data and relevant source code are published on the pages of University of Stavanger and GitHub.

From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world

Soils represent the largest terrestrial reservoir of organic carbon, and the balance between soil organic carbon (SOC) formation and loss will drive powerful carbon-climate feedbacks over the coming century. To date, efforts to predict SOC dynamics have rested on pool-based models, which assume classes of SOC with internally homogenous physicochemical properties. However, emerging evidence suggests that soil carbon turnover is not dominantly controlled by the chemistry of carbon inputs, but rather by restrictions on microbial access to organic matter in the spatially heterogeneous soil environment. The dynamic processes that control the physicochemical protection of carbon translate poorly to pool-based SOC models; as a result, we are challenged to mechanistically predict how environmental change will impact movement of carbon between soils and the atmosphere. Here, we propose a novel conceptual framework to explore controls on belowground carbon cycling: Probabilistic Representation of Organic Matter Interactions within the Soil Environment (PROMISE). In contrast to traditional model frameworks, PROMISE does not attempt to define carbon pools united by common thermodynamic or functional attributes. Rather, the PROMISE concept considers how SOC cycling rates are governed by the stochastic processes that influence the proximity between microbial decomposers and organic matter, with emphasis on their physical location in the soil matrix. We illustrate the applications of this framework with a new biogeochemical simulation model that traces the fate of individual carbon atoms as they interact with their environment, undergoing biochemical transformations and moving through the soil pore space. We also discuss how the PROMISE framework reshapes dialogue around issues related to SOC management in a changing world. We intend the PROMISE framework to spur the development of new hypotheses, analytical tools, and model structures across disciplines that will illuminate mechanistic controls on the flow of carbon between plant, soil, and atmospheric pools.

Organisational Innovation Systems for multi-actor co-innovation in European agriculture, forestry and related sectors: Diversity and common attributes

Innovation rests not only on discovery but also on cooperation and interactive learning. In agriculture, forestry and related sectors, multi-actor partnerships for ‘co-innovation’ occur in many forms, from international projects to informal ‘actor configurations’. Common attributes are that they include actors with ‘complementary forms of knowledge’ who collaborate in an innovation process, engage with a ‘larger periphery’ of stakeholders in the Agricultural Knowledge and Innovation System (AKIS) and are shaped by institutions. Using desk research and interviews, we reviewed, according to the Organisational Innovation Systems framework, the performance of 200 co-innovation partnerships from across Europe, selected for their involvement of various actors ‘all along the process’. Many of the reviewed partnerships were composed of actors that had previously worked together and most interviewees believed that no relevant actors had been excluded. In almost all cases, project targets and objectives were co-designed to a great or some extent, and the mechanisms applied to foster knowledge sharing between partners were considered to be very effective. Great importance was attached to communication beyond the partnership, not simply for dissemination but also for dialogue, and most interviewees evaluated the communication/outreach performance of their partnership very highly. Most partnerships received external funding, most did not use innovation brokers during the proposal writing process and two thirds had access to information they needed. We discuss the implications of these findings and question whether the AKIS concept as currently interpreted by many policy makers can adequately account for the regional differences encountered by co-innovation partnerships across Europe.

Straw Type and Nitrogen Fertilization Influence Winter Common Bean Yield and Quality

The common bean grown under straw of intercropped plants improves the cropping environment in no-tillage systems. In addition, nutrient supply from straw could reduce the use of topdressing nitrogen in common bean. Thus, the objective of this work was to evaluate under no-tillage system the effect of straw type and topdressing nitrogen doses in the irrigated common bean productive and qualitative attributes. The experimental design was in randomized blocks arranged in split-plots with four replications. The plots were composed by three straw types from different cropping systems, represented by sole maize, maize-intercropped with brachiaria and maize-intercropped with crotalaria. The subplots consisted of five topdressing N doses applied on common bean 0, 50, 100, 150 and 200 kg ha−1 of nitrogen via urea (coated with polymers). The common bean grown under the straw of maize intercropped with crotalaria showed higher number of pods per plant, grain yield and lower time for maximum grain hydration when compared to those plants grown under the straw of sole maize. Moreover, the crude protein content in the common bean grains was greater when plants were grown under the straw of maize intercropped with crotalaria than the straw of maize intercropped with brachiaria. The agronomic efficiency of common bean was higher when the plants were grown under the straw of maize intercropped with crotalaria in the lowest nitrogen fertilization rate tested (50 kg ha−1). Leaf nitrogen content, grain yield, sieve yield and crude protein content in the grain increased with increasing nitrogen availability via topdressing. Therefore, the results suggest that agronomic and qualitative characteristics of common bean are benefited when cultivated on the maize straw combined with crotalaria even reducing up to 50% the requirement of nitrogen fertilization.

Polygyny and Intimate Partner Violence (IPV) Among Ethiopian Women

Polygyny is marriage between a man and multiple women and is mostly practiced in sub-Saharan African countries. Some of the common attributes of this family structure include perennial conflicts and marital discord, which can expose women to intimate partner violence (IPV). Nonetheless, there is limited research on the intersection between such family structures and IPV in the Ethiopian context. Thus, we examined data from ever-married women (n = 4469) between 15 and 49 years of age collected in the 2016 Ethiopia Demographic and Health Survey (EDHS-2016). Overall, the proportion of polygynous marriages among ever-married Ethiopian women was 10.4% (confidence interval (CI): 8.7–12.4). The prevalences of emotional, physical, and sexual abuse among ever-married women were 24%, 23.1%, and 10.1%, respectively. Women who reported being in polygynous marriages were 1.82 times more likely to report emotional abuse by their husbands (odds ratio (OR) = 1.82; CI: 1.256–2.628). However, the type of marriage had no significant association with physical and sexual violence among ever-married women. The results suggest that polygynous family structures may expose women to IPV. Thus, health and social service providers should be aware of the vulnerability to psychological distress of women in such unions.

COVID-19 Vaccines A Global Common Good

COVID-19 Vaccines A Global Common Good

How are Professional Programs from Diverse Disciplines Approaching the Development and Assessment of Competence at a Mid-Sized Canadian University?

Time-honoured university policies, such as the credit-hour and academic freedom, present challenges for professional education programs tasked with operationalizing entry-to-practice competence frameworks for professional accreditation. A single embedded case study was used to explore how professional programs from one mid-sized Canadian university are approaching and perhaps problematizing the development and assessment of competence. Semi-structured interviews were conducted with educational leaders (faculty and staff, n=21) from a sample of nine programs. Following a grounded theory approach to qualitative analysis, the constant comparative method was used to inductively discern similarities and differences across programs, and to begin building theory about approaches to operationalization. While limited in scope given the use of a single university, our findings highlight: (a) diversity in approaches to operationalization across programs, (b) common attributes which can be used to classify the manner in which these programs operationalize competence, and (c) challenges with supporting faculty to buy in to competency-informed pedagogy and assessment. Given these findings, it is recommended that professional accrediting bodies and education programs spend time to consider the role university-based programs play in determining competence for entry-to-practice, as well their intents for implementing a competence framework, to ensure sufficiency in the approaches being used.

Supporter Profiling in Recycled Water Reuse: Evidence from Meta-Analysis

Recycled water is considered as a viable alternative water source, and its use is of great significance in dealing with the shortage of water resources. However, it is often rejected by the public during its promotion. To identity the common social attributes of proponents of recycled water, it is essential to study the profiled customers’ willingness to accept recycled water during the most difficult initial stage of promoting recycled water use projects. A meta-analysis was conducted in this study to deal with data concerning the influence of different social demographic factors on the public’s acceptance of recycled water use. Three steps of meta-analysis were mainly used in this paper. First, a random-effect model was used to measure the effect size of influencing factors, such as age, gender, and education. It was found that younger women were more likely to accept recycled water use than older men, and individuals with higher education were more willing to accept recycled water. Then, a cumulative meta-analysis showed that it was feasible to select young women with higher education as target profile customers in the initial stage of recycled water promotion. Finally, according to a meta-regression analysis, it was revealed that different research areas and selected model methods have important regulatory effects on the intention of the target population to use recycled water.

A Comprehensive Scientific Survey of Excipients Used in Currently Marketed, Therapeutic Biological Drug Products

PurposeThe steady development of biotechnology-derived therapeutic biologics over the last few decades has generated drugs that are now standard medical treatments for a range of indications. While the development of protein products has surged in recent years, the formulation and delivery of these complex molecules have relied on drug-specific studies and, in some instances, data from non-proteinaceous drug products. The commonalities, trends, and gaps in excipient technologies used to support the development of therapeutic proteins largely remain unexplored due to the drug-specific nature of many formulations.MethodsUsing a comprehensive and relational database approach, we aimed to provide a scientific survey of all approved or licensed biotechnology-derived drug products with the goal of providing evidence-based information on common attributes and trending features in protein product excipients. We examined 665 formulations, and 395 unique formulations based on having unique excipients within them, that supported 211 therapeutic proteins as of June 2020.ResultsWe report the prevalence of each excipient class and excipient chemical used in eight different drug types including monoclonal antibodies, antibody conjugates, cytokines and growth factors, enzymes, polypeptide hormones, pulmonary surfactants, recombinant fusion proteins, and toxins. We also report the prevalence by excipient type among all therapeutic proteins, in the context of each drug’s recommended pH range, concentration ranges for excipients, and route of administration.ConclusionsThe results of our analyses indicate certain excipients common to monoclonal antibodies, cytokines, and polypeptide hormones. We also report on excipients unique to protein drug products, such as amino acids, solubilizers, and lyoprotectants. Overall, our report summarizes the current landscape of excipients used in marketed biotechnology-derived therapeutic biologic products.