bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social network analysis reveals specialized trade in an Endangered songbird
Authors: Sánchez‐Mercado A. , Cardozo‐Urdaneta A. , Moran L. , Ovalle L. , Arvelo M. Á. , Morales‐Campos J. , Coyle B. , Braun M. J. , Rodríguez‐Clark K. M. 
Published: August 2019

AbstractUnsustainable harvest is one of the most important threats to biodiversity, and birds are highly impacted, but avian markets remain poorly understood. When species value and corruption/criminality are high, the “parallel trafficking” hypothesis predicts that illegal animal items will move through networks used for other illicit products. Alternatively, when particular demands, logistical skills or access limits trade, “specialized trafficking” hypotheses predict that few, expert actors will control markets. Here, we use social network analysis of trade in an Endangered songbird, the Red Siskin Spinus cucullatus, originating in Venezuela, to examine the generality of the parallel trafficking hypothesis in a setting where corruption/criminality and species value are high. In spite of these circumstances, of 2575 Red Siskin (RS) records compiled from 2010 to 2017, we found just six reports consistent with parallel trafficking. Instead, we discovered an independent network of 15 actor types, and a trade structure consistent with specialized trafficking. Just two intermediary types (national vendors to intermediaries and to consumers) and one consumer type (national breeders) had the highest exposure to the flow of birds, and the most trade connectivity. Use of wild‐caught over captive‐bred birds was high (67% of records), as was use of natural‐phenotype birds over hybrid or mutant‐phenotype birds (65% of records). Geographically, Spain and Venezuela had the highest exposure to the flow of birds, but Brazil and Colombia had the most direct connections with other countries. The unexpected lack of evidence for parallel trafficking suggests that combined flows of illicit products are not inevitable, even in adverse settings. In a context where law enforcement may not be feasible, our results suggest that it may be possible to reduce unsustainable harvest using breeder connectivity in informational campaigns to stimulate peer‐to‐peer interactions and accelerate behavior change.
Heavy cannabis use, dependence and the brain: a clinical perspective
Authors: Kroon Emese , Kuhns Lauren , Hoch Eva , Cousijn Janna 
Published: August 2019

AbstractAimsTo summarize and evaluate our knowledge of the relationship between heavy cannabis use, cannabis use disorder (CUD) and the brain.MethodsNarrative review of relevant literature identified through existing systematic reviews, meta‐analyses and a PubMed search. Epidemiology, clinical representations, potential causal mechanisms, assessments, treatment and prognosis are discussed.ResultsAlthough causality is unclear, heavy and dependent cannabis use is consistently associated with a high prevalence of comorbid psychiatric disorders and learning and memory impairments that seem to recover after a period of abstinence. Evidence regarding other cognitive domains and neurological consequences, including cerebrovascular events, is limited and inconsistent. Abstinence after treatment is only achieved in a minority of cases; treatment targeted at reduction in use appears have some success. Potential moderators of the impact of CUD on the brain include age of onset, heaviness of use, CUD severity, the ratio of ∆9‐tetrahydrocannabinol to cannabidiol and severity of comorbid disorders.ConclusionsCurrent evidence of long‐term effects of daily cannabis use and cannabis use disorder on brain‐related outcomes is suggestive rather than conclusive, but use is associated with psychiatric morbidity and with cognitive impairments that recover after a period of abstinence.
Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI
Authors: Shao Junneng , Dai Zhongpeng , Zhu Rongxin , Wang Xinyi , Tao Shiwan , Bi Kun , Tian Shui , Wang Huan , Sun Yurong , Yao Zhijian , Lu Qing 
Published: August 2019

AbstractObjectiveMisdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD.MethodThe module allegiance from resting‐fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD.ResultsBased on our classification model, difference mainly reflected in default‐mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well‐overlapped, except for cognitive control network (CCN).ConclusionThe functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.
Explanatory learner models: Why machine learning (alone) is not the answer
Authors: Rosé Carolyn P. , McLaughlin Elizabeth A. , Liu Ran , Koedinger Kenneth R. 
Published: August 2019

AbstractUsing data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving tens of thousands of parameters or more. Though strides towards interpretation of complex models are being made in core machine learning communities, it remains true in these cases of “black box” modeling that research teams may have little possibility to peer inside to try understand how, why, or even whether such models will work when applied beyond the data on which they were built. Rather than relying on AI expertise alone, we suggest that learning engineering teams bring interdisciplinary expertise to bear to develop explanatory learner models that provide interpretable and actionable insights in addition to accurate prediction. We describe examples that illustrate use of different kinds of data (eg, click stream and discourse data) in different course content (eg, math and writing) and toward different goals (eg, improving student models and generating actionable feedback). We recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning.
Practitioner NotesWhat is already known about this topic

Researchers in learning analytics and educational data mining have been successful in creating innovative models of data that optimize prediction.
Some of these models produce scientific or practical insights and fewer have been put into use and demonstrated to enhance student learning.
What this paper adds

We provide examples of development of explanatory models of learners that not only accurately predict data but also provide scientific insights and yield practical outcomes.
In particular, researchers with expertise in cognitive science and math education content use AI‐based data analytics to discover previously unrecognized barriers to geometry student learning. They use model‐derived insights to redesign an online tutoring system and “close‐the‐loop” by experimentally demonstrating that the new system produces better student learning than the original.
Implications for practice and/or policy

We define explanatory learning models and provide an articulation of a process for generating them that involves interdisciplinary teams employing human–computer interaction and learning engineering methods.
Based on our experiences, we recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning.
Practical ethics for building learning analytics
Authors: Kitto Kirsty , Knight Simon 
Published: August 2019

AbstractArtificial intelligence and data analysis (AIDA) are increasingly entering the field of education. Within this context, the subfield of learning analytics (LA) has, since its inception, had a strong emphasis upon ethics, with numerous checklists and frameworks proposed to ensure that student privacy is respected and potential harms avoided. Here, we draw attention to some of the assumptions that underlie previous work in ethics for LA, which we frame as three tensions. These assumptions have the potential of leading to both the overcautious underuse of AIDA as administrators seek to avoid risk, or the unbridled misuse of AIDA as practitioners fail to adhere to frameworks that provide them with little guidance upon the problems that they face in building LA for institutional adoption. We use three edge cases to draw attention to these tensions, highlighting places where existing ethical frameworks fail to inform those building LA solutions. We propose a pilot open database that lists edge cases faced by LA system builders as a method for guiding ethicists working in the field towards places where support is needed to inform their practice. This would provide a middle space where technical builders of systems could more deeply interface with those concerned with policy, law and ethics and so work towards building LA that encourages human flourishing across a lifetime of learning.
Practitioner NotesWhat is already known about this topic

Applied ethics has a number of well‐established theoretical groundings that we can use to frame the actions of ethical agents, including, deontology, consequentialism and virtue ethics.
Learning analytics has developed a number of checklists, frameworks and evaluation methodologies for supporting trusted and ethical development, but these are often not adhered to by practitioners.
Laws like the General Data Protection Regulation (GDPR) apply to fields like education, but the complexity of this field can make them difficult to apply.
What this paper adds

Evidence of tensions and gaps in existing ethical frameworks and checklists to support the ethical development and implementation of learning analytics.
A set of three edge cases that demonstrate places where existing work on the ethics of AI in education has failed to provide guidance.
A “practical ethics” conceptualisation that draws on virtue ethics to support practitioners in building learning analytics systems.
Implications for practice and/or policy

Those using AIDA in education should collect and share example edge cases to support development of practical ethics in the field.
A multiplicity of ethical approaches are likely to be useful in understanding how to develop and implement learning analytics ethically in practical contexts.
Tyrosine kinase inhibitor prophylaxis after transplant for Philadelphia chromosome‐positive acute lymphoblastic leukemia
Authors: Akahoshi Yu , Nishiwaki Satoshi , Mizuta Shuichi , Ohashi Kazuteru , Uchida Naoyuki , Tanaka Masatsugu , Fukuda Takahiro , Ozawa Yukiyasu , Takahashi Satoshi , Onizuka Makoto , Shiratori Souichi , Nakamae Hirohisa , Kanda Yoshinobu , Ichinohe Tatsuo , Atsuta Yoshiko , Kako Shinichi ,   the Adult Acute Lymphoblastic Leukemia Working Group of the Japan Society for Hematopoietic Cell Transplantation
Published: August 2019

AbstractTyrosine kinase inhibitor (TKI) administration after allogeneic hematopoietic stem cell transplantation (HSCT) may carry a survival benefit in Philadelphia chromosome‐positive acute lymphoblastic leukemia (Ph+ ALL). Therefore, we investigated whether TKI prophylaxis for negative‐minimal residual disease (MRD) after HSCT would improve patient outcomes in this nationwide retrospective cohort study. We included patients with Ph+ ALL who underwent their first allogeneic HSCT between 2001 and 2016, received TKI before HSCT, and achieved negative‐MRD status within 180 days after HSCT. Of 850 patients for inclusion, 50 patients received TKI prophylaxis, mostly imatinib or dasatinib (median dose: 400 mg with imatinib and 40 mg with dasatinib). In a multivariate analysis, disease status at HSCT was the sole risk factor for relapse (hazard ratio, 3.58; P < .001 for positive‐MRD with complete remission [CR] and hazard ratio, 6.13; P < .001 for active disease). TKI prophylaxis was not associated with a decreased risk of relapse or superior overall survival in either the whole cohort or in the analysis limited to negative‐MRD or positive‐MRD with CR1 at HSCT. Meanwhile, TKI prophylaxis limited to dasatinib might be associated with a decreased risk of relapse (hazard ratio, 0.34; P = .140), unlike imatinib. Alternative strategies using new‐generation TKI for high‐risk patients are warranted to improve the outcomes after allogeneic HSCT.
Association of ghrelin dynamics with beta cell function in Japanese subjects with normal glucose tolerance
Authors: Murai Norimitsu , Saito Naoko , Kodama Eriko , Iida Tatsuya , Mikura Kentaro , Imai Hideyuki , Kaji Mariko , Hashizume Mai , Kigawa Yasuyoshi , Koizumi Go , Tadokoro Rie , Sugisawa Chiho , Endo Kei , Iizaka Toru , Saiki Ryo , Otsuka Fumiko , Shun Ishibashi , Nagasaka Shoichiro 
Published: August 2019

AbstractBackgroundGhrelin is involved in feeding regulation and energy metabolism and is also known to inhibit insulin secretion (β). However, few clinical studies have demonstrated the relationship between β and ghrelin dynamics. This study tested the hypothesis that, in oral glucose tolerance tests (OGTT), ghrelin dynamics are associated with β.MethodsSubjects were 1145 healthy individuals <40 years old who tested normal on the 75‐g OGTT. The following indicators and the ghrelin suppression ratio (GSR) during OGTT were calculated: insulin sensitivity (SI) [1/homoeostasis model assessment of insulin resistance, insulin sensitivity index‐Matsuda and 1/fasting insulin (1/FIRI)]; and β [Stumvoll first‐phase index (Stumvoll‐1), Stumvoll second‐phase index and insulinogenic index]. From nine combinations of SI and β, combinations that produce hyperbolic relationships were identified.ResultsStumvoll‐1 and 1/FIRI showed a hyperbolic relationship in nonobese subjects, and the product of Stumvoll‐1 and 1/FIRI was used as the disposition index (DI). When analyzed by BMI quartiles, post‐loading glucose and insulin levels at each time point increased from Q1 (low BMI) through Q4 (high BMI), whereas the DI, ghrelin levels at each time point, and GSR decreased from Q1 to Q4. On multivariate and bivariate analysis, GSR and DI were positive and independent, and fasting ghrelin and FIRI were negatively and independently correlated.ConclusionsGhrelin dynamics were associated with beta cell function in subjects with normal glucose tolerance. Glucose intolerance in obesity may be due not only to insulin resistance but also to impaired beta cell function associated with abnormalities of ghrelin dynamics.
Chronic stress, depression and personality type in patients with myasthenia gravis
Authors: Bogdan A. , Barnett C. , Ali A. , AlQwaifly M. , Abraham A. , Mannan S. , Ng E. , Bril V. 
Published: August 2019

Background and purposeStress is a known risk factor for the onset and modulation of disease activity in autoimmune disorders. The aim of this cross‐sectional study was to determine any associations between myasthenia gravis (MG) severity and chronic stress, depression and personality type.MethodsIn all, 179 consecutive adult patients with confirmed MG attending the Neuromuscular Clinic between March 2017 and December 2017 were included. At baseline, patients were assessed clinically and they completed self‐administered scales for disease severity, perceived stress, depression and personality type.ResultsHigher disease severity [Myasthenia Gravis Impairment Index (MGII)] showed a moderate correlation with depression score (Beck's Depression Inventory, Second Edition, r = 0.52, P < 0.001) and a lower correlation with chronic stress (Trier Inventory for Assessment of Chronic Stress, r = 0.28, P = 0.001). Chronic stress scores were different according to personality types (anova, P = 0.02). The linear regression model with MGII score as the dependent variable showed R2 = 0.34, likelihood ratio chi‐squared 74.55, with P < 0.0001. The only variables that predicted disease severity were depression scores (P < 0.0001) and female sex (P = 0.003).ConclusionsA significant association of MG severity with depression and chronic stress was found, as well as with female gender. These findings should raise awareness that the long‐term management of MG should address depression and potential stress and consider behavioural management to prevent stress‐related immune imbalance.
Genomic biosurveillance of forest invasive alien enemies: A story written in code
Authors: Hamelin Richard C. , Roe Amanda D. 
Published: August 2019

AbstractThe world's forests face unprecedented threats from invasive insects and pathogens that can cause large irreversible damage to the ecosystems. This threatens the world's capacity to provide long‐term fiber supply and ecosystem services that range from carbon storage, nutrient cycling, and water and air purification, to soil preservation and maintenance of wildlife habitat. Reducing the threat of forest invasive alien species requires vigilant biosurveillance, the process of gathering, integrating, interpreting, and communicating essential information about pest and pathogen threats to achieve early detection and warning and to enable better decision‐making. This process is challenging due to the diversity of invasive pests and pathogens that need to be identified, the diverse pathways of introduction, and the difficulty in assessing the risk of establishment. Genomics can provide powerful new solutions to biosurveillance. The process of invasion is a story written in four chapters: transport, introduction, establishment, and spread. The series of processes that lead to a successful invasion can leave behind a DNA signature that tells the story of an invasion. This signature can help us understand the dynamic, multistep process of invasion and inform management of current and future introductions. This review describes current and future application of genomic tools and pipelines that will provide accurate identification of pests and pathogens, assign outbreak or survey samples to putative sources to identify pathways of spread, and assess risk based on traits that impact the outbreak outcome.
Climate variability impact on Bali sardine fishery: Ecology and fisheries perspective
Authors: Puspasari Reny , Rachmawati Puput Fitri , Muawanah Umi 
Published: August 2019

AbstractThe sardine fishery in Bali is influenced by fishing effort and environmental conditions including temperature and chlorophyll a (chl‐a). Bali's sardine, Sardinella lemuru Bleeker, production decreased significantly during the extreme conditions that occurred in 2010 and 2016. This study assesses the impact of extreme conditions on the sardine fishery of the Bali Strait. Fisheries data were collected from two landing places: Muncar fishing port (Banyuwangi District, East Java Province) and Pengambengan fishing port (Jembrana District, Bali Province) between January 2007 and December 2017. Temperature and chl‐a data were downloaded from satellite readings. Fishing locations were observed by an onboard observer to investigate shifts in fishing grounds. A modified Cobb–Douglas regression model and profile analyses were used to estimate the impact of environmental variables on sardine production and to assess how extreme periods affect the catch composition in the Bali Strait. Seawater temperature and chl‐a concentration had significant impacts on sardine production, but temperature is likely to be less correlated with sardine production than chl‐a concentration. To adapt to extreme weather, purse seiners prefer to modify the vessel type rather than change their fishing ground.
Fifteen important questions in the spatial ecology of diatoms
Authors: Soininen Janne , Teittinen Anette 
Published: August 2019

Abstract

Recently, studies about the spatial distribution of microbial species and communities have increased substantially. Freshwater diatoms are used as one of the most popular model taxa in such macroecological studies, but the most exciting patterns in the spatial ecology of diatoms have not been reviewed. Here, we identify 15 important questions about freshwater diatom ecology, review the major findings, and suggest novel research avenues.

We found that diatoms do not support classical latitudinal diversity gradient, species–area relationship or Rapoport's rule. We found some, albeit inconsistent support for an elevational diversity gradient, Bergmann's rule and niche conservation. Diatoms do support a positive interspecific abundance–occupancy relationship like nearly all other taxa. With regards to metacommunity dynamics, diatom communities are typically structured by species sorting, but neutral dynamics or mass effects are also evident at large or small spatial scale, respectively.

Taken together, this review suggests that diatoms follow some classical biogeographical patterns similarly to larger taxa, but patterns are typically weaker than for macro‐organisms. This may result because diatoms are typically relatively strongly controlled by local factors and often exhibit efficient stochastic dispersal processes both at small and large scales. More research is needed to build a more comprehensive view of diatom species–area relationships and latitudinal gradients in diversity, for example. In the future, well‐replicated macroecological field experiments, diatom studies with intercontinental or global coverage, trait‐based approaches, and DNA‐based species identification would be fruitful avenues to shed more light into the spatial ecology of diatoms.