bluuebunny/crossref_metadata_2025_split huggingface dataset:
Who benefits from university–industry collaboration for environmental sustainability?
Authors: Di Maria Eleonora , De Marchi Valentina , Spraul Katharina 
Published: August 2019

Purpose
This paper aims to analyze the characteristics and performance of university–industry (U-I) collaboration for knowledge transfer in relation to environmental sustainability, considering for both parties of the collaborations.

Design/methodology/approach
The study is explorative in nature, based on an original data set of more than 350 U-I research and consultancy contracts signed by more than 70 professors specializing in environmental sustainability-related academic disciplines at the University of Padova (Italy) for the period 2008-2012. A mixed-method approach is adopted. Social network analysis and regressions are used to explore the impact of U-I on performance considering for characteristics of the firms, the professors and the collaboration. Interviews with key informants at University of Padova is used to complement and validate the emerging evidence.

Findings
Results suggest that U-I positively impacts the performance of firms, but not of professors. Indeed, the hypothesis that professors’ performance (measures in terms of academic publications) is positively associated with academic engagement is not supported. On the contrary, firms’ financial performance is positively associated with U-I collaboration focused on knowledge transfer for environmental innovation; the higher the contracts activated the better the economic performance.

Originality/value
While most previous research has focused either on the university or the firm side of U-I, this study looks at both sides and focuses specifically on engagement in green contracts. The analysis of the geographical scope of U-I collaborations contributes to the growing body of literature by outlining geography’s role in U-I collaborations related to sustainability.
How mindfulness training cultivates introspection and competence development for sustainable consumption
Authors: Frank Pascal , Sundermann Anna , Fischer Daniel 
Published: August 2019

Purpose
This paper aims to explore the relationship between introspection and key competencies for sustainable consumption (KCSCs). It investigates whether mindfulness training can cultivate the ability to introspect and stimulate the development of KCSCs.

Design/methodology/approach
Two independent studies were analyzed. Data were retrieved from interviews with participants of a consumer-focused mindfulness training (Study 1, 11 participants), as well as from diaries of students attending a university seminar with mindfulness training (Study 2, 13 students), and made subject to qualitative content analysis.

Findings
Both studies show a clear intersection between both constructs and suggest that mindfulness training can contribute to the development of KCSCs and learners’ ability to introspect. The studies also demonstrated that introspection is not equally related to all competencies and that KCSCs must not be reduced to introspection.

Research limitations/implications
Both KCSCs and introspection are complex and latent constructs and hence challenging to observe. The research understands itself as a first exploratory approach for empirically investigating this complex relation.

Originality/value
While increasing (self-)reflectivity is at the core of competence-based education, a systematic engagement with the practice of introspection as a means to enhancing reflectivity is surprisingly lacking. Mindfulness training could be a promising way to cultivate introspective abilities and thus facilitate learning processes that are conducive to competence development.
Building a typology of the 100 smart cities in India
Authors: Praharaj Sarbeswar , Han Hoon 
Published: August 2019

Purpose
The Smart Cities Mission (SCM) in India is generating significant interest among researchers and policymakers globally. Cities under the SCM, irrespective of their locations, size, capacities or local needs, are heavily investing in technological solutions to improve civic conditions. The purpose of this paper is to build a typology and urban classification system of these 100 smart cities using a series of key performance indicators (KPIs) around urban development and access to public services. The paper also systematically recognises the diversity of challenges facing these cities and assess whether a generic technology-based approach is adequate to address them.

Design/methodology/approach
A two-stage statistical process is employed in this typology building exercise – first, a cluster analysis is conducted to classify the selected cities, then a multiple discriminant analysis is used to characterise each classified city.

Findings
The urban typology analysis finds that vast disparities remain across India’s urban centres, located in different geographical regions, in terms of access to social capital and physical infrastructure. The KPIs around education, health and social services emerged from the analysis as the most significant drivers in the urban typology building process. The lack of basic community infrastructure, especially in the small-to-medium-sized cities in India, exposes the shortcomings of a one-size-fits-all technocratic smart city development strategy that assumes foundational infrastructure is already in place for technology to take effect.

Originality/value
The research methodologies developed in this paper offers a novel planning approach for smart city policymakers to devise place-based smart city interventions, acknowledging diverse cultures and specific community needs.
Family of hereditary fibrosing poikiloderma with tendon contractures, myopathy and pulmonary fibrosis caused by a novel FAM111B mutation
Authors: Zhang Zhen , Zhang Jia , Chen Fuying , Zheng Luyao , Li Huaguo , Liu Ming , Li Ming , Yao Zhirong 
Published: August 2019

AbstractHereditary fibrosing poikiloderma with tendon contractures, myopathy and pulmonary fibrosis (POIKTMP) is a recently identified autosomal dominant genetic syndrome with mutations in FAM111B. Herein, we report a 14‐month‐old girl who presented with progressive poikiloderma on the face. Her 24‐year‐old mother had an identical facial poikiloderma, hyperpigmentation, mottling and Blaschko line hypopigmentation on the trunk and limbs, as well as severe tendon contractures. Next‐generation sequencing based on a targeted gene capture panel revealed a missense mutation in the FAM111B gene p.Phe416Ser (c.1247T>C). Her mother had the same mutation as the proband. Moreover, this mutation was absent in the unaffected father and maternal grandparents. Based on the clinical manifestations and genetic analysis, the proband and her mother were diagnosed with POIKTMP. Protein modeling indicated that the mutation p.Phe416Ser dramatically changed the protein structure, especially its structural stability, and affected the protein function. This is the first report of POIKTMP in a Chinese family due to a novel FAM111B mutation. Furthermore, we have reviewed the genotype–phenotype correlation, differential diagnoses and management of POIKTMP.
Ascoma genotyping and mating type analyses of mycorrhizas and soil mycelia of Tuber borchii in a truffle orchard established by mycelial inoculated plants
Authors: Leonardi Pamela , Murat Claude , Puliga Federico , Iotti Mirco , Zambonelli Alessandra 
Published: August 2019

SummaryTuber borchii (the Bianchetto truffle) is a heterothallic Ascomycete living in symbiotic association with trees and shrubs. Maternal and paternal genotype dynamics have already been studied for the black truffles Tuber melanosporum and Tuber aestivum but not yet for T. borchii. In this study, we analysed maternal and paternal genotypes in the first truffle orchard realized with plants inoculated with five different T. borchii mycelia. Our aims were to test the persistence of the inoculated mycelia, if maternal and/or paternal genotypes correspond to inoculated mycelia and to assess the hermaphroditism of T. borchii. The mating type of each isolate as well as those of mycorrhizas, ascomata and extraradical soil mycelia was determined. Moreover, simple sequence repeat (SSR) profiles of maternal and paternal genotypes were assessed in 18 fruiting bodies to investigate the sexual behaviour of this truffle. The maternal genotypes of the fruiting bodies corresponded to those of the inoculated mycelia with only two exceptions. This confirmed that the inoculated mycelia persisted 9 years after plantation. As regards paternal partner, only two had the same genotype as those of the inoculated mycelia, suggesting hermaphroditism. Most of the new paternal genotypes originated from a recombination of those of inoculated mycelia.
Reproducibility of Artificial Cut on Heel Area of Rubber Outsole
Authors: Liu Luoxi , Wu Jian , Luo Yaping , Lin Sheng 
Published: August 2019

AbstractRandomly acquired characteristics (RAC) are seldom used for exclusion in footwear examinations because they can disappear owing to wear. To help examiners explain discrepancies in RAC, this study investigated the reproducibility of cuts. One cut was made on the heel area of each shoe outsole and then measured. Changes in cuts were statistically evaluated, correlations between their variations and the weight/height of the subject were assessed, and the Support Vector Machine was used for the first time to study their discrimination probability. Most cuts became larger at first and then became smaller. The solidities of most cuts increased after 21 days, and the variation in the cut and the subject's weight/height were negatively correlated. Although the discrimination probability declined as the cut aged, 77% of the same‐source and 96.88% of the different‐source cuts could be identified correctly after 6 months, indicating that cuts on the heel area are relatively reliable.
Is the global prevalence rate of adult mental illness increasing? Systematic review and meta‐analysis
Authors: Richter D. , Wall A. , Bruen A. , Whittington R. 
Published: August 2019

ObjectivesThe question whether mental illness prevalence rates are increasing is a controversially debated topic. Epidemiological articles and review publications that look into this research issue are often compromised by methodological problems. The present study aimed at using a meta‐analysis technique that is usually applied for the analysis of intervention studies to achieve more transparency and statistical precision.MethodsWe searched PubMed, PsycINFO, CINAHL, Google Scholar and reference lists for repeated cross‐sectional population studies on prevalence rates of adult mental illness based on ICD‐ or DSM‐based diagnoses, symptom scales and distress scales that used the same methodological approach at least twice in the same geographical region. The study is registered with PROSPERO (CRD42018090959).ResultsWe included 44 samples from 42 publications, representing 1 035 697 primary observations for the first time point and 783 897 primary observations for the second and last time point. Studies were conducted between 1978 and 2015. Controlling for a hierarchical data structure, we found an overall global prevalence increase in odds ratio of 1.179 (95%‐CI: 1.065–1.305). A multivariate meta‐regression suggested relevant associations with methodological characteristics of included studies.ConclusionsWe conclude that the prevalence increase in adult mental illness is small, and we assume that this increase is mainly related to demographic changes.
Modulation of the functions of myeloid‐derived suppressor cells : a new strategy of hydrogen sulfide anti‐cancer effects
Authors: De Cicco Paola , Ercolano Giuseppe , Rubino Valentina , Terrazzano Giuseppe , Ruggiero Giuseppina , Cirino Giuseppe , Ianaro Angela 
Published: August 2019

Background and PurposeMyeloid‐derived suppressor cells (MDSCs) represent a major obstacle to cancer treatment, as they negatively regulate anti‐tumour immunity through the suppression of tumour‐specific T lymphocytes. Thus, the efficacy of immunotherapies may be improved by targeting MDSCs. In this study, we assessed the ability of hydrogen sulfide (H2S), a gasotransmitter whose anti‐cancer effects are well known, to inhibit the accumulation and immunosuppressive functions of MDSCs in melanoma.Experimental ApproachEffects of H2S on the host immune response to cancer were evaluated using an in vivo syngeneic model of murine melanoma. B16F10‐melanoma‐bearing mice were treated with the H2S donor, diallyl trisulfide (DATS) and analysed for content of MDSCs, dendritic cells (DCs) and T cells. Effects of H2S on expression of immunosuppressive genes in MDSCs and on T cell proliferation were evaluated.Key ResultsIn melanoma‐bearing mice, DATS inhibited tumour growth, and this effect was associated with a reduction in the frequency of MDSCs in the spleen, in the blood as well as in the tumour micro‐environment. In addition, we found that CD8+ T cells and DCs were increased. Furthermore, DATS reduced the immuno‐suppressive activity of MDSCs, restoring T cell proliferation.Conclusions and ImplicationsThe H2S donor compound, DATS, inhibited the expansion and the suppressive functions of MDSCs, suggesting a novel role for H2S as a modulator of MDSCs in cancer. Therefore, H2S donors may provide a novel approach for enhancing the efficacy of melanoma immunotherapy.Linked ArticlesThis article is part of a themed section on Hydrogen Sulfide in Biology & Medicine. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v177.4/issuetoc
Identification of potential drug–targets by combining evolutionary information extracted from frequency profiles and molecular topological structures
Authors: Wang Lei , You Zhu‐Hong , Li Li‐Ping , Yan Xin , Zhang Wei , Song Ke‐Jian , Song Chuan‐Dong 
Published: August 2019

AbstractIdentifying interactions among drug compounds and target proteins is the basis of drug research and plays a crucial role in drug discovery. However, determining drug–target interactions (DTIs) and potential protein–compound interactions by biological experiment‐based method alone is a very complicated, expensive, and time‐consuming process. Hence, there is an intense motivation to design in silico prediction methods to overcome these obstacles. In this work, we designed a novel in silico strategy to predict proteome‐scale DTIs based on the assumption that DTI pairs can be expressed through the evolutionary information derived from frequency profiles and drugs’ structural properties. To achieve this, drug molecules are encoded into the substructure fingerprints to represent certain fragments; target proteins are first converted into position‐specific scoring matrix (PSSM) and then encoded as 2‐dimensional principal component analysis (2DPCA) descriptors. In the prediction phase, the feature weighted rotation forest (RF) classifier is used to estimate whether drug and target interact with each other on four benchmark datasets,includingEnzymes, Ion Channels, GPCRs, andNuclear Receptors. The prediction accuracy of cross‐validation on the four datasets is 95.40%, 88.82%, 85.67%, and 82.22%, respectively. In order to have a clearer assessment of the proposed approach, we compared it with the discrete cosine transform (DCT) descriptor model, support vector machine (SVM) classifier model, and existing excellent approaches, including DBSI, NetCBP, KBMF2K, SIMCOMP, and RFDT. The excellent results of the experiment indicated that the proposed approach can effectively improve the DTI prediction accuracy and can be used as a practical tool for the research and design of new drugs.
Prediction of hERG K+ channel blockage using deep neural networks
Authors: Zhang Yanmin , Zhao Junnan , Wang Yuchen , Fan Yuanrong , Zhu Lu , Yang Yan , Chen Xingye , Lu Tao , Chen Yadong , Liu Haichun 
Published: August 2019

AbstractHuman ether‐a‐go‐go‐related gene (hERG) K+ channel blockage may cause severe cardiac side‐effects and has become a serious issue in safety evaluation of drug candidates. Therefore, improving the ability to avoid undesirable hERG activity in the early stage of drug discovery is of significant importance. The purpose of this study was to build predictive models of hERG activity by deep neural networks. For each combination of sampling methods and descriptors, deep neural networks with different architectures were implemented to build classification models. The optimal model M15 with three hidden layers, undersampling method, and 2D descriptors yielded the prediction accuracy of 0.78 and F1 score of 0.75 on the test set as well as accuracy of 0.77 and F1 score of 0.34 on the external validation set, outperforming the other 35 models including 9 random forest models. Particularly, the optimal model M15 achieved the highest F1 score and the second highest accuracy when compared with other five methods from four groups using different machine learning algorithms with the same external validation set. It can be believed that this model has powerful capability on prediction of hERG toxicity, which is of great benefit for developing novel drug candidates.
Sex differences in psychological distress, behavioural and emotional problems, and substance use in young people in out‐of‐home care
Authors: Cotton Sue M. , Rice Simon , Moeller‐Saxone Kristen , Magnus Anne , Harvey Carol , Mihalopoulos Cathy , Humphreys Cathy , Murray Lenice , Halperin Steve , McGorry Patrick D. , Herrman Helen 
Published: August 2019

AbstractThe aim of the study was to examine sex differences in self‐reported psychological distress, behavioural and emotional problems, and substance use in young people living in out‐of‐home care (OoHC). One hundred seventy‐six young people aged 12–17 years (females 53.4%) in OoHC in metropolitan Melbourne, Australia, were interviewed. Participants completed self‐report measures: Kessler Psychological Distress Scale, Strengths and Difficulties Questionnaire, and the World Health Organization Alcohol, Smoking, and Substance Involvement Screening Test—Youth Version. Girls had more OoHC placement instability over the past year compared with boys (p = .019). Compared with boys, the girls had significantly higher levels of distress (p < .001) (p = .007), were more likely to have self‐reported emotional symptoms (p < .001) and peer relationship problems (p = .043) and were more likely to use sedatives (p = .004). Girls had more psychological distress, behavioural disturbance, and sedative abuse; placement instability might contribute to these problems. Greater integration across OoHC, mental health, and substance use sectors is required. Girls in OoHC may benefit from interventions targeting problems with peer relationship and substance use and supporting prosocial behaviour; such targets may reduce distress and emotional symptoms and possibly prevent longer term problems.
p21 protein‐activated kinase 1 is associated with severe regressive autism, and epilepsy
Authors: Kernohan Kristin D. , McBride Arran , Hartley Taila , Rojas Samantha K. ,   Care4Rare Canada Consortium, Dyment David A. , Boycott Kym M. , Dyack Sarah 
Published: August 2019

AbstractThe p21‐activated kinase (PAK) family of proteins function as key effectors of RHO family GTPases in mammalian cells to regulate many pathways including Ras/Raf/MEK/ERK and Wnt/β‐catenin, amongst others. Here we report an individual with a novel autosomal dominant disorder characterized by severe regressive autism, intellectual disability, and epilepsy. Exome sequencing of the proband and her parents revealed a de novo variant in the PAK1 gene ([NM_001128620] c.362C>T/p.Pro121Leu). Studies in patient cells showed a clear effect on PAK1 protein function, including altered phosphorylation of targets (JNK and ERK), decreased abundance of β‐catenin, and concomitant altered expression downstream of these key regulators. Our findings add PAK1 to the list of PAK proteins and kinases which when mutated cause rare genetic diseases.
Inhibition of activator protein 1 attenuates neuroinflammation and brain injury after experimental intracerebral hemorrhage
Authors: Wei Chang‐Juan , Li Yu‐Lin , Zhu Zi‐Long , Jia Dong‐Mei , Fan Mo‐Li , Li Ting , Wang Xue‐Jiao , Li Zhi‐Guo , Ma Hong‐Shan 
Published: August 2019

AbstractAimsIntracerebral hemorrhage (ICH) is a devastating type of stroke without specific treatment. Activator protein 1 (AP‐1), as a gene regulator, initiates cytokine expression in response to environmental stimuli. In this study, we investigated the relationship between AP‐1 and neuroinflammation‐associated brain injury triggered by ICH.MethodsIntracerebral hemorrhage mice were developed by autologous blood or collagenase infusion. We measured the dynamics of AP‐1 in mouse brain tissues during neuroinflammation formation after ICH. The effects of the AP‐1 inhibitor SR11302 on brain injury and neuroinflammation as well as the underlying mechanisms were investigated in vivo and in vitro.ResultsAP‐1 was significantly upregulated in mouse brain tissue as early as 6 hours after ICH, accompanied by elevations in proinflammatory factors, including interleukin (IL)‐6, IL‐1β, and tumor necrosis factor (TNF)‐α. Inhibition of AP‐1 using SR11302 reduced neurodeficits and brain edema at day 3 after ICH. SR11302 ablated microglial IL‐6 and TNF‐α production and brain‐infiltrating leukocytes in ICH mice. In addition, SR11302 treatment diminished thrombin‐induced production of IL‐6 and TNF‐α in cultured microglia.ConclusionsInhibition of AP‐1 curbs neuroinflammation and reduces brain injury following ICH.
The Cognitive Architecture of Perceived Animacy: Intention, Attention, and Memory
Authors: Gao Tao , Baker Chris L. , Tang Ning , Xu Haokui , Tenenbaum Joshua B. 
Published: August 2019

AbstractHuman vision supports social perception by efficiently detecting agents and extracting rich information about their actions, goals, and intentions. Here, we explore the cognitive architecture of perceived animacy by constructing Bayesian models that integrate domain‐specific hypotheses of social agency with domain‐general cognitive constraints on sensory, memory, and attentional processing. Our model posits that perceived animacy combines a bottom–up, feature‐based, parallel search for goal‐directed movements with a top–down selection process for intent inference. The interaction of these architecturally distinct processes makes perceived animacy fast, flexible, and yet cognitively efficient. In the context of chasing, in which a predator (the “wolf”) pursues a prey (the “sheep”), our model addresses the computational challenge of identifying target agents among varying numbers of distractor objects, despite a quadratic increase in the number of possible interactions as more objects appear in a scene. By comparing modeling results with human psychophysics in several studies, we show that the effectiveness and efficiency of human perceived animacy can be explained by a Bayesian ideal observer model with realistic cognitive constraints. These results provide an understanding of perceived animacy at the algorithmic level—how it is achieved by cognitive mechanisms such as attention and working memory, and how it can be integrated with higher‐level reasoning about social agency.