25 years of knowledge creation processes in pharmaceutical contemporary trends

The pharmaceutical industry is knowledge and research-intensive. Due to technological, socio-political and organisational changes there has been a continuous evolution in the knowledge base utilized to achieve and maintain competitive advantages in this global industry. There is a gap in analysing the linkages and effects of those changes on knowledge creation processes associated with pharmaceutical R&D activities. Our paper looks to fill this gap. We built on an idiosyncratic research approach – the systematic literature review – and looked to unearth current trends affecting knowledge creation in international/global pharmaceutical R&D. We reviewed scientific papers published between 1980 and 2005. Key findings include promising trends in pharmaceutical innovation and human resource management, and their potential implications on current R&D practices within the pharmaceutical industry, from managerial and policy-making perspectives.

Combining Collaborative Networks and Knowledge Management: The SENAI Case

Part 19: Knowledge Management in Networks

Challenges with in vitro and in vivo experimental models of urinary bladder cancer for novel drug discovery

Urinary bladder cancer (UBC) is the second most frequent malignancy of the urinary system and the ninth most common cancer worldwide, affecting individuals over the age of 65. Several investigations have embarked on advancing knowledge of the mechanisms underlying urothelial carcinogenesis, understanding the mechanisms of antineoplastic drugs resistance and discovering new antineoplastic drugs. In vitro and in vivo models are crucial for providing additional insights into the mechanisms of urothelial carcinogenesis. With these models, various molecular pathways involved in urothelial carcinogenesis have been discovered, allowing therapeutic manipulation.

What Are Effective Program Characteristics of Self-Management Interventions in Patients With Heart Failure? : An Individual Patient Data Meta-analysis

Background To identify those characteristics of self-management interventions in patients with heart failure (HF) that are effective in influencing health-related quality of life, mortality, and hospitalizations. Methods and Results Randomized trials on self-management interventions conducted between January 1985 and June 2013 were identified and individual patient data were requested for meta-analysis. Generalized mixed effects models and Cox proportional hazard models including frailty terms were used to assess the relation between characteristics of interventions and health-related outcomes. Twenty randomized trials (5624 patients) were included. Longer intervention duration reduced mortality risk (hazard ratio 0.99, 95% confidence interval [CI] 0.97–0.999 per month increase in duration), risk of HF-related hospitalization (hazard ratio 0.98, 95% CI 0.96–0.99), and HF-related hospitalization at 6 months (risk ratio 0.96, 95% CI 0.92–0.995). Although results were not consistent across outcomes, interventions comprising standardized training of interventionists, peer contact, log keeping, or goal-setting skills appeared less effective than interventions without these characteristics. Conclusion No specific program characteristics were consistently associated with better effects of self-management interventions, but longer duration seemed to improve the effect of self-management interventions on several outcomes. Future research using factorial trial designs and process evaluations is needed to understand the working mechanism of specific program characteristics of self-management interventions in HF patients.

Knowledge-based management systems for the police force

Knowledge-Based Management Systems enable new ways to process and analyse knowledge to gain better insights to solve a problem and aid in decision making. In the police force such systems provide a solution for enhancing operations and improving client administration in terms of knowledge management. The main objectives of every police officer is to ensure the security of life and property, promote lawfulness, and avert and distinguish wrongdoing. The administration of knowledge and information is an essential part of policing, and the police ought to be proactive in directing both explicit and implicit knowledge, whilst adding to their abilities in knowledge sharing. In this paper the potential for a knowledge based system for the Mauritius police was analysed, and recommendations were also made, based on requirements captured from interviews with several long standing officers, and surveying of previous works in the area.

Occurrence and abundance models of threatened plant species: Applications to mitigate the impact of hydroelectric power dams

Species occurrence and abundance models are important tools that can be used in biodiversity conservation, and can be applied to predict or plan actions needed to mitigate the environmental impacts of hydropower dams. In this study our objectives were: (i) to model the occurrence and abundance of threatened plant species, (ii) to verify the relationship between predicted occurrence and true abundance, and (iii) to assess whether models based on abundance are more effective in predicting species occurrence than those based on presence–absence data. Individual representatives of nine species were counted within 388 randomly georeferenced plots (10 m × 50 m) around the Barra Grande hydropower dam reservoir in southern Brazil. We modelled their relationship with 15 environmental variables using both occurrence (Generalised Linear Models) and abundance data (Hurdle and Zero-Inflated models). Overall, occurrence models were more accurate than abundance models. For all species, observed abundance was significantly, although not strongly, correlated with the probability of occurrence. This correlation lost significance when zero-abundance (absence) sites were excluded from analysis, but only when this entailed a substantial drop in sample size. The same occurred when analysing relationships between abundance and probability of occurrence from previously published studies on a range of different species, suggesting that future studies could potentially use probability of occurrence as an approximate indicator of abundance when the latter is not possible to obtain. This possibility might, however, depend on life history traits of the species in question, with some traits favouring a relationship between occurrence and abundance. Reconstructing species abundance patterns from occurrence could be an important tool for conservation planning and the management of threatened species, allowing scientists to indicate the best areas for collection and reintroduction of plant germplasm or choose conservation areas most likely to maintain viable populations.

Organizational Cooperation and Knowledge Management in Research and Development Organizations

Organizational Cooperation (OC) is a current concept that responds to the growing interdependence among individuals and teams. Likewise, Knowledge Management (KM) accompanies specialization in all sectors of human activity. Most KM processes are cooperation-intensive, and the way both constructs relate to each other is relevant in understanding organizations and promoting performance. The present paper focuses on that relationship. The Organizational Cooperation Questionnaire (ORCOQ) and the Short form of the Knowledge Management Questionnaire (KMQ-SF) were applied to 639 members of research and development (R&D) organizations (Universities and Research Institutes). Descriptive, correlational, linear multiple regression and multivariate multiple regression analyses were performed. Results showed significant positive relationships between the ORCOQ and all the KMQ-SF dimensions. The prediction of KMQ-SF showed a large effect size (R2 = 62%). These findings will impact on how KM and OC are seen, and will be a step forward in the development of this field.

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

[between The Country And The Laboratory: The Dynamics Of The Production Of Knowledge At The Menopause Outpatients Clinic At Caism, Unicamp.]

This study investigates the practices involved in the production of knowledge about menopause at Caism, Unicamp, a reference center for public policies for women's health. Gynecological appointments and psychological support meetings were observed, and women and doctors were interviewed in order to identify what discourse circulates there and how different actors are brought in to ensure that the knowledge produced attains credibility and travels beyond the boundaries of the teaching hospital to become universal. The analysis is based on localized studies aligned with social studies of science and technology.

[between The Country And The Laboratory: The Dynamics Of The Production Of Knowledge At The Menopause Outpatients Clinic At Caism, Unicamp].

This study investigates the practices involved in the production of knowledge about menopause at Caism, Unicamp, a reference center for public policies for women's health. Gynecological appointments and psychological support meetings were observed, and women and doctors were interviewed in order to identify what discourse circulates there and how different actors are brought in to ensure that the knowledge produced attains credibility and travels beyond the boundaries of the teaching hospital to become universal. The analysis is based on localized studies aligned with social studies of science and technology.

Women's satisfaction with childbirth's experience in different models of care: a descriptive study

Com o objetivo de comparar a satisfação das mulheres com a experiência do parto em três modelos assistenciais, foi realizada pesquisa descritiva, com abordagem quantitativa, em dois hospitais públicos de São Paulo, um promovendo o modelo "Típico" e o outro com um centro de parto intra-hospitalar (modelo "CPNIH") e um peri-hospitalar (modelo "CPNPH"). A amostra foi constituída por 90 puérperas, 30 de cada modelo. A comparação entre os resultados referentes à satisfação das mulheres com o atendimento prestado pelos profissionais de saúde, com a qualidade da assistência e os motivos de satisfação e insatisfação, com a indicação ou recomendação dos serviços recebidos, com a sensação de segurança no processo e com as sugestões de melhorias, mostrou que o modelo CPHPH foi o melhor avaliado, vindo em seguida o CPNIH e por último o Típico. Conclui-se que o modelo peri-hospitalar de assistência ao parto deveria receber maior apoio do SUS, por se constituir em serviço em que as mulheres se mostram satisfeitas com a atenção recebida

Self-bound models of compact stars and recent mass-radius measurements

The exact composition of a specific class of compact stars, historically referred to as ""neutron stars,'' is still quite unknown. Possibilities ranging from hadronic to quark degrees of freedom, including self-bound versions of the latter, have been proposed. We specifically address the suitability of strange star models (including pairing interactions) in this work, in the light of new measurements available for four compact stars. The analysis shows that these data might be explained by such an exotic equation of state, actually selecting a small window in parameter space, but still new precise measurements and also further theoretical developments are needed to settle the subject.

Statistical models of mixtures with a biaxial nematic phase

We consider a simple Maier-Saupe statistical model with the inclusion of disorder degrees of freedom to mimic the phase diagram of a mixture of rodlike and disklike molecules. A quenched distribution of shapes leads to a phase diagram with two uniaxial and a biaxial nematic structure. A thermalized distribution, however, which is more adequate to liquid mixtures, precludes the stability of this biaxial phase. We then use a two-temperature formalism, and assume a separation of relaxation times, to show that a partial degree of annealing is already sufficient to stabilize a biaxial nematic structure.

Effect of grassland management on soil carbon sequestration in Rondonia and Mato Grosso states, Brazil

Grassland management affects soil organic carbon (SOC) content and a variety of management options have been proposed to sequester carbon. However, studies conducted in Brazilian pastures have shown divergent responses for the SOC depending on management practices. Our objective was to evaluate the effects of management on SOC stocks in grasslands of the Brazilian states of Rondonia and Mato Grosso, and to derive region-specific factors for soil C stock change associated with different management conditions. Compared to SOC stocks in native vegetation, degraded grassland management decreased SOC by a factor of 0.91 +/- 0.14, nominal grassland management reduced SOC stock for Oxisols by a relatively small factor of 0.99 +/- 0.08, whereas, SOC storage increased by a factor of 1.24 +/- 0.07 with nominal management for other soil types. Improved grassland management on Oxisols increased SOC storage by 1.19 +/- 0.07, relative to native stocks, but there were insufficient data to evaluate the impact of improved grassland management for other soil types. Using these results, we also evaluated the potential for grassland management to sequester or emit C to the atmosphere, and found that degraded grassland management decreased stocks by about 0.27-0.28 Mg C ha(-1) yr(-1); nominal management on Oxisols decreased C at a rate of 0.03 Mg C ha(-1) yr(-1), while nominal management on others soil types and improved management on Oxisols increased stocks by 0.72 Mg C ha(-1) yr(-1) and 0.61 Mg C ha(-1) yr(-1), respectively. Therefore, when well managed or improved, grasslands in Rondonia and Mato Grosso states have the potential to sequester C. (c) 2008 Elsevier B.V. All rights reserved.