Evaluating the resistance to sunn pest (Eurygaster integriceps Put) and its relationship with high-molecular-weight glutenin subunit in wheat

Trabalho final de mestrado integrado em Medicina, apresentado à Faculdade de Medicina da Universidade de Coimbra.Em consequência de um fenómeno global de envelhecimento populacional, é expectável um aumento na prevalência de demência. A demência vascular é a segunda causa mais comum de demência, depois da doença de Alzheimer. Trata-se de uma entidade clínica bastante heterogénea, sendo o acidente vascular cerebral (AVC) um dos seus mecanismos subjacentes. No entanto, nem todos os doentes desenvolvem demência após AVC, e nem sempre ela é do tipo vascular. Idade avançada, características do AVC, as suas complicações, e evidências neuro-imagiológicas de lesão cerebral acrescida, parecem determinar o desenvolvimento de demência. Evitar a recorrência do AVC, através do controlo de fatores de risco vasculares é, por enquanto, a única forma reconhecida de prevenção.As people live longer, an increase in prevalence and burden of dementia is to be expected worldwide. Vascular dementia is the second most common cause of dementia, second to Alzheimer’s disease. It is a heterogeneous clinical entity, with stroke being one of the responsible mechanisms. However, not all stroke patients develop dementia, and not always in the vascular form. Older age, stroke-related factors, its complications and other neuroimaging changes, seem to determine the occurrence of dementia after a stroke. Avoiding recurrence of stroke by careful monitoring and treatment of vascular risk factors is, for now, the only recognized preventive strategy of poststroke dementia

Efficacy of Tree-Based Models for Pipe Failure Prediction and Condition Assessment: A Comprehensive Review

This is the final version. Available on open access from ASCE via the DOI in this recordData Availability Statement: All data, models, and code generated or used during the study appear in the published article.This paper provides a comprehensive review of tree-based models and their application in condition assessment and prediction of water, wastewater, and sewer pipe failures. Tree-based models have gained significant attention in recent years due to their effectiveness in capturing complex relationships between parameters of systems and their ability in handling large data sets. This study explores a range of tree-based models, including decision trees and ensemble trees utilizing bagging, boosting, and stacking strategies. The paper thoroughly examines the strengths and limitations of these models, specifically in the context of assessing the pipes’ condition and predicting their failures. In most cases, tree-based algorithms outperformed other prevalent models. Random forest was found to be the most frequently used approach in this field. Moreover, the models successfully predicted the failures when augmented with a richer failure data set. Finally, it was identified that existing evaluation metrics might not be necessarily suitable for assessing the prediction models in the water and sewer networks.Datatecnics Corporation LimitedUKR

Customised-sampling approach for pipe failure prediction in water distribution networks

This is the final version. Available from Nature Research via the DOI in this record. Data availability. Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. All case study data is owned by the utility company and is subject to a non-disclosure agreement (NDA), thereby limiting its availability for public dissemination. Requests for non-commercial usage of the scripts will be evaluated on a case-by-case basis, by contacting the corresponding author at [email protected] paper presents a new methodology for addressing imbalanced class data for failure prediction in Water Distribution Networks (WDNs). The proposed methodology relies on existing approaches including under-sampling, over-sampling, and class weighting as primary strategies. These techniques aim to treat the imbalanced datasets by adjusting the representation of minority and majority classes. Under-sampling reduces data in the majority class, over-sampling adds data to the minority class, and class weighting assigns unequal weights based on class counts to balance the influence of each class during machine learning (ML) model training. In this paper, the mentioned approaches were used at levels other than “balance point” to construct pipe failure prediction models for a WDN with highly imbalanced data. F1-score, and AUC–ROC, were selected to evaluate model performance. Results revealed that under-sampling above the balance point yields the highest F1-score, while over-sampling below the balance point achieves optimal results. Employing class weights during training and prediction emphasises the efficacy of lower weights than the balance. Combining under-sampling and over-sampling to the same ratio for both majority and minority classes showed limited improvement. However, a more effective predictive model emerged when over-sampling the minority class and under-sampling the majority class to different ratios, followed by applying class weights to balance data.Datatecnics Corporation LimitedUKRIKnowledge Transfer Partnership (KTP) InnovateU

Geotechnical Characteristics of Fine-Grained Soils Stabilized with Fly Ash, a Review

This is the final version. Available on open access from MDPI via the DOI in this recordFly ash is a waste material obtained from burning of coal in thermal power plants. Coal consumption is still very high and is expected to remain above 38% globally. Therefore, large volumes of fly ash are produced every year that need to be managed as waste. Improper disposal of fly ash can lead to surface water and ground water pollution and adversely affect human health and environment. The use of fly ash as an agent to stabilize soil has recently become popular in geotechnical engineering due to its many benefits such as being eco-friendly and cost-effective, and improving the geotechnical characteristics of the soil. This paper presents a review of the geotechnical properties of fly ash-stabilized fine-grained soils. Several features of fly ash, including classification, physical, geotechnical, chemical, and mineralogical properties, health concerns, disposal, availability, and cost are analyzed. The effects of fly ash in improving a wide range of mechanical properties of soils including unconfined compressive strength, shear strength, CBR value, consolidation and/or swelling characteristics, and permeability are reviewed in detail. It is shown that fly ash can be a substitute material for use in soil stabilization, leading to substantial economic and environmental benefits.Turkish Ministry of National Education (MoNE)European Union Horizon 202

Impact of Safety-Related Dose Reductions or Discontinuations on Sustained Virologic Response in HCV-Infected Patients: Results from the GUARD-C Cohort.

BACKGROUND: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. METHODS: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. RESULTS: SVR24 rates were 46.1% (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1, 2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced ≥1 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with ≥1 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not ≥5. CONCLUSIONS: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginterferon alfa-2a/ribavirin.This study was sponsored by F. Hoffmann-La Roche Ltd, Basel, Switzerland. Support for third-party writing assistance for this manuscript, furnished by Blair Jarvis MSc, ELS, of Health Interactions, was provided by F. Hoffmann-La Roche Ltd, Basel, Switzerland

Impact of safety-related dose reductions or discontinuations on sustained virologic response in HCV-infected patients: Results from the GUARD-C Cohort

Background: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. Methods: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. Results: SVR24 rates were 46.1 % (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1,2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced 651 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with 651 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not 655. Conclusions: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginter-feron alfa-2a/ribavirin

Semi-supervised Clustering Approach for Pipe Failure Prediction with Imbalanced Dataset

This is the author accepted manuscript.Data Availability Statement: Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. All case study data is owned by the utility company and is subject to a non-disclosure agreement (NDA), thereby limiting its availability for public dissemination. Requests for non-commercial usage of the scripts will be evaluated on a case-by-case basis.In recent years, machine learning (ML) approaches have been widely used for water pipe condition assessment and failure prediction. These methods require a considerable amount of data from water distribution networks (WDNs). Imbalance and short data, either asset or failure data, compromise the model’s prediction performance. In this research, three ML methods, XGBoost, random forest and logistic regression, were utilised to prioritise the asset rehabilitation in a real WDN with 2 years of failure data. The imbalanced data were treated using random under- and over-sampling, SMOTE and class weight approaches. Also clustering methods were applied to divide the dataset into homogeneous categories. A semi-supervised clustering method was proposed to exploit the domain knowledge. Analysis of the results indicated that standard data science evaluation metrics were not able to clearly distinguish between different methods. To tackle this, an economic indicator was proposed to rank the pipes for rehabilitation based on their cost and likelihood of failure (LoF). Preventive maintenance using the results of economic indicator, reduces the number of failures with a small fraction of total replacement cost. Moreover, another indicator was developed to consider the consequence of the failures and LoF, simultaneously. This indicator mitigates the flow capacity reductions in WDNs caused by failures, in a cost-effective manner. The result of this study provides asset managers with a powerful tool to prioritise assets for rehabilitation.Datatecnics Corporation LimitedUKRIInnovate U