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  • 1
    Online Resource
    Online Resource
    Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations
    MDPI AG ; 2021
    In:  Animals Vol. 11, No. 6 ( 2021-06-01), p. 1638-
    Details
    In: Animals, MDPI AG, Vol. 11, No. 6 ( 2021-06-01), p. 1638-
    Abstract: Subclinical mastitis, an economically challenging disease of dairy cattle, is associated with an increased use of antimicrobials which reduces milk quantity and quality. It is more common than clinical mastitis and far more difficult to detect. Recently, much attention has been paid to the development of machine-learning expert systems for early detection of subclinical mastitis from milking features. However, differences between animals within a farm as well as between farms, particularly across multiple years, are major obstacles to the generalisation of machine learning models. Here, for the first time, we integrated scaling by quartiling with classification based on associations in a multi-year study to deal with farm heterogeneity by discovery of multiple patterns towards mastitis. The data were obtained from one farm comprising Holstein Friesian cows in Ongaonga, New Zealand, using an electronic automated monitoring system. The data collection was repeated annually over 3 consecutive years. Some discovered rules, such as when the milking peak flow is low, electrical conductivity (EC) of milk is low, milk lactose is low, milk fat is high, and milk volume is low, the cow has subclinical mastitis, reached high confidence ( 〉 70%) in multiple years. On averages, over 3 years, low level of milk lactose and high value of milk EC were part of 93% and 83.8% of all subclinical mastitis detecting rules, offering a reproducible pattern of subclinical mastitis detection. The scaled year-independent combinational rules provide an easy-to-apply and cost-effective machine-learning expert system for early detection of hidden mastitis using milking parameters.
    Type of Medium: Online Resource
    ISSN: 2076-2615
    URL: Article
    DOI: 10.3390/ani11061638
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2606558-7
    SSG: 23
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  • 2
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    An Integrated Planning Framework for Sustainable Water and Energy Supply
    MDPI AG ; 2020
    In:  Sustainability Vol. 12, No. 10 ( 2020-05-24), p. 4295-
    Details
    In: Sustainability, MDPI AG, Vol. 12, No. 10 ( 2020-05-24), p. 4295-
    Abstract: This study aims to reveal the economic, technical, and environmental impacts of different system configurations (centralized or decentralized, components, and technologies) on transition plans to achieve a higher share of renewable energy and desalination supplies for regions facing water scarcity. The main contribution of this research is the comparative evaluation of on-grid decentralized or distributed renewable-powered desalination systems for sustainable water and energy supply planning. Applying a novel nexus approach, an interactive multi-period planning model is developed to highlight synergies and to identify conflicts of planning both energy and water sectors at the same time as endogenous subsystems of one overall system. For studying these synergies in this study, the pace of technology deployment and the path of decline in overall costs are assumed to be a function of experience and knowledge as two-factor learning curves. Using data from 81 projects, the levelized cost and capacity factor of utility-scale photovoltaic and wind supplies in the Middle East were calculated. The results indicate that a scenario with a decentralized water sector and renewable-powered multiple-effect distillation technology has the best overall performance among the proposed scenarios.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su12104295
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2518383-7
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  • 3
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    Stochastic Operation of a Solar-Powered Smart Home: Capturing Thermal Load Uncertainties
    MDPI AG ; 2020
    In:  Sustainability Vol. 12, No. 12 ( 2020-06-22), p. 5089-
    Details
    In: Sustainability, MDPI AG, Vol. 12, No. 12 ( 2020-06-22), p. 5089-
    Abstract: This study develops a mixed-integer linear programming (MILP) model for the optimal and stochastic operation scheduling of smart buildings. The aim of this study is to match the electricity demand with the intermittent solar-based renewable resources profile and to minimize the energy cost. The main contribution of the proposed model addresses uncertainties of the thermal load in smart buildings by considering detailed types of loads such as hot water, heating, and ventilation loads. In smart grids, buildings are no longer passive consumers. They are controllable loads, which can be used for demand-side energy management. Smart homes, as a domain of Internet of Things (IoT), enable energy systems of the buildings to operate as an active load in smart grids. The proposed formulation is cast as a stochastic MILP model for a 24-h horizon in order to minimize the total energy cost. In this study, Monte Carlo simulation technique is used to generate 1000 random scenarios for two environmental factors: the outdoor temperature, and solar radiation. Therefore in the proposed model, the thermal load, the output power of the photovoltaic panel, solar collector power generation, and electricity load become stochastic parameters. The proposed model results in an energy cost-saving of 20%, and a decrease of the peak electricity demand from 7.6 KWh to 4.2 KWh.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su12125089
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2518383-7
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  • 4
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    Online Resource
    Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells
    MDPI AG ; 2021
    In:  Cells Vol. 10, No. 11 ( 2021-11-12), p. 3139-
    Details
    In: Cells, MDPI AG, Vol. 10, No. 11 ( 2021-11-12), p. 3139-
    Abstract: Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.
    Type of Medium: Online Resource
    ISSN: 2073-4409
    URL: Article
    DOI: 10.3390/cells10113139
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2661518-6
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  • 5
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    Online Resource
    Utility of MRI in Quantifying Tissue Injury in Cervical Spondylotic Myelopathy
    MDPI AG ; 2023
    In:  Journal of Clinical Medicine Vol. 12, No. 9 ( 2023-05-08), p. 3337-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 12, No. 9 ( 2023-05-08), p. 3337-
    Abstract: Cervical spondylotic myelopathy (CSM) is a progressive disease that worsens over time if untreated. However, the rate of progression can vary among individuals and may be influenced by various factors, such as the age of the patients, underlying conditions, and the severity and location of the spinal cord compression. Early diagnosis and prompt treatment can help slow the progression of CSM and improve symptoms. There has been an increased use of magnetic resonance imaging (MRI) methods in diagnosing and managing CSM. MRI methods provide detailed images and quantitative structural and functional data of the cervical spinal cord and brain, allowing for an accurate evaluation of the extent and location of tissue injury. This review aims to provide an understanding of the use of MRI methods in interrogating functional and structural changes in the central nervous system in CSM. Further, we identified several challenges hindering the clinical utility of these neuroimaging methods.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm12093337
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662592-1
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  • 6
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    Nonlinear Dynamic Assessment of a Steel Frame Structure Subjected to Truck Collision
    MDPI AG ; 2023
    In:  Buildings Vol. 13, No. 6 ( 2023-06-17), p. 1545-
    Details
    In: Buildings, MDPI AG, Vol. 13, No. 6 ( 2023-06-17), p. 1545-
    Abstract: The progressive collapse of structures subjected to a truck collision with ground floor columns is numerically investigated in this paper. For this purpose, a four-story steel building with a dual system (including an intermediate steel moment frame, with a special concentric steel bracing system in the longitudinal (x) direction, and an intermediate steel moment frame in the transversal (y) direction) is considered. The structure, which was designed according to AISC, ASCE7 and 2800 Iranian seismic standard guidelines, is located in seismic-prone area and subjected to eight different truck collision scenarios. The nonlinear dynamic analyses carried out in ABAQUS on a three-dimensional finite element (FE) numerical model include variations in collision features (i.e., mass and speed of the truck, the height of collision point), and are used to support the analysis of expected damage. The presented results confirm that increasing the truck mass and speed increases damage entity for the column and structure. Several influencing parameters are involved in damage location and progressive evolution. The height of the collision point from the ground also significantly affects the magnitude of structural damage, especially in terms of stress peaks in the panel zones for the target column. Finally, the perimeter columns are more vulnerable to impact than corner columns, in structures with dual system as with the examined four-story building. The presence of a bracing system parallel to the impacting vehicle can in fact reduce the deformation—and thus the expected damage—of the adjacent target column. Most importantly, it is shown that the numerically reproduced collision scenarios (and the associated damage configurations) based on truck impact are significantly more severe than those artificially created based on the conventional column removal method (i.e., alternate path (AP) analysis approach), which confirms the importance of more sophisticated numerical calculation procedures to investigate and assess the progressive collapse of structures.
    Type of Medium: Online Resource
    ISSN: 2075-5309
    URL: Article
    DOI: 10.3390/buildings13061545
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2661539-3
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  • 7
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    Online Resource
    Predicting Seismic Collapse Safety of Post-Fire Steel Moment Frames
    MDPI AG ; 2023
    In:  Buildings Vol. 13, No. 4 ( 2023-04-20), p. 1091-
    Details
    In: Buildings, MDPI AG, Vol. 13, No. 4 ( 2023-04-20), p. 1091-
    Abstract: This paper summarizes a study focused on evaluating the post-fire performance of steel Intermediate Moment Frames (IMFs) following earthquakes. To this aim, archetypes comprising 3-bay IMFs with three different heights were seismically designed, and their two-dimensional finite element models were created in OpenSees software. The post-fire mechanical properties of steel were inserted into the models based on 64 different fire scenarios. The effects of different cooling methods are scrutinized at system level. To develop seismic fragility curves, Incremental Dynamic Analysis (IDA) was performed using 50 suites of far-field and near-field records, according to FEMA-P695. Then, the Collapse Margin Ratio (CMR) of each model was calculated based on the data from the fragility analysis. The results show that the seismic resistance of structures that experienced fire declines to some extent. In addition, the lowest safety level was observed when the structures were subjected to pulse-like near-field records.
    Type of Medium: Online Resource
    ISSN: 2075-5309
    URL: Article
    DOI: 10.3390/buildings13041091
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2661539-3
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  • 8
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    A Transcription Regulatory Sequence in the 5′ Untranslated Region of SARS-CoV-2 Is Vital for Virus Replication with an Altered Evolutionary Pattern against Human Inhibitory MicroRNAs
    MDPI AG ; 2021
    In:  Cells Vol. 10, No. 2 ( 2021-02-04), p. 319-
    Details
    In: Cells, MDPI AG, Vol. 10, No. 2 ( 2021-02-04), p. 319-
    Abstract: Our knowledge of the evolution and the role of untranslated region (UTR) in SARS-CoV-2 pathogenicity is very limited. Leader sequence, originated from UTR, is found at the 5′ ends of all encoded SARS-CoV-2 transcripts, highlighting its importance. Here, evolution of leader sequence was compared between human pathogenic and non-pathogenic coronaviruses. Then, profiling of microRNAs that can inactivate the key UTR regions of coronaviruses was carried out. A distinguished pattern of evolution in leader sequence of SARS-CoV-2 was found. Mining all available microRNA families against leader sequences of coronaviruses resulted in discovery of 39 microRNAs with a stable thermodynamic binding energy. Notably, SARS-CoV-2 had a lower binding stability against microRNAs. hsa-MIR-5004-3p was the only human microRNA able to target the leader sequence of SARS and to a lesser extent, also SARS-CoV-2. However, its binding stability decreased remarkably in SARS-COV-2. We found some plant microRNAs with low and stable binding energy against SARS-COV-2. Meta-analysis documented a significant (p 〈 0.01) decline in the expression of MIR-5004-3p after SARS-COV-2 infection in trachea, lung biopsy, and bronchial organoids as well as lung-derived Calu-3 and A549 cells. The paucity of the innate human inhibitory microRNAs to bind to leader sequence of SARS-CoV-2 can contribute to its high replication in infected human cells.
    Type of Medium: Online Resource
    ISSN: 2073-4409
    URL: Article
    DOI: 10.3390/cells10020319
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2661518-6
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  • 9
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    The Role of Renewable Energy Resources in Sustainability of Water Desalination as a Potential Fresh-Water Source: An Updated Review
    MDPI AG ; 2020
    In:  Sustainability Vol. 12, No. 13 ( 2020-06-28), p. 5233-
    Details
    In: Sustainability, MDPI AG, Vol. 12, No. 13 ( 2020-06-28), p. 5233-
    Abstract: Desalination is becoming a practical option to meet water demand in an increasing number of locations that are facing water scarcity. Currently, more than 150 countries in the world are already using desalination technologies, which account for about one percent of the world’s drinking water. Although for specific regions, desalination is the only feasible solution to close the supply–demand gap (for example the production of desalinated seawater in the Middle East is predicted to rise almost fourteen-fold by 2040), the sustainability of desalination systems is still remarkably under question. This review aims first to investigate the technical and economic trends and environmental and social aspects of desalination systems and then, in the second stage, to give an overview of the role of renewable energy technologies in the sustainability of the future water systems with an increasing share of desalination.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su12135233
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2518383-7
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  • 10
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    Online Resource
    Long-Term Calorie Restriction Alters Anxiety-like Behaviour and the Brain and Adrenal Gland Transcriptomes of the Ageing Male Rat
    MDPI AG ; 2022
    In:  Nutrients Vol. 14, No. 21 ( 2022-11-04), p. 4670-
    Details
    In: Nutrients, MDPI AG, Vol. 14, No. 21 ( 2022-11-04), p. 4670-
    Abstract: Further examination of the molecular regulators of long-term calorie restriction (CR), reported to have an anxiolytic effect, may highlight novel therapeutic targets for anxiety disorders. Here, adult male Hooded Wistar rats were exposed to a 25% CR whilst anxiety-like behaviour was assessed at 6-, 12-, and 18-months of age via the elevated plus maze, open field, and acoustic startle tests. Next-generation sequencing was then used to measure transcriptome-wide gene expression in the hypothalamus, amygdala, pituitary, and adrenal glands. Results showed an anxiolytic behavioural profile across early, middle, and late adulthood by CR, with the strongest effects noted at 6-months. Transcriptomic analysis by seven attribute weighting algorithms, including Info Gain Ratio, Rule, Chi Squared, Gini Index, Uncertainty, Relief, and Info Gain, led to the development of a signature of long-term CR, independent of region. Complement C1q A chain (C1qa), an extracellular protein, expression was significantly decreased by CR in most regions examined. Furthermore, text mining highlighted the positive involvement of C1qa in anxiety, depression, neurodegeneration, stress, and ageing, collectively identifying a suitable biomarker candidate for CR. Overall, the current study identified anxiety-related phenotypic changes and a novel transcriptome signature of long-term CR, indicating potential therapeutic targets for anxiety, depression, and neurodegeneration.
    Type of Medium: Online Resource
    ISSN: 2072-6643
    URL: Article
    DOI: 10.3390/nu14214670
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518386-2
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