Search

Rainfall-runoff (RR) is a vital process as it is a key component of the Earth’s water cycle, which is required for the survival of life on our planet. It is responsible for water resource management as it will alter the water quality and availability for living things and environmental requirements. Most of the previous research, in this domain, focused on short-term modelling using data from a specific region. However, fewer studies have been conducted to predict water availability for longer periods. There is an urgent need to explore a model that can predict RR in diverse locations for varied periods and climate circumstances. In this context, predictive models for RR prediction in literature are reviewed in this study. The findings are highlighted, and the discussion of the results are condensed. The review has been carried out for 80 articles that were published within last 21 years (2003–2023) on the competency of the predictive models used in RR prediction in the analysis of the input variables and the data size of the time series. The publications include relevant information such as the model limitation and the suggestions for further research that will be useful to researchers who intend to perform similar studies in RR predictions in the future. In addition, researchers from previous studies found that the hybrid deep learning (DL) models are greater than the hybrid machine learning (ML) models, DL models and standalone ML models. In this study, four new models are suggested to forecast the RR.

Abstract Objective: The aim of this study is to assess the use of metformin with or without insulin for the treatment of Gestational Diabetes Mellitus compared to insulin alone. Data sources: This article consists of a systematic review of randomized clinical trials. The searches were carried out on MEDLINE including 7 studies, between 2010 to 2021. Study selection: Randomized clinical trials comparing metformin and insulin written in English, Spanish or Portuguese, with no time limit, were included. Data collection: Data was extracted from all the 7 articles and compared statistically when possible. Whenever data was not available or couldn't be statistically compared, the main results were described in detail. Data synthesis: Insulin alone is not superior than metformin with or without insulin on gestational diabetes mellitus. Conclusion: There is a potential viability of using metformin as an alternative compared to insulin alone in the treatment of Gestational Diabetes Mellitus. However, all assessed outcomes have a very low level of certainty of evidence and more studies are necessary to support these findings.

Water quality is influenced by a wide range of factors, but it is expensive and technically difficult to take into account every factor, which leaves out quality variations. The assessment process is made more difficult by the need for different evaluation indicators for various water uses. Furthermore, many water quality factors have complex nonlinear relationships that are difficult for these methods to handle. On the other hand, because machine learning can quickly identify underlying principles and handle complex data with efficiency, it offers a promising approach. The gap involves addressing complex relationship and environmental factors when predicting water quality in rivers. The purpose of this study is to evaluate the feasibility of estimating the Gombak River's Water Quality Index (WQI) using machine learning, and to identify appropriate models based on statistical performance metrics. The study looks into the possibility of estimating WQI solely using dissolved oxygen (DO) and pH as predictors because the chemical parameters in the current Malaysian WQI calculation method takes some time to compute. This research provides insight into the accuracy, precision, and general performance of these models in predicting water quality by looking at the residuals of various scenarios and evaluating performance metrics across different machine learning models. This study provides insights into the potential of machine learning for improving water quality assessment and management practices. Future studies should concentrate on resolving these issues and investigating other elements, such as environmental variables, land use patterns, and human activity, that may affect the forecast of water quality.

Purpose of the article: The aim of this multicenter observational study is to report data from real world on the use of bimekizumab in patients aged ≥ 65 years with moderate-to-severe plaque psoriasis. Elderly patients are poorly represented in clinical trials on bimekizumab for plaque psoriasis, and real-world studies are important to guide clinical choices.Materials and methods: A retrospective multicenter study was conducted in 33 dermatological outpatient clinics in Italy. Patients aged ≥ 65 years, with moderate-to-severe plaque psoriasis and treated with bimekizumab were enrolled. No exclusion criteria were applied. Bimekizumab was administered following the Italian Guidelines for the management of plaque psoriasis and according to the summary of product characteristics, in adult patients who were candidates for systemic treatments. Overall, 98 subjects were included, and received bimekizumab up to week 36. Clinical and demographic data were collected before the initiation of treatment with bimekizumab. At baseline and each dermatological examination (4, 16, and 36 weeks), clinical outcomes were measured by the following parameters: (1) PASI score; (2) site-specific (scalp, palmoplantar, genital, nail) Psoriasis Global Assessment (PGA). At each visit, the occurrence of any adverse events (AEs) was recorded, including serious AEs and AEs leading to bimekizumab discontinuation.Results: The mean PASI score was 16.6 ± 9.4 at baseline and significantly decreased to 4.3 ± 5.2 after 4 weeks (p

(1) Background/Objectives: Nail psoriasis (NP) is a chronic and difficult-to-treat disease, which causes significant social stigma and impairs the patients’ quality of life. Moreover, nail psoriasis is a true therapeutic challenge for clinicians. The presence of nail psoriasis can be part of a severe form of psoriasis and can have predictive value for the development of psoriatic arthritis. Our real-world-evidence multicenter study aims to evaluate the efficacy of bimekizumab in nail psoriasis. (2) Methods: A retrospective analysis of a multicenter observational study included 834 patients affected by moderate-to-severe psoriasis, in 33 Dermatologic Units in Italy, treated with bimekizumab from December 2022 to September 2023. Clinimetric assessments were based on Psoriasis Area and Severity Index (PASI), Dermatology Life Quality Index (DLQI), and Physician’s Global Assessment of Fingernail Psoriasis (PGA-F) for the severity of nail psoriasis at 0, 12, 24, and 36 weeks. (3) Results: Psoriatic nail involvement was present in 27.95% of patients. The percentage of patients who achieved a complete clearance of NP in terms of PGA-F 0 was 31.7%, 57%, and 88.5% at week 4, 16, and 36, respectively. PASI 100 was achieved by 32.03% of patients at week 4, by 61.8% at week 16, and by 78.92% of patients at week 36. The mean baseline PASI was 16.24. The mean DLQI values for the entire group of patients at baseline, at week 4, at week 16, and at week 36 were 14.62, 3.02, 0.83, and 0.5, respectively. (4) Conclusions: Therapies that promote the healing of both the skin and nails in a short time can also ensure a lower risk of subsequently developing arthritis which is disabling over time. Bimekizumab proved to be particularly effective to treat NP, with a fast response in terms of complete clearance, with over 88.5% of patients free from NP after 36 weeks. The findings of our real-world study showed that patients with moderate-to-severe PsO and concomitant NP had significantly faster and more substantial improvements in NP up to 36 weeks with respect to previous research findings. Considering the rapid healing of the nail, the dual inhibition of IL17 A and F might have a great value in re-establishing the dysregulation of keratin 17 at the nail level.

Riverbank erosion is a natural process of removal of earthen materials from the bank surface. The process of riverbank erosion that is induced naturally results in the formation of landforms such as valleys, canyons, and productive floodplains. However, riverbank erosion can also be considered a hazard when the process occurs at an alarming rate causing loss of land. The extent of erosion depends on many factors. One of the main factors responsible for riverbank erosion is the soil erodibility which is the resistance of soil to erosion. The aim of this study is to quantify the riverbank erosion rates and the potential magnitude of riverbank erosion in order to generate an empirical predictive model to estimate riverbank erosion from physical and geomorphic variables for rivers susceptible to riverbank erosion. Several models were trained using the Regression Learner application in MATLAB software. Models that include soil erodibility parameters perform better than the models without the soil erodibility parameters. The model with the highest accuracy was found to be Model 2, with Root Mean Square Error (RMSE) of 3.70E-08 and coefficient of determination, R2 of 0.55. The model produced in this study will be helpful to analyze and predict the effects of riverbank erosion and assist in the development of bank stabilization solution. ABSTRAK: Hakisan tebing sungai adalah proses semula jadi terhadap penyingkiran bahan tanah dari permukaan tebing. Proses hakisan tebing sungai yang terjadi secara semula jadi ini mengakibatkan pembentukan bentuk muka bumi seperti lembah, ngarai dan dataran banjir yang produktif. Bagaimanapun, hakisan tebing sungai juga boleh dianggap sebagai ancaman apabila proses berlaku pada kadar membimbangkan sehingga menyebabkan kehilangan tanah. Tahap hakisan bergantung pada banyak faktor. Salah satu faktor utama yang menyebabkan hakisan tebing sungai adalah kebolehhakisan tanah iaitu ketahanan tanah terhadap hakisan. Kajian ini bertujuan untuk mengukur kadar hakisan tebing sungai, mengkaji potensi magnitud hakisan tebing sungai dan menghasilkan model ramalan empirik bagi menganggarkan hakisan tebing sungai daripada pembolehubah fizikal dan geomorfik bagi sungai yang terdedah kepada hakisan tebing sungai. Beberapa model telah dilatih menggunakan aplikasi Regression Learner dalam perisian MATLAB. Dapatan menunjukkan model yang mengandungi parameter kebolehhakisan tanah adalah lebih baik berbanding model tanpa parameter kebolehhakisan tanah. Model 2 didapati mempunyai ketepatan tertinggi dengan ralat punca min kuasa dua (RMSE) sebanyak 3.70E-08 dan pekali penentuan, R2 sebanyak 0.55. Model dalam kajian ini dapat membantu dalam analisa berkaitan kesan hakisan tebing sungai dan penyelesaian kepada pembangunan kestabilan tebing.

Ancient DNA is highly degraded, resulting in very short sequences. Reads generated with modern high-throughput sequencing machines are generally longer than ancient DNA molecules, therefore the reads often contain some portion of the sequencing adaptors. It is crucial to remove those adaptors, as they can interfere with downstream analysis. Furthermore, overlapping portions when DNA has been read forward and backward (paired-end) can be merged to correct sequencing errors and improve read quality. Several tools have been developed for adapter trimming and read merging, however, no one has attempted to evaluate their accuracy and evaluate their potential impact on downstream analyses. Through the simulation of sequencing data, seven commonly used tools were analyzed in their ability to reconstruct ancient DNA sequences through read merging. The analyzed tools exhibit notable differences in their abilities to correct sequence errors and identify the correct read overlap, but the most substantial difference is observed in their ability to calculate quality scores for merged bases. Selecting the most appropriate tool for a given project depends on several factors, although some tools such as fastp have some shortcomings, whereas others like leeHom outperform the other tools in most aspects. While the choice of tool did not result in a measurable difference when analyzing population genetics using principal component analysis, it is important to note that downstream analyses that are sensitive to wrongly merged reads or that rely on quality scores can be significantly impacted by the choice of tool.

Resistance value (R-value) is one of the basic subgrade stiffness characterizations that express a material’s resistance to deformation. In this paper, artificial intelligence (AI)-based models—especially M5P, support vector machine (SVM), and Gaussian process regression (GPR) algorithms—are built for R-value evaluation that meets the high precision and rapidity requirements in highway engineering. The dataset of this study comprises seven parameters: hydrated lime-activated rice husk ash, liquid limit, plastic limit, plasticity index, optimum moisture content, and maximum dry density. The available data are divided into three parts: training set (70%), test set (15%), and validation set (15%). The output (i.e., R-value) of the developed models is evaluated using the performance measures coefficient of determination (R2), mean absolute error (MAE), relative squared error (RSE), root mean square error (RMSE), relative root mean square error (RRMSE), performance indicator (ρ), and visual framework (Taylor diagram). GPR is concluded to be the best performing model (R2, MAE, RSE, RMSE, RRMSE, and ρ equal to 0.9996, 0.0258, 0.0032, 0.0012, 0.0012, and 0.0006, respectively, in the validation phase), very closely followed by SVM, and M5P. The application used for the aforementioned approaches for predicting the R-value is also compared with the recently developed artificial neural network model in the literature. The analysis of performance measures for the R-value dataset demonstrates that all the AI-based models achieved comparatively better and reliable results and thus should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with maximum dry density being the highest.

O objetivo deste trabalho foi avaliar a suscetibilidade das lagartas Anticarsia gemmatalis (Lepidoptera: Erebidae) e Chrysodeixis includens (Lepidoptera: Noctuidae) às proteínas Cry1 e Vip3A, bem como determinar se há a interação entre essas proteínas no controle das duas espécies. Bioensaios com as proteínas isoladas e em combinações foram realizados, e as concentrações letais CL50 e CL90 foram estimadas para cada condição. As proteínas Cry1Aa, Cry1Ac e Vip3Af foram as mais efetivas no controle de A. gemmatalis, enquanto Cry1Ac, Vip3Aa e Vip3Af foram mais efetivas no de C. includens. As proteínas Cry1Ac e Cry1Ca causaram maior inibição do desenvolvimento das larvas sobreviventes à CL50, em ambas as espécies. Combinações entre Vip3A e Cry1 apresentam efeito sinérgico no controle das espécies e a combinação Vip3Aa+Cry1Ea destaca-se no controle de A. gemmatalis e C. includens. Essas proteínas combinadas são promissoras na construção de plantas piramidadas, para o controle simultâneo das pragas.

Dentre as doenças fúngicas do mamoeiro, a varíola (Asperisporium caricae) é umas das doenças mais importantes. Os objetivos deste trabalho foram avaliar a eficácia de fungicidas no controle da varíola em folhas e frutos do mamoeiro. O experimento foi conduzido em área comercial, no município de Taquarintiga - SP, em delineamento de blocos casualizados, com cinco tratamentos e quatro repetições. Os tratamentos foram tebuconazol + trifloxistrobina (0,06 L.100L-1 de água), piraclostrobina (0,04 L.100L-1 de água), difenoconazol (0,03 L.100L-1 de água), azoxistrobina (128 g.ha-1) e a testemunha. Foram realizadas três avaliações, nas quais foram avaliadas a severidade da varíola através de escala diagramática, nas folhas e nos frutos de três plantas previamente marcadas. Os dados registrados foram submetidos à análise de variância pelo teste F, e as médias, comparadas pelo teste de Scott-Knott (p

O objetivo desse estudo foi avaliar diferentes métodos de secagem de folhas para três diferentes frutíferas (maracujá, pêssego e abacate), com relação à determinação da matéria seca e os teores foliares de macronutrientes. Foram coletadas amostras de folhas recém expandida de três culturas, do pomar da fazenda de ensino e pesquisa da FCAV-UNESP, câmpus de Jaboticabal, no mês de janeiro de 2010, coletando-se para cada cultura 12 amostras com 25 folhas cada. Os tratamentos constituíram-se por dois métodos de secagem, estufa de circulação de ar forçada regulada a uma temperatura de 70°C e o forno microondas (FMO). Avaliou-se a massa da matéria seca e os teores foliares de macronutrientes. Os resultados sugerem que os dois métodos de secagem testados se assemelham na determinação de matéria seca e nos teores foliares de macronutrientes para as culturas análisadas, exceto os teores de cálcio na cultura do pêssego.The objective of this study was to evaluate different methods of drying of leaves for three different fruit (passion fruit, peach and avocado), with respect to the determination of dry matter and foliar nutrients. Samples were collected from recently expanded leaves of three crops orchard Farm education and research FCAV-UNESP, Jaboticabal campus, in January 2010, by collecting 12 samples for each culture with 25 leaves. The treatments consisted of two drying methods, greenhouse circulation of air regulated to a temperature of 70°C and FMO. We evaluated the mass of dry matter and foliar nutrients. The results suggest that the two drying methods tested did not interfere in the determination of dry matter and foliar nutrients to crops analyzed, except the calcium levels in peach.

Second generation Bt crops (insect resistant crops carrying Bacillus thuringiensis genes) combine more than one gene that codes for insecticidal proteins in the same plant to provide better control of agricultural pests. Some of the new combinations involve co-expression of cry and vip genes. Because Cry and Vip proteins have different midgut targets and possibly different mechanisms of toxicity, it is important to evaluate possible synergistic or antagonistic interactions between these two classes of toxins. Three members of the Cry1 class of proteins and three from the Vip3A class were tested against Heliothis virescens for possible interactions. At the level of LC50, Cry1Ac was the most active protein, whereas the rest of proteins tested were similarly active. However, at the level of LC90, Cry1Aa and Cry1Ca were the least active proteins, and Cry1Ac and Vip3A proteins were not significantly different. Under the experimental conditions used in this study, we found an antagonistic effect of Cry1Ca with the three Vip3A proteins. The interaction between Cry1Ca and Vip3Aa was also tested on two other species of Lepidoptera. Whereas antagonism was observed in Spodoptera frugiperda, synergism was found in Diatraea saccharalis. In all cases, the interaction between Vip3A and Cry1 proteins was more evident at the LC90 level than at the LC50 level. The fact that the same combination of proteins may result in a synergistic or an antagonistic interaction may be an indication that there are different types of interactions within the host, depending on the insect species tested.

Resistance value (R-value) is one of the basic subgrade stiffness characterizations that express a material's resistance to deformation. In this paper, artificial intelligence (AI)-based models—especially M5P, support vector machine (SVM), and Gaussian process regression (GPR) algorithms—are built for R-value evaluation that meets the high precision and rapidity requirements in highway engineering. The dataset of this study comprises seven parameters: hydrated lime-activated rice husk ash, liquid limit, plastic limit, plasticity index, optimum moisture content, and maximum dry density. The available data are divided into three parts: training set (70%), test set (15%), and validation set (15%). The output (i.e., R-value) of the developed models is evaluated using the performance measures coefficient of determination (R2), mean absolute error (MAE), relative squared error (RSE), root mean square error (RMSE), relative root mean square error (RRMSE), performance indicator (ρ), and visual framework (Taylor diagram). GPR is concluded to be the best performing model (R2, MAE, RSE, RMSE, RRMSE, and ρ equal to 0.9996, 0.0258, 0.0032, 0.0012, 0.0012, and 0.0006, respectively, in the validation phase), very closely followed by SVM, and M5P. The application used for the aforementioned approaches for predicting the R-value is also compared with the recently developed artificial neural network model in the literature. The analysis of performance measures for the R-value dataset demonstrates that all the AI-based models achieved comparatively better and reliable results and thus should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with maximum dry density being the highest. [ABSTRACT FROM AUTHOR]

O objetivo deste trabalho foi realizar uma revisão de literatura descrevendo as principais alterações fisiopatológicas, as possíveis causas, sintomas e tratamento do megacólon congênito (MC). Esta patologia caracteriza-se pela ausência total, parcial significativa ou deficiência das células ganglionares intestinais dos plexos submucoso e mioentérico do Sistema Nervoso Entérico, acarretando irregularidades anatômicas na porção distal do intestino grosso, reto e/ou intestino delgado. Na porção aganglionar do intestino grosso ocorre obstrução intestinal, acúmulo de fezes e dilatação, manifestando-se vômitos biliosos, diarréias, constipação intestinal e mau desenvolvimento físico da criança. A causa da desenervação intestinal, verificada no MC, vem da não migração das células nervosas para as porções mais distais do intestino grosso. Mutações em diferentes genes também são indicadas como causadoras dessa enfermidade. Se corretamente tratado desde as primeiras horas de nascimento, o portador de MC pode ter um desenvolvimento normal até a fase adulta evitando o óbito.

Alitretinoin is the only systemic agent approved to treat moderate‐severe chronic hand eczema (CHE) unresponsive to potent topical corticosteroids. No nationwide Italian data regarding real‐life efficacy, safety, and tolerability of treatment are available. The DECISA project (DErmatology Clinics in Italy: Survey on Alitretinoin) retrospectively examined data from a registry including 15 Dermatology Clinics authorized to prescription of alitretinoin for CHE patients. Disease severity was assessed at baseline, and after 3 and 6 months of treatment, using the 5‐point Physician Global Assessment (PGA) and the modified Total Lesion‐Symptoms‐Severity (mTLSS) scores. Between November 2010 and July 2018, data of 248 male and 190 female patients (mean age 49.71 ± 13.20 years) treated with alitretinoin were collected. Of them, 43.2% had irritant contact dermatitis, 22.2% allergic contact dermatitis, 18.0% atopic dermatitis, 16.7% mixed (irritant/allergic) type of eczema. At 3 months, the 420 re‐evaluated patients showed significantly reduced mTLSS and PGA (P <.0000001 vs baseline for both); PGA was clear/almost clear in 35.6% of cases. At 6 months, the 341 re‐evaluated patients showed significant (P <.0000001) improvement of mTLSS and PGA vs baseline and 3 months (PGA clear/almost clear: 41.4%). Relapses occurred in 125 patients; 58 underwent an additional course of alitretinoin, with similarly good results. No relevant safety issues were reported; 86 patients experienced adverse effects, which forced 40 to prematurely stop treatment. The DECISA project results confirm the real‐life efficacy, safety and tolerability of alitretinoin in the treatment of moderate to severe CHE refractory to standard topical therapies. [ABSTRACT FROM AUTHOR]