Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure–activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure–property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models

The prediction of suspended solids of river in forested catchment using artificial neural network

This study presents an artificial neural network (ANN) model that is able to predict suspended solids concentrations in forested catchment namely Berring River, Kelantan, Malaysia.The network was trained using data collected during a period of 13 days in April 2001. The sampling location was established in the middle section of the river for collecting water samples. The study was carried out for a duration of two weeks in April 2001. The water sample was collected at 60% of the total depth from the river bed for every two hours starting from 6:00 am to 12:00 midnight for the whole duration of the study period. In this study five parameters were selected as input parameter for the network which are turbidity, flow velocity, depth, width, and weather condition of during the sampling period, while suspended solids as desire output. The data fed to the neural network were divided into two set: a training set and testing set. 116 of the data were used in training set and 24 remained as testing set. A network of the model was detected automatically by the network to give good predictions for both training and testing data set. A partitioning method of the connection weights of the network was used to study the relative percentage contribution of each of the input variables. It was found that turbidity and river width gives 73.03% and 24.73% each. The performance of the neural network model was measured by computing the correlation coefficient which gives the value of 0.93. It’s shown that the neural network gives superior predictions. Based on the results of this study, ANN modeling appears to be a promising technique for the prediction of suspended solids. Dynamic Metadata(s

NanoSAR: In Silico Modelling of Nanomaterial Toxicity

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties of ENMs. Clearly, it is important to understand and ameliorate any risks to health or the environment posed by the presence of ENMs. However, there still exists a critical gap in the literature on the (eco)toxicological properties of ENMs and the particular characteristics that influence their toxic effects. Given their increasing industrial and technological use, it is important to assess their potential health and environmental impacts in a time and cost effective manner. One strategy to alleviate the problem of a large number and variety of ENMs is through the development of data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics. Although such structure-activity relationship (SAR) methods have proven to be effective in predicting the toxicity of substances in bulk form, their practical application to ENMs requires more research and further development. This study aimed to address this research need by investigating the application of data-driven toxicity modelling approaches (e.g. SAR) that are beneficial over animal testing from a cost, time and ethical perspective to ENMs. A large amount of data on ENM toxicity and properties was collected and analysed using quantitative methods to explore and explain the relationship between ENM properties and their toxic outcomes, as a part of this study. More specifically, multi-dimensional data visualisation techniques including heat maps combined with hierarchical clustering and parallel co-ordinate plots, were used for data exploration purposes while classification and regression based modelling tools, a genetic algorithm based decision tree construction algorithm and partial least squares, were successfully applied to explain and predict ENMs’ toxicity based on physicochemical characteristics. As a next step, the implementation of risk reduction measures for risks that are outside the range of tolerable limits was investigated. Overall, the results showed that computational methods hold considerable promise in their ability to identify and model the relationship between physicochemical properties and biological effects of ENMs, to make it possible to reach a decision more quickly and hence, to provide practical solutions for the risk assessment problems caused by the diversity of ENMs

Causes of variability in latent phenotypes of childhood wheeze

Background Latent class analysis (LCA) has been used extensively to identify (latent) phenotypes of childhood wheezing. However, the number and trajectory of discovered phenotypes differed substantially between studies. Objective We sought to investigate sources of variability affecting the classification of phenotypes, identify key time points for data collection to understand wheeze heterogeneity, and ascertain the association of childhood wheeze phenotypes with asthma and lung function in adulthood. Methods We used LCA to derive wheeze phenotypes among 3167 participants in the ALSPAC cohort who had complete information on current wheeze recorded at 14 time points from birth to age 16½ years. We examined the effects of sample size and data collection age and intervals on the results and identified time points. We examined the associations of derived phenotypes with asthma and lung function at age 23 to 24 years. Results A relatively large sample size (>2000) underestimated the number of phenotypes under some conditions (eg, number of time points <11). Increasing the number of data points resulted in an increase in the optimal number of phenotypes, but an identical number of randomly selected follow-up points led to different solutions. A variable selection algorithm identified 8 informative time points (months 18, 42, 57, 81, 91, 140, 157, and 166). The proportion of asthmatic patients at age 23 to 24 years differed between phenotypes, whereas lung function was lower among persistent wheezers. Conclusions Sample size, frequency, and timing of data collection have a major influence on the number and type of wheeze phenotypes identified by using LCA in longitudinal data

Identification and genetic characterization of Pseudomonas syringae pv. syringae from sweet cherry in Turkey

Pseudomonas syringae pv. syringae (Pss), which causes bacterial canker, is the most polyphagous bacterium in the P. syringae complex due to its broad host range. This pathogen is considered the major bacterial disease in cherry orchards. In this study, several samples were collected from infected sweet cherry trees in different locations of the Marmara region in Turkey between 2016-2018. Sixty-three isolates were identified as Pss by pathogenicity, LOPAT, GATTa, and MALDI-TOF MS tests. Total genomic DNA was extracted to confirm identity, followed by PCR amplification of syrB and cfl genes. Out of 63 isolates, 12 were randomly selected for Repetitive Element Sequence-based PCR (rep-PCR) and Multilocus Sequence Typing (MLST) analysis to gain insight into the relationships of those isolates. The cluster analysis of rep-PCR (ERIC-, REP- and BOX-PCR) could classify the isolates into two distinct clusters. Phylogenetic analysis was carried out to obtain the relation between isolates and the location.The MLST analysis of gyrB, rpoDp, rpoDs, and gltA genes allowed a clear allocation of the isolates into two separate main clusters. The relationship among the isolates were also evaluated by constructing a genealogical median-joining network (MJN). The isolates from six locations produced 11 haplotypes that were illustrated in the MJN. The results of this study proved that location could not be an indicator for showing the genetic diversity of Pss from cherry orchards. As the genetic variability of Pseudomonads has been demonstrated, the current study also showed high diversity among different isolates even within the populations. While more research is recommended, the results of this study contributed to a better understanding of the Pss evolutionary progress and genetic diversity of sweet cherry isolates

Senior Leonard Hayes Wins National Piano Competition

Lawrence University’s Leonard Hayes, a senior from Dallas, Texas, won the recent Young Artists’ Division of the 2011 Tourgee Debose National Piano Competition conducted at Southern University in Baton Rouge, La. This was Hayes’ second first-place showing in the competition having previously won the Tourgee Debose’s sophomore division in 2009. Hayes received a first-place prize of $1,000 for his winning performance of Beethoven’s “Piano Sonata Op. 90,” Cesar Franck’s “Poco Allegro and Fugue” and two movements from George Walker’s “Piano Sonata No. 2.” A third-place finisher in the 2010 National Association of Negro Musicians’ Piano Scholarship competition, Hayes studies in the piano studio of Catherine Kautsky

Chemical Control of Powdery Mildew of Bigleaf Hydrangea

The efficacy of the fungicide pydiflumetofen + difenoconazole (Postiva) was evaluated at varying application rates and intervals for the control of powdery mildew (Golovinomyces orontii, formerly Erysiphe polygoni) in bigleaf hydrangea (Hydrangea macrophylla ‘Nikko Blue’). Container-grown hydrangeas were arranged in a completely randomized design with six single-plant replications. Experiments were done in 2022 and 2023 under both greenhouse and shade house conditions (56% shade). Powdery mildew in hydrangea was developed naturally. Pydiflumetofen + difenoconazole at 1.1, 1.6, and 2.2 ml·L21 and a standard fungicide azoxystrobin + benzovindiflupyr (Mural) at 0.5 g·L21 were sprayed to runoff on 2-, 4-, and 6-week intervals. Plants that were not treated with fungicide served as the control. Plants were evaluated weekly for disease severity (0% to 100% foliage affected) and defoliation (0% to 100% defoliation). The season-long area under the disease progress curve (AUDPC) and defoliation progress curve (AUDFC) were calculated for the evaluation period. The initial and final plant height and width were recorded, and height and width increase were determined. Pydiflumetofen + difenoconazole and azoxystrobin + benzovindiflupyr significantly reduced final disease severity, AUDPC, and defoliation both in the greenhouse and shade house compared with control plants. In both greenhouse trials and the 2022 shade house trial, AUDFC was reduced in all treatments compared with the control plants. However, AUDFC was not reduced by all treatments in the 2023 shade house trial. Pooled over application intervals, the low rate of pydiflumetofen + difenoconazole was as effective as the medium and high rates of pydiflumetofen + difenoconazole and azoxystrobin + benzovindiflupyr in reducing final powdery mildew severity and AUDPC both in the greenhouse and shade house in both 2022 and 2023. No significant differences between application intervals were noted in final disease severity and progress. Control of powdery mildew with fungicides failed to increase plant dimensions (i.e., plant height and width) compared with the no fungicide control. Because all application rates and intervals of pydiflumetofen + difenoconazole provided comparable powdery mildew disease control, it is suggested that using a low rate of pydiflumetofen + difenoconazole with the longest application interval (6 weeks) is the most cost-effective approach for managing powdery mildew in bigleaf hydrangeas

Erosive arthritis in a patient with primary sjogren's syndrome: a case report

Primer Sjögren sendromu, sıklıkla ağız ve göz kuruluğu ile seyreden bir kronik otoimmun ekzokrinopatidir. Ağız içi bezleri ve göz yası bezleri dışında, nadir de olsa diğer ekzokrin bezler de etkilenebilir. En sık kas-iskelet sistem tutulusu ile karsımıza çıkmaktadır. Artralji, sabah tutukluğu ve romatoid artrite benzer kronik inflamatuvar poliartrit, eklem bulgularını oluşturmaktadır. Romatoid artrit'ten farklı olarak, Sjögren sendromunda sabah tutukluğu ve hareket kısıtlığı daha hafif olup, el ve el bilek deformasyonları görülmektedir. Romatoid artrit'ten ayıran en önemli özellik ise, direk grafi ve/veya magnetik rezonans görüntülerde, eklemlerde eroziv değişikliklerin olmamasıdır. Bu bildiride, primer Sjögren sendromu tanısı almış hastada, eroziv artrit rapor edilmiştir.Primary Sjogren's syndrome (SS) is an autoimmune exocrinopathy characterized by dry eyes and dry mouth. Exocrine glands other than salivary and lacrimal glands may be affected less frequently. The most common mode of presentation is musculoskeletal system involvement. Articular signs and symptoms include arthralgias, morning stiffness, and chronic polyarthritis that resemble those seen in rheumatoid arthritis (RA). Compared with RA, the arthritis tends to be more relapsing and remitting, and stiffness is less marked. The distinction fromRAis that, there is not any erosive changes neither on direct radiography nor magnetic resonance imaging (MRI).We report a patient of primary SS presented with erosive arthritis

Magnetic resonance ımaging of the sacroiliac joints in ankylosing spondilitis before and after therapy with anti-tumor necrosis factor alpha

AMAÇ: Çalışmanın amacı, dirençli AS'li hastalarda, anti-TNF-alfa ilaçların etkinliğini ve güvenirliğini yanısıra, manyetik rezonans (MR) görüntüleme ile tedavi öncesi ve sonrası sakroiliak eklem değişiklerini tespit etmektir. GEREÇ ve YÖNTEM: Modifiye New York tanı kriterlerine göre AS tanısı almış, 27 hasta çalışmaya dahil edildi. Sakroiilitis bulguları, anti-TNF-alfa tedavi öncesi ve sonrası, Gd-MR ile tespit edildi. Sekiz hastaya, 4 haftada bir İnfliximab 4 mg/kg i.v. infüzıon verildi. Diğer 19 hastaya ise Etanercept 2x25 mg/hafta s.c. verildi. Değerlendirilen klinik ve laboratuvar parametreler; BASDAı, BASFı, ağrı (VAS skoru), Schöber testi, göğüs ekspansiyonu, C-reaktif protein (CRP), eritrosit sedimentasyon hızı (ESH). BULGULAR: Hastaların çoğu, anti-TNF-alfa tedavilerine iyi yanıt verdi. 24. haftanın sonunda, takip edilen tüm parametrelerde iyileşme gözlendi. MR görüntüleme çalışmalarında, anti-TNF-alfa tedavi sonrası sadece 3 hastanın sakroiliak eklem inflamasyonunda gerileme gözlendi. SONUÇ: Aktif AS'li hastalarda, 24. hafta sonunda anti-TNF-alfa ilaçları güvenilir ve etkin bulundu. BASDAı, BASFı, ağrı skorlarında belirgin düşüş gözlendi. Fakat, sakroiliak eklemin akut inflamatuvar bulgularında, MR görüntüleme ile herhangi bir gerileme tespit edilmedi.OBJECTIVE: The goal of this study is to assess the changes in the sacroiliac joints (Sİ) by magnetic resonance imaging (MRI) in a 24-week follow-up period and to determine the efficacy and safety of anti-TNF-α therapies for refractory AS. MATERIALS and METHODS: Twenty-seven patients who met the modified New York criteria for AS were enrolled in this study. Activity of sacroiliitis was determined by Gd-MRI scan before and after anti-TNF-α treatment. Eight patients received infliximab at a dose of 4mg/kg by intravenous infusion over 2 hours at every 4 week. Other 19 patients were treated with 25mg subcutaneous etanercept twice weekly. Total observational period was 24 weeks. The clinical and laboratory variables included: Bath AS Disease Activity Index (BASDAI), Bath AS Functional Index (BASFI), pain on a visual analog scale, Schober's index, chest expansion, C-reactive protein(CRP), erythrocyte sedimentation rate (ESR). RESULTS: Most patients responded well to treatment of anti-TNF-α antagonists. At 24 weeks, there was an improvement in all of the following measures. Imaging studies showed decreased inflammation of the SI joints after 24 weeks of treatment with anti-TNF-α therapies in only 3 patients. CONCLUSION: The anti-TNF-α therapy was safe and effective in treating patients with active AS during 24-week study period. The BASDAI, BASFI, VAS of pain were decreased well. But we could not determine any regression of acute inflammatory changes of the SI joints as depicted by MR

Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice

Advances in big data analytics have created an opportunity for a step change in unraveling mechanisms underlying the development of complex diseases such as asthma, providing valuable insights that drive better diagnostic decision-making in clinical practice, and opening up paths to individualized treatment plans. However, translating findings from data-driven analyses into meaningful insights and actionable solutions requires approaches and tools which move beyond mining and patterning longitudinal data. The purpose of this review is to summarize recent advances in phenotyping of asthma, to discuss key hurdles currently hampering the translation of phenotypic variation into mechanistic insights and clinical setting, and to suggest potential solutions that may address these limitations and accelerate moving discoveries into practice. In order to advance the field of phenotypic discovery, greater focus should be placed on investigating the extent of within-phenotype variation. We advocate a more cautious modeling approach by “supervising” the findings to delineate more precisely the characteristics of the individual trajectories assigned to each phenotype. Furthermore, it is important to employ different methods within a study to compare the stability of derived phenotypes, and to assess the immutability of individual assignments to phenotypes. If we are to make a step change toward precision (stratified or personalized) medicine and capitalize on the available big data assets, we have to develop genuine cross-disciplinary collaborations, wherein data scientists who turn data into information using algorithms and machine learning, team up with medical professionals who provide deep insights on specific subjects from a clinical perspective