Anaerobic co-digestion of oil refinery wastewater and chicken manure to produce biogas, and kinetic parameters determination in batch reactors

ArticleIn order to improve the anaerobic fermentation of oil refinery wastewater (ORWW) via an appropriate nutrients pool for microbial and buffer capacity growth, a study was carried out on related anaerobic co-digestion (AcoD) with a rich organic carbon source, namely chicken manure (CM). The kinetic parameters were investigated (including cumulative biogas production, bio-methane content, retention time, and soluble chemical oxygen demand stabilisation rate) of batch AcoD experiments related to six ORWW:CM-ratio treatments (5:0, 4:1, 3:2, 2:3, 1:4, and 0:5) under mesophilic conditions. The highest soluble chemical oxygen demand removal rate was obtained for the 4:1-ratio treatment. However, the highest biogas production and bio-methane contents were achieved for the 1:4-ratio treatment. When taking into consideration the highest oil refinery wastewater portion in the AcoD mixtures and the statistical test results (LSD0.05) for the kinetic parameters, it can be seen that the 4:1-ratio treatment provided the maximum biogas production levels

A Formal Proof of PAC Learnability for Decision Stumps

We present a formal proof in Lean of probably approximately correct (PAC) learnability of the concept class of decision stumps. This classic result in machine learning theory derives a bound on error probabilities for a simple type of classifier. Though such a proof appears simple on paper, analytic and measure-theoretic subtleties arise when carrying it out fully formally. Our proof is structured so as to separate reasoning about deterministic properties of a learning function from proofs of measurability and analysis of probabilities.Comment: 13 pages, appeared in Certified Programs and Proofs (CPP) 202

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

Qualitative Indices of Istamaran Date Variety Affected by Various Drying Methods

Drying of fruits and vegetables is one of the oldest methods for preserving foods. Drying not only affects the moisture content of the product, but also changes other physical, chemical and biological properties of the product including enzymatic activity, microbial spoilage, viscosity, hardness, taste and aroma. In order to study the occurring changes in dried product, qualitative characteristics including shrinkage, color and water rehydration are commonly evaluated. The purpose of this research was to study the effect of drying methods on qualitative indices for dried Istamaran dates. The drying methods were hot air, microwave and vacuum drying. The photos of the final product were taken using a digital camera. Then, color parameters (L*, a* and b*) of the samples were measured using Photoshop software. The amount of shrinkage for dried product was determined by liquid displacement method. For evaluating rehydration ability, water absorption capacity (WAC), dry matter holding capacity (DHC), and rehydration ability (RA) were also estimated. Results showed that the effect of drying method on WAC, DHC, and RA was significant (p<0.01). Means comparison revealed that the structural damage into the final dried product occurred by microwave method was higher than that for hot air and vacuum drying methods. Drying method did not lead to any significant difference among shrinkage values. Drying temperature influenced shrinkage more than drying time. Analysis of variance showed that the effect of drying method on L*, a* and b* parameters was not significant. Since the temperature of drying in microwave method is very high, it is possible that caramelization occurs during this method. This phenomenon can be considered as the reason of color darkness caused by microwave method