Evaluation of double treated starches using thermal tools.

Starch is the main polysaccharide found in cereals, composed by amylose and amylopectin. Corn is the principal source of starches worldwide. Starches treatment, through physical, chemical and/or biological methods, can improve the applications range. Acid modification in alcoholic solution promotes minimally degradation in the granule. Ball mill is one physical method poorly explored. The aim was to treat the starches using HCl 0.5 mol L-1 for 1 hour in 100 ml of aqueous, ethanol or methanol solutions with subsequent ball milling processes. One sample was selected as native sample. The four others, one native sample and three acid modified samples, were treated by physical process with the oscillating ball mill. The DTG-60H equipment was used for the TG and DTA analysis. The TG curves showed three mass losses related to dehydration, decomposition and oxidation. The native sample without physical modification showed major resistance to total degradation. This occurs because the physical modification cleaves hydrogen bonds, leaving a weakened granule. The TGDTA results showed that the mass loss in the 2nd event was minor in the hydrolyzed samples compared with native samples. The acid modification can provide starch higher resistance to degradation up to 340 °C. These results showed that chemical and physical treatment changed the thermal behaviors of the starches

Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error

BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce human resources required for carrying out this step of a systematic review. METHODS: We applied ML approaches to a broad systematic review of animal models of depression at the citation screening stage. We tested two independently developed ML approaches which used different classification models and feature sets. We recorded the performance of the ML approaches on an unseen validation set of papers using sensitivity, specificity and accuracy. We aimed to achieve 95% sensitivity and to maximise specificity. The classification model providing the most accurate predictions was applied to the remaining unseen records in the dataset and will be used in the next stage of the preclinical biomedical sciences systematic review. We used a cross-validation technique to assign ML inclusion likelihood scores to the human screened records, to identify potential errors made during the human screening process (error analysis). RESULTS: ML approaches reached 98.7% sensitivity based on learning from a training set of 5749 records, with an inclusion prevalence of 13.2%. The highest level of specificity reached was 86%. Performance was assessed on an independent validation dataset. Human errors in the training and validation sets were successfully identified using the assigned inclusion likelihood from the ML model to highlight discrepancies. Training the ML algorithm on the corrected dataset improved the specificity of the algorithm without compromising sensitivity. Error analysis correction leads to a 3% improvement in sensitivity and specificity, which increases precision and accuracy of the ML algorithm. CONCLUSIONS: This work has confirmed the performance and application of ML algorithms for screening in systematic reviews of preclinical animal studies. It has highlighted the novel use of ML algorithms to identify human error. This needs to be confirmed in other reviews with different inclusion prevalence levels, but represents a promising approach to integrating human decisions and automation in systematic review methodology

Detection of Prion Protein Particles in Blood Plasma of Scrapie Infected Sheep

Prion diseases are transmissible neurodegenerative diseases affecting humans and animals. The agent of the disease is the prion consisting mainly, if not solely, of a misfolded and aggregated isoform of the host-encoded prion protein (PrP). Transmission of prions can occur naturally but also accidentally, e.g. by blood transfusion, which has raised serious concerns about blood product safety and emphasized the need for a reliable diagnostic test. In this report we present a method based on surface-FIDA (fluorescence intensity distribution analysis), that exploits the high state of molecular aggregation of PrP as an unequivocal diagnostic marker of the disease, and show that it can detect infection in blood. To prepare PrP aggregates from blood plasma we introduced a detergent and lipase treatment to separate PrP from blood lipophilic components. Prion protein aggregates were subsequently precipitated by phosphotungstic acid, immobilized on a glass surface by covalently bound capture antibodies, and finally labeled with fluorescent antibody probes. Individual PrP aggregates were visualized by laser scanning microscopy where signal intensity was proportional to aggregate size. After signal processing to remove the background from low fluorescence particles, fluorescence intensities of all remaining PrP particles were summed. We detected PrP aggregates in plasma samples from six out of ten scrapie-positive sheep with no false positives from uninfected sheep. Applying simultaneous intensity and size discrimination, ten out of ten samples from scrapie sheep could be differentiated from uninfected sheep. The implications for ante mortem diagnosis of prion diseases are discussed

A screening process for carbonation of vegetable oils using an aluminum(salen) complex with a further application as weldable polymers

Carbon dioxide (CO2) occurs naturally, though its emissions have been increasing due to anthropogenic activities, and its increasing atmospheric concentration levels are causing a greenhouse effect. In efforts to develop new carbon dioxide utilization (CDU) methodologies, the catalyzed reaction of CO2 with epoxidized vegetable oil, obtained from Brazilian macaw oil and Baru oil, to form carbonated oils for novel and sustainable monomers was explored. A screening process is carried out to develop the best reaction conditions, by varying catalyst/cocatalyst loading, reaction time, CO2 pressure, and the reaction temperature, resulting in conversions of 100%. The aluminum(salen) complex shows a selective and efficient catalyst activity. Both carbonated oils are reacted with amines (1,6-diaminohexane, lysine, and 4,4′-methylenebis [cyclohexylamine]) to provide weldable polyhydroxyurethanes. Polymers synthesized from lysine provide a more selective reaction and higher cross-linked structures, with fewer side reactions involving the glyceride groups. All the synthesized polymers are thermally stable above 200°C and differential scanning calorimetry (DSC) analysis shows two main thermal events, related to the glass transition (Tg) and the topology-freezing transition temperature (Tv). The Tv result indicates that the polymer has weldable properties due to chemical bond exchange. Thus, these polymers can be healed into different shapes upon exposure to red light (660 nm)