Automatic Leather Species Identification using Machine Learning Techniques

Content: Identification and classification of leather species becomes valuable and necessary due to concerns regarding consumer protection, product counterfeiting, and dispute settlement in the leather industry. Identification and classification of leather into species is carried out by histological examination or molecular analysis based on DNA. Manual method requires expertise, training and experience, and due to involvement of human judgment disputes are inevitable thus a need to automate the leather species identification. In the present investigation, an attempt has been made to automate leather species identification using machine learning techniques. A novel non-destructive leather species identification algorithm is proposed for the identification of cow, buffalo, goat and sheep leathers. Hair pore pattern was segmented efficiently using k-means clustering algorithm Significant features representing the unique characteristics of each species such as no.of hair pores, pore density, percent porosity, shape of the pores etc., were extracted. The generated features were used for training the Random forest classifier. Experimental results on the leather species image library database achieved an accuracy of 87 % using random forest as classifier, confirming the potentials of using the proposed system for automatic leather species classification. Take-Away: Novel technique to identify leather species Non destructive method Machine learning algorithms to automate leather species identificatio

A randomized phase III study of carfilzomib vs low-dose corticosteroids with optional cyclophosphamide in relapsed and refractory multiple myeloma (FOCUS)

This randomized, phase III, open-label, multicenter study compared carfilzomib monotherapy against low-dose corticosteroids and optional cyclophosphamide in relapsed and refractory multiple myeloma (RRMM). Relapsed and refractory multiple myeloma patients were randomized (1:1) to receive carfilzomib (10-min intravenous infusion; 20 mg/m(2) on days 1 and 2 of cycle 1; 27 mg/m(2) thereafter) or a control regimen of low-dose corticosteroids (84 mg of dexamethasone or equivalent corticosteroid) with optional cyclophosphamide (1400 mg) for 28-day cycles. The primary endpoint was overall survival (OS). Three-hundred and fifteen patients were randomized to carfilzomib (n=157) or control (n=158). Both groups had a median of five prior regimens. In the control group, 95% of patients received cyclophosphamide. Median OS was 10.2 (95% confidence interval (CI) 8.4-14.4) vs 10.0 months (95% CI 7.7-12.0) with carfilzomib vs control (hazard ratio=0.975; 95% CI 0.760-1.249; P=0.4172). Progression-free survival was similar between groups; overall response rate was higher with carfilzomib (19.1 vs 11.4%). The most common grade ⩾3 adverse events were anemia (25.5 vs 30.7%), thrombocytopenia (24.2 vs 22.2%) and neutropenia (7.6 vs 12.4%) with carfilzomib vs control. Median OS for single-agent carfilzomib was similar to that for an active doublet control regimen in heavily pretreated RRMM patients

Human Neutrophil Elastase Responsive Delivery from Poly(ethylene glycol) Hydrogels

A novel enzyme-responsive hydrogel drug delivery system was developed with the potential to treat inflammation locally. Human neutrophil elastase (HNE) is a serine protease secreted by neutrophils which are the first cells recruited to inflammatory sites. We exploited this cell-secreted enzyme as a biological cue for controlled release. HNE sensitive peptide linkers were immobilized within poly(ethylene glycol) hydrogels using photopolymerization techniques. The kinetics of the enzyme reaction within the gel was tailored by varying the amino acid residues present in the P1 and P1 ′ substrate positions (immediately adjacent to cleavage location). A novel FRET-based hydrogel platform was designed to characterize the accessibility of the substrate within the cross-linked, macroscopic hydrogel. Lastly, a diffusion-reaction mathematical model with Michaelis-Menten kinetics was developed to predict the overall release profile and captured the initial 80 % of the experimentally observed release. The hydrogel platform presented shows highly controlled release kinetics with potential applications in cellular responsive drug delivery. 1

The Use of Biomaterials in Islet Transplantation

Pancreatic islet transplantation is a therapeutic option to replace destroyed β cells in autoimmune diabetes. Islets are transplanted into the liver via the portal vein; however, inflammation, the required immunosuppression, and lack of vasculature decrease early islet viability and function. Therefore, the use of accessory therapy and biomaterials to protect islets and improve islet function has definite therapeutic potential. Here we review the application of niche accessory cells and factors, as well as the use of biomaterials as carriers or capsules, for pancreatic islet transplantation

Identification of Optimum Maturity Index for Quality of Red Flesh Guava (Psidium guajava L.)

This study conducted at Department of Postharvest Technology, Horticultural College and Research Institute, Periyakulam from 2015 – 2017, evaluated the optimal maturity stage for harvesting “Red flesh” guavas to maintain the postharvest quality. Four distinct maturity stages were identified: half maturity, green maturity, full maturity, and fully ripened based on day after fruit set (DAFS) and peel and pulp colour. Results indicate that “Red flesh” guavas reached half maturity at 91 days after fruit set, displaying characteristics like a deep green in peel and pale pink pulp, and showed largest fruit size measurements at green maturity (134 DAFS). At full maturity (147 DAFS), fruits achieved peak values in Total soluble solids (TSS) and TSS/acid ratio, with a decrease in firmness and acidity with changing peel in to yellowish green with bright pink pulp. The fully ripened stage (154 DAFS) was marked by the highest TSS content and maintained fruit size but slightly declines in specific gravity and firmness. Organoleptic evaluations rated guavas at fully maturity stage highest in terms of colour, texture, flavor, taste, and overall acceptability underscoring this stage as the most suitable for harvesting to ensure best quality

Automatic Leather Species Identification using Machine Learning Techniques

Content: Identification and classification of leather species becomes valuable and necessary due to concerns regarding consumer protection, product counterfeiting, and dispute settlement in the leather industry. Identification and classification of leather into species is carried out by histological examination or molecular analysis based on DNA. Manual method requires expertise, training and experience, and due to involvement of human judgment disputes are inevitable thus a need to automate the leather species identification. In the present investigation, an attempt has been made to automate leather species identification using machine learning techniques. A novel non-destructive leather species identification algorithm is proposed for the identification of cow, buffalo, goat and sheep leathers. Hair pore pattern was segmented efficiently using k-means clustering algorithm Significant features representing the unique characteristics of each species such as no.of hair pores, pore density, percent porosity, shape of the pores etc., were extracted. The generated features were used for training the Random forest classifier. Experimental results on the leather species image library database achieved an accuracy of 87 % using random forest as classifier, confirming the potentials of using the proposed system for automatic leather species classification. Take-Away: Novel technique to identify leather species Non destructive method Machine learning algorithms to automate leather species identificatio

Automatic Leather Species Identification using Machine Learning Techniques

Content: Identification and classification of leather species becomes valuable and necessary due to concerns regarding consumer protection, product counterfeiting, and dispute settlement in the leather industry. Identification and classification of leather into species is carried out by histological examination or molecular analysis based on DNA. Manual method requires expertise, training and experience, and due to involvement of human judgment disputes are inevitable thus a need to automate the leather species identification. In the present investigation, an attempt has been made to automate leather species identification using machine learning techniques. A novel non-destructive leather species identification algorithm is proposed for the identification of cow, buffalo, goat and sheep leathers. Hair pore pattern was segmented efficiently using k-means clustering algorithm Significant features representing the unique characteristics of each species such as no.of hair pores, pore density, percent porosity, shape of the pores etc., were extracted. The generated features were used for training the Random forest classifier. Experimental results on the leather species image library database achieved an accuracy of 87 % using random forest as classifier, confirming the potentials of using the proposed system for automatic leather species classification. Take-Away: Novel technique to identify leather species Non destructive method Machine learning algorithms to automate leather species identificatio