Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?

Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications

Bladder and upper urinary tract cancers as first and second primary cancers

Background Previous population-based studies on second primary cancers (SPCs) in urothelial cancers have focused on known risk factors in bladder cancer patients without data on other urothelial sites of the renal pelvis or ureter. Aims To estimate sex-specific risks for any SPCs after urothelial cancers, and in reverse order, for urothelial cancers as SPCs after any cancer. Such two-way analysis may help interpret the results. Methods We employed standardized incidence ratios (SIRs) to estimate bidirectional relative risks of subsequent cancer associated with urothelial cancers. Patient data were obtained from the Swedish Cancer Registry from years 1990 through 2015. Results We identified 46 234 urinary bladder cancers (75% male), 940 ureteral cancers (60% male), and 2410 renal pelvic cancers (57% male). After male bladder cancer, SIRs significantly increased for 9 SPCs, most for ureteral (SIR 41.9) and renal pelvic (17.2) cancers. In the reversed order (bladder cancer as SPC), 10 individual FPCs were associated with an increased risk; highest associations were noted after renal pelvic (21.0) and ureteral (20.9) cancers. After female bladder cancer, SIRs of four SPCs were significantly increased, most for ureteral (87.8) and pelvic (35.7) cancers. Female bladder, ureteral, and pelvic cancers associated are with endometrial cancer. Conclusions The risks of recurrent urothelial cancers were very high, and, at most sites, female risks were twice over the male risks. Risks persisted often to follow-up periods of >5 years, motivating an extended patient follow-up. Lynch syndrome-related cancers were associated with particularly female urothelial cancers, calling for clinical vigilance.Peer reviewe

A thermosensitive electromechanical model for detecting biological particles

Miniature electromechanical systems form a class of bioMEMS that can provide appropriate sensitivity. In this research, a thermo-electro-mechanical model is presented to detect biological particles in the microscale. Identification in the model is based on analyzing pull-in instability parameters and frequency shifts. Here, governing equations are derived via the extended Hamilton’s principle. The coupled effects of system parameters such as surface layer energy, electric field correction, and material properties are incorporated in this thermosensitive model. Afterward, the accuracy of the present model and obtained results are validated with experimental, analytical, and numerical data for several cases. Performing a parametric study reveals that mechanical properties of biosensors can significantly affect the detection sensitivity of actuated ultra-small detectors and should be taken into account. Furthermore, it is shown that the number or dimension of deposited particles on the sensing zone can be estimated by investigating the changes in the threshold voltage, electrode deflection, and frequency shifts. The present analysis is likely to provide pertinent guidelines to design thermal switches and miniature detectors with the desired performance. The developed biosensor is more appropriate to detect and characterize viruses in samples with different temperatures

The role of celecoxib in glioblastoma treatment: a review of literature

Objective: Glioblastoma (GB) is the most aggressive and lethal type of brain tumor. Despite the standard treatments and improvements, the overall survival (OS) and progression free survival (PFS) are not optimal. Celecoxib (CEL) has been considered as one of the adjuvant agents in patients with GB due to its different mechanisms in recent years. Materials and Methods: A systematic search was performed in EMBASE, MEDLINE, ClinicalTrials.gov, Web of Science, Google Scholar and Cochrane Central Register of the Controlled Trials databases to get access to the trials that investigated the potential benefits of CEL in the treatment regimen of patients with GB. Results: From 77 studies, twelve clinical trials with 690 patients from 2004 to 2015 were included. The trials were often in phase II and temozolamide was the main agent of the treatment regimen. CEL was administered mostly at high dose of 400 mg twice daily and it was well tolerated. CEL has shown some promising effects but only in studies which patients were not eligible for standard treatment due to their age or clinical conditions. Conclusions: CEL administration in tested doses is safe and practical for GBM patients. It could be considered as one of the choices in the therapeutic protocol of GB along with the main drugs commonly used in chemotherapy regimen especially in the elderly patients who are not eligible for standard treatment

Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition

© 2013 IEEE. Video-based face, expression, and scene recognition are fundamental problems in human-machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representation approach for face and texture recognition using short-length videos. First, it builds local linear subspaces of dynamic texture segments by computing spatiotemporal directional derivatives in a cylinder neighborhood within dynamic textures. Unlike traditional methods, a nonbinary texture coding technique is proposed to extract high-order derivatives using continuous circular and cylinder regions to avoid aliasing effects. Then, these local linear subspaces of texture segments are mapped onto a Grassmann manifold via sparse representation. A new joint sparse representation algorithm is developed to establish the correspondences of subspace points on the manifold for measuring the similarity between two dynamic textures. Extensive experiments on the Honda/UCSD, the CMU motion of body, the YouTube, and the DynTex datasets show that the proposed method consistently outperforms the state-of-the-art methods in dynamic texture recognition, and achieved the encouraging highest accuracy reported to date on the challenging YouTube face dataset. The encouraging experimental results show the effectiveness of the proposed method in video-based face recognition in human-machine system applications

Attitude of nurses towards euthanasia: a cross-sectional study in Iran

Background: Nurses play a major role in providing end-of-life care, and euthanasia is considered to be one of the most important ethical challenges that care providers can face. Aim: To assess the nurses' attitude towards euthanasia in Iran. Methods: The cross-sectional study included nurses who worked in intensive and critical care, as well as dialysis units of a teaching hospital affiliated to Zahedan University of Medical Sciences, who were selected by the census sampling technique. Data were collected using a two-part questionnaire encompassing the demographic characteristics of nurses and the 20-item Euthanasia Attitude Scale. Results: The overall score of nurses' attitudes towards euthanasia, ranging from one to five, was 2.71 +/- 0.45, indicating a negative attitude and opposition towards euthanasia. Alongside this, the results demonstrated that there was no significant relationship between demographic characteristics and nurses' attitudes toward euthanasia. Conclusion: In general, nurses in Iran oppose euthanasia. This can be attributed to the context of religious beliefs and culture in Iran as an Islamic country