Designing of banana shaped chromophores via molecular engineering of terminal groups to probe photovoltaic behavior of organic solar cell materials.

To meet the rising requirement of photovoltaic compounds for modernized hi-tech purpose, we designed six new molecules (DTPD1-DTPD6) from banana shaped small fullerene free chromophore (DTPR) by structural tailoring at terminal acceptors. Frontier molecular orbitals (FMOs), density of states (DOS), open circuit voltage (Voc), transition density matrix (TDM) analysis, optical properties, reorganization energy value of hole and electron were determined utilizing density function theory (DFT) and time-dependent density function theory (TD-DFT) approaches, to analyze photovoltaic properties of said compounds. Band gap contraction (∆E = 2.717-2.167 eV) accompanied by larger bathochromic shift (λmax = 585.490-709.693 nm) was observed in derivatives contrary to DTPR. The FMOs, DOS and TDMs investigations explored that central acceptor moiety played significant role for charge transformation. The minimum binding energy values for DTPD1-DTPD6 demonstrated the higher exciton dissociation rate with greater charge transferal rate than DTPR, which was further endorsed by TDM and DOS analyses. A comparable Voc (1.49-2.535 V) with respect to the HOMOPBDBT-LUMOacceptor for entitled compounds was investigated. In a nutshell, all the tailored chromophores can be considered as highly efficient compounds for promising OSCs with a good Voc response.

Personalising clinical pathways in a London breast cancer service.

Using interview and observational data from a busy and research-intensive breast cancer service in the United Kingdom, we discuss recent developments in personalised medicine. Specifically, we show how clinical and research practices meet in clinical pathways that are reconfigured in response to changing approaches of diagnosing, monitoring, treating and understanding cancers. Clinical pathways are increasingly sensitive to changes in evidence deduced through new technologies and therapies as well as decisions based on intensive, iterative analysis of data collected across a range of platforms. We contribute to existing research by showing how the organisation of clinical pathways both maintains established clinical practices and responds to new research evidence, managing a threshold between evidence-based and experimental medicine. Finally, we invite comparisons with other forms of personalisation to understand how they depend on the 'real time' collection, analysis and application of data.

Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis.

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%.

Breast Tumor Classification Using an Ensemble Machine Learning Method.

Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD.

Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) Model for Human Action Recognition.

Human action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although significant results have been achieved in simple scenarios, HAR is still a challenging task due to issues associated with view independence, occlusion and inter-class variation observed in realistic scenarios. In previous research efforts, the classical bag of visual words approach along with its variations has been widely used. In this paper, we propose a Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition without compromising the strengths of the classical bag of visual words approach. Expressions are formed based on the density of a spatio-temporal cube of a visual word. To handle inter-class variation, we use class-specific visual word representation for visual expression generation. In contrast to the Bag of Expressions (BoE) model, the formation of visual expressions is based on the density of spatio-temporal cubes built around each visual word, as constructing neighborhoods with a fixed number of neighbors could include non-relevant information making a visual expression less discriminative in scenarios with occlusion and changing viewpoints. Thus, the proposed approach makes the model more robust to occlusion and changing viewpoint challenges present in realistic scenarios. Furthermore, we train a multi-class Support Vector Machine (SVM) for classifying bag of expressions into action classes. Comprehensive experiments on four publicly available datasets: KTH, UCF Sports, UCF11 and UCF50 show that the proposed model outperforms existing state-of-the-art human action recognition methods in term of accuracy to 99.21%, 98.60%, 96.94 and 94.10%, respectively.

Artery to Cystic Duct: A Consistent Branch of Cystic Artery Seen in Laparoscopic Cholecystectomy.

Uncontrolled arterial bleeding during laparoscopic cholecystectomy is a serious problem and may increase the risk of bile duct damage. Therefore, accurate identification of the anatomy of the cystic artery is very important. Cystic artery is notoriously known to have a highly variable branching pattern. We reviewed the anatomy of the cystic artery and its branch to cystic duct as seen through the video laparoscope. A single artery to cystic duct with the classical "H-configuration" was demonstrated in 161 (91.47%) patients. This branch may cause troublesome bleeding during laparoscopic dissection in the hepatobiliary triangle. Careful identification of artery to cystic duct is helpful in the proper dissection of Calot's triangle as it reduces the chances of hemorrhage and thus may also be helpful in prevention of extrahepatic biliary radical injuries.

Laparoscopic interval appendectomy versus open interval appendectomy: a prospective randomized controlled trial.

This was a prospective randomized controlled study designed to compare laparoscopic and open interval appendectomy and involved 100 patients of appendicular phlegmon. After initial conservative management, patients were divided into 2 groups of 50 each and interval appendectomy was performed by laparoscopy in one of the groups and by open method in the other. Mean operative time in open surgery was 33.9 minutes and that in laparoscopic surgery was 57.64 minutes (P < 0.05). Concomitant pathology was observed in 16% and 2% of patients in the laparoscopic and open groups, respectively. Mean pain scores on the first postoperative day were 5.14 in the laparoscopic group and 6.01 in the open group (P < 0.05). Patients in the laparoscopic group had a shorter duration of ileus, postoperative stay, and returned to work earlier (P < 0.05). We conclude that laparoscopy offers a number of advantages over open interval appendectomy.

Epiploic appendagitis of caecum: a diagnostic dilemma.

Epiploic appendagitis is a rare cause of acute abdomen. Depending on the site of occurrence, it can mimic any cause of acute abdomen or disease of the colon and caecal appendix; making its preoperative diagnosis very difficult. We present here a case of a 7-year-old boy misdiagnosed preoperatively as acute appendicitis and later on, upon surgical exploration, found to have caecal appendagitis. The affected epiploic appendage was removed and the patient had an uneventful recovery. We also review the relevant literature and discuss the measures to overcome this diagnostic dilemma. General surgeons should be aware of this self-limiting disease and consider it as a differential diagnosis of acute abdomen.