DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI), 201

Numeracy and Home Blood Pressure Measurement Reporting

Background: Numeracy, a component of overall health literacy, affects the way patients understand and process numerical health information. Home blood pressure monitoring (HBPM), a valuable tool in predicting CVD risk and end-organ damage independent of office BPs, requires the self-measurement and reporting of numerical health information and may be limited by patient understanding. To my knowledge, the relationship between numeracy and quality of HBPM has not been previously described. In this study, I examined the association of numeracy level with the completeness of home BP reporting. Methods: A systematic review of recent literature was conducted to describe the relationship between low numeracy and health-related skills, self-efficacy, and other health-related outcomes among patients with chronic disease. Following the literature review, I analyzed data from 420 adults participating in a four week BP measurement study who performed HBPM and completed a validated 3-item numeracy assessment. Participants were asked to complete triplicate home BP measurements twice daily for 5 consecutive days during week 1 and week 3. Demographic information and health literacy assessments were also collected. Total percentages of completed home BP readings by low vs. high numeracy level were summarized using descriptive statistics, and I performed multivariate regression analyses to identify potential confounders that may mediate the effect of numeracy on completion of home BP reporting. Results: Four studies of numeracy published since 2010 were identified in the systematic review. Two studies measured health care utilization, one study measured diabetes self-efficacy, and two studies measured severity of diabetes as outcomes by numeracy level. The evidence was low for disease severity and was insufficient for self-efficacy and health care utilization. Among the 420 adults who performed HBPM, nearly one-third had low numeracy (score of 0 or 1) and two-thirds had adequate numeracy (score of 2 or 3). Those with adequate numeracy reported completing home BP readings more than those with low numeracy (96.2% vs. 93.7%; P=0.009), which held true after adjusting for potential confounders. Conclusion: There is insufficient evidence in the current literature describing the relationship between numeracy and health-related skills. Among patients with borderline high blood pressures, higher numeracy level is associated with more complete reporting of home BP readings, although the difference is small. More research is needed to assess whether higher numeracy is a predictor of more accurate BP readings, and whether this trend holds true in the context of other numerical health parameters.Master of Public Healt

Focal Left Atrial Tachycardia in a Patient with Left Ventricular Noncompaction

Left ventricular noncompaction (LVNC) is a rare disease caused by intrauterine failure of the myocardium to compact. The major clinical manifestations of LVNC include heart failure, ventricular tachyarrhythmia, thromboembolic event, and sudden deaths. Atrial arrhythmia usually seen is atrial fibrillation. We report a rare case of focal left atrial tachycardia in an 18-year-old patient who presented for evaluation of persistent tachycardia. Transthoracic echocardiogram showed severe systolic dysfunction and evidence of noncompaction of the left ventricle. A detailed review of ECG revealed the possibility of ectopic atrial tachycardia, most likely originating from the left side. Electrophysiology study showed sustained atrial tachycardia originating on the ridge anterior to the left sided pulmonary veins. A successful radiofrequency catheter ablation was performed at this site without any complications

Game Development using Panda 3D Game Engine

This paper explores the features of panda 3d game engine and the AI algorithm used in creating games. Here we propose the A* algorithm which is used in game development and explain its merits and demerits with other path finding algorithms. We describe the importance of AI in games and even understand how to A* algorithm works and also how to implement A* algorithm in python. DOI: 10.17762/ijritcc2321-8169.15022

Torpedo Maculopathy

This is a Photo Essay and does not have an abstract

An introduction to the WHO 5th edition 2022 classification of testicular tumours

The 5th edition of the World Health Organisation Blue Book was published recently and includes a comprehensive update on testicular tumours. This builds upon the work of the 4th edition, retaining its structure and main nomenclature, including the use of the term 'germ cell neoplasia in situ' (GCNIS) for the pre-invasive lesion of most germ cell tumours and division from those not derived from GCNIS. While there have been important developments in understanding the molecular underpinnings of testicular cancer, this updated classification paradigm and approach remains rooted in morphology. Nomenclature changes include replacement of the term 'primitive neuroectodermal tumour' by 'embryonic neuroectodermal tumour' based on the non-specificity of the former term and to separate these tumours clearly from Ewing sarcoma. Seminoma is placed in a germinoma family of tumours emphasising relation to those tumours at other sites. Criteria for the diagnosis of 'teratoma with somatic transformation' have been modified to not include variable field size assessments. The word 'carcinoid' has been changed to 'neuroendocrine tumour', with most examples in the testis now classified as 'prepubertal type testicular neuroendocrine tumour'. For sex cord-stromal tumours, the use of mitotic counts per high-power field has been changed to per mm2 for malignancy assessments, and the new entities, 'signet ring stromal tumour' and 'myoid gonadal stromal tumour', are defined. Well-differentiated papillary mesothelial tumour has now been defined as tumour type with a favourable prognosis. Sertoliform cystadenoma has been removed as an entity from testicular adnexal tumours and placed with Sertoli cell tumours

ARTIFICIAL NEURAL NETWORKS AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR WHEAT YIELD ANALYSIS AND PREDICTION

The current study evaluated the prediction of the yield of wheat crops in the Bagalkot district of Karnataka State, India. The study aimed to provide crop yield predictions to help farmers optimize their cultivation and marketing strategies. The model used various independent variables, such as temperature, humidity of air, and water resources, to predict growth in the yield of wheat crops. The correlation analysis helps determine the strength and direction of the relationship between the variables based on the results. The statistical analysis identifies the variables that have a significant impact on crop yield growth. The work developed and tested two different models (the Artificial Neural Network (ANN) model and the Adaptive Neuro-fuzzy Interference System (ANFIS) to predict crop yield growth based on the selected independent variables. The ANFIS model was particularly interesting as it can predict a mapping between the input and output parameters, which can be useful for understanding the relationships between different variables. ANFIS was considered a better predictor than ANN as the error percentage ranged from 0-3%. Overall, the work highlighted the importance of crop yield predictions and the potential benefits that simulations can generate for farmers and the agriculture sector in general

Primary Hepatic Leiomyosarcoma Report of a rare case with review of literature

Primary hepatic leiomyosarcoma is an extremely rare tumor with a dismal prognosis and difficulty in diagnosis. We present a 36-year-old female who presented with complaints of pain in right hypochondrium and epigastric region. Real-time ultrasonography revealed an enlarged liver with multiple hypoechoic lesions of varying sizes in both the lobes of the liver. USG guided core biopsy from the lesion showed an infiltrating malignant spindle cell neoplasm positive for smooth muscle actin and caldesmon-H confirming the diagnosis of leiomyosarcoma. It is vital to diagnose these lesions even on limited biopsies as early diagnosis can reduce hospital and operative morbidity and mortality rates in the patients

Crystal structure of binary and ternary complexes of serine hydroxymethyltransferase from Bacillus stearothermophilus

Serine hydroxymethyltransferase (SHMT), a member of the α-class of pyridoxal phosphate-dependent enzymes, catalyzes the reversible conversion of serine to glycine and tetrahydrofolate to 5,10-methylene tetrahydrofolate. We present here the crystal structures of the native enzyme and its complexes with serine, glycine, glycine, and 5-formyl tetrahydrofolate (FTHF) from Bacillus stearothermophilus. The first structure of the serine-bound form of SHMT allows identification of residues involved in serine binding and catalysis. The SHMT-serine complex does not show any significant conformational change compared with the native enzyme, contrary to that expected for a conversion from an "open" to "closed" form of the enzyme. However, the ternary complex with FTHF and glycine shows the reported conformational changes. In contrast to the Escherichia coli enzyme, this complex shows asymmetric binding of the FTHF to the two monomers within the dimer in a way similar to the murine SHMT. Comparison of the ternary complex with the native enzyme reveals the structural basis for the conformational change and asymmetric binding of FTHF. The four structures presented here correspond to the various reaction intermediates of the catalytic pathway and provide evidence for a direct displacement mechanism for the hydroxymethyl transfer rather than a retroaldol cleavage