Leptin signalling, obesity and prostate cancer: molecular and clinical perspective on the old dilemma

The prevalence of global obesity is increasing. Obesity is associated with general cancer-related morbidity and mortality and is a known risk factor for development of specific cancers. A recent large systematic review of 24 studies based on meta-analysis of 11,149 patients with prostate cancer showed a significant correlation between obesity and the risk of advanced prostate cancer. Further, a sustained reduction in BMI correlates with a decreased risk of developing aggressive disease. On the other hand, the correlation between consuming different products and prostate cancer occurrence/risk is limited. Here, we review the role of adipose tissue from an endocrine perspective and outline the effect of adipokines on cancer metabolism, with particular focus on leptin. Leptin exerts its physiological and pathological effects through modification of intracellular signalling, most notably activating the Janus kinase (JAK) 2/signal transducer and activator of transcription (STAT) 3 pathway and recently shown sphingolipid pathway. Both high levels of leptin in circulation and leptin receptor mutation are associated with prostate cancer risk in human patients; however, the in vivo mechanistic evidence is less conclusive. Given the complexity of metabolic cancer pathways, it is possible that leptin may have varying effects on prostate cancer at different stages of its development, a point that may be addressed by further epidemiological studies

Faktor-Faktor Yang Mendorong Masyarakat Baba Dan Nyonya Di Bandar Melaka Menceburi Bidang Keusahawanan

Kajian deskriptif ini dijalankan untuk mengenalpasti faktor-faktor yang mendorong masyarakat Baba dan Nyonya di Bandar Melaka menceburi bidang keusahawanan. Perkaitan dilakukan terhadap empat faktor yang dikhususkan kepada pengaruh faktor latar belakang keluarga, faktor sikap, faktor motivasi, dan faktor kemahiran-kemahiran yang dimiliki oleh usahawan. Instrumen kajian yang dijalankan adalah set soal selidik yang mengandungi 40 item. Skala Likert telah digunakan untuk tujuan pengumpulan data dan set soal selidik tersebut telah diedarkan secara rawak kepada 30 orang usahawan Baba dan Nyonya di sekitar Bandar Melaka. Maklumat-maklumat yang diperolehi kemudiannya dianalisis dengan menggunakan perisian Statistical Package for Social Science (SPSS) versi 12.0 bertujuan untuk mendapatkan nilai min, kekerapan, dan juga peratusan. Daripada dapatan kajian yang diperolehi, nilai min purata menunjukkan bahawa faktor sikap, faktor kemahiran serta faktor motivasi diri berada pada tahap yang tinggi. Cuma faktor latar belakang keluarga berada pada tahap sederhana. Faktor sikap usahawan merupakan faktor pendorong yang terkuat dalam mempengaruhi masyarakat Baba dan Nyonya di Bandar Melaka menceburi bidang keusahawanan. Beberapa cadangan telah dikemukakan bagi membolehkan kajian lanjutan dilakukan pada masa hadapan. Kata kunci: Baba dan Nyonya, Statistical Package for Social Science (SPSS

MicroRNA Expression Profiling of Human Respiratory Epithelium Affected by Invasive Candida Infection

Invasive candidiasis is potentially life-threatening systemic fungal infection caused by Candida albicans (C. albicans). Candida enters the blood stream and disseminate throughout the body and it is often observed in hospitalized patients, immunocompromised individuals or those with chronic diseases. This infection is opportunistic and risk starts with the colonization of C. albicans on mucocutaneous surfaces and respiratory epithelium. MicroRNAs (miRNAs) are small non-coding RNAs which are involved in the regulation of virtually every cellular process. They regulate and control the levels of mRNA stability and post-transcriptional gene expression. Aberrant expression of miRNAs has been associated in many disease states, and miRNA-based therapies are in progress. In this study, we investigated possible variations of miRNA expression profiles of respiratory epithelial cells infected by invasive Candida species. For this purpose, respiratory epithelial tissues of infected individuals from hospital laboratory were accessed before their treatment. Invasive Candida infection was confirmed by isolation of Candia albicans from the blood cultures of the same infected individuals. The purity of epithelial tissues was assessed by flow cytometry (FACSCalibur cytometer; BD Biosciences, Heidelberg, Germany) using statin antibody (S-44). TaqMan quantitative real-time PCR (in a TaqMan Low Density Array format) was used for miRNA expression profiling. MiRNAs investigated, the levels of expression of 55 miRNA were significantly altered in infected tissues. Some miRNAs showed dramatic increase (miR-16-1) or decrease of expression (miR-17-3p) as compared to control. Gene ontology enrichment analysis of these miRNA-targeted genes suggests that Candidal infection affect many important biological pathways. In summary, disturbance in miRNA expression levels indicated the change in cascade of pathological processes and the regulation of respiratory epithelial functions following invasive Candidal infection. These findings contribute to our understanding of host cell response to Candidal systemic infections

Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER. Our model utilises pre-trained models for multimodal feature extraction and is equipped with a dual attention mechanism including graph attention and co-attention to capture complex dependencies across different modalities and achieve improved cross-language SER results using minimal target language data. In addition, our model also exploits a transformer encoder layer for high-level feature representation to improve emotion classification accuracy. In this way, MDAT performs refinement of feature representation at various stages and provides emotional salient features to the classification layer. This novel approach also ensures the preservation of modality-specific emotional information while enhancing cross-modality and cross-language interactions. We assess our model's performance on four publicly available SER datasets and establish its superior effectiveness compared to recent approaches and baseline models.Comment: Under Review IEEE TM

Impact of Capital Structure on the Profitability in the Manufacturing and Non-Manufacturing Industries of Pakistan

The aim of this paper is to observe the connection between the capital structure and profitability and in fastidious, to measures their significance in manufacturing and non manufacturing industries of Pakistan. The paper adopts a quantitative data of different manufacturing and non manufacturing organizations in Pakistan. The financial statements were analyzed of manufacturing and non manufacturing organizations of Pakistan for the period of 2008-2013. The study reveals that there is a strong negative relationship between the profitability and debt in manufacturing industry and in the Non -manufacturing industry, there is a strong positive relationship between profitability and debt. The population of this study is Manufacturing and Non-Manufacturing industry of Pakistan and units of analysis are D.G Cement factory and AGTL from Manufacturing industry and, HBL & Bank Al-Falah from Non-Manufacturing industry. In this paper descriptive statistics were used to interpret the data. It is proved that manufacturing industry has found a strong negative regression between debts and profit and the non- manufacturing has found a strong positive regression between debt and profit. Keywords: Total debt; capital structure; profitability, performance; Return on Equity; Return on investment; Earning Per share and Price to Earnings Ratio, leverage

Leptomeningeal carcinomatosis as the primary presentation of relapse in breast cancer

Leptomeningeal metastasis (LM) is an uncommon presentation of relapse in breast cancer, which is associated with poor clinical outcomes and poor prognosis. Notably, LM most commonly occurs in breast cancer. The aim of the present review was to investigate the occurrence of LM as the primary presentation of relapse following remission in breast cancer patients and to determine whether specific histological subtypes are predisposed to meningeal metastases. In addition, the present review evaluated whether patients presenting with LM as the primary site of relapse exhibit differences in survival when compared with patients exhibiting metastasis to other sites. Cross-sectional studies have demonstrated that LM is commonly associated with other sites of distant metastasis including lung, liver and bone metastases. The histological breast cancer subtype most commonly associated with LM was invasive lobular carcinoma, while triple-negative breast cancer patients appear to be predisposed to the development of LM when considering the overall prevalence of histological breast cancer subtypes. At present, data regarding LM as the primary site of relapse are limited due to its rarity as the first site of metastasis in breast cancer. Case-controlled studies are required to investigate the incidence of LM as the primary site of recurrence in breast cancer patients as this would enable treatment standardization and identification of prognostic factors for improved survival

Multimodal framework based on audio‐visual features for summarisation of cricket videos

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166171/1/ipr2bf02094.pd

Instructor activity recognition through deep spatiotemporal features and feedforward Extreme Learning Machines

Human action recognition has the potential to predict the activities of an instructor within the lecture room. Evaluation of lecture delivery can help teachers analyze shortcomings and plan lectures more effectively. However, manual or peer evaluation is time-consuming, tedious and sometimes it is difficult to remember all the details of the lecture. Therefore, automation of lecture delivery evaluation significantly improves teaching style. In this paper, we propose a feedforward learning model for instructor's activity recognition in the lecture room. The proposed scheme represents a video sequence in the form of a single frame to capture the motion profile of the instructor by observing the spatiotemporal relation within the video frames. First, we segment the instructor silhouettes from input videos using graph-cut segmentation and generate a motion profile. These motion profiles are centered by obtaining the largest connected components and normalized. Then, these motion profiles are represented in the form of feature maps by a deep convolutional neural network. Then, an extreme learning machine (ELM) classifier is trained over the obtained feature representations to recognize eight different activities of the instructor within the classroom. For the evaluation of the proposed method, we created an instructor activity video (IAVID-1) dataset and compared our method against different state-of-the-art activity recognition methods. Furthermore, two standard datasets, MuHAVI and IXMAS, were also considered for the evaluation of the proposed scheme.We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for research work carried in Centre for Computer Vision Research (C2VR) at University of Engineering and Technology Taxila, Pakistan. Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509), and Banco Santander.We are also very thankful to participants, faculty, and postgraduate students of Computer Engineering Department who took part in the data acquisition phase.Without their consent, this work was not possible

Deep temporal motion descriptor (DTMD) for human action recognition

Spatiotemporal features have significant importance in human action recognition, as they provide the actor's shape and motion characteristics specific to each action class. This paper presents a new deep spatiotemporal human action representation, \Deep Temporal Motion Descriptor (DTMD)", which shares the attributes of holistic and deep learned features. To generate the DTMD descriptor, the actor's silhouettes are gathered into single motion templates through applying motion history images. These motion templates capture the spatiotemporal movements of the actor and compactly represents the human actions using a single 2D template. Then, deep convolutional neural networks are used to compute discriminative deep features from motion history templates to produce DTMD. Later, DTMD is used for learn a model to recognise human actions using a softmax classifier. The advantage of DTMD comes from (i) DTMD is automatically learned from videos and contains higher dimensional discriminative spatiotemporal representation as compared to handcrafted features; (ii) DTMD reduces the computational complexity of human activity recognition as all the video frames are compactly represented as a single motion template; (iii) DTMD works e ectively for single and multiview action recognition. We conducted experiments on three challenging datasets: MuHAVI-Uncut, iXMAS, and IAVID-1. The experimental findings reveal that DTMD outperforms previous methods and achieves the highest action prediction rate on the MuHAVI-Uncut datase