The Role of Multiparametric MRI in Detection, Localization and Characterization of Prostate Cancer

Our aim was to detect the performance characteristics of multiparametric magnetic resonance imaging (mp-MRI) in patients with clinical suspicion, or previous diagnosis, of prostate cancer. Mp-MRI (index test) comprised of T2-weighted, diffusion weighted and dynamic contrast enhanced imaging. Radiologists used Likert score 1-5 based on the likelihood of the presence of prostate cancer. Concordance was made between results of mp- MRI and template prostate mapping (TPM) biopsy (reference standard). This retrospective study included patients that had both the index test and reference standard between January 2007 to January 2011 at either University College London Hospital or London Urology Associates.. These were patients with; a) no prior prostate biopsy (n=129), b) prior negative prostate biopsy (n=54), c) previous positive prostate biopsy (n=194) and d) biochemical failure after radiotherapy (n=37). A set of target conditions was used and varied between the four groups of patients. These were either based on Gleason scoring, maximum cancer core length or a combination of both. In the first group, mp-MRI showed encouraging diagnostic performance results in ruling out clinically significant prostate cancer with sensitivity and negative predictive value (NPV) up to 94% and 89%, respectively. Accuracy figures were similar in the second group with sensitivity and NPV reaching up to 90% and 95%, respectively. In patients that underwent mp-MRI before reclassification TPM biopsy (third group), NPV for predicting that cancer remained low risk (as detected on previous TRUS-guided biopsy) reached up to 100%. Positive predictive value for upgrade of prostate cancer disease on subsequent TPM biopsy reached up to 75% with diagnostic odds ratio up to 2.86. In the last group, a combination of T2-weighted + high b-value showed optimum mp-MRI performance. These results suggest that mp-MRI can be used as a triage test among different patient populations, to select patients that can avoid biopsy and those that need re-biopsy before entering an active surveillance program. Time and cost can be saved by using only certain MRI sequences in patients with biochemical failure after radiotherapy

النظام القانوني للسفينة وموقف القانون السوري منها

كانت السفينة قديماً ترمز إلى الآلية البحرية التي تجوب البحار والمحيطات بقصد التجارة ونقل الأشخاص والبضائع، إلا أنها أصبحت في الوقت الحاضر تأخذ أشكالاً متعددة وتقوم بمهام متنوعة الأمر الذي يستدعي وضع تعريفٍ دقيق لها. يتناول البحث السفينة كعنصر هام وفعال له دورٌ حيوي في عمليات النقل التجاري البحري الدولي، الأمر الذي ترتب عليه أنها أصبحت العمود الفقري للملاحة البحرية. يستعرض البحث تعريف السفينة من وجهة نظر الاتفاقيات الدولية والتشريعات الوطنية وآراء الفقه الدولي، إضافةً إلى تسليط الضوء على الطبيعة القانونية والذاتية للمنشأة البحرية والتي تضفي عليها وصف السفينة، وبيان أنواع السفن وتميزها عن غيرها من المنشآت البحرية المماثلة لها. كما يهدف البحث إلى توضيح الحقوق الواردة على السفينة من ملكية ورهن وحجز، مع تحديد المركز القانوني للسفينة في المناطق البحرية الخاضعة للسيادة. وقد توصل البحث إلى العديد من النتائج كان من أهمها: هناك تناقض وتعارض ما بين الاتفاقيات الدولية والتشريعات الوطنية وآراء الفقه الدولي حول تحديد مفهوم السفينة. قسمت اتفاقية جامايكا للعام1982 البحر إلى مناطق بحرية مختلفة، ترتب على هذا التقسيم أنها نظمت المركز القانوني للسفينة أثناء تواجدها في هذه المناطق. ترتب على اعتبار السفينة مال منقول ذو طبيعة خاصة أنها أصبحت محلاً لأن ترد عليه بعض الحقوق الواردة على العقار كالرهن والحجز والتأمين

Development of Rapid and Accurate Method to Classify Malaysian Honey Samples using UV and Colour Image

The purpose of this paper is to classification of three main types of Malaysian honey (Acacia, Kelulut and Tualang) according to their botanical origin using UV–Vis Spectroscopy and digital camera. This paper presented the classification of the honey based on two characteristics from three (3) types of local honey, namely the antioxidant contents and colour variations. The former uses the UV spectroscopy of selected wavelength range, and the latter using RGB digital camera. Principal Component Analysis (PCA) was used for both methods to reduce the dimension of extracted data. The Support Vector Machine (SVM) was used for the classification of honey. The assessment was done separately for each of the methods, and also on the fusion of both data after features extraction and association. This paper shows that classification of the fusion method improved significantly compared to single modality Honey classification based on the fusion method was able to achieve 94% accuracy. Hence, the proposed methods have the ability to provide accurate and rapid classification of honey products in terms of origin. The proposed system can be applied in Malaysia honey industry and further improve the quality assessment and provide traceability

SLDPC: Towards Second Order Learning for Detecting Persistent Clusters in Data Streams

The main attention of research on data stream clustering algorithms so far has been focused on the adaptation of the algorithms for static datasets to the data streams and improvements of the existing adapted algorithms. Such algorithms fulfil the purpose of the first-order learning from data to clusters. This paper prompts a new question on second-order learning of cluster models from data streams and presents a learning algorithm that detects persistent clusters from consecutive clustering snapshots in data streams. In this work, we first collect a sequence of cluster snapshots as the output clusters at selected query points and then identify the persistent clusters within a given timeframe. The algorithm is evaluated on collections of synthetic datasets. The experimental results have demonstrated the effectiveness of the algorithm in detecting such persistent clusters

Inline 3D volumetric measurement of moisture content in rice using regression-based ML of RF tomographic imaging

The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors’ knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Wave runup estimates at gentle beaches in the northern Indian Ocean

The aim of this study is to estimate the wave runup on selected beaches around the northern Indian Ocean. The runup has been estimated using ERA-Interim, which is the latest global atmospheric re-analysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ECMWF uses the global Wave Model (WAM) model to calculate two dimensional wave spectra. The distances between the model grid points and the beaches have been calculated by a great circle calculator. The beach slopes have been calculated by Google Earth for all locations, except Maldives beach, which was assumed as an imaginary beach because the method of calculating the slope could not be used there. The significant wave height as well as the peak wave period at the grid points are assumed to be the same at the beach. The most frequent estimated runup is between 0.5 m and 1.0 m, which is produced by swell coming from the southern Indian Ocean for all locations except Sri Lanka, India and Maldives shores, where the most frequent runup value is less than 0.5 m. However, the extreme wave runup occurs with the largest wave heights during summer monsoon in July. Generally, the high wave height depends on wind sea. The mean elevation of the runup for all locations is 0.56 m. It is comparable to the measured values obtained by (Stochdon et al., 2006) at several beaches in USA and the Netherlands who found the mean value of dissipative sites (84 cm) for all experiments