PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

Non-nutritive sucking scoring system for preterm newborns

Aim: To validate a non-nutritive sucking (NNS) scoring system for oral feeding in preterm newborns (PTNB). Methods: A cohort study was carried out in two phases. In phase one of the study, 22 mastered speech-language pathologists received the protocol and procedure for a NNS scoring system to evaluate the content and presentation of the form and to define the grading scale. In phase two, six speech-language pathologists evaluated 51 PTNBs weekly, using the defined scoring system. Setting: This study was carried out in the Nursery Annex to the Maternity at the Intensive and Neonatal Pediatrics Service, Instituto da Crianca, Hospital das Clinicas, School of Medicine, University of Sao Paulo (FMUSP) during the period from May 2004 to May 2006. Participants: A total of 28 speech-language pathologist experts and 51 PTNBs. Results: In the first phase of the study, 22 speech-language pathologists selected the criteria, utilized in the NNS evaluation with 80% agreement. In the second phase of the study, the NNS evaluation was carried out on 51 PTNB, and a scoring system of 50 points was proposed, which corresponds to the smallest number of false positive and negative results regarding oral feeding ability. Conclusion: An NNS evaluation system was validated that was able to indicate when oral feeding could safely begin in PTNBs with a high level of agreement among the speech-language pathologists who have participated.

Improved contact load-bearing capacity of ultra-fine grained titanium : Multilayering and grading

The contact load-bearing response and surface damage resistance of multilayered hierarchical structured (MHSed) titanium were determined and compared to monolithic nanostructured titanium. The MHS structure was formed by combining cryorolling with a subsequent Surface Mechanical Attrition Treatment (SMAT) producing a surface structure consisted of an outer amorphous layer containing nanocrystals, an inner nanostructured layer and finally an ultra-fine grained core. The combination of a hard outer layer, a gradual transition layer and a compliant core results in reduced indentation depth, but a deeper and more diffuse sub-surface plastic deformation zone, compared to the monolithic nanostructured Ti. The redistribution of surface loading between the successive layers in the MHS Ti resulted in the suppression of cracking, whereas the monolithic nanograined (NG) Ti exhibited sub-surface cracks at the boundary of the plastic strain field. Finite element models with discrete layers and mechanically graded layersrepresenting the MHS system confirmed the absence of cracking and revealed a 38% decrease in shear stress in the sub-surface plastic strain field, compared to the monolithic NG Ti. Further, the mechanical gradation achieves a more gradual stress distribution which mitigates the interface failure and increases the interfacial toughness, thus providing strong resistance to loading damage. © 2014 Elsevier Ltd.

PRACTICAL LAND GRADING BASED ON LEAST SQUARES.

Practical methods for land grading design of a plane surface for rectangular and irregularly shaped fields based on a least squares analysis are presented. The least squares procedure leads to a system of three linear equations with three unknowns for determination of the best-fit plane. The equations can be solved by determinants (Cramer's rule) using a procedure capable of solution by many programmable calculators. The detailed computational process for determining the equation of the plane and a simple method to find the centroid location of an irregular field are also given. An illustrative example and design instructions are included to demonstrate the application of the design procedure.

Preoperative somatostatin analogues versus direct transsphenoidal surgery for newly-diagnosed acromegaly patients: a systematic review and meta-analysis using the GRADE system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Weather Inputs to a Warning System for Sooty Blotch and Flyspeck of Apple

Sooty blotch and flyspeck (SBFS) is a mid- to late-season disease of apple. SBFS fungi show up as dark smudges and clusters of black dots on the fruit surface. Since blemished fruit are downgraded, crop losses can exceed 90 percent of the fresh market value.

Testing a Warning System for Anthracnose Fruit Rot on Day-neutral Strawberry

Anthracnose fruit rot (AFR) of strawberry is caused by three Colletotrichum spp. In the Midwest, however, only Colletotrichum acutatum is found. This fungus can attach itself to apparently healthy plants and spread throughout without causing symptoms on the foliage. When fruit begin to ripen and weather conditions are rainy and warm, AFR can suddenly cause great damage to the fruit. To protect against AFR where it has appeared in the past, growers need to spray every 7 to 10 days beginning at the start of bloom until harvest.

Errors Associated to Keratoconus Grading using Systems based on Corneal Power

Purpose: To analyze and define the possible errors that may be introduced in keratoconus classification when the keratometric corneal power is used in such classification. Materials and methods: Retrospective study including a total of 44 keratoconus eyes. A comprehensive ophthalmologic examination was performed in all cases, which included a corneal analysis with the Pentacam system (Oculus). Classical keratometric corneal power (Pk), Gaussian corneal power (Pc Gauss), True Net Power (TNP) (Gaussian power neglecting the corneal thickness effect), and an adjusted keratometric corneal power (Pkadj) (keratometric power considering a variable keratometric index) were calculated. All cases included in the study were classified according to five different classification systems: Alió-Shabayek, Amsler-Krumeich, Rabinowitz-McDonnell, collaborative longitudinal evaluation of keratoconus (CLEK), and McMahon. Results: When Pk and Pkadj were compared, differences in the type of grading of keratoconus cases was found in 13.6% of eyes when the Alió-Shabayek or the Amsler-Krumeich systems were used. Likewise, grading differences were observed in 22.7% of eyes with the Rabinowitz-McDonnell and McMahon classification systems and in 31.8% of eyes with the CLEK classification system. All reclassified cases using Pkadj were done in a less severe stage, indicating that the use of Pk may lead to the classification of a cornea as keratoconus, being normal. In general, the results obtained using Pkadj, Pc Gauss or the TNP were equivalent. Differences between Pkadj and Pc Gauss were within ± 0.7D. Conclusion: The use of classical keratometric corneal power may lead to incorrect grading of the severity of keratoconus, with a trend to a more severe grading.

CORE: A Multi-Purpose Programming Contest Repository System

Яни Чаушев, Милослав Средков, Красимир Манев - Всяко състезание по програмиране използва множество софтуерни инструменти за управление на процесите по време на състезанието. Въпреки че тези инструменти обикновено покриват спецификите на конкретния вид състезание задоволително, те рядко адресират трудностите на дългосрочното съхранение на данни и оперативната съвместимост. В тази статия е представен един софтуерен инструмент адресиращ тези проблеми. Вместо комплексна система, касаеща всички аспекти на състезанието, CORE е централизирано хранилище за съхраняване и поддържане на необходимите за различни състезания данни. Представени са основните му елементи, текущото състояние на реализацията и перспективите за развитие на системата.

Assessment of a Planned Municipal Solid Waste Management System in Sri Lanka

Systematic Municipal Solid Waste Management (MSWM) authorities of Sri Lanka contributes to exchange some productive outputs with localities; however it is still not in a successful mode due to limitations and environmental failures in their operation. Most of these local administrations are directly dumping Municipal Solid Waste (MSW) to an open dumping site, this manner of inappropriate disposal of MSW is become a major threat to the environment and public health in developing countries like Sri Lanka. This study was conducted for the MSWM practices of Balangoda Urban Council. The research was performed based on analyzing information obtained from field observations; reports; literature; questionnaire distribution among community; and a series of formal interviews with major stakeholders. The ongoing MSWM practices of Balangoda Urban Council encompass six categories as waste minimization and handling; waste collection; on-site separation; waste transportation; further management including grading, composting, recycling, producing sludge fertilizer; and final disposal to an open dump site. Apart from those, training sessions on MSWM are also being conducted. The purpose of this paper is to assess current status of urban waste management scenario and highlight strengths and weaknesses to understand the sustainability of the system which would help any local authority to improve MSWM.

Detection and Grading of Prostate Cancer in Temporal Enhanced Ultrasound using Convolutional Neural Networks

Prostate cancer is the most common non-dermatological cancer amongst men in the developed world. The current definitive diagnosis is core needle biopsy guided by transrectal ultrasound. However, this method suffers from low sensitivity and specificity in detecting cancer. Recently, a new ultrasound based tissue typing approach has been proposed, known as temporal enhanced ultrasound (TeUS). In this approach, a set of temporal ultrasound frames is collected from a stationary tissue location without any intentional mechanical excitation. The main aim of this thesis is to implement a deep learning-based solution for prostate cancer detection and grading using TeUS data. In the proposed solution, convolutional neural networks are trained to extract high-level features from time domain TeUS data in temporally and spatially adjacent frames in nine in vivo prostatectomy cases. This approach avoids information loss due to feature extraction and also improves cancer detection rate. The output likelihoods of two TeUS arrangements are then combined to form our novel decision support system. This deep learning-based approach results in the area under the receiver operating characteristic curve (AUC) of 0.80 and 0.73 for prostate cancer detection and grading, respectively, in leave-one-patient-out cross-validation. Recently, multi-parametric magnetic resonance imaging (mp-MRI) has been utilized to improve detection rate of aggressive prostate cancer. In this thesis, for the first time, we present the fusion of mp-MRI and TeUS for characterization of prostate cancer to compensates the deficiencies of each image modalities and improve cancer detection rate. The results obtained using TeUS are fused with those attained using consolidated mp-MRI maps from multiple MR modalities and cancer delineations on those by multiple clinicians. The proposed fusion approach yields the AUC of 0.86 in prostate cancer detection. The outcomes of this thesis emphasize the viable potential of TeUS as a tissue typing method. Employing this ultrasound-based intervention, which is non-invasive and inexpensive, can be a valuable and practical addition to enhance the current prostate cancer detection.