Regulatory Change and the Quality of Compliance to Mandatory Disclosure Requirements: Evidence from Bangladesh

The present study investigates the effectiveness of changes in the regulatory environment on the quality of compliance to mandatory disclosure requirements in Bangladesh. Statistical analysis of the Mandatory Disclosure Index, as developed in this paper using annual reports of the exchangelisted firms before and after the changes in the regulatory environment, shows a significant improvement in the quality of compliance during the more regulated time period. The size of the firm, the qualification of its accounting staff that prepares financial statements and the reputation of its auditing firm have significant positive impact on the quality of compliance. The analysis presented in the study point to two additional important findings: lack of profitability of the firm does not seem to affect the quality of its compliance, and the performance of domestic firms are at par with foreign affiliated firms as far as the quality of the compliance is concerned. The findings reported in the present study lend support to the conventional notion that well packaged and timed regulations can foster sustainable development in the overall reporting environment of a country

Faecalibacterium prausnitzii Strain HTF-F and Its Extracellular Polymeric Matrix Attenuate Clinical Parameters in DSS-Induced Colitis

Date of Acceptance: 26/02/2015 Funding: The authors received no specific funding for this work.Peer reviewedPublisher PD

A Sierpinski Carpet Five Band Antenna for Wireless Applications

A compact Sierpinski Carpet square fractal multiband antenna operating at 3.9 (WiMAX) /6.6 (Satellite TV) /8.1/10.7/11.8 GHz (X-band) is presented. The proposed Microstrip Patch Antenna (MSPA) consists of a Sierpinski Carpet square fractal radiator in which square slots are etched out and a tapered microstrip feed line. The Sierpinski Carpet square fractal patch modifies the current resonant path thereby making the antenna to operate at five useful bands. Impedance matching at these bands are solely achieved by using Sierpinski square slot and tapered feedline, thus eliminating the need of any external matching circuit. The dimensions of the compact antenna is  and exhibits S11<-10dB bandwidth of about 4.8% (4.01-3.82 GHz), 2.1% (6.62-6.48 GHz), 2.7% (8.24-8.02 GHz), 2.1% (10.77-10.54 GHz) and 21% (12.1-11.60 GHz) with the gain of 7.57/3.91/3.77/6.74/1.33 dB at the operating frequencies 3.9/6.6/8.1/10.7 and 11.8 GHz, respectively under simulation analysis carried out by using HFSS v.13.0

A Sierpinski Carpet Five Band Antenna for Wireless Applications

A compact Sierpinski Carpet square fractal multiband antenna operating at 3.9 (WiMAX) /6.6 (Satellite TV) /8.1/10.7/11.8 GHz (X-band) is presented. The proposed Microstrip Patch Antenna (MSPA) consists of a Sierpinski Carpet square fractal radiator in which square slots are etched out and a tapered microstrip feed line. The Sierpinski Carpet square fractal patch modifies the current resonant path thereby making the antenna to operate at five useful bands. Impedance matching at these bands are solely achieved by using Sierpinski square slot and tapered feedline, thus eliminating the need of any external matching circuit. The dimensions of the compact antenna is  and exhibits S11&lt;-10dB bandwidth of about 4.8% (4.01-3.82 GHz), 2.1% (6.62-6.48 GHz), 2.7% (8.24-8.02 GHz), 2.1% (10.77-10.54 GHz) and 21% (12.1-11.60 GHz) with the gain of 7.57/3.91/3.77/6.74/1.33 dB at the operating frequencies 3.9/6.6/8.1/10.7 and 11.8 GHz, respectively under simulation analysis carried out by using HFSS v.13.0

CAROTIDNet: A Novel Carotid Symptomatic/Asymptomatic Plaque Detection System Using CNN-Based Tangent Optimization Algorithm in B-Mode Ultrasound Images

Deep learning methods have shown promise for automated medical image analysis tasks. However, class imbalance is a common challenge that can negatively impact model performance, especially for tasks with minority classes that are clinically significant. This study aims to address this challenge through a novel hyperparameter optimization technique for training convolutional neural networks on imbalanced data. We developed a custom Convolutional Neural Network (CNN) architecture and introduced a Tangent Optimization Algorithm (TOA) based on the trigonometric properties of the tangent function. The TOA optimizes hyperparameters during training without requiring data preprocessing or augmentation steps. We applied our approach to classifying B-mode ultrasound carotid artery plaque images as symptomatic or asymptomatic using a dataset with significant class imbalance. On k-fold cross-validation, our method achieved an average accuracy of 98.82%, a sensitivity of 99.41%, and a specificity of 95.74%. The proposed optimization technique provides a computationally efficient and interpretable solution for training deep learning models on unbalanced medical image datasets

Why the Data Revolution Needs Qualitative Thinking

This essay draws on qualitative social science to propose a critical intellectual infrastructure for data science of social phenomena. Qualitative sensibilities—interpretivism, abductive reasoning, and reflexivity in particular—could address methodological problems that have emerged in data science and help extend the frontiers of social knowledge. First, an interpretivist lens—which is concerned with the construction of meaning in a given context—can enable the deeper insights that are requisite to understanding high-level behavioral patterns from digital trace data. Without such contextual insights, researchers often misinterpret what they find in large-scale analysis. Second, abductive reasoning—which is the process of using observations to generate a new explanation, grounded in prior assumptions about the world—is common in data science, but its application often is not systematized. Incorporating norms and practices from qualitative traditions for executing, describing, and evaluating the application of abduction would allow for greater transparency and accountability. Finally, data scientists would benefit from increased reflexivity—which is the process of evaluating how researchers’ own assumptions, experiences, and relationships influence their research. Studies demonstrate such aspects of a researcher’s experience that typically are unmentioned in quantitative traditions can influence research findings. Qualitative researchers have long faced these same concerns, and their training in how to deconstruct and document personal and intellectual starting points could prove instructive for data scientists. We believe these and other qualitative sensibilities have tremendous potential to facilitate the production of data science research that is more meaningful, reliable, and ethical

Correlation Between Previous Caesarean Section and Adverse Maternal Outcomes Accordingly With Robson Classification: Systematic Review and Meta-Analysis

Background: The increasing rates of Caesarean section (CS) beyond the WHO standards (10–15%) pose a significant global health concern. Objective: Systematic review and meta-analysis to identify an association between CS history and maternal adverse outcomes for the subsequent pregnancy and delivery among women classified in Robson classification (RC). Search Strategy: PubMed/Medline, EbscoHost, ProQuest, Embase, Web of Science, BIOSIS, MEDLINE, and Russian Science Citation Index databases were searched from 2008 to 2018. Selection Criteria: Based on Robson classification, studies reporting one or more of the 14 adverse maternal outcomes were considered eligible for this review. Data Collection: Study design data, interventions used, CS history, and adverse maternal outcomes were extracted. Main Results: From 4,084 studies, 28 (n = 1,524,695 women) met the inclusion criteria. RC group 5 showed the highest proportion among deliveries followed by RC10, RC7, and RC8 (67.71, 32.27, 0.02, and 0.001%). Among adverse maternal outcomes, hysterectomy had the highest association after preterm delivery OR = 3.39 (95% CI 1.56–7.36), followed by Severe Maternal Outcomes OR = 2.95 (95% CI 1.00–8.67). We identified over one and a half million pregnant women, of whom the majority were found to belong to RC group 5. Conclusions: Previous CS was observed to be associated with adverse maternal outcomes for the subsequent pregnancies. CS rates need to be monitored given the prospective risks which may occur for maternal and child health in subsequent births

Climate Data Empathy

In the era of climate services, which provide globally complete data products in a ready-to-use form, the context of climate data is in danger of being neglected or forgotten. However, the historical and present-day context imprinted on this climate data is important in its own right. The data depend on political, economic and technological factors, as we show with a range of data coverage maps. We term awareness of and sensitivity to this context-dependence “climate data empathy,” and argue that context should be seen as a source of information to be communicated along with the data. Such context not only provides additional information about the data products, but may help in designing communication strategies and contribute more generally to raising awareness of the contingency of environmental data. Decision making should thus make use of both climate data and its context

Faecalibacterium prausnitzii A2-165 has a high capacity to induce IL-10 in human and murine dendritic cells and modulates T cell responses

Acknowledgements This work was financially supported by the EC FP7 Cross-talk project (PITN-GA-2008-215553). The authors thank the Histology Platform from GABI research unit and especially Abdelhak Boukadiri for their technical support in the histology sample preparation and Marlène Héry, Charline Pontlevoy, Jerome Pottier and André Tiffoche (UE0907 IERP, Jouy en Josas) for their help during animal experiments. The authors thank Rafael Muñoz-Tamayo (INRA) for his help in performing the PCA.Peer reviewedPublisher PD

DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies