Intrauterine Infection and Preterm Labor

Preterm birth remains the leading cause of perinatal mortality and morbidity. Evidence suggests that intrauterine infection plays an important role in the pathogenesis of preterm labor. This article reviews the clinical data supporting this theory and the cellular and biochemical mechanisms by which intrauterine infection may initiate uterine contractions. The clinical and laboratory methods of diagnosing clinical chorioamnionitis and asymptomatic bacterial invasion of the intraamniotic cavity are also reviewed. Finally, the management of clinical chorioamnionitis and asymptomatic microbial invasion of the amniotic fluid and the use of adjunctive antibiotic therapy in the treatment of preterm labor are presented

Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors

Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of \emph{class-specific} spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201

Connected OFCity Challenge: Addressing the Digital Divide in the Developing World

Over the past 50 years, the development of information and communications technology has provided unprecedented support to the steady economic growth of developed countries. In recent years, some of the largest growth has been reported in emerging economies, which, however, often lack adequate telecommunications infrastructure to further sustain their development. Although a number of service providers and system vendors have started to address the issue, the challenges they encounter are substantially different from those in the developed world, including an unreliable electricity grid, poor fiber infrastructure, low revenue expectations, and often a harsh climate environment. This paper reports use cases and solutions pertinent to the development of the networking infrastructure in emerging economies, provided by organizations directly involved in such activities. After providing some background information on the current state of network infrastructure and the main challenges for Africa and rural China, the paper provides details on two proposed solutions. The first focuses on the provisioning of services and network infrastructure through the development of low-cost data centers, whereas the second proposes cost-effective adaptation of both fiber and hybrid copper-fiber technology to rural areas. The article is concluded with a brief discussion on the complementarity of the two approaches

Iterative, Deep Synthetic Aperture Sonar Image Segmentation

Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learning is conditioned on having lots of labeled training data, but obtaining generous pixel-level annotations of SAS imagery is often practically infeasible. This challenge has thus far limited the adoption of deep learning methods for SAS segmentation. Algorithms exist to segment SAS imagery in an unsupervised manner, but they lack the benefit of state-of-the-art learning methods and the results present significant room for improvement. In view of the above, we propose a new iterative algorithm for unsupervised SAS image segmentation combining superpixel formation, deep learning, and traditional clustering methods. We call our method Iterative Deep Unsupervised Segmentation (IDUS). IDUS is an unsupervised learning framework that can be divided into four main steps: 1) A deep network estimates class assignments. 2) Low-level image features from the deep network are clustered into superpixels. 3) Superpixels are clustered into class assignments (which we call pseudo-labels) using $k$-means. 4) Resulting pseudo-labels are used for loss backpropagation of the deep network prediction. These four steps are performed iteratively until convergence. A comparison of IDUS to current state-of-the-art methods on a realistic benchmark dataset for SAS image segmentation demonstrates the benefits of our proposal even as the IDUS incurs a much lower computational burden during inference (actual labeling of a test image). Finally, we also develop a semi-supervised (SS) extension of IDUS called IDSS and demonstrate experimentally that it can further enhance performance while outperforming supervised alternatives that exploit the same labeled training imagery.Comment: arXiv admin note: text overlap with arXiv:2107.1456

Analyzing Transatlantic Network Traffic over Scientific Data Caches

Large scientific collaborations often share huge volumes of data around the world. Consequently a significant amount of network bandwidth is needed for data replication and data access. Users in the same region may possibly share resources as well as data, especially when they are working on related topics with similar datasets. In this work, we study the network traffic patterns and resource utilization for scientific data caches connecting European networks to the US. We explore the efficiency of resource utilization, especially for network traffic which consists mostly of transatlantic data transfers, and the potential for having more caching node deployments. Our study shows that these data caches reduced network traffic volume by 97% during the study period. This demonstrates that such caching nodes are effective in reducing wide-area network traffic

β-Catenin Activation in Hepatocellular Cancer: Implications in Biology and Therapy

Hepatocellular cancer (HCC), the most common primary liver tumor, has been gradually growing in incidence globally. The whole-genome and whole-exome sequencing of HCC has led to an improved understanding of the molecular drivers of this tumor type. Activation of the Wnt signaling pathway, mostly due to stabilizing missense mutations in its downstream effector β-catenin (encoded by CTNNB1) or loss-of-function mutations in AXIN1 (the gene which encodes for Axin-1, an essential protein for β-catenin degradation), are seen in a major subset of HCC. Because of the important role of β-catenin in liver pathobiology, its role in HCC has been extensively investigated. In fact, CTNNB1 mutations have been shown to have a trunk role. β-Catenin has been shown to play an important role in regulating tumor cell proliferation and survival and in tumor angiogenesis, due to a host of target genes regulated by the β-catenin transactivation of its transcriptional factor TCF. Proof-of-concept preclinical studies have shown β-catenin to be a highly relevant therapeutic target in CTNNB1-mutated HCCs. More recently, studies have revealed a unique role of β-catenin activation in regulating both tumor metabolism as well as the tumor immune microenvironment. Both these roles have notable implications for the development of novel therapies for HCC. Thus, β-catenin has a pertinent role in driving HCC development and maintenance of this tumor-type, and could be a highly relevant therapeutic target in a subset of HCC cases

Work ethics climate in relation to nurses’ commitment in a South African hospital

Orientation: Commitment, well-being and employer loyalty affect nurse retention. Literature shows that nurses are leaving the workforce at an alarming rate and that various factors are causing them to leave their employers. Research purpose: The main aim of this study was to investigate the influence of the ethical work climate in the organisation on nurses’ commitment. Motivation for the study: The health sector is essential in promoting mental, physical and emotional health but faces a shortage of skilled workers. The work ethics climate (WEC) can play a crucial role in retaining skills. Research approach/design and method: A quantitative research approach was adopted in a non-probability convenience sample of 208 permanent nurses from a South African public hospital. Participants completed self-assessments on an ethical climate questionnaire and an organisational commitment scale (OCS), and regression analysis was used to analyse the data. Main findings: Work ethics climate correlated with nurses’ affective, continuance and normative commitment. In addition, the results indicated that WEC predicted nurses’ commitment. Practical/managerial implications: Public hospitals in South Africa should create policies, laws and procedures that encourage ethical behaviour characterised by honesty, justice and dignity to boost nurse commitment. Thus, the South African hospital should foster an ethical workplace and implement an ethical code. Contribution/value add: This study contributes to the theory of ethical work climate and ethical behaviour by suggesting that nurses who positively perceive policies, rules and hospitals that have clear regulations are more likely to engage