Tubal damage, infertility and tubal ectopic pregnancy : chlamydia trachomatis and other microbial aetiologies

Infertility is a worldwide health problem with one in six couples suffering from this condition and with a major economic burden on the global healthcare industry. Estimates of the current global infertility rate suggest that 15% of couples are infertile (Zegers-Hochschild et al 2009) defined as: (1) failure to conceive after 12 months of unprotected sexual intercourse (i.e. infertility); (2) repeated implantation failure following ART cycles; or (3) recurrent miscarriage without difficulty conceiving (natural conceptions). Tubal factor infertility is among the leading causes of female factor infertility accounting for 7-9.8% of all female factor infertilities. Tubal disease directly causes from 36% to 85% of all cases of female factor infertility in developed and developing nations respectively and is associated with polymicrobial aetiologies. One of the leading global causes of tubal factor infertility is thought to be symptomatic (and asymptomatic in up to 70% cases) infection of the female reproductive tract with the sexually transmitted pathogen, Chlamydia trachomatis. Infection-related damage to the Fallopian tubes caused by Chlamydia accounts for more than 70% of cases of infertility in women from developing nations such as sub-Saharan Africa (Sharma et al 2009). Bacterial vaginosis, a condition associated with increased transmission of sexually transmitted infections including those caused by Neisseria gonorrhoeae and Mycoplasma genitalium is present in two thirds of women with pelvic inflammatory disease (PID). This review will focus on (1) the polymicrobial aetiologies of tubal factor infertility and (2) studies involved in screening for, and treatment and control of, Chlamydial infection to prevent PID and the associated sequelae of Fallopian tube inflammation that may lead to infertility and ectopic pregnancy.

A plasmonic 'ac Wheatstone bridge' circuit for high-sensitivity phase measurement and single-molecule detection

In this paper, a plasmonic “ac Wheatstone bridge” circuit is proposed and theoretically modeled for the first time. The bridge circuit consists of three metallic nanoparticles, shaped as rectangular prisms, with two nanoparticles acting as parallel arms of a resonant circuit and the third bridging nanoparticle acting as an optical antenna providing an output signal. Polarized light excites localized surface plasmon resonances in the two arms of the circuit, which generate an optical signal dependent on the phase-sensitive excitations of surface plasmons in the antenna. The circuit is analyzed using a plasmonic coupling theory and numerical simulations. The analyses show that the plasmonic circuit is sensitive to phase shifts between the arms of the bridge and has the potential to detect the presence of single molecules.

Proactive detection of high collision concentration locations on highways

In previous research (Chung et al., 2009), the potential of the continuous risk profile (CRP) to proactively detect the systematic deterioration of freeway safety levels was presented. In this paper, this potential is investigated further, and an algorithm is proposed for proactively detecting sites where the collision rate is not sufficiently high to be classified as a high collision concentration location but where a systematic deterioration of safety level is observed. The approach proposed compares the weighted CRP across different years and uses the cumulative sum (CUSUM) algorithm to detect the sites where changes in collision rate are observed. The CRPs of the detected sites are then compared for reproducibility. When high reproducibility is observed, a growth factor is used for sequential hypothesis testing to determine if the collision profiles are increasing over time. Findings from applying the proposed method using empirical data are documented in the paper together with a detailed description of the method.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.