Fixed bandwidth inference for fractional cointegration

In a fractional cointegration setting we derive the fixed bandwidth limiting theory of a class of estimators of the cointegrating parameter which are constructed as ratios of weighted periodogram averages. These estimators offer improved limiting properties over those of more standard approaches like OLS or NBLS estimation. These advantages have been justified by means of traditional asymptotic theory and here we explore whether these improvements still hold when considering the alternative fixed bandwidth theory and, more importantly, whether this latter approach provides a more accurate approximation to the sampling distribution of the corresponding test statistics. This appears to be relevant, especially in view of the typical oversizing displayed by Wald statistics when confronted to the standard limiting theory. A Monte Carlo study of finite-sample behaviour is included

The effects of metformin on maternal haemodynamics in gestational diabetes mellitus: A pilot study.

BACKGROUND: Gestational diabetes mellitus (GDM) is a major clinical challenge and is likely to remain so as the incidence of GDM continues to increase AIM: To assess longitudinal changes in maternal haemodynamics amongst women diagnosed with GDM requiring either metformin or dietary intervention in comparison to low-risk healthy controls. METHODOLOGY: Fifty-six pregnant women attending their first appointment at the GDM clinic and 60 low-risk healthy pregnant controls attending their routine antenatal clinics were recruited and assigned to three groups: GDM Metformin (GDM-M), GDM Diet (GDM-D) and Control. Non-invasive assessment of maternal haemodynamics, using recognised measures of arterial stiffness and central blood pressure (Arteriograph®), were undertaken under controlled conditions within four gestational windows: antenatal; AN1 (26-28 weeks), AN2 (32-34 weeks) and AN3 (37-40 weeks), and postnatal (PN) (6-8 weeks after delivery). Data were analysed using a linear mixed model incorporating gestational age and other relevant predictors, including age, blood pressure (BP), baseline bodyweight and pulse as fixed effects, and patient as a random effect. RESULTS: Fitted linear mixed models showed evidence of a two-way interaction effect between groups (GDM-D, GDM-M and Control) and stages of gestation (AN1, AN2, AN3 and PN) for maternal haemodynamic parameters: brachial artery augmentation index (AIx) (p=0.004), aortic AIx (p=0.008), and central systolic BP (p=0.001). However, differences in respect of aortic pulse wave velocity (p=0.001) and heart rate (p<0.001) were only significant for gestational stage. At AN2, we did not observe any evidence that the mean brachial Aix in the GDM-M was different from the control group (p=0.158). CONCLUSION: AIx and central systolic BP measures of arterial stiffness are adversely affected by GDM in comparison to controls during pregnancy. The possible beneficial effects of metformin therapy seen at 32 to 34 weeks of gestation require further exploration

Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

The potential of acoustic signatures to be used for State-of-Charge (SoC) estimation is demonstrated using artificial neural network regression models. This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual, and often arbitrary, waveform peak selection. For applications where computational economy is prioritised, simple metrics of statistical significance are used to formally identify the most informative waveform features. These alone can be exploited for SoC inference. It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value, challenging the more common peak selection methods which focus on the latter. Although later echoes represent greater through-thickness coverage, and are intuitively more information-rich, their presence is not guaranteed. Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states. It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly, while also improving the estimation accuracy. Most importantly, it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates, without any complementary voltage information. This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed. Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell. Mean estimation errors as low as 0.75% are achieved on a SoC scale of 0–100%

Diurnal variation and repeatability of arterial stiffness and cardiac output measurements in the third trimester of uncomplicated pregnancy.

AIM: To investigate same day repeated measures and diurnal variation of arterial stiffness, cardiac output (CO), stroke volume (SV) and total peripheral resistance (TPR) during the third trimester of normal pregnancy. METHODOLOGY: Pulse wave velocity (PWV) and augmentation index (AIx) were recorded using the Arteriograph, while CO, SV and TPR were recorded using noninvasive cardiac output monitoring. The measurements were obtained in the third trimester of pregnancy from 21 healthy pregnant women at four time points (morning, afternoon, evening and midnight) over a 24-h period. Triplicate measurements of 67 women were obtained at 5-min intervals to assess repeatability between measurements within a patient. RESULTS: Diurnal measurements of arterial stiffness for brachial AIx, aortic AIx and PWV were not statistically significantly different at any of the four time points. Estimated means (SD) for PWV at the four stated time points were 7.81 (2.05), 8.45 (1.68), 7.87 (1.74) and 7.64 m/s (1.15), respectively (P = 0.267). Estimates for AIx at those time points were 10.22 (15.62), 4.44 (10.07), 6.49 (10.92) and 8.40% (8.16), respectively (P = 0.295). Similarly, mean arterial pressure, SV, SV index and TPR did not show any evidence of diurnal variation. However, we observed that the mean CO, cardiac index (CI) and heart rate (HR) varied from morning to midnight; the mean CO, HR and CI increased significantly in the afternoon compared with the corresponding mean morning measurements in a similar fashion to HR. Mean (SD) CO estimates at the four stated time points were 5.90 (1.33), 6.38 (1.49), 6.18 (1.43) and 5.80 ml/min (1.19), respectively, (P < 0.001), whereas mean CI estimates were 3.65 (0.58), 3.93 (0.68), 3.81 (0.65), and 3.57 (0.48), respectively, (P < 0.001), and mean HR estimates were 95 (12), 98 (13), 95 (12) and 88 (12.98), respectively (P < 0.001). Triplicate measurements of 61 women in our repeatability study showed moderate-to-high correlation between observations on the same woman for all Arteriograph and noninvasive cardiac output monitoring variables (estimates of intraclass correlation ranged from 0.49 to 0.91). CONCLUSION: With the exception of CO, CI and HR which showed a diurnal variation, measurements of most haemodynamic parameters did not change significantly from morning to midnight, suggesting there was no evidence of systematic differences in the mean values of these variables at these time points. Multiple consecutive noninvasive measurements of vascular stiffness, CO, SV and TPR were highly correlated confirming repeatability of measurements in the third trimester of uncomplicated pregnancy, so these haemodynamic measurements do not need to be undertaken at a specific time period of the day

In Situ Ultrasound Acoustic Measurement of the Lithium-Ion Battery Electrode Drying Process

The electrode drying process is a crucial step in the manufacturing of lithium-ion batteries and can significantly affect the performance of an electrode once stacked in a cell. High drying rates may induce binder migration, which is largely governed by the temperature. Additionally, elevated drying rates will result in a heterogeneous distribution of the soluble and dispersed binder throughout the electrode, potentially accumulating at the surface. The optimized drying rate during the electrode manufacturing process will promote balanced homogeneous binder distribution throughout the electrode film; however, there is a need to develop more informative in situ metrologies to better understand the dynamics of the drying process. Here, ultrasound acoustic-based techniques were developed as an in situ tool to study the electrode drying process using NMC622-based cathodes and graphite-based anodes. The drying dynamic evolution for cathodes dried at 40 and 60 °C and anodes dried at 60 °C were investigated, with the attenuation of the reflective acoustic signals used to indicate the evolution of the physical properties of the electrode-coating film. The drying-induced acoustic signal shifts were discussed critically and correlated to the reported three-stage drying mechanism, offering a new mode for investigating the dynamic drying process. Ultrasound acoustic-based measurements have been successfully shown to be a novel in situ metrology to acquire dynamic drying profiles of lithium-ion battery electrodes. The findings would potentially fulfil the research gaps between acquiring dynamic data continuously for a drying mechanism study and the existing research metrology, as most of the published drying mechanism research studies are based on simulated drying processes. It shows great potential for further development and understanding of the drying process to achieve a more controllable electrode manufacturing process

Identification of signaling pathways in early mammary gland development by mouse genetics

The mammary gland develops as an appendage of the ectoderm. The prenatal stage of mammary development is hormone independent and is regulated by sequential and reciprocal signaling between the epithelium and the mesenchyme. A number of recent studies using human and mouse genetics, in particular targeted gene deletion and transgenic expression, have identified some of the signals that control specific steps in development. This process involves cell specification and proliferation, reciprocal tissue interactions and cell migration. Since some of these events are recapitulated during tumorigenesis, an understanding of these signaling pathways may contribute to the development of targeted therapies and novel drugs

A survey of opinion: When to start oral anticoagulants in patients with acute ischaemic stroke and atrial fibrillation?

Introduction: There is uncertainty regarding the optimal timing for initiation of oral anticoagulant treatment in patients with recent ischaemic stroke and atrial fibrillation. We surveyed the current UK practice and assessed clinician’s opinions of when to use oral anticoagulant in recent stroke patients with atrial fibrillation. Patients and methods: An online survey was sent to stroke physicians within the United Kingdom via their national societies. Results: One hundred and twenty-one clinicians responded to the survey. Ninety-five percent of responders agreed that there was uncertainty regarding timing of oral anticoagulant initiation after atrial fibrillation-related ischaemic stroke. Thirty-six percent of responders followed the ‘1–3–6–12’ European Society of Cardiology guidelines recommendation. Uncertainty was greater in cases of moderate stroke than in cases of transient ischaemic attack (TIA), mild or severe stroke. Eighty-eight percent of responders would be willing to participate in a clinical trial of early versus later initiation of oral anticoagulant after stroke. Direct-acting oral anticoagulants were the preferred oral anticoagulant of choice. Discussion and Conclusion: There is a lack of consensus amongst stroke physicians for when to initiate oral anticoagulant to prevent recurrence in stroke patients with atrial fibrillation. There is little uncertainty regarding TIA. A clinical trial assessing the use of early versus later initiation of direct-acting oral anticoagulant in patients with recent ischaemic stroke and atrial fibrillation would be beneficial

Microbiological, histological, immunological, and toxin response to antibiotic treatment in the mouse model of Mycobacterium ulcerans disease.

Mycobacterium ulcerans infection causes a neglected tropical disease known as Buruli ulcer that is now found in poor rural areas of West Africa in numbers that sometimes exceed those reported for another significant mycobacterial disease, leprosy, caused by M. leprae. Unique among mycobacterial diseases, M. ulcerans produces a plasmid-encoded toxin called mycolactone (ML), which is the principal virulence factor and destroys fat cells in subcutaneous tissue. Disease is typically first manifested by the appearance of a nodule that eventually ulcerates and the lesions may continue to spread over limbs or occasionally the trunk. The current standard treatment is 8 weeks of daily rifampin and injections of streptomycin (RS). The treatment kills bacilli and wounds gradually heal. Whether RS treatment actually stops mycolactone production before killing bacilli has been suggested by histopathological analyses of patient lesions. Using a mouse footpad model of M. ulcerans infection where the time of infection and development of lesions can be followed in a controlled manner before and after antibiotic treatment, we have evaluated the progress of infection by assessing bacterial numbers, mycolactone production, the immune response, and lesion histopathology at regular intervals after infection and after antibiotic therapy. We found that RS treatment rapidly reduced gross lesions, bacterial numbers, and ML production as assessed by cytotoxicity assays and mass spectrometric analysis. Histopathological analysis revealed that RS treatment maintained the association of the bacilli with (or within) host cells where they were destroyed whereas lack of treatment resulted in extracellular infection, destruction of host cells, and ultimately lesion ulceration. We propose that RS treatment promotes healing in the host by blocking mycolactone production, which favors the survival of host cells, and by killing M. ulcerans bacilli

SpeS: A Novel Superantigen and Its Potential as a Vaccine Adjuvant against Strangles.

Bacterial superantigens (sAgs) are powerful activators of the immune response that trigger unspecific T cell responses accompanied by the release of proinflammatory cytokines. Streptococcus equi (S. equi) and Streptococcus zooepidemicus (S. zooepidemicus) produce sAgs that play an important role in their ability to cause disease. Strangles, caused by S. equi, is one of the most common infectious diseases of horses worldwide. Here, we report the identification of a new sAg of S. zooepidemicus, SpeS, and show that mutation of the putative T cell receptor (TCR)-binding motif (YAY to IAY) abrogated TCR-binding, whilst maintaining interaction with major histocompatibility complex (MHC) class II molecules. The fusion of SpeS and SpeSY39I to six S. equi surface proteins using two different peptide linkers was conducted to determine if MHC class II-binding properties were maintained. Proliferation assays, qPCR and flow cytometry analysis showed that SpeSY39I and its fusion proteins induced less mitogenic activity and interferon gamma expression when compared to SpeS, whilst retaining Antigen-Presenting Cell (APC)-binding properties. Our data suggest that SpeSY39I-surface protein fusions could be used to direct vaccine antigens towards antigen-presenting cells in vivo with the potential to enhance antigen presentation and improve immune responses

A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models.

Background Surveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variety of health conditions. The purpose of this study was to utilise readily available patient, tumour, and treatment data to develop, validate and externally test machine learning models for predicting recurrence, recurrence-free survival (RFS) and overall survival (OS) at 2 years from treatment. Methods A retrospective, multicentre study of patients receiving curative-intent radiotherapy for NSCLC was undertaken. A total of 657 patients from 5 hospitals were eligible for inclusion. Data pre-processing derived 34 features for predictive modelling. Combinations of 8 feature reduction methods and 10 machine learning classification algorithms were compared, producing risk-stratification models for predicting recurrence, RFS and OS. Models were compared with 10-fold cross validation and an external test set and benchmarked against TNM-stage and performance status. Youden Index was derived from validation set ROC curves to distinguish high and low risk groups and Kaplan-Meier analyses performed. Findings Median follow-up time was 852 days. Parameters were well matched across training-validation and external test sets: Mean age was 73 and 71 respectively, and recurrence, RFS and OS rates at 2 years were 43% vs 34%, 54% vs 47% and 54% vs 47% respectively. The respective validation and test set AUCs were as follows: 1) RFS: 0·682 (0·575–0·788) and 0·681 (0·597–0·766), 2) Recurrence: 0·687 (0·582–0·793) and 0·722 (0·635–0·81), and 3) OS: 0·759 (0·663–0·855) and 0·717 (0·634–0·8). Our models were superior to TNM stage and performance status in predicting recurrence and OS. Interpretation This robust and ready to use machine learning method, validated and externally tested, sets the stage for future clinical trials entailing quantitative personalised risk-stratification and surveillance following curative-intent radiotherapy for NSCLC. Funding A full list of funding bodies that contributed to this study can be found in the Acknowledgements section