Generalised coherent point drift for group-wise registration of multi-dimensional point sets

In this paper we propose a probabilistic approach to group-wise registration of unstructured high-dimensional point sets. We focus on registration of generalised point sets which encapsulate both the positions of points on surface boundaries and corresponding normal vectors describing local surface geometry. Richer descriptions of shape can be especially valuable in applications involving complex and intricate variations in geometry, where spatial position alone is an unreliable descriptor for shape registration. A hybrid mixture model combining Student’s t and Von-Mises-Fisher distributions is proposed to model position and orientation components of the point sets, respectively. A group-wise rigid and non-rigid registration framework is then formulated on this basis. Two clinical data sets, comprising 27 brain ventricle and 15 heart shapes, were used to assess registration accuracy. Significant improvement in accuracy and anatomical validity of the estimated correspondences was achieved using the proposed approach, relative to state-of-the-art point set registration approaches, which consider spatial positions alone

Development of Aluminum LEKIDs for Balloon-Borne Far-IR Spectroscopy

We are developing lumped-element kinetic inductance detectors (LEKIDs) designed to achieve background-limited sensitivity for far-infrared (FIR) spectroscopy on a stratospheric balloon. The Spectroscopic Terahertz Airborne Receiver for Far-InfraRed Exploration (STARFIRE) will study the evolution of dusty galaxies with observations of the [CII] 158 $\mu$m and other atomic fine-structure transitions at $z=0.5-1.5$, both through direct observations of individual luminous infrared galaxies, and in blind surveys using the technique of line intensity mapping. The spectrometer will require large format ($\sim$1800 detectors) arrays of dual-polarization sensitive detectors with NEPs of $1 \times 10^{-17}$ W Hz$^{-1/2}$. The low-volume LEKIDs are fabricated with a single layer of aluminum (20 nm thick) deposited on a crystalline silicon wafer, with resonance frequencies of $100-250$ MHz. The inductor is a single meander with a linewidth of 0.4 $\mu$m, patterned in a grid to absorb optical power in both polarizations. The meander is coupled to a circular waveguide, fed by a conical feedhorn. Initial testing of a small array prototype has demonstrated good yield, and a median NEP of $4 \times 10^{-18}$ W Hz$^{-1/2}$.Comment: accepted for publication in Journal of Low Temperature Physic

What support do frontline workers want? A qualitative study of health and social care workers' experiences and views of psychosocial support during the COVID-19 pandemic

Background: The COVID-19 pandemic has placed a significant burden on the mental health and wellbeing of frontline health and social care workers. The need to support frontline staff has been recognised. However, there is to date little research specifically on how best to support the mental health needs of frontline workers, and none on their own experiences and views about what might be most helpful. Aims: We set out to redress this research gap by qualitatively exploring UK frontline health and social care workers’ own experiences and views of psychosocial support during the pandemic. Method: Frontline health and social care workers were recruited purposively through social media and by snowball sampling via healthcare colleagues. Workers who volunteered to take part in the study were interviewed remotely following a semi-structured interview guide. Transcripts of the interviews were analysed by the research team following the principles of Reflexive Thematic Analysis. Results: We conducted 25 interviews with frontline workers from a variety of professional groups working in health and social care settings across the UK. Themes derived from our analysis showed that workers’ experiences and views about psychosocial support were complex. Peer support was many workers’ first line of support but could also be experienced as a burden. Workers were ambivalent about support shown by organisations, media and the public. Whilst workers valued psychological support services, there were many disparities in provision and barriers to access. Conclusions: The results of this study show that frontline health and social care workers are likely to need a flexible system of support including peer, organisational and professional support. More research is needed to fully unpack the structural, systemic and individual barriers to accessing psychosocial support. Greater collaboration, consultation and co-production of support services and their evaluation is warranted

'He just gave up': an exploratory study into the perspectives of paid carers on supporting older people living in care homes with depression, self-harm, and suicide ideation and behaviours

This study explored the concept of ‘giving up’ from the perspective of care staff working in care homes, and their everyday communication and hidden knowledge concerning what they think about this taboo topic and the context it reflects. Moving to a care home is a major transition where cumulative losses can pose risks to mental health in later life. If not recognised, this vulnerability can lead to depression which extends to suicide ideation and behaviours in the form of self-harm and self-neglect. Care homes are a significant place of care until death, yet a discourse of silence means that self-harm and suicide is under-reported or not attended to with specialist expertise. The layperson’s concept of an older person ‘giving up’ on life is hardly discussed in the literature. This co-produced qualitative study used an inductive approach to explore this phenomenon through focus groups with 33 care staff across four care homes in South-East England. Findings paint a complex picture, highlighting tensions in providing the right support and creating spaces to respond to such challenging situations. ‘Giving up’ requires skilled detailed assessment to respond to risks alongside improved training and support for paid carers, to achieve a more holistic strategy which capitalises on significant relationships within a wider context

Effects of a Tailored Follow-Up Intervention on Health Behaviors, Beliefs, and Attitudes

Background: The high rates of relapse that tend to occur after short-term behavioral interventions indicate the need for maintenance programs that promote long-term adherence to new behavior patterns. Computer-tailored health messages that are mailed to participants or given in brief telephone calls offer an innovative and time-efficient alternative to ongoing face-to-face contact with healthcare providers. Methods: Following a 1-year behavior change program, 22 North Carolina health departments were randomly assigned to a follow-up intervention or control condition. Data were collected from 1999 to 2001 by telephone-administered surveys at preintervention and postintervention for 511 low-income, midlife adult women enrolled in the Well-Integrated Screening and Evaluation for Women Across the Nation (WISEWOMAN) program at local North Carolina health departments. During the year after the behavior change program, intervention participants were mailed six sets of computer-tailored health messages and received two computer-tailored telephone counseling sessions. Main outcomes of dietary and physical activity behaviors, beliefs, and attitudes were measured. Results: Intervention participants were more likely to move forward into more advanced stages of physical activity change (p = 0.02); control participants were more likely to increase their level of dietary social support at follow-up (p = 0.05). Both groups maintained low levels of reported saturated fat and cholesterol intake at follow-up. No changes were seen in physical activity in either group. Conclusions: Mailed computer-tailored health messages and telephone counseling calls favorably modified forward physical activity stage movement but did not appreciably affect any other psychosocial or behavioral outcomes

Sparse, interpretable and transparent predictive model identification for healthcare data analysis

Data-driven modelling approaches play an indispensable role in analyzing and understanding complex processes. This study proposes a type of sparse, interpretable and transparent (SIT) machine learning model, which can be used to understand the dependent relationship of a response variable on a set of potential explanatory variables. An ideal candidate for such a SIT representation is the well-known NARMAX (nonlinear autoregressive moving average with exogenous inputs) model, which can be established from measured input and output data of the system of interest, and the final refined model is usually simple, parsimonious and easy to interpret. The performance of the proposed SIT models is evaluated through two real healthcare datasets

A novel logistic-NARX model as a classifier for dynamic binary classification

System identification and data-driven modeling techniques have seen ubiquitous applications in the past decades. In particular, parametric modeling methodologies such as linear and nonlinear autoregressive with exogenous input models (ARX and NARX) and other similar and related model types have been preferably applied to handle diverse data-driven modeling problems due to their easy-to-compute linear-in-the-parameter structure, which allows the resultant models to be easily interpreted. In recent years, several variations of the NARX methodology have been proposed that improve the performance of the original algorithm. Nevertheless, in most cases, NARX models are applied to regression problems where all output variables involve continuous or discrete-time sequences sampled from a continuous process, and little attention has been paid to classification problems where the output signal is a binary sequence. Therefore, we developed a novel classification algorithm that combines the NARX methodology with logistic regression and the proposed method is referred to as logistic-NARX model. Such a combination is advantageous since the NARX methodology helps to deal with the multicollinearity problem while the logistic regression produces a model that predicts categorical outcomes. Furthermore, the NARX approach allows for the inclusion of lagged terms and interactions between them in a straight forward manner resulting in interpretable models where users can identify which input variables play an important role individually and/or interactively in the classification process, something that is not achievable using other classification techniques like random forests, support vector machines, and k-nearest neighbors. The efficiency of the proposed method is tested with five case studies