The Probability Density of the Higgs Boson Mass

The LEP Collaborations have reported a small excess of events in their combined Higgs boson analysis at center of mass energies up to about 208 GeV. In this communication, I present the result of a calculation of the probability distribution function of the Higgs boson mass which can be rigorously obtained if the validity of the Standard Model is assumed. It arises from the combination of the most recent set of precision electroweak data and the current results of the Higgs searches at LEP 2.Comment: 3 pages, 2 figure

Women's Perceptions of Contributory Factors for Not Achieving a Vaginal Birth After Cesarean (VBAC)

BACKGROUND: With cesarean rates around the world escalating, concern is growing around why women wanting a vaginal birth after cesarean (VBAC) are not achieving their goal. AIM: To gain an understanding of women’s perceptions of factors they felt contributed to not achieving a VBAC. SETTING AND PARTICIPANTS: Fifteen women were interviewed following a nonelective repeat cesarean section (NERCS). They had attended a Western Australian midwifery-led service, next birth after cesarean (NBAC), and labored but were not successful in achieving a VBAC because of reasons around delayed progress. Interview transcripts were analyzed using Colaizzi’s method of thematic analysis. FINDINGS: Five themes emerged: “Tentative commitment with lingering doubts,” “My body failed me,” “Compromised by a longer than tolerable labor,” “Unable to effectively self-advocate in a climate of power struggling and poor support,” and “The inflexibility of hospital processes.” The final theme included two subthemes: “Restrictive policies” on labor and use of the cardiotocography, “The CTG.” CONCLUSIONS: When labor did not progress as envisaged and hospital processes adversely affected how women were supported, women’s doubts around being able to achieve a VBAC were reinforced with a NERCS. Maternity services need to ensure clinical practice reflects best evidence while assuring staff are supportive of women’s choice

Differentiation of bipolar disorder versus borderline personality disorder: A machine learning approach

Background: Differentiation of bipolar disorder (BP) from borderline personality disorder (BPD) is a common diagnostic dilemma. We undertook a machine learning (ML) approach to distinguish the conditions. Methods: Participants meeting DSM criteria for BP or BPD were compared on measures examining cognitive and behavioral BPD constructs, emotion regulation strategies, and parental behaviors during childhood. Two analyses used continuous and dichotomised data, with ML-allocated diagnoses compared to DSM. Results: 82 participants met DSM criteria for BP and 52 for BPD. Accuracy of ML classification was 84.1% - 87.8% for BP, 50% - 57.7% for BPD, with overall accuracy of 73.1% - 73.9%. Importance of items differed between the analyses with the overall most important items including identity difficulties, relationship problems, female gender, feeling suicidal after a relationship breakdown and age. Limitations: Participants were volunteers, preponderance of bipolar II (BP II) participants, comorbidity of BP and BPD not examined, and small BPD sample contributed to the relatively low classification accuracies for this group Conclusions: Study findings may assist distinguishing BP and BPD based on differences in cognitive and behavioral domains, emotion regulation strategies and parental behaviors. Future studies using larger datasets could further improve predictive accuracy and assist in differential diagnosis

Longitudinal muon spin relaxation in high purity aluminum and silver

The time dependence of muon spin relaxation has been measured in high purity aluminum and silver samples in a longitudinal 2 T magnetic field at room temperature, using time-differential \musr. For times greater than 10 ns, the shape fits well to a single exponential with relaxation rates of \lambda_{\textrm{Al}} = 1.3 \pm 0.2\,(\textrm{stat.}) \pm 0.3\,(\textrm{syst.})\,\pms and \lambda_{\textrm{Ag}} = 1.0 \pm 0.2\,(\textrm{stat.}) \pm 0.2\,(\textrm{syst.})\,\pms

A mixed-method process evaluation of an East Midlands county summer 2021 holiday activities and food programme highlighting the views of programme co-ordinators, providers, and parents

BACKGROUND: The Holiday Activities and Food (HAF) Programme is a UK Government initiative created to alleviate food insecurity and promote health and well-being among children and their families, who are eligible for Free School Meals (FSM), during the school holidays. This process evaluation investigated factors that facilitated and acted as a barrier to the delivery of the HAF Programme from the perspectives of key stakeholders (Co-ordinators, Providers, and Parents) involved in the HAF Programme across an East Midlands county. METHODS: This evaluation utilized a mixed-methods approach, incorporating focus groups and online surveys to gain rich, multifaceted data. The focus groups were analyzed using a hybrid inductive-deductive thematic analysis and the online surveys were analyzed using mixed-methods approach due to the variation in question type (i.e., quantitative, Likert scale and open response) to align themes to the Government Aims and Standards of the HAF Programme. FINDINGS: The stakeholders highlighted several factors that facilitated and acted as a barrier to the delivery of the HAF Programme. Facilitating factors included existing and maintaining relationships between Co-ordinators, Providers, and facilities/schools/communities as this improved communication and attendance. Additionally, transport provision for those attending the Programme helped overcome barriers to attendance. The primary barrier of the Programme was the late awarding of the Programme contract as this limited the time available to prepare and organize the Programme. This in turn, had several “knock on” effects that created more barriers and resulted in some of the Government Aims and Standards not being met such as, nutrition education for children and parents. Despite the challenges faced, Co-ordinators and Providers were able to deliver the Programme and positively impact upon the children and their families that attended the Programme. CONCLUSION: Following the facilitators and barriers that were highlighted in this evaluation, several recommendations have been made to enhance the delivery of the HAF Programme and ensure Government Aims and Standards, to improve children and family's health and well-being, are attained

Index of T-wave variation as a predictor of sudden cardiac death in chronic heart failure patients with atrial fibrillation

Chronic heart failure (CHF) and atrial fibrillation (AF) are worldwide leading causes of morbidity and mortality in elders, a large part due to sudden cardiac deaths (SCD). The high irregularity of ventricular response in AF patients makes the use of standard SCD-risk markers inappropriate in this target population. The aim of this study was twofold: i) to propose a new index, suitable for AF patients, able to quantify ventricular repolarization changes; and ii) to evaluate its prognostic value in a CHF population with AF. Holter ECG recordings from 176 consecutive CHF patients with AF (22 SCD) were analyzed. The index of T-wave variation (ITV), quantifying the average T-wave changes in pairs of consecutive beats under stable rhythm conditions, was computed using a fully-automatic method. Survival analysis was performed considering SCD as an independent endpoint. ITVwas higher for SCD than non-SCD victims (median (Q1;Q3): 24.9 (14.4;85.4) μV vs 17.1 (11.3;28.2) μV, p=0.06). In a survival analysis where the threshold was set on the third quartile of ITVvalues, ITV(+) outcome was successfully associated to SCD (Hazard Ratio (CI):3.22 (1.36, 7.58)per μV, p=0.008). In conclusion, we show in this work that Ijy stratifies CHF patients with AF according to their risk of SCD, with larger ITVassociated to lower survival probability

Comparative Study of Human and Mouse Postsynaptic Proteomes Finds High Compositional Conservation and Abundance Differences for Key Synaptic Proteins

Direct comparison of protein components from human and mouse excitatory synapses is important for determining the suitability of mice as models of human brain disease and to understand the evolution of the mammalian brain. The postsynaptic density is a highly complex set of proteins organized into molecular networks that play a central role in behavior and disease. We report the first direct comparison of the proteome of triplicate isolates of mouse and human cortical postsynaptic densities. The mouse postsynaptic density comprised 1556 proteins and the human one 1461. A large compositional overlap was observed; more than 70% of human postsynaptic density proteins were also observed in the mouse postsynaptic density. Quantitative analysis of postsynaptic density components in both species indicates a broadly similar profile of abundance but also shows that there is higher abundance variation between species than within species. Well known components of this synaptic structure are generally more abundant in the mouse postsynaptic density. Significant inter-species abundance differences exist in some families of key postsynaptic density proteins including glutamatergic neurotransmitter receptors and adaptor proteins. Furthermore, we have identified a closely interacting set of molecules enriched in the human postsynaptic density that could be involved in dendrite and spine structural plasticity. Understanding synapse proteome diversity within and between species will be important to further our understanding of brain complexity and disease

Proposal for SPS beam time for the baby MIND and TASD neutrino detector prototypes

The design, construction and testing of neutrino detector prototypes at CERN are ongoing activities. This document reports on the design of solid state baby MIND and TASD detector prototypes and outlines requirements for a test beam at CERN to test these, tentatively planned on the H8 beamline in the North Area, which is equipped with a large aperture magnet. The current proposal is submitted to be considered in light of the recently approved projects related to neutrino activities with the SPS in the North Area in the medium term 2015-2020

Differentiating mania/hypomania from happiness using a machine learning analytic approach.

Background: This study aimed to improve the accuracy of bipolar disorder diagnoses by identifying symptoms that help to distinguish mania/hypomania in bipolar disorders from general ‘happiness’ in those with unipolar depression. Methods: An international sample of 165 bipolar and 29 unipolar depression patients (as diagnosed by their clinician) were recruited. All participants were required to rate a set of 96 symptoms with regards to whether they typified their experiences of manic/hypomanic states (for bipolar patients) or when they were ‘happy’ (unipolar patients). A machine learning paradigm (prediction rule ensembles; PREs) was used to derive rule ensembles that identified which of the 94 non-psychotic symptoms and their combinations best predicted clinically-allocated diagnoses. Results: The PREs were highly accurate at predicting clinician bipolar and unipolar diagnoses (92% and 91% respectively). A total of 20 items were identified from the analyses, which were all highly discriminating across the two conditions. When compared to a classificatory approach insensitive to the weightings of the items, the ensembles were of comparable accuracy in their discriminatory capacity despite the unbalanced sample. This illustrates the potential for PREs to supersede traditional classificatory approaches. Limitations: There were considerably less unipolar than bipolar patients in the sample, which limited the overall accuracy of the PREs. Conclusions: The consideration of symptoms outlined in this study should assist clinicians in distinguishing between bipolar and unipolar disorders. Future research will seek to further refine and validate these symptoms in a larger and more balanced sample

Categorical differentiation of the unipolar and bipolar disorders

There has been a longstanding debate as to whether the bipolar disorders differ categorically or dimensionally, with some dimensional or spectrum models including unipolar depressive disorders within a bipolar spectrum model. We analysed manic/hypomanic symptom data in samples of clinically diagnosed bipolar I, bipolar II and unipolar patients, employing latent class analyses to determine if separate classes could be identified. Mixture analyses were also undertaken to determine if a unimodal, bimodal or a trimodal pattern was present. For both a refined 15-item set and an extended 30-item set of manic/hypomanic symptoms, our latent class analyses favoured three-class solutions, while mixture analyses identified trimodal distributions of scores. Findings argue for a categorical distinction between unipolar and bipolar disorders, as well as between bipolar I and bipolar II disorders. Future research should aim to consolidate these results in larger samples, particularly given that the size of the unipolar group in this study was a salient limitation