The MPIfR-MeerKAT Galactic Plane Survey - I. System set-up and early results

Galactic plane radio surveys play a key role in improving our understanding of a wide range of astrophysical phenomena. Performing such a survey using the latest interferometric telescopes produces large data rates necessitating a shift towards fully or quasi-real-time data analysis with data being stored for only the time required to process them. We present here the overview and set-up for the 3000-h Max-Planck-Institut für Radioastronomie (MPIfR)-MeerKAT Galactic Plane Survey (MMGPS). The survey is unique by operating in a commensal mode, addressing key science objectives of the survey including the discovery of new pulsars and transients and studies of Galactic magnetism, the interstellar medium and star formation rates. We explain the strategy coupled with the necessary hardware and software infrastructure needed for data reduction in the imaging, spectral, and time domains. We have so far discovered 78 new pulsars including 17 confirmed binary systems of which two are potential double neutron star systems. We have also developed an imaging pipeline sensitive to the order of a few tens of micro-Jansky () with a spatial resolution of a few arcseconds. Further science operations with an in-house built S-band receiver operating between 1.7 and 3.5 GHz are about to commence. Early spectral line commissioning observations conducted at S-band, targeting transitions of the key molecular gas tracer CH at 3.3 GHz already illustrate the spectroscopic capabilities of this instrument. These results lay a strong foundation for future surveys with telescopes like the Square Kilometre Array (SKA)

Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets

Developing guidelines in low-income and middle-income countries: lessons from Kenya

There are few examples of sustained nationally organised, evidence-informed clinical guidelines development processes in Sub-Saharan Africa. We describe the evolution of efforts from 2005 to 2015 to support evidence-informed decision making to guide admission hospital care practices in Kenya. The approach to conduct reviews, present evidence, and structure and promote transparency of consensus-based procedures for making recommendations improved over four distinct rounds of policy making. Efforts to engage important voices extended from government and academia initially to include multiple professional associations, regulators and practitioners. More than 100 people have been engaged in the decision-making process; an increasing number outside the research team has contributed to the conduct of systematic reviews, and 31 clinical policy recommendations has been developed. Recommendations were incorporated into clinical guideline booklets that have been widely disseminated with a popular knowledge and skills training course. Both helped translate evidence into practice. We contend that these efforts have helped improve the use of evidence to inform policy. The systematic reviews, Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approaches and evidence to decision-making process are well understood by clinicians, and the process has helped create a broad community engaged in evidence translation together with a social or professional norm to use evidence in paediatric care in Kenya. Specific sustained efforts should be made to support capacity and evidence-based decision making in other African settings and clinical disciplines

What do we think we are doing? How might a clinical information network be promoting implementation of recommended paediatric care practices in Kenyan hospitals?

BACKGROUND: The creation of a clinical network was proposed as a means to promote implementation of a set of recommended clinical practices targeting inpatient paediatric care in Kenya. The rationale for selecting a network as a strategy has been previously described. Here, we aim to describe network activities actually conducted over its first 2.5 years, deconstruct its implementation into specific components and provide our 'insider' interpretation of how the network is functioning as an intervention. METHODS: We articulate key activities that together have constituted network processes over 2.5 years and then utilise a recently published typology of implementation components to give greater granularity to this description from the perspective of those delivering the intervention. Using the Behaviour Change Wheel we then suggest how the network may operate to achieve change and offer examples of change before making an effort to synthesise our understanding in the form of a realist context-mechanism-outcome configuration. RESULTS: We suggest our network is likely to comprise 22 from a total of 73 identifiable intervention components, of which 12 and 10 we consider major and minor components, respectively. At the policy level, we employed clinical guidelines, marketing and communication strategies with intervention characteristics operating through incentivisation, persuasion, education, enablement, modelling and environmental restructuring. These might influence behaviours by enhancing psychological capability, creating social opportunity and increasing motivation largely through a reflective pathway. CONCLUSIONS: We previously proposed a clinical network as a solution to challenges implementing recommended practices in Kenyan hospitals based on our understanding of theory and context. Here, we report how we have enacted what was proposed and use a recent typology to deconstruct the intervention into its elements and articulate how we think the network may produce change. We offer a more generalised statement of our theory of change in a context-mechanism-outcome configuration. We hope this will complement a planned independent evaluation of 'how things work', will help others interpret results of change reported more formally in the future and encourage others to consider further examination of networks as means to scale up improvement practices in health in lower income countries.</p

MeerKAT 1.3 GHz Observations of Supernova Remnants

We present full Stokes MeerKAT L -band (856–1712 MHz) observations of 36 high-latitude supernova remnants (SNRs). Sensitive, high-dynamic-range images show a wealth of structure. G15.1−1.6 appears to be a H ii region rather than an SNR. G30.7−2.0 consists of three background extragalactic sources which appear to form an arc when imaged with much lower resolution. At least half of the remnants in the sample contain “blowouts” or “ears,” showing these to be a common feature. Analysis of the polarimetric data reveals details of the magnetic field structure in the emitting regions of the remnants as well as magnetized thermal plasma in front of polarized emission. The chance alignment of G327.6+14.6 with a background active galactic nucleus with very extended polarized jets allows testing for the presence of Faraday effects in the interior of the remnant. Scant evidence of Faraday rotating material is found in the interior of this remnant

Robust Handwritten Text Recognition in Scarce Labeling Scenarios : Disentanglement, Adaptation and Generation

Els documents escrits a mà no només es conserven en arxius històrics, sinó que també s'utilitzen àmpliament en documents administratius, com ara xecs o formularis. Amb l'auge de de l'anomenat aprenentatge profund (Deep Learning), s'ha aconseguit un bon rendiment en conjunts de dades específics per al reconeixement de text manuscrit. Tot i això, encara és difícil resoldre casos d'ús reals a causa de la variació entre estils d'escriptura de diferents escriptors i el fet de tenir dades etiquetades limitades. Per tant, es requereix explorar arquitectures de reconeixement d'escriptura més sòlides així com proposar mètodes per disminuir la bretxa entre conjunts de dades font i objectiu de manera no supervisada. En aquesta tesi, en primer lloc, explorem noves arquitectures per al reconeixement de text manuscrit, un mètode Sequence-to-Sequence amb mecanisme d'atenció i un mètode basat en transformadors no recurrents. En segon lloc, ens centrem en la disminució de la bretxa de rendiment entre les dades d'origen i les de destinació de manera no supervisada. Finalment, proposem un grup de mètodes generatius per a imatges de text manuscrits, que es poden utilitzar per augmentar el conjunt d'entrenament per obtenir un reconeixement més robust. A més, simplement modificant el mètode generatiu i unint-lo amb un reconeixedor, acabem amb un mètode de desenredament eficaç per destil·lar contingut textual d'estils d'escriptura a mà per aconseguir un rendiment de reconeixement generalitzat. Superem el rendiment dels reconeixedors de text manuscrit de l'estat de l'art en els resultats experimentals entre diferents conjunts de dades científics i industrials, que demostren l'eficàcia dels mètodes proposats. Tant ell reconeixement no recurrent com el mètode de desenredament són les primeres contribucions al camp del reconeixement d'escriptura a mà. A més, hem esbossat les línies de recerca potencials, que serien interessants explorar en el futur.Los documentos manuscritos no solo se conservan en archivos históricos, sino que también se usan ampliamente en documentos administrativos como cheques y reclamaciones. Con el auge de las redes neuronales profundas, muchas técnicas del estado del arte han obtenido un buen rendimiento en conjuntos de datos específicos para el reconocimiento de texto manuscrito (HTR). Sin embargo, los casos de uso reales todavía son un desafío debido a la variabilidad de estilos de escritura de diferentes escritores y la cantidad limitada de datos etiquetados. Por lo tanto, es necesario explorar tanto arquitecturas para reconocimiento de texto manuscrito más robustas como proponer métodos para disminuir la brecha entre los datos de origen y destino de una manera no supervisada. En esta tesis, en primer lugar, exploramos arquitecturas novedosas para el HTR, desde el método secuencia-a-secuencia (Seq2Seq) con mecanismo de atención, hasta el método no recurrente basado en Transformers. En segundo lugar, nos centramos en reducir la brecha de rendimiento entre los datos de origen y de destino mediante métodos no supervisados. Finalmente, proponemos un grupo de métodos generativos para imágenes de texto manuscrito, que pueden usarse para aumentar el conjunto de entrenamiento y obtener un reconocedor más robusto. Además, simplemente modificando el método generativo y uniéndolo con un reconocedor, obtenemos un método eficaz para destilar el contenido textual de los estilos de escritura para lograr un rendimiento de reconocimiento generalizado. En resultados experimentales obtenemos rendimientos en HTR que superan los del estado del arte en diferentes conjuntos de datos científicos e industriales, los cuales demuestran la efectividad de los métodos propuestos. Hasta donde sabemos, el reconocedor no recurrente y el método de para destilar son contribuciones originales en el campo de reconocimiento de texto manuscrito. Finalmente, hemos esbozado posibles líneas de investigación que sería interesante explorar en el futuro.Handwritten documents are not only preserved in historical archives but also widely used in administrative documents such as cheques and claims. With the rise of the deep learning era, many state-of-the-art approaches have achieved good performance on specific datasets for Handwritten Text Recognition (HTR). However, it is still challenging to solve real use cases because of the varied handwriting styles across different writers and the limited labeled data. Thus, both exploring a more robust handwriting recognition architectures and proposing methods to diminish the gap between the source and target data in an unsupervised way are demanded. In this thesis, firstly, we explore novel architectures for HTR, from Sequence-to-Sequence (Seq2Seq) method with attention mechanism to non-recurrent Transformer-based method. Secondly, we focus on diminishing the performance gap between source and target data in an unsupervised way. Finally, we propose a group of generative methods for handwritten text images, which could be utilized to increase the training set to obtain a more robust recognizer. In addition, by simply modifying the generative method and joining it with a recognizer, we end up with an effective disentanglement method to distill textual content from handwriting styles so as to achieve a generalized recognition performance. We outperform state-of-the-art HTR performances in the experimental results among different scientific and industrial datasets, which prove the effectiveness of the proposed methods. To the best of our knowledge, the non-recurrent recognizer and the disentanglement method are the first contributions in the handwriting recognition field. Furthermore, we have outlined the potential research lines, which would be interesting to explore in the future.Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtic

Characteristics of admissions and variations in the use of basic investigations, treatments and outcomes in Kenyan hospitals within a new Clinical Information Network.

Lack of detailed information about hospital activities, processes and outcomes hampers planning, performance monitoring and improvement in low-income countries (LIC). Clinical networks offer one means to advance methods for data collection and use, informing wider health system development in time, but are rare in LIC. We report baseline data from a new Clinical Information Network (CIN) in Kenya seeking to promote data-informed improvement and learning.Data from 13 hospitals engaged in the Kenyan CIN between April 2014 and March 2015 were captured from medical and laboratory records. We use these data to characterise clinical care and outcomes of hospital admission.Data were available for a total of 30 042 children aged between 2 months and 15 years. Malaria (in five hospitals), pneumonia and diarrhoea/dehydration (all hospitals) accounted for the majority of diagnoses and comorbidity was found in 17 710 (59%) patients. Overall, 1808 deaths (6%) occurred (range per hospital 2.5%-11.1%) with 1037 deaths (57.4%) occurring by day 2 of admission (range 41%-67.8%). While malaria investigations are commonly done, clinical health workers rarely investigate for other possible causes of fever, test for blood glucose in severe illness or ascertain HIV status of admissions. Adherence to clinical guideline-recommended treatment for malaria, pneumonia, meningitis and acute severe malnutrition varied widely across hospitals.Developing clinical networks is feasible with appropriate support. Early data demonstrate that hospital mortality remains high in Kenya, that resources to investigate severe illness are limited, that care provided and outcomes vary widely and that adoption of effective interventions remains slow. Findings suggest considerable scope for improving care within and across sites

Adoption of recommended practices and basic technologies in a low-income setting.

OBJECTIVE: In global health considerable attention is focused on the search for innovations; however, reports tracking their adoption in routine hospital settings from low-income countries are absent. DESIGN AND SETTING: We used data collected on a consistent panel of indicators during four separate cross-sectional, hospital surveys in Kenya to track changes over a period of 11 years (2002-2012). MAIN OUTCOME MEASURES: Basic resource availability, use of diagnostics and uptake of recommended practices. RESULTS: There appeared little change in availability of a panel of 28 basic resources (median 71% in 2002 to 82% in 2012) although availability of specific feeds for severe malnutrition and vitamin K improved. Use of blood glucose and HIV testing increased but remained inappropriately low throughout. Commonly (malaria) and uncommonly (lumbar puncture) performed diagnostic tests frequently failed to inform practice while pulse oximetry, a simple and cheap technology, was rarely available even in 2012. However, increasing adherence to prescribing guidance occurred during a period from 2006 to 2012 in which efforts were made to disseminate guidelines. CONCLUSIONS: Findings suggest changes in clinical practices possibly linked to dissemination of guidelines at reasonable scale. However, full availability of basic resources was not attained and major gaps likely exist between the potential and actual impacts of simple diagnostics and technologies representing problems with availability, adoption and successful utilisation. These findings are relevant to debates on scaling up in low-income settings and to those developing novel therapeutic or diagnostic interventions