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Background/Objectives: Iron deficiency anemia is a widespread public health problem, particularly in low- and middle-income countries. Maternal iron status around and during pregnancy may influence infant iron status. We examined multiple biomarkers to determine the prevalence of iron deficiency and anemia among breastfed infants and explored its relationship with maternal and infant characteristics in Bhaktapur, Nepal. Subjects/Methods: In a cross-sectional survey, we randomly selected 500 mother–infant pairs from Bhaktapur municipality. Blood was analyzed for hemoglobin, ferritin, total iron-binding capacity, transferrin receptors and C-reactive protein. Results: The altitude-adjusted prevalence of anemia was 49% among infants 2–6-month-old (hemaglobin (Hb) <10.8 g/dl) and 72% among infants 7–12-month-old (Hb <11.3 g/dl). Iron deficiency anemia, defined as anemia and serum ferritin <20 or <12 μg/l, affected 9 and 26% of infants of these same age groups. Twenty percent of mothers had anemia (Hb <12.3 g/dl), but only one-fifth was explained by depletion of iron stores. Significant predictors of infant iron status and anemia were infant age, sex and duration of exclusive breastfeeding and maternal ferritin concentrations. Conclusions: Our findings suggest that iron supplementation in pregnancy is likely to have resulted in a low prevalence of postpartum anemia. The higher prevalence of anemia and iron deficiency among breastfed infants compared with their mothers suggests calls for intervention targeting newborns and infants.

The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β − -decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

Since the discovery of neutrino oscillations, we know that neutrinos have non-zero mass. However, the absolute neutrino-mass scale remains unknown. Here we report the upper limits on effective electron anti-neutrino mass, m ν , from the second physics run of the Karlsruhe Tritium Neutrino experiment. In this experiment, m ν is probed via a high-precision measurement of the tritium β -decay spectrum close to its endpoint. This method is independent of any cosmological model and does not rely on assumptions whether the neutrino is a Dirac or Majorana particle. By increasing the source activity and reducing the background with respect to the first physics campaign, we reached a sensitivity on m ν of 0.7 eV  c –2  at a 90% confidence level (CL). The best fit to the spectral data yields m ν 2  = (0.26 ± 0.34) eV 2   c –4 , resulting in an upper limit of m ν  < 0.9 eV  c –2  at 90% CL. By combining this result with the first neutrino-mass campaign, we find an upper limit of m ν  < 0.8 eV  c –2 at 90% CL. In its second measurement campaign, the Karlsruhe Tritium Neutrino experiment achieved a sub-electronvolt sensitivity on the effective electron anti-neutrino mass.

Childhood malnutrition is associated with high morbidity and mortality globally 1 . Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood 2 . Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0–59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards 3 – 5 . The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z -score, respectively, that is more than two standard deviations below the World Health Organization’s median growth reference standards for a healthy population 6 . Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces) 7 ; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes 8 . Building from our previous work mapping CGF in Africa 9 , here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99% of affected children live 1 , aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications. High-resolution subnational mapping of child growth failure indicators for 105 low- and middle-income countries between 2000 and 2017 shows that, despite considerable progress, substantial geographical inequalities still exist in some countries.

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after five years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN's main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous \\(^\\mathrm{83m}\\)Kr, which enables the neutrino-mass measurements in the SAP configuration.

The fact that neutrinos carry a non-vanishing rest mass is evidence of physics beyond the Standard Model of elementary particles. Their absolute mass bears important relevance from particle physics to cosmology. In this work, we report on the search for the effective electron antineutrino mass with the KATRIN experiment. KATRIN performs precision spectroscopy of the tritium \\(\\beta\\)-decay close to the kinematic endpoint. Based on the first five neutrino-mass measurement campaigns, we derive a best-fit value of \\(m_\\nu^{2} = {-0.14^{+0.13}_{-0.15}}~\\mathrm{eV^2}\\), resulting in an upper limit of \\(m_\\nu < {0.45}~\\mathrm{eV}\\) at 90 % confidence level. With six times the statistics of previous data sets, amounting to 36 million electrons collected in 259 measurement days, a substantial reduction of the background level and improved systematic uncertainties, this result tightens KATRIN's previous bound by a factor of almost two.

The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time-frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization - may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment - a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium \\(\\beta^-\\)-decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay spectroscopy. Project 8 seeks to use the CRES technique to measure the neutrino mass with a sensitivity of 40~meV, requiring a large supply of tritium atoms stored in a multi-cubic meter detector volume. Antenna arrays are one potential technology compatible with an experiment of this scale, but the capability of an antenna-based CRES experiment to measure the neutrino mass depends on the efficiency of the signal detection algorithms. In this paper, we develop efficiency models for three signal detection algorithms and compare them using simulations from a prototype antenna-based CRES experiment as a case-study. The algorithms include a power threshold, a matched filter template bank, and a neural network based machine learning approach, which are analyzed in terms of their average detection efficiency and relative computational cost. It is found that significant improvements in detection efficiency and, therefore, neutrino mass sensitivity are achievable, with only a moderate increase in computation cost, by utilizing either the matched filter or machine learning approach in place of a power threshold, which is the baseline signal detection algorithm used in previous CRES experiments by Project 8.

The Karlsruhe Tritium Neutrino (KATRIN) experiment is designed to measure a high-precision integral spectrum of the endpoint region of T2 beta decay, with the primary goal of probing the absolute mass scale of the neutrino. After a first tritium commissioning campaign in 2018, the experiment has been regularly running since 2019, and in its first two measurement campaigns has already achieved a sub-eV sensitivity. After 1000 days of data-taking, KATRIN's design sensitivity is 0.2 eV at the 90% confidence level. In this white paper we describe the current status of KATRIN; explore prospects for measuring the neutrino mass and other physics observables, including sterile neutrinos and other beyond-Standard-Model hypotheses; and discuss research-and-development projects that may further improve the KATRIN sensitivity.

To explore the influence of night blindness during pregnancy on nighttime work activities of women. A community based case-control study was used to compare nighttime activities of night blind (cases) and non-night blind pregnant women (controls) using a 24h recall method to measure work activities (n=116 pairs). Rural South-Eastern district in the plains of Nepal. Approximately one third of the night blind women reported being 'inactive' the previous night, not participating in any of the inquired work activities, as compared with only 15% of the control group (P < 0.031). The type of work that was significantly affected was the outdoor kind such as fetching water and washing dishes. Logistic regression analysis showed that night blind women were half as likely (odds ratio=0.49, 95% confidence interval=0.25-0.98) to work at night than women without night blindness after controlling for the effects of confounding variables including gestational age, season, and protein energy malnutrition which were significantly associated with nighttime work activity. Night blindness during pregnancy, an indicator of vitamin A deficiency, reduces the number and type of work activities women perform at night, thus impairing women's ability to participate in normal subsistence activities by reducing their 'work day'.