IMI TAMI Institute for Research and Development

Abstract Wheat straw, as a lignocellulosic residue, can be effectively converted into compost, representing a sustainable approach to agro-industrial waste management. This study investigates the effect of inoculating wheat straw with Bacillus strains on composting dynamics and organic matter degradation efficiency, with implications for soil quality improvement and environmental sustainability. Compost maturity and quality were evaluated through chemical and biological parameters over a 40-day period. Results showed that Bacillus -inoculated compost exhibited traits of a stable and mature product: the NH 4 ⁺-N/NO 3 ⁻-N ratio was 0.625, the C/N ratio was 25.88, and the pH was 7.26. The mineral composition of the inoculated compost was significantly increased compared to the control compost, namely: Ca (36%), Mg (58%), Cu (62%), and Zn (75%), with the greatest increase observed in Mn concentration (threefold). Phosphorus content in the treated compost increased eightfold compared to the initial value and exceeded that of the control. The germination index of white mustard seeds ( Sinapis alba L. ) was 30% higher in the inoculated sample. Bacillus strains accelerated the degradation of lignocellulosic material derived from wheat straw, yielding a mineral-rich, phytotoxin-free compost suitable for agricultural application and with enhanced biofertilization potential.

ObjectiveThis paper presents the results from a competition challenging participants to develop entity linking models using a subset of annotated MIMIC-IV-Note data and the SNOMED CT Terminology.Materials and MethodsAs a basis for this work, a large set of 74 808 annotations was curated across 272 discharge notes spanning 6624 unique clinical concepts. Submissions were evaluated using the mean Intersection-over-Union metric, evaluated at the character level with the 3 best performing solutions awarded a cash prize.ResultsThe winning solutions employed contrasting approaches: a dictionary-based method, an encoder-based method, and a decoder-based method.DiscussionOur analysis reveals that concept frequency in training data significantly impacts model performance, with rare concepts proving particularly challenging. High concept entropy and annotation ambiguity were also associated with decreased performance.ConclusionFindings from this work suggest that future projects should focus on improving entity linking for rare concepts and developing methods to better leverage contextual information when training examples are scarce.

Detecting and addressing potentially modifiable factors associated with healthy development is key to optimizing a child's potential. When investigating the outcomes of child development, it is important to account for disparities in feeding practices and avoid confounding bias. To estimate the independent association between breastfeeding and attainment of developmental milestones or neurodevelopmental conditions. This retrospective cohort study used data from a national network for routine child development surveillance in Israel linked with national social insurance financial entitlements for neurodevelopmental deficiencies. Participants were children born between January 2014 and December 2020 after at least 35 weeks' gestation without severe morbidity and with at least 1 follow-up surveillance visit at 2 to 3 years of age. Outcome data were collected in March 2023. Duration and exclusivity of breastfeeding in infancy. The primary outcomes were delays in attainment of developmental milestones and diagnosis of prespecified neurodevelopmental conditions. Multivariable regression, matching, and within-family analyses were used to estimate adjusted odds ratios (AORs) after accounting for potential confounding factors related to the child (gestational age, birth weight, multiple gestation, and child order in the family) and mother (age, socioeconomic status, educational level, marital status, employment, nationality, and postpartum depression). Of 570 532 children (291 953 [51.2%] male), 20 642 (3.6%) were preterm, 38 499 (6.7%) were small for gestational age, and 297 571 (52.1%) were breastfed for at least 6 months (123 984 [41.7%] were exclusively breastfed). Children who were breastfed for at least 6 months exhibited fewer delays in attaining language and social or motor developmental milestones compared with children exposed to less than 6 months of breastfeeding (AOR, 0.73 [95% CI, 0.71-0.76] for exclusive breastfeeding; AOR, 0.86 [95% CI, 0.83-0.88] for nonexclusive breastfeeding). Among 37 704 sibling pairs, children who were breastfed for at least 6 months were less likely to demonstrate milestone attainment delays (OR, 0.91 [95% CI, 0.86-0.97]) or be diagnosed with neurodevelopmental conditions (OR, 0.73 [95% CI, 0.66-0.82]) compared with their sibling with less than 6 months of breastfeeding or no breastfeeding. In this cohort study, exclusive or longer duration of breastfeeding was associated with reduced odds of developmental delays and language or social neurodevelopmental conditions. These findings may guide parents, caregivers, and public health initiatives in promoting early child development.

Predictive model performance may deteriorate when applied to data sources that were not used for training, thus, external validation is a key step in successful model deployment. As access to patient-level external data sources is typically limited, we recently proposed a method that estimates external model performance using only external summary statistics. Here, we benchmark the proposed method on multiple tasks using five large heterogeneous US data sources, where each, in turn, plays the role of an internal source and the remaining—external. Results showed accurate estimations for all metrics: 95th error percentiles for the area under the receiver operating characteristics (discrimination), calibration-in-the-large (calibration), Brier and scaled Brier scores (overall accuracy) of 0.03, 0.08, 0.0002, and 0.07, respectively. These results demonstrate the feasibility of estimating the transportability of prediction models using an internal cohort and external statistics. It may become an important accelerator of model deployment.

In this research, different Mg chips were produced via a mechanical chipping method in air to explore their potential as hydrogen storage materials. The production of fine magnesium chips was achieved with a laboratory-scale milling device, while coarse magnesium chips were fabricated by using a standard milling machine. The investigation of the first hydrogenation, at 300 °C under 50 atm, reveals that the fine chips require a shorter activation period of turbomolecular vacuum evacuation at 300 °C and exhibit a higher hydrogen absorption rate than the coarse chips. The activation stage anneals the dislocations formed during milling; therefore, they do not contribute to the hydrogenation. The enhanced performances of the fine chips are attributed to their larger specific surface area, smaller thickness, and the presence of macrostructural defects. Surface analysis of the outermost layers of the Mg chips, before and after activation procedures, sheds light on the surface reactions and thermal processes enabling the initiation of hydrogen absorption in Mg chips. The first hydrogen absorption kinetics appear to be characterized by a low-dimensional nucleation and growth mechanism. Apparent activation energies for absorption and desorption, determined using the Avrami and Kissinger equations, respectively, fall within the reported range for absorption and are lower than those reported for desorption. The mechanical chipping method in air is a simpler alternative to ball milling for producing Mg chips for hydrogen storage applications but requires an activation period before hydrogenation.

IntroductionAdvancing research and support for neurologically diverse populations requires novel data harmonisation methods that are capable of aligning with contemporary approaches to understanding health and disability.ObjectivesWe present the International Classification of Functioning, Disability and Health (ICF) as a conceptual framework to support harmonisation of mental health data and present a proof of principle within the Risk and Resilience in Developmental Diversity and Mental Health (R2D2-MH) consortium.Method138 measures from various mental health datasets were linked to the ICF following the WHO’s established linking rules.FindingsFindings support the notion that the ICF can assist in the harmonisation of mental health data. The high level of shared ICF codes provides indications of where items may be readily harmonised to develop datasets that may align more readily with contemporary approaches to understanding health and disability. Although the linking process necessarily entails an element of subjectivity, the application of established rules can increase rigour and transparency of the harmonisation process.ConclusionsWe present the first steps towards data harmonisation in mental health that is compatible with contemporary approaches in psychiatry, being more capable of capturing diversity and aligning with more transdiagnostic and neurodiversity-affirmative ways of understanding data.Clinical implicationsOur findings show promise, but future work is needed to address quantitative harmonisation. Similarly, issues related to the traditionally ‘pathophysiological’ frameworks that existing datasets are often embedded in can hinder the full potential of harmonisation based on the ICF.

The standard practice to account for expected developmental lags in preterm children is calculating their age as if born on their expected delivery date. We aimed to assess the accuracy of standard age correction in a large and diverse population. Routine surveillance data was extracted from a national network of mother-child clinics covering over 70% of the Israeli population. We included children with no developmental delay at age 2 years old, to exclude chronic dysfunctions. For each milestone assessed before age 2 years old we calculated the age of 90% and 95% population-milestone attainment, and compared attainment age between term and preterm children, before and after age correction. The study consisted of n = 656,986 and n = 52,662 term and preterm children respectively. Without age correction extensive gaps were observed in all domains, all degrees of prematurity and persisted throughout the first 2 years of life. With age correction most gaps were resolved among moderate/late preterm children, but not among extreme and very preterm, with residual gaps of at least 2 months for motor and 1 month for language-social development. While standard age correction accounts for maturational delay in late/moderate preterm children, it may underestimate the maturational delay among very/ extremely preterm children. Standard age correction is sufficient for late/moderate preterm children, and underestimates the maturational delay of extreme and very preterm children. Prior evidence on the accuracy of standard age correction across developmental domains and degrees of prematurity was limited to dated, small-scale data. Maturational delays persist throughout the first 2 years of life across all developmental domains and in all levels of prematurity. Developmental assessments without age correction may lead to unnecessary parental anxiety.

With the continuous increase in the prevalence of autistic spectrum disorder (ASD), effective early screening is crucial for initiating timely interventions and improving outcomes. To develop predictive models for ASD using routinely collected developmental surveillance data and to assess their performance in predicting ASD at different ages and in different clinical scenarios. This retrospective cohort study used nationwide data of developmental assessments conducted between January 1, 2014, and January 17, 2023, with minimal follow-up of 4 years and outcome collection in March 2023. Data were from a national program of approximately 1000 maternal child health clinics that perform routine developmental surveillance of children from birth to 6 years of age, serving 70% of children in Israel. The study included all children who were assessed at the maternal child health clinics (N = 1 187 397). Children were excluded if they were born at a gestational age of 33 weeks or earlier, had no record of gestational age, or were followed up for less than 4 years without an ASD outcome. The data set was partitioned at random into a development set (80% of the children) and a holdout evaluation set (20% of the children), both with the same prevalence of ASD outcome. For each child, demographic and birth-related covariates were extracted, as were per-visit growth measurements, quantified developmental milestone assessments, and referral summary covariates. Only information that was available before the prediction age was used for training and evaluating the models. The main outcome was eligibility for a governmental disabled child allowance due to ASD, according to administrative data of the National Insurance Institute of Israel. The performance of the models that predict the outcome was evaluated and compared with previous work on the Modified Checklist for Autism in Toddlers (M-CHAT). The study included 1 187 397 children (610 588 [51.4%] male). The performance of the ASD prediction models improved with prediction age, with fair accuracy already at 12 months of age. A model that combined longitudinal measures of developmental milestone assessments with a minimal set of demographic variables, which was applied at 18 to 24 months of age, achieved an area under the receiver operating characteristic curve of 0.83, with a sensitivity of 45.1% at a specificity of 95.0%. A model using single-visit assessments achieved an area under the receiver operating characteristic curve of 0.81 and a sensitivity of 41.2% at a specificity of 95.0%. The best performing prediction models surpassed the pooled performance of M-CHAT (sensitivity, 40%; specificity, 95%) reported in studies with a similar design. This cohort study found that ASD can be predicted from routine developmental surveillance data at an accuracy surpassing M-CHAT screening. This tool may be seamlessly integrated in the clinical workflow to improve early identification of children who may benefit from timely interventions.

Developmental surveillance, conducted routinely worldwide, is fundamental for timely identification of children at risk of developmental delays. It is typically executed by assessing age-appropriate milestone attainment and applying clinical judgment during health supervision visits. Unlike developmental screening and evaluation tools, surveillance typically lacks standardized quantitative measures, and consequently, its interpretation is often qualitative and subjective. Herein, we suggested a novel method for aggregating developmental surveillance assessments into a single score that coherently depicts and monitors child development. We described the procedure for calculating the score and demonstrated its ability to effectively capture known population-level associations. Additionally, we showed that the score can be used to describe longitudinal patterns of development that may facilitate tracking and classifying developmental trajectories of children. We described the Developmental Surveillance Score (DSS), a simple-to-use tool that quantifies the age-dependent severity level of a failure at attaining developmental milestones based on the recently introduced Israeli developmental surveillance program. We evaluated the DSS using a nationwide cohort of >1 million Israeli children from birth to 36 months of age, assessed between July 1, 2014, and September 1, 2021. We measured the score's ability to capture known associations between developmental delays and characteristics of the mother and child. Additionally, we computed series of the DSS in consecutive visits to describe a child's longitudinal development and applied cluster analysis to identify distinct patterns of these developmental trajectories. The analyzed cohort included 1,130,005 children. The evaluation of the DSS on subpopulations of the cohort, stratified by known risk factors of developmental delays, revealed expected relations between developmental delay and characteristics of the child and mother, including demographics and obstetrics-related variables. On average, the score was worse for preterm children compared to full-term children and for male children compared to female children, and it was correspondingly worse for lower levels of maternal education. The trajectories of scores in 6 consecutive visits were available for 294,000 children. The clustering of these trajectories revealed 3 main types of developmental patterns that are consistent with clinical experience: children who successfully attain milestones, children who initially tend to fail but improve over time, and children whose failures tend to increase over time. The suggested score is straightforward to compute in its basic form and can be easily implemented as a web-based tool in its more elaborate form. It highlights known and novel relations between developmental delay and characteristics of the mother and child, demonstrating its potential usefulness for surveillance and research. Additionally, it can monitor the developmental trajectory of a child and characterize it. Future work is needed to calibrate the score vis-a-vis other screening tools, validate it worldwide, and integrate it into the clinical workflow of developmental surveillance.