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Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

A third of patients with the DiGeorge syndrome have congenital kidney and urinary tract anomalies. This study provides evidence that haploinsufficiency of CRKL is associated with such anomalies in the DiGeorge syndrome and in sporadic congenital kidney and urinary tract anomalies. Deletions on chromosome 22q11.2 are the most common cause of the DiGeorge syndrome (Online Mendelian Inheritance in Man [OMIM] number, 188400) and the velocardiofacial syndrome (OMIM number, 192430) and constitute the most common microdeletion disorder in humans, with an estimated prevalence of 1 in 2000 to 4000 live births. 1 – 3 The DiGeorge syndrome is a debilitating, multisystemic condition that features (with variable expressivity) cardiac malformations, velopharyngeal insufficiency, hypoparathyroidism with hypocalcemia, and thymic aplasia with immune deficiency. Additional phenotypes include neurodevelopmental defects and urogenital malformations. 4 – 7 The long arm of chromosome 22 contains multiple segmental duplications (low-copy repeats) that confer a . . .

While patient-derived xenograft (PDX) models of hepatocellular carcinoma (HCC) have been successfully generated from resected tissues, no reliable methods have been reported for the generation of PDXs from patients who are not candidates for resection and represent the vast majority of patients with HCC. Here we compare two methods for the creation of PDXs from HCC biopsies and find that implantation of whole biopsy samples without the addition of basement membrane matrix favors the formation of PDX tumors that resemble Epstein-Barr virus (EBV)-driven B-cell lymphomas rather than HCC tumors. In contrast, implantation with Matrigel supports growth of HCC cells and leads to a high rate of HCC tumor formation from these biopsies. We validate the resulting PDXs, confirm their fidelity to the patients’ disease and conclude that minimally invasive percutaneous liver biopsies can be used with relatively high efficiency to generate PDXs of HCC.

Background Children with chronic kidney disease (CKD), requiring dialysis or kidney transplantation, have a mortality rate of up to 30-fold higher than the general aged-matched population, and severely impaired quality of life. Symptoms such as fatigue and pain are prevalent and debilitating. Children with CKD are at risk of cognitive impairment, and poorer educational, vocational, and psychosocial outcomes compared with their well peers, which have consequences through to adulthood. Treatment regimens for children with CKD are long-term, complex, and highly intrusive. While many trials have been conducted to improve outcomes in children with CKD, the outcomes measured and reported are often not relevant to patients and clinicians, and are highly variable. These problems can diminish the value of trials as a means to improve the lives of children with CKD. The Standardised Outcomes in Nephrology—Children and Adolescents (SONG-Kids) study aims to develop a core outcome set for trials in children and adolescents with any stage of CKD that is based on the shared priorities of all stakeholders. Methods/Design SONG-Kids involves five phases: a systematic review to identify outcomes (both domains and measures) that have been reported in randomised controlled trials involving children aged up to 21 years with CKD; focus groups (using nominal group technique) with adolescent patients and caregivers of paediatric patients (all ages) to identify outcomes that are relevant and important to patients and their family and the reasons for their choices; semistructured key informant interviews with health professionals involved in the care of children with CKD to ascertain their views on establishing core outcomes in paediatric nephrology; an international three-round online Delphi survey with patients, caregivers, clinicians, researchers, policy-makers, and members from industry to develop consensus on important outcome domains; and a stakeholder workshop to review and finalise the set of core outcome domains for trials in children with CKD (including nondialysis-dependent, dialysis, and kidney transplantation). Discussion Establishing a core outcome set to be reported in all trials conducted in children with any stage of CKD will enhance the relevance, transparency, and impact of research to improve the lives of children and adolescents with CKD.

Abstract BackgroundChildren with chronic kidney disease (CKD) generally have worse educational and psychosocial outcomes compared with their healthy peers. This can impair their ability to manage their treatment, which in turn can have long-term health consequences through to adulthood. We attempted to capture the experiences of children with CKD and to describe the perspectives of their parents and caregivers on access to educational and psychosocial support.MethodsChildren with CKD (n = 34) and their caregivers (n = 62) were sampled via focus groups from pediatric hospitals in Australia, Canada, and the USA. Sixteen focus groups were convened and the transcripts were analyzed thematically.ResultsWe identified four themes: disruption to self-esteem and identity (emotional turmoil of adolescence, wrestling with the sick self, powerlessness to alleviate child’s suffering, balancing normality and protection); disadvantaged by lack of empathy and acceptance (alienated by ignorance, bearing the burden alone); a hidden and inaccessible support system (excluded from formal psychological support, falling behind due to being denied special considerations); and building resilience (finding partners in the journey, moving towards acceptance of the illness, re-establishing childhood).ConclusionsChildren with CKD and their caregivers encountered many barriers in accessing psychosocial and educational support and felt extremely disempowered and isolated as a consequence. Improved availability and access to psychosocial and educational interventions are needed to improve the wellbeing and educational advancement of children with CKD. A higher resolution version of the Graphical abstract is available as Supplementary information.

Anorexia nervosa (AN) is a serious eating disorder characterized by restriction of energy intake relative to requirements, resulting in abnormally low body weight. It has a lifetime prevalence of approximately 1%, disproportionately affects females(1,2), and has no well replicated evidence of effective pharmacological or psychological treatments despite high morbidity and mortality(2). Twin studies support a genetic basis for the observed aggregation of AN in families(3), with heritability estimates of 48%-74%(4). Although initial genome-wide association studies (GWASs) were underpowered(5,6), evidence suggested that signals for AN would be detected with increased power(5). We present a GWAS of 3,495 AN cases and 10,982 controls with one genome-wide significant locus (index variant rs4622308, p=4.3x10-9) in a region (chr12:56,372,585-56,482,185) which includes six genes. The SNP-chip heritability (h_SNP^2) of AN from these data is 0.20 (SE=0.02), suggesting that a substantial fraction of the twin-based heritability stems from common genetic variation. Using these GWAS results, we also find significant positive genetic correlations with schizophrenia, neuroticism, educational attainment, and HDL cholesterol, and significant negative genetic correlations with body mass, insulin, glucose, and lipid phenotypes. Our results support the reconceptualization of AN as a disorder with both psychiatric and metabolic components.