Low zinc status and absorption exist in infants with jejunostomies or ileostomies which persists after intestinal repair.

There is very little data regarding trace mineral nutrition in infants with small intestinal ostomies. Here we evaluated 14 infants with jejunal or ileal ostomies to measure their zinc absorption and retention and biochemical zinc and copper status. Zinc absorption was measured using a dual-tracer stable isotope technique at two different time points when possible. The first study was conducted when the subject was receiving maximal tolerated feeds enterally while the ostomy remained in place. A second study was performed as soon as feasible after full feeds were achieved after intestinal repair. We found biochemical evidence of deficiencies of both zinc and copper in infants with small intestinal ostomies at both time points. Fractional zinc absorption with an ostomy in place was 10.9% ± 5.3%. After reanastamosis, fractional zinc absorption was 9.4% ± 5.7%. Net zinc balance was negative prior to reanastamosis. In conclusion, our data demonstrate that infants with a jejunostomy or ileostomy are at high risk for zinc and copper deficiency before and after intestinal reanastamosis. Additional supplementation, especially of zinc, should be considered during this time period

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared to its counterpart MAML for most experiments

Liquidity and the drivers of search, due diligence and transaction times for UK commercial real estate investments

Trading commercial real estate involves a process of exchange that is costly and which occurs over an extended and uncertain period of time. This has consequences for the performance and risk of real estate investments. Most research on transaction times has occurred for residential rather than commercial real estate. We study the time taken to transact commercial real estate assets in the UK using a sample of 578 transactions over the period 2004 to 2013. We measure average time to transact from a buyer and seller perspective, distinguishing the search and due diligence phases of the process, and we conduct econometric analysis to explain variation in due diligence times between assets. The median time for purchase of real estate from introduction to completion was 104 days and the median time for sale from marketing to completion was 135 days. There is considerable variation around these times and results suggest that some of this variation is related to market state, type and quality of asset, and type of participants involved in the transaction. Our findings shed light on the drivers of liquidity at an individual asset level and can inform models that quantify the impact of uncertain time on market on real estate investment risk

SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation

Processing medical data to find abnormalities is a time-consuming and costly task, requiring tremendous efforts from medical experts. Therefore, Ai has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. AI tools highly depend on data for training the models. However, there are several constraints to access to large amounts of medical data to train machine learning algorithms in the medical domain, e.g., due to privacy concerns and the costly, time-consuming medical data annotation process. To address this, in this paper we present a novel synthetic data generation pipeline called SinGAN-Seg to produce synthetic medical data with the corresponding annotated ground truth masks. We show that these synthetic data generation pipelines can be used as an alternative to bypass privacy concerns and as an alternative way to produce artificial segmentation datasets with corresponding ground truth masks to avoid the tedious medical data annotation process. As a proof of concept, we used an open polyp segmentation dataset. By training UNet++ using both the real polyp segmentation dataset and the corresponding synthetic dataset generated from the SinGAN-Seg pipeline, we show that the synthetic data can achieve a very close performance to the real data when the real segmentation datasets are large enough. In addition, we show that synthetic data generated from the SinGAN-Seg pipeline improving the performance of segmentation algorithms when the training dataset is very small. Since our SinGAN-Seg pipeline is applicable for any medical dataset, this pipeline can be used with any other segmentation datasets

VISEM-Tracking, a human spermatozoa tracking dataset

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa

Identification of Evening Complex Associated Proteins in Arabidopsis by Affinity Purification and Mass Spectrometry

Many species possess an endogenous circadian clock to synchronize internal physiology with an oscillating external environment. In plants, the circadian clock coordinates growth, metabolism and development over daily and seasonal time scales. Many proteins in the circadian network form oscillating complexes that temporally regulate myriad processes, including signal transduction, transcription, protein degradation and post-translational modification. In Arabidopsis thaliana, a tripartite complex composed of EARLY FLOWERING 4 (ELF4), EARLY FLOWERING 3 (ELF3), and LUX ARRHYTHMO (LUX), named the evening complex, modulates daily rhythms in gene expression and growth through transcriptional regulation. However, little is known about the physical interactions that connect the circadian system to other pathways. We used affinity purification and mass spectrometry (AP-MS) methods to identify proteins that associate with the evening complex in A. thaliana. New connections within the circadian network as well as to light signaling pathways were identified, including linkages between the evening complex, TIMING OF CAB EXPRESSION1 (TOC1), TIME FOR COFFEE (TIC), all phytochromes and TANDEM ZINC KNUCKLE/PLUS3 (TZP). Coupling genetic mutation with affinity purifications tested the roles of phytochrome B (phyB), EARLY FLOWERING 4, and EARLY FLOWERING 3 as nodes connecting the evening complex to clock and light signaling pathways. These experiments establish a hierarchical association between pathways and indicate direct and indirect interactions. Specifically, the results suggested that EARLY FLOWERING 3 and phytochrome B act as hubs connecting the clock and red light signaling pathways. Finally, we characterized a clade of associated nuclear kinases that regulate circadian rhythms, growth, and flowering in A. thaliana. Coupling mass spectrometry and genetics is a powerful method to rapidly and directly identify novel components and connections within and between complex signaling pathways

GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse datasets are the major challenge for clinical integration. This scarcity is also due to the legal restrictions and extensive manual efforts required for accurate annotations from clinicians. To address these challenges, we present \textit{GastroVision}, a multi-center open-access gastrointestinal (GI) endoscopy dataset that includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings (a total of 27 classes) from the GI tract. The dataset comprises 8,000 images acquired from B{\ae}rum Hospital in Norway and Karolinska University Hospital in Sweden and was annotated and verified by experienced GI endoscopists. Furthermore, we validate the significance of our dataset with extensive benchmarking based on the popular deep learning based baseline models. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification. Our dataset is available at \url{https://osf.io/84e7f/}

Effect of a Motivational Interviewing–Based Intervention on Initiation of Mental Health Treatment and Mental Health After an Emergency Department Visit Among Suicidal Adolescents

Abstract IMPORTANCE Emergency department (ED) visits present opportunities to identify and refer suicidal youth for outpatient mental health care, although this practice is not routine. OBJECTIVE To examine whether a motivational interviewing–based intervention increases linkage of adolescents to outpatient mental health services and reduces depression symptoms and suicidal ideation in adolescents seeking emergency care for non–mental health–related concerns who screen positive for suicide risk. DESIGN, SETTING, AND PARTICIPANTS In this randomized clinical trial, adolescents aged 12 to 17 years who screened positive on the Ask Suicide Screening Questions (ASQ) during a nonpsychiatric ED visit at 2 academic pediatric EDs in Ohio were recruited from April 2013 to July 2015. Intention-totreat analyses were performed from September 2018 to October 2019. INTERVENTIONS The Suicidal Teens Accessing Treatment After an Emergency Department Visit (STAT-ED) intervention included motivational interviewing to target family engagement, problem solving, referral assistance, and limited case management. The enhanced usual care (EUC) intervention consisted of brief mental health care consultation and referral. MAIN OUTCOMES AND MEASURES Primary outcomes were mental health treatment initiation and attendance within 2 months of ED discharge and suicidal ideation (assessed by the Suicidal Ideation Questionnaire JR) and depression symptoms (assessed by the Center for Epidemiologic Studies– Depression scale) at 2 and 6 months. Exploratory outcomes included treatment initiation and attendance and suicide attempts at 6 months. RESULTS A total of 168 participants were randomized and 159 included in the intention-to-treat analyses (mean [SD] age, 15.0 [1.5] years; 126 [79.2%] female; and 80 [50.3%] white). Seventy-nine participants were randomized to receive the STAT-ED intervention and 80 to receive EUC. At 2 months, youth in the STAT-ED group had similar rates of mental health treatment initiation compared with youth in the EUC group as assessed by parent report (29 [50.9%] vs 22 [34.9%]; adjusted odds ratio [OR], 2.08; 95% CI, 0.97-4.45) and administrative data from mental health care agencies (19 [29.7%] vs 11 [19.3%]; adjusted OR, 1.77; 95% CI, 0.76-4.15). At 2 months, youth in the STAT-ED group and the EUC group had similar rates of treatment attendance (1 appointment: 6 [9.7%] vs 2 [3.6%]; adjusted OR, 2.97; 95% CI, 0.56-15.73; 2 appointments: 10 [16.1%] vs 7 [12.7%]; adjusted OR, 1.43; 95% CI, 0.50-4.11). There were no significant group × time differences in suicidal ideation (F = 0.28; P = .72) and depression symptoms (F = 0.49; P = .60) during the 6-month follow-up period. In exploratory analyses, at 6 months, STAT-ED participants had significantly higher rates of agencyreported mental health treatment initiation (adjusted OR, 2.48; 95% CI, 1.16-5.28) and more completed appointments (t99.7 = 2.58; P = .01). CONCLUSIONS AND RELEVANCE This study’s findings indicate that no differences were found on any primary outcome by study condition. However, STAT-ED was more efficacious than EUC at increasing mental health treatment initiation and attendance at 6 months. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01779414 JAMA Network Open. 2019;2(12):e1917941. doi:10.1001/jamanetworkopen.2019.1794