The Developmental Origins of Health and Disease hypothesis holds that alterations to homeostasis during critical periods of development can predispose individuals to adult-onset chronic diseases such as diabetes and metabolic syndrome. It remains controversial whether preimplantation embryo manipulation, clinically used to treat patients with infertility, disturbs homeostasis and affects long-term growth and metabolism. To address this controversy, we have assessed the effects of in vitro fertilization (IVF) on postnatal physiology in mice. We demonstrate that IVF and embryo culture, even under conditions considered optimal for mouse embryo culture, alter postnatal growth trajectory, fat accumulation, and glucose metabolism in adult mice. Unbiased metabolic profiling in serum and microarray analysis of pancreatic islets and insulin sensitive tissues (liver, skeletal muscle, and adipose tissue) revealed broad changes in metabolic homeostasis, characterized by systemic oxidative stress and mitochondrial dysfunction. Adopting a candidate approach, we identify thioredoxin-interacting protein (TXNIP), a key molecule involved in integrating cellular nutritional and oxidative states with metabolic response, as a marker for preimplantation stress and demonstrate tissue-specific epigenetic and transcriptional TXNIP misregulation in selected adult tissues. Importantly, dysregulation of TXNIP expression is associated with enrichment for H4 acetylation at the Txnip promoter that persists from the blastocyst stage through adulthood in adipose tissue. Our data support the vulnerability of preimplantation embryos to environmental disturbance and demonstrate that conception by IVF can reprogram metabolic homeostasis through metabolic, transcriptional, and epigenetic mechanisms with lasting effects for adult growth and fitness. This study has wide clinical relevance and underscores the importance of continued follow-up of IVF-conceived offspring.

This study aims to utilize a deep convolutional neural network (DCNN) for synthesized CT image generation based on cone-beam CT (CBCT) and to apply the images to dose calculations for nasopharyngeal carcinoma (NPC). An encoder-decoder 2D U-Net neural network was produced. A total of 70 CBCT/CT paired images of NPC cancer patients were used for training (50), validation (10) and testing (10) datasets. The testing datasets were treated with the same prescription dose (70 Gy to PTVnx70, 68 Gy to PTVnd68, 62 Gy to the PTV62 and 54 Gy to the PTV54). The mean error (ME) and mean absolute error (MAE) for the true CT images were calculated for image quality evaluation of the synthesized CT. The dose-volume histogram (DVH) dose metric difference and 3D gamma pass rate for the true CT images were calculated for dose analysis, and the results were compared with those for the CBCT images (original CBCT images without any correction) and a patient-specific calibration (PSC) method. Compared with CBCT, the range of the MAE for synthesized CT images improved from (60, 120) to (6, 27) Hounsfield units (HU), and the ME improved from (−74, 51) to (−26, 4) HU. Compared with the true CT method, the average DVH dose metric differences for the CBCT, PSC and synthesized CT methods were 0.8%  ±  1.9%, 0.4%  ±  0.7% and 0.2%  ±  0.6%, respectively. The 1%/1 mm gamma pass rates within the body for the CBCT, PSC and synthesized CT methods were 90.8%  ±  6.2%, 94.1%  ±  4.4% and 95.5%  ±  1.6%, respectively, and the rates within the PTVnx70 were 80.3%  ±  16.6%, 87.9%  ±  19.7%, 98.6%  ±  2.9%, respectively. The DCNN model can generate high-quality synthesized CT images from CBCT images and be used for accurate dose calculations for NPC patients. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.

It was proposed that a diagnostic algorithm based on available clinical and endoscopic regression equation could improve the current sensitivity, specificity, and accuracy in differentiating between CD from ITB.

We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector can achieve a better than 1% energy linearity and a 3% effective energy resolution, required by the neutrino mass ordering determination.

No abstract

Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques,

JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day (cpd), therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz (i.e. ∼1 cpd accidental background) in the default fiducial volume, above an energy threshold of 0.7 MeV.

No abstract