Early life adversity induced third ventricular enlargement in young adult male patients suffered from major depressive disorder: a study of brain morphology

Background: Early life adversity (ELA) is not uncommon in major depressive disorder (MDD) patients. Childhood trauma has been reported more frequently in adult MDD patients relative to healthy controls. Recent researches have demonstrated that ELA could result in changes in brain morphology which might be an aetiological factor of MDD development.Materials and methods: We recruited 40 young adult patients suffered from MDD and made computed tomography scan. Subjects were divided in two groups: MDD patients with ELA experience (E+D) vs. MDD patients without ELAexperience (E–D) according to Chinese version-Childhood Trauma Questionnaire (CTQ). 17-item Hamilton Depression (HAMD) Scale and Neuropsychiatric Inventory (NPI) were also examined. Student’s t-test was used to compare the HAMD scores, NPI scores, CTQ subcomponents scores, third ventricular (TV) width and volume of patients from E+D and E–D groups.Results: Findings demonstrated that ELA might result in TV enlargement; furthermore, there was a correlation between physical neglect and TV volume.Conclusions: These findings supported the hypothesis that ELA could induce changes of structure around the TV, which might undermine the aetiology of MDD

On Nonlinear Bosonic Coherent States

Nonlinear coherent states are an interesting resource for quantum technologies. Here we investigate some critical features of the single-boson nonlinear coherent states, which are theoretically constructed as eigenstates of the annihilation operator and experimentally realized as stationary states of a trapped laser-driven ion. We show that the coherence and the minimum-uncertainty properties of such states are broken for values of the Lamb-Dicke parameter corresponding to the roots of the Laguerre polynomials, which enter their explicit expression. The case of the multiboson nonlinear coherent states is also discussed.Comment: published versio

WeatherProof: Leveraging Language Guidance for Semantic Segmentation in Adverse Weather

We propose a method to infer semantic segmentation maps from images captured under adverse weather conditions. We begin by examining existing models on images degraded by weather conditions such as rain, fog, or snow, and found that they exhibit a large performance drop as compared to those captured under clear weather. To control for changes in scene structures, we propose WeatherProof, the first semantic segmentation dataset with accurate clear and adverse weather image pairs that share an underlying scene. Through this dataset, we analyze the error modes in existing models and found that they were sensitive to the highly complex combination of different weather effects induced on the image during capture. To improve robustness, we propose a way to use language as guidance by identifying contributions of adverse weather conditions and injecting that as "side information". Models trained using our language guidance exhibit performance gains by up to 10.2% in mIoU on WeatherProof, up to 8.44% in mIoU on the widely used ACDC dataset compared to standard training techniques, and up to 6.21% in mIoU on the ACDC dataset as compared to previous SOTA methods.Comment: arXiv admin note: substantial text overlap with arXiv:2312.0953

An ionizing radiation acoustic imaging (iRAI) technique for real‐time dosimetric measurements for FLASH radiotherapy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163494/2/mp14358_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163494/1/mp14358.pd