Deterministic Online Classification: Non-iteratively Reweighted Recursive Least-Squares for Binary Class Rebalancing

Deterministic solutions are becoming more critical for interpretability. Weighted Least-Squares (WLS) has been widely used as a deterministic batch solution with a specific weight design. In the online settings of WLS, exact reweighting is necessary to converge to its batch settings. In order to comply with its necessity, the iteratively reweighted least-squares algorithm is mainly utilized with a linearly growing time complexity which is not attractive for online learning. Due to the high and growing computational costs, an efficient online formulation of reweighted least-squares is desired. We introduce a new deterministic online classification algorithm of WLS with a constant time complexity for binary class rebalancing. We demonstrate that our proposed online formulation exactly converges to its batch formulation and outperforms existing state-of-the-art stochastic online binary classification algorithms in real-world data sets empirically

SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images

Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time-consuming process. In recent years, deep convolutional neural networks have become the de facto standard for automated image segmentation. However, due to the expensive computational cost associated with enlarging the field of view in DCNNs, their ability to model long-range dependency is still limited, and this can result in sub-optimal segmentation performance for objects with background context spanning over long distances. On the other hand, Transformer models have demonstrated excellent capabilities in capturing such long-range information in several semantic segmentation tasks performed on medical images. Inspired by the recent success of Vision Transformers and advances in multi-modal image analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate cross-modal feature extraction at multiple resolutions.To validate the effectiveness of the proposed method, we performed experiments on the HECKTOR 2021 challenge dataset and compared it with the nnU-Net (the backbone of the top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based methods such as UNETR, and Swin UNETR. The proposed method is experimentally shown to outperform these comparing methods thanks to the ability of the CMA module to capture better inter-modality complimentary feature representations between PET and CT, for the task of head-and-neck tumor segmentation.Comment: 9 pages, 3 figures. Med Phys. 202

Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising

Position emission tomography (PET) is widely used in clinics and research due to its quantitative merits and high sensitivity, but suffers from low signal-to-noise ratio (SNR). Recently convolutional neural networks (CNNs) have been widely used to improve PET image quality. Though successful and efficient in local feature extraction, CNN cannot capture long-range dependencies well due to its limited receptive field. Global multi-head self-attention (MSA) is a popular approach to capture long-range information. However, the calculation of global MSA for 3D images has high computational costs. In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs. Experiments based on datasets of different PET tracers, i.e., $^{18}$F-FDG, $^{18}$F-ACBC, $^{18}$F-DCFPyL, and $^{68}$Ga-DOTATATE, were conducted to evaluate the proposed framework. Quantitative results show that the proposed Spach Transformer can achieve better performance than other reference methods.Comment: 10 page

Quaternary structures of Vac8 differentially regulate the Cvt and PMN pathways.

Armadillo (ARM) repeat proteins constitute a large protein family with diverse and fundamental functions in all organisms, and armadillo repeat domains share high structural similarity. However, exactly how these structurally similar proteins can mediate diverse functions remains a long-standing question. Vac8 (vacuole related 8) is a multifunctional protein that plays pivotal roles in various autophagic pathways, including piecemeal microautophagy of the nucleus (PMN) and cytoplasm-to-vacuole targeting (Cvt) pathways in the budding yeast Saccharomyces cerevisiae. Vac8 comprises an H1 helix at the N terminus, followed by 12 armadillo repeats. Herein, we report the crystal structure of Vac8 bound to Atg13, a key component of autophagic machinery. The 70-angstrom extended loop of Atg13 binds to the ARM domain of Vac8 in an antiparallel manner. Structural, biochemical, and in vivo experiments demonstrated that the H1 helix of Vac8 intramolecularly associates with the first ARM and regulates its self-association, which is crucial for Cvt and PMN pathways. The structure of H1 helix-deleted Vac8 complexed with Atg13 reveals that Vac8[Delta 19-33]-Atg13 forms a heterotetramer and adopts an extended superhelical structure exclusively employed in the Cvt pathway. Most importantly, comparison of Vac8-Nvj1 and Vac8-Atg13 provides a molecular understanding of how a single ARM domain protein adopts different quaternary structures depending on its associated proteins to differentially regulate 2 closely related but distinct cellular pathways

Neurocutaneous Melanosis Presenting as Chronic Partial Epilepsy

BACKGROUND: Neurocutaneous melanosis (NCM) is a rare neurocutaneous syndrome characterized by the presence of multiple congenital melanocytic nevi (CMN) and the proliferation of melanocytes in the central nervous system, usually involving the leptomeninges. Chronic partial epilepsy as a sole manifestation is rare in NCM. CASE REPORT: A 32-year-old man suffering from chronic partial epilepsy presented with multiple CMN on his trunk and scalp. Brain MRI demonstrated a focal lesion in the right amygdala that was consistent with interictal epileptiform discharges in the right temporal region on electroencephalography (EEG). An anterior temporal lobectomy was performed, and the pathology investigation revealed numerous melanophages in the amygdala. The patient was seizure-free after surgery. CONCLUSIONS: We report a patient with NCM presenting as chronic partial epilepsy who was successfully treated by anterior temporal lobectomy.ope

Inhibition of mTORC1 through ATF4-induced REDD1 and Sestrin2 expression by Metformin

Background Although the major anticancer effect of metformin involves AMPK-dependent or AMPK-independent mTORC1 inhibition, the mechanisms of action are still not fully understood. Methods To investigate the molecular mechanisms underlying the effect of metformin on the mTORC1 inhibition, MTT assay, RT-PCR, and western blot analysis were performed. Results Metformin induced the expression of ATF4, REDD1, and Sestrin2 concomitant with its inhibition of mTORC1 activity. Treatment with REDD1 or Sestrin2 siRNA reversed the mTORC1 inhibition induced by metformin, indicating that REDD1 and Sestrin2 are important for the inhibition of mTORC1 triggered by metformin treatment. Moreover, REDD1- and Sestrin2-mediated mTORC1 inhibition in response to metformin was independent of AMPK activation. Additionally, lapatinib enhances cell sensitivity to metformin, and knockdown of REDD1 and Sestrin2 decreased cell sensitivity to metformin and lapatinib. Conclusions ATF4-induced REDD1 and Sestrin2 expression in response to metformin plays an important role in mTORC1 inhibition independent of AMPK activation, and this signalling pathway could have therapeutic value.This research was supported by grants from the Korea Institute of Radiological and Medical Sciences (KIRAMS), funded by the Ministry of Science and ICT (MSIT), Republic of Korea (Nos. 50336–2021; 50531–2021; and 50544–2021)