Demonstration of In-Memory Biosignal Analysis: Novel High-Density and Low-Power 3D Flash Memory Array for Arrhythmia Detection.

Smart healthcare systems integrated with advanced deep neural networks enable real-time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND-type flash memory array with a rounded double channel for computing-in-memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low-power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life-threatening arrhythmias. Incorporated with a simplified spike-timing-dependent plasticity learning rule, the CIM architecture is suitable for robust, area- and energy-efficient in-memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm.

Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosis.

Susceptibility source separation, or χ-separation, estimates diamagnetic (χdia) and paramagnetic susceptibility (χpara) signals in the brain using local field and R2' (= R2* - R2) maps. Recently proposed R2*-based χ-separation methods allow for χ-separation using only multi-echo gradient echo (ME-GRE) data, eliminating the need for additional data acquisition for R2 mapping. Although this approach reduces scan time and enhances clinical utility, the impact of missing R2 information remains a subject of exploration. In this study, we evaluate the viability of two previously proposed R2*-based χ-separation methods as alternatives to their R2'-based counterparts: model-based R2*-χ-separation versus χ-separation and deep learning-based χ-sepnet-R2* versus χ-sepnet-R2'. Their performances are assessed in individuals with multiple sclerosis (MS), comparing them with their corresponding R2'-based counterparts (i.e., R2*-χ-separation vs. χ-separation and χ-sepnet-R2* vs. χ-sepnet-R2'). The evaluations encompass qualitative visual assessments by experienced neuroradiologists and quantitative analyses, including region of interest analyses and linear regression analyses. Qualitatively, R2*-χ-separation tends to report higher χpara and χdia values compared with χ-separation, leading to less distinct lesion contrasts, while χ-sepnet-R2* closely aligns with χ-sepnet-R2'. Quantitative analysis reveals a robust correlation between both R2*-based methods and their R2'-based counterparts (r ≥ 0.88). Specifically, in the whole-brain voxels, χ-sepnet-R2* exhibits higher correlation and better linearity than R2*-χ-separation (χdia/χpara from R2*-χ-separation: r = 0.88/0.90, slope = 0.79/0.86; χdia/χpara from χ-sepnet-R2*: r = 0.90/0.92, slope = 0.99/0.97). In MS lesions, both R2*-based methods display comparable correlation and linearity (χdia/χpara from R2*-χ-separation: r = 0.90/0.91, slope = 0.98/0.91; χdia/χpara from χ-sepnet-R2*: r = 0.88/0.88, slope = 0.91/0.95). Notably, χ-sepnet-R2* demonstrates negligible offsets, whereas R2*-χ-separation exhibits relatively large offsets (0.02 ppm in the whole brain and 0.01 ppm in the MS lesions), potentially indicating the false presence of myelin or iron in MS lesions. Overall, both R2*-based χ-separation methods demonstrated their viability as alternatives to their R2'-based counterparts. χ-sepnet-R2* showed better alignment with its R2'-based counterpart with minimal susceptibility offsets, compared with R2*-χ-separation that reported higher χpara and χdia values compared with R2'-based χ-separation.

So You Want to Image Myelin Using MRI: Magnetic Susceptibility Source Separation for Myelin Imaging.

In MRI, researchers have long endeavored to effectively visualize myelin distribution in the brain, a pursuit with significant implications for both scientific research and clinical applications. Over time, various methods such as myelin water imaging, magnetization transfer imaging, and relaxometric imaging have been developed, each carrying distinct advantages and limitations. Recently, an innovative technique named as magnetic susceptibility source separation has emerged, introducing a novel surrogate biomarker for myelin in the form of a diamagnetic susceptibility map. This paper comprehensively reviews this cutting-edge method, providing the fundamental concepts of magnetic susceptibility, susceptibility imaging, and the validation of the diamagnetic susceptibility map as a myelin biomarker that indirectly measures myelin content. Additionally, the paper explores essential aspects of data acquisition and processing, offering practical insights for readers. A comparison with established myelin imaging methods is also presented, and both current and prospective clinical and scientific applications are discussed to provide a holistic understanding of the technique. This work aims to serve as a foundational resource for newcomers entering this dynamic and rapidly expanding field.

Identification and pathogen detection of a Neocypholaelaps species (Acari: Mesostigmata: Ameroseiidae) from beehives in the Republic of Korea.

In this study, we identified a new strain of the genus Neocypholaelaps from the beehives of Apis mellifera colonies in the Republic of Korea (ROK). The Neocypholaelap sp. KOR23 mites were collected from the hives of honeybee apiaries in Wonju, Gangwon-do, in May 2023. Morphological and molecular analyses based on 18S and 28S rRNA gene regions conclusively identified that these mites belong to the genus Neocypholaelaps, closely resembling Neocypholaelaps sp. APGD-2010 that was first isolated from the United States. The presence of 9 of 25 honeybee pathogens in these mite samples suggests that Neocypholaelaps sp. KOR23 mite may act as an intermediate vector and carrier of honeybee diseases. The identification of various honeybee pathogens within this mite highlights their significance in disease transmission among honeybee colonies. This comprehensive study provides valuable insights into the taxonomy and implications of these mites for bee health management and pathogen dissemination.

Randomized controlled trial of digital therapeutics for temporomandibular disorder: A pilot study.

OBJECTIVES: Temporomandibular disorder (TMD) is a common condition that affects the temporomandibular joint (TMJ) and the muscles of the jaw, resulting in pain and dysfunction. TMD is affected by both behavioral and psychological factors. Digital therapeutics (DTx) can exert therapeutic effects by controlling behavioral factors through the delivery of appropriate interventions. Here, we report an open-label randomized control trial to evaluate the efficacy of DTx for TMD. METHODS: We recruited 40 participants diagnosed with TMD. Participants were randomly divided into an intervention group (DTx use, n = 20) and a control group (n =20). The intervention group received the usual treatment process for TMD in addition to the use of the DTx. The control group received the usual treatments only. Patients in both groups were followed up for 3-4 weeks, and outcome data were collected and analyzed. RESULT: The intervention group showed a significant reduction in pain scores as measured by the numerical rating scale (NRS) (p = 0.016). Additionally, the intervention group showed a statistically significant increase in maximal mouth opening compared to the control group (p = 0.0079). However, there were no significant differences in improvement in the Jaw Functional Limitation Scale, Oral Behavior Checklist, and Patient Health Questionnaire-4 between the two groups (p = 0503, = 0.820, and = 0.943, respectively). CONCLUSION: This RCT reveals DTx potential in TMD, showing pain and mouth opening improvements with conventional treatment. But no significant changes were noted in other outcomes. The findings advocate for more extensive, long-term research to solidify DTx's role in TMD management. CLINICAL SIGNIFICANCE: This research underlines DTx potential to improve pain outcomes in TMD therapy, reinforcing its value as a complementary treatment modality.

Metformin Suppresses Both PD-L1 Expression in Cancer Cells and Cancer-Induced PD-1 Expression in Immune Cells to Promote Antitumor Immunity.

Background: Metformin, a drug prescribed for patients with type 2 diabetes, has potential efficacy in enhancing antitumor immunity; however, the detailed underlying mechanisms remain to be elucidated. Therefore, we aimed to identify the inhibitory molecular mechanisms of metformin on programmed death ligand 1 (PD-L1) expression in cancer cells and programmed death 1 (PD-1) expression in immune cells. Methods: We employed a luciferase reporter assay, quantitative real-time PCR, immunoblotting analysis, immunoprecipitation and ubiquitylation assays, and a natural killer (NK) cell-mediated tumor cell cytotoxicity assay. A mouse xenograft tumor model was used to evaluate the effect of metformin on tumor growth, followed by flow-cytometric analysis using tumor-derived single-cell suspensions. Results: Metformin decreased AKT-mediated ß-catenin S552 phosphorylation and subsequent ß-catenin transactivation in an adenosine monophosphate-activated protein kinase (AMPK) activation-dependent manner, resulting in reduced CD274 (encoding PD-L1) transcription in cancer cells. Tumor-derived soluble factors enhanced PD-1 protein stability in NK and T cells via dissociation of PD-1 from ubiquitin E3 ligases and reducing PD-1 polyubiquitylation. Metformin inhibited the tumor-derived soluble factor-reduced binding of PD-1 to E3 ligases and PD-1 polyubiquitylation, resulting in PD-1 protein downregulation in an AMPK activation-dependent manner. These inhibitory effects of metformin on both PD-L1 and PD-1 expression ameliorated cancer-reduced cytotoxic activity of immune cells in vitro and decreased tumor immune evasion and growth in vivo. Conclusions: Metformin blocks both PD-L1 and PD-1 within the tumor microenvironment. This study provided a mechanistic insight into the efficacy of metformin in improving immunotherapy in human cancer.

Multifunctional HfAlO thin film: Ferroelectric tunnel junction and resistive random access memory.

This study presents findings indicating that the ferroelectric tunnel junction (FTJ) or resistive random-access memory (RRAM) in one cell can be intentionally selected depending on the application. The HfAlO film annealed at 700 °C shows stable FTJ characteristics and can be converted into RRAM by forming a conductive filament inside the same cell, that is, the process of intentionally forming a conductive filament is the result of defect generation and redistribution, and applying compliance current prior to a hard breakdown event of the dielectric film enables subsequent RRAM operation. The converted RRAM demonstrated good memory performance. Through current-voltage fitting, it was confirmed that the two resistance states of the FTJ and RRAM had different transport mechanisms. In the RRAM, the 1/f noise power of the high-resistance state (HRS) was about ten times higher than that of the low-resistance state (LRS). This is because the noise components increase due to the additional current paths in the HRS. The 1/f noise power according to resistance states in the FTJ was exactly the opposite result from the case of the RRAM. This is because the noise component due to the Poole-Frenkel emission is added to the noise component due to the tunneling current in the LRS. In addition, we confirmed the potentiation and depression characteristics of the two devices and further evaluated the accuracy of pattern recognition through a simulation by considering a dataset from the Modified National Institute of Standards and Technology.

Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning.

BACKGROUND: Oral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific morbidity and mortality. Recently, there have been studies to apply the rapidly advancing technology of deep learning for diagnosing oral cavity cancer and OPMDs. However, several challenging issues such as class imbalance must be resolved to effectively train a deep learning model for medical imaging classification tasks. The aim of this study is to evaluate a new technique of artificial intelligence to improve the classification performance in an imbalanced tongue lesion dataset. METHODS: A total of 1,810 tongue images were used for the classification. The class-imbalanced dataset consisted of 372 instances of cancer, 141 instances of OPMDs, and 1,297 instances of noncancerous lesions. The EfficientNet model was used as the feature extraction model for classification. Mosaic data augmentation, soft labeling, and curriculum learning (CL) were employed to improve the classification performance of the convolutional neural network. RESULTS: Utilizing a mosaic-augmented dataset in conjunction with CL, the final model achieved an accuracy rate of 0.9444, surpassing conventional oversampling and weight balancing methods. The relative precision improvement rate for the minority class OPMD was 21.2%, while the relative [Formula: see text] score improvement rate of OPMD was 4.9%. CONCLUSIONS: The present study demonstrates that the integration of mosaic-based soft labeling and curriculum learning improves the classification performance of tongue lesions compared to previous methods, establishing a foundation for future research on effectively learning from imbalanced data.

Comparative Performance of Susceptibility Map-Weighted MRI According to the Acquisition Planes in the Diagnosis of Neurodegenerative Parkinsonism.

OBJECTIVE: To evaluate the diagnostic performance of susceptibility map-weighted imaging (SMwI) taken in different acquisition planes for discriminating patients with neurodegenerative parkinsonism from those without. MATERIALS AND METHODS: This retrospective, observational, single-institution study enrolled consecutive patients who visited movement disorder clinics and underwent brain MRI and 18F-FP-CIT PET between September 2021 and December 2021. SMwI images were acquired in both the oblique (perpendicular to the midbrain) and the anterior commissure-posterior commissure (AC-PC) planes. Hyperintensity in the substantia nigra was determined by two neuroradiologists. 18F-FP-CIT PET was used as the reference standard. Inter-rater agreement was assessed using Cohen's kappa coefficient. The diagnostic performance of SMwI in the two planes was analyzed separately for the right and left substantia nigra. Multivariable logistic regression analysis with generalized estimating equations was applied to compare the diagnostic performance of the two planes. RESULTS: In total, 194 patients were included, of whom 105 and 103 had positive results on 18F-FP-CIT PET in the left and right substantia nigra, respectively. Good inter-rater agreement in the oblique (κ = 0.772/0.658 for left/right) and AC-PC planes (0.730/0.741 for left/right) was confirmed. The pooled sensitivities for two readers were 86.4% (178/206, left) and 83.3% (175/210, right) in the oblique plane and 87.4% (180/206, left) and 87.6% (184/210, right) in the AC-PC plane. The pooled specificities for two readers were 83.5% (152/182, left) and 82.0% (146/178, right) in the oblique plane, and 83.5% (152/182, left) and 86.0% (153/178, right) in the AC-PC plane. There were no significant differences in the diagnostic performance between the two planes (P > 0.05). CONCLUSION: There are no significant difference in the diagnostic performance of SMwI performed in the oblique and AC-PC plane in discriminating patients with parkinsonism from those without. This finding affirms that each institution may choose the imaging plane for SMwI according to their clinical settings.

Customized three-dimensional printed ceramic bone grafts for osseous defects: a prospective randomized study.

Ridge resorption can result in insufficient bone volume for implant surgery, necessitating bone substitutes to restore the resorption area. Recent advances in computer-aided design and manufacturing enable the use of alloplastic bone graft materials with customizable compositions or shapes. This randomized study evaluated the clinical effectiveness of a customized three-dimensional (3D) printed alloplastic bone material. Sixty patients requiring guided bone regeneration for implant installation following tooth extraction due to alveolar bone resorption were recruited at two institutions. The participants were randomly allocated to either a group that received 3D-printed patient-customized bone graft material or a group that received conventional block bone graft material. Implant installation with bone harvesting was performed approximately 5 months after bone grafting. Histological and radiological assessments of the harvested bone area were performed. The experimental group had a significantly higher percent bone volume and a smaller tissue surface than the control group. Bone volume, bone surface, bone surface/volume ratio, bone surface density (bone surface/total volume), and bone mineral density did not differ significantly between groups. Patient-customized bone graft materials offer convenience and reduce patient discomfort. The findings suggest 3D-printed patient-customized bone graft materials could be used as an alternative for simpler bone grafting procedures.

Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group.

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.

Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health.

As the emerging treatments that target grey matter pathology in Alzheimer's Disease have limited effectiveness, there is a critical need to identify new neural targets for treatments. White matter's (WM) metabolic vulnerability makes it a promising candidate for new interventions. This study examined the age and sex differences in estimates of axonal content, as well the associations of with highly prevalent modifiable health risk factors such as metabolic syndrome and adiposity. We estimated intra-axonal volume fraction (ICVF) using the Neurite Orientation Dispersion and Density Imaging (NODDI) in a sample of 89 cognitively and neurologically healthy adults (20-79 years). We showed that ICVF correlated positively with age and estimates of myelin content. The ICVF was also lower in women than men, across all ages, which difference was accounted for by intracranial volume. Finally, we found no association of metabolic risk or adiposity scores with the current estimates of ICVF. In addition, the previously observed adiposity-myelin associations (Burzynska et al., 2023) were independent of ICVF. Although our findings confirm the vulnerability of axons to aging, they suggest that metabolic dysfunction may selectively affect myelin content, at least in cognitively and neurologically healthy adults with low metabolic risk, and when using the specific MRI techniques. Future studies need to revisit our findings using larger samples and different MRI approaches, and identify modifiable factors that accelerate axonal deterioration as well as mechanisms linking peripheral metabolism with the health of myelin.

3D Printed Cellulose Nanofiber Aerogel Scaffold with Hierarchical Porous Structures for Fast Solar-Driven Atmospheric Water Harvesting.

Hygroscopic salt-based composite sorbents are considered ideal candidates for solar-driven atmospheric water harvesting. The primary challenge for the sorbents lies in exposing more hygroscopically active sites to the surrounding air while preventing salt leakage. Herein, a hierarchically structured scaffold is constructed by integrating cellulose nanofiber and lithium chloride (LiCl) as building blocks through 3D printing combined with freeze-drying. The milli/micrometer multiscale pores can effectively confine LiCl and simultaneously provide a more exposed active area for water sorption and release, accelerating both water sorption and evaporation kinetics of the 3D printed structure. Compared to a conventional freeze-dried aerogel, the 3D printed scaffold exhibits a water sorption rate that is increased 1.6-fold, along with a more than 2.4-fold greater water release rate. An array of bilayer scaffolds is demonstrated, which can produce 0.63 g g-1 day-1 of water outdoors under natural sunlight. This article provides a sustainable strategy for collecting freshwater from the atmosphere.

Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric-Based Neuromorphic System.

Hardware neuromorphic systems are crucial for the energy-efficient processing of massive amounts of data. Among various candidates, hafnium oxide ferroelectric tunnel junctions (FTJs) are highly promising for artificial synaptic devices. However, FTJs exhibit non-ideal characteristics that introduce variations in synaptic weights, presenting a considerable challenge in achieving high-performance neuromorphic systems. The primary objective of this study is to analyze the origin and impact of these variations in neuromorphic systems. The analysis reveals that the major bottleneck in achieving a high-performance neuromorphic system is the dynamic variation, primarily caused by the intrinsic 1/f noise of the device. As the device area is reduced and the read bias (VRead ) is lowered, the intrinsic noise of the FTJs increases, presenting an inherent limitation for implementing area- and power-efficient neuromorphic systems. To overcome this limitation, an adaptive read-biasing (ARB) scheme is proposed that applies a different VRead to each layer of the neuromorphic system. By exploiting the different noise sensitivities of each layer, the ARB method demonstrates significant power savings of 61.3% and a scaling effect of 91.9% compared with conventional biasing methods. These findings contribute significantly to the development of more accurate, efficient, and scalable neuromorphic systems.

Disruption of phosphofructokinase activity and aerobic glycolysis in human bronchial epithelial cells by atmospheric ultrafine particulate matter.

Exposure to ambient ultrafine particulate matter (UPM) causes respiratory disorders; however, the underlying molecular mechanisms remain unclear. In this study, we synthesized simulated UPM (sUPM) with controlled physicochemical properties using the spark-discharge method. Subsequently, we investigated the biological effects of sUPM using BEAS-2B human bronchial epithelial cells (HBECs) and a mouse intratracheal instillation model. High throughput RNA-sequencing and bioinformatics analyses revealed that dysregulation of the glycolytic metabolism is involved in the inhibited proliferation and survival of HBECs by sUPM treatment. Furthermore, signaling pathway and enzymatic analyses showed that the treatment of BEAS-2B cells with sUPM induces the inactivation of extracellular signal-regulated kinase (ERK) and protein kinase B (PKB, also known as AKT), resulting in the downregulation of phosphofructokinase 2 (PFK2) S483 phosphorylation, PFK enzyme activity, and aerobic glycolysis in HBECs in an oxidative stress-independent manner. Additionally, intratracheal instillation of sUPM reduced the phosphorylation of ERK, AKT, and PFK2, decreased proliferation, and increased the apoptosis of bronchial epithelial cells in mice. The findings of this study imply that UPM induces pulmonary toxicity by disrupting aerobic glycolytic metabolism in lung epithelial cells, which can provide novel insights into the toxicity mechanisms of UPM and strategies to prevent their toxic effects.

Analysis of Differences in Single-Joint Movement of Dominant and Non-Dominant Hands for Human-like Robotic Control.

Although several previous studies on laterality of upper limb motor control have reported functional differences, this conclusion has not been agreed upon. It may be conjectured that the inconsistent results were caused because upper limb motor control was observed in multi-joint tasks that could generate different inter-joint motor coordination for each arm. Resolving this, we employed a single wrist joint tracking task to reduce the effect of multi-joint dynamics and examined the differences between the dominant and non-dominant hands in terms of motor control. Specifically, we defined two sections to induce feedback (FB) and feedforward (FF) controls: the first section involved a visible target for FB control, and the other section involved an invisible target for FF control. We examined the differences in the position errors of the tracer and the target. Fourteen healthy participants performed the task. As a result, we found that during FB control, the dominant hand performed better than the non-dominant hand, while we did not observe significant differences in FF control. In other words, in a single-joint movement that is not under the influence of the multi-joint coordination, only FB control showed laterality and not FF control. Furthermore, we confirmed that the dominant hand outperformed the non-dominant hand in terms of responding to situations that required a change in control strategy.

The effect of different depth planes during a manual tracking task in three-dimensional virtual reality space.

Unlike ballistic arm movements such as reaching, the contribution of depth information to the performance of manual tracking movements is unclear. Thus, to understand how the brain handles information, we investigated how a required movement along the depth axis would affect behavioral tracking performance, postulating that it would be affected by the amount of depth movement. We designed a visually guided planar tracking task that requires movement on three planes with different depths: a fronto-parallel plane called ROT (0), a sagittal plane called ROT (90), and a plane rotated by 45° with respect to the sagittal plane called ROT (45). Fifteen participants performed a circular manual tracking task under binocular and monocular visions in a three-dimensional (3D) virtual reality space. As a result, under binocular vision, ROT (90), which required the largest depth movement among the tasks, showed the greatest error in 3D. Similarly, the errors (deviation from the target path) on the depth axis revealed significant differences among the tasks. Under monocular vision, significant differences in errors were observed only on the lateral axis. Moreover, we observed that the errors in the lateral and depth axes were proportional to the required movement on these axes under binocular vision and confirmed that the required depth movement under binocular vision determined depth error independent of the other axes. This finding implies that the brain may independently process binocular vision information on each axis. Meanwhile, the required depth movement under monocular vision was independent of performance along the depth axis, indicating an intractable behavior. Our findings highlight the importance of handling depth movement, especially when a virtual reality situation, involving tracking tasks, is generated.

AMPK-HIF-1α signaling enhances glucose-derived de novo serine biosynthesis to promote glioblastoma growth.

BACKGROUND: Cancer cells undergo cellular adaptation through metabolic reprogramming to sustain survival and rapid growth under various stress conditions. However, how brain tumors modulate their metabolic flexibility in the naturally serine/glycine (S/G)-deficient brain microenvironment remain unknown. METHODS: We used a range of primary/stem-like and established glioblastoma (GBM) cell models in vitro and in vivo. To identify the regulatory mechanisms of S/G deprivation-induced metabolic flexibility, we employed high-throughput RNA-sequencing, transcriptomic analysis, metabolic flux analysis, metabolites analysis, chromatin immunoprecipitation (ChIP), luciferase reporter, nuclear fractionation, cycloheximide-chase, and glucose consumption. The clinical significances were analyzed in the genomic database (GSE4290) and in human GBM specimens. RESULTS: The high-throughput RNA-sequencing and transcriptomic analysis demonstrate that the de novo serine synthesis pathway (SSP) and glycolysis are highly activated in GBM cells under S/G deprivation conditions. Mechanistically, S/G deprivation rapidly induces reactive oxygen species (ROS)-mediated AMP-activated protein kinase (AMPK) activation and AMPK-dependent hypoxia-inducible factor (HIF)-1α stabilization and transactivation. Activated HIF-1α in turn promotes the expression of SSP enzymes phosphoglycerate dehydrogenase (PHGDH), phosphoserine aminotransferase 1 (PSAT1), and phosphoserine phosphatase (PSPH). In addition, the HIF-1α-induced expression of glycolytic genes (GLUT1, GLUT3, HK2, and PFKFB2) promotes glucose uptake, glycolysis, and glycolytic flux to fuel SSP, leading to elevated de novo serine and glycine biosynthesis, NADPH/NADP+ ratio, and the proliferation and survival of GBM cells. Analyses of human GBM specimens reveal that the levels of overexpressed PHGDH, PSAT1, and PSPH are positively correlated with levels of AMPK T172 phosphorylation and HIF-1α expression and the poor prognosis of GBM patients. CONCLUSION: Our findings reveal that metabolic stress-enhanced glucose-derived de novo serine biosynthesis is a critical metabolic feature of GBM cells, and highlight the potential to target SSP for treating human GBM.

Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.

BACKGROUND: Chest x-ray is commonly used for pulmonary abnormality screening. However, since the image characteristics of x-rays highly depend on the machine specifications, an artificial intelligence (AI) model developed for specific equipment usually fails when clinically applied to various machines. To overcome this problem, we propose an image manipulation pipeline. METHODS: A total of 15,010 chest x-rays from systems with different generators/detectors were retrospectively collected from five institutions from May 2020 to February 2021. We developed an AI model to classify pulmonary abnormalities using x-rays from a single system. Then, we externally tested its performance on chest x-rays from various machine specifications. We compared the area under the receiver operating characteristics curve (AUC) of AI models developed using conventional image processing pipelines (histogram equalization [HE], contrast-limited histogram equalization [CLAHE], and unsharp masking [UM] with common data augmentations) with that of the proposed manipulation pipeline (XM-pipeline). RESULTS: The XM-pipeline model showed the highest performance for all the datasets of different machine specifications, such as chest x-rays acquired from a computed radiography system (n = 356, AUC 0.944 for XM-pipeline versus 0.917 for HE, 0.705 for CLAHE, 0.544 for UM, p [Formula: see text] 0.001, for all) and from a mobile x-ray generator (n = 204, AUC 0.949 for XM-pipeline versus 0.933 for HE, p = 0.042, 0.932 for CLAHE (p = 0.009), 0.925 for UM (p = 0.001). CONCLUSIONS: Applying the XM-pipeline to AI training increased the diagnostic performance of the AI model on the chest x-rays of different machine configurations. RELEVANCE STATEMENT: The proposed training pipeline would successfully promote a wide application of the AI model for abnormality screening when chest x-rays are acquired using various x-ray machines. KEY POINTS: • AI models developed using x-rays of a specific machine suffer from generalization. • We proposed a new image processing pipeline to address the generalization problem. • AI models were tested using multicenter external x-ray datasets of various machines. • AI with our pipeline achieved the highest diagnostic performance than conventional methods.

Efficient Hybrid Training Method for Neuromorphic Hardware Using Analog Nonvolatile Memory.

Neuromorphic hardware using nonvolatile analog synaptic devices provides promising advantages of reducing energy and time consumption for performing large-scale vector-matrix multiplication (VMM) operations. However, the reported training methods for neuromorphic hardware have appreciably shown reduced accuracy due to the nonideal nature of analog devices, and use conductance tuning protocols that require substantial cost for training. Here, we propose a novel hybrid training method that efficiently trains the neuromorphic hardware using nonvolatile analog memory cells, and experimentally demonstrate the high performance of the method using the fabricated hardware. Our training method does not rely on the conductance tuning protocol to reflect weight updates to analog synaptic devices, which significantly reduces online training costs. When the proposed method is applied, the accuracy of the hardware-based neural network approaches to that of the software-based neural network after only one-epoch training, even if the fabricated synaptic array is trained for only the first synaptic layer. Also, the proposed hybrid training method can be efficiently applied to low-power neuromorphic hardware, including various types of synaptic devices whose weight update characteristics are extremely nonlinear. This successful demonstration of the proposed method in the fabricated hardware shows that neuromorphic hardware using nonvolatile analog memory cells becomes a more promising platform for future artificial intelligence.