Epigenetic Activation of ASCT2 in the Hippocampus Contributes to Depression-Like Behavior by Regulating D-Serine in Mice

The roles of D-serine in depression are raised concerned recently as an intrinsic co-agonist for the NMDA receptor. However, the mechanisms underlying its regulation are not fully elucidated. ASCT2 is a Na+-dependent D-serine transporter. We found that decreased D-serine and increased hippocampal ASCT2 levels correlated with chronic social defeat stress (CSDS) in mice. Lentivirus-mediated shRNA-mediated knockdown of ASCT2 and the administration of exogenous D-serine in the hippocampus alleviated CSDS-induced social avoidance and immobility. In vivo and in vitro experiments revealed that upregulation of ASCT2 expression in CSDS was regulated through histone hyper-acetylation, not DNA methylation in its promoter region. Immunohistochemistry demonstrated the co-localization of ASCT2 and D-serine. Uptake of D-serine by ASCT2 was demonstrated by in vivo and in vitro experiments. Our results indicate that CSDS induces ASCT2 expression through epigenetic activation and decreases hippocampal D-serine levels, leading to social avoidance, and immobility. Thus, targeting D-serine transport represents an attractive new strategy for treating depression

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach

BackgroundIdentification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM.ObjectivesWe aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators.MethodsIn this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms.PatientsRegarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling.ResultsThe indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185.ConclusionThis work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients

Targeting a thrombopoietin-independent strategy in the discovery of a novel inducer of megakaryocytopoiesis, DMAG, for the treatment of thrombocytopenia

Thrombocytopenia is a thrombopoietin (TPO)-related disorder with very limited treatment options, and can be lifethreatening. There are major problems with typical thrombopoietic agents targeting TPO signaling, so it is urgent to discover a novel TPO-independent mechanism involving thrombopoiesis and potential druggable targets. We developed a drug screening model by the multi-grained cascade forest (gcForest) algorithm and found that 3,8-di-O-methylellagic acid 2- O-glucoside (DMAG) (10, 20 and 40 μM) promoted megakaryocyte differentiation in vitro. Subsequent investigations revealed that DMAG (40 mM) activated ERK1/2, HIF-1b and NF-E2. Inhibition of ERK1/2 blocked megakaryocyte differentiation and attenuated the upregulation of HIF-1b and NF-E2 induced by DMAG. Megakaryocyte differentiation induced by DMAG was inhibited via knockdown of NF-E2. In vivo studies showed that DMAG (5 mg/kg) accelerated platelet recovery and megakaryocyte differentiation in mice with thrombocytopenia. The platelet count of the DMAG-treated group recovered to almost 72% and 96% of the count in the control group at day 10 and 14, respectively. The platelet counts in the DMAG-treated group were almost 1.5- and 1.3-fold higher compared with those of the irradiated group at day 10 and 14, respectively. Moreover, DMAG (10, 25 and 50 mM) stimulated thrombopoiesis in zebrafish. DMAG (5 mg/kg) could also increase platelet levels in c-MPL knockout (c-MPL-/-) mice. In summary, we established a drug screening model through gcForest and demonstrated that DMAG promotes megakaryocyte differentiation via the ERK/HIF1/NF-E2 pathway which, importantly, is independent of the classical TPO/c-MPL pathway. The present study may provide new insights into drug discovery for thrombopoiesis and TPO-independent regulation of thrombopoiesis, as well as a promising avenue for thrombocytopenia treatment

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper

An information diffusion-based recommendation framework for micro-blogging

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches

A Loading Correction Model for GPS Measurements Derived from Multiple-Data Combined Monthly Gravity

Time-dependent loading deformations of the Earth’s surface, due to nontidal changes in the atmosphere, ocean, land water/ice, etc., contribute significantly to the seasonal and secular Global Positioning System (GPS) site displacements, especially for the up component. While loading deformations derived from general circulation model (GCM) outputs are usually used to correct loading signals in the GPS site displacements, this study aims to provide a loading correction model based on the multiple-data combined monthly gravity products LDCmgm90. We have adopted GPS measurements from 249 IGS reference frame stations and 3 different GCM-based loading models to test the reliability of the LDCmgm90 model. Compared to the GCM-based models, the LDCmgm90 loading correction is more effective in attenuating seasonal (especially annual) loading signals and can bring more significant improvements to most stations for both the data-trend-removed and the data-trend-retained cases. Thus, we have validated the LDCmgm90 model from the loading aspect and proved it to be a reliable loading-correction model for GPS displacements. The relatively better secular loading signals provided by the LDCmgm90 loading model may provide us a chance to study the long-term, nonloading signals in GPS data

Efficacy of Transistor Interleaving in DICE Flip-Flops at a 22 nm FD SOI Technology Node

Fully Depleted Silicon on Insulator (FD SOI) technology nodes provide better resistance to single event upsets than comparable bulk technologies, but upsets are still likely to occur at nano-scale feature sizes, and additional hardening techniques should be explored. Three flip-flop designs were implemented using Dual Interlocked Cell (DICE) latches in a 22 m FD SOI technology node. Additional hardening was implemented in the layout of each design by using transistor spacing and interleaving. Comparisons were made between a standard DICE design and two other designs making use of the new Continuous Active (CnRx) Diffusion construct and guard-gate transistor stacking through alpha particle and heavy ion irradiation. Designs making use of the CnRx construct for performance improvements were more likely to experience upsets due to higher collected charges in the increased diffusion regions. Conversely, transistor stacking showed strong soft error rate resilience because of the natural isolation between transistors in the FD SOI technology. Overall, the efficacy of transistor interleaving in flip-flops using DICE latches was found to be extremely robust in the 22 nm FD SOI technology node

Efficacy of Transistor Interleaving in DICE Flip-Flops at a 22 nm FD SOI Technology Node

Fully Depleted Silicon on Insulator (FD SOI) technology nodes provide better resistance to single event upsets than comparable bulk technologies, but upsets are still likely to occur at nano-scale feature sizes, and additional hardening techniques should be explored. Three flip-flop designs were implemented using Dual Interlocked Cell (DICE) latches in a 22 m FD SOI technology node. Additional hardening was implemented in the layout of each design by using transistor spacing and interleaving. Comparisons were made between a standard DICE design and two other designs making use of the new Continuous Active (CnRx) Diffusion construct and guard-gate transistor stacking through alpha particle and heavy ion irradiation. Designs making use of the CnRx construct for performance improvements were more likely to experience upsets due to higher collected charges in the increased diffusion regions. Conversely, transistor stacking showed strong soft error rate resilience because of the natural isolation between transistors in the FD SOI technology. Overall, the efficacy of transistor interleaving in flip-flops using DICE latches was found to be extremely robust in the 22 nm FD SOI technology node

Comparison of Total Ionizing Dose Effects in 22-nm and 28-nm FD SOI Technologies

Total ionizing dose (TID) effects from Co-60 gamma ray and heavy ion irradiation were studied at the 22-nm FD SOI technology node and compared with the testing results from the 28-nm FD SOI technology. Ring oscillators (RO) designed with inverters, NAND2, and NOR2 gates were used to observe the output frequency drift and current draw. Experimental results show a noticeable increased device current draw and decreases in RO frequencies where NOR2 ROs have the most degradation. As well, the functionality of a 256 kb SRAM block and shift-register chains were evaluated during C0-60 irradiation. SRAM functionality deteriorated at 325 krad(Si) of the total dosage, while the FF chains remained functional up to 1 Mrad(Si). Overall, the 22-nm FD SOI results show better resilience to TID effects compared to the 28-nm FD SOI technology node

Comparison of Total Ionizing Dose Effects in 22-nm and 28-nm FD SOI Technologies

Total ionizing dose (TID) effects from Co-60 gamma ray and heavy ion irradiation were studied at the 22-nm FD SOI technology node and compared with the testing results from the 28-nm FD SOI technology. Ring oscillators (RO) designed with inverters, NAND2, and NOR2 gates were used to observe the output frequency drift and current draw. Experimental results show a noticeable increased device current draw and decreases in RO frequencies where NOR2 ROs have the most degradation. As well, the functionality of a 256 kb SRAM block and shift-register chains were evaluated during C0-60 irradiation. SRAM functionality deteriorated at 325 krad(Si) of the total dosage, while the FF chains remained functional up to 1 Mrad(Si). Overall, the 22-nm FD SOI results show better resilience to TID effects compared to the 28-nm FD SOI technology node