Efficient Constrained Codes That Enable Page Separation in Modern Flash Memories

The pivotal storage density win achieved by solid-state devices over magnetic devices recently is a result of multiple innovations in physics, architecture, and signal processing. Constrained coding is used in Flash devices to increase reliability via mitigating inter-cell interference. Recently, capacity-achieving constrained codes were introduced to serve that purpose. While these codes result in minimal redundancy, they result in non-negligible complexity increase and access speed limitation since pages cannot be read separately. In this paper, we suggest new constrained coding schemes that have low-complexity and preserve the desirable high access speed in modern Flash devices. The idea is to eliminate error-prone patterns by coding data either only on the left-most page (binary coding) or only on the two left-most pages ($4$-ary coding) while leaving data on all the remaining pages uncoded. Our coding schemes are systematic and capacity-approaching. We refer to the proposed schemes as read-and-run (RR) constrained coding schemes. The $4$-ary RR coding scheme is introduced to limit the rate loss. We analyze the new RR coding schemes and discuss their impact on the probability of occurrence of different charge levels. We also demonstrate the performance improvement achieved via RR coding on a practical triple-level cell Flash device.Comment: 30 pages (single column), 5 figures, submitted to the IEEE Transactions on Communications (TCOM). arXiv admin note: substantial text overlap with arXiv:2111.0741

Spatio-Temporal Modeling for Flash Memory Channels Using Conditional Generative Nets

We propose a data-driven approach to modeling the spatio-temporal characteristics of NAND flash memory read voltages using conditional generative networks. The learned model reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the specified program/erase (P/E) cycling time stamp. We evaluate the model over a range of time stamps using the cell read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects. We conclude that the model accurately captures the spatial and temporal features of the flash memory channel

Revealing the pathogenesis of gastric intestinal metaplasia based on the mucosoid air-liquid interface

Background: Gastric intestinal metaplasia (GIM) is an essential precancerous lesion. Although the reversal of GIM is challenging, it potentially brings a state-to-art strategy for gastric cancer therapeutics (GC). The lack of the appropriate in vitro model limits studies of GIM pathogenesis, which is the issue this work aims to address for further studies. Method: The air-liquid interface (ALI) model was adopted for the long-term culture of GIM cells in the present work. This study conducted Immunofluorescence (IF), quantitative real-time polymerase chain reaction (qRT-PCR), transcriptomic sequencing, and mucoproteomic sequencing (MS) techniques to identify the pathways for differential expressed genes (DEGs) enrichment among different groups, furthermore, to verify novel biomarkers of GIM cells. Result: Our study suggests that GIM-ALI model is analog to the innate GIM cells, which thus can be used for mucus collection and drug screening. We found genes MUC17, CDA, TRIM15, TBX3, FLVCR2, ONECUT2, ACY3, NMUR2, and MAL2 were highly expressed in GIM cells, while GLDN, SLC5A5, MAL, and MALAT1 showed down-regulated, which can be used as potential biomarkers for GIM cells. In parallel, these genes that highly expressed in GIM samples were mainly involved in cancer-related pathways, such as the MAPK signal pathway and oxidative phosphorylation signal pathway. Conclusion: The ALI model is validated for the first time for the in vitro study of GIM. GIM-ALI model is a novel in vitro model that can mimic the tissue micro-environment in GIM patients and further provide an avenue for studying the characteristics of GIM mucus. Our study identified new markers of GIM as well as pathways associated with GIM, which provides outstanding insight for exploring GIM pathogenesis and potentially other related conditions

Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors

Precision medicine, characterized by the personalized integration of a patient’s genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual’s genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applications of these related fields in anesthesiology by reviewing them, exploring the potential of advanced technologies in the implementation and development of personalized anesthesia, realizing the future integration of new technologies into clinical practice, and promoting multidisciplinary collaboration between anesthesiology and disciplines such as genomics and artificial intelligence

Rapid inactivation of human respiratory RNA viruses by deep ultraviolet irradiation from light-emitting diodes on a high-temperature-annealed AlN/Sapphire template

Efficient and eco-friendly disinfection of air-borne human respiratory RNA viruses is pursued in both public environment and portable usage. The AlGaN-based deep ultraviolet (DUV) light-emission diode (LED) has high practical potentials because of its advantages of variable wavelength, rapid sterilization, environmental protection, and miniaturization. Therefore, whether the emission wavelength has effects on the disinfection as well as whether the device is feasible to sterilize various respiratory RNA viruses under portable conditions is crucial. Here, we fabricate AlGaN-based DUV LEDs with different wavelength on high-temperature-annealed (HTA) AlN/Sapphire templates and investigate the inactivation effects for several respiratory RNA viruses. The AlN/AlGaN superlattices are employed between the template and upper n-AlGaN to release the strong compressive stress (SCS), improving the crystal quality and interface roughness. DUV LEDs with the wavelength of 256, 265, and 278 nm, corresponding to the light output power of 6.8, 9.6, and 12.5 mW, are realized, among which the 256 nm-LED shows the most potent inactivation effect in human respiratory RNA viruses, including SARS-CoV-2, influenza A virus (IAV), and human parainfluenza virus (HPIV), at a similar light power density (LPD) of ~0.8 mW/cm2 for 10 s. These results will contribute to the advanced DUV LED application of disinfecting viruses with high potency and broad spectrum in a portable and eco-friendly use

Long Non-Coding MALAT1 Functions as a Competing Endogenous RNA to Regulate Vimentin Expression by Sponging miR-30a-5p in Hepatocellular Carcinoma

Background/Aims: Hepatocellular carcinoma (HCC) has a high morbidity as well as mortality and is believed to be one of the most prevalent cancers worldwide. The long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is involved in numerous cancers, including HCC. This study aimed to explore the role of MALAT1 in HCC progression. Methods: The expression levels of MALAT1 and Vimentin in HCC tissues and relative pair-matched adjacent normal liver tissues were analyzed by RT-PCR, and immunohistochemistry. Using bioinformatics analysis and dual-luciferase assay, we examined the correlation between MALAT1 and miR-30a-5p. Dual-luciferase assay and western blotting suggested that Vimentin was a target of miR-30a-5p. A wound healing assay and transwell assays were employed to determine the effect of MALAT1 and miR-30a-5p on cell migration and invasion in HCC. Results: Our data demonstrated that the levels of MALAT1 and Vimentin were upregulated in HCC tissues and that miR-30a-5p was a direct target of MALAT1. Silenced MALAT1 and overexpressed miR-30a-5p each inhibited cell migration and invasion. Additionally, dual-luciferase assay and western blotting demonstrated that MALAT1 could competitively sponge miR-30a-5p and thereby regulate Vimentin. Conclusion: Our data suggest that MALAT1 acts as an oncogenic lncRNA that promotes HCC migration and invasion. Therefore, the MALAT1-miR-30a-5p-Vimentin axis is a potential therapeutic target and molecular biomarker in HCC

Machine Learning Techniques for Data Storage Systems: Modeling, Coding, and Detection

As data explodes in modern applications, such as the Internet of Things (IoT), the needs for storage devices drastically increase. Solid state drives (SSDs) and hard disk drives (HDDs) are two main data storage devices. SSDs store data in flash memories, while HDDs store data in magnetic disks. To extend the lifetime and enhance the reliability of data storage devices, we utilize machine learning as the fundamental tool for three data storage modules: modeling in flash memory systems, error correction and constrained coding schemes, and detection method in magnetic recording channels. The modeling part of the dissertation is devoted to proposing a novel data-driven approach, referred to as Flash-Gen, to generating NAND flash memory read voltages in both space and time using conditional generative networks. This generative modeling method reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the time stamp, in a time-efficient, resource-saving, and function-comprehensive manner. As the needs for data-dependent channel models, we further extend the generative modeling approach to the coded storage channel. We train the generative models via transferring knowledge from models pre-trained with pseudo-random data. This technique can accelerate the training process and improve model accuracy in reconstructing the read voltages induced by constrained input data throughout the flash memory lifetime. The coding part of the dissertation designs practical coding workflow and proposes new constrained and shaping coding schemes for flash memories. We propose a flash system optimization procedure, referred to as the Flash-Gen coding workflow, that leverages reconstructed read voltages from Flash-Gen for the development of error correction codes (ECCs) and constrained codes. Flash-Gen coding workflow can effectively address a range of important tasks, including threshold determination, coding performance estimation, and pattern characterization. We then formulate inter-cell interference (ICI)-mitigation constrained codes and distribution-matching shaping codes. The proposed coding schemes both achieve remarkable lifetime improvement.The detection part of the dissertation builds recurrent neural network (RNN)-based detection for magnetic recording channels with partial-response equalization, which is referred as Partial-Response Neural Network (PR-NN). PR-NN could beat classical detection methods, such as the Viterbi detector, under multiple ``realistic'' environments and preserve the detection performance across different channel conditions

Safety Needs Mediate Stressful Events Induced Mental Disorders

“Safety first,” we say these words almost every day, but we all take this for granted for what Maslow proposed in his famous theory of Hierarchy of Needs: safety needs come second to physiological needs. Here we propose that safety needs come before physiological needs. Safety needs are personal security, financial security, and health and well-being, which are more fundamental than physiological needs. Safety worrying is the major reason for mental disorders, such as anxiety, phobia, depression, and PTSD. The neural basis for safety is amygdala, LC/NE system, and corticotrophin-releasing hormone system, which can be regarded as a “safety circuitry,” whose major behavior function is “fight or flight” and “fear and anger” emotions. This is similar to the Appraisal theory for emotions: fear is due to the primary appraisal, which is related to safety of individual, while anger is due to secondary appraisal, which is related to coping with the unsafe situations. If coping is good, the individual will be happy; if coping failed, the individual will be sad or depressed

Salvia miltiorrhiza inhibits the expressions of transcription factor T-bet (T-bet) and tumor necrosis factor α (TNFα) in the experimental colitis in mice

Salvia miltiorrhiza (SM) is mainly used for the treatment of coronary heart disease in china and it also represses inflammation, but the mechanism is not well known. This study aimed to investigate the effects of SM powder for injection on the expressions of transcription factor T-bet (T-bet) and tumor necrosis factor α (TNFα) in the experimental colitis in mice. Mice were grouped and treated with SM powder for injection at the time of colonic instillation of trinitrobenzene sulfonic acid (TNBS). Expression studies were performed by real-time polymerase chain reaction (PCR), western blot (WB) and immunohistochemistry (IHC), and histology studies were performed by hematoxylin and eosin stain (H&E). The survival of mice was also monitored. The expressions of TNFα in the colon, T-bet messenger ribonucleic acid (mRNA) and T-bet protein in the spleen decreased in the groups treated with SM powder for injection. The inflamed colonic lesions were relieved and the survival of mice also increased in the treated groups. SM powder for injection repressed the expressions of T-bet and TNFα in the experimental colitis in mice, which could relieve the inflamed colonic lesions and elevate the survival of mice.Keywords: Salvia miltiorrhiza, T-bet, tumor necrosis factor α, colitis, mice, inflammatory bowel disease, Crohn’s disease, ulcerative coliti