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"Gao, Bin"
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Carbon dioxide capture : an effective way to combat global warming
This topical brief summarizes the various options available for carbon capture and presents the current strategies involved in CO₂ reduction. The authors focus on current CO₂ capture technologies that facilitate the reduction of greenhouse gas (CO₂) emissions and reduce global warming. This short study will interest environmental researchers, teachers and students who have an interest in global warming.
Book
Basic liver immunology
The liver is the largest solid organ in the body and has many unique immunological properties, including induction of immune tolerance, strong innate immunity, poor adaptive immune response versus overreactive autoimmunity and hematopoiesis in the fetal liver. Thus, the liver has been proposed as ＇an immunological organ＇. Although the primary functions of the liver are not traditionally considered to be immunological, the liver also performs many essential immune tasks. For example, hepatocytes are responsible for the production of 80-90% of the circulating innate immunity proteins in the body,
Journal Article
Alcohol-associated liver disease
Alcohol-associated liver disease (ALD) is a major cause of chronic liver disease worldwide, and comprises a spectrum of several different disorders, including simple steatosis, steatohepatitis, cirrhosis, and superimposed hepatocellular carcinoma. Although tremendous progress has been made in the field of ALD over the last 20 years, the pathogenesis of ALD remains obscure, and there are currently no FDA-approved drugs for the treatment of ALD. In this Review, we discuss new insights into the pathogenesis and therapeutic targets of ALD, utilizing the study of multiomics and other cutting-edge approaches. The potential translation of these studies into clinical practice and therapy is deliberated. We also discuss preclinical models of ALD, interplay of ALD and metabolic dysfunction, alcohol-associated liver cancer, the heterogeneity of ALD, and some potential translational research prospects for ALD.
Journal Article
MicroRNAs as regulators, biomarkers and therapeutic targets in liver diseases
MicroRNAs (miRNAs) are small, non-coding RNAs that post-transcriptionally regulate gene expression by binding to specific mRNA targets and promoting their degradation and/or translational inhibition. miRNAs regulate both physiological and pathological liver functions. Altered expression of miRNAs is associated with liver metabolism dysregulation, liver injury, liver fibrosis and tumour development, making miRNAs attractive therapeutic strategies for the diagnosis and treatment of liver diseases. Here, we review recent advances regarding the regulation and function of miRNAs in liver diseases with a major focus on miRNAs that are specifically expressed or enriched in hepatocytes (miR-122, miR-194/192), neutrophils (miR-223), hepatic stellate cells (miR-29), immune cells (miR-155) and in circulation (miR-21). The functions and target genes of these miRNAs are emphasised in alcohol-associated liver disease, non-alcoholic fatty liver disease, drug-induced liver injury, viral hepatitis and hepatocellular carcinoma, as well liver fibrosis and liver failure. We touch on the roles of miRNAs in intercellular communication between hepatocytes and other types of cells via extracellular vesicles in the pathogenesis of liver diseases. We provide perspective on the application of miRNAs as biomarkers for early diagnosis, prognosis and assessment of liver diseases and discuss the challenges in miRNA-based therapy for liver diseases. Further investigation of miRNAs in the liver will help us better understand the pathogeneses of liver diseases and may identify biomarkers and therapeutic targets for liver diseases in the future.
Journal Article
YTHDF2 is a potential target of AML1/ETO-HIF1α loop-mediated cell proliferation in t(8;21) AML
The t(8;21) fusion product, AML1/ETO, and hypoxia-inducible factor 1α (HIF1α) form a feed-forward transcription loop that cooperatively transactivates the
DNA methyltransferase 3a
gene promoter that leads to DNA hypermethylation and drives leukemia cell growth. Suppression of the RNA N
6
-methyladenosine (m
6
A)-reader enzyme YTH N
6
-methyladenosine RNA binding protein 2 (YTHDF2) specifically compromises cancer stem cells in acute myeloid leukemia (AML) but promotes hematopoietic stem cell expansion without derailing normal hematopoiesis. However, the relevance of expression between
AML1/ETO
-
HIF1α
loop and
YTHDF2
, and its functional relationship with t(8;21) AML have not been documented. Here, we show that
YTHDF2
is highly expressed in t(8;21) AML patients and associated with a higher risk of relapse and inferior relapse-free survival. Knockdown of YTHDF2 in leukemia cells causes an impaired cell proliferation rate in vitro and in mice. Mechanistically, HIF1α is able to bind to the hypoxia-response elements of the 5′-untranslated region of the
YTHDF2
gene and promotes the transactivity of the
YTHDF2
promoter. Knockdown and overexpression of either
AML1/ETO
or
HIF1α
resulted in decreased and increased YTHDF2 protein and mRNA expression in t(8;21) AML cells. In particular, knockdown of YTHDF2 resulted in increased global mRNA m
6
A levels in t(8;21) AML cells, accompanied by increased
TNF receptor superfamily member 1b
(
TNFRSF1b
) mRNA and protein expression levels. Last, we demonstrated that the m
6
A methylation and expression levels of the
TNFRSF1b
gene were both negatively correlated with
HIF1α
expression levels. In conclusion, YTHDF2 is a downstream target of the AML1/ETO-HIF1α loop and promotes cell proliferation probably by modulating the global m
6
A methylation in t(8;21) AML.
Journal Article
Fully hardware-implemented memristor convolutional neural network
Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks
1
–
4
. However, convolutional neural networks (CNNs)—one of the most important models for image recognition
5
—have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device at each intersection. Moreover, achieving software-comparable results is highly challenging owing to the poor yield, large variation and other non-ideal characteristics of devices
6
–
9
. Here we report the fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs, which integrate eight 2,048-cell memristor arrays to improve parallel-computing efficiency. In addition, we propose an effective hybrid-training method to adapt to device imperfections and improve the overall system performance. We built a five-layer memristor-based CNN to perform MNIST
10
image recognition, and achieved a high accuracy of more than 96 per cent. In addition to parallel convolutions using different kernels with shared inputs, replication of multiple identical kernels in memristor arrays was demonstrated for processing different inputs in parallel. The memristor-based CNN neuromorphic system has an energy efficiency more than two orders of magnitude greater than that of state-of-the-art graphics-processing units, and is shown to be scalable to larger networks, such as residual neural networks. Our results are expected to enable a viable memristor-based non-von Neumann hardware solution for deep neural networks and edge computing.
A fully hardware-based memristor convolutional neural network using a hybrid training method achieves an energy efficiency more than two orders of magnitude greater than that of graphics-processing units.
Journal Article
Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
Reservoir computing is a highly efficient network for processing temporal signals due to its low training cost compared to standard recurrent neural networks, and generating rich reservoir states is critical in the hardware implementation. In this work, we report a parallel dynamic memristor-based reservoir computing system by applying a controllable mask process, in which the critical parameters, including state richness, feedback strength and input scaling, can be tuned by changing the mask length and the range of input signal. Our system achieves a low word error rate of 0.4% in the spoken-digit recognition and low normalized root mean square error of 0.046 in the time-series prediction of the Hénon map, which outperforms most existing hardware-based reservoir computing systems and also software-based one in the Hénon map prediction task. Our work could pave the road towards high-efficiency memristor-based reservoir computing systems to handle more complex temporal tasks in the future.
Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction error of 0.046 in the Hénon map.
Journal Article
Tumor microenvironment characteristics association with clinical outcome in patients with resected intestinal-type gastric cancer
Background
Tumor microenvironment (TME) characteristics including tumor stroma ratio (TSR), tumor budding (TB), and tumor-infiltrating lymphocytes (TILs) were examined in resected gastric cancer. These TME features have been shown to indicate metastatic potential in colon cancer, and intestinal-type gastric cancer (IGC) has pathological similarities with that malignancy.
Methods
TSR, TB, and TILs were quantified in routine histological sections from 493 patients with IGC who underwent radical resection at 2 university hospitals in China from 2010 to 2016. TME variables were dichotomized as follows: TSR (50%), TILs (median), TB per international guidelines (4 buds/0.785mm2), and platelet-lymphocyte ratio (PLR) per survival ROC. Association of TME features with patient clinicopathological characteristics, time-to-recurrence (TTR), and cancer-specific-survival (CSS) were examined using univariate and multivariate analysis, including a relative contribution analysis by Cox regression.
Results
Patients whose tumors showed high TSR or high TB or low TILs were each significantly associated with increased T and N stage, higher histological grade, and poorer TTR and CSS at 5 years. Only TSR and N stage were independently associated with TTR and CSS after adjustment for covariates. PLR was only independently associated with TTR after adjustment for covariates. Among the variables examined, only TSR was significantly associated with both TTR (HR 1.72, 95% CI, 1.14-2.60, P = .01) and CSS (HR 1.62, 95% CI, 1.05-2.51, P = .03) multivariately. Relative contribution to TTR revealed that the top 3 contributors were N stage (45.1%), TSR (22.5%), and PLR (12.9%), while the top 3 contributors to CSS were N stage (59.9%), TSR (14.7%), and PLR (10.9%).
Conclusions
Among the examined TME features, TSR was the most robust for prognostication and was significantly associated with both TTR and CSS. Furthermore, the relative contribution of TSR to patient TTR and CSS was second only to nodal status.
Tumor microenvironment characteristics, which have been shown to indicate metastatic potential in colon cancer, were examined in the setting of resected gastric cancer.
Graphical Abstract
Graphical Abstract
Journal Article
Strategies for enhancing thermal conductivity of polymer-based thermal interface materials: a review
Thermal management has been considered as a key issue for high-power electronics. Thermal interface materials (TIMs) play an extremely important role in the field of thermal management. Owing to their excellent insulation, mechanical properties and low processing costs, functional polymers have become the popular candidate for preparing TIMs. In order to develop high thermally conductive TIMs, the inorganic fillers with high thermal conductivity are generally composited with polymers. For this purpose, some key technologies are needed to improve the dispersibility of fillers to reduce interfacial thermal resistance and increase thermal conduction channels. This paper reviews recent progresses on effective methods for improving thermal conductivity, which mainly include filler functionalization and processing, filler hybridization and coating, filler orientation and network. After implementing these strategies, the interfacial interaction between fillers and polymers, the synergy effect of different fillers and the thermal conduction pathway inside the matrix can be highly improved, hence enhancing the thermal conductivity of TIMs.
Graphic abstract
Journal Article
A compute-in-memory chip based on resistive random-access memory
Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM)
1
promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory
2
–
5
. Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware
6
–
17
, it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to support diverse models and software-comparable accuracy. Although efficiency, versatility and accuracy are all indispensable for broad adoption of the technology, the inter-related trade-offs among them cannot be addressed by isolated improvements on any single abstraction level of the design. Here, by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM—a RRAM-based CIM chip that simultaneously delivers versatility in reconfiguring CIM cores for diverse model architectures, energy efficiency that is two-times better than previous state-of-the-art RRAM-CIM chips across various computational bit-precisions, and inference accuracy comparable to software models quantized to four-bit weights across various AI tasks, including accuracy of 99.0 percent on MNIST
18
and 85.7 percent on CIFAR-10
19
image classification, 84.7-percent accuracy on Google speech command recognition
20
, and a 70-percent reduction in image-reconstruction error on a Bayesian image-recovery task.
A compute-in-memory neural-network inference accelerator based on resistive random-access memory simultaneously improves energy efficiency, flexibility and accuracy compared with existing hardware by co-optimizing across all hierarchies of the design.
Journal Article