Structure Embedded Nucleus Classification for Histopathology Images.

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the previous methods. Code and data are made available via https://github.com/lhaof/SENC.

Live-attenuated virus vaccine defective in RNAi suppression induces rapid protection in neonatal and adult mice lacking mature B and T cells.

Global control of infectious diseases depends on the continuous development and deployment of diverse vaccination strategies. Currently available live-attenuated and killed virus vaccines typically take a week or longer to activate specific protection by the adaptive immunity. The mosquito-transmitted Nodamura virus (NoV) is attenuated in mice by mutations that prevent expression of the B2 viral suppressor of RNA interference (VSR) and consequently, drastically enhance in vivo production of the virus-targeting small-interfering RNAs. We reported recently that 2 d after immunization with live-attenuated VSR-disabled NoV (NoVΔB2), neonatal mice become fully protected against lethal NoV challenge and develop no detectable infection. Using Rag1-/- mice that produce no mature B and T lymphocytes as a model, here we examined the hypothesis that adaptive immunity is dispensable for the RNAi-based protective immunity activated by NoVΔB2 immunization. We show that immunization of both neonatal and adult Rag1-/- mice with live but not killed NoVΔB2 induces full protection against NoV challenge at 2 or 14 d postimmunization. Moreover, NoVΔB2-induced protective antiviral immunity is virus-specific and remains effective in adult Rag1-/- mice 42 and 90 d after a single-shot immunization. We conclude that immunization with the live-attenuated VSR-disabled RNA virus vaccine activates rapid and long-lasting protective immunity against lethal challenges by a distinct mechanism independent of the adaptive immunity mediated by B and T cells. Future studies are warranted to determine whether additional animal and human viruses attenuated by VSR inactivation induce similar protective immunity in healthy and adaptive immunity-compromised individuals.

Mn-Based Mullites for Environmental and Energy Applications.

Mn-based mullite oxides AMn2O5 (A = lanthanide, Y, Bi) is a novel type of ternary catalyst in terms of their electronic and geometric structures. The coexistence of pyramid Mn3+-O and octahedral Mn4+-O makes the d-orbital selectively active toward various catalytic reactions. The alternative edge- and corner-sharing stacking configuration constructs the confined active sites and abundant active oxygen species. As a result, they tend to show superior catalytic behaviors and thus gain great attention in environmental treatment and energy conversion and storage. In environmental applications, Mn-based mullites have been demonstrated to be highly active toward low-temperature oxidization of CO, NO, volatile organic compounds (VOCs), etc. Recent research further shows that mullites decompose O3 and ozonize VOCs from -20 °C to room temperature. Moreover, mullites enhance oxygen reduction reactions (ORR) and sulfur reduction reactions (SRR), critical kinetic steps in air-battery and Li-S batteries, respectively. Their distinctive structures also facilitate applications in gas-sensitive sensing, ionic conduction, high mobility dielectrics, oxygen storage, piezoelectricity, dehydration, H2O2 decomposition, and beyond. A comprehensive review from basic physicochemical properties to application certainly not only gains a full picture of mullite oxides but also provides new insights into designing heterogeneous catalysts.

UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration Enhancement.

Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fluorescein angiography (UWF-FA) necessitates the administration of a fluorescent dye via injection into the patient's hand or elbow unlike UWF scanning laser ophthalmoscopy (UWF-SLO). To mitigate potential adverse effects associated with injections, researchers have proposed the development of cross-modality medical image generation algorithms capable of converting UWF-SLO images into their UWF-FA counterparts. Current image generation techniques applied to fundus photography encounter difficulties in producing high-resolution retinal images, particularly in capturing minute vascular lesions. To address these issues, we introduce a novel conditional generative adversarial network (UWAFA-GAN) to synthesize UWF-FA from UWF-SLO. This approach employs multi-scale generators and an attention transmit module to efficiently extract both global structures and local lesions. Additionally, to counteract the image blurriness issue that arises from training with misaligned data, a registration module is integrated within this framework. Our method performs non-trivially on inception scores and details generation. Clinical user studies further indicate that the UWF-FA images generated by UWAFA-GAN are clinically comparable to authentic images in terms of diagnostic reliability. Empirical evaluations on our proprietary UWF image datasets elucidate that UWAFA-GAN outperforms extant methodologies. The code is accessible at https://github.com/Tinysqua/UWAFA-GAN.

Associations between non-insulin-based insulin resistance indices and heart failure prevalence in overweight/obesity adults without diabetes mellitus: evidence from the NHANES 2001-2018.

BACKGROUND: The triglyceride glucose (TyG) index and triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio are recognized as simple non-insulin-based insulin resistance indices. Our study aimed to explore the relationship between these two indicators and heart failure (HF) in overweight or obesity individuals without diabetes. METHODS: This cross-sectional study selected 13,473 participants from the National Health and Nutrition Examination Survey (NHANES) 2001-2018 dataset. Weighted multivariable logistic regression and subgroup analysis were employed to evaluate the relationships between TyG index, TG/HDL-C ratio, and HF prevalence, respectively. Additionally, smooth curve fitting was utilized to analyze the dose-response relationships. RESULTS: A total of 13,473 obesity or overweight people without diabetes were included in this study through screening, among whom 291 (2.16%) had comorbid HF. The results of multivariable logistic regression suggested that the highest TyG index (OR = 2.4, 95% CI = 1.4-4.2, p = 0.002) and the highest TG/HDL-C ratio (OR = 1.2, 95% CI = 1.1-1.3, p < 0.001) both increased the prevalence of HF, especially in the non-Hispanic population. Dose-response relationships suggested nonlinear relationships between these two indicators and HF. CONCLUSION: Our study demonstrated that elevated TyG index and TG/HDL-C ratio were closely associated with the prevalence of HF, and both exhibited nonlinear relationships with HF prevalence in overweight/obesity adults without diabetes. Based on these findings, additional prospective studies are needed for further validation.

Efficacy and safety of neoadjuvant sintilimab in combination with FLOT chemotherapy in patients with HER2-negative locally advanced gastric or gastroesophageal junction adenocarcinoma: an investigator-initiated, single-arm, open-label, phase II study.

BACKGROUND: The addition of immune checkpoint inhibitors to neoadjuvant chemotherapy in operable advanced gastric or gastroesophageal junction (G/GEJ) cancer aroused wide interest. This study was designed to assess the efficacy and safety of neoadjuvant sintilimab, a programmed cell death protein-1 (PD-1) inhibitor, in combination with fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT) chemotherapy for HER2-negative locally advanced G/GEJ cancer. METHODS: Eligible patients with clinical stage cT4 and/or cN+M0 G/GEJ cancer were enroled in this phase II study. Patients received neoadjuvant sintilimab (200 mg every 3 weeks) for three cycles plus FLOT (50 mg/m 2 docetaxel, 80 mg/m 2 oxaliplatin, 200 mg/m 2 calcium levofolinate, 2600 mg/m 2 5-fluorouracil every 2 weeks) for four cycles before surgery, followed by four cycles of adjuvant FLOT with same dosages after resection. The primary endpoint was the pathological complete response (pCR) rate. RESULTS: Thirty-two patients were enroled between August 2019 and September 2021, with a median follow-up of 34.8 (95% CI, 32.8-42.9) months. Thirty-two (100%) patients received neoadjuvant therapy, and 29 underwent surgery with an R0 resection rate of 93.1%. The pCR (TRG0) was achieved in 5 (17.2%; 95% CI, 5.8-35.8%) patients, and the major pathological response was 55.2%. Twenty-three (79.3%) patients had T downstaging, 21 (72.4%) had N downstaging, and 19 (65.5%) had overall TNM downstaging. Six (20.7%) patients experienced recurrence. Patients achieving pCR showed better event-free survival (EFS), disease-free survival (DFS), and overall survival (OS) than non-pCR. The estimated 3-year EFS rate, 3-year DFS rate, and 3-year OS rate were 71.4% (95% CI, 57.2-89.2%), 78.8% (95% CI, 65.1-95.5%), and 70.9% (95% CI, 54.8-91.6%), respectively. The objective response rate and disease control rate were 84.4% (95% CI, 68.3-93.1%) and 96.9% (95% CI, 84.3-99.5%), respectively. Twenty-five (86.2%) received adjuvant therapy. The main grade ≥3 treatment-related adverse events (TRAEs) were lymphopenia (34.4%), neutropenia (28.1%), and leukopenia (15.6%). no patients died from TRAE. The LDH level exhibited a better predictive value to pathological responses than PD-L1 and MSI status. CONCLUSIONS: The study demonstrated an encouraging efficacy and manageable safety profile of neoadjuvant sintilimab plus FLOT in HER2-negative locally advanced G/GEJ cancer, which suggested a potential therapeutic option for this population.

Development of a universal method for vibrational analysis of the terminal alkyne C≡C stretch.

The terminal alkyne C≡C stretch has a large Raman scattering cross section in the "silent" region for biomolecules. This has led to many Raman tag and probe studies using this moiety to study biomolecular systems. A computational investigation of these systems is vital to aid in the interpretation of these results. In this work, we develop a method for computing terminal alkyne vibrational frequencies and isotropic transition polarizabilities that can easily and accurately be applied to any terminal alkyne molecule. We apply the discrete variable representation method to a localized version of the C≡C stretch normal mode. The errors of (1) vibrational localization to the terminal alkyne moiety, (2) anharmonic normal mode isolation, and (3) discretization of the Born-Oppenheimer potential energy surface are quantified and found to be generally small and cancel each other. This results in a method with low error compared to other anharmonic vibrational methods like second-order vibrational perturbation theory and to experiments. Several density functionals are tested using the method, and TPSS-D3, an inexpensive nonempirical density functional with dispersion corrections, is found to perform surprisingly well. Diffuse basis functions are found to be important for the accuracy of computed frequencies. Finally, the computation of vibrational properties like isotropic transition polarizabilities and the universality of the localized normal mode for terminal alkynes are demonstrated.

Upfront Umbilical Cord Blood Transplantation Versus Immunosuppressive Therapy for Pediatric Patients With Idiopathic Severe Aplastic Anemia.

Umbilical cord blood transplantation (UCBT) has been rarely reported as a first-line treatment for idiopathic severe aplastic anemia (SAA) patients lacking HLA-matched sibling donors (MSD). Our study aimed to compare the clinical outcomes of pediatric SAA patients who received UCBT and immunosuppressive therapy (IST) upfront. A retrospective analysis was performed on 43 consecutive patients who received frontline IST (n = 17) or UCBT (n = 26) between July 2017 and April 2022. The 3-year overall survival (OS) was comparable between the UCBT and IST groups (96.2% versus 100%, P = .419), while the 3-year event-free survival (EFS) was significantly better in the former than in the latter (88.5% versus 58.8%, P = .048). In the UCBT group, 24 patients achieved successful engraftment, 2 patients developed severe acute graft-versus-host disease (aGVHD), no extensive chronic GVHD (cGVHD), and a high GVHD-free, failure-free survival (GFFS) of 84.6% at 3 years. After 1 year of treatment, 12 patients in the IST group responded, while 5 patients did not achieve remission and 2 patients had disease relapse. At both 3 and 6 months after treatment, the proportion of transfusion-independent patients was higher in the UCBT group than in the IST group. Faster immune recovery and earlier transfusion independence further reduced the risk of infection and bleeding, thereby improving health-related quality of life in the UCBT-treated group. Our results suggested that UCBT as upfront therapy may be an effective and safe option for pediatric SAA patients, with favorable outcomes in experienced centers.

Epitope delimitation: A new method for defining epitopes of human IgG-reactive antigenic peptides based on rabbit-recognized epitope motifs.

The use of precise epitope peptides as antigens is essential for accurate serological diagnosis of viral-infected individuals, but now it remains an unsolvable problem for mapping precise B cell epitopes (BCEs) recognized by human serum. To address this challenge, we propose a novel epitope delimitation (ED) method to uncover BCEs in the delineated human IgG-reactive (HR) antigenic peptides (APs). Specifically, the method based on the rationale of similarities in humoral immune responses between mammalian species consists of a pair of elements: experimentally delineated HR-AP and rabbit-recognized (RR) BCE motif and corresponding pair of sequence alignment analysis. As a result of using the ED approach, after decoding four RR-epitomes of human papillomavirus types 16/18-E6 and E7 proteins utilizing rabbit serum against each recombinant protein and sequence alignment analysis of HR-APs and RR-BCEs, 19 fine BCEs in 17 of 22 known HR-APs were defined based on each corresponding RR-BCE motifs, including the type-specificity of each delimited BCE in homologous proteins. The test with 22 known 16/20mer HR-APs demonstrated that the ED method is effective and efficient, indicating that it can be used as an alternative method to the conventional identification of fine BCEs using overlapping 8mer peptides.

Document-level Relation Extraction with Relation Correlations.

Document-level relation extraction faces two often overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into the document-level relation extraction task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of long-tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular datasets (i.e., DocRED and DWIE) are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. The data and code are released at https://github.com/RidongHan/DocRE-Co-Occur.

LSM14B coordinates protein component expression in the P-body and controls oocyte maturation.

The generation of mature and healthy oocytes is the most critical event in the entire female reproductive process, and the mechanisms regulating this process remain to be studied. Here, we demonstrate that Smith-like (LSM) family member 14B (LSM14B) regulates oocyte maturation, and the loss of LSM14B in mouse ovaries leads to abnormal oocyte MII arrest and female infertility. Next, we find the aberrant transcriptional activation, indicated by abnormal non-surrounded nucleolus and surrounded nucleolus oocyte proportions, and abnormal chromosome assembly and segregation in Lsm14b-deficient mouse oocytes. The global transcriptome analysis suggests that many transcripts involved in cytoplasmic processing body (P-body) function are altered in Lsm14b-deficient mouse oocytes. Deletion of Lsm14b results in the expression and/or localization changes of P-body components (such as LSM14A, DCP1A, and 4E-T). Notably, DDX6, a key component of the P-body, is downregulated and accumulates in the nuclei in Lsm14b-deficient mouse oocytes. Taken together, our data suggest that LSM14B links mouse oocyte maturation to female fertility through the regulation of the P-body.

A novel capsid-XL32-derived adeno-associated virus serotype prompts retinal tropism and ameliorates choroidal neovascularization.

Gene therapy has been adapted, from the laboratory to the clinic, to treat retinopathies. In contrast to subretinal route, intravitreal delivery of AAV vectors displays the advantage of bypassing surgical injuries, but the viral particles are more prone to be nullified by the host neutralizing factors. To minimize such suppression of therapeutic effect, especially in terms of AAV2 and its derivatives, we introduced three serine-to-glycine mutations, based on the phosphorylation sites identified by mass spectrum analysis, to the XL32 capsid to generate a novel serotype named AAVYC5. Via intravitreal administration, AAVYC5 was transduced more effectively into multiple retinal layers compared with AAV2 and XL32. AAVYC5 also enabled successful delivery of anti-angiogenic molecules to rescue laser-induced choroidal neovascularization and astrogliosis in mice and non-human primates. Furthermore, we detected fewer neutralizing antibodies and binding IgG in human sera against AAVYC5 than those specific for AAV2 and XL32. Our results thus implicate this capsid-optimized AAVYC5 as a promising vector suitable for a wide population, particularly those with undesirable AAV2 seroreactivity.

Mapping medical image-text to a joint space via masked modeling.

Recently, masked autoencoders have demonstrated their feasibility in extracting effective image and text features (e.g., BERT for natural language processing (NLP) and MAE in computer vision (CV)). This study investigates the potential of applying these techniques to vision-and-language representation learning in the medical domain. To this end, we introduce a self-supervised learning paradigm, multi-modal masked autoencoders (M3AE). It learns to map medical images and texts to a joint space by reconstructing pixels and tokens from randomly masked images and texts. Specifically, we design this approach from three aspects: First, taking into account the varying information densities of vision and language, we employ distinct masking ratios for input images and text, with a notably higher masking ratio for images; Second, we utilize visual and textual features from different layers for reconstruction to address varying levels of abstraction in vision and language; Third, we develop different designs for vision and language decoders. We establish a medical vision-and-language benchmark to conduct an extensive evaluation. Our experimental results exhibit the effectiveness of the proposed method, achieving state-of-the-art results on all downstream tasks. Further analyses validate the effectiveness of the various components and discuss the limitations of the proposed approach. The source code is available at https://github.com/zhjohnchan/M3AE.

Phase separation in DNA double-strand break response.

DNA double-strand break (DSB) is the most dangerous type of DNA damage, which may lead to cell death or oncogenic mutations. Homologous recombination (HR) and nonhomologous end-joining (NHEJ) are two typical DSB repair mechanisms. Recently, many studies have revealed that liquid-liquid phase separation (LLPS) plays a pivotal role in DSB repair and response. Through LLPS, the crucial biomolecules are quickly recruited to damaged sites with a high concentration to ensure DNA repair is conducted quickly and efficiently, which facilitates DSB repair factors activating downstream proteins or transmitting signals. In addition, the dysregulation of the DSB repair factor's phase separation has been reported to promote the development of a variety of diseases. This review not only provides a comprehensive overview of the emerging roles of LLPS in the repair of DSB but also sheds light on the regulatory patterns of phase separation in relation to the DNA damage response (DDR).

CiRS-7 Enhances the Liquid-liquid Phase Separation of miRISC and Promotes DNA Damage Repair.

Noncoding RNAs have been found to play important roles in DNA damage repair, whereas the participation of circRNA remains undisclosed. Here, we characterized ciRS-7, a circRNA containing over 70 putative miR-7-binding sites, as an enhancer of miRISC condensation and DNA repair. Both in vivo and in vitro experiments confirmed the condensation of TNRC6B and AGO2, two core protein components of human miRISC. Moreover, overexpressing ciRS-7 largely increased the condensate number of TNRC6B and AGO2 in cells, while silencing ciRS-7 reduced it. Additionally, miR-7 overexpression also promoted miRISC condensation. Consistent with the previous report that AGO2 participated in RAD51-mediated DNA damage repair, the overexpression of ciRS-7 significantly promoted irradiation-induced DNA damage repair by enhancing RAD51 recruitment. Our results uncover a new role of circRNA in liquid-liquid phase separation and provide new insight into the regulatory mechanism of ciRS-7 on miRISC function and DNA repair.

Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope.

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.

ECC-PolypDet: Enhanced CenterNet With Contrastive Learning for Automatic Polyp Detection.

Accurate polyp detection is critical for early colorectal cancer diagnosis. Although remarkable progress has been achieved in recent years, the complex colon environment and concealed polyps with unclear boundaries still pose severe challenges in this area. Existing methods either involve computationally expensive context aggregation or lack prior modeling of polyps, resulting in poor performance in challenging cases. In this paper, we propose the Enhanced CenterNet with Contrastive Learning (ECC-PolypDet), a two-stage training & end-to-end inference framework that leverages images and bounding box annotations to train a general model and fine-tune it based on the inference score to obtain a final robust model. Specifically, we conduct Box-assisted Contrastive Learning (BCL) during training to minimize the intra-class difference and maximize the inter-class difference between foreground polyps and backgrounds, enabling our model to capture concealed polyps. Moreover, to enhance the recognition of small polyps, we design the Semantic Flow-guided Feature Pyramid Network (SFFPN) to aggregate multi-scale features and the Heatmap Propagation (HP) module to boost the model's attention on polyp targets. In the fine-tuning stage, we introduce the IoU-guided Sample Re-weighting (ISR) mechanism to prioritize hard samples by adaptively adjusting the loss weight for each sample during fine-tuning. Extensive experiments on six large-scale colonoscopy datasets demonstrate the superiority of our model compared with previous state-of-the-art detectors.

Liquid-liquid phase separation in DNA double-strand breaks repair.

DNA double-strand breaks (DSBs) are the fatal type of DNA damage mostly induced by exposure genome to ionizing radiation or genotoxic chemicals. DSBs are mainly repaired by homologous recombination (HR) and nonhomologous end joining (NHEJ). To repair DSBs, a large amount of DNA repair factors was observed to be concentrated at the end of DSBs in a specific spatiotemporal manner to form a repair center. Recently, this repair center was characterized as a condensate derived from liquid-liquid phase separation (LLPS) of key DSBs repair factors. LLPS has been found to be the mechanism of membraneless organelles formation and plays key roles in a variety of biological processes. In this review, the recent advances and mechanisms of LLPS in the formation of DSBs repair-related condensates are summarized.

A predictive model combining clinical characteristics and nutritional risk factors for overall survival after umbilical cord blood transplantation.

BACKGROUND: Umbilical cord blood transplantation (UCBT) is a curable therapy for hematological disease; however, the impact of nutritional status on UCBT outcomes remains controversial. To evaluate the joint effect of clinical characteristics and nutritional status on the prognosis of patients who underwent UCBT, we screened various factors to establish a predictive model of overall survival (OS) after UCBT. METHODS: We performed an integrated clinical characteristic and nutritional risk factor analysis and established a predictive model that could be used to identify UCBT recipients with poor OS. Internal validation was performed by using the bootstrap method with 500 repetitions. RESULTS: Four factors, including disease status, conditioning regimen, calf skinfold thickness and albumin level, were identified and used to develop a risk score for OS, which showed a positive predictive value of 84.0%. A high-risk score (≥ 2.225) was associated with inferior 3-year OS post-UCBT [67.5% (95% CI 51.1-79.4%), P = 0.001]. Then, we built a nomogram based on the four factors that showed good discrimination with a C-index of 0.833 (95% CI 0.743-0.922). The optimism-corrected C-index value of the bootstrapping was 0.804. Multivariate analysis suggested that a high calf skinfold thickness (≥ 20.5 mm) and a low albumin level (< 33.6 g/L) conferred poor disease-free survival (DFS). CONCLUSION: The predictive model combining clinical and nutritional factors could be used to predict OS in UCBT recipients, thereby promoting preemptive treatment.

Digital image processing realized by memristor-based technologies.

Today performance and operational efficiency of computer systems on digital image processing are exacerbated owing to the increased complexity of image processing. It is also difficult for image processors based on complementary metal-oxide-semiconductor (CMOS) transistors to continuously increase the integration density, causing by their underlying physical restriction and economic costs. However, such obstacles can be eliminated by non-volatile resistive memory technologies (known as memristors), arising from their compacted area, speed, power consumption high efficiency, and in-memory computing capability. This review begins with presenting the image processing methods based on pure algorithm and conventional CMOS-based digital image processing strategies. Subsequently, current issues faced by digital image processing and the strategies adopted for overcoming these issues, are discussed. The state-of-the-art memristor technologies and their challenges in digital image processing applications are also introduced, such as memristor-based image compression, memristor-based edge and line detections, and voice and image recognition using memristors. This review finally envisages the prospects for successful implementation of memristor devices in digital image processing.