Carbonization of quercetin into nanogels: a leap in anticoagulant development.

Quercetin, a flavonoid abundantly found in onions, fruits, and vegetables, is recognized for its pharmacological potential, especially for its anticoagulant properties that work by inhibiting thrombin and coagulation factor Xa. However, its clinical application is limited due to poor water solubility and bioavailability. To address these limitations, we engineered carbonized nanogels derived from quercetin (CNGsQur) using controlled pyrolysis and polymerization techniques. This led to substantial improvements in its anticoagulation efficacy, water solubility, and biocompatibility. We generated a range of CNGsQur by subjecting quercetin to varying pyrolytic temperatures and then assessed their anticoagulation capacities both in vitro and in vivo. Coagulation metrics, including thrombin clotting time (TCT), activated partial thromboplastin time (aPTT), and prothrombin time (PT), along with a rat tail bleeding assay, were utilized to gauge the efficacy. CNGsQur showed a pronounced extension of coagulation time compared to uncarbonized quercetin. Specifically, CNGsQur synthesized at 270 °C (CNGsQur270) exhibited the most significant enhancement in TCT, with a binding affinity to thrombin exceeding 400 times that of quercetin. Moreover, variants synthesized at 310 °C (CNGsQur310) and 290 °C (CNGsQur290) showed the most substantial delays in PT and aPTT, respectively. Our findings indicate that the degree of carbonization significantly influences the transformation of quercetin into various CNGsQur forms, each affecting distinct coagulation pathways. Additionally, both intravenous and oral administrations of CNGsQur were found to extend rat tail bleeding times by up to fivefold. Our studies also demonstrate that CNGsQur270 effectively delays and even prevents FeCl3-induced vascular occlusion in a dose-dependent manner in mice. Thus, controlled pyrolysis offers an innovative approach for generating quercetin-derived CNGs with enhanced anticoagulation properties and water solubility, revealing the potential for synthesizing self-functional carbonized nanomaterials from other flavonoids for diverse biomedical applications.

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS: The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION: The DLR model is an effective method for identifying the primary source of liver metastases.

Real-Time Monitoring of the Evaporation and Fission of Electrospray-Ionized Polystyrene Beads and Bacterial Pellets at Elevated Temperatures.

This study uses real-time monitoring, at microsecond time scales, with a charge-sensing particle detector to investigate the evaporation and fission processes of methanol/micrometer-sized polystyrene beads (PS beads) droplets and bacterial particles droplets generated via electrospray ionization (ESI) under elevated temperatures. By incrementally raising capillary temperatures, the solvent, such as methanol on 0.75 µm PS beads, experiences partial evaporation. Further temperature increase induces fission, and methanol molecules continue to evaporate until PS ions are detected after this range. Similar partial evaporation is observed on 3 µm PS beads. However, the shorter period of the fission temperature range is necessary compared to 0.75 µm PS beads. For the spherical-shaped bacterium, Staphylococcus aureus, the desolvation process shows a similar fission period as compared to 0.75 µm PS beads. Comparably, the rod-shaped bacteria, Escherichia coli EC11303, and E. coli strain W have shorter fission periods than S. aureus. This research provides insights into the evaporation and fission mechanisms of ESI droplets containing different sizes and shapes of micrometer-sized particles, contributing to a better understanding of gaseous macroion formation.

Equalization system of low differential mode delay few-mode fibers based on the neural network MIMO algorithm.

In recent years, with the development of information networks, higher requirements for transmission capacity have been recommended. Yet, at the same time, the capacity of single-mode fiber is rapidly approaching the theoretical limit. The multidimensional multiplexing technique is an effective way to solve this problem. Since the high differential mode delay (DMD) of transmission fiber increases the complexity of demultiplexing in equalization algorithms, we use an intelligent design method to optimize the trench-assisted gradient refractive index structure in this paper. The maximum DMD of the optimized optical fiber structure is 19.6 ps/km. A least mean squares-feedforward neural network constant modulus algorithm (LMS-FNNCMA) is also designed by using the theory of the least mean squares (LMS), constant modulus algorithm (CMA), and the multiple input multiple output (MIMO) neural networks. In order to verify the accuracy of the algorithm, a polarization division multiplexing-wavelength division multiplexing-mode division multiplexing (PDM-WDM-MDM) optical transmission system is constructed through simulation. The algorithm successfully realizes the de-crosstalk over a transmission distance of 1200 km at a rate of 1.2 Tbps under simulation conditions.

Fluorescent nanodiamond immunosensors for clinical diagnostics of tuberculosis.

Fluorescent nanodiamonds (FNDs) are carbon nanoparticles containing a dense ensemble of nitrogen-vacancy defects as color centers. These centers have exceptional photostability and unique quantum properties, making them useful for ultrasensitive biosensing applications. This work employed FNDs conjugated with antibodies as magneto-optical immunosensors for tuberculosis (TB) diagnostics using competitive spin-enhanced lateral flow immunoassay (SELFIA). ESAT6 (6-kDa early secretory antigenic target) of Mycobacterium tuberculosis is a clinical marker of TB. We evaluated the assay's performance using the recombinant ESAT6 antigen and its antibodies noncovalently coated on FNDs. A detection limit of ∼0.02 ng mL-1 was achieved with the lateral flow membrane strip pre-structured with a narrow channel of 1 mm width. Adopting a cut-off value of 24.0 ng mm-1 for 100-nm FNDs on the strips, the method detected 49 out of 50 clinical samples with Mycobacterium tuberculosis complexes. In contrast, none of the assays for 10 clinical samples with non-tuberculous mycobacteria (NTM) isolates exhibited the presence of ESAT6. These results suggest that the SELFIA platform is applicable for TB detection and can differentiate TB from NTM infections, which also affect the human respiratory system. The FND-enabled immunosensing techniques are versatile and promising for early detection of TB and other diseases, opening a new avenue for biomedical applications of carbon-based nanomaterials.

DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system.

The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.

A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning.

Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach to transform the manual interpretation method into a human-machine collaborative intelligent interpretation method. This low-cost and high-efficiency framework aims to solve the problem of weak model generalization caused by the lack of annotated data in change detection. The proposed framework can effectively incorporate expert domain knowledge and reduce the cost of data annotation while improving model performance. To ensure data quality, a crowdsourcing quality control model is constructed to evaluate the annotation qualification of the annotators and check their annotation results. Furthermore, a prototype of automatic detection and crowdsourcing collaborative annotation management platform is developed, which integrates annotation, crowdsourcing quality control, and change detection applications. The proposed framework and platform can help natural resource departments monitor land cover changes efficiently and effectively.

ABCC4 suppresses glioblastoma progression and recurrence by restraining cGMP-PKG signalling.

BACKGROUND: Cyclic nucleotides are critical mediators of cellular signalling in glioblastoma. However, the clinical relevance and mechanisms of regulating cyclic nucleotides in glioblastoma progression and recurrence have yet to be thoroughly explored. METHODS: In silico, mRNA, and protein level analyses identified the primary regulator of cyclic nucleotides in recurrent human glioblastoma. Lentiviral and pharmacological manipulations examined the functional impact of cyclic nucleotide signalling in human glioma cell lines and primary glioblastoma cells. An orthotopic xenograft mice model coupled with aspirin hydrogels verified the in vivo outcome of targeting cyclic nucleotide signalling. RESULTS: Elevated intracellular levels of cGMP, instead of cAMP, due to a lower substrate efflux from ATP-binding cassette sub-family C member 4 (ABCC4) is engaged in the recurrence of glioblastoma. ABCC4 gene expression is negatively associated with recurrence and overall survival outcomes in glioblastoma specimens. ABCC4 loss-of-function activates cGMP-PKG signalling, promoting malignancy in glioblastoma cells and xenografts. Hydrogels loaded with aspirin, inhibiting glioblastoma progression partly by upregulating ABCC4 expressions, augment the efficacy of standard-of-care therapies in orthotopic glioblastoma xenografts. CONCLUSION: ABCC4, repressing the cGMP-PKG signalling pathway, is a tumour suppressor in glioblastoma progression and recurrence. Aspirin hydrogels impede glioblastoma progression through ABCC4 restoration and constitute a viable translational approach.

OAM mode-division multiplexing IM/DD transmission at 4.32†Tbit/s with a low-complexity adaptive-network-based fuzzy inference system nonlinear equalizer.

Stochastic nonlinear impairment is the primary factor that limits the transmission performance of high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication systems. This Letter presents a low-complexity adaptive-network-based fuzzy inference system (LANFIS) nonlinear equalizer for OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with three OAM modes and 15 wavelength division multiplex (WDM) channels. The LANFIS equalizer could adjust the probability distribution functions (PDFs) of the distorted pulse amplitude modulation (PAM) symbols to fit the statistical characteristics of the WDM-OAM-MDM transmission channel. Therefore, although the transmission symbols in the WDM-OAM-MDM system are subjected to a stochastic nonlinear impairment, the proposed LANFIS equalizer can effectively compensate the distorted signals. The proposed equalizer outperforms the Volterra equalizer with improvements in receiver sensitivity of 2, 1.5, and 1.3 dB for three OAM modes at a wavelength of 1550.12 nm, respectively. It also outperforms a CNN equalizer, with improvements in receiver sensitivity of 1, 0.5, and 0.3 dB, respectively. Moreover, complexity reductions of 67%, 74%, and 99.9% are achieved for the LANFIS equalizer compared with the Volterra, CNN, and ANFIS equalizers, respectively. The proposed equalizer has high performance and low complexity, making it a promising candidate for a high-speed WDM-OAM-MDM system.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

Augmenting Neutrophil Extracellular Traps with Carbonized Polymer Dots: A Potential Treatment for Bacterial Sepsis.

Sepsis is a life-threatening condition that can progress to septic shock as the body's extreme response to pathogenesis damages its own vital organs. Staphylococcus aureus (S. aureus) accounts for 50% of nosocomial infections, which are clinically treated with antibiotics. However, methicillin-resistant strains (MRSA) have emerged and can withstand harsh antibiotic treatment. To address this problem, curcumin (CCM) is employed to prepare carbonized polymer dots (CPDs) through mild pyrolysis. Contrary to curcumin, the as-formed CCM-CPDs are highly biocompatible and soluble in aqueous solution. Most importantly, the CCM-CPDs induce the release of neutrophil extracellular traps (NETs) from the neutrophils, which entrap and eliminate microbes. In an MRSA-induced septic mouse model, it is observed that CCM-CPDs efficiently suppress bacterial colonization. Moreover, the intrinsic antioxidative, anti-inflammatory, and anticoagulation activities resulting from the preserved functional groups of the precursor molecule on the CCM-CPDs prevent progression to severe sepsis. As a result, infected mice treated with CCM-CPDs show a significant decrease in mortality even through oral administration. Histological staining indicates negligible organ damage in the MRSA-infected mice treated with CCM-CPDs. It is believed that the in vivo studies presented herein demonstrate that multifunctional therapeutic CPDs hold great potential against life-threatening infectious diseases.

Ultrahigh-Efficacy VEGF Neutralization Using Carbonized Nanodonuts: Implications for Intraocular Anti-Angiogenic Therapy.

Ocular angiogenesis, associated with diseases such as retinopathy of prematurity and diabetic retinopathy, is a leading cause of irreversible vision loss. Herein, carbon nanodonuts (CNDs) with a donut-shaped structure are synthesized using sodium alginate (SA) and 1,8-diaminooctane (DAO) through a one-step thermal process. The formation of SA/DAO-CNDs occurs through a crosslinking reaction between SA and DAO, creating amide bonds followed by partial carbonization. In human retinal pigment epithelial cells exposed to H2 O2 or lipopolysaccharide, the SA/DAO-CNDs display a more than fivefold reduction in reactive oxygen species and proinflammatory cytokines, such as IL-6 and IL-1ß, when compared to carbonized nanomaterials produced exclusively from SA. Furthermore, the CNDs effectively inhibit vascular endothelial growth factor A-165 (VEGF-A165 )-induced cell migration and tube formation in human umbilical vein endothelial cells due to their strong affinity for VEGF-A165 , with a dissociation constant of 2.2 × 10-14  M, over 1600 times stronger than the commercial drug bevacizumab (Avastin). Trypsin digestion coupled with LC-MS/MS analysis reveals that VEGF-A165 interacts with SA/DAO-CNDs through its heparin-binding domain, leading to activity loss. The SA/DAO-CNDs demonstrate excellent biocompatibility and potent anti-angiogenic effects in chicken embryos and rabbit eyes. These findings suggest that SA/DAO-CNDs hold promise as a therapeutic agent for treating various angiogenesis-related ocular diseases.

Low-complexity characterized-long-short-term-memory-aided channel modeling for optical fiber communications.

In this work, a low-complexity data-driven characterized-long-short-term-memory (C-LSTM)-aided channel modeling technique is proposed for optical single-mode fiber (SMF) communications. To fully utilize the sequence correlation learning ability of traditional long short-term memory (LSTM) networks and solve the gradient explosion problem, the feature information is introduced into the traditional LSTM input layer to better characterize the intersymbol interference caused by dispersion in SMF modeling. The simulation results show that the proposed C-LSTM can effectively alleviate the gradient explosion problem with a stable and ultimately lower mean square error (MSE) than traditional LSTM. Compared with the split-step Fourier method (SSFM) and the conditional generative adversarial network (CGAN), the proposed C-LSTM has superior computational complexity. Moreover, due to the sequence correlation learning ability inherent to C-LSTM, coupled with the flexibility of feature information selection, the proposed C-LSTM-aided modeling technique has a higher modeling accuracy than traditional LSTM. Moreover, the C-LSTM-aided modeling technique can be effectively extended to other channel modeling applications with strong sequence correlations.

Bayesian generative adversarial network emulator based end-to-end learning strategy of the probabilistic shaping for OAM mode division multiplexing IM/DD transmission.

Orbital angular momentum (OAM) mode division multiplexing (MDM) has emerged as a new multiplexing technology that can significantly increase transmission capacity. In addition, probabilistic shaping (PS) is a well-established technique that can increase the transmission capacity of an optical fiber to close to the Shannon limit. However, both the mode coupling and the nonlinear impairment lead to a considerable gap between the OAM-MDM channel and the conventional additive white Gaussian noise (AWGN) channel, meaning that existing PS technology is not suitable for an OAM-MDM intensity-modulation direct-detection (IM-DD) system. In this paper, we propose a Bayesian generative adversarial network (BGAN) emulator based on an end-to-end (E2E) learning strategy with probabilistic shaping (PS) for an OAM-MDM IM/DD transmission with two modes. The weights and biases of the BGAN emulator are treated as a probability distribution, which can be accurately matched to the stochastic nonlinear model of OAM-MDM. Furthermore, a BGAN emulator based on an E2E learning strategy is proposed to find the optimal probability distribution of PS for an OAM-MDM IM/DD system. An experiment was conducted on a 200 Gbit/s two OAM modes carrierless amplitude phase-32(CAP-32) signal over a 5 km ring-core fiber transmission, and the results showed that the proposed BGAN emulator outperformed a conventional CGAN emulator, with improvements in modelling accuracy of 29.3% and 26.3% for the two OAM modes, respectively. Moreover, the generalized mutual information (GMI) of the proposed E2E learning strategy outperformed the conventional MB distribution and the CGAN emulator by 0.31 and 0.33 bits/symbol and 0.16 and 0.2 bits/symbol for the two OAM modes, respectively. Our experimental results demonstrate that the proposed E2E learning strategy with the BGAN emulator is a promising candidate for OAM-MDM IM/DD optic fiber communication.

Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning.

Introduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and Methods: We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People's Hospital from the renal disease treatment information system. IDH was defined as a systolic blood pressure drop ≥20 mm Hg, a mean arterial pressure drop ≥10 mm Hg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation was used to explain the contribution of each variable to the best predictive model. Results: A total of 3,906 patients and 314,534 dialysis sessions were included, of which 142,237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval [CI], 0.811-0.813), 0.748 (95% CI, 0.747-0.749), and 0.743 (95% CI, 0.742-0.744), respectively. Conclusion: Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions.

NIR-activated quercetin-based nanogels embedded with CuS nanoclusters for the treatment of drug-resistant biofilms and accelerated chronic wound healing.

We have developed multifunctional nanogels with antimicrobial, antioxidant, and anti-inflammatory properties, facilitating rapid wound healing. To prepare the multifunctional nanogels, we utilized quercetin (Qu) and a mild carbonization process to form carbonized nanogels (CNGs). These CNGs possess excellent antioxidative and bacterial targeting properties. Subsequently, we utilized the Qu-CNGs as templates to prepare nanogels incorporating copper sulfide (CuS) nanoclusters, further enhancing their functionality. Notably, the CuS/Qu-CNGs nanocomposites demonstrated an exceptional minimum inhibitory concentration against tested bacteria, approximately 125-fold lower than monomeric Qu or Qu-CNGs. This enhanced antimicrobial effect was achieved by leveraging near-infrared II (NIR-II) light irradiation. Additionally, the CuS/Qu-CNGs exhibited efficient penetration into the extracellular biofilm matrix, eradicating methicillin-resistant Staphylococcus aureus-associated biofilms in diabetic mice wounds. Furthermore, the nanocomposites were found to suppress proinflammatory cytokines, such as IL-1ß, at the wound sites while regulating the expression of anti-inflammatory factors, including IL-10 and TGF-ß1, throughout the recovery process. The presence of CuS/Qu-CNGs promoted angiogenesis, epithelialization, and collagen synthesis, thereby accelerating wound healing. Our developed CuS/Qu-CNGs nanocomposites have great potential in addressing the challenges associated with delayed wound healing caused by microbial pathogenesis.

Imaging Extreme Ultraviolet Radiation Using Nanodiamonds with Nitrogen-Vacancy Centers.

Extreme ultraviolet (EUV) radiation with wavelengths of 10-121 nm has drawn considerable attention recently for its use in photolithography to fabricate nanoelectronic chips. This study demonstrates, for the first time, fluorescent nanodiamonds (FNDs) with nitrogen-vacancy (NV) centers as scintillators to image and characterize EUV radiations. The FNDs employed are ∼100 nm in size; they form a uniform and stable thin film on an indium-tin-oxide-coated slide by electrospray deposition. The film is nonhygroscopic and photostable and can emit bright red fluorescence from NV0 centers when excited by EUV light. An FND-based imaging device has been developed and applied for beam diagnostics of 50 nm and 13.5 nm synchrotron radiations, achieving a spatial resolution of 30 µm using a film of ∼1 µm thickness. The noise equivalent power density is 29 µW/(cm2 Hz1/2) for the 13.5 nm radiation. The method is generally applicable to imaging EUV radiation from different sources.

Hidden conditional random field-based equalizer for the 3D-CAP-64 transmission of OAM mode-division multiplexed ring-core fiber communication.

As a key technique for achieving ultra-high capacity optical fiber communications, orbital angular momentum (OAM) mode-division multiplexing (MDM) is affected by severe nonlinear impairments, including modulation related nonlinearities, square-law nonlinearity and mode-coupling-induced nonlinearity. In this paper, an equalizer based on a hidden conditional random field (HCRF) is proposed for the nonlinear mitigation of OAM-MDM optical fiber communication systems with 20 GBaud three-dimensional carrierless amplitude and phase modulation-64 (3D-CAP-64) signals. The HCRF equalizer extracts the stochastic nonlinear feature of the OAM-MDM 3D-CAP-64 signals by estimating the conditional probabilities of the hidden variables, thereby enabling the signals to be classified into subclasses of constellation points. The nonlinear impairment can then be mitigated based on the statistical probability distribution of the hidden variables of the OAM-MDM transmission channel in the HCRF equalizer. Our experimental results show that compared with a convolutional neural network (CNN)-based equalizer, the proposed HCRF equalizer improves the receiver sensitivity by 2 dB and 1 dB for the two OAM modes used here, with l = + 2 and l = + 3, respectively, at the 7% forward error correction (FEC) threshold. When compared with a Volterra nonlinear equalizer (VNE) and CNN-based equalizer, the computational complexity of the proposed HCRF equalizer was found to be reduced by 30% and 41%, respectively. The bit error ratio (BER) performance and reduction in computational complexity indicate that the proposed HCRF equalizer has great potential to mitigate nonlinear distortions in high-speed OAM-MDM fiber communication systems.

Nanodiamonds-in-oil emulsions elicit potent immune responses for effective vaccination and therapeutics.

Background: The use of nanodiamonds (NDs) and fluorescent nanodiamonds (FNDs) as nonallergenic biocompatible additives in incomplete Freund's adjuvant (IFA) to elicit immune responses in vivo was investigated. Methods: C57BL/6 mice were immunized with chicken egg ovalbumin (OVA) in IFA and also OVA-conjugated NDs (or OVA-conjugated FNDs) in IFA to produce antibodies. OVA-expressing E.G7 lymphoma cells and OVA-negative EL4 cells were inoculated in mice to induce tumor formation. Results: The new formulation significantly enhanced immune responses and thus disease resistance. It exhibited specific therapeutic activities, effectively inhibiting the growth of E.G7 tumor cells in mice over 35 days. Conclusion: The high biocompatibility and multiple functionalities of NDs/FNDs render them applicable as active and trackable vaccine adjuvants and antitumor agents.