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Background: Given that there is no specific drug to treat Alzheimer’s disease, non-pharmacologic interventions in people with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are one of the most important treatment strategies. Objective: To clarify the efficacy of blue-green (500 nm) light therapy on sleep, mood, and physiological parameters in patients with SCD and aMCI is an interesting avenue to explore. Methods: This is a monocentric, randomized, and controlled trial that will last for 4 weeks. We will recruit 150 individuals aged 45 years or older from memory clinics and divide them into 5 groups: SCD treatment (n = 30), SCD control (n = 30), aMCI treatment (n = 30), aMCI control (n = 30), and a group of healthy adult subjects (n = 30) as a normal control (NC). Results: The primary outcome is the change in subjective and objective cognitive performance between baseline and postintervention visits (4 weeks after baseline). Secondary outcomes include changes in performance assessing from baseline, postintervention to follow-up (3 months after the intervention), as well as sleep, mood, and physiological parameters (including blood, urine, electrophysiology, and neuroimaging biomarkers). Conclusion: This study aims to provide evidence of the impact of light therapy on subjective and objective cognitive performance in middle-aged and older adults with SCD or aMCI. In addition, we will identify possible neurophysiological mechanisms of action underlying light therapy. Overall, this trial will contribute to the establishment of light therapy in the prevention of Alzheimer’s disease.

We present a computational framework for modeling and optimizing complex assemblies using cone joints. Cone joints are integral joints that generalize traditional single-direction joints such as mortise and tenon joints to support a general cone of directions for assembly. This additional motion flexibility not just reduces the risk of deadlocking for complex joint arrangements, but also simplifies the assembly process, in particular for automatic assembly by robots. On the other hand, compared to planar contacts, cone joints restrict relative part movement for improved structural stability. Cone joints can be realized in the form of curved contacts between associated parts, which have demonstrated good mechanical properties such as reduced stress concentration. To find the best trade-off between assemblability and stability, we propose an optimization approach that first determines the optimal motion cone for each part contact and subsequently derives a geometric realization of each joint to match this motion cone. We demonstrate that our approach can optimize cone joints for assemblies with a variety of geometric forms, and highlight several application examples.

In recent years, artificial intelligence supported by big data has gradually become more dependent on deep reinforcement learning. However, the application of deep reinforcement learning in artificial intelligence is limited by prior knowledge and model selection, which further affects the efficiency and accuracy of prediction, and also fails to realize the learning ability of autonomous learning and prediction. Metalearning came into being because of this. Through learning the information metaknowledge, the ability to autonomously judge and select the appropriate model can be formed, and the parameters can be adjusted independently to achieve further optimization. It is a novel method to solve big data problems in the current neural network model, and it adapts to the development trend of artificial intelligence. This article first briefly introduces the research process and basic theory of metalearning and discusses the differences between metalearning and machine learning and the research direction of metalearning in big data. Then, four typical applications of metalearning in the field of artificial intelligence are summarized: few-shot learning, robot learning, unsupervised learning, and intelligent medicine. Then, the challenges and solutions of metalearning are analyzed. Finally, a systematic summary of the full text is made, and the future development prospect of this field is assessed.

BACKGROUND: Given the high prevalence of coronavirus disease 2019 (COVID-19) globally, and the increased number of patients being discharged, it is necessary to understand the health consequences of COVID-19 to formulate and manage public policy concerning convalescent patients. METHODS: A longitudinal follow-up investigation of 25 patients from a tertiary hospital in Henan provincial was conducted 8 weeks after discharge. Of these patients, 15 attended a second follow-up appointment 8 weeks after that. A throat swab reverse transcription-polymerase chain reaction (RT-PCR) analysis for SARS-CoV-2 and chest computerized tomography (CT) scans were implemented at the first follow-up appointment, and a total of 40 blood samples (25 from the first and 15 from the second follow-up appointment) were collected. Patients’ levels of Immunoglobulin G (IgG) antibody against S-Receptor binding domain (S-RBD) and Nucleocapsid Protein (NP) of SARS-CoV-2 and the subpopulation of lymphocytes were evaluated using an enzyme-linked immunoassay (ELISA) test and flow cytometry, respectively. RESULTS: At the first follow-up appointment, 10 of the 25 patients (40.0%) showed complete radiological resolution. Of these patients, 80.0% were classified as moderate, and 80.0% were younger (those whose age was ≤ the median age of all the patients). The predominant patterns of abnormalities included an irregular line (12/25, 48.0%), ground-glass opacity (GGO) (44.0%), and multiple GGOs (28.0%). At the first follow-up appointment, 40.0% (10/25) of patients still had lymphopenia. Of the 15 patients who were followed-up with twice, the ratio of lymphopenia was 80% (12/15), 60.0% (9/15), and 46.7% (7/15) at 0, 8, and 16 weeks after discharge, respectively. This was mainly due to a decrease in the cluster of differentiation (CD) 4+ T lymphocyte, which was observed in 60% (9/15), 60% (9/15), and 46.7% (7/15) of total patients at 0, 8, and 16 weeks after discharge, respectively. All of the patients were S-RBD and NP IgG antibody positive at the first follow-up appointment. 40.0% (6/15) and 66.7% (10/15) of patients showed a decrease over 50.0% in the level of NP and S-RBD IgG antibodies, respectively, at the second follow-up appointment. The NP and S-RBD IgG antibodies’ levels declined to 44.6% (P=0.044) and 28.1% (P=0.18), respectively. 0 and 26.7% (4/15) of patients turned from NP and S-RBD IgG antibody positive to negative, respectively. CONCLUSIONS: About half of the patients still showed at least 1 abnormality in chest CT scans 8 weeks after discharge and lymphopenia 16 weeks after discharge. The level of the IgG antibody had declined by the follow-up appointment. Notably, the S-RBD IgG antibody declined more dramatically than that of NP. These results may have implications in the making of policies related to disease prevention, the long-term management of discharged patients, and vaccines’ development.

Alternating current light-emitting diodes (AC-LEDs) set up a new era of economic semiconductor-based lighting. However, the flicker of AC-LEDs in the AC cycles is unhealthy for human eyes. In this work, the red-emitting afterglow phosphor Ca4Nb2O9:Pr3+ was successfully synthesized via a traditional high temperature solid-state reaction method. The phase composition, photoluminescence spectra, afterglow curve, and thermoluminescence were investigated. It was revealed that the red emission (617 nm) is ascribed to the 3P0 → 3H6 transition of Pr3+ ions, and the afterglow time was in the second range after removal of the excitation source. In order to gain a deeper understanding of the origin of afterglow, a subsequent thermoluminescence analysis was performed and a relatively shallow trap within the host was found. These results indicated that Ca4Nb2O9:Pr3+ phosphors could compensate for the flickering behavior of AC-LEDs and may be a candidate for application in AC-LEDs.

Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism. Seven tasks from the Movement Disorders Society - Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification.

We report on a comprehensive analysis of the gut microbiomes of patients with gastrointestinal (GI) cancer receiving anti–PD-1/PD-L1 treatment. The human gut microbiota has been associated with clinical responses to anti–PD-1/PD-L1 immunotherapy in melanoma, non–small cell lung cancer, and renal cell carcinoma. We aimed to investigate this association in GI cancers. We also identified bacterial taxa with patient stratification potential. We recruited 74 patients with advanced-stage GI cancer receiving anti–PD-1/PD-L1 treatment and collected their fecal samples prior to and during immunotherapy, along with clinical evaluations. Our 16S rRNA taxonomy survey on the fecal samples revealed an elevation of the Prevotella/Bacteroides ratio in patients, with a preferred response to anti–PD-1/PD-L1 treatment, and a particular subgroup of responders harboring a significantly higher abundance of Prevotella, Ruminococcaceae, and Lachnospiraceae. The shotgun metagenomes of the same samples showed that patients exhibiting different responses had differential abundance of pathways related to nucleoside and nucleotide biosynthesis, lipid biosynthesis, sugar metabolism, and fermentation to short-chain fatty acids (SCFA). Gut bacteria that were capable of SCFA production, including Eubacterium, Lactobacillus, and Streptococcus, were positively associated with anti–PD-1/PD-L1 response across different GI cancer types. We further demonstrated that the identified bacterial taxa were predictive of patient stratification in both our cohort and melanoma patients from two previously published studies. Our results thus highlight the impact of gut microbiomes on anti–PD-1/PD-L1 outcomes, at least in a subset of patients with GI cancer, and suggest the potential of the microbiome as a marker for immune-checkpoint blockade responses. See articles by Tomita et al., p. 1236, and Hakozaki et al., p. 1243

The biomass direct-fired power plant with carbon capture and sequestration is promising to remove CO2 from air whilst generate electricity. However, the efficiencies of such power plants are usually low, and the life cycle CO2 emission of such power plants is seldom determined. To solve these issues, a novel chemical looping oxy-fired power plant with carbon capture and sequestration is proposed in this work. The proposed system is then modeled and compared with the conventional biomass air-fired plant in terms of thermodynamics and economics. All the sub-unit models of the two power plants are validated by reported data in literature. Sensitivity analyses are then implemented to investigate the effects of different key operation parameters on the system essential performance indicators. Under the optimum conditions, the power generation efficiency, the levelized cost of electricity, the CO2 capture rate, the annual power generation and the annual CO2 mitigation of the proposed system (or the conventional system) are 35.7% (31.5%), 0.0522$/kWh (0.0601$/kWh), 100% (98%), 1443.7 × 109 kWh/year (1241.81 × 109 kWh/year) and 1.191 × 109 t/year (1.159 × 109 t/year), respectively. The key findings of this work are of reference value for the construction, operation and optimization of the biomass direct-fired power plants with carbon capture and sequestration.

Self-powered wearable sweat-lactate analyzer has been developed for training analysis of rowing. Tetrapodshaped ZnO nanowires are attached onto ordinary textiles to form a device. Based on the coupling of enzymatic reaction (lactate oxidase and lactic acid) and piezoelectric effect, the device can be attached to the tester to monitor the driving frequency, rowing distance and sweat lactic acid concentration of the tester in real-time. The relationship between the rowing frequency and physiological state is obtained by monitoring the rowing process of the tester, which is helpful to formulate a training plan belonging to the rowing tester. This study provides a new direction for the scientific monitoring of kinematics and accelerates the development of wearable sports equipment.

Image-guided surgery plays a crucial role in realizing complete tumor removal, reducing postoperative recurrence and increasing patient survival. However, imaging of tumor lesion in the typical metabolic organs, e.g., kidney and liver, still has great challenges due to the intrinsic nonspecific accumulation of imaging probes in those organs. Herein, we report an in situ self-assembled near-infrared (NIR) peptide probe with tumor-specific excretion-retarded (TER) effect in tumor lesions, enabling high-performance imaging of human renal cell carcinoma (RCC) and achieving complete tumor removal, ultimately reducing postoperative recurrence. The NIR peptide probe first specifically recognizes αvβ3 integrin overexpressed in renal cancer cells, then is cleaved by MMP-2/9, which is up-regulated in the tumor microenvironment. The probe residue spontaneously self-assembles into nanofibers that exhibit an excretion-retarded effect in the kidney, which contributes to a high signal-to-noise (S/N) ratio in orthotopic RCC mice. Intriguingly, the TER effect also enables precisely identifying eye-invisible tiny lesions (

Background A retrospective analysis verified the role of gene mutations in brain metastasis in patients with non‐small cell lung cancer (NSCLC). Methods Data from 552 patients with advanced NSCLC treated from January 2015 to June 2017 in the Affiliated Cancer Hospital of Zhengzhou University were retrospectively analyzed. Next‐generation sequencing was used to detect mutations in eight reported driver genes and various risk factors were evaluated. Results Of the 552 patients with advanced NSCLC, 153 (27.7%) had brain metastases. The univariate analysis showed that age (P = .008), gender (P = .016), smoking history (P = .010), lymph node metastasis (P = .003), and three driver genes, positive epidermal growth factor receptor (EGFR) mutation (P = .001), positive anaplastic lymphoma kinase (ALK) gene fusion (P = .021), and positive rearranged during transfection (RET) gene fusion (P = .003), were the factors influencing the incidence of brain metastasis. Logistic multivariate regression analysis revealed that positive EGFR mutation (P = .012), positive ALK gene fusion (P = .015), positive RET gene fusion (P = .003), pathological type (P = .009), lymph node N2‐3 metastasis (P, The incidence of brain metastasis in non‐small cell lung cancer (NSCLC) patients is 30%‐40%. Brain metastasis (BM) in NSCLC correlated with age, pathological type and lymph node N2‐3 metastasis. Epidermal growth factor receptor, anaplastic lymphoma kinase, and rearranged during transfection (RET) mutations were associated with BM in NSCLC patients.

Metabolic dysfunction (MD) is a major health problem threatening the life quality of menopausal women. Saffron has been widely used in herb prescriptions for treating menopausal syndrome. However, the pharmacological effects and mechanisms of saffron are poorly understood. Here, we investigated the effect of crocin, the major ingredient of saffron and its active metabolite in blood, crocetin, on MD and lipid metabolism in ovariectomized (OVX) mice and 3T3-L1 adipocytes. The present study showed that intragastric treatment of crocin prevented weight gain, fat accumulation, and insulin resistance in OVX mice by increasing energy expenditure and fat oxidation. Mechanistically, crocin influenced adipose tissue homeostasis by regulating adipogenic and lipolytic factors, which was strongly associated with the restoration of the downregulated ERβ function in white adipose tissue (WAT). In vitro, crocetin facilitated lipid metabolism in an ERβ-dependent manner. Our results demonstrated the beneficial effects of crocetin/crocin-mediated intervention against metabolic dysfunction, revealing a prospective therapeutic application in menopausal women.

Laser tweezers Raman spectroscopy (LTRS) combines optical tweezers technology and Raman spectroscopy to obtain biomolecular compositional information from a single cell without invasion or destruction, so it can be used to “fingerprint” substances to characterize numerous types of biological cell samples. In the current study, LTRS was combined with two machine learning algorithms, principal component analysis (PCA)-linear discriminant analysis (LDA) and random forest, to achieve high-precision multi-species blood classification at the single-cell level. The accuracies of the two classification models were 96.60% and 96.84%, respectively. Meanwhile, compared with PCA-LDA and other classification algorithms, the random forest algorithm is proved to have significant advantages, which can directly explain the importance of spectral features at the molecular level.

Castration-resistant prostate cancer (CRPC) and its metastases are the main reasons for the high mortality of prostate cancer. Currently, paclitaxel (PTX)-based chemotherapeutics are used as first-line drugs to treat CRPC, but this treatment does not show good effects and is accompanied by serious side effects, which may be because intravenously injected chemotherapeutic drugs have difficulties gathering at the tumor site. Therefore, a safe and effective drug delivery carrier is urgently needed to enhance the therapeutic effects of chemotherapeutic drugs against CRPC. Methoxy polyethylene glycol-polylacticco-glycolic acid-polylysine (mPEG-PLGA-PLL) nanoparticles (NPs) have shown high drug encapsulation efficiency and good therapeutic effects against ovarian cancer and pancreatic cancer, but there are few studies on their treatment against CRPC. To expand the applications of mPEG-PLGA-PLL NPs, in this study, mPEG-PLGA-PLL NPs loaded with PTX (PTX-NPs) were synthesized. The synthesized PTX-NPs had a uniform particle size and no obvious aggregation. PTX-NPs can be uptaked by PC-3 cells, which significantly promotes the inhibition of proliferation and apoptosis effects of PTX on cells and reduces the expression levels of CDK6, Cyclin D1 and Bcl-2 (cyclins and an apoptosis inhibitor), and these effects can be further enhanced by ultrasound-induced microbubble cavitation (UIMC). Our research provides a new nanocarrier for the treatment of CRPC, laying the foundation for further research in the future.

Text classification is a basic problem in the field of natural language processing. It is a very active research direction in the field of machine learning, and has many important practical applications. This paper mainly studies the application of semantic analysis in text classification of computer technology. In view of the fact that most current methods ignore the important correlation between features, this paper adopts the LSA method, which fully considers the semantic relationship between features and reduces the dimension of feature space, so as to construct a new semantic space. Experimental results show that the proposed method can effectively reduce the dimension of feature space and improve the performance of text classification.

The study of protein–protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardized terms that describe gene products from molecular functions, biological processes, and cell components. Previous studies on assessing the similarity of GO terms were primarily based on Information Content (IC) between GO terms to measure the similarity of proteins. However, these methods tend to ignore the structural information between GO terms. Therefore, considering the structural information of GO terms, we systematically analyze the performance of the GO graph and GO Annotation (GOA) graph in calculating the similarity of proteins using different graph embedding methods. When applied to the actual Human and Yeast datasets, the feature vectors of GO terms and proteins are learned based on different graph embedding methods. To measure the similarity of the proteins annotated by different GO numbers, we used Dynamic Time Warping (DTW) and cosine to calculate protein similarity in GO graph and GOA graph, respectively. Link prediction experiments were then performed to evaluate the reliability of protein similarity networks constructed by different methods. It is shown that graph embedding methods have obvious advantages over the traditional IC-based methods. We found that random walk graph embedding methods, in particular, showed excellent performance in calculating the similarity of proteins. By comparing link prediction experiment results from GO(DTW) and GOA(cosine) methods, it is shown that GO(DTW) features provide highly effective information for analyzing the similarity among proteins.

The ability to capture frequent (per millisecond) persistent snapshots to NVM would enable a number of compelling use cases. Unfortunately, existing NVM snapshotting techniques suffer from a combination of persistence barrier stalls, write amplification to NVM, and/or lack of scalability beyond a single socket. In this paper, we present NVOverlay, which is a scalable and efficient technique for capturing frequent persistent snapshots to NVM such that they can be randomly accessed later. NVOverlay uses Coherent Snapshot Tracking to efficiently track changes to memory (since the previous snapshot) across multi-socket parallel systems, and it uses Multi-snapshot NVM Mapping to store these snapshots to NVM while avoiding excessive write amplification. Our experiments demonstrate that NVOverlay successfully hides the overhead of capturing these snapshots while reducing write amplification by 29%-47% compared with state-of-the-art logging-based snapshotting techniques.

The app economy is largely reliant on data collection as its primary revenue model. To comply with legal requirements, app developers are often obligated to notify users of their privacy practices in privacy policies. However, prior research has suggested that many developers are not accurately disclosing their apps’ privacy practices. Evaluating discrepancies between apps’ code and privacy policies enables the identification of potential compliance issues. In this study, we introduce the Mobile App Privacy System (MAPS) for conducting an extensive privacy census of Android apps. We designed a pipeline for retrieving and analyzing large app populations based on code analysis and machine learning techniques. In its first application, we conduct a privacy evaluation for a set of 1,035,853 Android apps from the Google Play Store. We find broad evidence of potential non-compliance. Many apps do not have a privacy policy to begin with. Policies that do exist are often silent on the practices performed by apps. For example, 12.1% of apps have at least one location-related potential compliance issue. We hope that our extensive analysis will motivate app stores, government regulators, and app developers to more effectively review apps for potential compliance issues.

Here, we review systematically the applications of exciplex and thermally activated delayed fluorescence (TADF) materials as the host in organic light-emitting diodes (OLEDs), who is the charge transfer host based inter-molecular and intra-molecular, respectively. Exciplex and TADF materials present many advantages of natural bipolarity, small singlet-triplet splitting energy and low driving voltage to act as the host rather than emitter. The applications of exciplex and TADF materials as the host in phosphorescent, fluorescent and white OLEDs are given and new challenges, suggestions are also provided finally for further research and potential development of this area.

Developing a high-performance catalyst and establishing a catalytic mechanism for understanding the catalytic activity are crucial to new generation photovoltaic technology. In this work, we present a feasible and general route to synthesize a bio-based porous carbon (BPC) supported ZnNb2O6 hybrid catalyst with a unique network structure, providing an effective means for electron transport between the electrode and the external circuit. Benefitting from the synergistic effect of ZnNb2O6 and BPC, a photovoltaic device assembled with the nanohybrid yields a power conversion efficiency of 8.83%, which is superior to that of pristine ZnNb2O6-based and conventional Pt-based cells (7.15% and 7.14%). Systematic electrochemical evaluations of the hybrid catalysts exhibit promising stability for practical application in photovoltaics. Contraposing the two vital functions of the counter electrode catalyst, collecting electrons and catalyzing I-3(-) reduction, we propose a general strategy to understand the potential catalytic mechanism from the band structure and surface adsorption by using first-principles density functional theory (DFT) calculations. The theoretical investigations clearly indicate that the splendid catalytic performance originates from the zero band-gap of surface metal atoms and the surface chemical adsorption interaction between I-3(-) and exposed metal atoms. The proposed general strategy in this work for synthesizing a hybrid material with a unique network structure and understanding the catalytic mechanism of the electrocatalyst can guide the design of expected catalytic nanohybrids applied in various energy fields.

The increased availability of on-body sensors gives researchers access to rich time-series data, many of which are related to human health conditions. Sharing such data can allow cross-institutional collaborations that create advanced data-driven models to make inferences on human well-being. However, such data are usually considered privacy-sensitive, and publicly sharing this data may incur significant privacy concerns. In this work, we seek to protect clinical time-series data against membership inference attacks, while maximally retaining the data utility. We achieve this by adding an imperceptible noise to the raw data. Known as adversarial perturbations, the noise is specially trained to force a deep learning model to make inference mistakes (in our case, mispredicting user identities). Our preliminary results show that our solution can better protect the data from membership inference attacks than the baselines, while succeeding in all the designed data quality checks.

A 70-year-old man with a history of invasive anal squamous cell carcinoma treated with excision and chemoradiation presented to the emergency department with right-sided neck pain and submandibular lymphadenopathy. CT imaging of the head and neck was unrevealing. The patient eventually developed cranial nerves X and XI dysfunction, manifesting as severe vocal cord paralysis (dysphonia), dysphagia, asymmetric palate elevation/deviation and trapezius muscle atrophy, in addition to scalene muscle atrophy. After an extensive workup, the patient’s symptoms were determined to be due to sequelae of varicella zoster infection, which was confirmed with antibody titers. The patient’s dysphagia and dysphonia eventually improved with vocal cord medialisation injection and Botox injection. However, despite delayed treatment with acyclovir and valacyclovir, the patient continued to have neuropathic pain and exhibit signs of CN X and CN XI paresis, in addition to scalene muscle atrophy.

Chest X-rays play a significant role in diagnosing pneumonia due to the technology's cost-effectiveness and rapid development times. Detecting pneumonia in chest Xrays is a challenging process that relies heavily upon the availability of trained radiologists and high-quality imagery. Training qualified interpreters require significant resources, while medical imaging remains prone to a wide variety of deficiencies. Therefore, an automated system for pneumonia diagnosis consisting of three phases is proposed. An initial sorting phase consisting of a trained ResNet-18 convolutional neural network separates the dataset according to the interpretive quality of the images, creating a high and low-quality class. The unique image translation capabilities of the CycleGAN network are leveraged in the enhancement phase to translate low-quality images into improved versions. A final ResNet-18 network serves to classify pneumonia in the diagnosis phase. The enhancement system improved mixed quality diagnosis accuracy by 12.1% to 86.7%, with training sets composed of enhanced images achieving an accuracy 15.8% higher than their low-quality counterparts. The system's generalized method for image augmentation successfully mitigates the deficiencies of low-quality data, allowing for a higher accuracy diagnosis than otherwise possible.

Currently chemotherapy drugs are usually used as first-line treatments for castration-resistant prostate cancer (CRPC), but they are ineffective and accompanied by serious side effects. MicroRNA-34a (miR-34a) simultaneously targets multiple genes related to the cell apoptosis in CRPC cells without obvious side effects. It has shown great potential in the treatment of CRPC. Previous studies focused on miR-34a increasing the sensitivity of chemotherapy drugs to chemoresistant prostate cancer cells. There are few researches on miR-34a alone in the treatment of CRPC. But the macromolecular miR-34a is difficult to enter the cell and is easily degraded by nuclease. Therefore, we constructed methoxy polyethylene glycol-polylacticco-glycolic acid-polylysine (mPEG-PLGA-PLL) nanoparticles to encapsulate miR-34a (miR-34a/NP). The results showed that miR-34a/NP protects miR-34a from degradation by nucleases and can be phagocytized by PC-3 CRPC cells. Ultrasound induces microbubble cavitation (UIMC) improves cell membrane permeability and capillary gaps, and further promotes miR-34a/NP to enter cells PC-3 and prostate cancer xenografts. The miR-34a/NP that enters the cell and tumor tissue releases miR-34a, which suppressed CRPC cells PC-3 proliferation, promoted its apoptosis, and inhibited the growth of CRPC xenografts. Our research verified that miR-34a/NP, especially combined with UIMC, has a significant anti-tumor effect on CRPC.

Since December 2019, the novel coronavirus SARS-CoV-2 pandemic (COVID-19) outbroke in Wuhan and spread in China. Here we aimed to investigate the clinical and radiological characteristics of COVID-19 cases. We collected and analyzed the clinical data of 172 hospitalized cases of COVID-19 who were diagnosed via qRT-PCR of nasopharyngeal swabs during January 2020 and February 2020. The chest images were reviewed by radiologists and respirologists. The older patients with COVID-19 in Henan Province had more severe disease and worse prognosis. The male sex, smoking history and Wuhan exposure of patients are not related to the severity or prognosis of COVID-19. Family gatherings were showed among 26.7% of patients. A greater proportion of patients in the severe group suffer from combined chronic diseases. CT results showed that most patients had bilateral lung lesions and multiple lung lobes. The lungs of severe patients are more damaged. Both the infection range and inflammatory factor levels are related to the poor prognosis. Antiviral drugs, immunoglobulin and traditional Chinese medicine are mainly used for the treatment of COVID-19 patients. The discharge rate of COVID-19 patients was 93.0%, and the mortality rate was 2.3%. Case type, lymphocyte ratio grade, and respiratory failure at admission are risk factors for poor prognosis, except for the number of infiltrating lung lobes. The results showed that severe disease process, lymphopenia and respiratory failure are risk factors for the COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s13577-021-00499-y.

In domain generalization, multiple labeled non-independent and non-identically distributed source domains are available during training while neither the data nor the labels of target domains are. Currently, learning so-called domain invariant representations (DIRs) is the prevalent approach to domain generalization. In this work, we define DIRs employed by existing works in probabilistic terms and show that by learning DIRs, overly strict requirements are imposed concerning the invariance. Particularly, DIRs aim to perfectly align representations of different domains, i.e. their input distributions. This is, however, not necessary for good generalization to a target domain and may even dispose of valuable classification information. We propose to learn so-called hypothesis invariant representations (HIRs), which relax the invariance assumptions by merely aligning posteriors, instead of aligning representations. We report experimental results on public domain generalization datasets to show that learning HIRs is more effective than learning DIRs. In fact, our approach can even compete with approaches using prior knowledge about domains., accepted at ICPR 2020

Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data. To this end, we propose CLEVE, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to learn event semantics and a graph encoder to learn event structures respectively. Specifically, the text encoder learns event semantic representations by self-supervised contrastive learning to represent the words of the same events closer than those unrelated words; the graph encoder learns event structure representations by graph contrastive pre-training on parsed event-related semantic structures. The two complementary representations then work together to improve both the conventional supervised EE and the unsupervised "liberal" EE, which requires jointly extracting events and discovering event schemata without any annotated data. Experiments on ACE 2005 and MAVEN datasets show that CLEVE achieves significant improvements, especially in the challenging unsupervised setting. The source code and pre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE., Comment: Accepted at ACL 2021

Patients undergoing many forms of cardiovascular surgery typically enter the cardiac intensive care unit (CICU) after surgery, transfer to a step down (SDn) unit, and then are ultimately either discharged or bounce back to the CICU because of deterioration. Randomness and unpredictability exist in these processes, especially the bounce back process. Underestimation of bounce back rates will result in a lack of bed capacity for patients and patient deferrals. Adding beds, however, will lead to an increase in cost. Therefore, trade-offs must be carefully considered between decreasing patients denied versus potential system costs when bounce back is introduced to the system. We present a discrete-event simulation model to assess how bounce back will impact assessment of bed capacity in the CICU and SDn and other major metrics of the system. We present analyses utilizing data from our collaborators at the Samuel and Jean Frankel Cardiovascular Center at Michigan Medicine.

An ability to detect, classify, and locate complex acoustic events can be a powerful tool to help smart systems build context-awareness, e.g., to make rich inferences about human behaviors in physical spaces. Conventional methods to measure acoustic signals employ microphones as sensors. As signals from multiple acoustic sources are blended during propagation to a sensor, such methods impose a dual challenge of separating the signal for an acoustic event from background noise and from other acoustic events of interest. Recent research has proposed using radio-frequency (RF) signals, e.g., Wi-Fi and millimeter-wave (mmWave), to sense sound directly from source vibrations. Whereas these works allow separating an acoustic event from background noise, they cannot monitor multiple sound sources simultaneously. In this paper, we present UWHear, a system that simultaneously recovers and separates sounds from multiple sources. Unlike previous works using continuous-wave RF, UWHear employs Impulse Radio Ultra-Wideband (IR-UWB) technology, in order to construct an enhanced audio sensing system tackling the above challenges. Further, IR-UWB radios can penetrate light building materials, which enables UWHear to operate in some non-line-of-sight (NLOS) conditions. In addition to providing a theoretical guarantee for audio recovery using RF pulses, we also implement an audio sensing prototype exploiting a commercial-off-the-shelf IR-UWB radar. Our experiments show that UWHear can effectively separate the content of two speakers that are placed only 25cm apart. UWHear can also capture and separate multiple sounds and vibrations of household appliances while being immune to non-target noise coming from other directions.

This paper based on literature review introduces two powerful techniques-mapper and persistent homology in topological data analysis (TDA) to compute a topological abstraction of the printing model in 3D printing area. After leading in the theory of these methods and application of mapper for 3D printing, this paper also learns about the advantage of TDA than traditional methods for capturing the whole structure of targeted objects especially in high dimensional data. At last this paper learns about an example which combines mapper and persistent homology using dragon dataset to get evaluation of the quality of3D printed objects and reaches certain results. In conclusion, TDA in 3D printing is very potential. However, this field need to be further investigated.

Collaboration in neuroscience is impeded by the difficulty of sharing primary data, results, and software across labs. Here we introduce Neuroscience Data Interface (NDI), a platform-independent standard that allows an analyst to use and create software that functions independently from the format of the raw data or the manner in which the data is organized into files. The interface is rooted in a simple vocabulary that describes common apparatus and storage devices used in neuroscience experiments. Results of analyses – and analyses of analyses – are stored as documents in a scalable, queryable database that stores the relationships and history among the experiment elements and documents. The interface allows the development of an application ecosystem where applications can focus on calculation rather than data format or organization. This tool can be used by individual labs to exchange and analyze data, and it can serve to curate neuroscience data for searchable archives.

Applying latent variable evolution to game level design has become more and more popular as little human expert knowledge is required. However, defective levels with illegal patterns may be generated due to the violation of constraints for level design. A traditional way of repairing the defective levels is programming specific rule-based repairers to patch the flaw. However, programming these constraints is sometimes complex and not straightforward. An autonomous level repairer which is capable of learning the constraints is needed. In this paper, we propose a novel approach, CNet, to learn the probability distribution of tiles giving its surrounding tiles on a set of real levels, and then detect the illegal tiles in generated new levels. Then, an evolutionary repairer is designed to search for optimal replacement schemes equipped with a novel search space being constructed with the help of CNet and a novel heuristic function. The proposed approaches are proved to be effective in our case study of repairing GAN-generated and artificially destroyed levels of Super Mario Bros. game. Our CNet-assisted evolutionary repairer can also be easily applied to other games of which the levels can be represented by a matrix of objects or tiles., Accepted at IEEE CEC2020

With the shift in hip fracture epidemiology toward older individuals as well as the shift in demographics toward nonagenarians, it is important to understand the outcomes of treatment for these patients.Geriatric patients (≥65 years old) who underwent surgery for hip fracture were identified in the 2005-2017 National Surgical Quality Improvement Program database and stratified into 2 age groups:90 and ≥90 years old (nonagenarians). Preoperative and procedural characteristics were compared. Multivariate regressions were used to compare risk for complications and 30-day readmissions. Risk factors for serious adverse events (SAEs) and 30-day mortality in nonagenarians were characterized.This study included 51,32790 year olds and 15,798 nonagenarians. Overall rate of SAEs in nonagenarians was 19.89% while in90 year olds was 14.80%. Multivariate analysis revealed higher risk for blood transfusion (relative risk [RR] = 1.21), death (RR = 1.74), pneumonia (RR = 1.24), and cardiac complications (RR = 1.33) in nonagenarians (all P.001). Risk factors for SAEs in nonagenarians include American Society of Anesthesiologists ≥3, dependent functional status, admitted from nursing home/chronic/intermediate care, preoperative hypoalbuminemia, and male gender (all P.05), but not time to surgery (P.05). In fact, increased time to surgery in nonagenarians was associated with lower risk of 30-day mortality (RR = 0.90, P = .048).Overall complication risk after hip fracture fixation in nonagenarians remains relatively low but higher than their younger counterparts. Interestingly, since time to surgery was not associated with adverse outcomes in nonagenarians, the commonly accepted 48-hour operative window may not be critical to this population. Additional time for preoperative medical optimization in this vulnerable population appears prudent.

Pneumothorax is a severe respiratory disease. In this study, an algorithm using a Deep Convolutional Neural Network (DCNN) is proposed to detect visual signs for Pneumothorax within x-ray images and conduct a diagnosis. Detecting and diagnosing Pneumothorax remains challenging despite of its prevalence. The deep residual network ResNet-101 was adapted through transfer learning. A database of 5,302 Pneumothorax radiographs was utilized for training, and a preliminary diagnosis accuracy of 86.26% was obtained. The area under the receiver operating characteristic curve (AUC) was 92.13%.

The flow fields generated by the acoustic behavior of microbubbles can significantly increase cell permeability. This facilitates the cellular uptake of external molecules in a process known as ultrasound-mediated drug delivery. To promote its clinical translation, this study investigated the relationships among the ultrasound parameters, acoustic behavior of microbubbles, flow fields, and delivery results. SonoVue microbubbles were activated by 1 MHz pulsed ultrasound with 100 Hz pulse repetition frequency, 1:5 duty cycle, and 0.20/0.35/0.70 MPa peak rarefactional pressure. Micro-particle image velocimetry was used to detect the microbubble behavior and the resulting flow fields. Then HeLa human cervical cancer cells were treated with the same conditions for 2, 4, 10, 30, and 60 s, respectively. Fluorescein isothiocyanate and propidium iodide were used to quantitate the rates of sonoporated cells with a flow cytometer. The results indicate that (1) microbubbles exhibited different behavior in ultrasound fields of different peak rarefactional pressures. At peak rarefactional pressures of 0.20 and 0.35 MPa, the dispersed microbubbles clumped together into clusters, and the clusters showed no apparent movement. At a peak rarefactional pressure of 0.70 MPa, the microbubbles were partially broken, and the remainders underwent clustering and coalescence to form bubble clusters that exhibited translational oscillation. (2) The flow fields were unsteady before the unification of the microbubbles. After that, the flow fields showed a clear pattern. (3)The delivery efficiency improved with the shear stress of the flow fields increased. Before the formation of the microbubble/bubble cluster, the maximum shear stresses of the 0.20, 0.35, and 0.70 MPa groups were 56.0, 87.5 and 406.4 mPa, respectively, and the rates of the reversibly sonoporated cells were 2.4% ± 0.4%, 5.5% ± 1.3%, and 16.6% ± 0.2%. After the cluster formation, the maximum shear stresses of the three groups were 9.1, 8.7, and 71.7 mPa, respectively. The former two could not mediate sonoporation, whereas the last one could. These findings demonstrate the critical role of flow fields in ultrasound-mediated drug delivery and contribute to its clinical applications.

In the near future, the Space Information Network (SIN) will evolve into Space-earth Integrated Network, which will realize the interconnection among the Space-based Network, traditional Internet and Mobile Communication Network. The construction of the future SIN will provide the ability of global real-time communication and comprehensive information services, which makes it possess a wide application prospect. The future SIN will realize inter-satellite routing and its network capacity will be flexible, its network topology and link status will change dynamically as well, which will increase the complexity of the network management. In addition, the openness of communication link and the resources limitation of the devices will both bring security threats to the SIN. We study on the characteristics of future SIN and present a dynamic security protection architecture based on SDN (Software Defined Network). We study on the trusted authentication mechanism of dynamic networking entities and the technique of path optimization and risky-path isolated transmission. At last, we design a network intelligent security management mechanism at the top level. Our scheme can greatly improve the security of future SIN.

Background: SARS-CoV-2 is causing a worldwide health crisis We aimed to present clinical and CT features for a cohort of patients in Henan, China with more foc

Interfacial evaporation is gaining popularity as a facile and effective method for harvesting solar energy and yielding freshwater from sewage and seawater. However, challenges exist for achieving a balance among a plethora of performance metrics, e.g., low cost, high evaporation efficiency, off-grid deployment, and negligible environmental impact. In this study, a hydrogel evaporator is prepared by combining agar, naturally abundant ocean biomass, with titanium nitride nanoparticles. Due to the excellent photothermal conversion effect of titanium nitride nanoparticles, the evaporator shows a solar absorptance of 0.98. This evaporator has vertically aligned water channels and is fabricated by an ice template-induced self-assembly method, enabling the formation of a biomimetic wood structure. The rapid water transport and salt drainage within its aligned channels, effective water activation in hydrogel molecular meshes, and efficient heat localization allow this hydrogel evaporator to achieve an evaporation rate of 5.15 kg m−2 h−1 under the irradiance of one sun (1 kW m−2). The indirect water supply method using cotton wipe as the water transportation channels and polystyrene foam as the thermal barrier efficiently localize heat within the top small evaporation region, which ensures such a high evaporation rate. Moreover, this hydrogel evaporator is easy to be recycled without performance reduction to achieve an extended lifespan with the advantage of facile thermal recyclability after long-term utilization. The freeze-thawing fabrication of this hydrogel evaporator is feasible for scalable deployment. This work offers new possibilities for high-quality freshwater yields with cost-effective raw materials and deployable solar desalination systems for industrial implementations.

Cost-effective and spectrally selective solar absorbers that possess high solar absorptance, low thermal emittance, and superior thermal stability are essential for photothermal conversion applications, e.g., industrial heating, solar desalination, photothermal catalysis, and concentrating solar power systems. Here, a new strategy using a selective leaching reaction is demonstrated for transfiguring the broad-spectrum and highly reflective aluminum alloys into plasmonic-nanostructure selective solar absorbers (PNSSAs). Enabled by surface plasmon resonance, this strategy via assembling copper nanostructured thin film on an alloy mirror yields tunable manipulation of the spectral selectivity, high and omnidirectional solar absorptance (0.94 from 0 to 60°), low thermal emittance (0.03 at 100 °C), and excellent thermomechanical stability. Featured with merits of competitive performance of spectral selectivity, the feasibility of solution-processed fabrication, and cost-effectiveness of raw materials and chemicals, selective-leaching-alloy to achieve PNSSAs is a promising and universal approach for achieving high photothermal efficiency (85%) of solar thermal energy harvesting. The compatibility of this strategy with other metal alloys, such as steel and superalloys, extends its applications to fabricating mid- and high-temperature selective solar absorbers.

We aim to simplify the device structure due to the low concentration precision and repeatability of white organic light-emitting diodes (WOLEDs) based on exciplex host with phosphorescent dopant. Two layers of exciplex used here play the role of blue emitter and excitons adjustment layer, respectively, to manage the excitons. Green and red phosphorescent ultrathin layer are inserted into exciplex layer to form white light-emitting. The maximum efficiency values of current efficiency, power efficiency and EQE reach to 38.8 cd/A, 34.9 lm/W and 15.3%, and remain 29.7 cd/A, 20.4 lm/W and 11.6% at 1000 cd/m2, respectively. Besides, the change of Commission Internationale de I'Eclairage coordinates is only (0.024, 0.013) with the enhanced driving voltages from 4 V to 6 V. Such a high performance of warm WOLEDs with simple structure demonstrates the reasonable excitons management and efficient energy transfer from exciplex and excitons adjustment layer to ultrathin phosphorescent emitters.

Highly efficient red and white organic light-emitting diodes (OLEDs) employing blue thermally activated delayed fluorescent (TADF) emitter of mSOAD based acridine/sulfone derivative as the host are realized by efficient management for singlet and triplet excitons. The red OLEDs reach a high maximum current efficiency, power efficiency and EQE of 32.7 cd A−1, 31.0 lm W−1 and 20.3%, respectively. And the white OLEDs with different dopant concentrations exhibit ultra-high color stability of ±(0.008, 0.001), ±(0.000, 0.003) and ±(0.002, 0.005), respectively. Meantime, the high efficiencies are also achieved with maximum current efficiencies, power efficiencies and EQEs of 28.2–32.0 cd A−1, 26.3–30.5 lm W−1 and 12.2–17.4%, respectively. The analysis of energy level and transient decay characteristics demonstrate the excellent device performances are mainly stemmed from the sufficient confinement for charges and excitons and efficient energy transfer from the TADF host to dopant.

The creation of an artificial solid-state ion pump that mimics the delicate ion transport behaviors of a biological protein-based ion pump is drawing more and more research attention due to its potential applications in energy conversion, biosensor, and desalination. However, the reported bioinspired double-gated ion pump systems are generally very primary and can only realize nonselective ion pumping functions with no directionality and uncontrollable ion gating functions, which are far from their biological counterparts. To make the bioinspired device "smart" in a real sense, the implementation of high-level selectivity and directionality in the ion pumping process, while achieving great controllability in the ion gating process, is a necessity. Here, we developed a bioinspired heterogeneous ion pump membrane by combining block copolymer membrane sacrificial coating and plasma grafting technique. The system has unidirectional selective ion pumping and controllable ion gating properties. The introduction of asymmetric ionic group distribution is the key reason for its novel transport behaviors. Such a heterogeneous ion pump could not only provide a basic platform that potentially sparks further efforts to simulate the smart ion transport processes in living bodies but also promote the application of artificial nanofluidic devices in energy conversion, water treatment, and biosensing.

Background/Aims: Emerging novel optical imaging techniques with cancer-specific molecular imaging agents offer a powerful and promising platform for cancer detection and resection. White-light cystoscopy and random bladder biopsies remain the most appropriate but nonetheless suboptimal diagnostic technique for bladder cancer, which is associated with high morbidity and recurrence. However, white-light cystoscopy has intrinsic shortcomings. Although current optical imaging technologies hold great potential for improved diagnostic accuracy, there are few imaging agents for specific molecular targeting. Carbonic anhydrase IX (CAIX) plays a pivotal role in tumorigenesis and tumor progression with potential value as an imaging target. Here, we investigated the feasibility of CAIX as a target and validated the diagnostic performance and significance of CAIX as an imaging agent. Methods: We first analyzed the data from The Cancer Genome Atlas (TCGA). Pairs of samples comprising bladder cancer and adjacent normal tissue were collected. All tissue samples were used for real-time PCR and immunohistochemistry to compare CAIX expression in normal and cancer tissue. Using blue-light cystoscopy, we observed the optical distribution of fluorescently labeled CAIX antibody in freshly excised human bladders and obtained random bladder biopsies to assess sensitivity and specificity. Results: The TCGA data revealed that CAIX expression was significantly higher in bladder cancer specimens than in normal tissue. The outcome was similar in quantitative real-time PCR analysis. In immunohistochemical analysis, bladder cancer specimens classified in four pathological subtypes presented a variety of positive staining intensities, whereas no benign specimens showed CAIX staining. Using blue-light cystoscopy, we distinguished bladder cancers that were mainly papillary, some variants of urothelial carcinoma, and less carcinoma in situ, from benign tissue, despite the presence of suspicious-appearing mucosa. The sensitivity and specificity for CAIX-targeted imaging were 88.00% and 93.75%, respectively. Conclusions: CAIX-targeted molecular imaging could be a feasible and adaptive alternative approach for the accurate diagnosis and complete resection of bladder cancer.

High color stability and CRI (>80) pure exciplex WOLEDs with merely complementary colors of orange- and blue-exciplexes are realized with the application of spacers. The WOLEDs with a three spacer structure achieve a maximum current efficiency, power efficiency and external quantum efficiency (EQE) of 16.2 cd A−1, 11.3 lm W−1 and 7.92%, respectively. Besides, a standard white light point close to Commission Internationale de l’Eclairage (CIE) coordinates of (0.31 ± 0.00, 0.37 ± 0.02) and a very high color rendering index (CRI) of ∼83 with two colors emitting are obtained simultaneously. The balanced emission and natural broad emission band of the exciplex are responsible for the stable white light spectra and high CRI. We also find that the location of the spacers, and the amount of them, play a key role in the electroluminescence performance of the WOLEDs and more detailed discussions are given below.

Pure fluorescent WOLEDs with low efficiency roll-off and high color stability were realized by employing an exciplex host. Due to incomplete energy transfer from the blue exciplex host of mCP : PO-T2T to the orange fluorescent dopant of rubrene, the WOLEDs showed a maximum current efficiency, power efficiency and EQE of 14.2 cd A−1, 12.8 lm W−1 and 4.90%, respectively. To our surprise, a rather small roll-off ratio of 8.2% from the maximum EQE to the EQE at 1000 cd m−2 and stable white light-emitting spectra with CIE coordinates of (0.384 ± 0.001, 0.439 ± 0.002) from 4 V to 8 V were obtained simultaneously. The bipolarity and triplet exciton up-conversion of the exciplex host played key roles in delivering excellent performance. More detailed discussions are also provided.