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Three-dimensional printing technology with the rapid development of printing materials are widely recognized as a promising way to fabricate bioartificial bone tissues. In consideration of the disadvantages of bone substitutes, including poor mechanical properties, lack of vascularization and insufficient osteointegration, functional modification strategies can provide multiple functions and desired characteristics of printing materials, enhance their physicochemical and biological properties in bone tissue engineering. Thus, this review focuses on the advances of functional engineering strategies for 3D printed biomaterials in hard tissue replacement. It is structured as introducing 3D printing technologies, properties of printing materials (metals, ceramics and polymers) and typical functional engineering strategies utilized in the application of bone, cartilage and joint regeneration.

ObjectivesTo investigate the short-term efficacy and radiotoxicity 3.543of chronoradiotherapy in patients with cervical cancer. We also examined the overall symptom score and quality of life (QOL) of patients who underwent morning radiotherapy and evening radiotherapy.MethodsWe conducted a multicenter randomized controlled trial to compare the effects of morning radiotherapy (9:00–11:00 AM) with evening radiotherapy (7:00–9:00 PM) in cervical cancer patients receiving radiotherapy. From November 2021 to June 2022, 114 cervical cancer patients admitted to eight cancer center hospitals in Tianjin, Chongqing, Hubei, Shanxi, Shandong, Shaanxi, Hebei, and Cangzhou were randomly divided into the morning radiotherapy group (MG; N = 61) and the evening radiotherapy group (EG; N = 53). The short-term efficacy of radiotherapy on cervical cancer patients at different time points and the occurrence of radiotoxicity were explored after patients had undergone radiotherapy.ResultsThe total effective response (partial remission [PR] + complete remission [CR]) rate was similar across the two groups (93.5% vs. 96.3%, p > 0.05). However, the incidence of bone marrow suppression and intestinal reaction in the two groups were significantly different (p < 0.05). The patients in the MG had significantly higher Anderson symptom scores than patients in the EG (21.64 ± 7.916 vs. 18.53 ± 4.098, p < 0.05). In terms of physical activity, functional status, and overall QOL, the MG had significantly lower scores than the EG (p < 0.05). No other measures showed a significant difference between the groups.ConclusionThe radiotherapy effect of the MG was consistent with that of the EG. The incidence of radiation enteritis and radiation diarrhea in the MG was significantly higher than that in the EG; however, bone marrow suppression and blood toxicity in the EG were more serious than in the MG. Because of the small sample size of the study, we only examined the short-term efficacy of radiotherapy. Therefore, further clinical trials are needed to verify the efficacy and side effects of chronoradiotherapy.Clinical Trial Registrationhttp://www.chictr.org.cn/searchproj.aspx, Registration Number: ChiCTR2100047140.

Triple-negative breast cancer (TNBC) accounts for 15–20% of all breast cancer. TNBC does not express the estrogen receptor, progesterone receptor, or human epidermal growth factor receptor 2. Cytotoxic chemotherapy and surgery are the current therapeutic strategies for TNBC patients, but the chemoresistance of TNBC limits the efficiency of this strategy and shortens the lifespan of patients. The exploration of targeted therapy is ongoing in TNBC research. The aim of the present study was to identify the mechanism underlying acquired resistance in TNBC through the exploration of the relationship between the expression of USP7 and of ABCB1. We found that ubiquitin specific protease 7 (USP7) is a potential therapeutic target for overcoming the chemoresistance of TNBC. USP7 overexpression increased the chemoresistance of TNBC, while the knockdown of USP7 effectively increased the chemosensitivity of chemoresistant TNBC. A USP7 inhibitor effectively induced apoptosis and suppressed metastasis in chemoresistant TNBC. We further clarified that USP7 is a specific deubiquitinating enzyme for ABCB1 that plays an essential role in drug resistance. USP7 directly interacted with ABCB1 and regulated its stability. We concluded that USP7 promotes the chemoresistance of TNBC by stabilizing the ABCB1 protein.

Bacteriophage T4 has enormous potential for biomedical applications due to its large size, capsid architecture, and high payload capability for protein and DNA delivery. However, it is not very easy to genetically engineer its genome heavily modified by cytosine hydroxymethylation and glucosylation. The glucosyl hydroxymethyl cytosine (ghmC) genome of phage is completely resistant to most restriction endonucleases and exhibits various degrees of resistance to CRISPR-Cas systems. Here, we found that the type V CRISPR-Cas12a system, which shows efficient cleavage of ghmC-modified genome when compared to the type II CRISPR-Cas9 system, can be synergistically employed to generate recombinant T4 phages. Focused on surface display, we analyzed the ability of phage T4 outer capsid proteins Hoc (highly antigenic outer capsid protein) and Soc (small outer capsid protein) to tether, in vivo, foreign peptides and proteins to T4 capsid. Our data show that while these could be successfully expressed and displayed during the phage infection, shorter peptides are present at a much higher copy number than full-length proteins. However, the copy number of the latter could be elevated by driving the expression of the transgene using the strong T7 RNA polymerase expression system. This CRISPR-inspired approach has the potential to expand the application of phages to various basic and translational research projects.

Liver metastasis (LM) severely affects gastric cancer (GC) patients' prognosis. Small extracellular vesicles (sEVs) play key roles in intercellular communication. Specific sEV-miRNAs from several types of cancer were found to induce a premetastatic niche in target organs before tumor cell arrive. However, whether the primary GC affects hepatic microenvironment or the role of sEV-miRNAs in GC-LM is yet unclear. We report that GC-derived sEVs are primarily absorbed by Kupffer cells (KCs). sEV-miR-151a-3p is highly expressed in GC-LM patients' plasma and presents poor prognosis. Treating mice with sEVs-enriched in miR-151a-3p promotes GC-LM, whereas has no influence on the proliferation of GC cells in situ. Mechanistically, sEV-miR-151a-3p inhibits SP3 in KCs. Simultaneously, sEV-miR-151a-3p targets YTHDF3 to decrease the transcriptional inhibitory activity of SP3 by reducing SUMO1 translation in a N6-methyladenosine-dependent manner. These factors contribute to TGF-β1 transactivation in KCs, subsequently activating the SMAD2/3 pathway and enhancing the stem cell-like properties of incoming GC cells. Furthermore, sEV-miR-151a-3p induces miR-151a-3p transcription in KCs to form a positive feedback loop. In summary, our results reveal a previously unidentified regulatory axis initiated by sEV-miR-151a-3p that establishes a hepatic stemness-permissive niche to support GC-LM. sEV-miR-151a-3p could be a promising diagnostic biomarker and preventive treatment candidate for GC-LM.

As patients are dissatisfied with the excessive weight of Co-based knee femoral components, it is an urgent need to reduce their weight without compromising the excellent mechanical properties. In the present study, femoral components of knee with Ti6Al4V alloy (86 g) or Co–Cr alloy (192 g) were printed by selective laser melting (SLM). In order to improve the mechanical properties and biocompatibility of 3D-printed Ti6Al4V, TiN and TiCrN coating layers were deposited on the surface of SLM Ti6Al4V using arc ion plating (AIP) system. The surface characterizations were analyzed by scanning electron microscopy (SEM) equipped with energy dispersive spectrometer (EDS), X-ray diffraction (XRD), contact angle tensiometer, and contact profilometer. The wear property, hardness value and corrosion resistance in phosphate buffered saline (PBS) were also investigated through vickers hardness, sliding wear test, electrochemical impedance spectroscopy and potentiodynamic polarization. The antibacterial capability and biocompatibility were assessed with Staphylococcus aureus and mesenchymal stem cells (MSCs) in vitro. Results illustrated that, after coating SLM Ti6Al4V with thin TiN and TiCrN layers, the hardness, anti-wear performance and corrosion resistance of SLM Ti6Al4V were significantly improved and became comparable to SLM CoCr. The TiN and TiCrN coated SLM Ti6Al4V also showed moderate antibacterial ability, and excellent biocompatibility in comparation with SLM Ti6Al4V and SLM CoCr.

Background Exposure to tobacco product marketing increases tobacco use among young adults, especially those from vulnerable communities (VCs). Purpose This study examined real-time tobacco marketing exposure among young adults from vulnerable and non-vulnerable communities using Ecological Momentary Assessment (EMA). Methods This study used EMA data to assess context (e.g., location and activity) of tobacco marketing exposure using four text-messaging surveys per day over 2 weeks. Young adult non-current tobacco users living in Washington, D.C. (n = 146; ages 18–24) recorded 5,285 surveys, including 20 participants (13.2%) from VCs with high proportions of lower income and racial/ethnic minorities, and high smoking rates. Unadjusted and adjusted multilevel logistic regressions were used to assess the associations between exposure to any and flavored tobacco marketing, VC residence, and real-time context. Results Fifty-nine participants (40.4%) reported at least one tobacco marketing exposure and recorded 94 exposure moments. In adjusted models, odds of exposure were higher among VC residents (AOR = 2.6, 95% CI = 1.2–5.4), in the presence of anyone using tobacco versus no use (AOR = 4.0, 95% CI = 2.4–6.7), at store/retail (AOR = 17.0, 95% CI = 6.4–44.8), or outside/in transit (AOR = 4.1, 95% CI = 2.1–7.8) versus at home. VC residence (AOR = 7.2, 95% CI = 2.3–22.2) was the strongest predictor of flavored tobacco marketing exposure among all covariates examined. Conclusions Young adults are predominantly exposed to tobacco marketing in their daily lives through retail advertisements. Young adults from VCs are at increased risks of seeing any tobacco and especially flavored tobacco marketing. Policies that curtail tobacco retailer density and advertisement displays may reduce overall and differential tobacco marketing exposure.

Time-series forecasting is an important problem across a wide range of domains. Designing accurate and prompt forecasting algorithms is a non-trivial task, as temporal data that arise in real applications often involve both non-linear dynamics and linear dependencies, and always have some mixtures of sequential and periodic patterns, such as daily, weekly repetitions, and so on. At this point, however, most recent deep models often use Recurrent Neural Networks (RNNs) to capture these temporal patterns, which is hard to parallelize and not fast enough for real-world applications especially when a huge amount of user requests are coming. Recently, CNNs have demonstrated significant advantages for sequence modeling tasks over the de-facto RNNs, while providing high computational efficiency due to the inherent parallelism. In this work, we propose HyDCNN, a novel hybrid framework based on fully Dilated CNN for time-series forecasting tasks. The core component in HyDCNN is a proposed hybrid module, in which our proposed position-aware dilated CNNs are utilized to capture the sequential non-linear dynamics and an autoregressive model is leveraged to capture the sequential linear dependencies. To further capture the periodic temporal patterns, a novel hop scheme is introduced in the hybrid module. HyDCNN is then composed of multiple hybrid modules to capture the sequential and periodic patterns. Each of these hybrid modules targets on either the sequential pattern or one kind of periodic patterns. Extensive experiments on five real-world datasets have shown that the proposed HyDCNN is better compared with state-of-the-art baselines and is at least 200% better than RNN baselines. The datasets and source code will be published in Github to facilitate more future work.

Recent years have witnessed the proliferation of mobile computing and Internet-of-Things (IoT), where billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend and the development of wireless communication, edge computing, an emerging computing paradigm, has received tremendous amount of interest. By pushing data storage, computing, and control closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, prompt response, and energy efficiency. In the meanwhile, with the development of neural networks, Artificial Intelligence (AI) has been applied to a variety of disciplines and proved highly successful in a vast class of intelligent applications cross many domains. Edge intelligence, aiming to facilitate the deployment of neural networks on edge computing, has received significant attention. However, there are many challenges existing for a novel design of edge computing architecture to AI applications, and their co-optimization. For instance, conventional neural networks techniques usually entail powerful computing facilities (e.g., cloud computing platforms), while the entities at the edge may have only limited resources for computations and communications. This suggests that AI algorithms should be revisited for edge computing to AI models into the edge device for efficient processing. On the other hand, the adapted deployments of neural networks at the edge empower the efficient learning systems that can provide the “smartification” across different layers, e.g., from network communications to applications, and also involve collaborations across edge to cloud. Finally, designing algorithms for small-scale edge devices in a learning ambience is all the challenging as there are several conflicting issues to account for. These include, memory management, power management, and compute capability of a node, etc.

Adding nucleotide pairs to a DNA alphabet not only increases the linear information density of DNA molecules, but also may increase the diversity of folds available to the molecular system. Reported here is an example of this. A single additional nucleotide, with the 5-nitro-6-aminopyridone ("Z") heterocycle, selected from an artificially expanded genetic information system (AEGIS), was incorporated at twelve sites into a DNA strand 23 nucleotides in length. With a fluor at its 5'-end and a quencher at its 3'-end, this molecule is shown by fluorescence quenching to fold in mildly alkaline (pH 8-9) conditions. This folded structure is quite stable, melting at Tm = 66.5 ℃ at pH 8.5. Spectroscopic, gel, and 2-dimensional NMR (800 MHz) analyses show that the structure is held together by "reverse" Z-:Z base pairs, which have been previously seen in a crystal structure of the free heterocycle, 5-nitro-6-aminopyridone. This, in turn, requires that the strands joined by Z-:Z reverse pairs be oriented in parallel. This, in turn, requires the fold to have a four stranded compact "down-up-down-up" fold. The structure does not form when various Zs are replaced or scrambled. Of two possible structures, we also considered the possibility of "intercalated" Z-:Z base pairs, a fold that resemble the i-motif of standard DNA where an analogous "down-up-down-up" fold is held together by C:C+ pairing at lower pH between parallel strands. An alternative model has a "down-up-down-up" fold without intercalation.

Aims To explore how fermented barley extracts could affect obesity-associated inflammatory responses to ameliorate high-fat diet (HFD)-induced obesity, and investigate whether their anti-inflammatory properties were affected by modulating the gut microbiota. Methods and Results Twenty-four male rats were assigned randomly to three groups for 8 weeks. Inflammatory status and gut microbiota in HFD-induced obese rats were measured by enzyme linked immunosorbent assay and 16sRNA sequencing technology. The dietary supplementation of Extract of fermented barley with L. plantarum JDM1 (LFBE) reduced HFD-induced obesity and improved insulin sensitivity. LFBE significantly decreased the levels of lipopolysaccharide (LPS) and pro-inflammatory cytokines (tumour necrosis factor-α, interleukin [IL]-6, IL-1β, monocyte chemotactic protein-1), and increased anti-inflammatory cytokines (IL-10) in serum. In addition, LFBE suppressed the activation of nuclear factor-κB (NF-κB) by inhibiting the inhibitor of NF-κB alpha degradation and phosphorylation of JNK/p38 mitogen-activated protein kinases in adipose tissue. Combined with changes in gut microbiota, these results illustrated that LFBE treatment markedly decreased the proportion of the LPS-producing opportunistic pathogens and increased the proportion of Bifidobacterium. Conclusions Administration of LFBE has beneficial effects on ameliorating HFD-induced obesity and insulin resistance, lessening HFD-induced gut microbiota dysbiosis and pro-inflammatory cytokines secretion. Significance and Impact of this Study The results suggest that fermented barley extracts may be a useful functional compound and beneficial to improve inflammatory status and gut microflora.

Transformer model architectures have recently received great interest in natural language, machine translation, and computer vision, where attention mechanisms are their building blocks. However, the attention mechanism is expensive because of its intensive matrix computations and complicated data flow. The existing hardware architecture has some disadvantages for the computing structure of attention, such as inflexibility and low efficiency. Most of the existing papers accelerate attention by reducing the amount of computation through various pruning algorithms, which will affect the results in a certain extent with different sparsity. This paper proposes the hardware accelerator for the multi-head attention (MHA) on field-programmable gate arrays (FPGAs) with reconfigurable architecture, efficient systolic array, and hardware-friendly radix-2 softmax. We propose a novel method called Four inputs Processing Element(FPE) to double the computation rate of the data-aware systolic array (SA) and make it efficient and load balance. Especially, the computation framework is well designed to ensure the utilization of SA efficiently. Our design is evaluated on a Xilinx Alveo U250 card, and the proposed architecture achieves 51.3×, 17.3× improvement in latency, and 54.4×, 17.9× energy savings compared to CPU and GPU.

The sternum is one of the most important components of the chest wall. However, to the best of our knowledge, at present there is no reference value of the sternum length in normal Han Chinese children that has been published in the literature. The aim of the present study was therefore to establish the reference value of sternum length in a normal Han Chinese population of different ages and sex groups. Chest computed tomography scans and three-dimensional reconstruction images of 1,080 individuals who were younger than 18 years old and without congenital or acquired structural anomalies or congenital metabolic diseases were retrospectively reviewed. The length of the sternum was measured for each individual and comparisons of the sternum length according to sex were conducted using the Mann-Whitney test. Age or region group comparisons were performed using the ANOVA test, and the association between the length of the sternum and age was assessed by regression analysis. A significant association between the age (x) of the individual and the length of the sternum (y) was confirmed, although different regression patterns were identified for the sexes (the regression equation for males was y=5.616x+60.408; P0.001; R

Deep learning services based on cloud computing have deficiencies in latency, privacy, etc. To meet the requirements of low latency, researchers have begun to consider the deployment of deep learning services in edges, \textit{i.e.}, edge intelligence service. Deploying deep learning models on multiple processors or devices so that the computation of a deep learning model can be conducted in parallel is a possible solution to improve the efficiency of edge intelligence services. In this paper, we propose a novel latency-driven deep learning model placement method for efficient edge intelligence service. Model placement contains two procedures: model partition and sub-models assignment. In our method, we first convert the model into execution graphs and propose a novel latency-driven multilevel graph partition for the model. Then the partitioned sub-models are heuristically assigned to available processors. To the best of our knowledge, it is the first work that proposes latency-driven graph partition algorithms for model placement. Extensive experiments on several commonly used DNN (deep neural network) models and synthetic datasets show that our method can achieve the lowest execution latency with low complexity compared with other state-of-the-art model placement methods.

Macrophage activation is a key contributing factor for excessive inflammatory responses of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). Geranylgeranyl diphosphate synthase (GGPPS) plays a key role in the development of inflammatory diseases. Our group previously showed that GGPPS in alveolar epithelium have deleterious effects on acute lung injury induced by LPS or mechanical ventilation. Herein, we examined the role of GGPPS in modulating macrophage activation in ALI/ARDS. We found significant increased GGPPS expression in alveolar macrophages in patients with ARDS compared with healthy volunteers and in ALI mice induced by LPS. GGPPS-floxed control ( GGPPSfl/fl) and myeloid-selective knockout ( GGPPSfl/fl LysMcre) mice were then generated. Interestingly, using an LPS-induced ALI mouse model, we showed that myeloid-specific GGPPS knockout significantly increased mortality, aggravated lung injury, and increased the accumulation of inflammatory cells, total protein, and inflammatory cytokines in BALF. In vitro, GGPPS deficiency upregulated the production of LPS-induced IL-6, IL-1β, and TNF-α in alveolar macrophages, bone marrow-derived macrophages (BMDMs), and THP-1 cells. Mechanistically, GGPPS knockout increased phosphorylation and nuclear translocation of NF-κB p65 induced by LPS. In addition, GGPPS deficiency increased the level of GTP-Rac1, which was responsible for NF-κB activation. In conclusion, decreased expression of GGPPS in macrophages aggravates lung injury and inflammation in ARDS, at least partly by regulating Rac1-dependent NF-κB signaling. GGPPS in macrophages may represent a novel therapeutic target in ARDS.

To model the context history in multi-turn conversations has become a critical step towards a better understanding of the user query in question answering systems. To utilize the context history, most existing studies treat the whole context as input, which will inevitably face the following two challenges. First, modeling a long history can be costly as it requires more computation resources. Second, the long context history consists of a lot of irrelevant information that makes it difficult to model appropriate information relevant to the user query. To alleviate these problems, we propose a reinforcement learning based method to capture and backtrack the related conversation history to boost model performance in this paper. Our method seeks to automatically backtrack the history information with the implicit feedback from the model performance. We further consider both immediate and delayed rewards to guide the reinforced backtracking policy. Extensive experiments on a large conversational question answering dataset show that the proposed method can help to alleviate the problems arising from longer context history. Meanwhile, experiments show that the method yields better performance than other strong baselines, and the actions made by the method are insightful.

To identify the risk factors of mortality for the coronavirus disease 19 (COVID-19) patients admitted to intensive care units (ICUs) through a retrospective analysis. The demographic, clinical, laboratory, and chest imaging data of patients admitted to the ICU of Huoshenshan Hospital from February 10 to April 10, 2020 were retrospectively analyzed. Student's t-test and Chi-square test were used to compare the continuous and categorical variables, respectively. The logistic regression model was employed to ascertain the risk factors of mortality. This retrospective study involved 123 patients, including 64 dead and 59 survivors. Among them, 57 people were tested for interleukin-6 (IL-6) (20 died and 37 survived). In all included patients, the oxygenation index (PaO2/FiO2) was identified as an independent risk factor (odd ratio [OR] = 0.96, 95% confidence interval [CI]: 0.928–0.994, p = 0.021). The area under the curve (AUC) was 0.895 (95% CI: 0.826–0.943, p 2/FiO2 (OR = 0.955, 95%CI: 0.915–0.996, p = 0.032) and IL-6 (OR = 1.013, 95%CI: 1.001–1.025, p = 0.028) were identified as independent risk factors. The AUC was 0.9 (95% CI: 0.791–0.964, p p 2/FiO2. PaO2/FiO2 and IL-6 could potentially serve as independent risk factors for predicting death in COVID-19 patients requiring intensive care.

Anomaly detection based on time series data is of great importance in many fields. Time series data produced by man-made systems usually include two parts: monitored and exogenous data, which respectively are the detected object and the control/feedback information. In this paper, a so-called G-CNN architecture that combined the gated recurrent units (GRU) with a convolutional neural network (CNN) is proposed, which respectively focus on the monitored and exogenous data. The most important is the introduction of a complementary double-referenced thresholding approach that processes prediction errors and calculates threshold, achieving balance between the minimization of false positives and the false negatives. The outstanding performance and extensive applicability of our model is demonstrated by experiments on two public datasets from aerospace and a new server machine dataset from an Internet company. It is also found that the monitored data is close associated with the exogenous data if any, and the interpretability of the G-CNN is discussed by visualizing the intermediate output of neural networks.

Synthetic bone graft has gained considerable attention because of an increase in the aged population. In the present study, a biphasic bone graft composed of hydroxyapatite (HAp) and calcium sulfate (CS) is prepared using a gel-foaming technique. The gel is prepared at a temperature as low as 35 °C; many pores are introduced into the gel through an agitation process. The foamed gel can be cut into any shape and size. After sintering, the porosity of the biphasic bone graft is approximately 70%. Depending on the HAp/CS ratio, the amount of macropores and micropores can be tailored. The macropores of approximately 130 μm diameter are interconnected with each other through openings of 40 μm size. The biphasic bone graft exhibits no cytotoxicity; preosteoblast cells can adhere and proliferate on the surface of the fabricated bone graft. However, migration of these cells into the bone graft is considerably limited in the in vitro study. When the biphasic bone graft is implanted into the distal femur of rats, with the combined effect of osteoblast and osteoclast cells, more than 90% of the bone graft is degraded after 3 months. New trabeculae bone and bone marrow are observed within the bone defect. The HAp/CS composite can thus be used as degradable bone grafts.

Background Starting in 2019 policies restricting the availability of flavored e-cigarette products were proposed or implemented in the United States to curb vaping by youth. People took to Twitter to voice their opposition, referencing the phrase “Flavors Save Lives.” This study documented the emerging themes pertaining to “Flavors Saves Lives” over a 12-month period. Methods The study period was from May 1, 2019, to May 1, 2020. A stratified sampling procedure supplied 2500 tweets for analysis. Posts were classified by one or more of the following themes: (1) Political Referendum; (2) Institutional Distrust; (3) Individual Rights; (4) Misinformation; (5) THC Vaping is the Real Problem; (6) Smoking Cessation; (7) Adult Use; and (8) Not a Bot. The temporal pattern of tweets over the year was examined. Results Political Referendum (76.5%) and Institutional Distrust (31.3%) were the most prominent themes, followed by Not a Bot (11.0%), Individual Rights (10.4%), Adult Use (8.0%), Smoking Cessation (6.6%), Misinformation (5.9%), and THC Vaping is the Real Problem (3.5%). Total tweet frequencies increased in September 2019 and peaked in November 2019 before returning to relatively low numbers. Political Referendum and Institutional Distrust were consistently the most prevalent themes over time. Conclusion Twitter posts with the phrase “Flavors Save Lives” commonly discussed voting against political incumbents and mentioned distrust of government representatives. Findings demonstrated the possibility of near real-time Twitter monitoring of public opposition to flavor bans. These data may be valuable for designing tobacco control information campaigns in the future. Implications (a) Starting in 2019 policies restricting the availability of flavored e-cigarette products were proposed or implemented in the United States to curb vaping by youth. (b) This study content analyzed Twitter posts with the phrase “Flavors Save Lives” from a 12-month period to understand opposition to flavor restrictions. (c) Twitter posts commonly discussed voting against political incumbents and mentioned distrust of government representatives. (d) Findings demonstrated the possibility of near real-time Twitter monitoring of public opposition to flavor bans, and contribute to a more comprehensive assessment of different sub-population's responses to current and proposed tobacco control information policies.

This study was conducted to investigate the effects of incorporating varying proportions of nutraceutical-rich mushroom (Cordycepes militaris) flour (1%, 3% and 5%) on the physicochemical, antioxidant and sensory properties of cookies. Results showed that the contents of crude protein, ash and fiber of supplemented coo kies were higher than that of control cookies. Spread ratio and hardness decreased as the C.militaris flour incorporation level increased. The color values of cookies also varied with the increasing levels of C. militaris flour. Furthermore, fortification of C. militaris flour improved the functional quality of cookies by significantly enhancing the phenolic contents and antioxidant activities (P < 0.05). Sensory evaluation of cookies indicated that the addition of 3% C. militaris flour was more acceptable. Overall, enriched cookies have more functional components and effective antioxidant capacity than plain wheat cookies. Their supplementation could provide the consumers a novel cereal-based product with health-promoting benefits.

Although stochastic modeling methods can achieve multiple implementations of sedimentary microfacies model in dense well blocks, it is difficult to realize continuous convergence of well spacing. Taking the small high-sinuosity meandering river sediments of the third member of Quantou Formation in Songliao Basin as an example, a deterministic modeling method based on geological vector information was explored in this article. Quantitative geological characteristics of point bar sediments were analyzed by field outcrops, modern sediments, and dense well block anatomy. The lateral extension distance, length, and spacing parameters of the point bar were used to quantitatively characterize the thickness, dip angle, and frequency of the lateral layer. In addition, the three-dimensional architecture modeling of the point bar was carried out in the study. The established three-dimensional architecture model of well X24-1 had continuous convergence near all wells, which conformed to the geological knowledge of small high-sinuosity meandering river, and verified the reliability of this method in the process of geological modeling in dense well blocks.

Introduction Racial/ethnic minorities have a higher prevalence of past 30–day cigar smoking than White, non-Hispanics. Little is known, however, about racial/ethnic differences in advanced cigar-smoking patterns by cigar types. This research explores whether cigar-smoking patterns differ by race/ethnicity and cigar types. Methods This study used a nationally representative sample of adults (aged ≥18 years; N=28,148) from the Wave 3 survey (2015–2016) of the Population Assessment of Tobacco and Health Study for analysis. Cigar-smoking patterns included past 30–day use, daily use, established use, past 12–month blunt use, use within 30 minutes of waking, and the number of cigars used per day. Weighted multivariable regressions were conducted in 2019 to examine the associations between race/ethnicity and cigar-smoking patterns by cigar types (traditional cigars, cigarillos, and filtered cigars), controlling for covariates. Results Compared with White, non-Hispanics, Black, non-Hispanics were more likely to smoke cigars in the past 30 days (AOR=2.27, 95% CI=2.03, 2.54) and daily (AOR=2.65, 95% CI=1.89, 3.70), have established cigar smoking (AOR=1.95, 95% CI=1.66, 2.29), and smoke blunts in the past 12 months (AOR=2.30, 95% CI=1.84, 2.88). This pattern was generally consistent across cigar types and was especially pronounced for cigarillos. Compared with White, non-Hispanics, Hispanics were more likely to smoke cigars within 30 minutes of waking (AOR=1.50, 95% CI=1.10, 2.06). Conclusions This study finds that Black, non-Hispanics and Hispanics have more advanced patterns of cigar smoking than White, non-Hispanics. Interventions and policies for minimizing cigar smoking may differentially benefit these populations and reduce disparities.

We have exemplified a pretargeted approach to interrogate hypoxia in live cells using radioactive bioorthogonal inverse electron demand Diels–Alder (IEDDA) “click” chemistry. Our novel 18F-tetrazine probe ([18F]FB-Tz) and 2-nitroimidazole-based TCO targeting molecule (8) showed statistically significant (P < 0.0001) uptake in hypoxic cells (ca. 90 %ID per mg) vs. normoxic cells (, Bioorthogonal IEDDA “click” can interrogate intracellular hypoxia using a radioactive reporter molecule.

Dictionary-based fault diagnosis methods, focusing on storing feature patterns of known faults, have been widely used for electromechanical systems. The state of component degradation caused soft faults, however, are continuously changeable. Thus, conventional dictionaries cannot be applied for diagnosis of soft faults with multi-degradation levels. To address this issue, this article develops a new type of dictionary by combining the unit residual signal vector (URV) and the linear discriminant analysis (LDA) for feature transformation, which is referred to as URV-LDA dictionary. The unit residual signal vector keeps the fault feature growth trends but eliminates the degradation severity influence. The linear discriminant analysis is then implemented to find the best projection directions for classification. Specifically, two dictionaries named as the URV-MLDA binary-value dictionary and the URV-SLDA unique-value dictionary are proposed. To validate the efficiency of two developed dictionaries, an electromagnetic relay is carried out and two conventional methods are compared. The comparison results show the developed dictionaries can better solve the soft faults issues with significant increases on diagnostic accuracy.

Bearing incipient fault diagnosis is very crucial to the timely condition-based maintenance (CBM), but it is also relatively difficult because of the faint feature and environmental interference. In this article, an adaptive optimized time-varying filtering-based empirical mode decomposition (TVF-EMD) (AO-TVF-EMD) is put forward to extract more explicit and abundant incipient fault features by optimizing the parameter combination in conventional TVF-EMD. First, a novel integrated indicator termed sparsity-impact measure index (SIMI) constructed from dispersion entropy (DE) and envelope characteristic frequency ratio (ECFR) is designed as the parameter selection criterion. This index fully guarantees the optimal sparsity and impact properties of the obtained modes simultaneously, considering both time- and frequency-domain characteristics. Simulation verified that SIMI can not only sensitively characterize the variation of fault feature but also is robust to the environmental disturbance. Subsequently, we employ the Salp swarm algorithm (SSA) to realize the accurate search of parameter combinations where the minimum SIMI is defined as the fitness function. Moreover, comparative analysis in two runs to failure tests of bearings demonstrate that AO-TVF-EMD is more effective to extract incipient fault features than the maximum weighted Kurtosis (MWK) optimized TVF-EMD, the conventional TVF-EMD with fixed parameter, and some other advanced signal processing methods, which highlights the superiority of the proposed method.

In smart cities, region-based prediction (e.g. traffic flow and electricity flow) is of great importance to city management and public safety, and it remains a daunting challenge that involves complicated spatial-temporal-related factors such as weather, holidays, events, etc. Region-based forecasting aims to predict the future situation for regions in a city based on historical data. In the existing literature, the state-of-the-art method solve region-based problems with long short-term memory (LSTM) algorithms that extract the temporal view and local convolutional neural network (CNN) algorithms that extract the spatial view (local spatial correlation via local CNN). In this paper, we propose a deep learning-based method for region-based prediction for smart cities. First, we divide the cities into regions based on the space dimension and model the situation of the cities in 3D volumes. Based on the constructed 3D volumes, we design a model called multiple local 3D CNN spatial-temporal residual networks (LMST3D-ResNet) for region-based prediction in smart cities. LMST3D-ResNet can extract multiple temporal dependencies (including trend, period and closeness) for local regions and then predict the future citywide activities according to the learned multiple spatial-temporal features. LMST3D-ResNet can also combine the spatial-temporal features with external factors. LMST3D-ResNet includes 3D CNNs and ResNet mechanisms for processing spatial-temporal information. In particular, 3D CNNs have the ability to model 3-dimensional information due to 3D convolution and 3D pooling operations, while ResNet enables the connection of the convolutional neural network across layers to obtain a deeper network structure. Specifically, in our proposed model, a novel region-based information extraction mechanism and an end-to-end multiple spatial-temporal dependency learning structure are designed for local regions. Extensive experimental results on two datasets, i.e., MLElectricity and BJTaxi demonstrate the superior performance of our proposed method over the exisiting state-of-the-art methods.

Analysis of high dimensional biomedical data such as microarray gene expression data and mass spectrometry images, is crucial to provide better medical services including cancer subtyping, protein homology detection,etc. Clustering is a fundamental cognitive task which aims to group unlabeled data into multiple clusters based on their intrinsic similarities. The K-means algorithm is one of the most widely used clustering heuristics that aims at grouping the data objects into meaningful clusters such that the sum of squared Euclidean distances within each cluster is minimized. Its conceptual simplicity and computational efficiency make it easy to be used for wide applications of different data types. However, all features of data in K-means are considered equally in relevance, which distorts the performance when clustering high-dimensional data such as microarray gene expression data, mass spectrometry images, where there exist many redundant variables and correlated variables. In this paper, we propose a new correlation induced clustering, CoIn, to capture complex correlations among high dimensional data and guarantee the correlation consistency within each cluster. We evaluate the proposed method on a high dimensional mass spectrometry dataset of liver cancer tumor to explore the metabolic differences on tissues and discover the intra-tumor heterogeneity (ITH). By comparing the results of baselines and ours, it has been found that our method produces more explainable and understandable results for clinical analysis, which demonstrates the proposed clustering paradigm has the potential with application to knowledge discovery in high dimensional bioinformatics data.

Mucosal vaccines that can induce local mucosal immune responses and combat the pathogens at entry sites are considered to be the most effective way to prevent infection. A universal platform that can be customized for development of mucosal vaccines against any given pathogen is therefore highly desired. Here, we demonstrate an efficient approach to develop nasal mucosal vaccines through genetic engineering of T4 phage to generate antigen-decorated nanoparticles. The antigen coding sequence was inserted into T4 genome in-frame at the C terminus of Soc (small outer capsid protein) using the CRISPR-Cas phage editing technology. During the propagation of recombinant T4 phages in E. coli, the Soc-antigen fusion proteins self-assemble on T4 capsids to form antigen-decorated nanoparticles that have intrinsic adjuvant activity and mucosal adhesive property. As a proof of concept, we showed that intranasal immunization with Flu viral M2e-decorated T4 nanoparticles efficiently induced local mucosal as well as systemic immune responses and provided complete protections against divergent influenza viruses in a mouse model. Potentially, our platform can be customized for any respiratory pathogen to rapidly generate mucosal vaccines against future emerging epidemics and pandemics.

Recent studies have shown that prompt-based fine-tuning improves the performance of large Pre-trained Language Models (PLMs) for few-shot text classification. Specifically, this type of method transforms the text classification task into the inherent Masked Language Modeling (MLM) with some natural language prompts and verbalizers to bridge the gap between the pre-training stage and the fine-tuning stage. Yet, it is unclear how the prompting knowledge can be transferred across similar or distant NLP tasks, for the purpose of mutual reinforcement. Based on continuous prompt embeddings, we propose TransPrompt v2, a novel transferable prompting framework for few-shot learning across similar or distant text classification tasks. For transferable prompt learning across similar tasks, we employ a multi-task meta-knowledge acquisition procedure to train a meta-learner that captures the cross-task transferable knowledge. For learning across distant tasks, we further introduce the task type descriptions and propose intra-type and inter-type prompt encoders to capture implicit mutual relations among multiple distant tasks. Additionally, two de-biasing techniques are further designed to make the trained meta-learner more task-agnostic and unbiased towards any tasks. After that, the meta-learner can be fine-tuned for specific tasks with better parameters initialization. Extensive experiments show that TransPrompt v2 outperforms single-task and cross-task strong baselines over multiple NLP tasks and datasets. We further show that the meta-learner can effectively improve the performance of PLMs on previously unseen tasks. In addition, TransPrompt v2 also outperforms strong fine-tuning baselines when learning with full training sets.

The stimulator of interferon genes (STING) pathway is important for anticancer immune responses. However, the relative contributions of host and tumour STING in anti-programmed cell death protein 1 (anti-PD-1) inhibitor responses in non-small cell lung cancer (NSCLC) are unknown. STING expression in tumour and blood was associated with anti-PD-1 therapy in NSCLC patients; Moreover, loss of PD-1 inhibitor therapeutic potency was demonstrated in STING KO (knock out) splenocytes and STING KO mice. STING knock-down in tumour cells had no effect. STING on CD8