Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360° Image Outpainting.

360° images, with a field-of-view (FoV) of $180^{\circ}\times 360^{\circ}$, provide immersive and realistic environments for emerging virtual reality (VR) applications, such as virtual tourism, where users desire to create diverse panoramic scenes from a narrow FoV photo they take from a viewpoint via portable devices. It thus brings us to a technical challenge: 'How to allow the users to freely create diverse and immersive virtual scenes from a narrow FoV image with a specified viewport?' To this end, we propose a transformer-based 360° image outpainting framework called Dream360, which can generate diverse, high-fidelity, and high-resolution panoramas from user-selected viewports, considering the spherical properties of 360° images. Compared with existing methods, e.g., [3], which primarily focus on inputs with rectangular masks and central locations while overlooking the spherical property of 360° images, our Dream360 offers higher outpainting flexibility and fidelity based on the spherical representation. Dream360 comprises two key learning stages: (I) codebook-based panorama outpainting via Spherical-VQGAN (S-VQGAN), and (II) frequency-aware refinement with a novel frequency-aware consistency loss. Specifically, S-VQGAN learns a sphere-specific codebook from spherical harmonic (SH) values, providing a better representation of spherical data distribution for scene modeling. The frequency-aware refinement matches the resolution and further improves the semantic consistency and visual fidelity of the generated results. Our Dream360 achieves significantly lower Frechet Inception Distance (FID) scores and better visual fidelity than existing methods. We also conducted a user study involving 15 participants to interactively evaluate the quality of the generated results in VR, demonstrating the flexibility and superiority of our Dream360 framework.

A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities.

Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe cities' bird's-eye view. We further gather population, nighttime light, survey, and built environment data to depict SDGs regarding poverty, health, education, inequality, and living environment. We anticipate the dataset to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities.

A human mobility dataset collected via LBSLab.

Location-Based Services (LBS) have been prosperous owing to technological advancements of smart devices. Analyzing location-based user-generated data is a helpful way to understand human mobility patterns, further fueling applications such as recommender systems and urban computing. This dataset documents user activities of location-based services through LBSLab, a smartphone-based system implemented as a mini-program in the WeChat app. The dataset contains activity data of multiple types including logins, profile viewing, weather checking, and check-ins with location information (latitude and longitude), POI and mood indicated, collected from 467 users over a period of 11 days. We also present some temporal and spatial data analysis and believe the reuse of the data will allow researchers to better understand user behaviors of LBS, human mobility, and also temporal and spatial characteristics of people's moods.

Regenerable sensor based on tris(4,7'-diphenyl-1,10-phenanthroline)ruthenium (II) for anodic and cathodic electrochemiluminescence applications.

Tris(4,7'-diphenyl-1,10-phenanthroline) ruthenium (II) dichloride [Ru(dpp)32+] was used for the first time to construct a regenerable electrochemiluminescence (ECL) sensor. The Ru(dpp)32+-modified carbon paste electrode (CPE) showed several unique features in comparison with commonly studied Ru(bpy)32+-modified electrodes. On the one hand, a quite reversible reduction peak was observed at -0.96 V where no obvious hydrogen evolution occured, enabling the sensitive detection of S2O82-. Moreover, our proposed S2O82- sensor showed a good linear range from 3 × 10-9 to 3 × 10-4 M with a detection limit of 2 nM, indicating higher sensitivity for the same analyte than previously reported ECL methods by about two orders of magnitude. On the other hand, the Ru(dpp)32+-modified electrode showed an irreversible oxidation peak because electrogenerated Ru(dpp)33+ is very reactive in aqueous solutions, while Ru(bpy)32+-modified electrode showed a reversible oxidation peak. Moreover, the present sensor showed a good linear range from 10-7 M to 10-3 M for oxalate with a detection limit of 60 nM. It detected oxalate in urine samples with nice recoveries. The regenerable ECL sensor presented good characteristics, such as low cost, simple fabrication procedure and fast response time. The Ru(dpp)32+ based regenerable sensor is an attractive alternative to Ru(bpy)32+-based regenerable sensor, as it can be used for both anodic and cathodic ECL analysis with high sensitivity in aqueous media.

Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity.

Balancing social utility and equity in distributing limited vaccines is a critical policy concern for protecting against the prolonged COVID-19 pandemic and future health emergencies. What is the nature of the trade-off between maximizing collective welfare and minimizing disparities between more and less privileged communities? To evaluate vaccination strategies, we propose an epidemic model that explicitly accounts for both demographic and mobility differences among communities and their associations with heterogeneous COVID-19 risks, then calibrate it with large-scale data. Using this model, we find that social utility and equity can be simultaneously improved when vaccine access is prioritized for the most disadvantaged communities, which holds even when such communities manifest considerable vaccine reluctance. Nevertheless, equity among distinct demographic features may conflict; for example, low-income neighbourhoods might have fewer elder citizens. We design two behaviour-and-demography-aware indices, community risk and societal risk, which capture the risks communities face and those they impose on society from not being vaccinated, to inform the design of comprehensive vaccine distribution strategies. Our study provides a framework for uniting utility and equity-based considerations in vaccine distribution and sheds light on how to balance multiple ethical values in complex settings for epidemic control.

Exponential amplification by redox cross-catalysis and unmasking of doubly protected molecular probes.

The strength of autocatalytic reactions lies in their ability to provide a powerful means of molecular amplification, which can be very useful for improving the analytical performances of a multitude of analytical and bioanalytical methods. However, one of the major difficulties in designing an efficient autocatalytic amplification system is the requirement for reactants that are both highly reactive and chemically stable in order to avoid limitations imposed by undesirable background amplifications. In the present work, we devised a reaction network based on a redox cross-catalysis principle, in which two catalytic loops activate each other. The first loop, catalyzed by H2O2, involves the oxidative deprotection of a naphthylboronate ester probe into a redox-active naphthohydroquinone, which in turn catalyzes the production of H2O2 by redox cycling in the presence of a reducing enzyme/substrate couple. We present here a set of new molecular probes with improved reactivity and stability, resulting in particularly steep sigmoidal kinetic traces and enhanced discrimination between specific and nonspecific responses. This translates into the sensitive detection of H2O2 down to a few nM in less than 10 minutes or a redox cycling compound such as the 2-amino-3-chloro-1,4-naphthoquinone down to 50 pM in less than 30 minutes. The critical reason leading to these remarkably good performances is the extended stability stemming from the double masking of the naphthohydroquinone core by two boronate groups, a counterintuitive strategy if we consider the need for two equivalents of H2O2 for full deprotection. An in-depth study of the mechanism and dynamics of this complex reaction network is conducted in order to better understand, predict and optimize its functioning. From this investigation, the time response as well as detection limit are found to be highly dependent on pH, nature of the buffer, and concentration of the reducing enzyme.

SEAR: Scaling Experiences in Multi-user Augmented Reality.

In this paper, we present the design, implementation, and evaluation of SEAR, a collaborative framework for Scaling Experiences in multi-user Augmented Reality (AR). Most AR systems benefit from computer vision (CV) algorithms to detect, classify, or recognize physical objects for augmentation. A widely used acceleration method for mobile AR is to offload the compute-intensive tasks (e.g., CV algorithms) to the network edge. However, we show that the end-to-end latency, an important metric of mobile AR, may dramatically increase when offloading AR tasks from a large number of concurrent users to the edge. SEAR tackles this scalability issue through the innovation of a lightweight collaborative local caching scheme. Our key observation is that nearby AR users may share some common interests, and may even have overlapped views to augment (e.g., when playing a multi-user AR game). Thus, SEAR opportunistically exchanges the results of offloaded AR tasks among users when feasible and leverages compute resources on mobile devices to relieve, if necessary, the edge workload by intelligently reusing these results. We build a prototype of SEAR to demonstrate its efficacy in scaling AR experiences. We conduct extensive evaluations through both real-world experiments and trace-driven simulations. We observe that SEAR not only reduces the end-to-end latency, by up to 130×, compared to the state-of-the-art adaptive edge offloading scheme, but also achieves high object-recognition accuracy for mobile AR.

An autocatalytic organic reaction network based on cross-catalysis.

Here we report a simple autocatalytic organic reaction network based on the redox chemistry of quinones and reactive oxygen species. Autocatalysis arises from the cross-activation between the H2O2-catalyzed deprotection of a pro-benzoquinone arylboronic ester probe and the benzoquinone-catalyzed H2O2 production through redox cyling with ascorbate in an aerated buffered solution.

IGF1 receptor inhibition amplifies the effects of cancer drugs by autophagy and immune-dependent mechanisms.

BACKGROUND: Pharmacological autophagy enhancement constitutes a preclinically validated strategy for preventing or treating most major age-associated diseases. Driven by this consideration, we performed a high-content/high-throughput screen on 65 000 distinct compounds on a robotized fluorescence microscopy platform to identify novel autophagy inducers. RESULTS: Here, we report the discovery of picropodophyllin (PPP) as a potent inducer of autophagic flux that acts on-target, as an inhibitor of the tyrosine kinase activity of the insulin-like growth factor-1 receptor (IGF1R). Thus, PPP lost its autophagy-stimulatory activity in cells engineered to lack IGF1R or to express a constitutively active AKT serine/threonine kinase 1 (AKT1) mutant. When administered to cancer-bearing mice, PPP improved the therapeutic efficacy of chemoimmunotherapy with a combination of immunogenic cytotoxicants and programmed cell death 1 (PDCD1, better known as PD-1) blockade. These PPP effects were lost when tumors were rendered PPP-insensitive or autophagy-incompetent. In combination with chemotherapy, PPP enhanced the infiltration of tumors by cytotoxic T lymphocytes, while reducing regulatory T cells. In human triple-negative breast cancer patients, the activating phosphorylation of IGF1R correlated with inhibited autophagy, an unfavorable local immune profile, and poor prognosis. CONCLUSION: Altogether, these results suggest that IGF1R may constitute a novel and druggable therapeutic target for the treatment of cancer in conjunction with chemoimmunotherapies.

Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic.

BACKGROUND: Unprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions. METHODS AND FINDINGS: Population and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences. INTERPRETATION: Compared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.

Overcome the tumor immunotherapy resistance by combination of the HDAC6 inhibitors with antitumor immunomodulatory agents.

Tumor immunotherapy is currently subject of intense scientific and clinical developments. In previous decade, therapists used natural immune system from the human body to treat several diseases. Although tumor immune disease is a big challenge, combinatorial therapeutic strategy has been succeeded to show the clinical significance. In this context, we discuss the HDAC6 and tumor immune diseases relationship. Also, we summarized the current state of knowledge that based on the combination treatments of the HDAC6 inhibitors (HDAC6is) with antitumor immunomodulatory agents. We observed that, the combination therapies slow down the tumor immune diseases by blocking the aggresome and proteasome pathway. The combination therapy was able to reduce M2 macrophage and increasing PD-L1 blockade sensitivity. Most importantly, multiple combinations of HDAC6is with other agents may consider as potential strategies to treat tumor immune diseases, by reducing the side effects and improve efficacy for the future clinical development.

A Review of Progress in Histone Deacetylase 6 Inhibitors Research: Structural Specificity and Functional Diversity.

Histone deacetylases (HDACs) are essential for maintaining homeostasis by catalyzing histone deacetylation. Aberrant expression of HDACs is associated with various human diseases. Although HDAC inhibitors are used as effective chemotherapeutic agents in clinical practice, their applications remain limited due to associated side effects induced by weak isoform selectivity. HDAC6 displays unique structure and cellular localization as well as diverse substrates and exhibits a wider range of biological functions than other isoforms. HDAC6 inhibitors have been effectively used to treat cancers, neurodegenerative diseases, and autoimmune disorders without exerting significant toxic effects. Progress has been made in defining the crystal structures of HDAC6 catalytic domains which has influenced the structure-based drug design of HDAC6 inhibitors. This review summarizes recent literature on HDAC6 inhibitors with particular reference to structural specificity and functional diversity. It may provide up-to-date guidance for the development of HDAC6 inhibitors and perspectives for optimization of therapeutic applications.

The Impact of Covid-19 on Smartphone Usage.

The outbreak of Covid-19 changed the world as well as human behavior. In this article, we study the impact of Covid-19 on smartphone usage. We gather smartphone usage records from a global data collection platform called Carat, including the usage of mobile users in North America from November 2019 to April 2020. We then conduct the first study on the differences in smartphone usage across the outbreak of Covid-19. We discover that Covid-19 leads to a decrease in users' smartphone engagement and network switches, but an increase in WiFi usage. Also, its outbreak causes new typical diurnal patterns of both memory usage and WiFi usage. Additionally, we investigate the correlations between smartphone usage and daily confirmed cases of Covid-19. The results reveal that memory usage, WiFi usage, and network switches of smartphones have significant correlations, whose absolute values of Pearson coefficients are greater than 0.8. Moreover, smartphone usage behavior has the strongest correlation with the Covid-19 cases occurring after it, which exhibits the potential of inferring outbreak status. By conducting extensive experiments, we demonstrate that for the inference of outbreak stages, both Macro-F1 and Micro-F1 can achieve over 0.8. Our findings explore the values of smartphone usage data for fighting against the epidemic.

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically ill patients within 24 h of intensive care unit (ICU) admission using only vital signs. METHODS: We used data from the Medical Information Mart for Intensive Care III database and the eICU Collaborative Research Database to develop a vasopressor prediction model. We performed systematic data preprocessing using matching of cohorts, oversampling, and imputation to control for bias, class imbalance, and missing data. Bidirectional long short-term memory (Bi-LSTM), a multivariate time series model, was used to predict the need for vasopressor therapy using serial physiological data collected 21 h prior to prediction time. RESULTS: Using data from 10,941 critically ill patients from 209 ICUs, our model achieved an initial area under the curve of 0.96 (95% CI 0.96-0.96) to predict the need for vasopressor therapy in 2 h within the first day of ICU admission. After matching to control class imbalance, the Bi-LSTM model had area under the curve of 0.83 (95% CI 0.82-0.83). Heart rate, respiratory rate, and mean arterial pressure contributed most to the model. CONCLUSIONS: We used Bi-LSTM to develop a model to predict the need for vasopressor for critically ill patients for the first 24 h of ICU admission. With attention mechanism, respiratory rate, mean arterial pressure, and heart rate were identified as key sequential determinants of vasopressor requirements.

Determinants of survival in advanced non-small cell lung cancer patients treated with anti-PD-1/PD-L1 therapy.

BACKGROUND: The present study aimed to investigate the determinant factors of survival in patients with pretreated advanced stage non-small cell lung cancer (NSCLC) who received anti-PD-1/PD-L1 therapy. METHODS: In this observational retrospective study, the clinical profiles and laboratory parameters of patients with NSCLC treated with anti-PD-1/PD-L1 therapy were consecutively collected. Lung Immune Prognostic Index (LIPI) was calculated based on the derived neutrophil-to-lymphocyte ratio (dNLR) and lactate dehydrogenase level (LDH). Modified Glasgow Prognostic Score (mGPS) was calculated based on serum C reactive protein and albumin, and tumor mutation burden (TMB) was calculated using a targeted next-generation sequencing panel based on 422 cancer-relevant genes. The primary and secondary end points were overall survival (OS) and progression-free survival (PFS), respectively. The Cox regression model was used to identify the potential determinant factors of survival benefit. Trained oncologists at Sun Yat-sen University Cancer Center followed all of the participants through visits to doctors' offices or via telephone calls to determine their clinical status. RESULTS: Seventy-three patients were included in our study. With a median follow up time of 637 days, there was a significant difference in PFS between patients with high TMB compared to those with low TMB (3.7 vs. 2.1 months; P=0.004), while no significant difference was found in OS (14.0 vs. 16.4 months; P=0.972). Patients with a good LIPI score had a significantly longer OS compared to patients with a poor LIPI score (19.2 vs. 12.6 months; P=0.010). The median OS in patients with a good and a poor mGPS was 16.8 and 4.3 months, respectively (P=0.029). In multivariate analysis, TMB was found to be significantly associated with PFS (HR, 0.38; 95% CI: 0.21-0.69; P=0.002), while LIPI score was found to be significantly associated with OS (HR, 0.50; 95% CI: 0.28-0.89; P=0.012). CONCLUSIONS: In the present study, LIPI score was a significant determinant of OS in patients with advanced NSCLC who received ICIs; however, TMB was only associated with PFS and not associated with OS.

Dioscin Reduces Vascular Damage in the Retina of db/db Mice by Inhibiting the VEGFA Signaling Pathway.

Diabetic retinopathy (DR) is a complication of diabetes that has a serious impact on the quality of life of patients. VEGFA is necessary in the physiological state to maintain endothelial activity and physical properties of blood vessels. VEGFA plays an important role in the promotion of neovascularization; therefore, inhibition of VEGFA can degrade the structure of blood vessels and reduce neovascularization. In the present study, HERB, a high-throughput experimental and reference-oriented database of herbal medicines, was used for compound mining targeting VEGFA. The compounds most likely to interact with VEGFA were screened by molecular docking. Next, the compounds were used to verify whether it could inhibit the activity of the VEGF signaling pathway in vitro and neovascularization in vivo. In vitro, we found that dioscin could inhibit the activation of the VEGFA-VEGFR2 signaling pathway and cell proliferation of human retinal microvascular endothelial cells in a high-glucose (HG) environment. A more important dioscin intervention inhibits the expression of pro-angiogenic factors in the retinas of db/db mice. In conclusion, our study indicates that dioscin reduces the vascular damage and the expression of pro-angiogenic factors in the retina of db/db mice and implies an important and potential application of dioscin for treatment of DR in clinics.

Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches.

Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding high risk late intubation. This study evaluates whether machine learning can predict the need for intubation within 24 h using commonly available bedside and laboratory parameters taken at critical care admission. We extracted data from 2 large critical care databases (MIMIC-III and eICU-CRD). Missing variables were imputed using autoencoder. Machine learning classifiers using logistic regression and random forest were trained using 60% of the data and tested using the remaining 40% of the data. We compared the performance of logistic regression and random forest models to predict intubation in critically ill patients. After excluding patients with limitations of therapy and missing data, we included 17,616 critically ill patients in this retrospective cohort. Within 24 h of admission, 2,292 patients required intubation, whilst 15,324 patients were not intubated. Blood gas parameters (PaO2, PaCO2, HCO3-), Glasgow Coma Score, respiratory variables (respiratory rate, SpO2), temperature, age, and oxygen therapy were used to predict intubation. Random forest had AUC 0.86 (95% CI 0.85-0.87) and logistic regression had AUC 0.77 (95% CI 0.76-0.78) for intubation prediction performance. Random forest model had sensitivity of 0.88 (95% CI 0.86-0.90) and specificity of 0.66 (95% CI 0.63-0.69), with good calibration throughout the range of intubation risks. The results showed that machine learning could predict the need for intubation in critically ill patients using commonly collected bedside clinical parameters and laboratory results. It may be used in real-time to help clinicians predict the need for intubation within 24 h of intensive care unit admission.

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives.

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

Thiosemicarbazone-based lead optimization to discover high-efficiency and low-toxicity anti-gastric cancer agents.

In this paper, a series of thiosemicarbazone derivatives containing different aromatic heterocyclic groups were synthesized and the tridentate donor system of the lead compound was optimized. Most of the target compounds showed improved antiproliferative activity against MGC803 cells. SAR studies revealed that compound 5d displayed significant advantages in inhibition effect with an IC50 value of 0.031 µM, and better selectivity between cancer and normal cells than 3-AP and DpC (about 15- and 5-fold improved respectively). Besides, compound 5d showed selective antiproliferative activity in not only other cancer cells but also different gastric cancer cell lines. In-depth mechanism studies showed that compound 5d could induce mitochondria-related apoptosis which might be related to the elevation of intracellular ROS level, and cause cell cycle arrest at S phase. Moreover, 5d could evidently suppress the cell migration and invasion by blocking the EMT (epithelial-mesenchymal transition) process. Consequently, our studies provided a lead optimization strategy of thiosemicarbazone derivatives which would contribute to discover high-efficiency and low-toxicity agents for the treatment of gastric cancer.