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.

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.

DUSP1 and KCNJ2 mRNA upregulation can serve as a biomarker of mechanical asphyxia-induced death in cardiac tissue.

The incidence of death by asphyxia is second to the incidence of death by mechanical injury; however, death by mechanical asphyxia may be difficult to prove in court, particularly in cases in which corpses do not exhibit obvious signs of asphyxia. To identify a credible biomarker of asphyxia, we first examined the expression levels of 47,000 mRNAs in human cardiac tissue specimens from individuals who died of mechanical asphyxia and compared the expression levels with the levels of the corresponding mRNAs in specimens from individuals who died of craniocerebral injury using microarray. We selected 119 differentially expressed mRNAs, examined the expression levels of these mRNAs in 44 human cardiac tissue specimens of individuals who died of mechanical asphyxia, craniocerebral injury, hemorrhagic shock, or other causes. That the expression of dual-specificity phosphatase 1 (DUSP1) and potassium voltage-gated channel subfamily J member 2 (KCNJ2) was upregulated in human cardiac tissues from the mechanical asphyxia group compared with control tissues, regardless of age, environmental temperature, and postmortem interval (PMI), indicating that DUSP1 and KCNJ2 may be associated with mechanical asphyxia-induced death and can thus serve as useful biomarkers of death by mechanical asphyxia.

Detection of 1,3-dihydroxyacetone by tris(2,2'-bipyridine)ruthenium(II) electrochemiluminescence.

1,3-Dihydroxyacetone, a common cosmetic material and food additive, has been successfully explored as an efficient electrochemiluminescence coreactant of Ru(bpy)32+ for the first time. It is about 25 times more effective than the well-known coreactant sodium oxalate. The high electrochemiluminescence efficiency allows sensitive detection of 1,3-dihydroxyacetone without any derivatization. The electrochemiluminescence method shows two linear electrochemiluminescence responses over the range of 5.0-500 µM and 500 µM-6.0 mM with a detection limit of 1.79 µM. The proposed method is at least two orders of magnitude more sensitive than other reported methods. Graphical abstract ECL intensity-potential profile of 1,3-dihydroxyacetone (DHA) and oxalate.

Predicting search time when hunting for multiple moving targets: A recursive harmonic law.

We investigate searching for multiple mobile objects on networks and introduce the concept of mean random search time (MRST) to quantify the expected time a searcher takes to capture moving targets specified in advance. We consider this quantity averaged over all initial conditions for a searcher and multiple targets called the global MRST. We find that the growth of global MRST follows a recursive harmonic law with respect to that of stalking the individuals. In particular, when the diffusive laws of moving targets are identical, the global MRST shows a logarithmic increase with the number of moving targets. Moreover, utilizing the recursive harmonic law, we can accurately predict the expected successive time interval for capturing a new moving target. The recursive harmonic law unveils the underlying mechanism governing the search time when hunting for multiple moving targets on networks.

Surfactant-free synthesis of three-dimensional nitrogen-doped hierarchically porous carbon and its application as an electrode modification material for simultaneous sensing of ascorbic acid, dopamine and uric acid.

Three-dimensional N-doped hierarchically porous carbon (3D NHPC) was synthesized successfully without using any surfactant or etching agent. This simple and effective synthesis method was accomplished by solvothermal synthesis followed by pyrolysis. The physical morphology and chemical composition of 3D NHPC were verified by scanning electron microscopy, nitrogen adsorption-desorption, and X-ray photoelectron spectroscopy. Its structural studies reveal that it exhibits a micro-mesoporous hierarchical structure and contains C, O and N atoms forming different functional groups. The characterization also reveals that 3D NHPC has a Brunauer-Emmett-Teller (BET) surface area of 594.774 m2 g-1 and the micropore volume was calculated to be 0.192 cm3 g-1 using the t-plot method. Its catalytic activity was investigated by studying its application for simultaneous determination of ascorbic acid (AA), dopamine (DA) and uric acid (UA) by cyclic voltammetry and square-wave voltammetry at physiological pH. A 3D NHPC-modified glassy carbon electrode exhibits linear ranges from 0.05 to 14.50 µM, 1.0 to 120.0 µM, and 2.0 to 30.0 µM for DA, AA and UA, respectively. It also exhibits low detection limits (0.02, 0.10, and 0.14 µM for DA, AA, and UA, respectively), good reproducibility and stability. The urine sample analysis results show good recoveries ranging between 96.30% and 105.40%.

Multitarget search on complex networks: A logarithmic growth of global mean random cover time.

We investigate multitarget search on complex networks and derive an exact expression for the mean random cover time that quantifies the expected time a walker needs to visit multiple targets. Based on this, we recover and extend some interesting results of multitarget search on networks. Specifically, we observe the logarithmic increase of the global mean random cover time with the target number for a broad range of random search processes, including generic random walks, biased random walks, and maximal entropy random walks. We show that the logarithmic growth pattern is a universal feature of multi-target search on networks by using the annealed network approach and the Sherman-Morrison formula. Moreover, we find that for biased random walks, the global mean random cover time can be minimized, and that the corresponding optimal parameter also minimizes the global mean first passage time, pointing towards its robustness. Our findings further confirm that the logarithmic growth pattern is a universal law governing multitarget search in confined media.

Distance-Based Opportunistic Mobile Data Offloading.

Cellular network data traffic can be offload onto opportunistic networks. This paper proposes a Distance-based Opportunistic Publish/Subscribe (DOPS) content dissemination model, which is composed of three layers: application layer, decision-making layer and network layer. When a user wants new content, he/she subscribes on a subscribing server. Users having the contents decide whether to deliver the contents to the subscriber based on the distance information. If in the meantime a content owner has traveled further in the immediate past time than the distance between the owner and the subscriber, the content owner will send the content to the subscriber through opportunistic routing. Simulations provide an evaluation of the data traffic offloading efficiency of DOPS.

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.

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.

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.

Safety of recombinant human growth hormone in treating children with growth hormone deficiency and idiopathic short stature.

Recombinant human growth hormone is generally safe in treating children with growth hormone deficiency and idiopathic short stature. However, side effects such as sodium and water retention, benign intracranial hypertension, insulin insensitivity, increasing risk of secondary neoplasm, scoliosis, and slipped capital femoral epiphysis may occur occasionally, although the overall incidence remains low.

Advances in insulin like growth factor binding protein 1 in the prognosis of acute myocardial infarction.

Insulin-like growth factor (IGF) is closely associated with cardiovascular diseases. Low IGF-I level and high insulin-like growth factor binding protein-1 (IGFBP-1) level in serum can be used as a marker in predicting the long term morbidity and mortality of acute myocardial infarction (AMI). The article reviews the recent advances in IGFBP-1 in the prognosis of AMI.

Application of positron emission tomography in the clinical evaluation and treatment of hypothalamic-pituitary diseases.

Positron emission tomography (PET) and PET/CT are playing increasingly important roles in the clinical evaluation and treatment of tumors. As neuroendocrine tissues, the hypothalamus and pituitary gland have their unique features, and PET can be valuable in evaluating hypothalamic-pituitary diseases lesions. This article reviews the application of PET in the clinical evaluation and treatment of hypothalamic-pituitary diseases.

Clinical analysis of misdiagnosis in 70 children with primary hypothyroidism.

OBJECTIVE: To analyze the causes of misdiagnosis of primary hypothyroidism (PH), with an attempt to reduce the misdiagnosis or mistreatment. METHODS: Totally 70 PH children with a history of misdiagnosis but whose conditions were confirmed in Peking Union Medical College Hospital and the First Hospital of Jilin University from July 2000 to May 2009 were enrolled in this study. The clinical data were collected and the causes of misdiagnoses were analyzed. RESULTS: Of these 70 patients, 19 were misdiagnosed as anaemia and dystrophy, 18 as pituitary tumors, 10 as adiposities, 6 as myocarditis or pericardial effusion, 4 as Downs syndrome, 3 as hepatitis, 3 as amyasthenia, 3 as cerebral palsy, 2 as cystis thyrolingualis, and 2 as congenital megacolon. The duration of misdiagnoses ranged from 6 to 72 months. The clinical manifestations of these patients were complicated, involving multiple organs and systems. CONCLUSIONS: PH has complicated clinical manifestations and individual variations, and therefore can be easily misdiagnosed. Good knowledge, sufficient history-taking, and cautious physical examinations can help avoid misdiagnosis. Neonatal screening is helpful for diagnosis and treatment of congenital hypothyroidism.

Growth and development of adolescent patients with Cushing's syndrome.

OBJECTIVE: To elucidate the growth and development of patients with Cushing's syndrome (CS) in adolescence. METHODS: We analyzed the clinical data of 19 patients aged under 18 with CS. We divided the patients into two groups according to the height at diagnosis. The patients under the 3rd percentile were in the short stature group (n=12), and the others in the non-short stature group (n=7). The intergroup differences in disease course, age of onset, and 24-hour urinary free cortisol were analyzed. RESULTS: The median age at diagnosis was 13 years. The median disease course was 0.9 years. All those patients presented with typical clinical characteristics of CS. Twelve cases (63.2%) were in short stature group. The disease course of the short stature group was found significantly longer than that of the non-short stature group [(2.7∓1.7) years vs. (0.8∓0.6) years, P=0.013], but no significant difference was found in the age of onset (P=0.530) or 24-hour urinary free cortisol (P=0.919) between the two groups. CONCLUSIONS: The data suggest that short stature is common in adolescent CS patients. The growth delay may be correlated to the disease course of CS.