Roles and interactions of tumor microenvironment components in medulloblastoma with implications for novel therapeutics.

Medulloblastomas, the most common malignant pediatric brain tumors, can be classified into the wingless, sonic hedgehog (SHH), group 3, and group 4 subgroups. Among them, the SHH subgroup with the TP53 mutation and group 3 generally present with the worst patient outcomes due to their high rates of recurrence and metastasis. A novel and effective treatment for refractory medulloblastomas is urgently needed. To date, the tumor microenvironment (TME) has been shown to influence tumor growth, recurrence, and metastasis through immunosuppression, angiogenesis, and chronic inflammation. Treatments targeting TME components have emerged as promising approaches to the treatment of solid tumors. In this review, we summarize progress in research on medulloblastoma microenvironment components and their interactions. We also discuss challenges and future research directions for TME-targeting medulloblastoma therapy.

Direct and indirect treatment effects in the presence of semicompeting risks.

Semicompeting risks refer to the phenomenon that the terminal event (such as death) can censor the nonterminal event (such as disease progression) but not vice versa. The treatment effect on the terminal event can be delivered either directly following the treatment or indirectly through the nonterminal event. We consider 2 strategies to decompose the total effect into a direct effect and an indirect effect under the framework of mediation analysis in completely randomized experiments by adjusting the prevalence and hazard of nonterminal events, respectively. They require slightly different assumptions on cross-world quantities to achieve identifiability. We establish asymptotic properties for the estimated counterfactual cumulative incidences and decomposed treatment effects. We illustrate the subtle difference between these 2 decompositions through simulation studies and two real-data applications in the Supplementary Materials.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Bridging the Green Gap: Monochromatic InP-Based Quantum-Dot-on-Chip LEDs with over 50% Color Conversion Efficiency.

Solid-state light-emitting diodes (LEDs) emit nearly monochromatic light, yet seamless tuning of emission color throughout the visible region remains elusive. Color-converting powder phosphors are therefore used for making LEDs with a bespoke emission spectrum, yet broad emission lines and low absorption coefficients compromise the formation of small-footprint monochromatic LEDs. Color conversion by quantum dots (QDs) can address these issues, but high-performance monochromatic LEDs made using QDs free of restricted, hazardous elements remain to be demonstrated. Here, we show green, amber, and red LEDs formed using InP-based QDs as on-chip color convertor for blue LEDs. Implementing QDs with near-unity photoluminescence efficiency yields a color conversion efficiency over 50% with little intensity roll-off and nearly complete blue light rejection. Moreover, as the conversion efficiency is mostly limited by package losses, we conclude that on-chip color conversion using InP-based QDs can provide spectrum-on-demand LEDs, including monochromatic LEDs that bridge the green gap.

Unconditional and Robust Quantum Metrological Advantage beyond N00N States.

Quantum metrology employs quantum resources to enhance the measurement sensitivity beyond that can be achieved classically. While multiphoton entangled N00N states can in principle beat the shot-noise limit and reach the Heisenberg limit, high N00N states are difficult to prepare and fragile to photon loss which hinders them from reaching unconditional quantum metrological advantages. Here, we combine the idea of unconventional nonlinear interferometers and stimulated emission of squeezed light, previously developed for the photonic quantum computer Jiuzhang, to propose and realize a new scheme that achieves a scalable, unconditional, and robust quantum metrological advantage. We observe a 5.8(1)-fold enhancement above the shot-noise limit in the Fisher information extracted per photon, without discounting for photon loss and imperfections, which outperforms ideal 5-N00N states. The Heisenberg-limited scaling, the robustness to external photon loss, and the ease-of-use of our method make it applicable in practical quantum metrology at a low photon flux regime.

Solving Graph Problems Using Gaussian Boson Sampling.

Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the generated samples from the GBS could be harnessed to enhance the classical stochastic algorithms in searching some graph features. Here, we use Jiǔzhang, a noisy intermediate-scale quantum computer, to solve graph problems. The samples are generated from a 144-mode fully connected photonic processor, with photon click up to 80 in the quantum computational advantage regime. We investigate the open question of whether the GBS enhancement over the classical stochastic algorithms persists-and how it scales-with an increasing system size on noisy quantum devices in the computationally interesting regime. We experimentally observe the presence of GBS enhancement with a large photon-click number and a robustness of the enhancement under certain noise. Our work is a step toward testing real-world problems using the existing noisy intermediate-scale quantum computers and hopes to stimulate the development of more efficient classical and quantum-inspired algorithms.

Gaussian Boson Sampling with Pseudo-Photon-Number-Resolving Detectors and Quantum Computational Advantage.

We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical spoofing mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ∼600 yr using exact methods, whereas our quantum computer, Jiǔzhang 3.0, takes only 1.27 µs to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier∼3.1×10^{10} yr.

Maintaining higher grain production with less reactive nitrogen losses in China: A meta-analysis study.

Managing reactive nitrogen (Nr) in agricultural production is crucial for addressing the triple challenges of food security, climate change and environmental degradation. Intensive work has been conducted to investigate the effects of mitigation strategies on reducing Nr losses by ammonia emission (Nr-NH3), nitrous oxide emission (Nr-N2O) and nitrate leaching (Nr-NO3-) separately. This meta-analysis evaluated the efficiency of each strategy in mitigating Nr losses coupled with grain yield responses. The results indicate that producing one Megagram (Mg) of wheat grains caused higher Nr losses, twice that of rice and 17% that of maize. The Nr-NH3 and Nr-NO3- were the dominant sources of Nr losses of the three crops (96%), while Nr-NH3 only presented 86% of the total Nr losses for rice. Reducing the N rate strategy decreased the yield by 33% and the Nr losses by 62% compared with the conventional rate (150-250 kg N ha-1) as an average of the three crops. In contrast, increasing the N rate higher than 250 kg N ha-1 amplified the yield by 15% but also caused a 71% increase in Nr losses compared with the conventional rate. Although subsurface application decreased Nr losses by 5%, this study rejected this approach as an effective strategy due to a 4% yield decline on average of the grain crops. Slow-release fertilizers decreased Nr-NH3 and Nr-N2O losses by 41-58% and 54-89%, respectively, of the highest losses under urea in the three crops, but also led to yield reductions. Organic amendments achieved the highest drop in Nr-NO3- loss by 66% in maize coupled with yield declines. Biochar increased wheat and maize yields by 0.3 and 0.1 Mg, respectively, coupled with 1 kg reduction in Nr losses. On average, inhibitors augmented the grain yields by 0.2 Mg ha-1 for each 1 kg decline in Nr losses. In conclusion, for sustainable agricultural intensification, biochar (for wheat only) and inhibitors (for the three crops) are strongly recommended as mitigation strategies for Nr losses from grain crop production systems in China.

Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light.

We report phase-programmable Gaussian boson sampling (GBS) which produces up to 113 photon detection events out of a 144-mode photonic circuit. A new high-brightness and scalable quantum light source is developed, exploring the idea of stimulated emission of squeezed photons, which has simultaneously near-unity purity and efficiency. This GBS is programmable by tuning the phase of the input squeezed states. The obtained samples are efficiently validated by inferring from computationally friendly subsystems, which rules out hypotheses including distinguishable photons and thermal states. We show that our GBS experiment passes a nonclassicality test based on inequality constraints, and we reveal nontrivial genuine high-order correlations in the GBS samples, which are evidence of robustness against possible classical simulation schemes. This photonic quantum computer, Jiuzhang 2.0, yields a Hilbert space dimension up to ∼10^{43}, and a sampling rate ∼10^{24} faster than using brute-force simulation on classical supercomputers.

Analysis of misidentifications in TEM characterisation of organic-inorganic hybrid perovskite material.

Organic-inorganic hybrid perovskites (OIHPs) have recently emerged as groundbreaking semiconductor materials owing to their remarkable properties. Transmission electron microscopy (TEM), as a very powerful characterisation tool, has been widely used in perovskite materials for structural analysis and phase identification. However, the perovskites are highly sensitive to electron beams and easily decompose into PbX2 (X = I, Br, Cl) and metallic Pb. The electron dose of general high-resolution TEM is much higher than the critical dose of MAPbI3 , which results in universal misidentifications that PbI2 and Pb are incorrectly labelled as perovskite. The widely existed mistakes have negatively affected the development of perovskite research fields. Here misidentifications of the best-known MAPbI3 perovskite are summarised and corrected, then the causes of mistakes are classified and ascertained. Above all, a solid method for phase identification and practical strategies to reduce the radiation damage for perovskite materials have also been proposed. This review aims to provide the causes of mistakes and avoid misinterpretations in perovskite research fields in the future.

Estimation of incubation period and generation time based on observed length-biased epidemic cohort with censoring for COVID-19 outbreak in China.

The incubation period and generation time are key characteristics in the analysis of infectious diseases. The commonly used contact-tracing-based estimation of incubation distribution is highly influenced by the individuals' judgment on the possible date of exposure, and might lead to significant errors. On the other hand, interval censoring-based methods are able to utilize a much larger set of traveling data but may encounter biased sampling problems. The distribution of generation time is usually approximated by observed serial intervals. However, it may result in a biased estimation of generation time, especially when the disease is infectious during incubation. In this paper, the theory from renewal process is partially adopted by considering the incubation period as the interarrival time, and the duration between departure from Wuhan and onset of symptoms as the mixture of forward time and interarrival time with censored intervals. In addition, a consistent estimator for the distribution of generation time based on incubation period and serial interval is proposed for incubation-infectious diseases. A real case application to the current outbreak of COVID-19 is implemented. We find that the incubation period has a median of 8.50 days (95% confidence interval [CI] [7.22; 9.15]). The basic reproduction number in the early phase of COVID-19 outbreak based on the proposed generation time estimation is estimated to be 2.96 (95% CI [2.15; 3.86]).

A powerful test for the maximum treatment effect in thorough QT/QTc studies.

Parallel-group thorough QT/QTc studies focus on the change of QT/QTc values at several time-matched points from a pretreatment day (baseline) to a posttreatment day for different groups of treatment. The International Council for Harmonisation E14 stresses that QTc prolongation beyond a threshold represents high cardiac risk and calls for a test on the largest time-matched treatment effect (QTc prolongation). QT/QTc analysis usually assumes a jointly multivariate normal (MVN) distribution of pretreatment and posttreatment QT/QTc values, with a blocked compound symmetry covariance matrix. Existing methods use an analysis of covariance (ANCOVA) model including day-averaged baseline as a covariate to deal with the MVN model. However, the ANCOVA model tends to underestimate the variation of the estimator for treatment effects, resulting in the inflation of empirical type I error rate when testing whether the largest QTc prolongation is beyond a threshold. In this article, we propose two new methods to estimate the time-matched treatment effects under the MVN model, including maximum likelihood estimation and ordinary-least-square-based two-stage estimation. These two methods take advantage of the covariance structure and are asymptotically efficient. Based on these estimators, powerful tests for QT/QTc prolongation are constructed. Simulation shows that the proposed estimators have smaller mean square error, and the tests can control the type I error rate with high power. The proposed methods are applied on testing the carryover effect of diltiazem to inhibit dofetilide in a randomized phase 1 trial.

Prognostic value of liver biochemical parameters for COVID-19 mortality.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has brought great challenges to global public health. However, a comprehensive analysis of the relationship between liver biochemical parameters and COVID-19 mortality is quite limited. METHODS: We searched the following electronic databases: PubMed, Embase, Cochrane Library, Web of Science, Scopus, Wanfang and China National Knowledge Infrastructure database until May 5, 2020. STATA software was used for the statistical analyses. RESULTS: A total of 25 studies involving 5971 COVID-19 patients were included in our analysis. Compared with non-survivors, survivors had lower levels of aspartate aminotransferase (AST) (weighted mean difference [WMD]=-16.71U/L, 95%CI=[-21.03,-12.40], P<0.001), alanine transaminase (ALT) (WMD=-5.20U/L, 95%CI=[-8.00,-2.41], P<0.001), total bilirubin (TBIL) (WMD=4.40µmol/L, 95%CI=[-5.11,-3.70], P<0.001) and lactic dehydrogenase (LDH) (WMD=-252.44U/L, 95%CI=[-289.57,-215.30], P<0.001), and higher albumin (ALB) level (WMD=4.47g/L, 95%CI=[3.47,5.47], P<0.001). Besides, survivors had lower proportions of these abnormally increased parameters (AST: OR=0.25, 95%CI=[0.15,0.41], P<0.001; ALT: OR=0.49, 95%CI=[0.37,0.64], P<0.001; TBIL: (OR=0.20, 95%CI=[0.12,0.34], P<0.001; LDH, OR=0.09, 95%CI=[0.06,0.14], P<0.001), and lower proportion of abnormally decreased ALB (OR=0.16, 95%CI=[0.07,0.38], P<0.001). Meta-analysis based on standard mean difference and sensitivity analysis did not change the conclusions. Egger test did not detect the presence of publication bias. CONCLUSIONS: Liver biochemical parameters were strongly correlated with COVID-19 mortality. Measurement of these liver biochemical parameters might assist clinicians to evaluate the prognosis of COVID-19.

Quantum Beat between Sunlight and Single Photons.

We demonstrate fourth-order quantum beat between sunlight and single photons from a quantum dot. With a fast time-resolved detection system, we observed high-visibility quantum beat between the independent photons of different frequencies from the two astronomically separated light sources. The temporal dynamics of the beat oscillation indicate the coherent behavior of the interfering photons, and the raw visibility of two-photon interference shows violation of the classical limit with a frequency mismatch of three-times the line width.

Quantum Interference between Light Sources Separated by 150 Million Kilometers.

We report an experiment to test quantum interference, entanglement, and nonlocality using two dissimilar photon sources, the Sun and a semiconductor quantum dot on the Earth, which are separated by ∼150 million kilometers. By making the otherwise vastly distinct photons indistinguishable in all degrees of freedom, we observe time-resolved two-photon quantum interference with a raw visibility of 0.796(17), well above the 0.5 classical limit, providing unambiguous evidence of the quantum nature of thermal light. Further, using the photons with no common history, we demonstrate postselected two-photon entanglement with a state fidelity of 0.826(24) and a violation of Bell inequality by 2.20(6). The experiment can be further extended to a larger scale using photons from distant stars and open a new route to quantum optics experiments at an astronomical scale.

Optically Levitated Nanodumbbell Torsion Balance and GHz Nanomechanical Rotor.

Levitated optomechanics has great potential in precision measurements, thermodynamics, macroscopic quantum mechanics, and quantum sensing. Here we synthesize and optically levitate silica nanodumbbells in high vacuum. With a linearly polarized laser, we observe the torsional vibration of an optically levitated nanodumbbell. This levitated nanodumbbell torsion balance is a novel analog of the Cavendish torsion balance, and provides rare opportunities to observe the Casimir torque and probe the quantum nature of gravity as proposed recently. With a circularly polarized laser, we drive a 170-nm-diameter nanodumbbell to rotate beyond 1 GHz, which is the fastest nanomechanical rotor realized to date. Smaller silica nanodumbbells can sustain higher rotation frequencies. Such ultrafast rotation may be used to study material properties and probe vacuum friction.

Vesicle formation by proton transfer driven short-tailed fatty acids of C4-C8 chain length in water.

Ultrashort single-chain fatty acids self-assemble to form vesicles under certain proton-driven conditions. The protonation provides a larger charge area around the hydrophilic carbonyl headgroups, and proton shift as the key driving parameter was studied. The ultrashort fatty acids (C4-C8) formed stable unilamellar vesicles predominantly through out the whole range of tested pH levels (6.5-9.5). A proton-driven self-assembly process and effects on the phase transition were characterized by dynamic light scattering, transmission electron microscopy and cryo-transmission electron microscopy. In particular, we studied in greater detail the molecular packing characteristics of FA vesicles for geometric reasons and the protonation effect changes the molecular surface charge and further carboxylic acid headgroup motion. This study enhances the understanding of the physicochemical specificity of these membrane vesicles, and may facilitate the alteration of membrane function caused by FAs.

Is tea consumption associated with the serum uric acid level, hyperuricemia or the risk of gout? A systematic review and meta-analysis.

BACKGROUND: The aim of this study was to examine the associations of tea consumption with the serum uric acid (SUA) level, hyperuricemia (HU) and the risk of gout. METHODS: A comprehensive literature search up to June 2016, using PUBMED and EMBASE databases, was conducted to identify the relevant observational studies that examined the associations of tea consumption with the SUA level, HU and the risk of gout. RESULTS: A total of fifteen observational studies were included in this study, and nine studies were extracted for meta-analysis. For the SUA level, seven studies were included. According to the combined weighted mean difference (WMD), there was no significant difference between the highest and the lowest tea intake category in terms of the SUA level (WMD = 7.41 µmol/L, 95%CI: -2.34 to 17.15; P = 0.136). In subgroup analysis including three studies, green tea consumption was positively associated with the SUA level (WMD = 17.20 µmol/L, 95%CI: 7.00 to 27.40; P = 0.01). For the prevalence of HU, five studies were included. The overall multi-variable adjusted odds ratio (OR) for the highest versus the lowest category of tea consumption was 0.98 (95%CI: 0.77 to 1.24; P = 0.839). For the risk of gout, two prospective cohort studies showed that there was no relationship between tea consumption and the risk of gout in males and females, respectively. CONCLUSION: The current evidences suggest that tea consumption does not seem to be associated with the SUA level, HU and the risk of gout. However, due to the limited number of studies, green tea consumption might be positively associated with the SUA level. More well-designed prospective cohort studies are needed to elaborate these issues further.

Profiles of attitudes toward inclusive education among Chinese in-service teachers: their relationships with demographic factors and organizational commitment.

Introduction: The increased diversity of students (e.g., students with special educational needs) has presented teachers with unprecedented challenges. Teachers' attitudes toward inclusive education play a crucial role in teachers' organizational well-being. However, existing studies mostly explored attitudes toward inclusive education based on a variable-centered approach. This study used a person-centered approach to identify teachers' attitude profile membership and explored the relationships of attitude profiles with demographic factors (i.e., gender, years of teaching experience, subject taught, and in-service training) and organizational commitment. Methods: Nine hundred and seventy-two in-service teachers from forty-nine inclusive education schools in Beijing responded to the Revised Multidimensional Attitudes toward Inclusive Education Scale and the Organizational Commitment Inventory. Latent profile analyses, multinomial logistic regression, and univariate analysis of variance were used to analyze data. Results and discussion: The results revealed four attitude profiles: involuntary participation, behavior avoidance, neutral, and proactive involvement. Years of teaching experience and in-service training were significant predictors of teachers' latent profile membership. Teachers belonging to the involuntary participation profile showed the highest levels of maladaptive commitments to inclusive education schools. Teachers belonging to the proactive involvement and the behavior avoidance profiles showed higher levels of adaptive commitments. However, teachers belonging to the neutral profile had the lowest levels of adaptive commitments. The theoretical contributions, practical implications, and limitations are discussed.