[Risk factors for recurrence of childhood acute lymphoblastic leukemia after treatment with the Chinese Children's Cancer Group ALL-2015 protocol]. / CCCG-ALL-2015æ–¹æ¡ˆæ²»ç–—å„¿ç«¥æ€¥æ€§æ·‹å·´ç»†èƒžç™½è¡€ç— å¤å‘çš„å±é™©å› ç´ åˆ†æž.

OBJECTIVES: To investigate the cumulative incidence of recurrence (CIR) in children with acute lymphoblastic leukemia (ALL) after treatment with the Chinese Children's Cancer Group ALL-2015 (CCCG-ALL-2015) protocol and the risk factors for recurrence. METHODS: A retrospective analysis was conducted on the clinical data of 852 children who were treated with the CCCG-ALL-2015 protocol from January 2015 to December 2019. CIR was calculated, and the risk factors for the recurrence of B-lineage acute lymphoblastic leukemia (B-ALL) were analyzed. RESULTS: Among the 852 children with ALL, 146 (17.1%) experienced recurrence, with an 8-year CIR of 19.8%±1.6%. There was no significant difference in 8-year CIR between the B-ALL group and the acute T lymphocyte leukemia group (P>0.05). For the 146 children with recurrence, recurrence was mainly observed in the very early stage (n=62, 42.5%) and the early stage (n=46, 31.5%), and there were 42 children with bone marrow recurrence alone (28.8%) in the very early stage and 27 children with bone marrow recurrence alone (18.5%) in the early stage. The Cox proportional-hazards regression model analysis showed that positive MLLr fusion gene (HR=4.177, 95%CI: 2.086-8.364, P<0.001) and minimal residual disease≥0.01% on day 46 (HR=2.013, 95%CI: 1.163-3.483, P=0.012) were independent risk factors for recurrence in children with B-ALL after treatment with the CCCG-ALL-2015 protocol. CONCLUSIONS: There is still a relatively high recurrence rate in children with ALL after treatment with the CCCG-ALL-2015 protocol, mainly bone marrow recurrence alone in the very early stage and the early stage, and minimal residual disease≥0.01% on day 46 and positive MLLr fusion gene are closely associated with the recurrence of B-ALL.

[The Efficacy and Influencing Factors of Cyclosporine Alone in the Treatment of Children with Acquired Aplastic Anemia].

OBJECTIVE: To analyze the efficacy and influencing factors of cyclosporine (CsA) alone in the treatment of children with acquired aplastic anemia (AA). METHODS: The clinical data of children diagnosed with AA and treated with CsA alone from January 1, 2016 to December 31, 2020 in the Children's Hospital of Chongqing Medical University were collected, and the efficacy and influencing factors of CsA treatment were evaluated. RESULTS: Among the 119 patients, there were 62 male and 57 female, with a median age of 7 years and 1 month. There were 45 cases of very severe AA (VSAA), 47 cases of severe AA (SAA), and 27 cases of non-severe AA (NSAA). At 6 months after treatment, the efficacy of VSAA was lower than that of SAA and NSAA, and there was a statistical difference (P < 0.01). 6 cases died early, 16 cases relapsed, 2 cases progressed to AML and ALL. The results of univariate analysis showed that the high proportion of lymphocyte in the bone marrow at 6 months was an adverse factor for the efficacy of CsA, while high PLT count was a protective factor (P =0.008, P =0.002). The ROC curve showed that the cut-off values of PLT count and the proportion of bone marrow lymphocyte at 6 months were 16.5×109 /L, 68.5%, respectively. Multivariate analysis showed that the high proportion of lymphocyte in bone marrow at 6 months was an independent adverse factor for IST (P =0.020, OR =0.062), and high PLT count was a protective factor (P =0.044, OR =1.038). At 3 months of treatment, CsA response and NSAA were the risk factor for recurrence (P =0.001, 0.031). CONCLUSION: The efficacy of NSAA was higher than that of SAA and VSAA after 6 months of treatment with CsA alone. A high PLT count at the initial diagnosis was a good factor for the effectiveness of CsA, and a high proportion of bone marrow lymphocyte was an unfavorable factor. CsA response at 3 months and NSAA were risk factors for recurrence.

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks.

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

Isolation, identification, and tolerance analysis of yeast during the natural fermentation process of Sidamo coffee beans.

Yeast, which plays a pivotal role in the brewing, food, and medical industries, exhibits a close relationship with human beings. In this study, we isolated and purified 60 yeast strains from the natural fermentation broth of Sidamo coffee beans to screen for indigenous beneficial yeasts. Among them, 25 strains were obtained through morphological characterization on nutritional agar medium from Wallerstein Laboratory (WL), with molecular biology identifying Saccharomyces cerevisiae strain YBB-47 and the remaining 24 yeast strains identified as Pichia kudriavzevii. We investigated the fermentation performance, alcohol tolerance, SO2 tolerance, pH tolerance, sugar tolerance, temperature tolerance, ester production capacity, ethanol production capacity, H2S production capacity, and other brewing characteristics of YBB-33 and YBB-47. The results demonstrated that both strains could tolerate up to 3% alcohol by volume at a high sucrose mass concentration (400 g/L) under elevated temperature conditions (40 ℃), while also exhibiting a remarkable ability to withstand an SO2 mass concentration of 300 g/L at pH 3.2. Moreover, S. cerevisiae YBB-47 displayed a rapid gas production rate and strong ethanol productivity. whereas P. kudriavzevii YBB-33 exhibited excellent alcohol tolerance. Furthermore, this systematic classification and characterization of coffee bean yeast strains from the Sidamo region can potentially uncover additional yeasts that offer high-quality resources for industrial-scale coffee bean production.

Improving Buried Interface Contact by Bidentate Anchoring for Inverted Perovskite Solar Cells.

Nickel oxide (NiOx) is a promising hole transport layer (HTL) to fabricate efficient and large-scale inverted perovskite solar cells (PSCs) due to its low cost and superior chemical stability. However, inverted PSCs based on NiOx are still lagging behind that of other HTL because of the poor quality of buried interface contact. Herein, a bidentate ligand, 4,6-bis (diphenylphosphino) phenoxazine (2DPP), is used to regulate the NiOx surface and perovskite buried interface. The diphosphine Lewis base in the 2DPP molecule can coordinate both with NiOx and lead ions at NiOx/perovskite interface, leading to high-quality perovskite films with minimized defects. It is found that the inverted PSCs with 2DPP-modified buried interface exhibit double advantages of being both fast charge extraction and reduced nonradiative recombination, which is a combination of multiple factors including favorable energetic alignment, improved interface contact and strong binding between NiOx/2DPP and perovskite. The optimal PSC based on 2DPP modification yields a champion power conversion efficiency (PCE) of 21.9%. The unencapsulated PSC maintains above 75% of its initial PCE in the air with a relative humidity (RH) of 30-40% for 1000 h.

Investigation on screening, identification, and fermentation characteristics of Yunnan olive in the fermented liquid utilizing five strains of Saccharomyces cerevisiae.

Refined indigenous Saccharomyces cerevisiae can enhance refinement, sophistication, and subtlety of fruit wines by showcasing exceptional regional characteristics. In order to identify exceptional indigenous S. cerevisiae strains from Yunnan olive, this study isolated 60 yeast strains from wild Yunnan olive fermentation mash. The five S. cerevisiae strains were subjected to morphological and molecular biological identification, followed by evaluation of their fermentation performance, ethanol production capacity, ester production capacity, H2S production capacity, killing capacity, and tolerance. Strains LJM-4, LJM-10, and LJM-26 exhibited robust tolerance to 6% ethanol volume fraction, pH 2.8, sucrose concentration of 400 g/L, SO2 concentration of 0.3 g/L, glucose concentration of 400 g/L at both 40 °C and 15 °C. Additionally, strain LJM-10 demonstrated a faster fermentation rate compared to the other strains. Among the tested S. cerevisiae strains evaluated in this study for olive wine fermentation process in Yunnan region; strain LJM-10 displayed superior abilities in terms of ester and ethanol production while exhibiting the lowest H2S production levels. These findings suggest that strain LJM-10 holds great potential as an excellent candidate for optimizing fruit wine S. cerevisiae fermentation processes in Yunnan olive fruit wine.

Psychromicrobium Xiongbiense sp. nov., an Actinobacterium Isolated from Forest Soil.

A novel Gram-stain-positive, oval-shaped, and non-flagellated bacterial strain YIM S02556T was isolated from forest soil in Xiongbi Town, Shizong County, Qujing City, Yunnan Province, southwestern China. The strain exhibited high pairwise 16 S rRNA gene sequence similarity with Psychromicrobium lacuslunae (97.3%) and Psychromicrobium silvestre (96.3%). Strain YIM S02556T exhibited an average nucleotide identity (ANI) of 72.5% with P. lacuslunae IHBB 11,108T and 72.8% ANI with P. silvestre AK 20-18T. The digital DNA-DNA hybridization (dDDH) value between strain YIM S02556T and P. lacuslunae IHBB 11,108T was 20.2%, while with P. silvestre AK 20-18T, the dDDH value was 20.8%. Strain YIM S02556T exhibited optimal growth at 28 °C, pH 7.0, without NaCl. Growth occurred within 10-37 ℃, pH 5.0-8.0, and in the presence of up to 5% w/v NaCl concentration. The genome size was 3.1 Mbp with 64.2% G + C content. The predominant menaquinone was MK-8(H4). The major cellular fatty acid was anteiso-C15:0. Based on the polyphasic analysis, strain YIM S02556T (= KCTC 49,805T = CCTCC AB2020166T) represents a novel Psychromicrobium species in which the name Psychromicrobium xiongbiense sp.nov. was proposed.

High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit.

Reinforcement learning (RL) stands as one of the three fundamental paradigms within machine learning and has made a substantial leap to build general-purpose learning systems. However, using traditional electrical computers to simulate agent-environment interactions in RL models consumes tremendous computing resources, posing a significant challenge to the efficiency of RL. Here, we propose a universal framework that utilizes a photonic integrated circuit (PIC) to simulate the interactions in RL for improving the algorithm efficiency. High parallelism and precision on-chip optical interaction calculations are implemented with the assistance of link calibration in the hybrid architecture PIC. By introducing similarity information into the reward function of the RL model, PIC-RL successfully accomplishes perovskite materials synthesis task within a 3472-dimensional state space, resulting in a notable 56% improvement in efficiency. Our results validate the effectiveness of simulating RL algorithm interactions on the PIC platform, highlighting its potential to boost computing power in large-scale and sophisticated RL tasks.

Observation of a Photonic Orbital Gauge Field.

Gauge field is widely studied in natural and artificial materials. With an effective magnetic field for uncharged particles, many intriguing phenomena are observed in several systems like photonic Floquet topological insulator. However, previous researches about the gauge field mostly focus on limited dimensions such as the Dirac spinor in graphene materials. Here, an orbital gauge field based on photonic triangular lattices is first proposed and experimentally observed. Disclination defects with Frank angle Ω created on such lattices breaks the original lattice symmetry and generates purely geometric gauge field operating on orbital basis functions. Interestingly, it is found that bound states near zero energy with the orbital angular momentum (OAM) l = 2 are intensively confined at the disclination as gradually expanding Ω. Moreover, the introduction of a vector potential field breaks the time-reversal symmetry of the orbital gauge field, experimentally manifested by the chiral transmission of light on helical waveguides. The orbital gauge field further suggests fantastic applications of manipulating the vortex light in photonic integrated devices.

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel.

Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel.

U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model's performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

The effect of lipid metabolism disorder on patients with hyperuricemia using Multi-Omics analysis.

A multiomics study was conducted to investigate how lipid metabolism disorders affect the immune system in Xinjiang patients with hyperuricemia. The serum of 60 healthy individuals and 60 patients with hyperuricemia was collected. This study used LC-MS and HPLC to analyze differential lipid metabolites and enrichment pathways. It measured levels of immune factors tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), carnitine palmitoyltransferase-1 (CPT1), transforming growth factor-ß1 (TGF-ß1), glucose (Glu), lactic acid (LD), interleukin 10 (IL-10), and selenoprotein 1 (SEP1) using ELISA, as well as to confirm dysregulation of lipid metabolism in hyperuricemia. 33 differential lipid metabolites were significantly upregulated in patients with hyperuricemia. These lipid metabolites were involved in arachidonic acid metabolism, glycerophospholipid metabolism, linoleic acid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and alpha-Linolenic acid metabolism pathways. Moreover, IL-10, CPT1, IL-6, SEP1, TGF-ß1, Glu, TNF-α, and LD were associated with glycerophospholipid metabolism. In patients with hyperuricemia of Han and Uyghur nationalities, along with healthy individuals, significant differences in CPT1, TGF-ß1, Glu, and LD were demonstrated by ELISA (P < 0.05). Furthermore, the levels of SEP1, IL-6, TGF-ß1, Glu, and LD differed considerably between groups of the same ethnicity (P < 0.05). It was found that 33 kinds of lipid metabolites were significantly different in patients with hyperuricemia, which mainly involved 5 metabolic pathways. According to the results of further studies, it is speculated that CPT1, TGF-ß1, SEP1, IL-6, Glu and LD may increase fatty acid oxidation and mitochondrial oxidative phosphorylation in patients through glycerophospholipid pathway, reduce the rate of glycolysis, and other pathways to change metabolic patterns, promote different cellular functions, and thus affect the disease progression in patients with hyperuricemia.

Coordination of carbon assimilation, allocation, and utilization for systemic improvement of cereal yield.

The growth of yield outputs is dwindling after the first green revolution, which cannot meet the demand for the projected population increase by the mid-century, especially with the constant threat from extreme climates. Cereal yield requires carbon (C) assimilation in the source for subsequent allocation and utilization in the sink. However, whether the source or sink limits yield improvement, a crucial question for strategic orientation in future breeding and cultivation, is still under debate. To narrow the knowledge gap and capture the progress, we focus on maize, rice, and wheat by briefly reviewing recent advances in yield improvement by modulation of i) leaf photosynthesis; ii) primary C allocation, phloem loading, and unloading; iii) C utilization and grain storage; and iv) systemic sugar signals (e.g., trehalose 6-phosphate). We highlight strategies for optimizing C allocation and utilization to coordinate the source-sink relationships and promote yields. Finally, based on the understanding of these physiological mechanisms, we envisage a future scenery of "smart crop" consisting of flexible coordination of plant C economy, with the goal of yield improvement and resilience in the field population of cereals crops.

[Effect of electroacupuncture on proliferation and differentiation of endogenous neural stem cells and Jagged1/Notch1 pathway in hippocampus of APP/PS1 model mice].

OBJECTIVE: To investigate the effect of electroacupuncture(EA) stimulation on proliferation and diffe-rentiation of endogenous neural stem cells as well as Jagged1/Notch1 pathway in AD model mice, so as to explore its mechanism underlying amelioration of AD. METHODS: A total of 40 6-week-old male APP/PS1 transgenic AD mice were randomly divided into EA group (n=20) and AD model group ( n=20), and other 20 normal C57BL/6J mice of the same age were used as the normal control group. The mice in the EA group received EA (10 Hz, 2 mA) at "Baihui"(GV20), "Fengfu"(GV16) and bilateral "Shenshu" (BL23) for 20 min, once daily, 6 days a week for 16 weeks. The mice's learning-memory ability was detected by Morris water maze tests. The Aß senile plaques in the hippocampal CA1 region were detected by Congo red staining, the immunofluorescence double label of BrdU, neuronal nuclear antigen (NeuN) and astrocyte specific protein GFAP in dentate gyrus of hippocampus was performed for detecting the proliferation and differentiation of the endogenous neural stem cells. The expression levels of Nestin (neuron specific protein) and GFAP were detected by Western blot, and those of Jagged1 and Notch1 mRNAs and proteins in the hippocampus were detected by real-time fluorescence quantifative PCR and Western blot. RESULTS: Compared with the normal control group, the escape latencies at 2nd, 3rd and 4th day, and Aß senile plaques were significantly increased (P<0.05, P<0.01), whereas the platform crossing times and time spent in the target quadrant, the expression levels of Jagged1 mRNA and Nestin protein were remarkably down-regulated (P<0.05) in the model group. Following EA intervention, the escape latencies at the 3rd and 4th day, Aß senile plaques, immunofluorescence density of BrdU/GFAP, and GFAP protein expression were pronouncedly decreased (P<0.05, P<0.01), while the platform crossing times, platform quadrant residence time, immunofluorescence density of BrdU/NeuN, expression levels of Jagged1 and Notch1 mRNAs and proteins and Nestin protein evidently increased (P<0.05, P<0.01), suggesting an enhancement of proliferation and diffe-rentiation of endogenous neural stem cells into neurons and a suppression of the proliferation and differentiation towards astrocytes in the hippocampus. CONCLUSION: EA at GV20, GV16 and BL23 can improve the learning-memory ability, promote the proliferation and differentiation of endogenous neural stem cells towards neurons and inhibit the proliferation and differentiation of endogenous neural stem cells towards astrocytes in the hippocampus, which may be achieved by regulating Jagged1/Notch1 pathway.

BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations.

Computer-aided Diagnosis (CADx) based on explainable artificial intelligence (XAI) can gain the trust of radiologists and effectively improve diagnosis accuracy and consultation efficiency. This paper proposes BI-RADS-Net-V2, a novel machine learning approach for fully automatic breast cancer diagnosis in ultrasound images. The BI-RADS-Net-V2 can accurately distinguish malignant tumors from benign ones and provides both semantic and quantitative explanations. The explanations are provided in terms of clinically proven morphological features used by clinicians for diagnosis and reporting mass findings, i.e., Breast Imaging Reporting and Data System (BI-RADS). The experiments on 1,192 Breast Ultrasound (BUS) images indicate that the proposed method improves the diagnosis accuracy by taking full advantage of the medical knowledge in BI-RADS while providing both semantic and quantitative explanations for the decision.

[Genetic Subtypes and Pretreatment Drug Resistance in the Newly Reported Human Immunodeficiency Virus-Infected Men Aged≥50 Years Old in Guangxi].

Objective To analyze the genetic subtypes of human immunodeficiency virus (HIV) and the prevalence of pretreatment drug resistance in the newly reported HIV-infected men in Guangxi. Methods The stratified random sampling method was employed to select the newly reported HIV-infected men aged≥50 years old in 14 cities of Guangxi from January to June in 2020.The pol gene of HIV-1 was amplified by nested reverse transcription polymerase chain reaction and then sequenced.The mutation sites associated with drug resistance and the degree of drug resistance were then analyzed. Results A total of 615 HIV-infected men were included in the study.The genetic subtypes of CRF01_AE,CRF07_BC,and CRF08_BC accounted for 57.4% (353/615),17.1% (105/615),and 22.4% (138/615),respectively.The mutations associated with the resistance to nucleoside reverse transcriptase inhibitors (NRTI),non-nucleoside reverse transcriptase inhibitors (NNRTI),and protease inhibitors occurred in 8 (1.3%),18 (2.9%),and 0 patients,respectively.M184V (0.7%) and K103N (1.8%) were the mutations with the highest occurrence rates for the resistance to NRTIs and NNRTIs,respectively.Twenty-two (3.6%) patients were resistant to at least one type of inhibitors.Specifically,4 (0.7%),14 (2.3%),4 (0.7%),and 0 patients were resistant to NRTIs,NNRTIs,both NRTIs and NNRTIs,and protease inhibitors,respectively.The pretreatment resistance to NNRTIs had much higher frequency than that to NRTIs (2.9% vs.1.3%;χ2=3.929,P=0.047).The prevalence of pretreatment resistance to lamivudine,zidovudine,tenofovir,abacavir,rilpivirine,efavirenz,nevirapine,and lopinavir/ritonavir was 0.8%, 0.3%, 0.7%, 1.0%, 1.3%, 2.8%, 2.9%, and 0, respectively. Conclusions CRF01_AE,CRF07_BC,and CRF08_BC are the three major strains of HIV-infected men≥50 years old newly reported in Guangxi,2020,and the pretreatment drug resistance demonstrates low prevalence.

Experimental 61-partite entanglement on a three-dimensional photonic chip.

Multipartite entanglements are essential resources for proceeding tasks in quantum information science and technology. However, generating and verifying them present significant challenges, such as the stringent requirements for manipulations and the need for a huge number of building-blocks as the systems scale up. Here, we propose and experimentally demonstrate the heralded multipartite entanglements on a three-dimensional photonic chip. Integrated photonics provide a physically scalable way to achieve an extensive and adjustable architecture. Through sophisticated Hamiltonian engineering, we are able to control the coherent evolution of shared single photon in the multiple spatial modes, dynamically tuning the induced high-order W-states of different orders in a single photonic chip. Using an effective witness, we successfully observe and verify 61-partite quantum entanglements in a 121-site photonic lattice. Our results, together with the single-site-addressable platform, offer new insights into the accessible size of quantum entanglements and may facilitate the developments of large-scale quantum information processing applications.

Impact of a novel prognostic model on allogeneic hematopoietic stem cell transplantation outcomes in patients with CMML.

Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic stem cell malignancy, and allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only curable treatment. The outcomes after transplant are influenced by both disease characteristics and patient comorbidities. To develop a novel prognostic model to predict the post-transplant survival of CMML patients, we identified risk factors by applying univariable and multivariable Cox proportional hazards regression to a derivation cohort. In multivariable analysis, advanced age (hazard ratio [HR] 3.583), leukocyte count (HR 3.499), anemia (HR 3.439), bone marrow blast cell count (HR 2.095), and no chronic graft versus host disease (cGVHD; HR 4.799) were independently associated with worse survival. A novel prognostic model termed ABLAG (Age, Blast, Leukocyte, Anemia, cGVHD) was developed and the points were assigned according to the regression equation. The patients were categorized into low risk (0-1), intermediate risk (2, 3), and high risk (4-6) three groups and the 3-year overall survival (OS) were 93.3% (95%CI, 61%-99%), 78.9% (95%CI, 60%-90%), and 51.6% (95%CI, 32%-68%; p < .001), respectively. In internal and external validation cohort, the area under the receiver operating characteristic (ROC) curves of the ABLAG model were 0.829 (95% CI, 0.776-0.902) and 0.749 (95% CI, 0.684-0.854). Compared with existing models designed for the nontransplant setting, calibration plots, and decision curve analysis showed that the ABLAG model revealed a high consistency between predicted and observed outcomes and patients could benefit from this model. In conclusion, combining disease and patient characteristic, the ABLAG model provides better survival stratification for CMML patients receiving allo-HSCT.

Experimental Boson Sampling Enabling Cryptographic One-Way Function.

Boson sampling is a computational problem, which is commonly believed to be a representative paradigm for attaining the milestone of quantum advantage. So far, massive efforts have been made to the experimental large-scale boson sampling for demonstrating this milestone, while further applications of the machines remain a largely unexplored area. Here, we investigate experimentally the efficiency and security of a cryptographic one-way function that relies on coarse-grained boson sampling, in the framework of a photonic boson-sampling machine fabricated by a femtosecond laser direct writing technique. Our findings demonstrate that the implementation of the function requires moderate sample sizes, which can be over 4 orders of magnitude smaller than the ones predicted by the Chernoff bound; whereas for numbers of photons n≥3 and bins d∼poly(m,n), the same output of the function cannot be generated by nonboson samplers. Our Letter is the first experimental study that deals with the potential applications of boson sampling in the field of cryptography and paves the way toward additional studies in this direction.