Succinct quantum testers for closeness and kk-wise uniformity of probability distributions

We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and $k$-wise uniformity of probability distributions. \textit{Closeness testing} is the problem of distinguishing whether two $n$-dimensional distributions are identical or at least $\varepsilon$-far in $\ell^1$- or $\ell^2$-distance. We show that the quantum query complexities for $\ell^1$- and $\ell^2$-closeness testing are O\rbra{\sqrt{n}/\varepsilon} and O\rbra{1/\varepsilon}, respectively, both of which achieve optimal dependence on $\varepsilon$, improving the prior best results of \hyperlink{cite.gilyen2019distributional}{Gily{\'e}n and Li~(2019)}. \textit{$k$-wise uniformity testing} is the problem of distinguishing whether a distribution over \cbra{0, 1}^n is uniform when restricted to any $k$ coordinates or $\varepsilon$-far from any such distributions. We propose the first quantum algorithm for this problem with query complexity O\rbra{\sqrt{n^k}/\varepsilon}, achieving a quadratic speedup over the state-of-the-art classical algorithm with sample complexity O\rbra{n^k/\varepsilon^2} by \hyperlink{cite.o2018closeness}{O'Donnell and Zhao (2018)}. Moreover, when $k = 2$ our quantum algorithm outperforms any classical one because of the classical lower bound \Omega\rbra{n/\varepsilon^2}. All our quantum algorithms are fairly simple and time-efficient, using only basic quantum subroutines such as amplitude estimation.Comment: We have added the proof of lower bounds and have polished the languag

How to Survive between "Standardized Resident Training " and "Professional Master" -On the Difficulties Encountered in Undergraduate Clinical Practice

To review on the difficulties encountered by medical bachelor students for their career development after graduation, and to explore potential solutions to their current situation, thus provide them possibilities of making good use of professional training and skills acquired in campus

Insect Neuropeptide Bursicon Homodimers Induce Innate Immune and Stress Genes during Molting by Activating the NF-κB Transcription Factor Relish

BACKGROUND: Bursicon is a heterodimer neuropeptide composed of two cystine knot proteins, bursicon α (burs α) and bursicon β (burs β), that elicits cuticle tanning (melanization and sclerotization) through the Drosophila leucine-rich repeats-containing G protein-coupled receptor 2 (DLGR2). Recent studies show that both bursicon subunits also form homodimers. However, biological functions of the homodimers have remained unknown until now. METHODOLOGY/PRINCIPAL FINDINGS: In this report, we show in Drosophila melanogaster that both bursicon homodimers induced expression of genes encoding antimicrobial peptides (AMPs) in neck-ligated adults following recombinant homodimer injection and in larvae fat body after incubation with recombinant homodimers. These AMP genes were also up-regulated in 24 h old unligated flies (when the endogenous bursicon level is low) after injection of recombinant homodimers. Up-regulation of AMP genes by the homodimers was accompanied by reduced bacterial populations in fly assay preparations. The induction of AMP expression is via activation of the NF-κB transcription factor Relish in the immune deficiency (Imd) pathway. The influence of bursicon homodimers on immune function does not appear to act through the heterodimer receptor DLGR2, i.e. novel receptors exist for the homodimers. CONCLUSIONS/SIGNIFICANCE: Our results reveal a mechanism of CNS-regulated prophylactic innate immunity during molting via induced expression of genes encoding AMPs and genes of the Turandot family. Turandot genes are also up-regulated by a broader range of extreme insults. From these data we infer that CNS-generated bursicon homodimers mediate innate prophylactic immunity to both stress and infection during the vulnerable molting cycle

GPX8 regulates pan-apoptosis in gliomas to promote microglial migration and mediate immunotherapy responses

IntroductionGliomas have emerged as the predominant brain tumor type in recent decades, yet the exploration of non-apoptotic cell death regulated by the pan-optosome complex, known as pan-apoptosis, remains largely unexplored in this context. This study aims to illuminate the molecular properties of pan-apoptosis-related genes in glioma patients, classifying them and developing a signature using machine learning techniques.MethodsThe prognostic significance, mutation features, immunological characteristics, and pharmaceutical prediction performance of this signature were comprehensively investigated. Furthermore, GPX8, a gene of interest, was extensively examined for its prognostic value, immunological characteristics, medication prediction performance, and immunotherapy prediction potential. ResultsExperimental techniques such as CCK-8, Transwell, and EdU investigations revealed that GPX8 acts as a tumor accelerator in gliomas. At the single-cell RNA sequencing level, GPX8 appeared to facilitate cell contact between tumor cells and macrophages, potentially enhancing microglial migration. ConclusionsThe incorporation of pan-apoptosis-related features shows promising potential for clinical applications in predicting tumor progression and advancing immunotherapeutic strategies. However, further in vitro and in vivo investigations are necessary to validate the tumorigenic and immunogenic processes associated with GPX8 in gliomas

Badanie wpływu wielkości cząstek na dokładność identyfikacji węgla i skały płonnej

In order to explore the impact of coal and gangue particle size changes on recognition accuracy and to improve the single particle size of coal and gangue identification accuracy of sorting equipment, this study established a database of different particle sizes of coal and gangue through image gray and texture feature extraction, using a relief feature selection algorithm to compare different particle size of coal and gangue optimal features of the combination, and to identify the points and particle size of coal and gangue. The results show that the optimal features and number of coal and gangue are different with different particle sizes. Based on visible-light coal and gangue separation technology, the change of coal and gangue particle size cause fluctuations in the recognition accuracy, and the fluctuation of recognition accuracy will gradually decrease with increases in the number of features. In the process of particle size classification, if the training model has a single particle size range, the recognition accuracy of each particle size range is low, with the highest recognition accuracy being 98% and the average recognition rate being only 97.2%. The method proposed in this paper can effectively improve the recognition accuracy of each particle size range. The maximum recognition accuracy is 100%, the maximum increase is 4%, and the average recognition accuracy is 99.2%. Therefore, this method has a high practical application value for the separation of coal and gangue with single particle size.W celu zbadania wpływu zmian wielkości cząstek węgla i skały płonnej na dokładność rozpoznawania oraz poprawienia dokładności identyfikacji pojedynczych cząstek węgla i skały płonnej przez urządzenia sortujące, w ramach tej pracy utworzono bazę danych różnych rozmiarów cząstek węgla i skały płonnej za pomocą obrazów szarych i ekstrakcję cech tekstury przy użyciu algorytmu wyboru cech reliefowych w celu porównania różnych rozmiarów cząstek węgla i skały płonnej przy optymalnych cechach kombinacji oraz identyfikacji punktów i wielkości cząstek węgla i skały płonnej. Wyniki pokazują, że optymalne liczby cech węgla i skały płonnej są różne dla różnych rozmiarów cząstek. W oparciu o technologię separacji węgla i skały płonnej w świetle widzialnym, zmiana wielkości cząstek węgla i skały płonnej powoduje fluktuacje dokładności rozpoznawania, a te z kolei będą stopniowo zmniejszać się wraz ze wzrostem liczby cech. W procesie klasyfikacji wielkości cząstek, jeśli model uczący ma jeden zakres wielkości cząstek, dokładność rozpoznawania każdego zakresu wielkości cząstek jest niska, przy czym najwyższa dokładność rozpoznawania wynosi 98%, a średni wskaźnik rozpoznawania wynosi tylko 97,2%. Metoda zaproponowana w tym artykule może skutecznie poprawić dokładność rozpoznawania każdego zakresu wielkości cząstek. Maksymalna dokładność rozpoznawania wynosi 100%, maksymalny wzrost to 4%, a średnia dokładność rozpoznawania to 99,2%. Dlatego ta metoda ma dużą praktyczną wartość użytkową do oddzielania węgla i skały płonnej według rozmiaru pojedynczej cząstki

Accuracy assessment of four cloud-free snow cover products over the Qinghai-Tibetan Plateau

Four up-to-date daily cloud-free snow products – IMS (Interactive Multisensor Snow products), MOD-SSM/I (combination of the MODIS and SSM/I snow products), MOD-B (Blending method basing on the MODIS snow cover products) and TAI (Terra–Aqua–IMS) – with high-resolutions over the Qinghai-Tibetan Plateau (QTP) were comprehensively assessed. Comparisons of the IMS, MOD-SSM/I, MOD-B and TAI cloud-free snow products against meteorological stations observations over 10 snow seasons (2004–2013) over the QTP indicated overall accuracies of 76.0%, 89.3%, 92.0% and 92.0%, respectively. The Khat values of the IMS, MOD-SSM/I, MOD-B and TAI products were 0.084, 0.463, 0.428 and 0.526, respectively. The TAI products appear to have the best cloud-removal ability among the four snow products over the QTP. Based on the assessment, an I-TAI (Improvement of Terra–Aqua–IMS) snow product was proposed, which can improve the accuracy to some extent. However, the algorithms of the MODIS series products show instability when identifying wet snow and snow under forest cover over the QTP. The snow misclassification is an important limitation of MODIS snow cover products and requires additional improvements

Promotion of Differentiating Bone Marrow Mesenchymal Stromal Cells (BMSCs) into Cardiomyocytes via HCN2 and HCN4 Cotransfection

Aim. Investigation of the influences HCN2 and HCN4 has on bone marrow mesenchymal stromal cells (BMSCs) on cardiomyocyte differentiation. Methods. Miniature adult pigs were used for bone marrow extraction and isolation of BMSCs. The identification of these BMSCs was done by using flow cytometry for the detection of expressed surface antigens CD45, CD11B, CD44, and CD90. Using HCN2 and HCN4 genes cotransfected into BMSCs as group HCN2+HCN4 while myocardial induction solution was used to induced BMSC differentiation in the BMSC induction group. Myocardial marker proteins α-actin and cTnT were detected by immunofluorescence staining, while α-actin, cTnT, and Desmin myocardial marker proteins expressed were detected by Western blot. The whole-cell patch-clamp technique was used to identify and detect cellular HCN2 channels, HCN4 channel current activation curve, and the inhibitory effect of CsCl on heterologous expression currents. Results. Flow cytometry results showed that CD45 and CD11B were expressed negatively while CD90 and CD44 were positive. Post HCN2 and HCN4 gene transfection, immunofluorescence staining, and Western blot showed significantly increased HCN2, HCN4, α-actin, and cTnT expressed in group HCN2+HCN4 were, which could be compared to the expression levels in the BMSC-induced group. The HCN2+HCN4 group was able to document cell membrane channel ion currents that were similar to If properties. Conclusion. HCN2 and HCN4 overexpression can considerably enhance the MSC ability to differentiate into cardiomyocytes in vitro and restore the ionic current

Targeting MYH9 represses USP14-mediated NAP1L1 deubiquitination and cell proliferation in glioma

Abstract Myosin heavy chain 9 (MYH9) plays an important role in a number of diseases. Nevertheless, the function of MYH9 in glioma is unclear. The present research aimed to investigate the role of MYH9 in glioma and determine whether MYH9 is involved in the temozolomide chemoresistance of glioma cells. Our results showed that MYH9 increased the proliferation and temozolomide resistance of glioma cells. The mechanistic experiments showed that the binding of MYH9 to NAP1L1, a potential promoter of tumor proliferation, inhibited the ubiquitination and degradation of NAP1L1 by recruiting USP14. Upregulation of NAP1L1 increased its binding with c-Myc and activated c-Myc, which induced the expression of CCND1/CDK4, promoting glioma cell temozolomide resistance and proliferation. Additionally, we found that MYH9 upregulation was strongly related to patient survival and is therefore a negative factor for patients with glioma. Altogether, our results show that MYH9 plays a role in glioma progression by regulating NAP1L1 deubiquitination. Thus, targeting MYH9 is a potential therapeutic strategy for the clinical treatment of glioma in the future

Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration

Objective: To investigate the immune cell infiltration status in glioblastoma multiforme (GBM) and construct a novel prognostic risk model that can predict patients’ prognosis. Methods: The Cancer Genome Atlas (TCGA) database was used to obtain RNA-sequence information and relevant clinical data. We performed Pearson correlation, univariate Cox regression to screen m6A-related prognostic lncRNA. GMB patients’ samples were separated into different clusters through the ConsensusClusterPlus package. The risk score model was established through LASSO regression analysis. Besides, KEGG pathway enrichment analysis was implemented. CIBERSORT algorithm was used to analyze the difference of 22 types of immune cell infiltration in different cluster of GBM patient. Cox regression analyses were used to verify the independence of the model and correlation analysis was performed to demonstrate the link between our model and clinical characteristics of GBM patients. Experiments were used to validate the differential expression of the model lncRNA in patients with different prognosis. Results: 17 lncRNA related to prognosis were screened from 1021 m6A-related lncRNAs. Further, four m6A-related lncRNAs that were significantly correlated with GBM prognosis were selected to establish our prognostic risk model, which had excellent accuracy and can independently predict the prognosis of GBM patients. The infiltration fractions of T regulatory cells, T cells CD4 memory activated and neutrophils were positively associated with risk score, which suggested a significant relationship between the model and tumor immune microenvironment. Conclusion: The m6A-related RNA risk model offered potential for identifying biomarkers of therapy and predicting prognosis of GBM patients