Reconfigurable matrix multiplier with on-site reinforcement learning.

Matrix multiplication is a fundamental building block for modern information processing and artificial intelligence algorithms. Photonics-based matrix multipliers have recently attracted much attention due to their advantages of low energy and ultrafast speed. Conventionally, achieving matrix multiplication relies on bulky Fourier optical components, and the functionalities are unchangeable once the design is determined. Furthermore, the bottom-up design strategy cannot easily be generalized into concrete and practical guidelines. Here, we introduce a reconfigurable matrix multiplier driven by on-site reinforcement learning. The constituent transmissive metasurfaces incorporating varactor diodes serve as tunable dielectrics based on the effective medium theory. We validate the viability of tunable dielectrics and demonstrate the performance of matrix customization. This work represents a new avenue in realizing reconfigurable photonic matrix multipliers for on-site applications.

Tris(butadiene) Metal Complexes of the First-Row Transition Metals versus Coupling of Butadiene to Eight- and Twelve-Carbon Hydrocarbon Chains.

The role that zerovalent nickel plays in catalyzing the trimerization of butadiene to 1,5,9-cyclododecatriene conveys interest in the properties of the tris(butadiene)metal complexes (C4H6)3M. In this connection the complexes (C4H6)3M (M = Ti-Ni) of the first-row transition metals have been investigated by density functional theory. The intermediate C12H18Ni which has been isolated in the nickel-catalyzed trimerization of butadiene but is too unstable for X-ray crystallography is suggested here to have an open-chain hexahapto η3,3-C12H18 ligand rather than the octahapto such ligand suggested by some investigators. The lowest energy (C4H6)3M structures of the other first-row transition metals from vanadium to cobalt are found to have related structures with open-chain C12H18 ligands having hapticities ranging from four to eight with hexahapto structures being most common. The nickel and cobalt (C12H18)M derivatives favor low-spin singlet and doublet spin states, respectively, whereas the manganese derivative (C12H18)Mn favors the high-spin sextet state corresponding to the half-filled d5 shell of Mn(II). A (C4H6)3Cr structure with three separate tetrahapto butadiene ligands analogous to the very stable (η4-C4H6)3M (M = Mo, W) with the favored 18-electron metal configuration is found to be a very high energy structure relative to isomers containing an open-chain C12H18 ligand.

[Studies on the Analytical Potential Energies for Partial Electronic States of Li(2) with Variational Algebraic Energy Consistent Method].

The full vibrational spectra especially those high-lying vibrational energies in the dissociation region of four specific electronic states 1(3)Δ(g), 33Σ(+)(g), 1(3)Σ-(g) and b(3)Π(u) have been obtained by using the improved variational algebraic method (VAM). The analytical potential energy functions (APEFs) of these electronic states are also determined with corresponding adjustable parameter λ by using the variational algebraic energy consistent method (VAECM) based on the VAM vibrational spectra. The full vibrational energies, vibrational spectroscopic constants, force constants f(n), and expansion coefficients a(n) of the VAECM potential are also tabulated for each electronic state in this study. The results show that the VAECM analytical potentials are superior to some other widely used analytical ones, and do not have the unphysical tiny barriers existing in the precious AECM potentials.

Arbitrary Polarization Readout with Dual-Channel Neuro-Metasurfaces.

Polarization, as a vector nature of the electromagnetic wave, plays a fundamental role in optics. Determining the polarization state of light is required by many applications, spanning from remote sensing and material analysis to biology and microscopy. To achieve this goal, conventional methods necessitate cascading of multiple optical components and consequential measurements to estimate the Stokes parameters, rendering the entire optical system bulky, complex, and sensitive. Here a brand-new strategy is introduced for direct polarization readout based on dual-channel neuro-metasurfaces. Neuro-metasurfaces can independently manipulate two orthogonal linearly-polarized waves that can synthesize arbitrary polarization waves with a linear combination. By judiciously designing the output focus points, a unique polarization atlas is created that allows one-to-one correspondence from intensity ratio to polarization state. To implement this, polarization-sensitive metasurfaces are designed and the spatial layout is optimized using a diffractive neural network. The feasibility of this strategy is validated by numerical simulation and microwave experiments. These results pave a new avenue in realizing integrated and multifunctional detectors and demonstrate the potential of neuro-metasurfaces as an add-on for discomposing and composing spatial basis.

Exploring the significance of tumor volume in endometrial cancer: Clinical pathological features, prognosis, and adjuvant therapies.

To assist clinicians in formulating treatment strategies for endometrial cancer (EC), this retrospective study explores the relationship between tumor volume and clinical pathological features, as well as prognosis, in patients undergoing staging surgery. Preoperative pelvic MRI examinations were conducted on 234 histologically confirmed EC patients. The ITK-SNAP software was employed to manually delineate the region of interest in the MRI images and calculate the tumor volume (MRI-TV). The analysis focused on investigating the relationship between MRI-TV and the clinical pathological features and prognosis of EC patients. Larger MRI-TV was found to be associated with various adverse prognostic factors (G3, deep myometrial invasion, cervical stromal invasion, lymphovascular space invasion, lymph node metastasis, advanced international federation of gynecology and obstetrics staging, and receipt of adjuvant therapy). The receiver operating characteristic curve indicated that MRI-TV ≥ 8 cm3 predicted deep myometrial invasion, and MRI-TV ≥ 12 cm3 predicted lymph node metastasis. Penalized spline (P-spline) regression analysis identified 14 cm3 of MRI-TV as the optimal prognostic cutoff value. MRI-TV ≥ 14 cm3 was an independent prognostic factor for overall survival and disease-free survival. For patients with MRI-TV ≥ 14 cm3, the disease-free survival rate with adjuvant therapy was superior to that of the sole staging surgery group. This study demonstrates a significant correlation between MRI-TV and clinical pathological features and prognosis in EC. For patients with MRI-TV ≥ 14 cm3, staging surgery followed by adjuvant therapy was superior to sole staging surgery.

Theoretical study on the vibrational spectra and potential energy curves for SiC (X3Π) and SiS (X1Σ+) molecules.

In this work, the vibrational constants (ωe,ωexe) calculated by the variational algebraic method (VAM) and some other molecular constants (De,re,Be,αe) were used to construct the improved Hulburt-Hirschfelder (IHH) analytical potential energy function (APEF). Not only that, but the calculated VAM potential points are used as the 'true' energies to determine the value of the variational parameter λ which is the pivotal fitting parameter in the IHH potential. With limited experimental data, high-precision IHH potential can be achieved by combining the VAM and the IHH APEF. This combination of the VAM and the IHH APEF is referred to be VAIHH APEF, which is employed to study the vibrational energies and potential energy curves (PECs) of SiC (X3Π) and SiS (X1Σ+) molecules, yielding full vibrational spectra and spectroscopic constants. The calculational results indicate that the VAIHH APEFs of SiC (X3Π) and SiS (X1Σ+) molecules are in good agreement with the experimental RKR potential points. Accurate PECs of SiC (X3Π) and SiS (X1Σ+) molecules imply that the VAIHH APEF is of high quality.

A knowledge-inherited learning for intelligent metasurface design and assembly.

Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics, recurring in various applications of material design, system optimization, and automation control. Deep learning-enabled on-demand metasurface design has been the subject of extensive expansion, as it can alleviate the time-consuming, low-efficiency, and experience-orientated shortcomings in conventional numerical simulations and physics-based methods. However, collecting samples and training neural networks are fundamentally confined to predefined individual metamaterials and tend to fail for large problem sizes. Inspired by object-oriented C++ programming, we propose a knowledge-inherited paradigm for multi-object and shape-unbound metasurface inverse design. Each inherited neural network carries knowledge from the "parent" metasurface and then is freely assembled to construct the "offspring" metasurface; such a process is as simple as building a container-type house. We benchmark the paradigm by the free design of aperiodic and periodic metasurfaces, with accuracies that reach 86.7%. Furthermore, we present an intelligent origami metasurface to facilitate compatible and lightweight satellite communication facilities. Our work opens up a new avenue for automatic metasurface design and leverages the assemblability to broaden the adaptability of intelligent metadevices.

The prognostic value of the tertiary lymphoid structure in gastrointestinal cancers.

Background: Numerous studies and research papers have provided evidence suggesting that tertiary lymphoid structures (TLS) play a crucial role in combating and suppressing tumor growth and progression. Despite the wealth of information on the significance of TLS in various types of cancer, their prognostic value in gastrointestinal (GI) cancers remains uncertain. Therefore, this meta-analysis investigated the prognostic value of TLS in GI cancers. Methods: We searched Web of science, Pubmed, Embase and Cochrane Library for studies that met the requirements as of May 1, 2023, and the hazard ratio (HR) and the corresponding 95% confidence interval (CI) were included in the analysis. The bioinformatics analysis results based on the TCGA database are used to supplement our research. Results: The meta-analysis included 32 studies involving 5778 patients. The results of comprehensive analysis showed that TLS-High is associated with prolonged OS (HR=0.525,95%CI:0.447-0.616 (P < 0.001), RFS (HR=0.546,95%CI:0.461-0.647, P < 0.001), DFS (HR=0.519,95%CI:0.417-0.646, P < 0.001) and PFS (HR=0.588,95%CI:0.406-0.852, P=0.005) in GI cancer. Among the patients who received immunotherapy, TLS-High is associated with significantly prolonged OS (HR=0.475, 95%CI:0.282-0.799, P=0.005) and PFS(HR=0.576, 95%CI:0.381-0.871, P=0.009). It is worth noting that subgroup analysis showed that there was no significant relationship between TLS and OS(HR=0.775, 95%CI:0.570-1.053,P=0.103) in CRC. And when Present is used as the cut-off criteria of TLS, there is no significant correlation between TLS and OS (HR=0.850, 95%CI:0.721-1.002, P=0.053)in HCC. Conclusion: TLS is a significant predictor of the prognosis of GI cancers and has the potential to become a prognostic biomarker of immunotherapy-related patients. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/#recordDetails, identifier CRD42023443562.

Construction and Validation of a Novel Immune Checkpoint-Related Model in Clear Cell Renal Cell Carcinoma.

Background: With the highest mortality and metastasis rate, kidney renal clear cell carcinoma (KIRC) is one of the most common urological malignant tumors and not sensitive to chemotherapy and radiotherapy. Immunotherapy, which proves to be effective and a big progression, such as PD-1/PD-L1 inhibitors, is not sensitive to all KIRC patients. To predict prognosis and immunotherapy response, a novel immune checkpoint gene- (ICG-) related model is essential in clinics. Methods: From the public database-downloaded dataset, a novel ICG-related model for predicting prognosis and immunotherapy response in KIRC patients was built up and verified with R packages and Cox regression analysis. The Kaplan-Meier curve was plotted. Results: 39 ICGs were identified to have different expression in KIRC patients and enriched in immune-related biological pathways and activities. Three ICGs (CTLA4, TNFSF14, and HHLA2) were screened to generate KIRC-ICG model. The KIRC-ICG model was verified to be effective. With conducting KIRC-SYS model, KIRC-ICGscore was verified to be an independent factor regardless of age, gender, stage, grade, and TNM stage. Compared to the ICG-low subgroup, the ICG-high subgroup had more immune activities. KIRC-ICGscore was significantly positively correlated with the expression of Treg markers. KIRC-ICG model could also be reliable to predict immunotherapy response. Conclusion: The KIRC-ICG model was reliable to predict prognosis and immunotherapy response for KIRC patients and could be an independent factor regardless of clinical characteristics.

A method for predicting the molar heat capacities of HBr and HCl gases based on the full set of molecular rovibrational energies.

A new method is presented for one to obtain the molar heat capacities of diatomic macroscopic gas with a full set of microscopic molecular rovibrational energies. Based on an accurate experimental vibrational energies subset of a diatomic electronic ground state, the full vibrational energies can be obtained by using the variational algebraic method (VAM), the potential energy curves (PECs) will be constructed by the Rydberg-Klein-Rees (RKR) method, the full set of rovibrational energies will be calculated by the LEVEL program, and then the partition functions and the molar heat capacities of macroscopic gas can be calculated with the help of the quantum statistical ensemble theory. Applying the method to the ground state HBr and HCl gases, it is found that the relative errors of the partition functions calculated in the temperature range of 300 âˆ¼ 6000 K are in excellent agreement with those obtained from TIPS database, and the calculated molar heat capacities are closer to the experimental values than those calculated by other methods without considering the energy levels of highly excited quantum states. The present method provides an effective new way for one to obtain the full set of molecular rovibrational energies and the molar heat capacities of macroscopic gas through the microscopic spectral information of a diatomic system.

A data- and model-driven strategy for the evaluation of the experimental transition lines: Theoretical prediction for the ground state of 12C16O.

An analytical formula that relates the molecular constants of the Herzberg expression and experimental transition lines is developed herein with a difference algebraic approach (DAA) model. Based on the data-driven strategy, the DAA model is able to deal with the tiny uncertainties that exhibit behind the experimental transition lines, which is applied to the P branch emission spectra of some first overtone bands of the ground electronic state of 12C16O. The relationship can be used to generate transition lines with sufficient accuracy, as evident from the high J of agreement with the HITRAN database, Velichko data, Goorvitch data and quantum-mechanical data. In addition, line intensities, absorption oscillator strengths and Einstein A coefficients of these lines, which are introduced to enhance the dataset and are in good agreement with those of other authors, are also reported to validate our results. These various comparative results show that the proposed data-driven strategy based on the DAA model is expecting to be a good algorithm that relies on relatively limited data for training.

Homeostatic neuro-metasurfaces for dynamic wireless channel management.

The physical basis of a smart city, the wireless channel, plays an important role in coordinating functions across a variety of systems and disordered environments, with numerous applications in wireless communication. However, conventional wireless channel typically necessitates high-complexity and energy-consuming hardware, and it is hindered by lengthy and iterative optimization strategies. Here, we introduce the concept of homeostatic neuro-metasurfaces to automatically and monolithically manage wireless channel in dynamics. These neuro-metasurfaces relieve the heavy reliance on traditional radio frequency components and embrace two iconic traits: They require no iterative computation and no human participation. In doing so, we develop a flexible deep learning paradigm for the global inverse design of large-scale metasurfaces, reaching an accuracy greater than 90%. In a full perception-decision-action experiment, our concept is demonstrated through a preliminary proof-of-concept verification and an on-demand wireless channel management. Our work provides a key advance for the next generation of electromagnetic smart cities.

Study on potential energy curves and ro-vibrational energies of DT, HT and T2 molecules.

Accurately monitoring and effectively controlling the tritium compounds based on their ro-vibrational energy structure are important issues in various nuclear systems. Because of their radioactivity, it is difficult to obtain the corresponding energies directly through experiments. In this paper, the potential energy curves and the corresponding ro-vibrational full spectrum of DT, HT and T2 systems are derived by ab initio methods. However, it is difficult to verify the reliability of the calculated results due to the lack of direct experimental support. Therefore, a data-driven reliability analysis method is proposed, which can confirm the reliability by extracting information from the relevant calculations and multiple experimental data (the vibrational level, rotational level, and molar heat capacity) of similar systems (HD, H2, D2). The results show that: 1) The potential energy curves obtained by the ab initio method can provide the full ro-vibrational energy spectrum with an accuracy of approximately 10 cm-1; 2) Macroscopic heat capacity information can be used to distinguish and calibrate the overall reliability of microscopic ro-vibrational energies; 3) For the isotopic energy level structure of hydrogen, the influence of isotopes is mainly mass effect.

Spectroscopy learning: A machine learning method for study diatomic vibrational spectra including dissociation behavior.

Molecular spectroscopy plays an important role in the study of physical and chemical phenomena at the atomic level. However, it is difficult to acquire accurate vibrational spectra directly in theory and experiment, especially these vibrational levels near the dissociation energy. In our previous study (Variational Algebraic Method), dissociation energy and low energy level data are employed to predict the ro-vibrational spectra of some diatomic system. In this work, we did the following: 1) We expand the method to a more rigorous combined model-driven and data-driven machine learning approach (Spectroscopy Learning Method). 2) Extracting information from a wide range of existing data can be used in this work, such as heat capacity. 3) Reliable vibrational spectra and dissociation energy can be predicted by using heat capacity and the reliability of this method is verified by the ground states of CO and Br2 system.

A joint data and model driven method for study diatomic vibrational spectra including dissociation behavior.

The details of quantum multi-body interactions are so rich and subtle which make it difficult to accurately model for some situations such as the behavior of diatomic long-range vibrations. In recent years, data-driven machine learning has made remarkable achievements in capturing complex relationships that are subtle. Combining the characteristics of these two fields, we propose a joint machine learning method to obtain reliable diatomic vibrational spectra including dissociation energy by using accessible heterogeneous micro/macro information such as low lying vibrational energy levels and heat capacity. Applications of this method to CO and Br2 in the ground state yield their state of the art of vibrational spectra including dissociation limit. The strategy introduced here is an exploration of combining the model-driven and data-driven method to cover subtle physical details that are difficult to study in a single way.

An analytical formula for predicting R-branch high-lying transition lines of BiLi molecule.

A concise and convenient analytical formula without any spectral constant is derived from the conventional expression of R-branch transitional energies for calculating the R-branch high-lying rovibrational emission spectral lines of diatomic molecule. This is based on the thought of Sun's difference converging method (DCM) in 2011. This formula can correctly predict the high-lying transitional emission spectral lines only using 11 known experimental transition lines and a set of physical criteria when any spectral constants of the system are not available. Furthermore, a new method for analyzing the accuracy of prediction with the improved formula is proposed, which point out the core problem of the predicted spectrum and give a quantitative conclusion on the reliability of our work. In this work, the new method is applied to study the R-branch transitional emission spectra of the (0-0) band of the A20+ → X10+ and A20+ → X21 transition systems of BiLi molecule. A series of experimental and theoretical comparisons and discussions show that under the condition of lack of any spectral constants, our method can still use 11 known experimental transition spectral data to reproduce the R-branch emission spectral lines reliably including the high excited rovibrational transition spectral lines.

BAP1 promotes breast cancer cell proliferation and metastasis by deubiquitinating KLF5.

The transcription factor KLF5 is highly expressed in basal-like breast cancer and promotes breast cancer cell proliferation, survival, migration and tumour growth. Here we show that, in breast cancer cells, KLF5 is stabilized by the deubiquitinase (DUB) BAP1. With a genome-wide siRNA library screen of DUBs, we identify BAP1 as a bona fide KLF5 DUB. BAP1 interacts directly with KLF5 and stabilizes KLF5 via deubiquitination. KLF5 is in the BAP1/HCF-1 complex, and this newly identified complex promotes cell cycle progression partially by inhibiting p27 gene expression. Furthermore, BAP1 knockdown inhibits tumorigenicity and lung metastasis, which can be rescued partially by ectopic expression of KLF5. Collectively, our findings not only identify BAP1 as the DUB for KLF5, but also reveal a critical mechanism that regulates KLF5 expression in breast cancer. Our findings indicate that BAP1 could be a potential therapeutic target for breast and other cancers.

Correction: Prevalence and Molecular Characterization of Glucose-6-Phosphate Dehydrogenase Deficiency at the China-Myanmar Border.

[This corrects the article DOI: 10.1371/journal.pone.0134593.].

Prevalence and Molecular Characterization of Glucose-6-Phosphate Dehydrogenase Deficiency at the China-Myanmar Border.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is an X-linked hereditary disease that predisposes red blood cells to oxidative damage. G6PD deficiency is particularly prevalent in historically malaria-endemic areas. Use of primaquine for malaria treatment may result in severe hemolysis in G6PD deficient patients. In this study, we systematically evaluated the prevalence of G6PD deficiency in the Kachin (Jingpo) ethnic group along the China-Myanmar border and determined the underlying G6PD genotypes. We surveyed G6PD deficiency in 1770 adult individuals (671 males and 1099 females) of the Kachin ethnicity using a G6PD fluorescent spot test. The overall prevalence of G6PD deficiency in the study population was 29.6% (523/1770), among which 27.9% and 30.6% were males and females, respectively. From these G6PD deficient samples, 198 unrelated individuals (147 females and 51 males) were selected for genotyping at 11 known G6PD single nucleotide polymorphisms (SNPs) in Southeast Asia (ten in exons and one in intron 11) using a multiplex SNaPshot assay. Mutations with known association to a deficient phenotype were detected in 43.9% (87/198) of cases, intronic and synonymous mutations were detected alone in 34.8% (69/198) cases and no mutation were found in 21.2% (42/198) cases. Five non-synonymous mutations, Mahidol 487G>A, Kaiping 1388G>A, Canton 1376G>T, Chinese 4 392G>T, and Viangchan 871G>A were detected. Of the 87 cases with known deficient mutations, the Mahidol variant was the most common (89.7%; 78/87), followed by the Kaiping (8.0%; 7/87) and the Viangchan (2.2%; 2/87) variants. The Canton and Chinese 4 variants were found in 1.1% of these 87 cases. Among them, two females carried the Mahidol/Viangchan and Mahidol/Kaiping double mutations, respectively. Interestingly, the silent SNPs 1311C>T and IVS11nt93T>C both occurred in the same 95 subjects with frequencies at 56.4% and 23.5% in tested females and males, respectively (P<0.05). It is noteworthy that 24 subjects carrying the Mahidol mutation and two carrying the Kaiping mutation also carried the 1311C>T/IVS11nt93T>C SNPs. Further studies are needed to determine the enzyme levels of the G6PD deficient people and presence of additional G6PD mutations in the study population.