Enhancing multiple myeloma staging: a novel cell death risk model approach.

The prognostication of survival trajectories in multiple myeloma (MM) patients presents a substantial clinical challenge. Leveraging transcriptomic and clinical profiles from an expansive cohort of 2,088 MM patients, sourced from the Gene Expression Omnibus and The Cancer Genome Atlas repositories, we applied a sophisticated nested lasso regression technique to construct a prognostic model predicated on 28 gene pairings intrinsic to cell death pathways, thereby deriving a quantifiable risk stratification metric. Employing a threshold of 0.15, we dichotomized the MM samples into discrete high-risk and low-risk categories. Notably, the delineated high-risk cohort exhibited a statistically significant diminution in survival duration, a finding which consistently replicated across both training and external validation datasets. The prognostic acumen of our cell death signature was further corroborated by TIME ROC analyses, with the model demonstrating robust performance, evidenced by AUC metrics consistently surpassing the 0.6 benchmark across the evaluated arrays. Further analytical rigor was applied through multivariate COX regression analyses, which ratified the cell death risk model as an independent prognostic determinant. In an innovative stratagem, we amalgamated this risk stratification with the established International Staging System (ISS), culminating in the genesis of a novel, refined ISS categorization. This tripartite classification system was subjected to comparative analysis against extant prognostic models, whereupon it manifested superior predictive precision, as reflected by an elevated C-index. In summation, our endeavors have yielded a clinically viable gene pairing model predicated on cellular mortality, which, when synthesized with the ISS, engenders an augmented prognostic tool that exhibits pronounced predictive prowess in the context of multiple myeloma.

EO nonlinear function generator.

An electro-optical programmable nonlinear function generator (PNFG) is developed on a multimode waveguide with four parallel thermal electrodes. The current on one electrode is chosen as the input, while the rest serve as function-defining units to modulate the multimode interference. The electro-thermo-optical effects are analyzed step by step and the impact on the eigenmode properties is derived. It shows that the optical output power variation by altered interference, in response to the input current, manifests as a complex ensemble of functions in general. The PNFG aims to find the special setting under which such relation can be simplified into some basic functions. Through an optimization program, a variety of such functions are found, including Sigmoid, SiLU, and Gaussian. Furthermore, the shape of these functions can be adjusted by finetuning the defining units. This device may be integrated in a large-scale photonic computing network that can tackle complex problems with nonlinear function adaptability.

0D Pyramid-intercalated 2D Bimetallic Halides with Tunable Electronic Structures and Enhanced Emission under Pressure.

Hybrid metal halides are emerging semiconductors as promising candidates for optoelectronics. The pursuit of hybridizing various dimensions of metal halides remains a desirable yet highly complex endeavor. By utilizing dimension engineering, a diverse array of new materials with intrinsically different electronic and optical properties has been developed. Here, we report a new family of 2D-0D hybrid bimetallic halides, (C6 N2 H14 )2 SbCdCl9 ⋅ 2H2 O (SbCd) and (C6 N2 H14 )2 SbCuCl9 ⋅ 2H2 O (SbCu). These compounds adopt a new layered structure, consisting of alternating 0D square pyramidal [SbCl5 ] and 2D inorganic layers sandwiched by organic layers. SbCd and SbCu have optical band gaps of 3.3 and 2.3 eV, respectively. These compounds exhibit weak photoluminescence (PL) at room temperature, and the PL gradually enhances with decreasing temperature. Density functional theory (DFT) calculations reveal that SbCd and SbCu are direct gap semiconductors, where first-principles band gaps follow the experimental trend. Moreover, given the different pressure responses of 0D and 2D components, these materials exhibit highly tunable electronic structures during compression, where a remarkable 11 times enhancement in PL emission is observed for SbCd at 19 GPa. This work opens new avenues for designing new layered bimetallic halides and further manipulating their structures and optoelectronic properties via pressure.

The evolution of data pricing: From economics to computational intelligence.

Data pricing, which aids in articulating the worth and advantages of data and encourages its opening, sharing, and circulation, is an indispensable component of data trading. Studies pertinent to the topic of data pricing are continuously developing. To undertake a thorough analysis of the literature in data pricing, we use bibliometric and statistical methods for the first time. The 373 data pricing publications from 1990 to 2023 are the study's research object. We mainly analyze the external characteristics, keyword co-occurrence and co-citation networks of data pricing. We find that data pricing has been progressing fairly quickly during the last decade. Two basic approaches have been used by researchers to study how to price various data objects: one is based on economics theory, and the other on computer science algorithms. We provide an in-depth study of the overall evolution of data pricing and give the future directions of data pricing.

Hydrogen Storage with Aluminum Formate, ALF: Experimental, Computational, and Technoeconomic Studies.

Long-duration storage of hydrogen is necessary for coupling renewable H2 with stationary fuel cell power applications. In this work, aluminum formate (ALF), which adopts the ReO3-type structure, is shown to have remarkable H2 storage performance at non-cryogenic (>120 K) temperatures and low pressures. The most promising performance of ALF is found between 120 K and 160 K and at 10 bar to 20 bar. The study illustrates H2 adsorption performance of ALF over the 77 K to 296 K temperature range using gas isotherms, in situ neutron powder diffraction, and DFT calculations, as well as technoeconomic analysis (TEA), illustrating ALF's competitive performance for long-duration storage versus compressed hydrogen and leading metal-organic frameworks. In the TEA, it is shown that ALF's storage capacity, when combined with a temperature/pressure swing process, has advantages versus compressed H2 at a fraction of the pressure (15 bar versus 350 bar). Given ALF's performance in the 10 bar to 20 bar regime under moderate cooling, it is particularly promising for use in safe storage systems serving fuel cells.

As-Grown Miniaturized True Zero-Order Waveplates Based on Low-Dimensional Ferrocene Crystals.

As basic optical elements, waveplates with anisotropic electromagnetic responses are imperative for manipulating light polarization. Conventional waveplates are manufactured from bulk crystals (e.g., quartz and calcite) through a series of precision cutting and grinding steps, which typically result in large size, low yield, and high cost. In this study, a bottom-up method is used to grow ferrocene crystals with large anisotropy to demonstrate self-assembled ultrathin true zero-order waveplates without additional machining processing, which is particularly suited for nanophotonic integration. The van der Waals ferrocene crystals exhibit high birefringence (Δn (experiment) = 0.149  ±  0.002 at 636 nm), low dichroism Δκ (experiment) = -0.0007 at 636 nm), and a potentially broad operating range (550 nm to 20 µm) as suggested by Density Functional Theory (DFT) calculations. In addition, the grown waveplate's highest and the lowest principal axes (n1 and n3 , respectively) are in the a-c plane, where the fast axis is along one natural edge of the ferrocene crystal, rendering them readily usable. The as-grown, wavelength-scale-thick waveplate allows the development of further miniaturized systems via tandem integration.

Exclusive Recognition of CO2 from Hydrocarbons by Aluminum Formate with Hydrogen-Confined Pore Cavities.

Exclusive capture of carbon dioxide (CO2) from hydrocarbons via adsorptive separation is an important technology in the petrochemical industry, especially for acetylene (C2H2) production. However, the physicochemical similarities between CO2 and C2H2 hamper the development of CO2-preferential sorbents, and CO2 is mainly discerned via C recognition with low efficiency. Here, we report that the ultramicroporous material Al(HCOO)3, ALF, can exclusively capture CO2 from hydrocarbon mixtures, including those containing C2H2 and CH4. ALF shows a remarkable CO2 capacity of 86.2 cm3 g-1 and record-high CO2/C2H2 and CO2/CH4 uptake ratios. The inverse CO2/C2H2 separation and exclusive CO2 capture performance from hydrocarbons are validated via adsorption isotherms and dynamic breakthrough experiments. Notably, the hydrogen-confined pore cavities with appropriate dimensional size provide an ideal pore chemistry to specifically match CO2 via a hydrogen bonding mechanism, with all hydrocarbons rejected. This molecular recognition mechanism is unveiled by in situ Fourier-transform infrared spectroscopy, X-ray diffraction studies, and molecular simulations.

Noncryogenic Air Separation Using Aluminum Formate Al(HCOO)3 (ALF).

Separating oxygen from air to create oxygen-enriched gas streams is a process that is significant in both industrial and medical fields. However, the prominent technologies for creating oxygen-enriched gas streams are both energy and infrastructure intensive as they use cryogenic temperatures or materials that adsorb N2 from air. The latter method is less efficient than the methods that adsorb O2 directly. Herein, we show, via a combination of gas adsorption isotherms, gas breakthrough experiments, neutron and synchrotron X-ray powder diffraction, Raman spectroscopy, and computational studies, that the metal-organic framework, Al(HCOO)3 (ALF), which is easily prepared at low cost from commodity chemicals, exhibits substantial O2 adsorption and excellent time-dependent O2/N2 selectivity in a range of 50-125 near dry ice/solvent (≈190 K) temperatures. The effective O2 adsorption with ALF at ≈190 K and ≈0.21 bar (the partial pressure of O2 in air) is ≈1.7 mmol/g, and at ice/salt temperatures (≈250 K), it is ≈0.3 mmol/g. Though the kinetics for full adsorption of O2 near 190 K are slower than at temperatures nearer 250 K, the kinetics for initial O2 adsorption are fast, suggesting that O2 separation using ALF with rapid temperature swings at ambient pressures is a potentially viable choice for low-cost air separation applications. We also present synthetic strategies for improving the kinetics of this family of compounds, namely, via Al/Fe solid solutions. To the best of our knowledge, ALF has the highest O2/N2 sorption selectivity among MOF adsorbents without open metal sites as verified by co-adsorption experiments..

Self-supervised structural similarity-based convolutional neural network for cardiac diffusion tensor image denoising.

BACKGROUND: Diffusion tensor imaging (DTI) is a promising technique for non-invasively investigating the myocardial fiber structures of human heart. However, low signal-to-noise ratio (SNR) has been a major limit of cardiac DTI to prevent us from detecting myocardium structure accurately. Therefore, it is important to remove the effect of noise on diffusion weighted (DW) images. PURPOSE: Although the conventional and deep learning-based denoising methods have shown the potential to deal with effectively the noise in DW images, most of them are redundant information dependent or require the noise-free images as golden standard. In addition, the existed DW image denoising methods often suffer from problems of over-smoothing. To address these issues, we propose a self-supervised learning model, structural similarity based convolutional neural network with edge-weighted loss (SSECNN), to remove the noise effectively in cardiac DTI. METHODS: Considering that the DW images acquired along different diffusion directions have structural similarity, and the noise in these DW images is independent and identically distributed, the structural similarity-based matching algorithm is proposed to search for the most similar DW images. Such similar noisy DW image pairs are then used as the input and target of the denoising network SSECNN, which consists of several convolutional and residual blocks. Through the self-supervised training with these image pairs, the network can restore the clean DW images and retain the correlations between the denoised DW images along different directions. To avoid the over-smoothing problem, we design a novel edge-weighted loss which enables the network to adaptively adjust the loss weights with iterations and therefore to improve the detail preserve ability of the model. To verify the superiority of the proposed method, comparisons with state-of-the-art (SOTA) denoising methods are performed on both synthetic and real acquired DTI datasets. RESULTS: Experimental results show that SSECNN can effectively reduce the noise in the DW images while preserving detailed texture and edge information and therefore achieve better performance in DTI reconstruction. For synthetic dataset, compared to the SOTA method, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structure similarity index measure (SSIM) between the denoised DW images obtained with SSECNN and noise-free DW images are improved by 6.94%, 1.98%, and 0.76% respectively when the noise level is 10%. As for the acquired cardiac DTI dataset, the SSECNN method could significantly improve SNR and contrast to noise ratio (CNR) of cardiac DW images and achieve more regular helix angle (HA) and transverse angle (TA) maps. The ablation experimental results validate that using the structure similarity-based method to search the similar DW image pairs yield the smallest loss, and with the help of the edge-weighted loss, the denoised DW images and diffusion metric maps can preserve more details. CONCLUSIONS: The proposed SSECNN method can fully explore the similarity between the DW images along different diffusion directions. Using such similarity and an edge-weighted loss enable us to denoise cardiac DTI effectively in a self-supervised manner. Our method can overcome the redundancy information dependence and over-smoothing problem of the SOTA methods.

Achieving Near-unity Photoluminescence Quantum Yields in Organic-Inorganic Hybrid Antimony (III) Chlorides with the [SbCl5 ] Geometry.

Hybrid organic-inorganic antimony halides have attracted increasing attention due to the non-toxicity, stability, and high photoluminescence quantum yield (PLQY). To shed light on the structural factors that contribute to the high PLQY, five pairs of antimony halides with general formula A2 SbCl5 and A2 Sb2 Cl8 are synthesized via two distinct methods and characterized. The A2 SbCl5 type adopts square pyramidal [SbCl5 ] geometry with near-unity PLQY, while the A2 Sb2 Cl8 adopts seesaw dimmer [Sb2 Cl8 ] geometry with PLQY≈0 %. Through combined data analysis with the literature, we have found that A2 SbCl5 series with square pyramidal geometry generally has much longer Sb⋅⋅⋅Sb distances, leading to more expressed lone pairs of SbIII . Additional factors including Sb-Cl distance and stability of antimony chlorides may also affect PLQY. Our targeted synthesis and correlated insights provide efficient tools to precisely form highly emissive materials for optoelectronic applications.

Aluminum formate, Al(HCOO)3: An earth-abundant, scalable, and highly selective material for CO2 capture.

A combination of gas adsorption and gas breakthrough measurements show that the metal-organic framework, Al(HCOO)3 (ALF), which can be made inexpensively from commodity chemicals, exhibits excellent CO2 adsorption capacities and outstanding CO2/N2 selectivity that enable it to remove CO2 from dried CO2-containing gas streams at elevated temperatures (323 kelvin). Notably, ALF is scalable, readily pelletized, stable to SO2 and NO, and simple to regenerate. Density functional theory calculations and in situ neutron diffraction studies reveal that the preferential adsorption of CO2 is a size-selective separation that depends on the subtle difference between the kinetic diameters of CO2 and N2. The findings are supported by additional measurements, including Fourier transform infrared spectroscopy, thermogravimetric analysis, and variable temperature powder and single-crystal x-ray diffraction.

Micro Light Flow Controller on a Programmable Waveguide Engine.

A light flow controller that can regulate the three-port optical power in both lossless and lossy modus is realized on a programmable multimode waveguide engine. The microheaters on the waveguide chip mimic the tunable "pixels" that can continuously adjust the local refractive index. Compared to the conventional method where the tuning takes place only on single-mode waveguides, the proposed structure is more compact and requires less electrodes. The local index changes in a multimode waveguide can alter the mode numbers, field distribution, and propagation constants of each individual mode, all of which can alter the multimode interference pattern significantly. However, these changes are mostly complex and not governed by analytical equations as in the single-mode case. Though numerical simulations can be performed to predict the device response, the thermal and electromagnetic computing involved is mostly time-consuming. Here, a multi-level search program is developed based on experiments only. It can reach a target output in real time by adjusting the microheaters collectively and iteratively. It can also jump over local optima and further improve the cost function on a global level. With only a simple waveguide structure and four microheaters, light can be routed freely into any of the three output ports with arbitrary power ratios, with and without extra attenuation. This work may trigger new ideas in developing compact and efficient photonic integrated devices for applications in optical communication and computing.

Statistical information enhanced robust design method of optical thin film.

A novel merit function was constructed using the spectral coefficient average error and standard deviation, which can simultaneously optimize the expectation of spectral coefficient error and the envelope of standard deviation. Thus, a multi-objective optimization strategy based on Non-Dominated Sorting Genetic Algorithm and Sequential quadratic programming was proposed. By comparing result of wideband anti-reflection film, cut-off filter and Infrared dual-band filter designed by the conventional algorithm and the new algorithm, the control effect of the new algorithm on sensitivity of film parameters error was verified. The results show that the novel design method has the characteristics of time-efficient calculations and is capable of effectively improving the production yield of the film system, which has practical significance.

Hybrid Germanium Bromide Perovskites with Tunable Second Harmonic Generation.

Ge-based hybrid perovskite materials have demonstrated great potential for second harmonic generation (SHG) due to the geometry and lone-pair induced non-centrosymmetric structures. Here, we report a new family of hybrid 3D Ge-based bromide perovskites AGeBr3 , A=CH3 NH3 (MA), CH(NH2 )2 (FA), Cs and FAGe0.5 Sn0.5 Br3 , crystallizing in polar space groups. These compounds exhibit tunable SHG responses, where MAGeBr3 shows the strongest SHG intensity (5×potassium dihydrogen phosphate, KDP). Structural and theoretical analysis indicate the high SHG efficiency is attributed to the displacement of Ge2+ along [111] direction and the relatively strong interactions between lone pair electrons of Ge2+ and polar MA cations along the c-axis. This work provides new structural insights for designing and fine-tuning the SHG properties in hybrid metal halide materials.

Fundamental investigations on the sodium-ion transport properties of mixed polyanion solid-state battery electrolytes.

Lithium and sodium (Na) mixed polyanion solid electrolytes for all-solid-state batteries display some of the highest ionic conductivities reported to date. However, the effect of polyanion mixing on the ion-transport properties is still not fully understood. Here, we focus on Na1+xZr2SixP3-xO12 (0 ≤ x ≤ 3) NASICON electrolyte to elucidate the role of polyanion mixing on the Na-ion transport properties. Although NASICON is a widely investigated system, transport properties derived from experiments or theory vary by orders of magnitude. We use more than 2000 distinct ab initio-based kinetic Monte Carlo simulations to map the compositional space of NASICON over various time ranges, spatial resolutions and temperatures. Via electrochemical impedance spectroscopy measurements on samples with different sodium content, we find that the highest ionic conductivity (i.e., about 0.165 S cm-1 at 473 K) is experimentally achieved in Na3.4Zr2Si2.4P0.6O12, in line with simulations (i.e., about 0.170 S cm-1 at 473 K). The theoretical studies indicate that doped NASICON compounds (especially those with a silicon content x ≥ 2.4) can improve the Na-ion mobility compared to undoped NASICON compositions.

Superionic Conduction in the Plastic Crystal Polymorph of Na4P2S6.

Sodium thiophosphates are promising materials for large-scale energy storage applications benefiting from high ionic conductivities and the geopolitical abundance of the elements. A representative of this class is Na4P2S6, which currently shows two known polymorphs-α and ß. This work describes a third polymorph of Na4P2S6, γ, that forms above 580 °C, exhibits fast-ion conduction with low activation energy, and is mechanically soft. Based on high-temperature diffraction, pair distribution function analysis, thermal analysis, impedance spectroscopy, and ab initio molecular dynamics calculations, the γ-Na4P2S6 phase is identified to be a plastic crystal characterized by dynamic orientational disorder of the P2S6 4- anions translationally fixed on a body-centered cubic lattice. The prospect of stabilizing plastic crystals at operating temperatures of solid-state batteries, with benefits from their high ionic conductivities and mechanical properties, could have a strong impact in the field of solid-state battery research.

Identification and validation of CALCRL-associated prognostic genes in acute myeloid leukemia.

In the past few decades, several advances have been made in the field of acute myeloid leukemia (AML), especially in the development of novel drugs. However, the overall survival rate remains particularly disappointing due to a high rate of chemotherapy resistance and relapse. The calcitonin receptor-like receptor (CALCRL) is a novel promising therapeutic target of AML and has been indicated to be strongly correlated with chemotherapy resistance and relapse driven by leukemic stem cells. Nevertheless, the CALCRL downstream genes associated with the drug resistance and relapse of AML remain to be elucidated. Within this study, we used multiple gene expression datasets from the Gene Expression Omnibus (GEO) database and cBioPortal to explore the candidate CALCRL-associated genes that could potentially mediate the chemoresistance and relapse of AML. Then, we investigated the prognostic value, coexpression relationship with CALCRL, and expression characteristics of these genes using independent data from The Cancer Genome Atlas (TCGA). Eventually, three genes were screened out as CALCRL-associated prognostic genes. The expression of AGTPBP1 and LYST was negatively correlated with CALCRL, high expression of which was associated with favorable prognosis in AML. In contrast, the expression of ETS2 was positively correlated with CALCRL, high expression of which was associated with poor prognosis in AML. The results indicated that the three prognostic genes are potential CALCRL downstream genes that mediate drug resistance and relapse in AML. This study helps to further explore the role and molecular pathways of CALCRL in mediating drug resistance and relapse of AML.