Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation

Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have demonstrated remarkable capabilities in generating high-quality images while maintaining strong 3D consistency. Notably, significant advancements have been made in the domain of face generation. However, most existing models prioritize view consistency over disentanglement, resulting in limited semantic/attribute control during generation. To address this limitation, we propose a conditional GNeRF model incorporating specific attribute labels as input to enhance the controllability and disentanglement abilities of 3D-aware generative models. Our approach builds upon a pre-trained 3D-aware face model, and we introduce a Training as Init and Optimizing for Tuning (TRIOT) method to train a conditional normalized flow module to enable the facial attribute editing, then optimize the latent vector to improve attribute-editing precision further. Our extensive experiments demonstrate that our model produces high-quality edits with superior view consistency while preserving non-target regions. Code is available at https://github.com/zhangqianhui/TT-GNeRF.Comment: 13 page

The Mechanism of Intragranular Acicular Ferrite Nucleation Induced by Mg-Al-O Inclusions

The features of inclusion and microstructure for carbon structural steel containing Mg-Al-O inclusions were studied through the scanning electron microscope (SEM) and Energy Dispersive Spectrometer (EDS). It can be seen that, in Mg-Al-O inclusions, the elements of Mn, Si, and S coexist, and their central mole ratio of Mg/Al varies in a wide range. This value for most inclusions is larger than 0.5, which suggests the formation of solid solution between MgAl2O4 and MgO. After etching, the typical microstructure of intragranular acicular ferrites is observed, which is due to the nucleation effect induced by Mg-Al-O inclusions. From the SEM-EDS mapping images, it is found that the element of sulfur accumulates on the periphery of nucleation inclusion. Moreover, line EDS analysis hints that Mn-depletion zone (MDZ) exists in steel matrix, which is adjacent to the complex inclusion. Combined with the theoretical analysis, this phenomenon can be explained by the absorption of Mn due to the magnesium vacancy in MgAl2O4, and this MDZ promotes the nucleation of intragranular acicular ferrite. Through statistical analysis of SEM images for microstructure, the probabilistic nature of inducing nucleation effect is revealed. These results may be helpful to clarify the nature of oxide metallurgy

Towards Effective Management: Toxicity, Causal Mechanism and Controlling Strategy of Toxic Rangeland Weeds in Western China

Toxic rangeland weeds (TRWs) pose a great threat to animal husbandry. Currently, an estimated 33 million hectares of pasture (10%) in western China is infested by a variety of toxic weeds, including Stellera chamaejasme, Oxytropis spp., Astragalus spp., Achnatherum inebrians. The spread of these toxic weeds results in huge annual economic losses of more than $2.4 billion USD (direct and indirect). A combination of ecology, molecular biology, biochemistry and field practise methods will be used to identify and evaluate TRWs, explore the mechanism of toxicity, and more importantly, understand the causal mechanism by which TRWs flourish. The knowledge will underpin the development of effective management strategies

Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort

Osteosarcoma, a rare malignant tumor, has a poor prognosis. This study aimed to find the best prognostic model for osteosarcoma. There were 2912 patients included from the SEER database and 225 patients from Hebei Province. Patients from the SEER database (2008-2015) were included in the development dataset. Patients from the SEER database (2004-2007) and Hebei Province cohort were included in the external test datasets. The Cox model and three tree-based machine learning algorithms (survival tree [ST], random survival forest [RSF] and gradient boosting machine [GBM]) were used to develop the prognostic models by 10-fold cross-validation with 200 iterations. Additionally, performance of models in the multivariable group was compared with the TNM group. The 3-year and 5-year cancer specific survival (CSS) were 72.71% and 65.92% in the development dataset, respectively. The predictive ability in the multivariable group was superior to that in the TNM group. The calibration curves and consistency in the multivariable group were superior to those in the TNM group. The Cox and RSF models performed better than the ST and GBM models. A nomogram was constructed to predict the 3-year and 5-year CSS of osteosarcoma patients. The RSF model can be used as a nonparametric alternative to the Cox model. The constructed nomogram based on the Cox model can provide reference for clinicians to formulate specific therapeutic decisions both in America and China

Achieving 19% efficiency in non-fused ring electron acceptor solar cells via solubility control of donor and acceptor crystallization

Non-fused ring electron acceptors (NFREAs) potentially have lower synthetic costs than their fused counterparts. However, the low backbone planarity and the presence of bulky substituents adversely affect the crystallinity of NFREAs, impeding charge transport and the formation of bicontinuous morphology in organic solar cells. Here we show that a binary solvent system can individually control the crystallization and phase separation of the donor polymer (for example, D18) and the NFREA (for example, 2BTh-2F-C2). We select solvents such as chloroform and o-xylene that evaporate at different temperatures and rates and have different solubility for D18. Upon evaporation of chloroform, D18 starts to assemble into fibrils. Then, the evaporation of o-xylene induces the rapid formation of a fibril network that phase segregates 2BTh-2F-C2 into pure domains and leads to a bicontinuous morphology. The well-defined interpenetrating network morphology affords an efficiency of 19.02% on small-area cells and 17.28% on 1 cm2 devices

One-step Preparation of ZnO Electron Transport Layers Functionalized with Benzoic Acid Derivatives

We present a "one-step" approach to modify ZnO electron transport layers (ETLs) used in organic solar cells. This approach involves adding benzoic acid (BZA) derivatives directly to the ZnO precursor solution, which are then present at the surface of the resulting ZnO film. We demonstrate this approach for three different BZA derivatives, namely benzoic acid, chlorobenzoic acid, and 4-hydrazinobenzoic acid. For all molecules, improved device performance and stability is demonstrated in solar cells using an active layer blend of PTQ10 (donor) and ITIC-Br (non-fullerene acceptor) compared to such cells prepared using untreated ZnO. Furthermore, similar or improved device performance and stability is demonstrated compared to conventional PEIE treatment of ZnO. The presence of the BZA derivatives at the surface after processing is established using X-ray photoelectron spectroscopy and near-edge X-ray absorption fine-structure spectroscopy. From atomic force microscopy analysis and X-ray diffraction studies, the addition of BZA derivatives appears to restrict ZnO grain growth; however, this does not negatively impact device performance. ZnO layers treated with BZA derivatives also exhibit higher water contact angle and lower work function compared to untreated ZnO. This approach enables simplification of device manufacture while still allowing optimization of the surface properties of metal oxide ETLs. Keywords: electron transport layers, zinc oxide, organic solar cells, surface modificationComment: Manuscript: 25 pages, 8 figures, 5 tables. Supplementary Material: 36 pages, 22 figures, 13 tables. Submitted to Solar Energy Materials and Solar Cell

Molecular mechanism underlying transport and allosteric inhibition of bicarbonate transporter SbtA

SbtA is a high-affinity, sodium-dependent bicarbonate transporter found in the cyanobacterial CO2-concentrating mechanism (CCM). SbtA forms a complex with SbtB, while SbtB allosterically regulates the transport activity of SbtA by binding with adenyl nucleotides. The underlying mechanism of transport and regulation of SbtA is largely unknown. In this study, we report the three-dimensional structures of the cyanobacterial Synechocystis sp. PCC 6803 SbtA–SbtB complex in both the presence and absence of HCO3− and/or AMP at 2.7 Å and 3.2 Å resolution. An analysis of the inward-facing state of the SbtA structure reveals the HCO3−/Na+ binding site, providing evidence for the functional unit as a trimer. A structural comparison found that SbtA adopts an elevator mechanism for bicarbonate transport. A structure-based analysis revealed that the allosteric inhibition of SbtA by SbtB occurs mainly through the T-loop of SbtB, which binds to both the core domain and the scaffold domain of SbtA and locks it in an inward-facing state. T-loop conformation is stabilized by the AMP molecules binding at the SbtB trimer interfaces and may be adjusted by other adenyl nucleotides. The unique regulatory mechanism of SbtA by SbtB makes it important to study inorganic carbon uptake systems in CCM, which can be used to modify photosynthesis in crops.</jats:p

Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-P\'erot laser with saturable absorber

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.Comment: 10 pages, 8 figure

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p