Catalysts Promoted with Niobium Oxide for Air Pollution Abatement

Pt-containing catalysts are currently used commercially to catalyze the conversion of carbon monoxide (CO) and hydrocarbon (HC) pollutants from stationary chemical and petroleum plants. It is well known that Pt-containing catalysts are expensive and have limited availability. The goal of this research is to find alternative and less expensive catalysts to replace Pt for these applications. This study found that niobium oxide (Nb2O5), as a carrier or support for certain transition metal oxides, promotes oxidation activity while maintaining stability, making them candidates as alternatives to Pt. The present work reports that the orthorhombic structure of niobium oxide (formed at 800 °C in air) promotes Co3O4 toward the oxidation of both CO and propane, which are common pollutants in volatile organic compound (VOC) applications. This was a surprising result since this structure of Nb2O5 has a very low surface area (about 2 m2/g) relative to the more traditional Al2O3 support, with a surface area of 150 m2/g. The results reported demonstrate that 1% Co3O4/Nb2O5 has comparable fresh and aged catalytic activity to 1% Pt/γ-Al2O3 and 1% Pt/Nb2O5. Furthermore, 6% Co3O4/Nb2O5 outperforms 1% Pt/Al2O3 in both catalytic activity and thermal stability. These results suggest a strong interaction between niobium oxide and the active component—cobalt oxide—likely by inducing an oxygen defect structure with oxygen vacancies leading to enhanced activity toward the oxidation of CO and propane

Information seeking as chasing anticipated prediction errors

When faced with delayed, uncertain rewards, humans and other animals usually prefer to know the eventual outcomes in advance. This preference for cues providing advance information can lead to seemingly suboptimal choices, where less reward is preferred over more reward. Here, we introduce a reinforcement-learning model of this behavior, the anticipated prediction error (APE) model, based on the idea that prediction errors themselves can be rewarding. As a result, animals will sometimes pick options that yield large prediction errors, even when the expected rewards are smaller. We compare the APE model against an alternative information-bonus model, where information itself is viewed as rewarding. These models are evaluated against a newly collected dataset with human participants. The APE model fits the data as well or better than the other models, with fewer free parameters, thus providing a more robust and parsimonious account of the suboptimal choices. These results suggest that anticipated prediction errors can be an important signal underpinning decision making

Challenges in QCD matter physics - The Compressed Baryonic Matter experiment at FAIR

Substantial experimental and theoretical efforts worldwide are devoted to explore the phase diagram of strongly interacting matter. At LHC and top RHIC energies, QCD matter is studied at very high temperatures and nearly vanishing net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was created at experiments at RHIC and LHC. The transition from the QGP back to the hadron gas is found to be a smooth cross over. For larger net-baryon densities and lower temperatures, it is expected that the QCD phase diagram exhibits a rich structure, such as a first-order phase transition between hadronic and partonic matter which terminates in a critical point, or exotic phases like quarkyonic matter. The discovery of these landmarks would be a breakthrough in our understanding of the strong interaction and is therefore in the focus of various high-energy heavy-ion research programs. The Compressed Baryonic Matter (CBM) experiment at FAIR will play a unique role in the exploration of the QCD phase diagram in the region of high net-baryon densities, because it is designed to run at unprecedented interaction rates. High-rate operation is the key prerequisite for high-precision measurements of multi-differential observables and of rare diagnostic probes which are sensitive to the dense phase of the nuclear fireball. The goal of the CBM experiment at SIS100 (sqrt(s_NN) = 2.7 - 4.9 GeV) is to discover fundamental properties of QCD matter: the phase structure at large baryon-chemical potentials (mu_B > 500 MeV), effects of chiral symmetry, and the equation-of-state at high density as it is expected to occur in the core of neutron stars. In this article, we review the motivation for and the physics programme of CBM, including activities before the start of data taking in 2022, in the context of the worldwide efforts to explore high-density QCD matter.Comment: 15 pages, 11 figures. Published in European Physical Journal

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network

Opium poppy is a medicinal plant, and its cultivation is illegal without legal approval in China. Unmanned aerial vehicle (UAV) is an effective tool for monitoring illegal poppy cultivation. However, targets often appear occluded and confused, and it is difficult for existing detectors to accurately detect poppies. To address this problem, we propose an opium poppy detection network, YOLOHLA, for UAV remote sensing images. Specifically, we propose a new attention module that uses two branches to extract features at different scales. To enhance generalization capabilities, we introduce a learning strategy that involves iterative learning, where challenging samples are identified and the model’s representation capacity is enhanced using prior knowledge. Furthermore, we propose a lightweight model (YOLOHLA-tiny) using YOLOHLA based on structured model pruning, which can be better deployed on low-power embedded platforms. To evaluate the detection performance of the proposed method, we collect a UAV remote sensing image poppy dataset. The experimental results show that the proposed YOLOHLA model achieves better detection performance and faster execution speed than existing models. Our method achieves a mean average precision (mAP) of 88.2% and an F1 score of 85.5% for opium poppy detection. The proposed lightweight model achieves an inference speed of 172 frames per second (FPS) on embedded platforms. The experimental results showcase the practical applicability of the proposed poppy object detection method for real-time detection of poppy targets on UAV platforms

Information Seeking as Chasing Anticipated Prediction Errors

When faced with delayed, uncertain rewards, humans andother animals usually prefer to know the eventual outcomesin advance. This preference for cues providing advance infor-mation can lead to seemingly suboptimal choices, where lessreward is preferred over more reward. Here, we introduce areinforcement-learning model of this behavior, the anticipatedprediction error (APE) model, based on the idea that predic-tion errors themselves can be rewarding. As a result, animalswill sometimes pick options that yield large prediction errors,even when the expected rewards are smaller. We compare theAPE model against an alternative information-bonus model,where information itself is viewed as rewarding. These mod-els are evaluated against a newly collected dataset with humanparticipants. The APE model fits the data as well or betterthan the other models, with fewer free parameters, thus provid-ing a more robust and parsimonious account of the suboptimalchoices. These results suggest that anticipated prediction er-rors can be an important signal underpinning decision making

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

Abstract The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a “digital biopsy”. As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment

Main Chain Dendronized Polymers: Design, Synthesis, and Application in the Second-Order Nonlinear Optical (NLO) Area

For the first time, two nonlinear optical (NLO) main chain dendronized polymers, <b>MDPG1</b> and <b>MDPG2</b>, were prepared through the Suzuki coupling reaction by utilizing low-generation dendrimers as the monomers. These polymers inherit the advantages of both main chain polymers (which usually demonstrate good stability of the NLO effect) and dendronized polymers (which usually possess a large NLO coefficient) simultaneously. For <b>MDPG2</b>, its NLO coefficients <i>d</i>₃₃ value and <i>d</i>_33(∞) value are up to 116 and 20 pm/V, respectively, very similar to those of the normal dendronized polymer <b>DPG2</b>. Interestingly, thanks to its main chain structure, the onset temperature for the decay of its <i>d</i>₃₃ value was tested to be 100 °C, 30 °C higher than that of <b>DPG2</b>