Interactive Feature Embedding for Infrared and Visible Image Fusion

General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well designed loss function, which cannot guarantee that all vital information of source images is sufficiently extracted. In this work, we propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of self-supervised learning framework, hierarchical representations of source images can be efficiently extracted. In particular, interactive feature embedding models are tactfully designed to build a bridge between the self-supervised learning and infrared and visible image fusion learning, achieving vital information retention. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods

A Novel Probabilistic Model Based Fingerprint Recognition Algorithm

AbstractA novel fingerprint recognition algorithm based on the probabilistic graphical model is proposed in this paper. First, minutiae in query fingerprint are viewed as random variables with the minutiae in template print as the realizations. According to the random variables, a 2-tree model is built by selecting two signal points from the query set. Second, the model is converted into a Junction Tree, and the potentials of the tree nodes are defined according to the intrinsic characters of fingerprint. After that, Junction Tree (J.T.) algorithm is performed to obtain the correspondence of the two sets of minutiae. To deal with many-to-one corresponding problem caused by the outliers, we repeat the process by exchanging two sets. Finally, the similarity of the two fingerprints is evaluated using the number of common matching pairs and the maximal posteriori probability generated by the J.T. algorithm. Experiments performed on databases of FVC2004 achieve the perfect performance

Elucidate microbial characteristics in a fullscale treatment plant for offshore oil produced wastewater

Oil-produced wastewater treatment plants, especially those involving biological treatment processes, harbor rich and diverse microbes. However, knowledge of microbial ecology and microbial interactions determining the efficiency of plants for oil-produced wastewater is limited. Here, we performed 16S rDNA amplicon sequencing to elucidate the microbial composition and potential microbial functions in a full-scale well-worked offshore oil-produced wastewater treatment plant. Results showed that microbes that inhabited the plant were diverse and originated from oil and marine associated environments. The upstream physical and chemical treatments resulted in low microbial diversity. Organic pollutants were digested in the anaerobic baffled reactor (ABR) dominantly through fermentation combined with sulfur compounds respiration. Three aerobic parallel reactors (APRs) harbored different microbial groups that performed similar potential functions, such as hydrocarbon degradation, acidogenesis, photosynthetic assimilation, and nitrogen removal. Microbial characteristics were important to the performance of oil-produced wastewater treatment plants with biological processes

Adenosine generation catalyzed by CD39 and CD73 expressed on regulatory T cells mediates immune suppression

The study of T regulatory cells (T reg cells) has been limited by the lack of specific surface markers and an inability to define mechanisms of suppression. We show that the expression of CD39/ENTPD1 in concert with CD73/ecto-5′-nucleotidase distinguishes CD4+/CD25+/Foxp3+ T reg cells from other T cells. These ectoenzymes generate pericellular adenosine from extracellular nucleotides. The coordinated expression of CD39/CD73 on T reg cells and the adenosine A2A receptor on activated T effector cells generates immunosuppressive loops, indicating roles in the inhibitory function of T reg cells. Consequently, T reg cells from Cd39-null mice show impaired suppressive properties in vitro and fail to block allograft rejection in vivo. We conclude that CD39 and CD73 are surface markers of T reg cells that impart a specific biochemical signature characterized by adenosine generation that has functional relevance for cellular immunoregulation

The large area detector onboard the eXTP mission

The Large Area Detector (LAD) is the high-throughput, spectral-timing instrument onboard the eXTP mission, a flagship mission of the Chinese Academy of Sciences and the China National Space Administration, with a large European participation coordinated by Italy and Spain. The eXTP mission is currently performing its phase B study, with a target launch at the end-2027. The eXTP scientific payload includes four instruments (SFA, PFA, LAD and WFM) offering unprecedented simultaneous wide-band X-ray timing and polarimetry sensitivity. The LAD instrument is based on the design originally proposed for the LOFT mission. It envisages a deployed 3.2 m2 effective area in the 2-30 keV energy range, achieved through the technology of the large-area Silicon Drift Detectors - offering a spectral resolution of up to 200 eV FWHM at 6 keV - and of capillary plate collimators - limiting the field of view to about 1 degree. In this paper we will provide an overview of the LAD instrument design, its current status of development and anticipated performance

Overview on Multienzymatic Cascades for the Production of Non-canonical α-Amino Acids

SM-R thanks the University of Granada for the support provided by project PPJI2017-1 and the European Cooperation in Science and Technology (COST Action CA15133). Authors are also grateful to the Andalusian Regional Government through Endocrinology & Metabolism Group (CTS-202).The 22 genetically encoded amino acids (AAs) present in proteins (the 20 standard AAs together with selenocysteine and pyrrolysine), are commonly referred as proteinogenic AAs in the literature due to their appearance in ribosome-synthetized polypeptides. Beyond the borders of this key set of compounds, the rest of AAs are generally named imprecisely as non-proteinogenic AAs, even when they can also appear in polypeptide chains as a result of post-transductional machinery. Besides their importance as metabolites in life, many of D-α- and L-α-“non-canonical” amino acids (NcAAs) are of interest in the biotechnological and biomedical fields. They have found numerous applications in the discovery of new medicines and antibiotics, drug synthesis, cosmetic, and nutritional compounds, or in the improvement of protein and peptide pharmaceuticals. In addition to the numerous studies dealing with the asymmetric synthesis of NcAAs, many different enzymatic pathways have been reported in the literature allowing for the biosynthesis of NcAAs. Due to the huge heterogeneity of this group of molecules, this review is devoted to provide an overview on different established multienzymatic cascades for the production of non-canonical D-α- and L-α-AAs, supplying neophyte and experienced professionals in this field with different illustrative examples in the literature. Whereas the discovery of new or newly designed enzymes is of great interest, dusting off previous enzymatic methodologies by a “back and to the future” strategy might accelerate the implementation of new or improved multienzymatic cascades.University of Granada PPJI2017-1European Cooperation in Science and Technology (COST) CA15133Andalusian Regional Government through Endocrinology & Metabolism Group CTS-20

CT and MR Image Fusion Based on Adaptive Structure Decomposition

Computed tomography (CT) has an excellent performance in detecting dense structure, such as bones and implants, while magnetic resonance (MR) provides high-resolution information for soft issues. To obtain sufficient and accurate information for diagnosis, we propose a CT and MR image fusion method via adaptive structure decomposition to combine the complementary information. First, on the basis of different scales of issues, we adaptively decompose the source images into sub-bands (bands of small, middle, and large issues) by a spectral total variation method. Second, based on the interpretability of sub-bands, for the small scale and middle scale of issues, we extract the edge information from the sub-bands and design the fusion weight by the local edge energy. And for the large scale of issues, we design the fusion weight by the local intensity energy. Third, we reconstruct the fused image. The experimental results demonstrate the superiority of the proposed method on both subjective and objective assessments