Efficacy of omadacycline in the treatment of Legionella pneumonia: a case report.

Legionella, one of the main pathogens that causes community-acquired pneumonia, can lead to Legionella pneumonia, a condition characterized predominantly by severe pneumonia. This disease, caused by the bacterium Legionella pneumophila, can quickly progress to critical pneumonia and is often associated with damage to multiple organs. As a result, it requires close attention in terms of clinical diagnosis and treatment. Omadacycline, a new type of tetracycline derivative belonging to the aminomethylcycline class of antibiotics, is a semi-synthetic compound derived from minocycline. Its key structural feature, the aminomethyl modification, allows omadacycline to overcome bacterial resistance and broadens its range of effectiveness against bacteria. Clinical studies have demonstrated that omadacycline is not metabolized in the body, and patients with hepatic and renal dysfunction do not need to adjust their dosage. This paper reports a case of successful treatment of Legionella pneumonia with omadacycline in a patient who initially did not respond to empirical treatment with moxifloxacin. The patient also experienced electrolyte disturbance, as well as dysfunction in the liver and kidneys, delirium, and other related psychiatric symptoms.

Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies.

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

The Burgeoning Significance of Liquid-Liquid Phase Separation in the Pathogenesis and Therapeutics of Cancers.

Liquid-liquid phase separation (LLPS) is a physiological phenomenon that parallels the mixing of oil and water, giving rise to compartments with diverse physical properties. Biomolecular condensates, arising from LLPS, serve as critical regulators of gene expression and control, with a particular significance in the context of malignant tumors. Recent investigations have unveiled the intimate connection between LLPS and cancer, a nexus that profoundly impacts various facets of cancer progression, including DNA repair, transcriptional regulation, oncogene expression, and the formation of critical membraneless organelles within the cancer microenvironment. This review provides a comprehensive account of the evolution of LLPS from the molecular to the pathological level. We explore the mechanisms by through which biomolecular condensates govern diverse cellular physiological processes, encompassing gene expression, transcriptional control, signal transduction, and responses to environmental stressors. Furthermore, we concentrate on potential therapeutic targets and the development of small-molecule inhibitors associated with LLPS in prevalent clinical malignancies. Understanding the role of LLPS and its interplay within the tumor milieu holds promise for enhancing cancer treatment strategies, particularly in overcoming drug resistance challenges. These insights offer innovative perspectives and support for advancing cancer therapy.

PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy.

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

Equilibrative nucleotide transporter ENT3 (SLC29A3): A unique transporter for inherited disorders and cancers.

As a crucial gene associated with diseases, the SLC29A3 gene encodes the equilibrative nucleoside transporter 3 (ENT3). ENT3 plays an essential regulatory role in transporting intracellular hydrophilic nucleosides, nucleotides, hydrophilic anticancer and antiviral nucleoside drugs, energy metabolism, subcellular localization, protein stability, and signal transduction. The mutation and inactivation of SLC29A3 are intimately linked to the occurrence, development, and prognosis of various human tumors. Moreover, many hereditary human diseases, such as H syndrome, pigmentary hypertrichosis and non-autoimmune insulin-dependent diabetes mellitus (PHID) syndrome, Faisalabad histiocytosis (FHC), are related to SLC29A3 mutations. This review explores the mechanisms of SLC29A3 mutations and expression alterations in inherited disorders and cancers. Additionally, we compile studies on the inhibition of ENT3, which may serve as an effective strategy to potentiate the anticancer activity of chemotherapy. Thus, the synopsis of genetics, permeant function and drug therapy of ENT3 provides a new theoretical and empirical foundation for the diagnosis, prognosis of evaluation and treatment of various related diseases.

Modeling Cardinality in Image Hashing.

Cardinality constraint, namely, constraining the number of nonzero outputs of models, has been widely used in structural learning. It can be used for modeling the dependencies between multidimensional labels. In hashing, the final outputs are also binary codes, which are similar to multidimensional labels. It has been validated that estimating how many 1's in a multidimensional label vector is easier than directly predicting which elements are 1 and estimating cardinality as a prior step will improve the classification performance. Hence, in this article, we incorporate cardinality constraint into the unsupervised image hashing problem. The proposed model is divided into two steps: 1) estimating the cardinalities of hashing codes and 2) then estimating which bits are 1. Unlike multidimensional labels that are known and fixed in the training phase, the hashing codes are generally learned through an iterative method and, therefore, their cardinalities are unknown and not fixed during the learning procedure. We use a neural network as a cardinality predictor and its parameters are jointly learned with the hashing code generator, which is an autoencoder in our model. The experiments demonstrate the efficiency of our proposed method.

Strip SAR image simulation of a composite vehicle-ground model.

In this paper, the strip synthetic aperture radar (SAR) image of a composite vehicle-ground model is simulated by combining the electromagnetic scattering algorithm and radar imaging algorithm. A linear frequency modulated wave is incident on the composite model, which is partitioned into a mass of triangular patches. In the "stop-and-go" radar mode, for each patch, the amplitude of scattered echo in the frequency domain is solved by a hybrid method of physical optics (PO)-shooting and bouncing ray (SBR)-physical theory of diffraction (PTD). For the composite model, the total scattered echo in terms of range frequency and azimuth time is obtained by the vector superposition of echo on patches. Then the SAR image of the composite model is generated by the range-Doppler algorithm. In numerical simulations, both the electromagnetic scattering of a target by the SBR-PTD method and composite scattering by the PO-SBR-PTD method are validated and evaluated by comparing with the multilevel fast multipole method (MLFMM) in FEKO software. Moreover, the SAR image of the composite vehicle-ground model is also compared with the real image in Moving and Stationary Target Acquisition and Recognition database, which verifies the feasibility of the proposed method. SAR images of the composite model for different incident angles are also presented and analyzed.

Erratum: Eightfold Degenerate Fermions in Two Dimensions [Phys. Rev. Lett. 127, 176401 (2021)].

This corrects the article DOI: 10.1103/PhysRevLett.127.176401.

Eightfold Degenerate Fermions in Two Dimensions.

Multifold degenerate fermions have attracted a lot of research interest in condensed matter physics and materials science, but always lack in two dimensions. In this Letter, from symmetry analysis and lattice model construction, we demonstrate that eightfold degenerate fermions can be realized in two-dimensional systems. In nonmagnetic materials with negligible spin-orbit coupling, the gray magnetic space groups together with SU(2) spin rotation symmetry can protect the two-dimensional eightfold degenerate fermions on a certain high-symmetry axis in the Brillouin zone, no matter whether the system is centrosymmetric or noncentrosymmetric. In antiferromagnetic materials, the eightfold degenerate fermions can also be protected by certain "spin space groups." Furthermore, by first-principles electronic structure calculations, we predict that the paramagnetic phase of the monolayer LaB_{8} on a suitable substrate is a two-dimensional eightfold degenerate as well as Dirac node-line semimetal. Especially, the eightfold degenerate points are close to the Fermi level, which makes monolayer LaB_{8} a good platform to study the exotic physical properties of two-dimensional eightfold degenerate fermions.

Interval Type-2 Fuzzy Logic for Semisupervised Multimodal Hashing.

Retrieving nearest neighbors across correlated data in multiple modalities, such as image-text pairs on Facebook and video-tag pairs on YouTube, has become a challenging task due to the huge amount of data. Multimodal hashing methods that embed data into binary codes can boost the retrieving speed and reduce storage requirements. Since unsupervised multimodal hashing methods are usually inferior to supervised ones and the supervised ones require too much manually labeled data, the proposed method in this paper utilizes partially available labels to design a semisupervised multimodal hashing method. The labels for unlabeled data are treated as an interval type-2 fuzzy set and estimated by the available labels. By defuzzifying the estimated labels using hard partitioning, a supervised multimodal hashing method is used to generate binary codes. Experiments show that the proposed semisupervised method with 50% labels can get a medium performance among the compared supervised ones and achieve approximate performance to the best supervised method with 90% labels. With only 10% labels, the proposed method can still compete with the worst compared supervised one. Furthermore, the proposed label estimation method has been experimentally proven to be more feasible for a multilabeled MIRFlickr data set in a hash lookup task.

Improving the Topside Profile of Ionosonde with TEC Retrieved from Spaceborne Polarimetric SAR.

Signals from spaceborne polarimetric synthetic aperture radar will suffer from Faraday rotations when they propagate through the ionosphere, especially those at L-band or lower frequencies, such as signals from the Phased Array type L-band Synthetic Aperture Radar (PALSAR). For this reason, Faraday rotation compensation should be considered. On the other hand, Faraday rotation could also be retrieved from distorted echoes. Moreover, combining Faraday rotation with the radar parameters and the model of magnetic field, we could derive the total electron content (TEC) along the signal path. Benefiting from the high spatial resolution of the SAR system, TEC obtained from PALSAR could be orders of magnitude higher in spatial resolution than that from GPS. Besides, we demonstrated that the precision of TEC from PALSAR is also much higher than that from GPS. With the precise TEC available, we could fuse it with data from other ionosphere detection devices to improve their performances. In this paper, we adopted it to help modify the empirically modeled topside profile of ionosonde. The results show that the divergence between the modified profile and the referenced incoherent scattering radar profile reduced by about 30 percent when compared to the original ionosonde topside profile.

Modulation of resting-state brain functional connectivity by exposure to acute fourth-generation long-term evolution electromagnetic field: An fMRI study.

By now, the neurophysiological effect of electromagnetic field (EMF) exposure and its underlying regulating mechanisms are not well manifested. In this study, we aimed to investigate whether acute long-term evolution (LTE) EMF exposure could modulate brain functional connectivity using regional homogeneity (ReHo) method and seed-based analysis on resting-state functional magnetic resonance imaging (fMRI). We performed the LTE-EMF exposure experiment and acquired the resting-state brain activities before and after EMF exposure. Then we applied ReHo index to characterize the localized functional connectivity and seed-based method to evaluate the inter-regional functional connectivity. Statistical comparisons were conducted to identify the possible evidence of brain functional connectivity modulation induced by the acute LTE-EMF exposure. We found that the acute LTE-EMF exposure modulated localized intra-regional connectivity (p < 0.05, AlphaSim corrected, voxel size ≥ 18) and inter-regional connectivity in some brain regions (p < 0.05, AlphaSim corrected, voxel size ≥ 18). Our results may indicate that the approaches relying on network-level inferences could provide deeper insight into the acute effect on human functional activity induced by LTE-EMF exposure. Bioelectromagnetics. 40:42-51, 2019. © 2018 Wiley Periodicals, Inc.