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ObjectiveCo-occurring anti-tripartite motif-containing protein 9 and 67 autoantibodies (TRIM9/67-IgG) have been reported in only a very few cases of paraneoplastic cerebellar syndrome. The value of these biomarkers and the most sensitive methods of TRIM9/67-IgG detection are not known.MethodsWe performed a retrospective, multicenter study to evaluate the cerebrospinal fluid and serum of candidate TRIM9/67-IgG cases by tissue-based immunofluorescence, peptide phage display immunoprecipitation sequencing, overexpression cell-based assay (CBA), and immunoblot. Cases in which TRIM9/67-IgG was detected by at least 2 assays were considered TRIM9/67-IgG positive.ResultsAmong these cases (n = 13), CBA was the most sensitive (100%) and revealed that all cases had TRIM9 and TRIM67 autoantibodies. Of TRIM9/67-IgG cases with available clinical history, a subacute cerebellar syndrome was the most common presentation (n = 7/10), followed by encephalitis (n = 3/10). Of these 10 patients, 70% had comorbid cancer (7/10), 85% of whom (n = 6/7) had confirmed metastatic disease. All evaluable cancer biopsies expressed TRIM9 protein (n = 5/5), whose expression was elevated in the cancerous regions of the tissue in 4 of 5 cases.InterpretationTRIM9/67-IgG is a rare but likely high-risk paraneoplastic biomarker for which CBA appears to be the most sensitive diagnostic assay. ANN NEUROL 2023;94:1086-1101.

Introduction: Bovine mastitis caused by Escherichia coli compromises animal health and inflicts substantial product losses in dairy farming. It may manifest as subclinical through severe acute disease and can be transient or persistent in nature. Little is known about bacterial factors that impact clinical outcomes or allow some strains to outcompete others in the mammary gland (MG) environment. Mastitis-associated E. coli (MAEC) may have distinctive characteristics which may contribute to the varied nature of the disease. Given their high levels of intraspecies genetic variability, virulence factors of commonly used MAEC model strains may not be relevant to all members of this group. Methods: In this study, we sequenced the genomes of 96 MAEC strains isolated from cattle with clinical mastitis (CM). We utilized clinical severity data to perform genome-wide association studies to identify accessory genes associated with strains isolated from mild or severe CM, or with high or low competitive fitness during in vivo competition assays. Genes associated with mastitis pathogens or commensal strains isolated from bovine sources were also identified. Results: A type-2 secretion system (T2SS) and a chitinase (ChiA) exported by this system were strongly associated with pathogenic isolates compared with commensal strains. Deletion of chiA from MAEC isolates decreased their adherence to cultured bovine mammary epithelial cells. Discussion: The increased fitness associated with strains possessing this gene may be due to better attachment in the MG. Overall, these results provide a much richer understanding of MAEC and suggest bacterial processes that may underlie the clinical diversity associated with mastitis and their adaptation to this unique environment. [ABSTRACT FROM AUTHOR]

Piezoelectric resonators have shown promise as efficient, power-dense energy storage element alternatives to continue the miniaturization of DC-DC converters. However, practical piezoelectric resonators diverge from their ideal behavior due to spurious modes that cause high loss regions throughout their operating frequency range. Typically, control of piezoelectric resonator DC-DC converters is constrained to unidirectional power flow each resonant cycle for maximum efficiency operation. However, output power depends on the frequency with such control, which means the converter cannot operate at loads corresponding to spurious mode frequencies. This paper presents a fixed-frequency control mode utilizing the high-quality factor of piezoelectric resonators to avoid spurious modes. The fixed-frequency control enables efficient operation spanning the converter's full load range, demonstrated through a prototype DC-DC converter with a custom fabricated lithium niobate resonator. At a conversion ratio of 60 V to 30 V, spurious modes limit the converter's operating range from 33 W to 51 W using unidirectional power-constrained control, yet the fixed-frequency control extends operation from 33 W to 2 W.

The performance of inductors at high frequencies and small sizes is one of the largest limiting factors in the continued miniaturization of dc-dc converters. Piezoelectric resonators can have a very high quality factor and provide an inductive impedance between their series and parallel resonant frequencies, making them a promising technology for further miniaturizing dc-dc converters. In this paper we analyze the impact of resonator parameters on the performance of the piezoelectric resonator based dc-dc converter, derive the optimal load impedance and efficiency limits, and analyze the impacts of varying conversion ratio and load impedance. This work is accompanied by a prototype dc-dc converter using a piezoelectric resonator fabricated from lithium niobate. The piezoelectric resonator has a quality factor of 4178 and a coupling coefficient, $k_t^2$, of 29%. The converter is able to achieve high efficiency zero voltage switching and a continuously variable conversion ratio without the use of any discrete inductors. It achieves a maximum power output of 30.9 W at an efficiency of 95.2% with a power density of 6.76 $\frac{\text {W}}{\text {cm}^3}$.

Abstract Background Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks. Methods and Results To identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature. Conclusions Constrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

Introduction: The microbial interactions within the human microbiome are complex, and few methods are available to identify these interactions within a longitudinal microbial abundance framework. Existing methods typically impose restrictive constraints, such as requiring long sequences and equal spacing, on the data format which in many cases are violated. Methods: To identify microbial interaction networks (MINs) with general longitudinal data settings, we propose a stationary Gaussian graphical model (SGGM) based on 16S rRNA gene sequencing data. In the SGGM, data can be arbitrarily spaced, and there are no restrictions on the length of data sequences from a single subject. Based on the SGGM, EM -type algorithms are devised to compute the L1-penalized maximum likelihood estimate of MINs. The algorithms employ the classical graphical LASSO algorithm as the building block and can be implemented efficiently. Results: Extensive simulation studies show that the proposed algorithms can significantly outperform the conventional algorithms if the correlations among the longitudinal data are reasonably high. When the assumptions in the SGGM areviolated, e.g., zero inflation or data from heterogeneous microbial communities, the proposed algorithms still demonstrate robustness and perform better than the other existing algorithms. The algorithms are applied to a 16S rRNA gene sequencing data set from patients with cystic fibrosis. The results demonstrate strong evidence of an association between the MINs and the phylogenetic tree, indicating that the genetically related taxa tend to have more/ stronger interactions. These results strengthen the existing findings in literature. Discussion: The proposed algorithms can potentially be used to explore the network structure in genome, metabolome etc. as well. [ABSTRACT FROM AUTHOR]