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The nature of the neutrino is one of the major open questions in experimental nuclear and particle physics. The most sensitive known method to establish the Majorana nature of the neutrino is detection of the ultra-rare process of neutrinoless double beta decay. However, identification of one or a handful of decay events within a large mass of candidate isotope, without obfuscation by backgrounds is a formidable experimental challenge. One hypothetical method for achieving ultra- low-background neutrinoless double beta decay sensitivity is the detection of single 136 Ba ions produced in the decay of 136 Xe (“barium tagging”). To implement such a method, a single-ion-sensitive barium detector must be developed and demonstrated in bulk liquid or dry gaseous xenon. This paper reports on the development of two families of dry-phase barium chemosensor molecules for use in high pressure xenon gas detectors, synthesized specifically for this purpose. One particularly promising candidate, an anthracene substituted aza-18-crown-6 ether, is shown to respond in the dry phase with almost no intrinsic background from the unchelated state, and to be amenable to barium sensing through fluorescence microscopy. This interdisciplinary advance, paired with earlier work demonstrating sensitivity to single barium ions in solution, opens a new path toward single ion detection in high pressure xenon gas.

Using Commercial Off-The-Shelf (COTS) Operational Amplifiers (OpAmps) and Complementary Metal-Oxide Semiconductor (CMOS) transistors, we present a demonstration of the Q-Pix front-end architecture, a novel readout solution for kiloton-scale Liquid Argon Time Projection Chamber (LArTPC) detectors. The Q-Pix scheme employs a Charge-Integrate/Reset process based on the Least Action principle, enabling pixel-scale self-triggering charge collection and processing, minimizing energy consumption, and maximizing data compression. We examine the architecture's sensitivity, linearity, noise, and other features at the circuit board level and draw comparisons to SPICE simulations. Furthermore, we highlight the resemblance between the Q-Pix front-end and Sigma-Delta modulator, emphasizing that digital data processing techniques for Sigma-Delta can be directly applied to Q-Pix, resulting in enhanced signal-to-noise performance. These insights will inform the development of Q-Pix front-end designs in integrated circuits (IC) and guide data collection and processing for future large-scale LArTPC detectors in neutrino physics and other high-energy physics experiments.

A widely held concern of screening is that its psychological harms may outweigh the benefits of early detection and treatment. This study describes pregnant women's perceptions of possible harms and benefits of mental health screening and factors associated with identifying screening as harmful or beneficial. This study analyzed a subgroup of women who had undergone formal or informal mental health screening from our larger multi-site, cross-sectional study. Pregnant women >16 years of age who spoke/read English were recruited (May-December 2013) from prenatal classes and maternity clinics in Alberta, Canada. Descriptive statistics were generated to summarize harms and benefits of screening and multivariable logistic regression identified factors associated with reporting at least one harm or affirming screening as a positive experience (January-December 2014). Overall study participation rate was 92% (N = 460/500). Among women screened for mental health concerns (n = 238), 63% viewed screening as positive, 69% were glad to be asked, and 87% took it as evidence their provider cared about them. Only one woman identified screening as a negative experience. Of the 6 harms, none was endorsed by >7% of women, with embarrassment being most cited. Women who were very comfortable (vs somewhat/not comfortable) with screening were more likely to report it as a positive experience. Women were largely Caucasian, well-educated, partnered women; thus, findings may not be generalizable to women with socioeconomic risk. Most women perceived prenatal mental health screening as having high benefit and low harm. These findings dispel popular concerns that mental health screening is psychologically harmful.

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [ 1 ]. Multiple peer-reviewed papers independently verified or extended our findings [ 2 – 12 ]. Given this impact, we carefully considered concerns by Gihawi et al. [ 13 ] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [ 14 ]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [ 15 ], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [ 13 ] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [ 13 ] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Microorganisms in the human gut play a role in health and disease, and in adults higher gut biodiversity has been linked to better health. Since gut microorganisms may be pivotal in the development of microbial therapies, understanding the factors that shape gut biodiversity is of utmost interest. We performed large-scale analyses of the relationship of age and sex to gut bacterial diversity in adult cohorts from four geographic regions: the United States, the United Kingdom, Colombia, and China. In the U.S., U.K., and Colombian cohorts, bacterial biodiversity correlated positively with age in young adults but plateaued at about age 40 years, with no positive association being found in middle-aged adults. Young, but not middle-aged, adult women had higher gut bacterial diversity than men, a pattern confirmed via supervised machine learning. Interestingly, in the Chinese cohort, minimal associations were observed between gut biodiversity and age or sex. Our results highlight the patterns of adult gut biodiversity and provide a framework for future research. Gut microbial diversity changes throughout the human life span and is known to be associated with host sex. We investigated the association of age, sex, and gut bacterial alpha diversity in three large cohorts of adults from four geographical regions: subjects from the United States and United Kingdom in the American Gut Project (AGP) citizen-science initiative and two independent cohorts of Colombians and Chinese. In three of the four cohorts, we observed a strong positive association between age and alpha diversity in young adults that plateaued after age 40 years. We also found sex-dependent differences that were more pronounced in younger adults than in middle-aged adults, with women having higher alpha diversity than men. In contrast to the other three cohorts, no association of alpha diversity with age or sex was observed in the Chinese cohort. The association of alpha diversity with age and sex remained after adjusting for cardiometabolic parameters in the Colombian cohort and antibiotic usage in the AGP cohort. We further attempted to predict the microbiota age in individuals using a machine-learning approach for the men and women in each cohort. Consistent with our alpha-diversity-based findings, U.S. and U.K. women had a significantly higher predicted microbiota age than men, with a reduced difference being seen above age 40 years. This difference was not observed in the Colombian cohort and was observed only in middle-aged Chinese adults. Together, our results provide new insights into the influence of age and sex on the biodiversity of the human gut microbiota during adulthood while highlighting similarities and differences across diverse cohorts. IMPORTANCE Microorganisms in the human gut play a role in health and disease, and in adults higher gut biodiversity has been linked to better health. Since gut microorganisms may be pivotal in the development of microbial therapies, understanding the factors that shape gut biodiversity is of utmost interest. We performed large-scale analyses of the relationship of age and sex to gut bacterial diversity in adult cohorts from four geographic regions: the United States, the United Kingdom, Colombia, and China. In the U.S., U.K., and Colombian cohorts, bacterial biodiversity correlated positively with age in young adults but plateaued at about age 40 years, with no positive association being found in middle-aged adults. Young, but not middle-aged, adult women had higher gut bacterial diversity than men, a pattern confirmed via supervised machine learning. Interestingly, in the Chinese cohort, minimal associations were observed between gut biodiversity and age or sex. Our results highlight the patterns of adult gut biodiversity and provide a framework for future research.

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.

Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

Major international guidelines recommend mental health screening during the perinatal period. However, substantial barriers to screening have been reported by pregnant and postpartum women and perinatal care providers. E-screening offers benefits that may address implementation challenges. The primary objective of this randomized controlled trial was to evaluate the feasibility and acceptability of Web-based mental health e-screening compared with paper-based screening among pregnant women. A secondary objective was to identify factors associated with women's preferences for e-screening and disclosure of mental health concerns. Pregnant women recruited from community and hospital-based antenatal clinics and hospital-based prenatal classes were computer-randomized to a fully automated Web-based e-screening intervention group or a paper-based control group. Women were eligible if they spoke or read English, were willing to be randomized to e-screening, and were willing to participate in a follow-up diagnostic interview. The intervention group completed the Antenatal Psychosocial Health Assessment and the Edinburgh Postnatal Depression Scale on a tablet computer, while controls completed them on paper. All women completed self-report baseline questions and were telephoned 1 week after randomization by a blinded research assistant for a MINI International Neuropsychiatric Interview. Renker and Tonkin's tool of feasibility and acceptability of computerized screening was used to assess the feasibility and acceptability of e-screening compared with paper-based screening. Intention-to-treat analysis was used. To identify factors associated with preference for e-screening and disclosure, variables associated with each outcome at P<.20 were simultaneously entered into final multivariable models to estimate adjusted odds ratios (AORs) and 95% CIs. Of the 675 eligible women approached, 636 agreed to participate (participation rate 94.2%) and were randomized to the intervention (n=305) or control (n=331) groups. There were no significant baseline differences between groups. More women in the e-screening group strongly or somewhat agreed that they would like to use a tablet for answering questions on emotional health (57.9%, 175/302 vs 37.2%, 121/325) and would prefer using a tablet to paper (46.0%, 139/302 vs 29.2%, 95/325), compared with women in the paper-based screening group. There were no differences between groups in women's disclosure of emotional health concerns (94.1%, 284/302 vs 90.2%, 293/325). Women in the e-screening group consistently reported the features of e-screening more favorably than controls (more private or confidential, less impersonal, less time-consuming). In the multivariable models, being in the e-screening group was significantly associated with preferring e-screening (AOR 2.29, 95% CI 1.66-3.17), while no factors were significantly associated with disclosure. The findings suggest that mental health e-screening is feasible and acceptable to pregnant women. Clinicaltrials.gov NCT01899534; https://clinicaltrials.gov/ct2/show/NCT01899534 (Archived by WebCite at http://www.webcitation.org/6ntWg1yWb).

BACKGROUND[18F]FluorThanatrace ([18F]FTT) is a radiolabeled poly (adenosine diphosphate-ribose) polymerase inhibitor (PARPi) that enables noninvasive quantification of PARP with potential to serve as a biomarker for patient selection for PARPi therapy. Here we report for the first time to our knowledge noninvasive in vivo visualization of drug-target engagement during PARPi treatment.METHODSTwo single-arm, prospective, nonrandomized clinical trials were conducted at the University of Pennsylvania from May 2017 to March 2020. PARP expression in breast cancer was assessed in vivo via [18F]FTT PET before and after initiation of PARPi treatment and in vitro via [125I]KX1 (an analog of [18F]FTT) binding to surgically removed breast cancer.RESULTSThirteen patients had baseline [18F]FTT PET. Nine of these then had resection and in vitro evaluation of [18F]FTT uptake with an analog and uptake was blocked with PARPi. Of the other 4 patients, 3 had [18F]FTT PET uptake, and all had uptake blocked with treatment with a therapeutic PARPi. Initial in vivo [18F]FTT tumor uptake ranged from undetectable to robust. Following initiation of PARPi therapy, [18F]FTT uptake was not detectable above background in all cases. In vitro tumor treatment with a PARPi resulted in 82% reduction in [125I]KX1 binding.CONCLUSION[18F]FTT noninvasively quantifies PARP-1 expression. Early results indicate ability to visualize PARPi drug-target engagement in vivo and suggest the utility of further study to test [18F]FTT PET as a predictive and pharmacodynamic biomarker.TRIAL REGISTRATIONClinicalTrials.gov identifiers NCT03083288 and NCT03846167.FUNDINGMetavivor Translational Research Award, Susan G. Komen for the Cure (CCR 16376362), Department of Defense BC190315, and Abramson Cancer Center Breakthrough Bike Challenge.