Explore the vast range of titles available.

We demonstrate optical cycling and laser cooling of a cryogenic buffer-gas beam of calcium monohydride (CaH) molecules. We measure vibrational branching ratios for laser cooling transitions for both excited electronic states A and B . Furthermore, we measure that repeated photon scattering via the A ← X transition is achievable at a rate of ∼ 1.6 × 1 0 6 photons s −1 and demonstrate interaction-time limited scattering of ∼ 200 photons by repumping the largest vibrational decay channel. We also demonstrate a sub-Doppler cooling technique, namely the magnetically assisted Sisyphus effect, and use it to cool the transverse temperature of a molecular beam of CaH. Using a standing wave of light, we lower the transverse temperature from 12.2(1.2) mK to 5.7(1.1) mK. We compare these results to a model that uses optical Bloch equations and Monte Carlo simulations of the molecular beam trajectories. This work establishes a clear pathway for creating a magneto-optical trap (MOT) of CaH molecules. Such a MOT could serve as a starting point for production of ultracold hydrogen gas via dissociation of a trapped CaH cloud.

Abstract Background Rapid, reliable, and widespread testing is required to curtail the ongoing COVID-19 pandemic. Current gold-standard nucleic acid tests are hampered by supply shortages in critical reagents including nasal swabs, RNA extraction kits, personal protective equipment, instrumentation, and labor. Methods To overcome these challenges, we developed a rapid colorimetric assay using reverse-transcription loop-mediated isothermal amplification (RT-LAMP) optimized on human saliva samples without an RNA purification step. We describe the optimization of saliva pretreatment protocols to enable analytically sensitive viral detection by RT-LAMP. We optimized the RT-LAMP reaction conditions and implemented high-throughput unbiased methods for assay interpretation. We tested whether saliva pretreatment could also enable viral detection by conventional reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Finally, we validated these assays on clinical samples. Results The optimized saliva pretreatment protocol enabled analytically sensitive extraction-free detection of SARS-CoV-2 from saliva by colorimetric RT-LAMP or RT-qPCR. In simulated samples, the optimized RT-LAMP assay had a limit of detection of 59 (95% confidence interval: 44–104) particle copies per reaction. We highlighted the flexibility of LAMP assay implementation using 3 readouts: naked-eye colorimetry, spectrophotometry, and real-time fluorescence. In a set of 30 clinical saliva samples, colorimetric RT-LAMP and RT-qPCR assays performed directly on pretreated saliva samples without RNA extraction had accuracies greater than 90%. Conclusions Rapid and extraction-free detection of SARS-CoV-2 from saliva by colorimetric RT-LAMP is a simple, sensitive, and cost-effective approach with broad potential to expand diagnostic testing for the virus causing COVID-19.

In order to facilitate understanding of pigment cell biology, we developed a method to concomitantly purify melanocytes, iridophores, and retinal pigmented epithelium from zebrafish, and analyzed their transcriptomes. Comparing expression data from these cell types and whole embryos allowed us to reveal gene expression co-enrichment in melanocytes and retinal pigmented epithelium, as well as in melanocytes and iridophores. We found 214 genes co-enriched in melanocytes and retinal pigmented epithelium, indicating the shared functions of melanin-producing cells. We found 62 genes significantly co-enriched in melanocytes and iridophores, illustrative of their shared developmental origins from the neural crest. This is also the first analysis of the iridophore transcriptome. Gene expression analysis for iridophores revealed extensive enrichment of specific enzymes to coordinate production of their guanine-based reflective pigment. We speculate the coordinated upregulation of specific enzymes from several metabolic pathways recycles the rate-limiting substrate for purine synthesis, phosphoribosyl pyrophosphate, thus constituting a guanine cycle. The purification procedure and expression analysis described here, along with the accompanying transcriptome-wide expression data, provide the first mRNA sequencing data for multiple purified zebrafish pigment cell types, and will be a useful resource for further studies of pigment cell biology.

Klebsiella pneumoniae has been classified into two types, classical K. pneumoniae (cKP) and hypervirulent K. pneumoniae (hvKP). cKP isolates are highly diverse and important causes of nosocomial infections; they include globally disseminated antibiotic-resistant clones. hvKP isolates are sensitive to most antibiotics but are highly virulent, causing community-acquired infections in healthy individuals. The virulence phenotype of hvKP is associated with pathogenicity loci responsible for siderophore and hypermucoid capsule production. Recently, convergent strains of K. pneumoniae , which possess features of both cKP and hvKP, have emerged and are cause of much concern. Here, we screen the genomes of 2,608 multidrug-resistant K. pneumoniae isolates from the United States and identify 47 convergent isolates. We perform phenotypic and genomic characterization of 12 representative isolates. These 12 convergent isolates contain a variety of antimicrobial resistance plasmids and virulence plasmids. Most convergent isolates contain aerobactin biosynthesis genes and produce more siderophores than cKP isolates but not more capsule. Unexpectedly, only 1 of the 12 tested convergent isolates has a level of virulence consistent with hvKP isolates in a murine pneumonia model. These findings suggest that additional studies should be performed to clarify whether convergent strains are indeed more virulent than cKP in mouse and human infections. Convergent strains, those containing characteristics of both multidrug-resistant & hypervirulent Klebsiella pneumoniae , are a global threat to public health. In this work, authors analyse convergent isolates from the United States and reveal unexpectantly low virulence.

Background Klebsiella pneumoniae strains have been divided into two major categories: classical K. pneumoniae, which are frequently multidrug-resistant and cause hospital-acquired infections in patients with impaired defenses, and hypervirulent K. pneumoniae, which cause severe community-acquired and disseminated infections in normal hosts. Both types of infections may lead to bacteremia and are associated with significant morbidity and mortality. The relative burden of these two types of K. pneumoniae among bloodstream isolates within the United States is not well understood. Methods We evaluated consecutive K. pneumoniae isolates cultured from the blood of hospitalized patients at Northwestern Memorial Hospital (NMH) in Chicago, Illinois between April 2015 and April 2017. Bloodstream isolates underwent whole genome sequencing, and sequence types (STs), capsule loci (KLs), virulence genes, and antimicrobial resistance genes were identified in the genomes using the bioinformatic tools Kleborate and Kaptive. Patient demographic, comorbidity, and infection information, as well as the phenotypic antimicrobial resistance of the isolates were extracted from the electronic health record. Candidate hypervirulent isolates were tested in a murine model of pneumonia, and their plasmids were characterized using long-read sequencing. We also extracted STs, KLs, and virulence and antimicrobial resistance genes from the genomes of bloodstream isolates submitted from 33 United States institutions between 2007 and 2021 to the National Center for Biotechnology Information (NCBI) database. Results Consecutive K. pneumoniae bloodstream isolates (n = 104, one per patient) from NMH consisted of 75 distinct STs and 51 unique capsule loci. The majority of these isolates (n = 58, 55.8%) were susceptible to all tested antibiotics except ampicillin, but 17 (16.3%) were multidrug-resistant. A total of 32 (30.8%) of these isolates were STs of known high-risk clones, including ST258 and ST45. In particular, 18 (17.3%) were resistant to ceftriaxone (of which 17 harbored extended-spectrum beta-lactamase genes) and 9 (8.7%) were resistant to meropenem (all of which harbored a carbapenemase genes). Four (3.8%) of the 104 isolates were hypervirulent K. pneumoniae, as evidenced by hypermucoviscous phenotypes, high levels of virulence in a murine model of pneumonia, and the presence of large plasmids similar to characterized hypervirulence plasmids. These isolates were cultured from patients who had not recently traveled to Asia. Two of these hypervirulent isolates belonged to the well characterized ST23 lineage and one to the re-emerging ST66 lineage. Of particular concern, two of these isolates contained plasmids with tra conjugation loci suggesting the potential for transmission. We also analyzed 963 publicly available genomes of K. pneumoniae bloodstream isolates from locations within the United States. Of these, 465 (48.3%) and 760 (78.9%) contained extended-spectrum beta-lactamase genes or carbapenemase genes, respectively, suggesting a bias towards submission of antibiotic-resistant isolates. The known multidrug-resistant high-risk clones ST258 and ST307 were the predominant sequence types. A total of 32 (3.3%) of these isolates contained aerobactin biosynthesis genes and 26 (2.7%) contained at least two genetic features of hvKP strains, suggesting elevated levels of virulence. We identified 6 (0.6%) isolates that were STs associated with hvKP: ST23 (n = 4), ST380 (n = 1), and ST65 (n = 1). Conclusions Examination of consecutive isolates from a single center demonstrated that multidrug-resistant high-risk clones are indeed common, but a small number of hypervirulent K. pneumoniae isolates were also observed in patients with no recent travel history to Asia, suggesting that these isolates are undergoing community spread in the United States. A larger collection of publicly available bloodstream isolate genomes also suggested that hypervirulent K. pneumoniae strains are present but rare in the USA; however, this collection appears to be heavily biased towards highly antibiotic-resistant isolates (and correspondingly away from hypervirulent isolates).

In the field of coastal geomorphology, advancements in space technology have revolutionized coastal mapping and understanding. Satellite‐derived bathymetry (SDB) research has progressed, focusing on various estimation methods using high‐resolution satellite imagery and in‐situ data. Challenges arise when applying these methods to the Indian coastline due to its turbid waters and intricate features such as creeks and deltas, laden with sediment and submerged rocks. A study aims to assess multivariate machine learning (ML) regression techniques for estimating bathymetric data. The study employs ground‐truth data and imagery from Aster, Landsat‐8, and Sentinel‐2 at distinct sites known for complex underwater landscapes. Several algorithms including Multiple Linear Regression, Support Vector Regressor, Gaussian Process Regression (GPR), Decision Tree Regression, K‐Neighbors Regressor, k‐fold cross‐validation with Decision Tree Regression, and Random Forest (RF) are evaluated for SDB. Results from the Vengurla Site show that using the Landsat‐8 data set with the GPR algorithm achieves R2 0.94, root mean squared error (RMSE) 1.53 m, and MAE 1.14 m, utilizing visible spectrum bands. At Mormugao, the Sentinel‐2 data set with GPR and RF algorithms attains R2 0.97 and RMSE 1.23 m, with GPR outperforming RF, having an MAE of 1.05 m compared to RF's 1.22 m. This study underscores the potential of ML regression techniques in overcoming challenges with using SDB for mapping coastal geomorphology, particularly in intricate underwater terrains and turbid waters by assimilating sophisticated algorithms and their refined cartographic representation. Plain Language Summary Researchers are leveraging advancements in space technology to enhance the mapping and comprehension of coastal regions, particularly focusing on satellite‐derived bathymetry (SDB). SDB utilizes high‐resolution satellite imagery in conjunction with on‐site data to estimate the submerged terrain. However, the application of these methodologies along the Indian coastline poses challenges due to factors such as turbidity and the presence of complex geological formations like creeks and deltas. This study explored the utilization of multivariate machine learning (ML) regression techniques to improve the estimation of SDB. Various algorithms were tested using data sourced from satellites such as Aster, Landsat‐8, and Sentinel‐2 across two different sites with diverse underwater landscapes. Results demonstrate promising accuracy, particularly when employing Landsat‐8 data in conjunction with Gaussian Process Regression (GPR), yielding an R2 value of 0.94. Similarly, at another site, the combination of SENTINEL‐2 data with GPR and RF achieved an R2 value of 0.97, underscoring the potential of ML techniques in mapping intricate coastal terrains despite challenges like turbid waters. Key Points Advancements in space technology for coastal bathymetry mapping Predicting coastal geomorphology with satellite‐derived bathymetry (SDB) Multivariate machine learning regression for estimating SDB

Distinguishing between Crohn's Disease (CD) and Intestinal Tuberculosis (ITB) has been a challenging task for clinicians due to their similar presentation. CD4+FOXP3+ T regulatory cells (Tregs) have been reported to be increased in patients with pulmonary tuberculosis. However, there is no such data available in ITB. The aim of this study was to investigate the differential expression of FOXP3+ T cells in patients with ITB and CD and its utility as a biomarker. The study prospectively recruited 124 patients with CD, ITB and controls: ulcerative colitis (UC) and patients with only haemorrhoidal bleed. Frequency of CD4+CD25+FOXP3+ Tregs in peripheral blood (flow cytometry), FOXP3 mRNA expression in blood and colonic mucosa (qPCR) and FOXP3+ T cells in colonic mucosa (immunohistochemistry) were compared between controls, CD and ITB patients. Frequency of CD4+CD25+FOXP3+ Treg cells in peripheral blood was significantly increased in ITB as compared to CD. Similarly, significant increase in FOXP3+ T cells and FOXP3 mRNA expression was observed in colonic mucosa of ITB as compared to CD. ROC curve showed that a value of >32.5% for FOXP3+ cells in peripheral blood could differentiate between CD and ITB with a sensitivity of 75% and a specificity of 90.6%. Phenotypic enumeration of peripheral CD4+CD25+FOXP3+ Treg cells can be used as a non-invasive biomarker in clinics with a high diagnostic accuracy to differentiate between ITB and CD in regions where TB is endemic.

Mutations reducing the function of MYT1L, a neuron-specific transcription factor, are associated with a syndromic neurodevelopmental disorder. MYT1L is used as a pro-neural factor in fibroblast-to-neuron transdifferentiation and is hypothesized to influence neuronal specification and maturation, but it is not clear which neuron types are most impacted by MYT1L loss. In this study, we profile 412,132 nuclei from the forebrains of wild-type and MYT1L-deficient mice at three developmental stages: E14 at the peak of neurogenesis, P1 when cortical neurons have been born, and P21 when neurons are maturing, to examine the role of MYT1L levels on neuronal development. MYT1L deficiency disrupts cortical neuron proportions and gene expression, primarily affecting neuronal maturation programs. Effects are mostly cell autonomous and persistent through development. While MYT1L can both activate and repress gene expression, the repressive effects are most sensitive to haploinsufficiency, likely mediating MYT1L syndrome. These findings illuminate MYT1L’s role in orchestrating gene expression during neuronal development, providing insights into the molecular underpinnings of MYT1L syndrome. Mutations reducing the function of MYT1L, a neuron-specific transcription factor, are associated with a syndromic neurodevelopmental disorder, yet it remains unclear which cell types are most impacted by MYT1L loss. Here authors use single-nuclei RNA sequencing to profile the forebrains of MYT1L-deficient mice at three developmental stages and reveal MYT1L deficiency disrupts cortical neuron proportions and gene expression, primarily affecting excitatory neuron maturation programs.

Wide variation of responses to identical stimuli presented to genetically inbred mice suggests the hypothesis that stochastic non-genetic variation, such as in chromatin state or enhancer activity during neurodevelopment, can mediate such phenotypic differences. However, this hypothesis is largely untested since capturing pre-existing molecular states requires non-destructive, longitudinal recording. Therefore, we tested the potential of Calling Cards (CC) to record transient neuronal enhancer activity during postnatal development in mice, and thereby associate such non-genetic variation with a subsequent phenotypic presentation – degree of seizure response to the pro-convulsant pentylenetetrazol. We show that recorded differences in enhancer activity at 243 loci predict a severe vs. mild response, and that these are enriched near genes associated with human epilepsy. We also validated pharmacologically a seizure-modifying role for two previously unassociated genes, Htr1f and Let7c . This proof-of-principle supports using CC broadly to discover predisposition loci for other neuropsychiatric traits and behaviors. Finally, as human disease is also influenced by non-genetic factors, similar epigenetic predispositions are possible in humans. Linking prior epigenetic status to future outcomes remains a challenge. Here, authors show recording neuronal enhancer activity across postnatal development in mice reveals loci that predict and can be manipulated to modify acute seizure response.

Pathogenic mutations in APP, PSEN1, PSEN2, MAPT and GRN have previously been linked to familial early onset forms of dementia. Mutation screening in these genes has been performed in either very small series or in single families with late onset AD (LOAD). Similarly, studies in single families have reported mutations in MAPT and GRN associated with clinical AD but no systematic screen of a large dataset has been performed to determine how frequently this occurs. We report sequence data for 439 probands from late-onset AD families with a history of four or more affected individuals. Sixty sequenced individuals (13.7%) carried a novel or pathogenic mutation. Eight pathogenic variants, (one each in APP and MAPT, two in PSEN1 and four in GRN) three of which are novel, were found in 14 samples. Thirteen additional variants, present in 23 families, did not segregate with disease, but the frequency of these variants is higher in AD cases than controls, indicating that these variants may also modify risk for disease. The frequency of rare variants in these genes in this series is significantly higher than in the 1,000 genome project (p = 5.09 × 10⁻⁵; OR = 2.21; 95%CI = 1.49-3.28) or an unselected population of 12,481 samples (p = 6.82 × 10⁻⁵; OR = 2.19; 95%CI = 1.347-3.26). Rare coding variants in APP, PSEN1 and PSEN2, increase risk for or cause late onset AD. The presence of variants in these genes in LOAD and early-onset AD demonstrates that factors other than the mutation can impact the age at onset and penetrance of at least some variants associated with AD. MAPT and GRN mutations can be found in clinical series of AD most likely due to misdiagnosis. This study clearly demonstrates that rare variants in these genes could explain an important proportion of genetic heritability of AD, which is not detected by GWAS.