The present study was designed to report the prevalence of spotted fever group Rickettsia and Anaplasma in ticks from Pakistan. To address this knowledge gap, ticks were collected from October 2019 to November 2020 from livestock hosts. 390 ticks from Punjab, Khyber Pakhtunkhwa, and Islamabad were investigated for the presence of Rickettsia and Anaplasma. The collected ticks were subjected to molecular studies for detection and characterization of spotted fever group Rickettsia and Anaplasma in ticks from Pakistan. PCR amplification of the ompA gene was used for detection of Rickettsia and portions of the 16S rDNA gene for detection of Anaplasma. Nine species of ticks were tested. 7/390 (2.58%) of ticks were positive for Rickettsia. Rickettsia spp. were detected in Haemaphysalis punctata, Hyalomma anatolicum, Hyalomma scupense, Rhipicephalus microplus, and Rhipicephalus sanguineus. Unknown Rickettsia was detected in Hy. scupense. 57 (14.6%) ticks were also positive for Anaplasma spp. Anaplasma ovis was detected in Hy. anatolicum, Hy. scupense, Hy. excavatum, Rhipicephalus decoloratus, R. microplus, and R. sanguineus. Anaplasma marginale was detected in Hy. anatolicum, Hy. scupense, R. microplus, R. decoloratus, and R. sanguineus. The Anaplasma sequences obtained from this experiment were 99–100% similar to those of documented strains. This study provides information and confirms the presence of spotted fever group Rickettsia and Anaplasma spp. in different tick species. It also highlights the need for control programs to prevent health risks. Further investigation to determine the prevalence and disease burden of these pathogens in Pakistan is necessary.

Premenstrual symptoms are a cyclic set of symptoms that affect women’s psychological and physical wellbeing. Growing evidence suggests that micronutrients may contribute to the risk and severity of premenstrual symptoms such as depression. Yet the relationship between folate and premenstrual symptoms remains inconclusive. The objective of this study was to determine the association between folate intake and MTHFR genotype with premenstrual symptoms. Females (n=678) aged 20-29 years from the Toronto Nutrigenomics and Health Study self-reported fifteen premenstrual symptoms. Dietary intake was measured using a validated 196-item Toronto-modified Harvard food frequency questionnaire. DNA was isolated from peripheral white blood cells and genotyped for the C677T MTHFR (rs1801133) polymorphism. Using logistic regression, the odds of experiencing premenstrual symptoms were compared between total folate intake below and above the median (647 mcg/d), and between MTHFR genotypes. We found associations between MTHFR genotype and some premenstrual symptoms. Among women with low folate intake, an additive association was observed for the T allele of MTHFR and premenstrual depression. Compared to those with the CC genotype, the OR (95% CI) for depression was 1.66 (0.98, 2.87) for those with the CT genotype and 2.41 (1.08, 5.38) for those with the TT genotype. No associations were observed between MTHFR genotype and premenstrual depression among those with higher habitual intakes of folate. Since the MTHFR genotype is involved in the folate metabolic pathway, these findings suggest that folate or its metabolites may be related to risk of premenstrual depression.

Leptospirosis in NZ has historically been associated with male workers in livestock industries; however, the disease epidemiology is changing. This study identified risk factors amid these shifts. Participants (95 cases:300 controls) were recruited nationwide between 22 July 2019 and 31 January 2022, and controls were frequency-matched by sex (90% male) and rurality (65% rural). Multivariable logistic regression models, adjusted for sex, rurality, age, and season—with one model additionally including occupational sector—identified risk factors including contact with dairy cattle (aOR 2.5; CI: 1.0–6.0), activities with beef cattle (aOR 3.0; 95% CI: 1.1–8.2), cleaning urine/faeces from yard surfaces (aOR 3.9; 95% CI: 1.5–10.3), uncovered cuts/scratches (aOR 4.6; 95% CI: 1.9–11.7), evidence of rodents (aOR 2.2; 95% CI: 1.0–5.0), and work water supply from multiple sources—especially creeks/streams (aOR 7.8; 95% CI: 1.5–45.1) or roof-collected rainwater (aOR 6.6; 95% CI: 1.4–33.7). When adjusted for occupational sector, risk factors remained significant except for contact with dairy cattle, and slaughter without gloves emerged as a risk (aOR 3.3; 95% CI: 0.9–12.9). This study highlights novel behavioural factors, such as uncovered cuts and inconsistent glove use, alongside environmental risks from rodents and natural water sources.

This study characterises the radio luminosity functions (RLFs) for SFGs and AGN using statistical redshift estimation in the absence of comprehensive spectroscopic data. Sensitive radio surveys over large areas detect many sources with faint optical and infrared counterparts, for which redshifts and spectra are unavailable. This challenges our attempt to understand the population of radio sources. Statistical tools are often used to model parameters (such as redshift) as an alternative to observational data. Using the data from GAMA G23 and EMU early science observations, we explore simple statistical techniques to estimate the redshifts in order to measure the RLFs of the G23 radio sources as a whole and for SFGs and AGN separately. Redshifts and AGN/SFG classifications are assigned statistically for those radio sources without spectroscopic data. The calculated RLFs are compared with existing studies, and the results suggest that the RLFs match remarkably well for low redshift galaxies with an optical counterpart. We use a more realistic high redshift distribution to model the redshifts of (most likely) high redshift radio sources and find that the LFs from our approach match well with measured LFs. We also look at strategies to compare the RLFs of radio sources without an optical counterpart to existing studies.

We present the Evolutionary Map of the Universe (EMU) survey conducted with the Australian Square Kilometre Array Pathfinder (ASKAP). EMU aims to deliver the touchstone radio atlas of the southern hemisphere. We introduce EMU and review its science drivers and key science goals, updated and tailored to the current ASKAP five-year survey plan. The development of the survey strategy and planned sky coverage is presented, along with the operational aspects of the survey and associated data analysis, together with a selection of diagnostics demonstrating the imaging quality and data characteristics. We give a general description of the value-added data pipeline and data products before concluding with a discussion of links to other surveys and projects and an outline of EMU’s legacy value.

Task sharing is endorsed as one of the strategies to address the treatment gap in common perinatal mental health conditions. There is a well-established body of evidence on the effectiveness of psychological interventions delivered by nonspecialist health workers (NSHWs); however, there is a dearth of evidence documenting factors determining the feasibility, acceptability and sustainability of integrating and implementing these interventions. This systematic review aims to synthesize the implementation outcomes and implementation process of NSHWs-delivered psychological interventions for the management of perinatal depression and anxiety using Proctor’s implementation science framework outlining eight constructs: feasibility, acceptability, appropriateness, adoption, cost, fidelity, penetration and sustainability. We searched PubMed, Web of Science and Cochrane Center Register of Controlled Trials for studies published in English and between 2000 and 2022 using search terms under five broad categories: (a) “perinatal”; (b) “common mental disorders”; (c) “psychological interventions”; (d) “nonspecialist” and (e) “implementation outcomes.” Secondary publications were also hand-searched for data extraction. Two authors independently reviewed abstracts and full-text articles. Data for included articles were extracted using a standard data extraction sheet. A narrative synthesis of qualitative evidence was conducted. Initial searches identified 885 articles of which full text of 128 articles were screened for eligibility, with 56 studies meeting the inclusion criteria. Out of the eight constructs of Proctor’s framework, “feasibility,” “acceptability,” “appropriateness” and “fidelity” were the most evaluated outcomes. None of the studies reported “penetration” and very few reported “sustainability,” “adoption” or “cost.” None of the studies used any implementation science framework for the study evaluation. Despite the well-established evidence on the effectiveness of psychosocial interventions for perinatal depression and anxiety by NSHWs, these interventions are rarely adopted into the health system. More studies applying systems thinking are needed to explore facilitators, barriers and mechanisms for integrating interventions in the health system. Using implementation science frameworks to design, plan, execute and evaluate psychosocial interventions by NSHWs can address this gap in evidence.

We introduce a new approach to quantifying dust in galaxies by combining information from the Balmer decrement (BD) and the dust mass (Md). While there is no explicit correlation between these two properties, they jointly probe different aspects of the dust present in galaxies. We explore two new parameters that link BD with Md by using star formation rate sensitive luminosities at several wavelengths (ultraviolet, Hα, and far-infrared). This analysis shows that combining the BD and Md in these ways provides new metrics that are sensitive to the degree of optically thick dust affecting the short wavelength emission. We show how these new “dust geometry” parameters vary as a function of galaxy mass, star formation rate, and specific star formation rate. We demonstrate that they are sensitive probes of the dust geometry in galaxies, and that they support the “maximal foreground screen” model for dust in starburst galaxies.

The scatter in global atomic hydrogen (Hi) scaling relations is partly attributed to differences in how Hi and stellar properties are measured, with Hi reservoirs typically extending beyond the inner regions of galaxies where star formation occurs. Using pilot observations from the Widefield ASKAP L-band Legacy All-sky Blind Survey (WALLABY), we present the first measurements of Hi mass enclosed within the stellar-dominated regions of galaxies for a statistical sample of 995 local gas-rich systems, investigating the factors driving its variation. We examine how global Hi scaling relations change when measurements are restricted to $R_{\text{25}}$ and $R_{\text{24}}$ – the isophotal radii at 25 and 24 mag arcsec$^{-2}$ in the i-band – and explore how the fraction of Hi mass and Hi surface density within these radii correlate with other galaxy properties. On average, 68% of the total Hi mass is enclosed within $R_{\text{25}}$ and 54% within $R_{\text{24}}$, though significant variation exists between galaxies, ranging from $\sim$20% to 100%. The fraction of Hi mass within $R_{\text{25}}$ shows a mild correlation with stellar properties, with galaxies of higher stellar mass, greater stellar surface density, or redder colours enclosing a larger fraction of their Hi reservoirs. These correlations do not significantly strengthen when considering $R_{\text{24}}$. Conversely, global Hi surface densities show no significant correlation with stellar mass or stellar surface density, but trends start emerging when these are measured within the inner regions of galaxies. The strongest correlation is observed with optical colour, with bluer galaxies having higher average Hi surface densities within $R_{\text{25}}$. This trend of the average Hi surface density with optical colour strengthens when we restrict from $R_{\text{25}}$ to $R_{\text{24}}$, suggesting a closer connection between inner Hi reservoirs and star formation. This study underscores the value of (at least marginally) resolved Hi surveys of statistical samples for advancing our understanding of the gas-star formation cycle in galaxies.

Objectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping visualizes regions of heightened neural activity for OUD. We conduct fivefold cross-validation classification experiments (OUD vs. HC) to assess the discriminative power of functional network features with and without incorporating demographic features. Demographic features are ranked based on ML classification importance. Follow-up Boruta analysis is performed to study the medial prefrontal cortex (mPFC), posterior cingulate cortex, and temporoparietal junctions in the DMN. Results/Anticipated Results: Boruta ML analysis identifies the DMN as the most salient functional network for differentiating OUD from HC, with 33% of DMN features found significant (p < 0.05), compared to 10% and 0% for the SN and ECN, respectively. The Boruta ML algorithm identifies age and education as the most significant demographic features. Brain activity mapping shows heightened neural activity in the DMN for OUD. The DMN exhibits the greatest discriminative power, with a mean AUC of 69.74%, compared to 47.14% and 54.15% for the SN and ECN, respectively. Fusing DMN BOLD features with the most important demographic features improves the mean AUC to 80.91% and the F1 score to 73.97%. Follow-up Boruta analysis highlights the mPFC as the most important functional hub within the DMN, with 65% significant features. Discussion/Significance of Impact: Our study enhances the understanding of OUD neurobiology, identifying the DMN as the most significant network using ML rs-fMRI BOLD feature analysis. Ethnicity, education, and age rank are the most important demographic features and the mPFC emerges as a key functional hub for OUD. Future research can build on these findings to inform treatment of OUD.

Objectives/Goals: In mice, it has been shown that loss of Cib2 (calcium and integrin-binding protein 2) results in progressive retinal disease that recapitulates many characteristics of age-related macular degeneration (AMD). This study aims to characterize transcriptional changes in the retinal pigment epithelium (RPE) that underlie this disease process. Methods/Study Population: RPE tissue samples, pooled from 2–3 mice for each biological group, were collected from Cib2-KO and wildtype (WT) mice at two (young) and eight (aged) months of age. Bulk mRNA sequencing was performed using the Illumina HiSeq 4000. Reads were aligned to the UCSC mouse reference genome and quantified using HTSeq. Significant differentially expressed genes (DEGs) between mouse genotype and age groups were assessed using DESeq. CLICK unsupervised clustering followed by gene ontology analysis was performed to identify cellular processes and molecular pathways affected by loss of Cib2 as well as age. Results/Anticipated Results: CLICK analysis revealed several functional pathways that are differentially expressed between sample groups. For example, in both young and aged mice, pathways upregulated in Cib2-KO samples included calcium signaling, RhoA signaling, and integrin signaling. Uniquely downregulated DEGs in young Cib2-KO animals were related to complement and coagulation cascades, LXR/RXR activation (related to lipid synthesis and transport), and phagosomes. Aged Cib2-KO mice displayed the most significant downregulation of genes in the phototransduction pathway, indicating temporal changes in functional pathways that correlate with disease progression. Next steps in analysis include investigating patterns in RPE- and AMD-signature gene sets that may identify molecular pathways more specific to human disease. Discussion/Significance of Impact: Many current studies investigate the role of complement activation, vesicle trafficking, and ion transport as top contributors to AMD development. We identified DEGs paralleling many of these molecular pathways in Cib2-KO mice, highlighting their potential as a model to study age-related RPE pathologies and evaluate therapeutic interventions.

Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patients using an automated pipeline. The dataset is divided into training (357 cases), calibration (63 cases), and testing (105 cases). Our probabilistic model is a convolutional neural network (CNN) with dropout regularization. It generates a prediction set containing high-probability correct predictions using conformal prediction techniques, which add an uncertainty layer to the CNN. Additionally, gradient-based localization mapping is employed to identify brain regions associated with low uncertainty cases. Results/Anticipated Results: The performance of the computational conformal model is evaluated using training and testing data with varying dropout rates from 0.1 to 0.5. The best results are achieved with a dropout rate of 0.5, yielding a fivefold cross-validated average precision of 72.19% and an F1-score of 70.66%. Additionally, the model provides probabilistic uncertainty quantification along with gradient-based localization maps that identify key brain regions, including the temporal lobe, putamen, caudate, and occipital lobe, relevant to obesity prediction. Comparisons with standard segmented brain atlases and existing literature highlight that our model’s uncertainty quantification mapping offers complementary evidence linking obesity to structural brain regions. Discussion/Significance of Impact: This research offers two significant advancements. First, it introduces a probabilistic model for predicting obesity from structural magnetic resonance imaging data, focusing on uncertainty quantification for reliable results. Second, it improves interpretability using localization maps to identify key brain regions linked to obesity.