Search

Abstract Wolverine distribution contracted along the southern periphery of its range in North America during the 19th and 20th centuries due primarily to human influences. This history, along with low densities, sensitivity to climate change, and concerns about connectivity among fragmented habitats spurred the recent US federal listing of threatened status and special concern status in Canada. To help inform large scale landscape connectivity, we collected 882 genetic samples genotyped at 19 microsatellite loci. We employed multiple statistical models to assess the landscape factors (terrain complexity, human disturbance, forest configuration, and climate) associated with wolverine genetic connectivity across 2.2 million km2 of southwestern Canada and the northwestern contiguous United States. Genetic similarity (positive spatial autocorrelation) of wolverines was detected up to 555 km and a high-to-low gradient of genetic diversity occurred from north-to-south. Landscape genetics analyses confirmed that wolverine genetic connectivity has been negatively influenced by human disturbance at broad scales and positively influenced by forest cover and snow persistence at fine- and broad–scales, respectively. This information applied across large landscapes can be used to guide management actions with the goal of maintaining or restoring population connectivity.

Abstract Early peritoneal dialysis (PD)-related infection is a severe complication. This study investigated the relationship between patient–doctor contact (PDC) duration and early PD-related infection. In the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS) Korea, incident dialysis patients receiving PD were divided into two groups based on PDC duration (

Abstract In this study, we explore the utilization of penalized likelihood estimation for the analysis of sparse photon counting data obtained from distributed target lidar systems. Specifically, we adapt the Poisson Total Variation processing technique to cater to this application. By assuming a Poisson noise model for the photon count observations, our approach yields denoised estimates of backscatter photon flux and related parameters. This facilitates the processing of raw photon counting signals with exceptionally high temporal and range resolutions (demonstrated here to 50 Hz and 75 cm resolutions), including data acquired through time-correlated single photon counting, without significant sacrifice of resolution. Through examination involving both simulated and real-world 2D atmospheric data, our method consistently demonstrates superior accuracy in signal recovery compared to the conventional histogram-based approach commonly employed in distributed target lidar applications.

Abstract We estimated the operating characteristics of ICD-10 code U07.1, introduced by the World Health Organization in 2020, to identify lab-confirmed SARS-CoV-2. CCEDRRN is a national research registry of adults (March 2020–August 2021) with suspected/confirmed SARS-CoV-2 identified in Canadian emergency departments (EDs) using chart review (symptoms, clinical information, and lab test results including SARS-CoV-2 polymerase chain reaction, PCR results). CCEDRRN data were linked to administrative hospitalization discharge and ED ICD-10 diagnostic codes (accessed centrally via the Canadian Institute for Health Information). We identified ICD-10 diagnostic codes in CCEDRRN participants. We defined lab-confirmed SARS-CoV-2 based on at least one positive PCR in the 0–14 days before the ED presentation and/or during hospitalization (in those admitted from ED). We performed separate analyses for CCEDRRN participants discharged from ED and those hospitalized from the ED. Additional analyses were stratified by province, sex, age, and (for hospitalized patients) timing of the first PCR test. The sensitivity of ICD-10 code U07.1 for a positive SARS-CoV-2 test was 93.6% (95% CI 93.0–94.1%) in those hospitalized from ED and 83.0% (95% CI 82.1–83.9%) in those discharged from the ED. Sensitivity was similar across provinces and demographics, but in each stratified analysis, values were higher in those hospitalized versus those discharged from ED. The ICD-10 diagnostic code for U07.1 within administrative data identified most lab-confirmed SARS-CoV-2 within persons hospitalized from ED, although a significant number of cases discharged from ED were missed. This should be considered when using administrative data for research and public health planning.

Abstract Efficacious stem cell-based therapies for traumatic brain injury (TBI) depend on successful delivery, migration, and engraftment of stem cells to induce neuroprotection. L-myc expressing human neural stem cells (LMNSC008) demonstrate an inherent tropism to injury sites after intranasal (IN) administration. We hypothesize that IN delivered LMNSC008 cells migrate to primary and secondary injury sites and modulate biomarkers associated with neuroprotection and tissue regeneration. To test this hypothesis, immunocompetent adult female rats received either controlled cortical impact injury or sham surgery. LMNSC008 cells or a vehicle were administered IN on postoperative days 7, 9, 11, 13, 15, and 17. The distribution and migration of eGFP-expressing LMNSC008 cells were quantified over 1 mm-thick optically cleared (CLARITY) coronal brain sections from TBI and SHAM controls. NSC migration was observed along white matter tracts projecting toward the hippocampus and regions of TBI. ELISA and Nanostring assays revealed a shift in tissue gene expression in LMNSC008 treated rats relative to controls. LMNSC008 treatment reduced expression of genes and pathways involved in inflammatory response, microglial function, and various cytokines and receptors. Our proof-of-concept studies, although preliminary, support the rationale of using intranasal delivery of LMNSC008 cells for functional studies in preclinical models of TBI and provide support for potential translatability in TBI patients.

Abstract The high antibiotic resistance of Pseudomonas aeruginosa (PA) makes it critical to develop alternative antimicrobial agents that are effective and affordable. One of the many applications of silver nanoparticles (Ag NPs) is their use as an antimicrobial agent against bacteria resistant to common antibiotics. The key purpose of this research was to assess the antibacterial and antibiofilm effectiveness of biosynthesized Ag NPs against six biofilm-forming clinically isolated strains of PA and one reference strain (ATCC 27853). Ag NPs were biosynthesized using a seed extract of Peganum harmala as a reducing agent. Ag NPs were characterized by Ultraviolet–visible (UV–Vis) spectroscopy and scanning transmission electron microscopy (STEM). The effect of Ag NPs on biofilm formation and eradication was examined through micro-titer plate assays, and the minimal inhibitory (MIC) and minimum bactericidal (MBC) concentrations determined. In addition, real-time polymerase chain reactions (RT-PCR) were performed to examine the effects of Ag NPs on the expression of seven PA biofilm-encoding genes (LasR, LasI, LssB, rhIR, rhII, pqsA and pqsR). The biosynthesized Ag NPs were spherically-shaped with a mean diameter of 11 nm. The MIC for each PA strain was 15.6 µg/ml, while the MBC was 31.25 µg/ml. All PA strains exposed to Ag NPs at sub-inhibitory concentrations (0.22–7.5 µg/ml) showed significant inhibitory effects on growth and biofilm formation. Biomass and biofilm metabolism were reduced dependent on Ag NP concentration. The expression of the quorum-sensing genes of all strains were significantly reduced at an Ag NP concentration of 7.5 µg/ml. The results demonstrate the extensive in-vitro antibacterial and antibiofilm performance of Ag NPs and their potential in the treatment of PA infection. It is recommended that future studies examine the possible synergy between Ag NPs and antibiotics.

Abstract Many health authorities differentiate hospitalizations in patients infected with SARS-CoV-2 as being “for COVID-19” (due to direct manifestations of SARS-CoV-2 infection) versus being an “incidental” finding in someone admitted for an unrelated condition. We conducted a retrospective cohort study of all SARS-CoV-2 infected patients hospitalized via 47 Canadian emergency departments, March 2020-July 2022 to determine whether hospitalizations with “incidental” SARS-CoV-2 infection are less of a burden to patients and the healthcare system. Using a priori standardized definitions applied to hospital discharge diagnoses in 14,290 patients, we characterized COVID-19 as (i) the “Direct” cause for the hospitalization (70%), (ii) a potential “Contributing” factor for the hospitalization (4%), or (iii) an “Incidental” finding that did not influence the need for admission (26%). The proportion of incidental SARS-CoV-2 infections rose from 10% in Wave 1 to 41% during the Omicron wave. Patients with COVID-19 as the direct cause of hospitalization exhibited significantly longer LOS (mean 13.8 versus 12.1 days), were more likely to require critical care (22% versus 11%), receive COVID-19-specific therapies (55% versus 19%), and die (17% versus 9%) compared to patients with Incidental SARS-CoV-2 infections. However, patients hospitalized with incidental SARS-CoV-2 infection still exhibited substantial morbidity/mortality and hospital resource use.

Abstract There is a limitation in the range of effectual antibiotics due to the Pseudomonas aeruginosa (PA) infection due to its innate antimicrobial resistance. Researchers have therefore been concentrating their efforts to discover advanced and cost effective antibacterial agents among the ever-increasing PA bacterial resistance strains. It has been discovered that various nanoparticles can be employed as antimicrobial agents. Here, we evaluated the antibacterial properties of the Zinc Oxide nanoparticles (ZnO NPs), which was biosynthesized, being examined on six hospital strains of PA alongside a reference strain (ATCC 27853). A chemical approach was applied to biosynthesize the ZnO NPs from Olea europaea was performed, and confirmed by using X-ray diffraction and Scanning Electron Microscopes. The nanoparticles then applied their antibacterial properties to examine them against six clinically isolated PA strains alongside the reference strain. This process tested for the results of the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC). The Growth, biofilm formation and eradication were analyzed. The influence of the differentiating degrees ZnO NPs in regard to Quorom sensing gene expression were further examined. The ZnO NPs exhibited a crystalline size and diameter (Dc) of 40–60 nm and both the MIC and MBC tests revealed positive outcomes of concentrations of 3 and 6 mg/ml for each PA strain, respectively. At sub inhibitory concentration, The ZnO NPs were found to significantly inhibit the growth and biofilm formation of all PA strains and decreases in the biomass and metabolic behavior of PA established biofilms; these decreases varied depending on the dosage. At ZnO NPs concentrations of 900 µg/ml, the expression of majority of quorum sensing genes of all strains were significantly reduced, at ZnO NPs concentrations of 300 µg/ml, few genes were significantly impacted. In conclusion, the treatment of PA and could be other antibiotic resistant bacteria can therefore be approached by using ZnO NPs as it has been uncovered that they withhold advanced antibacterial properties.

Abstract Molecular and stable isotope compositions of hydrate-bound gases collected from 59 hydrate-bearing sites between 2005 to 2019 in the southern and central sub-basins of Lake Baikal are reported. The δ2H of the hydrate-bound methane is distributed between − 310‰ and − 270‰, approximately 120‰ lower than its value in the marine environment, due to the difference in δ2H between the lake water and seawater. Hydrate-bound gases originate from microbial (primary and secondary), thermogenic, and mixed gas sources. Gas hydrates with microbial ethane (δ13C: − 60‰, δ2H: between − 310‰ and − 250‰) were retrieved at approximately one-third of the total sites, and their stable isotope compositions were lower than those of thermogenic ethane (δ13C: − 25‰, δ2H: − 210‰). The low δ2H of ethane, which has rarely been reported, suggests for the first time that lake water with low hydrogen isotope ratios affects the formation process of microbial ethane as well as methane. Structure II hydrates containing enclathrated methane and ethane were collected from eight sites. In thermogenic gas, hydrocarbons heavier than ethane are biodegraded, resulting in a unique system of mixed methane-ethane gases. The decomposition and recrystallization of the hydrates that enclathrate methane and ethane resulted in the formation of structure II hydrates due to the enrichment of ethane.

Abstract The collapse of the Maya civilization in the late 1st/early 2nd millennium CE has been attributed to multiple internal and external causes including overpopulation, increased warfare, and environmental deterioration. Yet the role hurricanes may have played in the fracturing of Maya socio-political networks, site abandonment, and cultural reconfiguration remains unexplored. Here we present a 2200 yearlong hurricane record developed from sediment recovered from a flooded cenote on the northeastern Yucatan peninsula. The sediment archive contains fine grain autogenic carbonate interspersed with anomalous deposits of coarse carbonate material that we interpret as evidence of local hurricane activity. This interpretation is supported by the correlation between the multi-decadal distribution of recent coarse beds and the temporal distribution of modern regional landfalling storms. In total, this record allows us to reconstruct the variable hurricane conditions impacting the northern lowland Maya during the Late Preclassic, Classic, and Postclassic Periods. Strikingly, persistent above-average hurricane frequency between ~ 700 and 1450 CE encompasses the Maya Terminal Classic Phase, the declines of Chichén Itza, Cobá, and subsequent rise and fall of the Mayapán Confederacy. This suggests that hurricanes may have posed an additional environmental stressor necessary of consideration when examining the Postclassic transformation of northern Maya polities.

Abstract LHF-535 is a small molecule antiviral currently in development for the treatment of Lassa fever, a zoonotic disease endemic in West Africa that generates significant morbidity and mortality. Current treatment options are inadequate, and there are no approved therapeutics or vaccines for Lassa fever. LHF-535 was evaluated in a lethal guinea pig model of Lassa pathogenesis, using once-daily administration of a fixed dose (50 mg/kg/day) initiating either 1 or 3 days after inoculation with a lethal dose of Lassa virus. LHF-535 reduced viremia and clinical signs and protected all animals from lethality. A subset of surviving animals was rechallenged four months later with a second lethal challenge of Lassa virus and were found to be protected from disease. LHF-535 pharmacokinetics at the protective dose in guinea pigs showed plasma concentrations well within the range observed in clinical trials in healthy volunteers, supporting the continued development of LHF-535 as a Lassa therapeutic.

Abstract We evaluated whether whole-body dual-energy X-ray absorptiometry (DXA) measures of lean body mass can be used as biomarkers for disease progression and treatment effects in patients with Duchenne muscular dystrophy. This post hoc analysis utilized data from a randomized, 2-period study of domagrozumab versus placebo in 120 ambulatory boys with DMD. DXA measures of lean body mass were obtained from the whole body (excluding head), arms, legs and appendicular skeleton at baseline and every 16 weeks. Treatment effects on DXA measures for domagrozumab versus placebo were assessed at Week 49. At Week 49, domagrozumab statistically significantly increased lean body mass versus placebo in the appendicular skeleton (p = 0.050) and arms (p

Abstract Human cancers display a restricted set of expression profiles, despite diverse mutational drivers. This has led to the hypothesis that select sets of transcription factors act on similar target genes as an integrated network, buffering a tumor’s transcriptional state. Noninvasive papillary urothelial carcinoma (NIPUC) with higher cell cycle activity has higher risk of recurrence and progression. In this paper, we describe a transcriptional network of cell cycle dysregulation in NIPUC, which was delineated using the ARACNe algorithm applied to expression data from a new cohort (n = 81, RNA sequencing), and two previously published cohorts. The transcriptional network comprised 121 transcription factors, including the pluripotency factors SOX2 and SALL4, the sex hormone binding receptors ESR1 and PGR, and multiple homeobox factors. Of these 121 transcription factors, 65 and 56 were more active in tumors with greater and less cell cycle activity, respectively. When clustered by activity of these transcription factors, tumors divided into High Cell Cycle versus Low Cell Cycle groups. Tumors in the High Cell Cycle group demonstrated greater mutational burden and copy number instability. A putative mutational driver of cell cycle dysregulation, such as homozygous loss of CDKN2A, was found in only 50% of High Cell Cycle NIPUC, suggesting a prominent role of transcription factor activity in driving cell cycle dysregulation. Activity of the 121 transcription factors strongly associated with expression of EZH2 and other members of the PRC2 complex, suggesting regulation by this complex influences expression of the transcription factors in this network. Activity of transcription factors in this network also associated with signatures of pluripotency and epithelial-to-mesenchymal transition (EMT), suggesting they play a role in driving evolution to invasive carcinoma. Consistent with this, these transcription factors differed in activity between NIPUC and invasive urothelial carcinoma.

Abstract Studies of microbiota reveal inter-relationships between the microbiomes of the gut and lungs. This relationship may influence the progression of lung disease, particularly in patients with cystic fibrosis (CF), who often experience extraoesophageal reflux (EOR). Despite identifying this relationship, it is not well characterised. Our hypothesis is that the gastric and lung microbiomes in CF are related, with the potential for aerodigestive pathophysiology. We evaluated gastric and sputum bacterial communities by culture and 16S rRNA gene sequencing in 13 CF patients. Impacts of varying levels of bile acids, pepsin and pH on patient isolates of Pseudomonas aeruginosa (Pa) were evaluated. Clonally related strains of Pa and NTM were identified in gastric and sputum samples from patients with symptoms of EOR. Bacterial diversity was more pronounced in sputa compared to gastric juice. Gastric and lung bile and pepsin levels were associated with Pa biofilm formation. Analysis of the aerodigestive microbiomes of CF patients with negative sputa indicates that the gut can be a reservoir of Pa and NTM. This combined with the CF patient’s symptoms of reflux and potential aspiration, highlights the possibility of communication between microorganisms of the gut and the lungs. This phenomenon merits further research.

Abstract The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8676 children, 3–4 months of age, born with HLA-susceptibility genotypes for islet autoimmunity (IA) and type 1 diabetes (T1D). Whole-genome sequencing (WGS) was performed in 1119 children in a nested case–control study design. Telomere length was estimated from WGS data using five tools: Computel, Telseq, Telomerecat, qMotif and Motif_counter. The estimated median telomere length was 5.10 kb (IQR 4.52–5.68 kb) using Computel. The age when the blood sample was drawn had a significant negative correlation with telomere length (P = 0.003). European children, particularly those from Finland (P = 0.041) and from Sweden (P = 0.001), had shorter telomeres than children from the U.S.A. Paternal age (P = 0.019) was positively associated with telomere length. First-degree relative status, presence of gestational diabetes in the mother, and maternal age did not have a significant impact on estimated telomere length. HLA-DR4/4 or HLA-DR4/X children had significantly longer telomeres compared to children with HLA-DR3/3 or HLA-DR3/9 haplogenotypes (P = 0.008). Estimated telomere length was not significantly different with respect to any IA (P = 0.377), IAA-first (P = 0.248), GADA-first (P = 0.248) or T1D (P = 0.861). These results suggest that telomere length has no major impact on the risk for IA, the first step to develop T1D. Nevertheless, telomere length was shorter in the T1D high prevalence populations, Finland and Sweden.

Abstract Recurrent high grade glioma patients face a poor prognosis for which no curative treatment option currently exists. In contrast to prescribing high dose hypofractionated stereotactic radiotherapy (HFSRT, $$\ge 6$$ ≥ 6 Gy $$\times$$ × 5 in daily fractions) with debulking intent, we suggest a personalized treatment strategy to improve tumor control by delivering high dose intermittent radiation treatment (iRT, $$\ge 6$$ ≥ 6 Gy $$\times$$ × 1 every 6 weeks). We performed a simulation analysis to compare HFSRT, iRT and iRT plus boost ( $$\ge 6$$ ≥ 6 Gy $$\times$$ × 3 in daily fractions at time of progression) based on a mathematical model of tumor growth, radiation response and patient-specific evolution of resistance to additional treatments (pembrolizumab and bevacizumab). Model parameters were fitted from tumor growth curves of 16 patients enrolled in the phase 1 NCT02313272 trial that combined HFSRT with bevacizumab and pembrolizumab. Then, iRT +/− boost treatments were simulated and compared to HFSRT based on time to tumor regrowth. The modeling results demonstrated that iRT + boost(− boost) treatment was equal or superior to HFSRT in 15(11) out of 16 cases and that patients that remained responsive to pembrolizumab and bevacizumab would benefit most from iRT. Time to progression could be prolonged through the application of additional, intermittently delivered fractions. iRT hence provides a promising treatment option for recurrent high grade glioma patients for prospective clinical evaluation.

Abstract The weathering front is the boundary beneath Earth’s surface where pristine rock is converted into weathered rock. It is the base of the “critical zone”, in which the lithosphere, biosphere, and atmosphere interact. Typically, this front is located no more than 20 m deep in granitoid rock in humid climate zones. Its depth and the degree of rock weathering are commonly linked to oxygen transport and fluid flow. By drilling into fractured igneous rock in the semi-arid climate zone of the Coastal Cordillera in Chile we found multiple weathering fronts of which the deepest is 76 m beneath the surface. Rock is weathered to varying degrees, contains core stones, and strongly altered zones featuring intensive iron oxidation and high porosity. Geophysical borehole measurements and chemical weathering indicators reveal more intense weathering where fracturing is extensive, and porosity is higher than in bedrock. Only the top 10 m feature a continuous weathering gradient towards the surface. We suggest that tectonic preconditioning by fracturing provided transport pathways for oxygen to greater depths, inducing porosity by oxidation. Porosity was preserved throughout the weathering process, as secondary minerals were barely formed due to the low fluid flow.

Abstract Multiple studies have reported a doubling in risk of Coronavirus Disease-2019 (COVID-19) among cancer patients. Here, we examine the potential biological rationale behind this recurrent epidemiological observation. By leveraging large-scale genome-wide transcriptional data of normal and malignant tissues from adults and children, we found evidence of increased expression of SARS-CoV-2 viral entry genes in the cancer state, particularly in respiratory, gastrointestinal, and genitourinary tract tissues, with decreased expression in pediatric vs. adult samples. Additionally, by interrogating the temporal effects of radiotherapy on human peripheral blood mononuclear and mucosal cells, we observed important treatment-related alterations in host innate immunity, specifically type I interferon responses. Overall, cancers enhance expression of critical viral entry genes, and innate viral defenses can be dysregulated transiently during radiation treatments. These factors may contribute to the observed increased susceptibility to SARS-CoV-2 entry and severity of COVID-19 in cancer patients.

Abstract Our aim was to investigate the associations between erythrocyte fatty acids and the risk of islet autoimmunity in children. The Environmental Determinants of Diabetes in the Young Study (TEDDY) is a longitudinal cohort study of children at high genetic risk for type 1 diabetes (n = 8676) born between 2004 and 2010 in the U.S., Finland, Sweden, and Germany. A nested case–control design comprised 398 cases with islet autoimmunity and 1178 sero-negative controls matched for clinical site, family history, and gender. Fatty acids composition was measured in erythrocytes collected at the age of 3, 6, and 12 months and then annually up to 6 years of age. Conditional logistic regression models were adjusted for HLA risk genotype, ancestry, and weight z-score. Higher eicosapentaenoic and docosapentaenoic acid (n − 3 polyunsaturated fatty acids) levels during infancy and conjugated linoleic acid after infancy were associated with a lower risk of islet autoimmunity. Furthermore, higher levels of some even-chain saturated (SFA) and monounsaturated fatty acids (MUFA) were associated with increased risk. Fatty acid status in early life may signal the risk for islet autoimmunity, especially n − 3 fatty acids may be protective, while increased levels of some SFAs and MUFAs may precede islet autoimmunity.

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

Abstract The northern Bahamas have experienced more frequent intense-hurricane impacts than almost anywhere else in the Atlantic since 1850 CE. In 2019, category 5 (Saffir-Simpson scale) Hurricane Dorian demonstrated the destructive potential of these natural hazards. Problematically, determining whether high hurricane activity levels remained constant through time is difficult given the short observational record (

Abstract The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an accurate patient specific risk prediction for one-year postoperative mortality or cardiac transplantation and prolonged length of hospital stay with the purpose of assisting clinicians and patients’ families in the preoperative decision making process. Currently available risk prediction models either do not provide patient specific risk factors or only predict in-hospital mortality rates. We apply machine learning models to predict and calculate individual patient risk for mortality and prolonged length of stay using the Pediatric Heart Network Single Ventricle Reconstruction trial dataset. We applied a Markov Chain Monte-Carlo simulation method to impute missing data and then fed the selected variables to multiple machine learning models. The individual risk of mortality or cardiac transplantation calculation produced by our deep neural network model demonstrated 89 ± 4% accuracy and 0.95 ± 0.02 area under the receiver operating characteristic curve (AUROC). The C-statistics results for prediction of prolonged length of stay were 85 ± 3% accuracy and AUROC 0.94 ± 0.04. These predictive models and calculator may help to inform clinical and organizational decision making.

Abstract Cannabis sativa, also known as marijuana or hemp, produces a non-psychoactive compound cannabidiol (CBD). To investigate the defensive role of CBD, a feeding preference assay was performed with tobacco hornworm Manduca sexta. The larvae clearly show feeding preference towards the Cannabis tissue containing low CBD over high CBD. While the larva avoided the high CBD diet, we investigated detrimental effects of CBD in the insects’ diet. Contrasted to the performance on low CBD-infused artificial diet (AD), larvae reared on the high CBD diet suffer significantly reduced growth and increased mortality. Through testing different carriers, we found that the increase of EtOH in the diet is negatively correlated with insect development and behaviors. Notably, CBD treatment significantly improved ethanol-intoxicated larval survival rate by 40% and also improved diet searching activity, resulting in increased diet consumption. Electrophysiology results revealed that the CBD-treated ganglia had delayed but much larger response with electric stimuli in comparison to the larvae reared on AD only and EtOH-added diet. Our results show CBDs’ defensive role against pest insects, which suggests its possible use as an insecticide. We also provide evidence that CBD alleviates alcohol-induced stress; consequently, improving the performance and viability of M. sexta larvae.

Abstract Rosemary extract is used in food additives and traditional medicine and has been observed to contain anti-tumor activity. In this study, rosemary extract is hypothesized to induce synthetic lethality in BRCA2 deficient cells by PARP inhibition. Chinese hamster lung V79 cells and its mutant cell lines, V-C8 (BRCA2 deficient) and V-C8 with BRCA2 gene correction were used. Rosemary extract and its major constituent chemicals were tested for their cytotoxicity by colony formation assay in cells of different BRCA2 status. The latter chemicals were tested for inhibitory effect of poly (ADP-ribose) polymerase (PARP) activity in vitro and in vivo. Rosemary has shown selective cytotoxicity against V-C8 cells (IC50 17 µg/ml) compared to V79 cells (IC50 26 µg/ml). Among tested chemicals, gallic acid and carnosic acid showed selective cytotoxicity to V-C8 cells along with PARP inhibitory effects. Carnosol showed comparative PARP inhibitory effects at 100 µM compared to carnosic acid and gallic acid, but the selective cytotoxicity was not observed. In conclusion, we predict that within rosemary extract two specific constituent components; gallic acid and carnosic acid were the cause for the synthetic lethality.

Evaluating the quality of isolated human islets before transplantation is crucial for predicting the success in treating Type 1 diabetes. The current gold standard involves time-intensive in vivo transplantation into diabetic immunodeficient mice. Given the susceptibility of isolated islets to hypoxia, we hypothesized that hypoxia present in islets before transplantation could indicate compromised islet quality, potentially leading to unfavorable outcomes. To test this hypothesis, we analyzed expression of 39 hypoxia-related genes in human islets from 85 deceased donors. We correlated gene expression profiles with transplantation outcomes in 327 diabetic mice, each receiving 1200 islet equivalents grafted into the kidney capsule. Transplantation outcome was post-transplant glycemic control based on area under the curve of blood glucose over 4 weeks. In linear regression analysis, DDIT4 (R = 0.4971, P

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Development of high yielding cowpea varieties coupled with good taste and rich in essential minerals can promote consumption and thus nutrition and profitability. The sweet taste of cowpea grain is determined by its sugar content, which comprises mainly sucrose and galacto-oligosaccharides (GOS) including raffinose and stachyose. However, GOS are indigestible and their fermentation in the colon can produce excess intestinal gas, causing undesirable bloating and flatulence. In this study, we aimed to examine variation in grain sugar and mineral concentrations, then map quantitative trait loci (QTLs) and estimate genomic-prediction (GP) accuracies for possible application in breeding. Grain samples were collected from a multi-parent advanced generation intercross (MAGIC) population grown in California during 2016-2017. Grain sugars were assayed using high-performance liquid chromatography. Grain minerals were determined by inductively coupled plasma-optical emission spectrometry and combustion. Considerable variation was observed for sucrose (0.6-6.9%) and stachyose (2.3-8.4%). Major QTLs for sucrose (QSuc.vu-1.1), stachyose (QSta.vu-7.1), copper (QCu.vu-1.1) and manganese (QMn.vu-5.1) were identified. Allelic effects of major sugar QTLs were validated using the MAGIC grain samples grown in West Africa in 2017. GP accuracies for minerals were moderate (0.4-0.58). These findings help guide future breeding efforts to develop mineral-rich cowpea varieties with desirable sugar content.

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lins concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Circulating miR-371a-3p has excellent performance in the detection of viable (non-teratoma) germ cell tumor (GCT) pre-orchiectomy; however, its ability to detect occult disease is understudied. To refine the serum miR-371a-3p assay in the minimal residual disease setting we compared performance of raw (Cq) and normalized (∆Cq, RQ) values from prior assays, and validated interlaboratory concordance by aliquot swapping. Revised assay performance was determined in a cohort of 32 patients suspected of occult retroperitoneal disease. Assay superiority was determined by comparing resulting receiver-operator characteristic (ROC) curves using the Delong method. Pairwise t-tests were used to test for interlaboratory concordance. Performance was comparable when thresholding based on raw Cq vs. normalized values. Interlaboratory concordance of miR-371a-3p was high, but reference genes miR-30b-5p and cel-miR-39-3p were discordant. Introduction of an indeterminate range of Cq 28-35 with a repeat run for any indeterminate improved assay accuracy from 0.84 to 0.92 in a group of patients suspected of occult GCT. We recommend that serum miR-371a-3p test protocols are updated to (a) utilize threshold-based approaches using raw Cq values, (b) continue to include an endogenous (e.g., miR-30b-5p) and exogenous non-human spike-in (e.g., cel-miR-39-3p) microRNA for quality control, and (c) to re-run any sample with an indeterminate result.