Germline genetic regulation of the colorectal tumor immune microenvironment.

OBJECTIVE: To evaluate the contribution of germline genetics to regulating the briskness and diversity of T cell responses in CRC, we conducted a genome-wide association study to examine the associations between germline genetic variation and quantitative measures of T cell landscapes in 2,876 colorectal tumors from participants in the Molecular Epidemiology of Colorectal Cancer Study (MECC). METHODS: Germline DNA samples were genotyped and imputed using genome-wide arrays. Tumor DNA samples were extracted from paraffin blocks, and T cell receptor clonality and abundance were quantified by immunoSEQ (Adaptive Biotechnologies, Seattle, WA). Tumor infiltrating lymphocytes per high powered field (TILs/hpf) were scored by a gastrointestinal pathologist. Regression models were used to evaluate the associations between each variant and the three T-cell features, adjusting for sex, age, genotyping platform, and global ancestry. Three independent datasets were used for replication. RESULTS: We identified a SNP (rs4918567) near RBM20 associated with clonality at a genome-wide significant threshold of 5 × 10- 8, with a consistent direction of association in both discovery and replication datasets. Expression quantitative trait (eQTL) analyses and in silico functional annotation for these loci provided insights into potential functional roles, including a statistically significant eQTL between the T allele at rs4918567 and higher expression of ADRA2A (P = 0.012) in healthy colon mucosa. CONCLUSIONS: Our study suggests that germline genetic variation is associated with the quantity and diversity of adaptive immune responses in CRC. Further studies are warranted to replicate these findings in additional samples and to investigate functional genomic mechanisms.

Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding.

Almost all state-of-the-art de novo peptide sequencing algorithms now use machine learning models to encode fragment peaks and hence identify amino acids in mass spectrometry (MS) spectra. Previous work has highlighted how the inherent MS challenges of noise and missing peptide peaks detrimentally affect the performance of these models. In the present research we extracted and evaluated the encoding modules from 3 state-of-the-art de novo peptide sequencing algorithms. We also propose a convolutional neural network-graph neural network machine learning model for encoding peptide ions in tandem MS spectra. We compared the proposed encoding module to those used in the state-of-the-art de novo peptide sequencing algorithms by assessing their ability to identify b-ions and y-ions in MS spectra. This included a comprehensive evaluation in both real and artificial data across various levels of noise and missing peptide peaks. The proposed model performed best across all data sets using two different metrics (area under the receiver operating characteristic curve (AUC) and average precision). The work also highlighted the effect of including additional features such as intensity rank in these encoding modules as well as issues with using the AUC as a metric. This work is of significance to those designing future de novo peptide identification algorithms as it is the first step toward a new approach.

Clinical implications of conflicting variant interpretations in the cancer genetics clinic.

PURPOSE: The aim of this study was to describe the clinical impact of commercial laboratories issuing conflicting classifications of genetic variants. METHODS: Results from 2000 patients undergoing a multigene hereditary cancer panel by a single laboratory were analyzed. Clinically significant discrepancies between the laboratory-provided test reports and other major commercial laboratories were identified, including differences between pathogenic/likely pathogenic and variant of uncertain significance (VUS) classifications, via review of ClinVar archives. For patients carrying a VUS, clinical documentation was assessed for evidence of provider awareness of the conflict. RESULTS: Fifty of 975 (5.1%) patients with non-negative results carried a variant with a clinically significant conflict, 19 with a pathogenic/likely pathogenic variant reported in APC or MUTYH, and 31 with a VUS reported in CDKN2A, CHEK2, MLH1, MSH2, MUTYH, RAD51C, or TP53. Only 10 of 28 (36%) patients with a VUS with a clinically significant conflict had a documented discussion by a provider about the conflict. Discrepant counseling strategies were used for different patients with the same variant. Among patients with a CDKN2A variant or a monoallelic MUTYH variant, providers were significantly more likely to make recommendations based on the laboratory-reported classification. CONCLUSION: Our findings highlight the frequency of variant interpretation discrepancies and importance of clinician awareness. Guidance is needed on managing patients with discrepant variants to support accurate risk assessment.

NCCN Guidelines® Insights: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 2.2024.

The NCCN Guidelines for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic focus primarily on assessment of pathogenic/likely pathogenic (P/LP) variants associated with increased risk of breast, ovarian, pancreatic, and prostate cancer, including BRCA1, BRCA2, CDH1, PALB2, PTEN, and TP53, and recommended approaches to genetic counseling/testing and care strategies in individuals with these P/LP variants. These NCCN Guidelines Insights summarize important updates regarding: (1) a new section for transgender, nonbinary and gender diverse people who have a hereditary predisposition to cancer focused on risk reduction strategies for ovarian cancer, uterine cancer, prostate cancer, and breast cancer; and (2) testing criteria and management associated with TP53 P/LP variants and Li-Fraumeni syndrome.

Psychosocial outcomes following germline multigene panel testing in an ethnically and economically diverse cohort of patients.

BACKGROUND: Little is known about the psychological outcomes of germline multigene panel testing, particularly among diverse patients and those with moderate-risk pathogenic variants (PVs). METHODS: Study participants (N = 1264) were counseled and tested with a 25- or 28-gene panel and completed a 3-month postresult survey including the Multidimensional Impact of Cancer Risk Assessment (MICRA). RESULTS: The mean age was 52 years, 80% were female, and 70% had cancer; 45% were non-Hispanic White, 37% were Hispanic, 10% were Asian, 3% were Black, and 5% had another race/ethnicity. Approximately 28% had a high school education or less, and 23% were non-English-speaking. The genetic test results were as follows: 7% had a high-risk PV, 6% had a moderate-risk PV, 35% had a variant of uncertain significance (VUS), and 52% were negative. Most participants (92%) had a total MICRA score ≤ 38, which corresponded to a mean response of "never," "rarely," or only "sometimes" reacting negatively to results. A multivariate analysis found that mean total MICRA scores were significantly higher (more uncertainty/distress) among high- and moderate-risk PV carriers (29.7 and 24.8, respectively) than those with a VUS or negative results (17.4 and 16.1, respectively). Having cancer or less education was associated with a significantly higher total MICRA score; race/ethnicity was not associated with the total MICRA score. High- and moderate-risk PV carriers did not differ significantly from one another in the total MICRA score, uncertainty, distress, or positive experiences. CONCLUSIONS: In a diverse population undergoing genetic counseling and multigene panel testing for hereditary cancer risk, the psychological response corresponded to test results and showed low distress and uncertainty. Further studies are needed to assess patient understanding and subsequent cancer screening among patients from diverse backgrounds. LAY SUMMARY: Multigene panel tests for hereditary cancer have become widespread despite concerns about adverse psychological reactions among carriers of moderate-risk pathogenic variants (mutations) and among carriers of variants of uncertain significance. This large study of an ethnically and economically diverse cohort of patients undergoing panel testing found that 92% "never," "rarely," or only "sometimes" reacted negatively to results. Somewhat higher uncertainty and distress were identified among carriers of high- and moderate-risk pathogenic variants, and lower levels were identified among those with a variant of uncertain significance or a negative result. Although the psychological response corresponded to risk, reactions to testing were favorable, regardless of results.

Chordomics: a visualization tool for linking function to phylogeny in microbiomes.

SUMMARY: The overarching aim of microbiome analysis is to uncover the links between microbial phylogeny and function in order to access ecosystem functioning. This can be done using several experimental strategies targeting different biomolecules, including DNA (metagenomics), RNA (metatranscriptomics) and proteins (metaproteomics). Despite the importance of linking microbial function to phylogeny there are currently no visualization tools that effectively integrate this information. Chordomics is a Shiny-based application for linked -omics data analysis, allowing users to visualize microbial function and phylogeny on a single plot and compare datasets across time and environments. AVAILABILITY AND IMPLEMENTATION: Chordomics is available on GitHub: https://github.com/kevinmcdonnell6/chordomics; software is coded in R and JavaScript and a demonstration version is available at https://kmcd.shinyapps.io/ChordomicsDemo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data.

A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services.

Lymphocytic infiltration in stage II microsatellite stable colorectal tumors: A retrospective prognosis biomarker analysis.

BACKGROUND: Identifying stage II patients with colorectal cancer (CRC) at higher risk of progression is a clinical priority in order to optimize the advantages of adjuvant chemotherapy while avoiding unnecessary toxicity. Recently, the intensity and the quality of the host immune response in the tumor microenvironment have been reported to have an important role in tumorigenesis and an inverse association with tumor progression. This association is well established in microsatellite instable CRC. In this work, we aim to assess the usefulness of measures of T-cell infiltration as prognostic biomarkers in 640 stage II, CRC tumors, 582 of them confirmed microsatellite stable. METHODS AND FINDINGS: We measured both the quantity and clonality index of T cells by means of T-cell receptor (TCR) immunosequencing in a discovery dataset (95 patients with colon cancer diagnosed at stage II and microsatellite stable, median age 67, 30% women) and replicated the results in 3 additional series of stage II patients from 2 countries. Series 1 and 2 were recruited in Barcelona, Spain and included 112 fresh frozen (FF, median age 69, 44% women) and 163 formalin-fixed paraffin-embedded (FFPE, median age 67, 39% women) samples, respectively. Series 3 included 270 FFPE samples from patients recruited in Haifa, Northern Israel, as part of a large case-control study of CRC (median age 73, 46% women). Median follow-up time was 81.1 months. Cox regression models were fitted to evaluate the prognostic value of T-cell abundance and Simpson clonality of TCR variants adjusting by sex, age, tumor location, and stage (IIA and IIB). In the discovery dataset, higher TCR abundance was associated with better prognosis (hazard ratio [HR] for ≥Q1 = 0.25, 95% CI 0.10-0.63, P = 0.003). A functional analysis of gene expression on these tumors revealed enrichment in pathways related to immune response. Higher values of clonality index (lower diversity) were not associated with worse disease-free survival, though the HR for ≥Q3 was 2.32 (95% CI 0.90-5.97, P = 0.08). These results were replicated in an independent FF dataset (TCR abundance: HR = 0.30, 95% CI 0.12-0.72, P = 0.007; clonality: HR = 3.32, 95% CI 1.38-7.94, P = 0.007). Also, the association with prognosis was tested in 2 independent FFPE datasets. The same association was observed with TCR abundance (HR = 0.41, 95% CI 0.18-0.93, P = 0.03 and HR = 0.56, 95% CI 0.31-1, P = 0.042, respectively, for each FFPE dataset). However, the clonality index was associated with prognosis only in the FFPE dataset from Israel (HR = 2.45, 95% CI 1.39-4.32, P = 0.002). Finally, a combined analysis combining all microsatellite stable (MSS) samples demonstrated a clear prognosis value both for TCR abundance (HR = 0.39, 95% CI 0.26-0.57, P = 1.3e-06) and the clonality index (HR = 2.13, 95% CI 1.44-3.15, P = 0.0002). These associations were also observed when variables were considered continuous in the models (HR per log2 of TCR abundance = 0.85, 95% CI 0.78-0.93, P = 0.0002; HR per log2 or clonality index = 1.16, 95% CI 1.03-1.31, P = 0.016). LIMITATIONS: This is a retrospective study, and samples had been preserved with different methods. Validation series lack complete information about microsatellite instability (MSI) status and pathology assessment. The Molecular Epidemiology of Colorectal Cancer (MECC) study had information about overall survival instead of progression-free survival. CONCLUSION: Results from this study demonstrate that tumor lymphocytes, assessed by TCR repertoire quantification based on a sequencing method, are an independent prognostic factor in microsatellite stable stage II CRC.

Inhibition of poly(ADP-ribose) polymerase induces synthetic lethality in BRIP1 deficient ovarian epithelial cells.

OBJECTIVE: Pathogenic variations in the homologous recombination (HR) gene, BRCA1 interacting protein C-terminal helicase 1 (BRIP1) increase the risk for ovarian cancer. PARP inhibitors (PARPi) exert a synthetic lethal effect in BRCA-mutated ovarian cancers. Effective HR requires cooperation between BRCA1 and BRIP1; therefore, BRIP1-incompetancy may predict vulnerability to synthetic lethality. Here we investigated the response of ovarian epithelial cells with defective BRIP1 function to PARPi, and compared these cells to those lacking BRCA1 activity. METHODS: We engineered Chinese Hamster ovarian (CHO) epithelial cells to express deficient BRIP1 or BRCA1, and exposed them to olaparib with or without carboplatin or cisplatin. We assessed cellular proliferation and survival; we calculated inhibitory concentrations and combination and reduction drug indices. RESULTS: BRIP1 and BRCA1 inactivation impedes HR activity, decreases cellular proliferation and compromises DNA damage recovery. Platinum agent exposure impairs cellular survival. Olaparib exposure alone decreases cell viability in BRCA1-deficient cells, although has no effect on BRIP1-deficient cells. Combining carboplatin or cisplatin with olaparib synergistically attenuates cellular survival, consistent with synthetic lethality. CONCLUSIONS: BRIP1-deficient ovarian epithelial cells exhibit defective HR, resulting in synthetic lethality when exposed to a platinum agent/PARPi combination. PARPi alone had no effect; this lack of effect may result from distinguishing molecular properties of BRIP1and/or consequences of genomic background. Our study identifies altered BRIP1 as a target for precision medicine-based therapies for ovarian cancers. This investigation supports consideration of the use of a platinum agent/PARPi combination in ovarian cancers depending upon genetic profile and genomic background.

Hepatic FcRn regulates albumin homeostasis and susceptibility to liver injury.

The neonatal crystallizable fragment receptor (FcRn) is responsible for maintaining the long half-life and high levels of the two most abundant circulating proteins, albumin and IgG. In the latter case, the protective mechanism derives from FcRn binding to IgG in the weakly acidic environment contained within endosomes of hematopoietic and parenchymal cells, whereupon IgG is diverted from degradation in lysosomes and is recycled. The cellular location and mechanism by which FcRn protects albumin are partially understood. Here we demonstrate that mice with global or liver-specific FcRn deletion exhibit hypoalbuminemia, albumin loss into the bile, and increased albumin levels in the hepatocyte. In vitro models with polarized cells illustrate that FcRn mediates basal recycling and bidirectional transcytosis of albumin and uniquely determines the physiologic release of newly synthesized albumin into the basal milieu. These properties allow hepatic FcRn to mediate albumin delivery and maintenance in the circulation, but they also enhance sensitivity to the albumin-bound hepatotoxin, acetaminophen (APAP). As such, global or liver-specific deletion of FcRn results in resistance to APAP-induced liver injury through increased albumin loss into the bile and increased intracellular albumin scavenging of reactive oxygen species. Further, protection from injury is achieved by pharmacologic blockade of FcRn-albumin interactions with monoclonal antibodies or peptide mimetics, which cause hypoalbuminemia, biliary loss of albumin, and increased intracellular accumulation of albumin in the hepatocyte. Together, these studies demonstrate that the main function of hepatic FcRn is to direct albumin into the circulation, thereby also increasing hepatocyte sensitivity to toxicity.

111In- and IRDye800CW-Labeled PLA-PEG Nanoparticle for Imaging Prostate-Specific Membrane Antigen-Expressing Tissues.

Targeted delivery of drug-encapsulated nanoparticles is a promising new approach to safe and effective therapeutics for cancer. Here we investigate the pharmacokinetics and biodistribution of a prostate-specific membrane antigen (PSMA)-targeted nanoparticle based on a poly(lactic acid)-polyethylene glycol copolymer by utilizing single photon emission computed tomography (SPECT) and fluorescence imaging of a low-molecular-weight, PSMA-targeting moiety attached to the surface and oriented toward the outside environment. Tissue biodistribution of the radioactive, PSMA-targeted nanoparticles in mice containing PSMA(+) PC3 PIP and PSMA(-) PC3 flu (control) tumors demonstrated similar accumulation compared to the untargeted particles within all tissues except for the tumor and liver by 96 h postinjection. For PSMA(+) PC3 PIP tumor, the targeted nanoparticle demonstrated retention of 6.58% injected dose (ID)/g at 48 h and remained nearly at that level out to 96 h, whereas the untargeted nanoparticle showed a 48 h retention of 8.17% ID/g followed by a significant clearance to 2.37% ID/g at 96 h (P < 0.02). On the other hand, for control tumor, both targeted and untargeted particles displayed similar 48 h retentions and rates of clearance over 96 h. Ex vivo microscopic analysis with near-infrared versions of the nanoparticles indicated retention within PSMA(+) tumor epithelial cells as well as tumor-associated macrophages for targeted particles and primarily macrophage-associated uptake for the untargeted particles. Retention in control tumor was primarily associated with tumor vasculature and macrophages. The data demonstrate the utility of radioimaging to assess nanoparticle biodistribution and suggest that active targeting has a modest positive effect on tumor localization of PSMA-targeted PLA-PEG nanoparticles that have been derivatized for imaging.

Low-Cost Intervention to Increase Influenza Vaccination Rate at a Comprehensive Cancer Center.

Influenza morbidity and mortality can be severe and costly. Vaccination rates remain suboptimal in cancer patients due to provider- and patient-related factors. The objective of this study was to evaluate whether low-cost provider- and patient-focused interventions would increase influenza vaccination rates at the University of Michigan Comprehensive Cancer Center (UMCCC). This quality improvement project included all patients without documentation of influenza vaccination prior to their first outpatient appointment during the 2011-2012 and 2012-2013 influenza seasons. The multi-stepped intervention included provider and patient reminders. Influenza vaccination rates were compiled using CPT-4 codes. Same-day (with appointment) vaccination rates during the intervention seasons were compared to historical (2005-2011 seasons) controls; vaccination rates were also compared to contemporary control population at the University of Michigan Health System (UMHS). Reasons for non-adherence with vaccination were explored. The cumulative same-day vaccination rate in eligible adults was 10.1 % (2011-2012) and 9.4 % (2012-2013) compared to an average 6.9 % during influenza seasons 2005-2011. Based on logistic regression analysis, there was a 37.6 % (95 % CI 35-40.3 %) and 56.1 % (95 % CI 40.9-73 %) relative increase in the adult vaccination rate associated with the intervention, with 399 and 697 additional vaccinations, respectively, for each season. During the 2012-2013 season, the UMCCC adult vaccination rate was higher compared to the remainder of that of the UMHS. The intervention was well accepted by providers. Reasons for no vaccination were provider- and patient-related. Increasing provider and patient awareness with a simple, inexpensive intervention was associated with higher influenza vaccination rates at a large academic cancer center. The intervention is permanently implemented during influenza seasons.

Towards an effective biosensor for monitoring AD leachate: a knockout E. coli mutant that cannot catabolise lactate.

Development of a biosensor for the convenient measurement of acetate and propionate concentrations in a two-phase anaerobic digestor (AD) requires a bacterium that will be unresponsive to the other organic acids present in the leachate, of which lactate is the most abundant. Successive gene knockouts of E.coli W3110 D-lactate dehydrogenase (dld), L-lactate dehydrogenase (lldD), glycolate oxidase (glcD) and a suspected L-lactate dehdrogenase (ykgF) were performed. The resulting quadruple mutant (IMD Wldgy) was incapable of growth on D- and L-lactate, whereas the wild type grew readily on these substrates. Furthermore, the O2 consumption rates of acetate-grown IMD Wldgy cell suspensions supplied with either acetate (0.1 mM) or a synthetic leachate including acetate (0.1 mM) and DL-lactate (1 mM) were identical (2.79 and 2.70 mg l(-1) min(-1), respectively). This was in marked contrast to similar experiments with the wild type which gave initial O2 consumption rates of 2.00, 2.36 and 2.97 mg l(-1) min(-1) when cell suspensions were supplied with acetate (0.1 mM), acetate (0.1 mM) plus D-lactate (1 mM) or acetate (0.1 mM) plus L-lactate (1 mM), respectively. The knockout strain provides a platform for the design of a biosensor that can accessibly monitor acetate and propionate concentrations in AD leachate via O2-uptake measurements.

Carbon dioxide utilisation of Dunaliella tertiolecta for carbon bio-mitigation in a semicontinuous photobioreactor.

Bio-fixation of carbon dioxide (CO2) by microalgae has been recognised as an attractive approach to offset anthropogenic emissions. Biological carbon mitigation is the process whereby autotrophic organisms, such as microalgae, convert CO2 into organic carbon and O2 through photosynthesis; this process through respiration produces biomass. In this study Dunaliella tertiolecta was cultivated in a semicontinuous culture to investigate the carbon mitigation rate of the system. The algae were produced in 1.2-L Roux bottles with a working volume of 1 L while semicontinuous production commenced on day 4 of cultivation when the carbon mitigation rate was found to be at a maximum for D. tertiolecta. The reduction in CO2 between input and output gases was monitored to predict carbon fixation rates while biomass production and microalgal carbon content are used to calculate the actual carbon mitigation potential of D. tertiolecta. A renewal rate of 45 % of flask volume was utilised to maintain the culture in exponential growth with an average daily productivity of 0.07 g L(-1) day(-1). The results showed that 0.74 g L(-1) of biomass could be achieved after 7 days of semicontinuous production while a total carbon mitigation of 0.37 g L(-1) was achieved. This represented an increase of 0.18 g L(-1) in carbon mitigation rate compared to batch production of D. tertiolecta over the same cultivation period.

Differences in Tumor-Associated T cell receptor repertoires between Early-Onset and Average-Onset colorectal cancer.

The incidence of colorectal cancer (CRC) among individuals younger than age 50 (early onset CRC; EOCRC) has substantially increased, yet the etiology and molecular mechanisms underlying this alarming rise remain unclear. We compared tumor-associated T cell repertoires between EOCRC and average-onset CRC (AOCRC) to uncover potentially unique immune microenvironment-related features by age of onset. Our discovery cohort included 242 patients who underwent surgical resection at Cleveland Clinic from 2000 to 2020. EOCRC was defined as age < 50 years at diagnosis (N = 126), and AOCRC as age ≥ 60 years (N = 116). T cell receptor (TCR) abundance and clonality were measured by immunosequencing of tumors. Logistic regression models were used to evaluate the associations between TCR repertoire features and age of onset, adjusting for sex, race, tumor location, and stage. Findings were replicated in 152 EOCRC and 1,984 AOCRC cases from the Molecular Epidemiology of Colorectal Cancer Study. EOCRC tumors had significantly higher TCR diversity compared to AOCRC tumors in the discovery cohort (Odds Ratio (OR):0.44, 95% Confidence Interval (CI):0.32-0.61, p < .0001). This association was also observed in the replication cohort (OR : 0.74, 95% CI : 0.62-0.89, p = .0013). No significant differences in TCR abundance were observed between EOCRC and AOCRC in either cohort. Higher TCR diversity, suggesting a more diverse intratumoral T cell response, is more frequently observed in EOCRC than AOCRC. Further studies are warranted to investigate the role of T cell diversity and the adaptive immune response more broadly in the etiology and outcomes of EOCRC.

Microbial community in the soil determines the forest recovery post-exposure to gamma irradiation.

Exposure of an ecosystem to ionizing radiation remains a possibility either due to accidents involving nuclear fuel rods or contamination with high-level radioactive wastes. While the short and long-term effect of ionizing radiation on higher eukaryotes has been well documented, we do not have an understanding on the recovery of the microbial community post radiation. Here we report that at a site within Brookhaven National Laboratory that was radiated from 1961 to 1978 with γ rays (Gamma Forest), the ecosystem has not yet fully recovered from the effects of radiation. The current vegetation type in the Gamma Forest varies as one goes away from the source of ionizing radiation, with the region closest to the source having no vegetation. The microbial tag-encoded FLX amplicon pyrosequencing analysis of the soil from different regions suggests that the current microbial community structure is identical in all the Zones. When soil samples from each vegetation zone of the Gamma Forest were radiated with 1.8 kGy γ radiation and survival microbial community analyzed, clear difference in the microbial communities were observed. It is evident based on the experimental data that the colonization of soil with Nitrosomonadaceae is critical for the higher plants in pine barrens to reestablish and grow after the area had been exposed to ionizing radiation.

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise.

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification.

Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

Development and comparative analysis of ANN and SVR-based models with conventional regression models for predicting spray drift.

As monitoring of spray drift during application can be expensive, time-consuming, and labor-intensive, drift predicting models may provide a practical complement. Several mechanistic models have been developed as drift prediction tool for various types of application equipment. Nevertheless, mechanistic models are quite often intricate and complex with a large number of input parameters required. Quite often, the detailed data needed for such models are not readily available. In this study, two advanced machine learning models (artificial neural network (ANN) and support vector regression (SVR)) were developed for pesticide drift prediction and compared with three conventional regression-based models: multiple linear regression (MLR), generalized linear model (GLM), and generalized nonlinear least squares (GNLS). The models were evaluated in fivefold cross-validation and by external validation using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute bias (MAB). From regression-based models, GLM and GNLS models performed very well when evaluated by cross-validation with R2 = 0.96 and 0.95 and RMSE = 0.70 and 0.82 respectively, while MLR performed less with R2 of 0.65 and RMSE of 2.25. Simultaneously, ANN and SVR models performed very well with R2 = 0.98 and 0.97 and RMSE = 0.58 and 0.71 respectively. Overall, ANN model performed best compared to the other four models followed by SVR. A comparison was also made between the high-performing model, ANN, and two previously published empirical models. The ANN model outperformed the two previously published empirical models and can be used to predict pesticide drift. Therefore, the ANN model is a potentially promising new approach for predicting ground drift that merits further study. In conclusion, our work demonstrated that the new approach, ANN and SVR-based models, for pesticide drift modeling has better predictive power than conventional regression models. Their ability to model complex relationships is a clear benefit in pesticide drift modeling where the variability in pesticide drift is often affected by a number of variables and the relationships between drift and predictors are very complicated. We believe such insights will pave better way for the application of machine learning towards spray drift modeling.

The establishment of winter wheat root system architecture in field soils: The effect of soil type on root development in a temperate climate.

Winter wheat (Triticum aestivum L.) is an important cereal crop in the temperate climates of western Europe. Root system architecture is a significant contributor to resource capture and plant resilience. However, the impact of soil type on root system architecture (RSA) in field structured soils is yet to be fully assessed. This work studied the development of root growth using deep cultivation (250 mm) during the tillering phase stage (Zadock stage 25) of winter wheat across three soil types. The three sites of contrasting soil types covered a geographical area in the UK and Ireland in October 2018. Root samples were analysed using two methods: X-ray computed tomography (CT) which provides 3D images of the undisturbed roots in the soil, and a WinRHIZO™ scanner used to generate 2D images of washed roots and to measure further root parameters. Important negative relationships existed between soil bulk density and root properties (root length density, root volume, surface area and length) across the three sites. The results revealed that despite reduced root growth, the clay (Southoe) site had a significantly higher crop yield irrespective of root depth. The loamy sand (Harper Adams) site had significantly higher root volume, surface area and root length density compared with the other sites. However, a reduction in grain yield of 2.42 Mt ha-1 was incurred compared with the clay site and 1.6 Mt ha-1 compared with the clay loam site. The significantly higher rooting characteristics found in the loamy sand site were a result of the significantly lower soil bulk density compared with the other two sites. The loamy sand site had a lower soil bulk density, but no significant difference in macroporosity between sites (p > 0.05). This suggests that soil type and structure directly influence crop yield to greater extent than root parameters, but the interactions between both need simultaneous assessment in field sites.