Risk Of False Positives Research Articles

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BackgroundIn the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.ResultsWe trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.Conclusions3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Screening drug-induced liver injury through two independent parameters of lipid droplets and peroxynitrite with a π-extended coumarin-based NIR fluorescent probe

Drug-induced liver injury (DILI) or hepatotoxicity is a significant concern for public health. However, relying on a single biomarker for early DILI diagnosis poses a high risk of false positives. In this study, we reported a near-infrared fluorescent probe (BCOU-S), which enables independent visualization of LDs status and ONOO fluctuations. BCOU-S was constructed by combining a π-extended coumarin core as the NIR fluorophore and a methyl thioether group as the ONOO recognition site. BCOU-S could emit 655 nm NIR fluorescence excited at 518 nm, with a low detection limit of 27 nM and a large Stokes shift of 137 nm. The response mechanism to ONOO was confirmed by molecular orbital density function theory (DFT) and time-dependent DFT (TD-DFT) calculations. BCOU-S monitors LDs status through co-localization, oleic acid (OA) incubation, and starvation induction experiments. It sensitively distinguishes subtle changes in ONOO induced by different drugs in DILI cell models. In the acetaminophen (APAP)-induced DILI model, BCOU-S reveals significant LDs accumulation and increase in ONOO levels, establishing a clear time-effect and dose-effect relationship. Simultaneously monitoring ONOO levels and LDs accumulation, BCOU-S proves to be a more sensitive and effective tool for early DILI prevention, effectively avoiding false positives caused by changes in a single parameter. BCOU-S is a useful tool for further monitoring early DILI development.

Reducing risk of false positives in the in vivo comet assay and improving result reliability

The risk of generating false positive in vivo comet assay results can be increased when procedural bias and/or technical variability is poorly controlled. This has been an ongoing concern since comet was first introduced into regulatory safety testing. But the proprietary nature of regulated studies and the 3Rs have limited the ability to conduct and publish the comparative in vivo studies necessary to determine the effect these factors can have on comet assay results when substances other than well characterized positive control compounds are evaluated in multiple tissues. That changed when Helix3 was asked to repeat for regulatory submission three independent in vivo comet studies with positive results generated by three other laboratories evaluating the effects of three different test substances on the liver, duodenum, and stomach. We repeated each study using the same test substance and experimental design as the original labs but with our standard quality control methods implemented to reduce procedural bias and variability. In every case, we generated negative results that regulatory authorities accepted over the initial positive results due to evidence of high technical variability and procedural bias in the original labs and studies. Meanwhile, the International Workshop on Genotoxicity (IWGT) compared >14 years of Helix3 comet historical control data (HCD) to HCD from 6 other experienced comet laboratories and concluded that our data exhibited the highest overall background % tail DNA levels with the lowest inter-study variability resulting in the highest quality HCD of all the labs evaluated. These case studies and the IWGT report suggest that our enhanced quality control methods and higher (>2 % mean of slide median tail DNA) background levels can effectively mitigate the nuisance factors that can generate false positive in vivo comet assay results. To facilitate a better understanding of the technical parameters that can significantly influence the comet results, we describe our enhanced procedures with justifications and examples.

Improved diagnostic accuracy with three lung tumor markers compared to six-marker panel.

Combining multiple tumor markers increases sensitivity for lung cancer diagnosis in the cost of false positive. However, some would like to check as many as tumor markers in the fear of missing cancer. We though to propose a panel of fewer tumor markers for lung cancer diagnosis. Patients with suspected lung cancer who simultaneously underwent all six tests [carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA), squamous cell carcinoma-associated antigen (SCC), neuron-specific enolase (NSE), pro-gastrin-releasing peptide (ProGRP), and sialyl Lewis-X antigen (SLX)] were included. Tumor markers with significant impact on the lung cancer in a logistic regression model were included in our panel. Area under the curve (AUC) was compared between our panel and the panel of all six. We included 1,733 [median 72 years, 1,128 men, 605 women, 779 (45%) confirmed lung cancer]. Logistic regression analysis suggested CEA, CYFRA, and NSE were independently associated with the lung cancer diagnosis. The panel of these three tumor markers [AUC =0.656, 95% confidence interval (CI): 0.630-0.682, sensitivity 0.650, specificity 0.662] had better (P<0.001) diagnostic performance than six tumor markers (AUC =0.575, 95% CI: 0.548-0.602, sensitivity 0.829, specificity 0.321). Compared to applying all six markers (at least one marker above the upper limit of normal), the panel with three markers (at least one marker above the upper limit of normal) led to a better predictive value by lowering the risk of false positives.

Enhancing Loop-Mediated Isothermal Amplification through Nonpowered Nanoelectric Preconcentration.

The global threat posed by the COVID-19 pandemic has catalyzed the development of point-of-care (POC) molecular diagnostics. While loop-mediated isothermal amplification (LAMP) stands out as a promising technique among FDA-approved methods, it is occasionally susceptible to a high risk of false positives due to nonspecific amplification of a primer dimer. In this work, we report an enhancing LAMP technique in terms of assay sensitivity and reliability through streamlined integration with a nonpowered nanoelectric preconcentration (NPP). The NPP, serving as a sample preparation tool, enriched the virus concentration in samples prior to the subsequent LAMP assay. This enrichment enabled not only to achieve more sensitive assay but also to shorten the assay time for all tested clinical samples by ∼10 min compared to the conventional LAMP. The shortened assay time suppresses the occurrence of nonspecific amplification by not providing the necessary incubation time, effectively suppressing misidentification by false positives. Utilizing this technique, we also developed a prototype of the POC NPP-LAMP kit. This kit offers a streamlined diagnostic process for nontrained individuals, from the sample enrichment, transfer of the enriched sample to LAMP assays, which facilitates on-site/on-demand diagnosis of SARS-CoV-2. This development holds the potential to contribute toward preventing not only the current outbreak but also future occurrences of pandemic viruses.

Screen-printed electrode modified with bismuth film and chemometric techniques for on-site detection and classification of gunshot residues

The analysis of gunshot residues (GSR) is important for forensic science to help understand what happened in a crime scene. Many works on this have been published; however, there are a number of questions that remain. An analytical method should ideally respond to these questions: 1 Can the residue from a shooting or the analysis present a false positive? 2. Is it possible to identify the gun used in a shooting and information about it such as caliber and type of ammunition used? An analysis, as well, must be precise, easy to use, reproducible and allowable on-site. To achieve these requirements, voltammograms registered on a portable potentiostat, and chemometrics analysis were used. Electroanalytical techniques provide meaningful chemical information about a sample. Chemometric tools make it possible to build models to identify even small differences and respond to these questions with data. A total of 90 GSR samples were collected with the help of the Institute of Criminalistics in the state of Pernambuco, Brazil. The GSR were taken from 3 types of firearms, 2 of them with distinct ammunition. To assess the risk of false positives, 45 non-shooting samples were analyzed. The model constructed achieved a classification accuracy of 100 % to respond to the question of false positives. There was an accuracy rate of over 85 % for the second question, of identifying information about the weapon. A blind test was performed and used to prove that the electroanalytical-chemometrics methodology was useful and effective for GSR forensic investigators, enabling the emission of reliable reports, and contributing to faster processing of legal proceedings.

A pilot study to investigate the clinically predictive values of copy number variations detected by next-generation sequencing of cell-free deoxyribonucleic acid in spent culture media

To investigate the positive predictive value and false positive risk of copy number variations (CNV's) detected in cell free deoxyribonucleic acid (DNA) from spent culture media for nonviable or aneuploid embryos. Diagnostic/prognostic accuracy study. Patients aged 35 and younger with an indication for IVF-ICSI and elective single frozen embryo transfer at a single, private IVF center. Embryo selection was performed according to the conventional grading, blinded to noninvasive preimplantation genetic testing for aneuploidy (niPGT-A) results. After clinical outcomes were established, spent culture media samples were analyzed. Prognostic accuracy of CNVs according to niPGT-A results to predict nonviability or clinical aneuploidy. One hundred twenty patients completed the study. Interpretations of next-generation sequencing (NGS) profiles were as follows: 7.5% (n = 9) failed quality control; 62.5% (n = 75) no CNVs detected; and 30% (n = 36) abnormal copy number detected. Stratification of abnormal NGS profiles was as follows: 15% (n = 18) whole chromosome and 15% (n = 18) uncertain reproductive potential. An intermediate CNV was evident in 27.8% (n = 5) of the whole chromosome abnormalities. The negative predictive value for samples with no detected abnormality was 57.3% (43/75). Whole chromosome abnormality was associated with a positive predictive value of 94.4% (17/18), lower sustained implantation rate (5.6%, 1/18), and higher relative risk (RR) for nonviability compared with no detected abnormalities (RR 2.21, 95% CI: 1.66-2.94). No other CNVs were associated with significant differences in the sustained implantation or RRs for nonviability. Unequal sex chromosome proportions suggested that maternal contamination was not uncommon. A secondary descriptive analysis of 705 supernumerary embryos revealed proportions of NGS profile interpretations similar to the transferred cohort. Significant median absolute pairwise differences between certain subcategories of CNV abnormalities were apparent. Whole chromosome abnormalities were associated with a high positive predictive value and significant RR for nonviability. Embryos associated with other CNVs had sustained implantation rates similar to those with no abnormalities detected. Further studies are required to validate the clinical applicability of niPGT-A. clinicaltrials.gov (NCT04732013).

Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

SummaryAttacks have a significant negative impact on the wireless sensor network's network operations. They target to weaken the network layer, if they are not completely eradicated, the networks' ability to execute their desired function becomes collapsed. While intended to monitor such unexpected attacks, anomaly‐based intrusion detection systems (AIDS) have a high risk of false positives. This article intends to propose a Deep Ensemble Intrusion detection with a Self‐adaptive Sewing Training‐Based Optimization (DIED‐SASTO) model that includes the following steps: (a) preprocessing, (b) feature extraction, (c) optimal feature selection, and (d) detection. Initially, an improved class imbalance processing will take place to solve the imbalance problem during the preprocessing step. Subsequently, the features are extracted including higher‐order statistical features, improved entropy, and correlation‐based features during the feature extraction step. From the extracted feature set, as the curse of dimensionality becomes a serious challenge, it is significant to choose the optimal features. In the optimal feature selection process, a new Self‐adaptive Sewing Training‐Based Optimization (SASTO) is used. In addition, the intrusion detection will be employed based on the selected optimal features and trained using deep ensemble models like Deep maxout, Deep Belief Network (DBN), and Bidirectional Long short‐term memory (Bi‐LSTM). Once the presence of an attack is detected, it is mandatory to mitigate the attacker node form the network. For Attack Mitigation, improved entropy will be progressed. The effectiveness of the DIED‐SASTO is evaluated over conventional methods in terms of various metrics.

MoCoLo: a testing framework for motif co-localization.

Sequence-level data offers insights into biological processes through the interaction of two or more genomic features from the same or different molecular data types. Within motifs, this interaction is often explored via the co-occurrence of feature genomic tracks using fixed-segments or analytical tests that respectively require window size determination and risk of false positives from over-simplified models. Moreover, methods for robustly examining the co-localization of genomic features, and thereby understanding their spatial interaction, have been elusive. We present a new analytical method for examining feature interaction by introducing the notion of reciprocal co-occurrence, define statistics to estimate it and hypotheses to test for it. Our approach leverages conditional motif co-occurrence events between features to infer their co-localization. Using reverse conditional probabilities and introducing a novel simulation approach that retains motif properties (e.g. length, guanine-content), our method further accounts for potential confounders in testing. As a proof-of-concept, motif co-localization (MoCoLo) confirmed the co-occurrence of histone markers in a breast cancer cell line. As a novel analysis, MoCoLo identified significant co-localization of oxidative DNA damage within non-B DNA-forming regions that significantly differed between non-B DNA structures. Altogether, these findings demonstrate the potential utility of MoCoLo for testing spatial interactions between genomic features via their co-localization.

Novel linkage approach to join community-acquired and national data

BackgroundCommunity optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools.MethodsAs the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices.ResultsA sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records.ConclusionsThe potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.

Evolving paradigms in breast cancer screening: Balancing efficacy, personalization, and equity

Breast cancer remains a significant global health challenge, with projections indicating a troubling increase in incidence. Breast cancer screening programs have long been hailed as life-saving initiatives, yet their true impact on mortality rates is a subject of ongoing debate. Screening poses the risk of false positives and the detection of indolent tumors, potentially leading to overtreatment. Bias factors, including lead time, length time, and selection biases, further complicate the assessment of screening efficacy. Recent studies suggest that AI-driven image analysis may revolutionize breast cancer screening, maintaining diagnostic accuracy while reducing radiologists' workload. However, the generalizability of these findings to diverse populations is a critical consideration. Personalized screening approaches and equitable access to advanced technologies are essential to mitigate disparities. In conclusion, the breast cancer screening landscape is evolving, emphasizing the need for risk stratification, appropriate imaging modalities, and a personalized approach to reduce overdiagnosis and focus on cancers with the potential to impact lives while prioritizing patient-centered care.

MAGICIAN: MAG simulation for investigating criteria for bioinformatic analysis

BackgroundThe possibility of recovering metagenome-assembled genomes (MAGs) from sequence reads allows for further insights into microbial communities and their members, possibly even analyzing such sequences with tools designed for single-isolate genomes. As result quality depends on sequence quality, performance of tools for single-isolate genomes on MAGs should be tested beforehand. Bioinformatics can be leveraged to quickly create varied synthetic test sets with known composition for this purpose.ResultsWe present MAGICIAN, a flexible, user-friendly pipeline for the simulation of MAGs. MAGICIAN combines a synthetic metagenome simulator with a metagenomic assembly and binning pipeline to simulate MAGs based on user-supplied input genomes, allowing users to test performance of tools on MAGs while having a ground truth to compare results to. Using MAGICIAN, we found that even very slight (1%) changes in depth of coverage can drastically affect whether a genome can be recovered. We also demonstrate the use of simulated MAGs by evaluating the suitability of such genomes obtained with MAGICIAN’s current default pipeline for analysis with the antimicrobial resistance gene identification tool ResFinder.ConclusionsUsing MAGICIAN, it is possible to simulate MAGs which, while generally high in quality, reflect issues encountered with real-world data, thus providing realistic best-case data. Evaluating the results of ResFinder analysis of these genomes revealed a risk for plausible-looking false positives, which underlines the need for pipeline validation so that researchers are aware of the potential issues when interpreting real-world data. Furthermore, the effects of fluctuations in depth of coverage on genome recovery in our simulated “random sequencing” warrant further investigation and indicate random subsampling of reads may affect discovery of more genomes.

Synergizing Neutrosophy and Randomized Blocks Design: Development and Analytical Insights

The design of the experiment is a strategy for effectively examining the relationship between input design parameters and process output and developing a greater understanding. A randomized block design is an experimental design that has two primary factors and is widely used in agriculture, environment, biological, animal, and food sciences, where experimental material is heterogeneous and precise. In a randomised block design, one or more observations may lose their true significance due to an accident, poor handling, pest infestations in agricultural trials, or other factors. It is prudent to treat this value as missing and estimate it. In today’s practical situations, uncertainty and inaccuracies are inevitable in most research areas. It is important to handle such data, which can lead to inaccurate and unreliable results. Neutrosophy is the branch of philosophy that provides an efficient method to study impreciseness among the data. Some of the common sources of Neutrosophy in randomised block design are incorrect blocking factor selection, measurement error, subjective factors, and natural variability. It is paramount to handle the Neutrosophy in a randomised block design; otherwise, it may lead to various problems, like a high risk of false positives. In this paper, the Neutrosophic Randomised Block Design (NRBD) is introduced to tackle data impreciseness. The study also, outlines a methodology for estimating missing observations in NRBD and presents its analysis. Additionally, the study compares the efficiency of NRBD to that of the Neutrosophic Completely Randomised Design (NCRD).

Chinese medicine in the treatment of non-alcoholic fatty liver disease based on network pharmacology: a review.

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by abnormalities in hepatic fat deposition, the incidence of which has been increasing year by year in recent years. It has become the largest chronic liver disease globally and one of the important causes of cirrhosis and even primary liver cancer formation. The pathogenesis of NAFLD has not yet been fully clarified. Modern medicine lacks targeted clinical treatment protocols for NAFLD, and most drugs lack efficacy and have high side effects. In contrast, Traditional Chinese Medicine (TCM) has significant advantages in the treatment and prevention of NAFLD, which have been widely recognized by scholars around the world. In recent years, through the establishment of a "medicine-disease-target-pathway" network relationship, network pharmacology can explore the molecular basis of the role of medicines in disease prevention and treatment from various perspectives, predicting the pharmacological mechanism of the corresponding medicines. This approach is compatible with the holistic view and treatment based on pattern differentiation of TCM and has been widely used in TCM research. In this paper, by searching relevant databases such as PubMed, Web of Science, and Embase, we reviewed and analyzed the relevant signaling pathways and specific mechanisms of action of single Chinese medicine, Chinese medicine combinations, and Chinese patent medicine for the treatment of NAFLD in recent years. These related studies fully demonstrated the therapeutic characteristics of TCM with multi-components, multi-targets, and multi-pathways, which provided strong support for the exact efficacy of TCM exerted in the clinic. In conclusion, we believe that network pharmacology is more in line with the TCM mindset of treating diseases, but with some limitations. In the future, we should eliminate the potential risks of false positives and false negatives, clarify the interconnectivity between components, targets, and diseases, and conduct deeper clinical or experimental studies.

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conservatism, potentially resulting in false–positive risk events in complicated real-world applications. In this paper, we combine two reachability analysis techniques, a backward reachable set (BRS) and a stochastic forward reachable set (FRS), and propose an integrated probabilistic collision–detection framework for highway driving. Within this framework, we can first use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step. Thus, the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data. The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising for real-world applications.

Statistically significant differences versus convincing evidence of real treatment effects: an analysis of the false positive risk for single-centre trials in anaesthesia

BackgroundThe American Statistical Association has highlighted problems with null hypothesis significance testing and outlined alternative approaches that may ‘supplement or even replace P-values’. One alternative is to report the false positive risk (FPR), which quantifies the chance the null hypothesis is true when the result is statistically significant. MethodsWe reviewed single-centre, randomised trials in 10 anaesthesia journals over 6 yr where differences in a primary binary outcome were statistically significant. We calculated a Bayes factor by two methods (Gunel, Kass). From the Bayes factor we calculated the FPR for different prior beliefs for a real treatment effect. Prior beliefs were quantified by assigning pretest probabilities to the null and alternative hypotheses. ResultsFor equal pretest probabilities of 0.5, the median (inter-quartile range [IQR]) FPR was 6% (1–22%) by the Gunel method and 6% (1–19%) by the Kass method. One in five trials had an FPR ≥20%. For trials reporting P-values 0.01–0.05, the median (IQR) FPR was 25% (16–30%) by the Gunel method and 20% (16–25%) by the Kass method. More than 90% of trials reporting P-values 0.01–0.05 required a pretest probability >0.5 to achieve an FPR of 5%. The median (IQR) difference in the FPR calculated by the two methods was 0% (0–2%). ConclusionsOur findings suggest that a substantial proportion of single-centre trials in anaesthesia reporting statistically significant differences provide limited evidence of real treatment effects, or, alternatively, required an implausibly high prior belief in a real treatment effect. Clinical trial registrationPROSPERO (CRD42023350783).

Interpreting frequentist hypothesis tests: insights from Bayesian inference

Randomized controlled trials are one of the best ways of quantifying the effectiveness of medical interventions. Therefore, when the authors of a randomized superiority trial report that differences in the primary outcome between the intervention group and the control group are “significant” (i.e., P ≤ 0.05), we might assume that the intervention has an effect on the outcome. Similarly, when differences between the groups are “not significant,” we might assume that the intervention does not have an effect on the outcome. Nevertheless, both assumptions are frequently incorrect.In this article, we explore the relationship that exists between real treatment effects and declarations of statistical significance based on P values and confidence intervals. We explain why, in some circumstances, the chance an intervention is ineffective when P ≤ 0.05 exceeds 25% and the chance an intervention is effective when P > 0.05 exceeds 50%.Over the last decade, there has been increasing interest in Bayesian methods as an alternative to frequentist hypothesis testing. We provide a robust but nontechnical introduction to Bayesian inference and explain why a Bayesian posterior distribution overcomes many of the problems associated with frequentist hypothesis testing.Notwithstanding the current interest in Bayesian methods, frequentist hypothesis testing remains the default method for statistical inference in medical research. Therefore, we propose an interim solution to the “significance problem” based on simplified Bayesian metrics (e.g., Bayes factor, false positive risk) that can be reported along with traditional P values and confidence intervals. We calculate these metrics for four well-known multicentre trials. We provide links to online calculators so readers can easily estimate these metrics for published trials. In this way, we hope decisions on incorporating the results of randomized trials into clinical practice can be enhanced, minimizing the chance that useful treatments are discarded or that ineffective treatments are adopted.

Solid-State Single-Molecule Sensing with the Electronic Life-Detection Instrument for Enceladus/Europa (ELIE).

Growing evidence of the potential habitability of Ocean Worlds across our solar system is motivating the advancement of technologies capable of detecting life as we know it-sharing a common ancestry or physicochemical origin with life on Earth-or don't know it, representing a distinct emergence of life different than our one known example. Here, we propose the Electronic Life-detection Instrument for Enceladus/Europa (ELIE), a solid-state single-molecule instrument payload that aims to search for life based on the detection of amino acids and informational polymers (IPs) at the parts per billion to trillion level. As a first proof-of-principle in a laboratory environment, we demonstrate the single-molecule detection of the amino acid L-proline at a 10 μM concentration in a compact system. Based on ELIE's solid-state quantum electronic tunneling sensing mechanism, we further propose the quantum property of the HOMO-LUMO gap (energy difference between a molecule's highest energy-occupied molecular orbital and lowest energy-unoccupied molecular orbital) as a novel metric to assess amino acid complexity. Finally, we assess the potential of ELIE to discriminate between abiotically and biotically derived α-amino acid abundance distributions to reduce the false positive risk for life detection. Nanogap technology can also be applied to the detection of nucleobases and short sequences of IPs such as, but not limited to, RNA and DNA. Future missions may utilize ELIE to target preserved biosignatures on the surface of Mars, extant life in its deep subsurface, or life or its biosignatures in a plume, surface, or subsurface of ice moons such as Enceladus or Europa. One-Sentence Summary: A solid-state nanogap can determine the abundance distribution of amino acids, detect nucleic acids, and shows potential for detecting life as we know it and life as we don't know it.

Presence of IgG antibodies is not a reliable marker of Toxoplasma gondii infection in feral mice

The single-celled parasite Toxoplasma gondii uses mice as a vector to reach its definitive host, the cat, where it can accomplish its sexual reproduction and produce oocysts, which will contaminate the environment. In this study, we have captured 103 feral house mice (Mus musculus) on Kangaroo Island, Australia. We have measured the level of exposure to T.gondii serologically with the Modified Agglutination Test and conjointly with a T.gondii B1 gene PCR. We have included stringent quality control steps in the molecular analysis to reduce the risk of false positivity and false negativity. Our results indicated a low seroprevalence of 0.97%, 95%CI [-0.36; 0.58] associated with the detection of T.gondii genetic material in 51.46%, 95%CI [41.93, 60.88] of mice brains. Neither sex nor mice body weight had an effect on the PCR outcome. We postulate that both the transmission route, horizontal or vertical, and natural selection processes could lead to this discordance which has been observed elsewhere in wild mice. The question of the biological mechanisms allowing the chronic infection of wild mice in the absence of a measurable humoral immune response remains. Our findings indicate that serological studies should not be used to measure the level of exposure to T.gondii in feral house mice.

Estimating the false discovery risk of (randomized) clinical trials in medical journals based on published p-values.

The influential claim that most published results are false raised concerns about the trustworthiness and integrity of science. Since then, there have been numerous attempts to examine the rate of false-positive results that have failed to settle this question empirically. Here we propose a new way to estimate the false positive risk and apply the method to the results of (randomized) clinical trials in top medical journals. Contrary to claims that most published results are false, we find that the traditional significance criterion of α = .05 produces a false positive risk of 13%. Adjusting α to.01 lowers the false positive risk to less than 5%. However, our method does provide clear evidence of publication bias that leads to inflated effect size estimates. These results provide a solid empirical foundation for evaluations of the trustworthiness of medical research.