Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons.

The extensive spread of harmful content, including hate speech, harassment, violent and adult material across online platforms and media channels poses significant challenges and has raised considerable concern within various societal groups. Government bodies, educators, and parents frequently find themselves in disagreement with media providers over the best methods to regulate and restrict such content. Automated content moderation technologies have become critical tools in addressing these challenges, particularly through natural language processing (NLP) techniques that can automatically detect and filter sensitive textual content, such as offensive language, violence, and adult materials, enabling platforms to enforce moderation policies on a large scale. Despite their widespread use, current moderation technologies face challenges related to detection accuracy, often resulting in substantial false positives and false negatives. To enhance content moderation systems, more advanced algorithms capable of accurately interpreting textual context are necessary. In this study, we assess current large language model (LLM)-based moderation solutions, specifically OpenAI moderation model and Llama-Guard-3, examining their effectiveness in detecting sensitive content. Additionally, we investigate the capabilities of contemporary LLMs, including OpenAI generative pre-trained transformer (GPT), Google Gemini, Meta Llama, Anthropic Claude, and small language models (SLMs) such as Google Gemma, in recognizing inappropriate content from diverse media sources. We also studied the performance of these models under adversarial attacks such as input perturbation and prompt injection. Our evaluation and comparative analysis utilize various textual datasets, such as X (Twitter) posts, Amazon product reviews, and news articles. The findings indicate that LLM-based approaches significantly demonstrate high accuracy and low rates of false positives and negatives. They also indicate the robustness of the models under various adversarial attacks. These results underscore the considerable potential for integrating advanced LLMs into websites and social media platforms, thereby enhancing content regulation and moderation effectiveness.

Motivation: Certain studies use short-TE MRS to estimate metabolites such as gamma-aminobutyric acid. The findings of such studies hinge on the correlation of this technique to the gold-standard edited MRS technique. Goal(s): We investigate how the false negative rate and false positive rate of studies vary as a function of correlation between short-TE and MEGA-edited metabolite estimates. Approach: Simulated data was used to numerically estimate the false positive and false negative rates, using parameters obtained from the literature. Results: We show that these studies can have largely inflated false positive and false negative rates as compared to their nominally designed values. Impact: The false negative and false positive rate can be markedly inflated when using short-TE MRS to estimate metabolites such as gamma-aminobutyric acid. This calls for increased caution in interpreting results, and the accommodation of these effects when designing statistical analyses.

Background: High plasma concentrations of lipoprotein (a) [Lp(a)] are associated with an increased cardiovascular risk. Current guidelines recommend measurement of only a single Lp(a) in an individual’s lifetime under specific circumstances to improve cardiovascular risk prediction. Accordingly, the question raised is the number of false positives and negatives missed through only a single measurement. Methods: All Lp(a) measurements between 2004 and March 2021 were retrieved from the laboratory database of the Erasme hospital. Only patients with repeated measurement were included. The first and subsequent Lp(a) measurement were compared. Two different cohorts were studied as a result of a change in Lp(a) determination methodology (n = 2049 and n = 309, respectively). The effects of a third Lp(a) measurement were assessed through binary analyses (n = 678). The 180 mg/dl (430 nmol/L) threshold recommended in the ESC guidelines was assessed first. Analysis was repeated for 100, 70 and 50 mg/dl thresholds of raised Lp(a) levels. Results: A low rate of false negatives (0.8%–1%) and false positives (0.6–0.3%) were revealed with two Lp(a) measurements. There was no difference in regards to the divergent Lp(a) thresholds nor the measurement of Lp(a) on two or three occasions. Conclusion: The present study showed Lp(a) determination to be reproducible. A single measurement is sufficient to assess if a patient exceeds various cut-off values of elevated Lp(a) levels.

Ranaviruses have been identified as the etiologic agent in many amphibian die-offs across the globe. Polymerase chain reaction (PCR) is commonly used to detect ranavirus infection in amphibian hosts, but the test results may vary between tissue samples obtained by lethal and non-lethal procedures. Testing liver samples for infection is a common lethal sampling technique to estimate ranavirus prevalence because the pathogen often targets this organ and the liver is easy to identify and collect. However, tail clips or swabs may be more practicable for ranavirus surveillance programs compared with collecting and euthanizing animals, especially for uncommon species. Using PCR results from liver samples for comparison, we defined false-positive test results as occurrences when a non-lethal technique indicated positive but the liver sample was negative. Similarly, we defined false-negative test results as occurrences when a non-lethal technique was negative but the liver sample was positive. Using these decision rules, we estimated false-negative and false-positive rates for tail clips and swabs. Our study was conducted in a controlled facility using American bullfrog Lithobates catesbeianus tadpoles; false-positive and false-negative rates were estimated after different periods of time following exposure to ranavirus. False-negative and false-positive rates were 20 and 6%, respectively, for tail samples, and 22 and 12%, respectively, for swabs. False-negative rates were constant over time, but false-positive rates decreased with post-exposure duration. Our results suggest that non-lethal sampling techniques can be useful for ranavirus surveillance, although the prevalence of infection may be underestimated when compared to results obtained with liver samples.

Peer-to-Peer system has achieved great success with millions of end users in the past several years. P2P traffic has occupied about 60–80 % of the total traffic volumes, which greatly consumes network bandwidth and causes congestions. To achieve the goal of efficacious P2P system management in the monitored network, in this paper we develop a framework named CUFTI (Core Users Finding and Traffic Identification). The core users are referred as long-lived peers, and we focus on life-time characteristics of coexisting peers within each snapshot of the overlay. Based on the analysis results of user’s behaviour in PPlive system, we develop an accurate model to forecast the peer’s residual life-time and identify the long-lived peers. Furthermore, we develop a flow identification model for P2P traffic management of those core users. Based on the analysis results of actual traffic traces, we find the P2P traffic flows are composed of data and control packets. Most of the control packets appear at the beginning and end of each flow to establish and close the communication between peers. We employ the direction and payload length of the control packets at the beginning of the flow as features to perform flow identification. Experimental results based on traces collected from the Northwest Region Center of CERNET (China Education and Research Network) show that the newly developed methods outperforms other existing methods with lower false negative rate (FNR) and false positive rate (FPR).

The Effect of Testing Reliability on Visual Field Sensitivity in Normal Eyes: The Singapore Chinese Eye Study

BackgroundGlaucoma referral filtering schemes have operated in the UK for many years. However, there is a paucity of data on the false-negative (FN) rate. This study evaluated the clinical effectiveness...

Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

To evaluate the false-negative and false-positive error rates both in a screening and a non-screening population. A total of 4192 prostatic biopsies were reported in a 6-year period by 15 consultant histopathologists, two of whom had an interest in uropathology and were deemed to be specialists (J.O. and C.S.). All biopsies were reviewed prior to the multidisciplinary team (MDT) meeting. The overall false-negative rate was 1.7% (screening 2.1%, non-screening 1.5%). The overall false-positive rate was 0.5% (screening 0.9%, non-screening 0.4%). These error rates varied among pathologists, with the false-negative rate ranging from 0% to 9.3%, and the false-positive rate ranging from 0% to 3.8%. The false-negative rate was three times greater than the false-positive rate, showing that detection of significant pathology is far greater in the negative biopsies. More errors occurred in the screening population than in the non-screening population. The consultants making the most errors were non-specialists, but the specialists also made false-negative errors, suggesting that just using specialist reporting alone would not have eradicated errors.

Cost-sensitive Feature Selection for Support Vector Machines

In a Delay-Tolerant Network (DTN), data originating from a source node may be delivered to the destination node, despite the non-existence of end-to-end connectivity between them at all times. In an adversarial environment such as a battlefield, DTN nodes could be compromised to launch Denial-of-Service (DoS) attacks by generating excess data, to cause an overflow of the limited resources of the legitimate nodes, hence decreasing the network throughput. A node may also display selfish behavior by generating more data than allowed, to increase its throughput and to decrease the latency of its data packets. In this paper, we term such a DoS attack and selfish data generation behavior, a resource-misuse attack. We study two types of resource-misuse attacks, breadth attacks and depth attacks. Accordingly, we propose different schemes to detect these attacks. Trace-driven simulations using both a synthetic and a real-world trace show that our detection schemes have low average detection latency and additionally, probabilistic detection of the depth attack has low false positive and false negative rates.

When detecting positive selection in proteins, the prevalence of errors resulting from misalignment and the ability of alignment filters to mitigate such errors are not well understood, but filters are commonly applied to try to avoid false positive results. Focusing on the sitewise detection of positive selection across a wide range of divergence levels and indel rates, we performed simulation experiments to quantify the false positives and false negatives introduced by alignment error and the ability of alignment filters to improve performance. We found that some aligners led to many false positives, whereas others resulted in very few. False negatives were a problem for all aligners, increasing with sequence divergence. Of the aligners tested, PRANK's codon-based alignments consistently performed the best and ClustalW performed the worst. Of the filters tested, GUIDANCE performed the best and Gblocks performed the worst. Although some filters showed good ability to reduce the error rates from ClustalW and MAFFT alignments, none were found to substantially improve the performance of PRANK alignments under most conditions. Our results revealed distinct trends in error rates and power levels for aligners and filters within a biologically plausible parameter space. With the best aligner, a low false positive rate was maintained even with extremely divergent indel-prone sequences. Controls using the true alignment and an optimal filtering method suggested that performance improvements could be gained by improving aligners or filters to reduce the prevalence of false negatives, especially at higher divergence levels and indel rates.

Machine learning based survival prediction of DLBCL: Using multimodal data.

Background Blast cells are immature white blood cells (WBC). The presence of blasts in the peripheral blood may be associated with certain diseases such as myelodysplastic syndrome and acute myelogenous leukemia. Variant lymphocytes (i.e., reactive lymphocytes, atypical lymphocytes) are WBCs produced by the immune system in reaction to infections caused by pathogens such as Epstein-Barr virus, cytomegalovirus, etc. It is clinically useful that the automated hematology analyzer is capable of flagging these cells. In this study, the blast (shown as ‘BLAST’ on the Alinity hq and ‘Blast/Abn Lympho?’ or ‘Blast?’ on XN-1000) and variant lymphocyte (shown as ‘VAR LYM’ on the Alinity hq, and ‘Atypical Lympho?’ on XN-1000) flagging was compared between the Alinity hq from Abbott and XN-1000 from Sysmex. Alinity hq has different sensitivity settings available for BLAST and VAR LYM flagging. In this study, the default setting for Alinity hq was used. For XN-1000, the Q-Flag default setting is 100. In this study, the setting was 120 for ‘Blast/Abn Lympho’ and 100 for “Atypical lympho?’. Methods Blood specimens were tested on the Alinity hq and XN-1000 analyzers. Two smears per specimen were made manually or by the Alinity hs slide maker stainer and read by two independent readers. The specimens showing any of the following flagging were included in the analysis: ‘BLAST’ on Alinity hq, ‘VAR LYM’ on Alinity hq, ‘Blast/Abn Lympho?’ or ‘Blast?’ on XN-1000, ‘Atypical lympho?’ on XN-1000. The flagging reports of these samples from both analyzers were compared to the smear review to determine if each analyzer's blast and variant lymphocyte flagging were true positive, false positive, true negative, or false negative. Results A total of 520 specimens were tested, yielding 41 specimens for blast flagging analysis and 80 specimens for variant lymphocyte flagging analysis. Due to the scarce availability of true positive samples from the manual review, true positive and false negative rate were not compared. For blast flagging using smear results of >=1% or >=5% as the positive criterion, Alinity hq yielded a 9.8% (=4/41) false positive rate and 87.8% (=36/41) true negative rate. XN-1000 yielded a 75.6% (=31/41) false positive rate and 22.0% (=9/41) true negative rate. For variant lymphocyte flagging using smear results of >=5% as the positive criterion, Alinity hq yielded a 6.3% (=5/80) false positive rate and 92.5% (=74/80) true negative rate. XN-1000 yielded a 91.3% (=73/80) false positive rate and 7.5% (=6/80) true negative rate. Conclusions This study demonstrated that Alinity hq achieved a much lower false positive rate and much higher true negative rate for both blast and variant lymphocyte flagging when compared to the XN-1000. Manual blast and variant lymphocyte morphology classification is laboratory- and technician-dependent. Both analyzers allow different sensitivity/specificity levels to flag blasts and variant lymphocytes. Each individual laboratory should evaluate and choose the setting that is most suitable for their specific patient populations to obtain the desired flagging rate.

Tandem mass spectrometry (MS/MS) has been widely used in proteomics studies. Multiple algorithms have been developed for assessing matches between MS/MS spectra and peptide sequences in databases. However, it is still a challenge to reduce false negative rates without compromising the high confidence of peptide identification. In this study, we developed the score, Oscore, by logistic regression using SEQUEST and AMASS variables to identify fully tryptic peptides. Since these variables showed complicated association with each other, combining them together rather than applying them to a threshold model improved the classification of correct and incorrect peptide identifications. Oscore achieved both a lower false negative rate and a lower false positive rate than PeptideProphet on datasets from 18 known protein mixtures and several proteome-scale samples of different complexity, database size and separation methods. By a three-way comparison among Oscore, PeptideProphet and another logistic regression model which made use of PeptideProphet's variables, the main contributor for the improvement made by Oscore is discussed.