Number Of False-positive Results Research Articles

Enhancing UAV-Based Precision Agriculture: A Deep Learning Approach to Targeted Maize Disease Management

Corn/maize is the third most globally produced crop, and there are over eighty diseases impacting it, creating significant concerns in the agricultural sector. As a result, Unmanned Aerial Vehicles (UAVs) and their ability to spray specifically diseased crops have become increasingly popular. When using UAVs, Normalized Difference Vegetation Index (NDVI) calculations are frequently used to determine whether a crop should be sprayed with pesticides. However, NDVI alone is not a sufficient metric. NDVI indicates plant stress in general, which could be drought or poor soil quality, and this will not be addressed by pesticides. The solution is then to create a custom deep learning model that uses a sufficiently low NDVI value as a starting point. Once a low NDVI value is detected, the same near infrared imagery used to retrieve the NDVI calculations will be used in the model where object detection will be used to determine whether there are diseased lesions on the maize. Near infrared imagery has been proven to increase the detection of maize diseases, achieving a higher accuracy than RGB imagery. The model will mimic aspects of YOLOv10 to facilitate rapid detection times, providing the opportunity for real-time spraying. While scholars have modified YOLOv5 to achieve detection times as low as 0.064 s/sheet, a custom deep learning model used only on the contingency of low NDVI values is less computationally intensive and should then take less time overall. Finally, Keyhole Markup Language (KML) and Geographic Information Systems (GIS) will be used to create an output map indicating where pesticides were sprayed. The research will reduce the number of false positive results, which will limit the quantity of pesticides used, consequently reducing financial costs and negative environmental impacts.

Detection of Antibodies against Feline Morbillivirus by Recombinant Matrix Enzyme-Linked Immunosorbent Assay.

Feline morbillivirus (FeMV) has been associated with feline health, although its exact role in pathogenesis is still debated. In this study, an indirect enzyme-linked immunosorbent assay (i-ELISA) targeting a recombinant matrix protein of FeMV (rFeMV-M) was developed and assessed in comparison to a Western blotting (WB) assay. The i-ELISA was evaluated using blood samples from 136 cats that were additionally tested with real-time reverse-transcription PCR (RT-qPCR). The i-ELISA exhibited a sensitivity of 90.1%, specificity of 75.6%, positive predictive value of 88.2%, and negative predictive value of 79.1%. The agreement between i-ELISA and WB analyses was substantial (a κ coefficient of 0.664 with a 95% confidence interval of 0.529 to 0.799). Within the study group, 68.4% (93/136) of the cats were serologically positive in the i-ELISA and 66.9% (91/136) in the WB assay, with 11.8% (11/93) of false positivity with the i-ELISA. However, only 8.1% (11/136) of the cats tested positive for FeMV using RT-qPCR (p < 0.001). The developed i-ELISA proved effective in identifying FeMV-infected cats and indicated the prevalence of FeMV exposure. Combining FeMV antibody detection through i-ELISA with FeMV RT-qPCR could offer a comprehensive method to determine and monitor FeMV infection status. Nevertheless, this assay still requires refinement due to a significant number of false positive results, which can lead to the misdiagnosis of cats without antibodies as having antibodies. This study also provided the first evidence of seroprevalence against FeMV among cat populations in Thailand, contributing valuable insights into the geographic distribution and prevalence of this virus.

Fat tails and the need to disclose distribution parameters of qEEG databases.

Neurometry (a.k.a. quantitative EEG or qEEG) is a popular method to assess clinically relevant abnormalities in the electroencephalogram. Neurometry is based on norm values for the distribution of specific EEG parameters and believed to show good psychometric properties such as test-retest reliability. Many psychometric properties only hold under the Gaussian distribution and become problematic when distributions are fat-tailed. EEG signals are typically fat-tailed and do not show fast convergence to a Gaussian distribution. To circumvent this property of EEG, log-transformations have frequently, but not always been employed. In Monte Carlo simulations, we investigated the impact of fat-tails (i.e. deviations from Gaussian) on the cut-off criteria and changeability of what in neurometry is termed "abnormal EEG". Even slight deviations from the Gaussian distribution as measured by skewness and kurtosis lead to large inflation in the number of false positive qEEG findings. The more stringent the cutoff value adopted, the larger the inflation. Moreover, "abnormal EEG" seems to recover spontaneously at rates not compatible with the alleged test-retest reliability of qEEG. Alternative methods should be employed to determine cut-off values for diagnostics purposes, since a large number of false positive results emerge even when slight deviations from the Gaussian distribution are present. We argue that distribution properties of qEEG databases should be disclosed in much more detail by commercial providers to avoid questionable research practices and promote diagnostic transparency. We provide recommendations for the improvement of psychometric properties of existing qEEG databases.

Strategic Retesting to Reduce False Positive SARS-CoV-2 PCR Test Results.

Nucleic acid amplification testing is the gold standard for SARS-CoV-2 diagnostics, although it may produce a certain number of false positive results. There has not been much published about the characteristics of false positive results. In this study, based on retesting, specimens that initially tested positive for SARS-CoV-2 were classified as true or false positive groups to characterize the distribution of cycle threshold (CT) values for N1 and N2 targets and number of targets detected for each group. Specimens that were positive for N-gene on retesting and accompanied with S-gene were identified as true positives (true positive based on retesting, rTP), while specimens that retested negative were classified as false positives (false positive based on retesting, rFP). Of the specimens retested, 85/127 (66.9%) were rFP, 16/47 (34.0%) specimens with both N1 and N2 targets initially detected were rFP, and the CT values for each target was higher in rFP than in rTP. ROC curve analysis showed that optimal cutoff values of CT to differentiate between rTP and rFP were 34.8 for N1 and 33.0 for N2. With the optimal cutoff values of CT for each target, out of the 24 specimens that were positive for both N1 and N2 targets and classified as rTP, 23 (95.8%) were correctly identified as true positives. rFP specimens had a single N1 target in 52/61 (85.2%) and a single N2 target in 17/19 (89.5%). Notably, no true positive results were obtained from any specimens with only N2 target detected. These results suggest that retesting should be performed for positive results with a CT value greater than optimal cutoff value for each target or with a single N1 target amplified, considering the possibility of a false positive. This may provide guidance on indications to perform retesting to minimize the number of false positives.

Accurate determination of Biotinidase activity in serum by HPLC and its utilization as second tier test for the confirmation of initial positive newborn screening results

Diagnosis of Biotinidase deficiency (BTD) is extremely important to avoid several neurodevelopmental problems in early childhood. Colorimetric and fluorometric methods lack specificity and selectivity due to several interferences resulting in a high number of false positive results. We developed an HPLC method for BTD activity in serum with fluorescent detection. In colorimetric assays, biotinidase attacks the amide linkage of the artificial substrate biotinidyl-4-aminobenzoic acid (B-PABA) and releases p-aminobenzoic acid (PABA), which is converted to a purple dye by diazotization reaction. The newly developed method injects the reaction mixture directly into the HPLC column and quantifies using a six-point calibration curve without coupling and diazotization reaction. The method is linear over the 5–1000 μmol/L range. The detection and quantitation limits were 2.5 μmol/L and 5.0 μmol/L, respectively. When compared with colorimetric assay, the correlation coefficient (R2) was 0.9963. The within-assay and between-assay precision was <10.0% for four levels of quality control samples. No significant variation in BTD activity was detected due to hemolysis, icteric, and lipemic samples. The newly developed method eliminates the potential interference due to the presence of aromatic amines and significantly reduces the false positive results observed with the colorimetric method. It is simple, specific, sensitive, faster in sample preparation, and requires a small sample volume. The newly developed HPLC method was used in our laboratory as a secondary tier test for initial positive BTD samples from newborn screening programs. To our knowledge, no similar HPLC method has been reported to date.

Proposed New Cutoff Value for Troponin in the Diagnosis of Acute Myocardial Infarction (AMI) in Patients with Congestive Heart Failure (CHF) and/or End Stage Renal Disease (ESRD)

Abstract Introduction/Objective In a recent publication, we assessed the efficacy of two serum biomarkers for AMI, cardiac Troponin I (cTnI) and creatine kinase-MB (CK-MB), for patients presenting to our emergency department (ED) with acute chest pain. We found that two conditions, CHF and ESRD in 85 patients, caused a significant number of false positive results that resulted in lowering the specificity and positive predictive value of both tests, which were already reduced by the low prevalence of AMI in our patient population. Removal of these patients resulted in significant increases in the two latter parameters. In this study, we focus on cTnI to determine if changing the cutoff value can increase the sensitivity and specificity of diagnosing acute MI in patients with CHF and ESRD. Methods/Case Report A cohort of 380 patients who presented with acute chest pain in the ED and worked up for acute MI were included, 85 of whom had CHF and/or ESRD. A total of 47 patients (15.4% prevalence) were definitively diagnosed with AMI. An initial serum cTnI sample was collected for each patient. Specificity, sensitivity, positive predictive value (PPV) and negative predictive value (NPV) were then compared using the routine cutoff cTnI value (0.05 ng/mL) and the higher cutoff of 0.2 ng/mL. Results (if a Case Study enter NA) Using the original cutoff of 0.05 ng/mL for cTnI in the patients with CHF or ESRD, the sensitivity and NPV were both 100%, while the specificity and PPV were low at 40.3% and 23%, respectively. Interestingly, when the cutoff was raised from 0.05 to 0.2 ng/mL, the sensitivity decreased by about 8% to 92.3 (12 out of 13 patients with AMI), and the specificity increased almost two-fold to 76.4%. The PPV increased to 40.4% and NPV was 98.2%. These values are comparable with the sensitivity, specificity, PPV and NPV of the total population at 85.3%, 82.4%, 38.7% and 97.7%, respectively. Conclusion The original cutoff value for cTnI in patients with ESRD and CHF produces a very low specificity and PPV. The results of this study suggest that an acceptable cutoff for cTnI to rule-out AMI patients who have CHF and or ESRD is 0.2 ng/mL which is four times higher than the original, but has comparable specificity, sensitivity, PPV and PPV to the total patient population. We note that this study must be regarded as a pilot study since the numbers of patients are small. We recommend a larger study to determine the validity of these conclusions.

Rendimiento diagnóstico de un nuevo software de aprendizaje profundo para corrección de atenuación en la imagen de perfusión miocárdica utilizando una cámara CZT cardiodedicada. Experiencia en la práctica clínica

PurposeTo evaluate the diagnostic performance of a novel deep learning attenuation correction software (SAPCA) for myocardial perfusion imaging (MPI) using a cadmium-zinc-telluride (CZT) cardio dedicated camera with invasive coronary angiography (ICA) correlation for the diagnosis of coronary artery disease (CAD) in a high-risk population. MethodsRetrospective study of 300 patients (196 males [65%], mean age 68 years) from September 2014 to October 2019 undergoing MPI, followed by ICA and evaluated by means of quantitative angiography software, within six months after the MPI. The mean pre-test probability score for coronary disease according to the European Society of Cardiology criteria was 37% for the whole cohort. The MPI was performed in a dedicated CZT cardio camera (D-SPECT® Spectrum Dynamics) with a two-day protocol, according to the European Association of Nuclear Medicine guidelines. MPI was retrospectively evaluated with and without the SAPCA. ResultsThe overall diagnostic accuracy of MPI without SAPCA to identify patients with any obstructive CAD at ICA was 87%, Sensitivity 94%, Specificity 57%, positive predictive value 91% and negative predictive value 64%. Using SAPCA the overall diagnostic accuracy was 90%, sensitivity 91%, specificity 86%, positive predictive value 97% and negative predictive value 66%. ConclusionUse of the novel SAPCA enhances performance of the MPI using the CZT D-SPECT® camera and achieves improved results, especially avoiding artefacts and reducing the number of false positive results.

PRELIMINARY FINDINGS ON THE USE OF GENOMIC SEQUENCING TO DIAGNOSE ORTHOPAEDIC INFECTIONS

AbstractObjectivesThe objective of this study is to investigate if genomic sequencing is a useful method to diagnose orthopaedic infections. Current methods used to identify the species of bacteria causing orthopaedic infections take considerable time and the results are frequently insufficient for guiding antibiotic treatment. The aim here is to investigate if genomic sequencing is a faster and more reliable method to identify the species of bacteria causing infections. Current methods include a combination of biochemical markers and microbiological cultures which frequently produce false positive results and false negative results.MethodsSamples of prosthetic fluid were obtained from surgical interventions to treat orthopaedic infections. DNA is extracted from these samples lab and nanopore genomic sequencing is performed. Initial investigations informed that a sequencing time of 15 minutes was sufficient. The resulting genomic sequence data was classified using Basic Local Alignment Tool (BLAST) against the NCBI bacterial database and filtered by only including reads with an identity score of 90 and E-value of 1e-50. An E-value of 1e-50 suggests a high-quality result and is commonly used when analysing genomic data. This data was then filtered in R Studio to identify if any species were associated with orthopaedic infections. The results from genomic sequencing were compared to microbiology results from the hospital to see if the same species had been identified. The whole process from DNA extraction to output took approximately 2 hours, which was faster than parallel microbiological cultures.ResultsIn these preliminary analyses, 15 samples have been collected from patients with confirmed/suspected orthopaedic infections. To date, 11 samples from confirmed infected patients have been sequenced and a summary of the findings are presented in the table attached. As well as finding bacteria species to match microbiological cultures, genomic sequencing has also identified bacteria when culture results have been negative, but the patient is known to have an infection due to clinical indication and previous culture results. This example suggests genomic sequencing may have higher sensitivity than microbiological cultures at detecting bacteria causing orthopaedic infections. Results in table indicate the identification of bacteria from genomic sequencing that match microbiological cultures are high quality.ConclusionsPreliminary data presented using genomic sequencing suggests that the technique may be useful to identify bacterial species causing orthopaedic infections and can do so in a shorter time frame than current microbial methods. The results from genomic sequencing all produced a number of false positive results which hopefully can be reduced by improving the bioinformatic techniques used and increasing the sample number to include individuals without an infection. Further analysis will also look at identifying antibiotic resistance genes in the sequencing data and seeing if this ca be used to predict which patients will and will not respond to antibiotic treatment. The aim at the end of this project is to demonstrate if genomic sequencing is a more sensitive method to identify bacteria causing orthopaedic infections that current methods and if it can be used to guide antibiotic treatment. Include limitations, next steps and bigger picture.Declaration of Interest(b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

Diagnostic performance of a novel deep learning attenuation correction software for MPI using a cardio dedicated CZT camera. Experience in the clinical practice

PurposeTo evaluate the diagnostic performance of a novel deep learning attenuation correction software (DLACS) for myocardial perfusion imaging (MPI) using a cadmium–zinc–telluride (CZT) cardio dedicated camera with invasive coronary angiography (ICA) correlation for the diagnosis of coronary artery disease (CAD) in a high-risk population. MethodsRetrospective study of 300 patients (196 males [65%], mean age 68 years) from September 2014 to October 2019 undergoing MPI, followed by ICA and evaluated by means of quantitative angiography software, within six months after the MPI. The mean pre-test probability score for coronary disease according to the European Society of Cardiology criteria was 37% for the whole cohort. The MPI was performed in a dedicated CZT cardio camera (D-SPECT Spectrum Dynamics) with a two-day protocol, according to the European Association of Nuclear Medicine guidelines. MPI was retrospectively evaluated with and without the DLACS. ResultsThe overall diagnostic accuracy of MPI without DLACS to identify patients with any obstructive CAD at ICA was 87%, sensitivity 94%, specificity 57%, Positive Predictive Value 91% and Negative Predictive Value 64%. Using DLACS the overall diagnostic accuracy was 90%, sensitivity 91%, specificity 86%, Positive Predictive Value 97% and Negative Predictive Value 66%. ConclusionUse of the novel DLACS enhances performance of the MPI using the CZT D-SPECT camera and achieves improved results, especially avoiding artefacts and reducing the number of false positive results.

Comparative performance evaluation of QIAreach QuantiFERON-TB and tuberculin skin test for diagnosis of tuberculosis infection in Viet Nam

Current WHO-recommended diagnostic tools for tuberculosis infection (TBI) have well-known limitations and viable alternatives are urgently needed. We compared the diagnostic performance and accuracy of the novel QIAreach QuantiFERON-TB assay (QIAreach; index) to the QuantiFERON-TB Gold Plus assay (QFT-Plus; reference). The sample included 261 adults (≥ 18 years) recruited at community-based TB case finding events. Of these, 226 underwent Tuberculin Skin Tests and 200 returned for interpretation (TST; comparator). QIAreach processing and TST reading were completed at lower-level healthcare facilities. We conducted matched-pair comparisons for QIAreach and TST with QFT-Plus, calculated sensitivity, specificity and area under a receiver-operating characteristic curve (AUC), and analyzed concordant-/discordant-pair interferon-gamma (IFN-γ) levels. QIAreach sensitivity and specificity were 98.5% and 72.3%, respectively, for an AUC of 0.85. TST sensitivity (53.2%) at a 5 mm induration threshold was significantly below QIAreach, while specificity (82.4%) was statistically equivalent. The corrected mean IFN-γ level of 0.08 IU/ml and corresponding empirical threshold (0.05) of false-positive QIAreach results were significantly lower than the manufacturer-recommended QFT-Plus threshold (≥ 0.35 IU/ml). Despite QIAreach’s higher sensitivity at equivalent specificity to TST, the high number of false positive results and low specificity limit its utility and highlight the continued need to expand the diagnostic toolkit for TBI.

Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model.

As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients' survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis.

Clinical Significance of Serum Cholinesterase in the Diagnosis and Assessment of Clinical Activity in Patients with IBD

Abstract Background Inflammatory bowel diseases (IBD), including Crohn’s disease and Ulcerative colitis, witnessed dramatic rise in incidence in the past few decades. Fecal calprotectin has been widely established as laboratory marker in assessment, diagnosis and severity of IBD. It showed low specificity and high number of false positive results. Cholinesterase (ChE) enzymatic glycoprotein used in hydrolosis of acetylcholine, showed significant correlation with IBD activity, it showed also potential role in diagnosis and assessing clinical severity in IBD patients. We will evaluate the role of serum cholinesterase in diagnosis and assessment of severity in IBD patients. Aim of the Work The aim of this study is to evaluate the role of serum cholinesterase (ChE) in diagnosis and assessment of activity in IBD patients Patients and Methods A Case control study in outpatients and inpatients in departments of Internal Medicine, Ain Shams University Hospital for Six months since beginning of data collection (till fulfilling the sample size) on 70 subjects divided into 3 groups: Group A: 25 patients with Crohn’s disease evidenced by colonoscopy and tissue biopsy, group B: 25 patients with Ulcerative Colitis disease evidenced by colonoscopy and tissue biopsy, group C: 20 age-matched, sex-matched healthy controls Results Our results confirmed that, compared with healthy controls, serum ChE levels were decreased in patients with IBD, and the decline in serum ChE levels was more pronounced as clinical activity in IBD increased. We also found that the serum ChE levels of patients with CD and those with UC were significantly different. It needs to be further clarified the relationship between serum ChE and endoscopic activity in the future. Conclusion Serum ChE levels can be used as a simple, economical method to help diagnose and distinguish the different stages of IBD. With the increasing biomedical research on the cholinergic anti-inflammatory pathway and ChE, serum ChE is expected to become a new immunotherapy target for IBD.

Searching for Neuronal Antibodies in Psychiatric Diseases: Uncertain Findings and Implications.

In recent years, neurology and psychiatry journals have been inundated with reports on individual symptoms of autoimmune encephalitis (AE) that are described as distinct entities such as autoimmune psychosis, obsessive-compulsive disorders, or depression. It is unquestionable that for AE the demonstration of antibodies against neuronal-surface proteins is intrinsically linked to distinct disorders (some defining new diseases) that are usually treatment-responsive and associate with comorbidities that vary according to the antigen. By contrast, for psychiatric diseases, the apparent detection of antibodies has not defined any disorder or affected the diagnosis and treatment of patients. Although these studies frequently use anti-N-methyl-D-aspartate receptor encephalitis to rationalize the findings, they rarely adopt the same rigorous investigations or address the clinical and pathogenic significance of the antibodies or discuss the limitations related to the biological sample or antibody-testing techniques. It is imperative to consider (1) some antibodies (GAD65, TPO) occur in serum of 8%-13% of healthy people; (2) VGKC antibodies are not useful unless LGI1 or CASPR2 are investigated; (3) commercial-clinical testing for Ma2, Zic4, and SOX1 antibodies causes a high number of false-positive results; (4) GlyR antibodies have unclear disease specificity when examined only in serum; and (5) the significance of antibodies against unknown antigens of endothelium, astrocytes, myelin fibers, or granule cells of hippocampus and cerebellum is questioned by the lack of disease specificity and appropriate controls. These limitations and problems are a frequent cause of neurologic consultations. Here we discuss some of these problems, emphasizing the importance of clinical judgment over antibody findings.

Concordance between liquid and tissue biopsy in participants with newly diagnosed recurrent breast cancer.

1028 Background: Tissue biopsy is recommended to confirm breast cancer (BC) recurrence. Liquid biopsy [including circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA)] is a non-invasive approach for detecting cancer that may provide information to identify treatment choices and replace invasive biopsies. This ongoing study aims to assess the concordance between tissue and liquid biopsy testing in subjects presenting with suspicion of distant recurrence from BC. Methods: Patients with suspected metastatic BC were enrolled; tumour characteristics and treatment were recorded. Blood samples were collected within 30 days before tissue biopsy, or within 7-28 days after tissue biopsy and before any systemic or radiation treatment. Samples were shipped to EPIC Sciences and processed within 96 hours; after plasma isolation, nucleated cells were plated; slides and plasma were banked. CTCs were identified using Epic Sciences digital imaging and machine learning algorithms. Single-cell isolation for genomic ctDNA analysis was performed. Cell free DNA was analyzed using a validated NGS panel to detect ctDNA alterations. The presence of metastases was classified as suspicious, highly suspicious, definitely metastatic BC or other by the treating oncologist based on the patient's clinical presentation and biopsy pathology results. Epic Sciences classified samples into similar categories based on CTC and ctDNA assay results. These classifications were performed independently. Sensitivity of the Epic Sciences methodology to detect metastatic BC (as determined by the treating oncologist), and its false positive rate were calculated. Results: 100 patients were enrolled from June 2020 to October 2022; shipping delays precluded EPIC assays in six patients; 94 patients were analyzed. Of 83 cases deemed suspicious, highly suspicious, or definitely metastatic BC by the treating oncologist, 61 were also deemed so by Epic Sciences (sensitivity of 73.5%, 95% confidence interval, CI 63.1% - 81.9%). Of 66 cases assigned as suspicious, highly suspicious, or definitely metastatic BC by the Epic Sciences, 4 had new primary cancers (3 lung cancer, 1 hepatocarcinoma), for a false positive rate of 6.1% (95% CI 1.9% - 15.0%). One additional case was classified as un-specified adenocarcinoma (possibly breast) by the treating oncologist; resolution awaits further follow-up. Conclusions: Preliminary results show that 73.5% of distant BC recurrences were correctly identified by liquid biopsy. A small number of false positive results occurred in patients with other new primary cancers. Additional analyses with CTC characterization are ongoing. [Table: see text]

Efficient False Positive Control Algorithms in Big Data Mining

The typical hypothesis testing issue in statistical analysis is determining whether a pattern is significantly associated with a specific class label. This usually leads to highly challenging multiple-hypothesis testing problems in big data mining scenarios, as millions or billions of hypothesis tests in large-scale exploratory data analysis can result in a large number of false positive results. The permutation testing-based FWER control method (PFWER) is theoretically effective in dealing with multiple hypothesis testing issues. In reality, however, this theoretical approach confronts a serious computational efficiency problem. It takes an extremely long time to compute an appropriate FWER false positive control threshold using PFWER, which is almost impossible to achieve in a reasonable amount of time using human effort on medium- or large-scale data. Although some methods for improving the efficiency of the FWER false positive control threshold calculation have been proposed, most of them are stand-alone, and there is still a lot of space for efficiency improvement. To address this problem, this paper proposes a distributed PFWER false-positive threshold calculation method for large-scale data. The computational effectiveness increases significantly when compared to the current approaches. The FP-growth algorithm is used first for pattern mining, and the mining process reduces the computation of invalid patterns by using pruning operations and index optimization for merging patterns with index transactions. The distributed computing technique is introduced on this basis, and the constructed FP tree is decomposed into a set of subtrees, each corresponding to a subtask. All subtrees (subtasks) are distributed to different computing nodes. Each node independently calculates the local significance threshold according to the designated subtasks. Finally, all local results are aggregated to compute the FWER false positive control threshold, which is completely consistent with the theoretical result. A series of experimental findings on 11 real-world datasets demonstrate that the distributed algorithm proposed in this paper can significantly improve the computation efficiency of PFWER while ensuring its theoretical accuracy.

Mammography in combination with breast ultrasonography versus mammography for breast cancer screening in women at average risk.

Mammography in combination with breast ultrasonography versus mammography for breast cancer screening in women at average risk.

Analysis of online parenting community posts on expanded newborn screening for metabolic disorders using topic modeling: a quantitative content analysis.

As more newborns have received expanded newborn screening (NBS) for metabolic disorders, the overall number of false-positive results has increased. The purpose of this study was to explore the psychological impacts experienced by mothers related to the NBS process. An online parenting community in Korea was selected, and questions regarding NBS were collected using web crawling for the period from October 2018 to August 2021. In total, 634 posts were analyzed. The collected unstructured text data were preprocessed, and keyword analysis, topic modeling, and visualization were performed. Of 1,057 words extracted from posts, the top keyword based on 'term frequency-inverse document frequency' values was "hypothyroidism," followed by "discharge," "close examination," "thyroid-stimulating hormone levels," and "jaundice." The top keyword based on the simple frequency of appearance was "XXX hospital," followed by "close examination," "discharge," "breastfeeding," "hypothyroidism," and "professor." As a result of LDA topic modeling, posts related to inborn errors of metabolism (IEMs) were classified into four main themes: "confirmatory tests of IEMs," "mother and newborn with thyroid function problems," "retests of IEMs," and "feeding related to IEMs." Mothers experienced substantial frustration, stress, and anxiety when they received positive NBS results. The online parenting community played an important role in acquiring and sharing information, as well as psychological support related to NBS in newborn mothers. Nurses can use this study's findings to develop timely and evidence-based information for parents whose children receive positive NBS results to reduce the negative psychological impact.

The Diagnostic Test: Goodness, Characteristics, and Interpretation: Under the Impact of the Corona Pandemic and Different SARS-CoV-2 Tests

Many diagnostic tests are currently being performed around the world to detect SARS-CoV-2 infection. Positive and negative test results are not one hundred percent accurate, but have far-reaching consequences. There are false positives (test positive, uninfected) and false negatives (test negative, infected). A positive/negative result does not necessarily mean that the test subject is actually infected/non-infected. This article has two objectives: 1. to explain the most important characteristics of diagnostic tests with binary outcome 2. to point out problems and phenomena of interpretation of diagnostic tests, on the basis of different scenarios. Presentation of the basic concepts of the quality of a diagnostic test (sensitivity, specificity) and pre-test probability (prevalence of test group). Calculation (including formulas) of further important quantities. In the basic scenario, sensitivity is 100%, specificity 98.8%, and pre-test probability of 1.0% (10 infected persons per 1,000 tested). For 1,000 diagnostic tests, the statistical mean is 22 positive cases, 10 of which are true-positive. The positive predictive probability is 45.7%. The prevalence calculated from this (22/1,000 tests) overestimates the actual prevalence (10/1,000 tests) by a factor of 2.2. All cases with a negative test outcome are true negative. The prevalence has a strong influence on the positive and negative predictive value. This phenomenon occurs even with otherwise very good test values of sensitivity and specificity. At a prevalence of only 5 infected persons per 10,000 (0.05%), the positive predictive probability drops to 4.0%. Lower specificity amplifies this effect, especially with small numbers of infected persons. If the sensitivity or specificity is below 100%, diagnostic tests are always error-prone. If the prevalence of infected persons is low, a large number of false positive results are to be expected - even if the test is of good quality with a high sensitivity and especially a high specificity. This is accompanied by low positive predictive values, i. e. positive tested persons are not infected. A false positive test result in the first test can be clarified by carrying out a second test.

Comparison of Tandem Mass Spectrometry and the Fluorometric Method-Parallel Phenylalanine Measurement on a Large Fresh Sample Series and Implications for Newborn Screening for Phenylketonuria.

Phenylketonuria (PKU) was the first disease to be identified by the newborn screening (NBS) program. Currently, there are various methods for determining phenylalanine (Phe) values, with tandem mass spectrometry (MS/MS) being the most widely used method worldwide. We aimed to compare the MS/MS method with the fluorometric method (FM) for measuring Phe in the dried blood spot (DBS) and the efficacy of both methods in the NBS program. The FM was performed using a neonatal phenylalanine kit and a VICTOR2TM D fluorometer. The MS/MS method was performed using a NeoBaseTM 2 kit and a Waters Xevo TQD mass spectrometer. The Phe values measured with the MS/MS method were compared to those determined by the FM. The cut-off value for the NBS program was set at 120 µmol/L for FM and 85 µmol/L for MS/MS. We analyzed 54,934 DBS. The measured Phe values varied from 12 to 664 µmol/L, with a median of 46 µmol/L for the MS/MS method and from 10 to 710 µmol/L, with a median of 70 µmol/L for the FM. The Bland-Altman analysis indicated a bias of -38.9% (-23.61 µmol/L) with an SD of 21.3% (13.89 µmol/L) when comparing the MS/MS method to the FM. The Phe value exceeded the cut-off in 187 samples measured with FM and 112 samples measured with MS/MS. The FM had 181 false positives, while the MS/MS method had 106 false positives. Our study showed that the MS/MS method gives lower results compared to the FM. Despite that, none of the true positives would be missed, and the number of false-positive results would be significantly lower compared to the FM.

OTP-NMS: Towards Optimal Threshold Prediction of NMS for Crowded Pedestrian Detection.

Pedestrian detection is still a challenging task for computer vision, especially in crowded scenes where the overlaps between pedestrians tend to be large. The non-maximum suppression (NMS) plays an important role in removing the redundant false positive detection proposals while retaining the true positive detection proposals. However, the highly overlapped results may be suppressed if the threshold of NMS is lower. Meanwhile, a higher threshold of NMS will introduce a larger number of false positive results. To solve this problem, we propose an optimal threshold prediction (OTP) based NMS method that predicts a suitable threshold of NMS for each human instance. First, a visibility estimation module is designed to obtain the visibility ratio. Then, we propose a threshold prediction subnet to determine the optimal threshold of NMS automatically according to the visibility ratio and classification score. Finally, we re-formulate the objective function of the subnet and utilize the reward-guided gradient estimation algorithm to update the subnet. Comprehensive experiments on CrowdHuman and CityPersons show the superior performance of the proposed method in pedestrian detection, especially in crowded scenes.