false positives
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2022 ◽  
Vol 31 (2) ◽  
pp. 1-37
Jiachi Chen ◽  
Xin Xia ◽  
David Lo ◽  
John Grundy

The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts ( SCs ) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and summarize the key reasons. Their feedback shows that 66.67% of the developers will deploy an updated contract to the Ethereum after destructing the old contract. According to this information, we propose a method to find the self-destructed contracts (also called predecessor contracts) and their updated version (successor contracts) by computing the code similarity. By analyzing the difference between the predecessor contracts and their successor contracts, we found five reasons that led to the death of the contracts; two of them (i.e., Unmatched ERC20 Token and Limits of Permission ) might affect the life span of contracts. We developed a tool named LifeScope to detect these problems. LifeScope reports 0 false positives or negatives in detecting Unmatched ERC20 Token . In terms of Limits of Permission , LifeScope achieves 77.89% of F-measure and 0.8673 of AUC in average. According to the feedback of developers who exclude selfdestruct functions, we propose suggestions to help developers use selfdestruct functions in Ethereum smart contracts better.

2022 ◽  
Jayanthi Shastri ◽  
Sachee Agrawal ◽  
Nirjhar Chatterjee ◽  
Harsha Gupta

Background: Accurate rapid antibody detection kits requiring minimum infrastructure are beneficial in detecting post-vaccination antibodies in large populations. ChAdOx1-nCOV (COVISHIELD) and BBV-152 (Covaxin) vaccines are primarily used in India. Methods: In this single-centre prospective study, performance of Meril ABFind was investigated by comparing with Abbott SARS-CoV-2 IgG II Quant (Abbott Quant), GenScript cPass SARS-CoV-2 neutralization antibody detection kit (GenScript cPass), and COVID Kawach MERILISA (MERILISA) in 62 vaccinated health care workers (HCW) and 40 pre-pandemic samples. Results: In the vaccinated subjects, Meril ABFind kit displayed high sensitivity of 93.3% (CI, 89.83%-96.77%), 94.92% (CI, 91.88%-97.96%), and 90.3% (CI, 86.20%-94.4%) in comparison to Abbott Quant, MERILISA, and GenScript cPass respectively. The results of the Meril ABFind in the COVISHIELD-vaccinated group were excellent with 100% sensitivity in comparison to the other three kits. In the Covaxin-vaccinated group, Meril ABFind displayed sensitivity ranging from 80% to 88.9%. In control samples, there were no false positives detected by Meril ABFind, while Abbott Quant, MERILISA, and GenScript cPass reported 2.5%, 10.0%, and 12.5% false positives, respectively. In the pre-pandemic controls, specificity of Meril ABFind was 100%, Abbott Quant 97.5%, MERILISA 90%, and GenScript cPass 87.5%. Conclusion: The Meril ABFind kit demonstrated satisfactory performance when compared with the three commercially available kits and was the only kit without false positives in the pre-pandemic samples. This makes it a viable option for rapid diagnosis of post vaccination antibodies.

2022 ◽  
Vol 10 (1) ◽  
K. Nebiolo ◽  
T. Castro-Santos

Abstract Introduction Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects. Methods BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s $$\kappa$$ κ . Results Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. BIOTAS classified between 94 and 99% of study tag detections as valid. Conclusion As part of a robust data management plan, BIOTAS is able to discriminate between detections from study tags and known false positives. BIOTAS works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. BIOTAS provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.

2022 ◽  
Roberto Fernandez-Maestre ◽  
Mahmoud Tabrizchi ◽  
Dairo Meza-Morelos

Ion mobility spectrometry is widely used for the detection of illegal substances and explosives in airports, ports, custom, some stations and many other important places. This task is usually complicated by false positives caused by overlapping the target peaks with that of interferents, commonly associated with samples of interest. Shift reagents (SR) are species that selectively change ion mobilities through adduction with analyte ions when they are introduced in IMS instruments. This characteristic can be used to discriminate false positives because the interferents and illegal substances respond differently to SR depending on the structure and size of analytes and their interaction energy with SR. This study demonstrates that ion mobility shifts upon introduction of SR depend, not only on the ion masses, but on the interaction energies of the ion:SR adducts. In this study, we introduced five different SRs using ESI-IMS-MS to study the effect of the interaction energy and size on mobility shifts. The mobility shifts showed a decreasing trend as the molecular weight increased for the series of compounds ethanolamine, valinol, serine, threonine, phenylalanine, tyrosine, tributylamine, tryptophan, desipramine, and tribenzylamine. It was proved that the decreasing trend was partially due to the inverse relation between the mobility and drift time and hence, the shift in drift time better reflects the pure effect of SR on the mobility of analytes. Yet the drift time shift exhibited a mild decrease with the mass of ions. Valinol pulled out from this trend because it had a low binding energy interaction with all the SR and, consequently, its clusters were short-lived. This short lifetime produced fewer collisions against the buffer gas and a drift time shorter compared to those of ions of similar molecular weight. Analyte ion:SR interactions were calculated using Gaussian. IMS with the introduction of SR could be the choice for the free-interferents detection of illegal drugs, explosives, and biological and warfare agents. The suppression of false positives could facilitate the transit of passengers and cargos, rise the confiscation of illicit substances, and save money and distresses due to needless delays. Keywords: Adduction, ion mobility spectrometry, mass spectrometry, shift reagent, valinol, buffer gas modifier

Foods ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 148
Stephanie Lam ◽  
Bethany Uttaro ◽  
Benjamin M. Bohrer ◽  
Marcio Duarte ◽  
Manuel Juárez

Commercial technologies for assessing meat quality may be useful for performing early in-line belly firmness classification. This study used 207 pork carcasses to measure predicted iodine value (IV) at the clear plate region of the carcass with an in-line near-infrared probe (NitFomTM), calculated IV of belly fat using wet chemistry methods, determined the belly bend angle (an objective method to measure belly firmness), and took dimensional belly measurements. A regression analysis revealed that NitFomTM predicted IV (R2 = 0.40) and belly fat calculated IV (R2 = 0.52) separately contributed to the partial variation of belly bend angle. By testing different NitFomTM IV classification thresholds, classifying soft bellies in the 15th percentile resulted in 5.31% false negatives, 5.31% false positives, and 89.38% correctly classified soft and firm bellies. Similar results were observed when the classification was based on belly fat IV calculated from chemically analyzed fatty acid composition. By reducing the level of stringency on the percentile of the classification threshold, an increase in false positives and decrease in false negatives was observed. This study suggests the IV predicted using the NitFomTM may be useful for early in-line presorting of carcasses based on expected belly firmness, which could optimize profitability by allocating carcasses to specific cutout specifications.

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 133
Sabine Skrebinska ◽  
Francis Megraud ◽  
Ilva Daugule ◽  
Daiga Santare ◽  
Sergejs Isajevs ◽  

Background. Discrepancies between histology and serology results for Helicobacter pylori detection could be caused by a variety of factors, including a biopsy sampling error, expertise of the pathologist, natural loss of infection due to advanced atrophy, or a false-positive serology in the case of a previous infection, since antibodies may be present in blood following recovery from the infection. Aims. To identify true H. pylori-positive individuals in discrepant cases by serology and histology using real time polymerase chain reaction (RT-PCR) as a gold standard. Methods. Study subjects with discrepant histology and serology results were selected from the GISTAR pilot study data base in Latvia. Subjects having received previous H. pylori eradication therapy or reporting use of proton pump inhibitors, antibacterial medications, or bismuth containing drugs one month prior to upper endoscopy were excluded. We compared the discrepant cases to the corresponding results of RT-PCR performed on gastric biopsies. Results. In total, 97 individuals with discrepant results were identified: 81 subjects were serology-positive/histology-negative, while 16 were serology-negative/histology-positive. Among the serology-positive/histology-negative cases, 64/81 (79.0%) were false-positives by serology and, for the majority, inflammation was absent in all biopsies, while, in the serology-negative/histology-positive group, only 6.2% were proven false-positives by histology. Conclusions. Among this high H. pylori prevalent, middle-aged population, the majority of discrepant cases between serology and histology were due to false positive-serology, rather than false-negative histology. This confirms the available evidence that the choice of treatment should not be based solely on the serological results, but also after excluding previous, self-reported eradication therapy.

2022 ◽  
Vol 12 (1) ◽  
pp. 489
Mizuki Yoshida ◽  
Atsushi Teramoto ◽  
Kohei Kudo ◽  
Shoji Matsumoto ◽  
Kuniaki Saito ◽  

Since recognizing the location and extent of infarction is essential for diagnosis and treatment, many methods using deep learning have been reported. Generally, deep learning requires a large amount of training data. To overcome this problem, we generated pseudo patient images using CycleGAN, which performed image transformation without paired images. Then, we aimed to improve the extraction accuracy by using the generated images for the extraction of cerebral infarction regions. First, we used CycleGAN for data augmentation. Pseudo-cerebral infarction images were generated from healthy images using CycleGAN. Finally, U-Net was used to segment the cerebral infarction region using CycleGAN-generated images. Regarding the extraction accuracy, the Dice index was 0.553 for U-Net with CycleGAN, which was an improvement over U-Net without CycleGAN. Furthermore, the number of false positives per case was 3.75 for U-Net without CycleGAN and 1.23 for U-Net with CycleGAN, respectively. The number of false positives was reduced by approximately 67% by introducing the CycleGAN-generated images to training cases. These results indicate that utilizing CycleGAN-generated images was effective and facilitated the accurate extraction of the infarcted regions while maintaining the detection rate.

2022 ◽  
Vol 2022 (1) ◽  
pp. pdb.prot103143
Edward A. Greenfield

Immunoprecipitation is rarely used for screening hybridoma fusions because the assays are tedious and time-consuming. However, it can be useful when working with complex antigens because the precipitated antigen is normally detected after sodium dodecyl sulfate (SDS)–polyacrylamide electrophoresis and thus it is simple to discriminate between true and false positives. Furthermore, the assay provides information regarding the molecular weight of the antigen.

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