FL System Research Articles

B-167 Quantification of Thyroglobulin in Serum for Clinical Research using SISCAPA Workflow Combined with LC-MS/MS

Abstract Background Accurate measurement of Thyroglobulin (Tg) using existing immunoassay-based techniques can be challenging in the presence of anti-Tg antibodies (TgAb), which can prevent the binding of Tg to assay antibodies thus leading to non-quantifiable Tg concentrations. The Stable Isotope Standards and Capture with Anti-Peptide Antibodies (SISCAPA™) Workflow combined with LC-MS/MS has been successfully employed to circumvent this problem by digesting the serum sample thus eliminating interfering TgAb and measuring a Tg-specific surrogate peptide instead. Methods Using a Andrew+™ Pipetting Robot, denaturation and reduction buffer was added to 250µL serum samples and incubated at 37°C. Internal standard and trypsin were added and samples incubated at 37°C. The samples were quenched with protease inhibitor and Anti-Tg FSP mAb SISCAPA was added to the serum samples and mixed for 60 minutes. Using a magnetic array plate, the beads were left to pull down, samples were discarded, and the beads were twice washed with PBS/CHAPS prior to elution with acetonitrile with formic acid. Using an ACQUITY™ UPLC™ I-Class PLUS FL System, samples were injected onto a Waters™ XSelect™ HSS T3 Column using a water/acetonitrile/formic acid gradient elution profile and the Tg FSP peptide was quantified using a Waters Xevo™ TQ Absolute Mass Spectrometer. Results The method demonstrated no significant carryover or matrix effects and was shown to be linear from 0.1 - 50 ng/mL. Analytical sensitivity investigations indicate the analytical sensitivity of this method would allow precise quantification (<20%) at 0.1 ng/mL with S/N (PtP) >10:1. Coefficients of variation (CV) for total precision and repeatability on 5 analytical runs for low, mid and high QCs were all < 9.5% (n = 25) for both automated and manual precision assessments. Comparison with samples from the UK NEQAS scheme demonstrated significant method bias of -40% for the developed LC-MS/MS method. Re-assignment of the calibrator concentrations using the EQA samples reduced the bias to -5.5%, indicating differences in the calibration materials used for these measurements. Conclusions A LC-MS/MS clinical research method for serum thyroglobulin was developed using SISCAPA, followed by analysis using ACQUITY UPLC I-Class PLUS FL System and the Xevo TQ Absolute Mass Spectrometer. The method provides analytical sensitivity down to 0.1 ng/mL from 250 µL serum, while providing sufficient sample for re-analysis. The method demonstrates excellent linearity across the calibration range, with no significant carryover, interferences, and matrix effects. Total reproducibility and repeatability of the method was ≤9.5% RSD for manual and automated sample preparation, using the Andrew+ Pipetting Robot. In addition, the Andrew+ Pipetting Robot can minimize user touch-time, allowing a full plate to be prepared within four hours without user intervention.

B-155 Analysis of Monoclonal Antibodies Using a Quantification LC-MS/MS Kit - Spotlight on Infliximab

Abstract Background The use of immunoassays for measuring mAbs is well established but has some limitations, including poor performance, lack of standardization, a high cost when processing a limited number of samples, limited dynamic range, and the potential for cross-reactivity. Using LC-MS/MS associated with the ready-to-use commercial mAbXmise Kit is a simple way to implement mAbs measurement for infliximab, providing high analytical performance, ease of use, and flexibility. Methods The Promise Proteomics Inflammation mAbXmise Kit contains all the calibrators, QCs, internal standard and sample preparation consumables to run the LC-MS/MS method for infliximab. Briefly, 20µL samples are dispensed into the mAbXmise plates containing lyophilized stable labelled infliximab. Samples are transferred to the PuriXmise plate to perform the immunocapture of infliximab and allow washing of the samples. Samples are eluted into a collection plate, evaporated to dryness, resuspended, and the protease (CutXmise) is added to the sample to digest infliximab overnight. The digestion is quenched, with the samples immediately ready for analysis. Using an ACQUITY™ UPLC™ I-Class PLUS FL System, samples were injected onto a Waters™ XSelect™ Premier HSS T3 Column, eluted using a water/acetonitrile/formic acid gradient profile and quantified with both a Waters Xevo™ TQ-XS and Xevo TQ-S micro Mass Spectrometer. Results The method was shown to be linear from 2 - 100 µg/mL. Analytical sensitivity at lowest calibrator of 2µg/mL provided S/N (PtP) ≥15:1 for all tryptic peptides on both mass spectrometers. Coefficients of variation (CV) at 4 and 25µg/mL QC concentrations were all ≤ 10.0% (n = 5). The relative concentrations of the tryptic peptides were compared across 29 anonymized samples and good agreement was observed for GLE, SAV, DIL and ASA peptides. Conclusions We have demonstrated a clinical research method for quantifying Infliximab in plasma using a commercially available kit for sample preparation followed by LC-MS/MS analysis. The Promise Proteomics mAbXmise Kit aids in the day-to-day reproducibility of the immunocapture and tryptic digestion of IFX for targeted LC-MS/MS analysis with the correct peptide selection. Compared to a direct digest surrogate peptide workflow, the process used in the kit reduces matrix interference, improves analytical sensitivity, and enables the use of lower sample volumes. The method can be run on both the Xevo TQ-XS and Xevo TQ-S micro Mass Spectrometers which provide the dynamic range to quantify IFX across the expected range, and the selectivity and analytical sensitivity to obtain low-level quantification of the IFX surrogate peptide in plasma samples. The data presented combines the use of a kit dedicated to the preparation of samples and the use of liquid chromatography and mass spectrometry instrumentation to perform the quantitative analyses. The mAbXmise Kit described has not been cleared by any regulatory entity for diagnostic purposes outside of Europe. Analytical performance depends on the instrument characteristics and its settings. The end user is responsible for completion of the method development and validation. Proteomics mAbXmise Kits are not available for sale in all countries. For Research Use Only. Not for use in diagnostic procedures.

A-067 Simultaneous Measurement of Aldosterone and Plasma Renin Activity in Human Serum Using Liquid Chromatography Coupled to Tandem Mass Spectrometry for Clinical Research

Abstract Background The combination of primary aldosteronism (PA) and alterations in plasma renin activity (PRA) is a leading cause of secondary hypertension. An accurate measurement of Renin Angiotensin-Aldosterone System (RAAS) pathway improves the recognition of symptoms related to stroke, coronary disease, heart failure and metabolic syndrome. Methods Previously, surrogate matrices have been used when demonstrating the analytical performance of LC-MS/MS clinical research methods for the measurement of aldosterone and angiotensin I. Here, we have developed an analytically sensitive and reliable analytical strategy for simultaneous measurement of Aldosterone and Angiotensin I in human matrices. In-house calibrators and quality control samples were prepared, containing varying ranges of both aldosterone (20-2000pg/mL; 55-5548pmol/L) and angiotensin 1 (0.6-240ng/mL; 0.46-185nmol/L). The samples were extracted using the Oasis™ MAX 96-well µElution plate and then separated through the ACQUITY™ UPLC™ I-Class FL System using a XBridge™ Premier Peptide BEH™ C18 Column, over a run time of 3 minutes. Samples were then injected to Xevo™ TQ Absolute Mass Spectrometer, using both positive and negative acquisition modes. The downstream LC-MS/MS data analysis was performed using MassLynx™ MS Software. The analytical performance was demonstrated by extracting and quantifying two replicates of samples on two occasions per day over five separate days. Additionally, the stability of aldosterone and angiotensin I over the period of incubation (3-5hr) was evaluated, which is necessary when investigating the plasma renin activity in human Serum/Plasma samples. Results High reproducibility and repeatability were both determined, indicating total precision and repeatability (<15%CV) for both analytes and no significant changes were found in Aldosterone and Angiotensin I concentrations over 3-5hr of incubation. No significant system carryover (<10% of the lowest calibrator) from high concentration samples into subsequent blank injections was observed. In addition, calibration lines in human serum/plasma were linear, with coefficient of determinations (r2) >0.992 for all analyses at different incubation times. The analytical sensitivity of the clinical research method allows for precise quantification at 55 pmol/L for aldosterone and 0.46 nmol/L/hr for angiotensin I, with both analytes providing S/N (PtP) ≥10:1 and %RSD of <15% at these concentrations. Conclusions An analytically sensitive and selective clinical research method has been developed for the analysis of aldosterone and angiotensin I, using the Xevo TQ Absolute Mass Spectrometer. The Xevo TQ Absolute Mass Spectrometer enables the simultaneous analysis of physiologically low levels of aldosterone and plasma renin activity in human samples, (20pg/mL; 55pmol/L), (0.6ng/mL; 0.46nmol/h/L). A single test that improves instrument utilization, reduces cost, and allows the user to expand their existing test menu on the same platform has been established.

Atomically Fe(Ⅲ) anchored metal-organic frameworks-based fluorescent nanozyme for smartphone-adopted chemiluminescence-fluorescence dual-mode analysis of Uric acid

BackgroundChemiluminescence (CL) analysis is a promising analytical method with advantages including easy operation, high sensitivity and simple instrument. However, the single CL mode usually suffered from poor stability and reproducibility as a result of the flash-type nature of luminescent molecules, leading to false positive or negative results in practical applications. Dual-mode detection is an advanced sensing methodology that identifies analytes through independent output signals. This approach has the ability to circumvent the inherent constraints of individual sensing modes while integrating their respective strengths, thereby yielding a synergistic enhancement in the detection system. ResultsHerein, a chemiluminescence-fluorescence (FL) dual-mode analysis and imaging system is designed by constructing an atomically Fe(Ⅲ) anchored PCN-224 peroxidase-mimicking nanozyme (PCN-224/Fe(Ⅲ)) and achieve an ultrasensitive detection of Uric Acid (UA). The multifunctional PCN-224/Fe(Ⅲ) serves as a high-efficiency co-reaction promoter in the generation of reactive oxygen species (ROS) in both the CL and FL system, while also demonstrating exceptional capabilities as fluorescent nanoprobes. Ultimately, a smartphone-adopted CL imaging device was developed to achieve a visual CL detection through the design of portable paper-based chips. Besides, with the assistance of the TMB-mediated fluorescence energy resonance transfer, the fluorescent PCN-224/Fe(Ⅲ) nanoprobes exhibited good fluorescence detection performance for UA. The limit of detection was achieved as low as 2.45 × 10−10 M and 1.99 × 10−9 M in the CL and FL mode, respectively. SignificanceThis study engineered an atomically Fe(Ⅲ)- MOF-based multifunctional nanozyme and developed an innovative approach for creating CL-FL dual-mode analysis and imaging detection for UA. The proposed CL-FL dual-mode detection system not only provided a portable and sensitive method for UA detection but also offered valuable insights into the mechanism of the co-reaction promoter enhanced CL and FL analysis.

Practice of distributed machine learning in clinical modeling for chronic obstructive pulmonary disease

BackgroundThe high prevalence, morbidity and mortality, and disease heterogeneity of chronic obstructive pulmonary disease (COPD) result in the scattered data derived from patient visits in different medical units. The huge cost of integrating the scattered data for analysis and modeling, as well as the legal demand for patient privacy protection lead to the emergence of data island. ObjectivesOn the premise of protecting patient privacy, integrating scattered data of patients from different medical units for high-quality modeling is beneficial to promoting the development of digital health. Based on this, we develop a distributed COPD disease diagnosis system termed COPD average federated learning (COPD_AVG_FL) using FedAvg. MethodsFirst, to build the COPD_AVG_FL, the clinical data of COPD patients from the real world is collected and the data pre-processing is performed to clean the incorrect data, outlier samples and missing values. Then, a classical federated learning architecture is designed as COPD_AVG_FL. Finally, to evaluate the established COPD_AVG_FL system, we develop Centralized Machine Learning (CML). ConclusionsOur results suggest that, with the assistance of COPD_AVG_FL, the absolute improvement rates are 13.4% (accuracy), 13.3% (precision), 12.8% (recall), 13.1% (F1-Score) and 12.9% (AUC) on the test data, respectively. The decoupling between model training and raw training data protects the patients' privacy, and helps to securely integrate more COPD data from different medical units to generate a more comprehensive model COPD_AVG_FL. This approach promotes the landing of wise information technology of medicine for COPD in the real clinical world. Code for our model will be made available at https://github.com/Cczhh/COPD_AVG_FL/tree/master.

Towards Energy-Aware Federated Learning via Collaborative Computing Approach

This research delves into the consequences of the high complexity of on-device operations executed during the federated learning process. We investigate how the varying computational capabilities and battery levels among mobile devices can introduce performance disparities and influence training quality. Hence, in order to deal with these challenges, we propose EAFL+, a novel energy optimization technique, that focuses on managing power consumption in devices with limited battery capacity. EAFL+ is a cloud–edge–terminal collaborative approach that provides a new architectural design for achieving power-aware FL training by leveraging resource diversity and computation offloading. The innovative scheme enables the efficient selection of an approximately-optimal offloading target, from a set of Cloud-tier, Edge-tier, and Terminal-tier resources and achieves the best cost-quality tradeoff for the devices taking part in the FL system. Our evaluation shows EAFL+ can help conserve the devices’ energy participating in training, which improves the participation rates and increases the clients’ contributions, hence achieving higher accuracy and faster convergence. Through experiments on real datasets and traces in an emulated FL environment, EAFL+ reduces the drop-out of clients to zero and enhances accuracy by up to 24% and 9% compared to EAFL and Oort, respectively.

Privacy preserving and secure robust federated learning: A survey

SummaryFederated learning (FL) has emerged as a promising solution to address the challenges posed by data silos and the need for global data fusion. It offers a distributed machine learning framework with privacy‐preserving features, allowing model training without the need to collect user data. However, FL also presents significant security and privacy threats that hinder its widespread adoption. The requirements of privacy and security in FL are inherently conflicting. Privacy necessitates the concealment of individual client updates, while security requires the disclosure of client updates to detect anomalies. While most existing research focused on the privacy and security aspects of FL, very few studies have addressed the compatibility of these two demands. In this work, we aim to bridge this gap by proposing a comprehensive defense scheme that ensures privacy, security, and compatibility in FL. We categorize the existing literature into two key directions: privacy defense and security defense. Privacy defense includes methods based on additive masks, differential privacy, homomorphic encryption, and trusted execution environment, whereas security defense encompasses distance‐, performance‐, clustering‐, and similarity‐based anomaly detection techniques and statistical information‐based anomaly update bypassing techniques when the server is trusted and privacy‐compatible anomaly update detection techniques when the server is not trusted. In addition, this article presents decentralized FL solutions based on blockchain. For each direction, we discuss specific technical solutions, their advantages, and disadvantages. By evaluating various defense methods, we identify the most suitable approach to address the primary challenge of “achieving a secure and robust FL system against malicious adversaries while protecting users' privacy.” We then propose a theoretical reference framework for end‐to‐end protection of privacy and security in FL for the key problem, which summarizes the attack surface of FL systems from the client to the server under the security model where the client and server are malicious. Leveraging the strengths and characteristics of existing schemes, our proposed framework integrates multiple techniques to strike a balance between privacy, usability, and efficiency. This framework serves as a valuable reference and provides insights for future work in the field. Finally, we also provide recommendations for future research directions in this field.

Fault Location with Increased Reliability for Overhead Transmission Lines

The double-ended traveling wave (TW) method is a promising tool for fault location on transmission lines. It implies that high-frequency transients during fault inception are recorded in specific devices, which identify the precise time, when a front of an electromagnetic traveling wave reaches the corresponding end of the transmission line. The principles of operation, methods of calculations and test data of the developed traveling wave FL (TWFL) system are presented. The results of field tests and experience of operation of the TWFL system on the double-circuit 220 kV Tommot-Maya transmission line including short-circuit faults caused by lightning strikes are discussed.

FREDY: Federated Resilience Enhanced with Differential Privacy

Federated Learning is identified as a reliable technique for distributed training of ML models. Specifically, a set of dispersed nodes may collaborate through a federation in producing a jointly trained ML model without disclosing their data to each other. Each node performs local model training and then shares its trained model weights with a server node, usually called Aggregator in federated learning, as it aggregates the trained weights and then sends them back to its clients for another round of local training. Despite the data protection and security that FL provides to each client, there are still well-studied attacks such as membership inference attacks that can detect potential vulnerabilities of the FL system and thus expose sensitive data. In this paper, in order to prevent this kind of attack and address private data leakage, we introduce FREDY, a differential private federated learning framework that enables knowledge transfer from private data. Particularly, our approach has a teachers–student scheme. Each teacher model is trained on sensitive, disjoint data in a federated manner, and the student model is trained on the most voted predictions of the teachers on public unlabeled data which are noisy aggregated in order to guarantee the privacy of each teacher’s sensitive data. Only the student model is publicly accessible as the teacher models contain sensitive information. We show that our proposed approach guarantees the privacy of sensitive data against model inference attacks while it combines the federated learning settings for the model training procedures.

Privacy-preserving federated learning for transportation mode prediction based on personal mobility data

• We build a federated learning framework for transportation mode prediction based on personal mobility data, which can benefit the public transportation data analysis and resources management. • Our framework can preserve user’s privacy by keeping all user’s personal mobility data in local without uploading to central nodes. • Our framework can train accurate deep neural network models for transportation mode prediction that is close to the centralized training performance. Personal daily mobility trajectories/traces like Google Location Service integrates many valuable information from individuals and could benefit a lot of application scenarios, such as pandemic control and precaution, product recommendation, customized user profile analysis, traffic management in smart cities, etc. However, utilizing such personal mobility data faces many challenges since users’ private information, such as home/work addresses, can be unintentionally leaked. In this work, we build an FL system for transportation mode prediction based on personal mobility data. Utilizing FL-based training scheme, all user’s data are kept in local without uploading to central nodes, providing high privacy preserving capability. At the same time, we could train accurate DNN models that is close to the centralized training performance. The resulted transportation mode prediction system serves as a prototype on user’s traffic mode classification, which could potentially benefit the transportation data analysis and help make wise decisions to manage public transportation resources.

GRNN: Generative Regression Neural Network—A Data Leakage Attack for Federated Learning

Data privacy has become an increasingly important issue in Machine Learning (ML) , where many approaches have been developed to tackle this challenge, e.g., cryptography ( Homomorphic Encryption (HE) , Differential Privacy (DP) ) and collaborative training (Secure Multi-Party Computation (MPC) , Distributed Learning, and Federated Learning (FL) ). These techniques have a particular focus on data encryption or secure local computation. They transfer the intermediate information to the third party to compute the final result. Gradient exchanging is commonly considered to be a secure way of training a robust model collaboratively in Deep Learning (DL) . However, recent researches have demonstrated that sensitive information can be recovered from the shared gradient. Generative Adversarial Network (GAN) , in particular, has shown to be effective in recovering such information. However, GAN based techniques require additional information, such as class labels that are generally unavailable for privacy-preserved learning. In this article, we show that, in the FL system, image-based privacy data can be easily recovered in full from the shared gradient only via our proposed Generative Regression Neural Network (GRNN) . We formulate the attack to be a regression problem and optimize two branches of the generative model by minimizing the distance between gradients. We evaluate our method on several image classification tasks. The results illustrate that our proposed GRNN outperforms state-of-the-art methods with better stability, stronger robustness, and higher accuracy. It also has no convergence requirement to the global FL model. Moreover, we demonstrate information leakage using face re-identification. Some defense strategies are also discussed in this work.

Integration Satellite Imagery with Fuzzy Logic for Potential Change Detection in Land Use/Land Cover

Considered survey systems for monitoring the earth’s surface of the techniques basic and developed for all countries because of their great importance in collecting and providing data and information required as engineering science and research applications can be an important database for many of the establishments. Remote sensing and fuzzy logic are employed to design systems for the survey, monitoring, and changes detect that have occurred in the study area in 2020. The studied area is in Baghdad and its extensions, with an area equivalent to approximately 2776.7628 km2. This paper consists of two parts; the first part when the remote sensing classified the study area in unsupervised methods to detect the ratios of components the land use and land cover via divide it into four parameters (vegetation, urban, empty area, and water). As for the second part of the study, the change’s detection results are the inputs of the fuzzy logic system. The four inputs were processed using hypothetical probabilities of the FL system. The results were reasonable and could be considered useful for predicting the future of the four parameters (vegetation, urban, empty area, and water) of the study area in the coming years.

Effects of rearing system and narasin on growth performance, gastrointestinal development, and gut microbiota of broilers

This study was conducted to evaluate the effects of 3 rearing systems (FL: flooring litter rearing, MC: multilayer cage rearing, PN: plastic net rearing) with or without supplemental narasin on growth performance, gastrointestine development and health of broilers. A total of 2,400 one-day-old Ross 308 mixed-sex broilers (1:1 ratio of males and females) were used in a completely randomized design utilizing a 3 × 2 factorial arrangement of treatments, with 12 replicates per treatment. Each replicate for FL, MC, and PN consisted of 34 birds per floor pen, 30 birds per cage, and 36 birds per net pen, respectively, ensuring the same stocking density (12 birds/m2) across the 3 systems. Results showed that lower ADG (average daily gain), ADFI (average daily feed intake), and FCR (feed conversation ratio) observed in the MC group than those of the other 2 systems from 1 to 36 d of age (P < 0.05). Narasin inclusion in the diets decreased ADFI and FCR significantly (P < 0.05). Multilayer cage and PN rearing systems reduced the relative weight of the gizzard significantly (P < 0.05). Compared with FL, MC reduced the relative weight of the duodenum, jejunum, and ileum (P < 0.05). The mRNA expression levels of the ileal IL-1β and IFN-γ in FL were higher than those in PN and MC (P < 0.05). Narasin decreased the ileal mRNA expression of TNF-α (P < 0.05). Different rearing systems changed the ileal microflora structure of broilers. The FL system increased the ileal microbial diversity of broilers and the relative abundance of Actinobacteria. Narasin combined with MC increased the relative abundance of Proteobacteria. In conclusion, birds reared in PN had a higher body weight. The MC birds had poorer intestinal development and health condition, higher abundance of Proteobacteria, but better FCR. The FL rearing appeared to be propitious for gastrointestinal development and health. Narasin inclusion in the diets improved FCR and changed the relative abundance Proteobacteria of broilers.

Acute Deep Vein Thrombosis and Association with Popliteal Fossa (Baker's) Cysts in Patients with Lower Extremity Duplex Ultrasound Examination

Background: Popliteal fossa cysts (PFCs aka Baker's cysts) are synovial cysts of the knee joint that can be symptomatic or asymptomatic and incidentally identified on ultrasound. Whether PFCs are associated with deep vein thrombosis (DVT) is unknown. Possible mechanisms for an association include direct compression of the popliteal vein, indirect compression on the popliteal vein with leg flexion, adjacent inflammation of the cyst, or relative immobility due to underlying joint disease itself. Methods: Lower extremity venous Duplex ultrasound radiology reports from the inception of electronic archiving through 11/14/2019 were evaluated across the Mayo Clinic Enterprise (Rochester MN, Jacksonville, FL, Scottsdale AZ, and Mayo Clinic Health System) in patients &amp;gt;18 years of age. Natural language processing (NLP) algorithms were created and validated to identify acute DVT (proximal or distal) and PFCs. A random sample of 1,752 ultrasound reports underwent manual review to calculate the sensitivity and specificity of the NLP algorithm. Cases (ultrasounds with acute DVT) were compared to controls (ultrasound without acute DVT) to examine the frequency of PFCs. IRB approval was obtained and patients lacking Minnesota research authorization were excluded. Results: A total of 332,016 lower extremity venous ultrasounds were performed in 223,035 patients; 156,846 unilateral and 175,170 bilateral lower extremities exams. The mean age at ultrasound was 63.3 (SD 16.5) and 54.7% were female. Ultrasound reports were available for analysis starting in 1992 with a significant increase in the number of ultrasounds performed over the study period across the enterprise (Figure 1). Overall, acute DVT was identified in 24,179 (7.3%) of ultrasounds, and PFCs were identified in 32,427 (9.8%) of ultrasounds. The sensitivity and specificity of the NLP algorithm in the full dataset to identify acute DVT was 86.0% and 97.2%, respectfully. The sensitivity and specificity of the NLP algorithm to identify PFCs was 97.8% and 99.5%, respectively. PFCs were present in 9.3% of ultrasounds with acute DVT and 9.8% of ultrasounds without acute DVT (p=0.007), OR 0.94 (95% CI 0.90-0.98). In a multivariate logistic regression model, after adjusting for age and sex, results remained significant (aOR 0.95, 95% CI 0.91-0.995). Comparing ultrasounds before and after 2010, there was a higher percentage of PFCs and acute DVT reported after 2010 (p&amp;lt;0.001 for both). Sensitivity analyses comparing results before or after 2010, by sex, and only in the first ultrasound performed per person, demonstrated similar results. Conclusions: PFCs are negatively associated with the presence of acute DVT on lower extremity venous Duplex ultrasound. This data does not support PFCs as a contributing or causative factor in the development of lower extremity DVT. Figure 1 Disclosures No relevant conflicts of interest to declare.

An efficient scheme for secure feature location using data fusion and data mining in internet of things environment

SummaryFeature location (FL) is performed to find the relationships between domain concepts and other software artifacts. One major problem in maintaining a software system is to understand how many functional features exist in a system and how these features are implemented. Also, poor security is the prime problem in the FL system. However, the existing recent FL techniques use a textual and dynamic approach, which is not found to be secure, keeping in view the changes in the description of security attacks. To overcome this drawback, this work proposed a novel secure approach for FL utilizing data fusion as well as data mining for the internet of things environment. Firstly, the repeated test cases (TC) are eradicated as of the labeled TC. Next, important attributes are selected using the artificial flora optimization algorithm from the removed labeled TC. Then, association rule mining is performed to ascertain closed attributes. Subsequently, encrypt the closed attributes utilizing Caesar Cipher‐Rivest, Shamirs, as well as Adelman algorithm. After that, the score value of the closed attributes counts was found utilizing entropy calculation. Finally, the score value is given as input to the normalized‐K‐Means (N‐[K‐Means]) algorithm, where the score value is normalized utilizing min‐max normalization and then grouped utilizing K‐Means algorithm (KMA). It proffers better results for FL in the source code. The proposed N‐(K‐Means) performance is found better in comparison to the KMA and latent semantic indexing methods. The proposed system proffered better FL results in comparison to the other prevailing methods.

A Hybridized Immune System for Avoidance of Wormhole Attacks in Manet

In Mobile Ad-hoc Networks (MANETs), the most challenging task is detecting and mitigating wormhole links during data transmission between the source and destination nodes. Since it requires special hardware, synchronized clocks, mobile nodes equipped with GPS, etc. To overcome these challenges, Artificial Immune System-based Improved Secure-aware Wormhole Attack Detection (AIS-ISWAD) technique has been proposed that considers maximum end-to-end delay, path length and system parameters for detecting wormhole attacks. To simplify the detection process, AIS has been used as a learning approach that learns those parameters to detect the wormhole links and select an alternative route for transmitting the data packets. However, an uncertainty problem is addressed in AIS during computation of affinity value between antibody and antigen. Also, route selection is still not satisfied since data transmission requires a high-performance stable path from source to destination nodes. Therefore, the main goal of this article is handling the uncertainty problem during affinity computation and selecting the high-performance stable paths to transmit the data. In this paper, a Fuzzy Logic and AIS-ISWAD (FLAIS-ISWAD) technique is proposed to improve the wormhole attack detection and mitigation. In this technique, all computed parameters are given to the FL system to handle the uncertainty problem and construct the high-performance stable paths among all available paths in the network. Also, AIS is applied as a learning method to identify and isolate the wormhole links/nodes in MANETs with the highest network performance. Finally, the performance of the FLAIS-ISWAD technique is evaluated and compared through simulation results in terms of different performance metrics.

Environmental and economic performance of beef farming systems with different feeding strategies in southern Brazil

Beef production is one of the contributors to emission of pollutants to the environment, and increasingly competes for natural resources. Beef producers can improve their environmental performance by adopting alternative feeding strategies. Adoption of alternative feeding strategies, however, might negatively impact farm profitability. The objective of this study was to evaluate the environmental and economic performance of four beef farming systems with different feeding strategies in southern Brazil: grazing on natural pasture (NP); grazing on improved pasture (IP); grazing on natural pasture and crop residues (CR); and grazing on natural pasture and feedlot fattening (FL). Environmental indicators used to compare these farming systems were global warming potential (GWP), fossil energy use, and land occupation per kilogram live weight (LW). Life cycle assessment (LCA) was used to quantify environmental indicators from cradle-to-farm gate. The indicator for economic performance was operating profit per farm. The IP system had lower GWP (18.7kgCO2-eq.·kg−1LW) and land occupation (37m2·kg−1LW) than other systems, whereas its fossil energy use (19.3MJ·kg−1LW) was higher. CR had the highest operating profit (1,567,800R$·farm−1) of the four systems, followed by the IP system (616,400R$·farm−1). Operating profit in the CR system was mainly from crop production (88%). The GWP of the CR system (26.8kgCO2-eq.·kg−1LW) was similar to the GWP of the NP system (27.3kgCO2-eq.·kg−1LW). Operating profit of the FL system (148,100R$·farm−1) was lower than in the NP system (184,400R$·farm−1). The outcomes of this research suggest that IP is a promising system to improve GWP, land occupation, and operating profit, whereas CR has the potential to improve economic performance of whole farms in southern Brazil.

Recent progress in the diagnosis of foot-and-mouth disease: Rapid field-based assays

The rapid and accurate diagnosis of foot-and-mouth disease (FMD) is critical for effective disease control. Most of laboratory-based methods can provide objective results within a few hours of sample receipt. However, the time taken to transport suspect material to the laboratory can be unacceptably long, often precluding laboratory confirmation in the event of an outbreak. Therefore, rapid and easy-to-perform tests, which can be used in the field (on-site) in case of a suspected disease outbreak, would be a valuable tool for veterinarians in the initial diagnosis of FMDV. The lateral flow immunochromatographic (LFI) test has been efficiently used for the detection of specific antibodies against FMDV non-structural proteins. FMDV serotypes O, A, Asia 1, SAT 2 and non-serotype-specific FMDV. Recently, several FMD-specific real-time RT-PCR (rRT-PCR) assays have been transferred to portable mobile platforms and evaluated for the detection of FMDV in the field: the Cepheid SmartCycler Real-time PCR machine, Enigma FL and LightCycler Nano System. The BioSeeq-Vet (Smiths Detection) was the first commercially available truly portable PCR instrument for the detection of viral RNA in the field. Additionally, a new portable amplification platform Genie I for the on-site detection of viral RNA by reverse-transcription loop-mediated isothermal amplification (RT-LAMP) has also been developed and evaluated. Infrared thermography (IRT), a quantitative method for the assessment of body surface temperature, may also be a useful tool for the early detection of FMDV in the field. In the absence of overt clinical signs, the IRT rapid screening test, which measures heat emission, can be essential in selecting potentially infected animals. Microarrays, a recently introduced technology for the laboratory diagnosis of FMD, offers greater screening capabilities for FMDV detection and can be regarded as an alternative to classical diagnostic methods.

Optimised class point approach for software effort estimation using adaptive neuro-fuzzy inference system model

The success of software development depends very much on proper estimation of effort required to develop the software. There is no simple way to make an accurate estimate of these parameters required to develop a software system. There are basically some points approach which are available for software effort estimation such as function point, use case point, class point, object point, etc. In this paper, our aim is to estimate the cost of various software projects using class point approach. The parameters are optimised using various soft computing techniques such as fuzzy logic and adaptive neuro-fuzzy logic so as to achieve better accuracy. Also, a comparative analysis of software effort estimation using the techniques such as artificial neural network ANN, fuzzy logic FL and adaptive neuro-fuzzy inference system ANFIS has been provided.

Optimization of cell line development in the GS-CHO expression system using a high-throughput, single cell-based clone selection system

Therapeutic antibodies are commonly produced by high-expressing, clonal and recombinant Chinese hamster ovary (CHO) cell lines. Currently, CHO cells dominate as a commercial production host because of their ease of use, established regulatory track record, and safety profile. CHO-K1SV is a suspension, protein-free-adapted CHO-K1-derived cell line employing the glutamine synthetase (GS) gene expression system (GS-CHO expression system). The selection of high-producing mammalian cell lines is a crucial step in process development for the production of therapeutic antibodies. In general, cloning by the limiting dilution method is used to isolate high-producing monoclonal CHO cells. However, the limiting dilution method is time consuming and has a low probability of monoclonality. To minimize the duration and increase the probability of obtaining high-producing clones with high monoclonality, an automated single cell-based clone selector, the ClonePixFL system, is available. In this study, we applied the high-throughput ClonePix FL system for cell line development using CHO-K1SV cells and investigated efficient conditions for single cell-based clone selection. CHO-K1SV cell growth at the pre-picking stage was improved by optimizing the formulation of semi-solid medium. The efficiency of picking and cell growth at the post-picking stage was improved by optimization of the plating time without decreasing the diversity of clones. The conditions for selection, including the medium formulation, were the most important factors for the single cell-based clone selection system to construct a high-producing CHO cell line.