Consensus Accuracy Research Articles

The Utility of Cardiac Computed Tomographic Angiography in Differentiating Between Papillary Fibroelastoma, Infective Endocarditis, and Degeneration of the Aortic Valve

Purpose Cardiac computed tomography angiography (CCTA) has significantly advanced the visualization of cardiac structures, particularly valves. We assessed the diagnostic performance of CCTA in diagnosing the most common disorders affecting the aortic valves requiring surgery—papillary fibroelastoma, infective endocarditis, and degeneration. Methods This retrospective study included patients who underwent aortic valve resection between 2016 and 2023 and had a preceding CCTA. Pathologic diagnoses were categorized into the following 3 groups: papillary fibroelastoma, infective endocarditis, and degeneration. Each case was independently evaluated by 3 cardiac-trained radiologists. Results The study group consisted of 50 patients with aortic valvular disorder—14 papillary fibroelastoma, 13 endocarditis, and 23 degeneration. Diagnostic accuracy varied from 70% to 74% among the 3 readers, with a pooled accuracy of 71% and consensus accuracy of 76%. Most errors involved small (≤7 mm) papillary fibroelastomas. Both papillary fibroelastomas and endocarditis-related vegetations typically presented with nodules on CT; however, nodule stalks were only observed with papillary fibroelastomas. Echocardiography aided in distinguishing between the disorders by detecting leaflet perforation and aortic regurgitation in cases of infective endocarditis. Conclusions This study evaluated cardiac CTA for noninvasive characterization of aortic valvular disorders prior to resection. Reader accuracy ranged from 70% to 74%, improving to 76% with consensus interpretations. Small papillary fibroelastomas (≤7 mm) and overlapping imaging features, such as nodular lesions and leaflet thickening, were diagnostic challenges. Greater awareness of these CT characteristics is essential for accurate diagnosis and clinical management.

Leveraging uncertainty in collective opinion dynamics with heterogeneity

Natural and artificial collectives exhibit heterogeneities across different dimensions, contributing to the complexity of their behavior. We investigate the effect of two such heterogeneities on collective opinion dynamics: heterogeneity of the quality of agents’ prior information and of degree centrality in the network. To study these heterogeneities, we introduce uncertainty as an additional dimension to the consensus opinion dynamics model, and consider a spectrum of heterogeneous networks with varying centrality. By quantifying and updating the uncertainty using Bayesian inference, we provide a mechanism for each agent to adaptively weigh their individual against social information. We observe that uncertainties develop throughout the interaction between agents, and capture information on heterogeneities. Therefore, we use uncertainty as an additional observable and show the bidirectional relation between centrality and information quality. In extensive simulations on heterogeneous opinion dynamics with Gaussian uncertainties, we demonstrate that uncertainty-driven adaptive weighting leads to increased accuracy and speed of consensus, especially with increasing heterogeneity. We also show the detrimental effect of overconfident central agents on consensus accuracy which can pose challenges in designing such systems. The opportunities for improved performance and observablility suggest the importance of considering uncertainty both for the study of natural and the design of artificial heterogeneous systems.

Adaptive Sliding Mode Resilient Control of Multi-Robot Systems with a Leader–Follower Model under Byzantine Attacks in the Context of the Industrial Internet of Things

In this paper, an adaptive and resilient consensus control mechanism for multi-robot systems under Byzantine attack, based on sliding mode control, is proposed. The primary aim of the article is to develop a finite-time consensus control strategy even in the presence of a Byzantine attack. In the start, a finite-time consensus control mechanism is proposed to identify the essential conditions required for ensuring consensus accuracy in multi-robot systems, even when faced with Byzantine attacks using Lyapunov theory. Subsequently, a sliding mode control is combined with an adaptive technique for multi-robot systems that lack prior knowledge of Byzantine attack. Later, an attack observer is proposed to estimate the performance of multi-robot systems in the presence of a Byzantine attack. Additionally, chattering effects are mitigated by employing integral sliding mode control. As a result, resilient consensus performance of multi-robot systems can be achieved in a finite time interval. A simulation example is also presented to validate the effectiveness of the proposed model. Furthermore, we delve into the data structure of the proposed method and explore its integration with Artificial Intelligence for seamless incorporation into the Industrial Internet of Things applications.

Distributed Fixed-Time Formation Tracking Control for the Multi-Agent System and an Application in Wheeled Mobile Robots

This work addresses the issue of multi-agent system (MAS) formation control under external disturbances and a directed communication topology. Firstly, a new disturbance observer is proposed to effectively reconstruct and compensate for external disturbances within a short period of time. Then, the integral terminal sliding mode technology is introduced to devise a novel distributed formation control protocol, ultimately realizing the stability of the MAS within a fixed time. Moreover, by means of rigorous Lyapunov theory analyses, a faster formation convergence rate and more accurate consensus accuracies are achieved in the proposed fixed-time strategy with variable exponent form. Finally, the formation tracking control scheme is applied to a multi-wheeled mobile robot (WMR) system. The experimental results strongly support the fine effectiveness of the control scheme designed in this work.

A Two-color Single-molecule Sequencing Platform and Its Clinical Applications.

DNA sequencers have become increasingly important research and diagnostic tools over the past 20 years. In this study, we developed a single-molecule desktop sequencer, GenoCare 1600 (GenoCare), which utilizes amplification-free library preparation and two-color sequencing-by-synthesis chemistry, making it more user-friendly compared with previous single-molecule sequencing platforms for clinical use. Using the GenoCare platform, we sequenced an Escherichia coli standard sample and achieved a consensus accuracy exceeding 99.99%. We also evaluated the sequencing performance of this platform in microbial mixtures and coronavirus disease 2019 (COVID-19) samples from throat swabs. Our findings indicate that the GenoCare platform allows for microbial quantitation, sensitive identification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and accurate detection of virus mutations, as confirmed by Sanger sequencing, demonstrating its remarkable potential in clinical application.

Deep learning to extract the meteorological by-catch of wildlife cameras.

Microclimate-proximal climatic variation at scales of metres and minutes-can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.

Chromosome-scale genome assembly of sweet tea (Lithocarpus polystachyus Rehder)

Lithocarpus, with >320 species, is the second largest genus of Fagaceae. However, the lack of a reference genome limits the molecular biology and functional study of Lithocarpus species. Here, we report the chromosome-scale genome assembly of sweet tea (Lithocarpus polystachyus Rehder), the first Lithocarpus species to be sequenced to date. Sweet tea has a 952-Mb genome, with a 21.4-Mb contig N50 value and 98.6% complete BUSCO score. In addition, the per-base consensus accuracy and completeness of the genome were estimated at 60.6 and 81.4, respectively. Genome annotation predicted 37,396 protein-coding genes, with repetitive sequences accounting for 64.2% of the genome. The genome did not undergo whole-genome duplication after the gamma (γ) hexaploidy event. Phylogenetic analysis showed that sweet tea diverged from the genus Quercus approximately at 59 million years ago. The high-quality genome assembly and gene annotation resources enrich the genomics of sweet tea, and will facilitate functional genomic studies in sweet tea and other Fagaceae species.

Machine Learning Diagnosis of Small-Bowel Crohn Disease Using T2-Weighted MRI Radiomic and Clinical Data.

BACKGROUND. Radiologists have variable diagnostic performance and considerable interreader variability when interpreting MR enterography (MRE) examinations for suspected Crohn disease (CD). OBJECTIVE. The purposes of this study were to develop a machine learning method for predicting ileal CD by use of radiomic features of ileal wall and mesenteric fat from noncontrast T2-weighted MRI and to compare the performance of the method with that of expert radiologists. METHODS. This single-institution study included retrospectively identified patients who underwent MRE for suspected ileal CD from January 1, 2020, to January 31, 2021, and prospectively enrolled participants (patients with newly diagnosed ileal CD or healthy control participants) from December 2018 to October 2021. Using axial T2-weighted SSFSE images, a radiologist selected two slices showing greatest terminal ileal wall thickening. Four ROIs were segmented, and radiomic features were extracted from each ROI. After feature selection, support-vector machine models were trained to classify the presence of ileal CD. Three fellowship-trained pediatric abdominal radiologists independently classified the presence of ileal CD on SSFSE images. The reference standard was clinical diagnosis of ileal CD based on endoscopy and biopsy results. Radiomic-only, clinical-only, and radiomic-clinical ensemble models were trained and evaluated by nested cross-validation. RESULTS. The study included 135 participants (67 female, 68 male; mean age, 15.2 ± 3.2 years); 70 were diagnosed with ileal CD. The three radiologists had accuracies of 83.7% (113/135), 88.1% (119/135), and 86.7% (117/135) for diagnosing CD; consensus accuracy was 88.1%. Interradiologist agreement was substantial (κ = 0.78). The best-performing ROI was bowel core (AUC, 0.95; accuracy, 89.6%); other ROIs had worse performance (whole-bowel AUC, 0.86; fat-core AUC, 0.70; whole-fat AUC, 0.73). For the clinical-only model, AUC was 0.85 and accuracy was 80.0%. The ensemble model combining bowel-core radiomic and clinical models had AUC of 0.98 and accuracy of 93.5%. The bowel-core radiomic-only model had significantly greater accuracy than radiologist 1 (p = .009) and radiologist 2 (p = .02) but not radiologist 3 (p > .99) or the radiologists in consensus (p = .05). The ensemble model had greater accuracy than the radiologists in consensus (p = .02). CONCLUSION. A radiomic machine learning model predicted CD diagnosis with better performance than two of three expert radiologists. Model performance improved when radiomic data were ensembled with clinical data. CLINICAL IMPACT. Deployment of a radiomic-based model including T2-weighted MRI data could decrease interradiologist variability and increase diagnostic accuracy for pediatric CD.

Trust-Preserving Mechanism for Blockchain Assisted Mobile Crowdsensing

Blockchain is envisioned as one of the promising technologies to address trust concern brought by mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. Nevertheless, blockchain cannot fundamentally guarantee that the valuable sensed data outside the chain can enter the chain, although data integrity and consistency can be ensured once it is confirmed inside the chain. In addition, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to fully enjoy the benefits of using blockchain in MCS. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize participants to maintain the trustworthiness of interactions. By inferring trust to aid decision-making, trust decision is further made, including leader election and transaction data generation, to filter untrusted nodes from participating in blockchain process. Finally, extensive simulations are conducted to validate the effectiveness and efficiency of TPM, and improve the performance in terms of contribution rate, consensus accuracy and system stability.

Differentially Private Average Consensus With Logarithmic Dynamic Encoding-Decoding Scheme.

This article is concerned with the differentially private average consensus (DPAC) problem for a class of multiagent systems with quantized communication. By constructing a pair of auxiliary dynamic equations, a logarithmic dynamic encoding-decoding (LDED) scheme is developed and then utilized during the process of data transmission, thereby eliminating the effect of quantization errors on the consensus accuracy. The primary purpose of this article is to establish a unified framework that integrates the convergence analysis, the accuracy evaluation, and the privacy level for the developed DPAC algorithm under the LDED communication scheme. By means of the matrix eigenvalue analysis method, the Jury stability criterion, and the probability theory, a sufficient condition (with respect to the quantization accuracy, the coupling strength, and the communication topology) is first derived to ensure the almost sure convergence of the proposed DPAC algorithm, and the convergence accuracy and privacy level are thoroughly investigated by resorting to the Chebyshev inequality and ϵ -differential privacy index. Finally, simulation results are provided to illustrate the correctness and validity of the developed algorithm.

ST-PBFT: An Optimized PBFT Consensus Algorithm for Intellectual Property Transaction Scenarios

For the current Intellectual Property (IP) transaction scenario, consensus nodes need to simultaneously consensus transactions of the same transaction type, resulting in low consensus efficiency, accuracy, and reliability, which seriously hinders the development of intellectual property. Based on the consortium chain, this paper proposes a secure and efficient blockchain-distributed consensus algorithm, ST-PBFT (Shard Transaction Practical Byzantine Fault Tolerance), applied to the IP transaction scenario. The main contributions of ST-PBFT include the following: first, a grouping method based on the principle of consistent hashing is proposed to group consensus nodes, and nodes group consensus, which reduces the complexity of communication. Second, the transaction consensus group can process IP transactions in parallel, which improves the throughput of the algorithm. Third, a node reputation evaluation model is proposed, which can prevent byzantine nodes from being repeatedly elected as primary nodes. The experimental results show that ST-PBFT can significantly improve the consensus efficiency and reliability and reduce consensus latency.

Differentiated Output-Based Privacy-Preserving Average Consensus

This letter investigates the differentiated output-based privacy-preserving average consensus problem over digraphs. A new stochastic obfuscation algorithm is proposed to achieve better privacy-preserving effect. When the output messages for at least one out-neighbour are not leaked, the algorithm can be designed to achieve any pre-given consensus accuracy and privacy-preserving level simultaneously by properly selecting the private weights. Even if all the output messages are leaked, the algorithm can still ensure that each agent’s initial state is protected to a certain extent. The mean square convergence of the algorithm is proved. The efficiency of the algorithm is verified by a numerical example.

Adaptive Appointed-Time Consensus Control of Networked Euler–Lagrange Systems With Connectivity Preservation

With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.

PSV-A-13 Using Canine Olfaction to Detect Bovine Respiratory Disease: An Analysis of two Dogs’ Performance

Abstract Bovine respiratory disease (BRD) detection is a major challenge in feedlot cattle management. In a pilot study, two dogs were trained using sets of three samples to identify nasal swabs from cattle that developed signs of BRD after feedlot arrival. Cattle arrived at the feedlot in four lots and included a mixture of bulls (n=306) and steers (n=89). In the final stage of training, Dog A accuracy was 0.82 (95% CI: 0.79-0.85) and Dog B accuracy was 0.63 (95% CI: 0.60-0.67). A Kruskal-Wallis test identified differences in accuracy across lots (P < 0.001). Differences between pairs were identified by Wilcoxon tests. Accuracy for Lot 2 (0.53, 95% CI: 0.46-0.59) was lower than all other lots. The greatest accuracy, 0.96 (95% CI: 0.92-1.00), was for sets where the positive sample came from a different lot than both negative samples, and differed from accuracy for all lots except Lot 4. A Wilcoxon test detected no differences (p = 0.76) in accuracy between sets that consisted of all bulls (0.73, 95% CI: 0.69-0.76) and sets that were a mixture of bulls and steers (0.72, 95% CI: 0.69-0.75). After training, in a double-blind detection test, unfamiliar samples (n=123) were presented to the dogs as 41 sets. Each set was evaluated twice by each dog. Accuracy for Dog A was 0.39 (95% CI: 0.28-0.50), no better than chance; however, Dog B’s accuracy was 0.45 (95% CI: 0.34-0.56), better than the 0.33 expected by chance. In the detection test, neither sample lot (p = 0.33) nor sex (p = 0.11) impacted performance for either dog. Dogs’ consensus accuracy was 0.54 (95% CI: 0.38-0.69). Overall, dogs showed some ability to discriminate between BRD-affected and healthy cattle using nasal swabs, however multiple compounding factors may have influenced canines’ decision making in this trial.

Distributed cooperative secondary control based on discrete consensus for DC microgrid

Aiming at overcoming the drawbacks of bus voltage deviation and current sharing accuracy degradation caused by conventional v-i droop control in primary control of DC microgrid, a distributed cooperative secondary control strategy is proposed in this paper. The strategy includes voltage restoration control and current sharing control. Discrete consensus is applied in the proposed method which considers the discrete nature of measurement, controller implementation, and communication. The controller on each converter exchanges data with only its neighbors on a sparse network of communication across the DC microgrid, further dynamically tracking the output voltage and current. In addition, the proposed control strategy can reduce the impact of communication noise on consensus convergence and accuracy by introducing the consistency gain function. The effectiveness of the proposed method is verified by a grid-connected DC microgrid system based on the MATLAB/Simulink.

MSRCall: a multi-scale deep neural network to basecall Oxford Nanopore sequences.

MinION, a third-generation sequencer from Oxford Nanopore Technologies, is a portable device that can provide long-nucleotide read data in real-time. It primarily aims to deduce the makeup of nucleotide sequences from the ionic current signals generated when passing DNA/RNA fragments through nanopores charged with a voltage difference. To determine nucleotides from measured signals, a translation process known as basecalling is required. However, compared to NGS basecallers, the calling accuracy of MinION still needs to be improved. In this work, a simple but powerful neural network architecture called multi-scale recurrent caller (MSRCall) is proposed. MSRCall comprises a multi-scale structure, recurrent layers, a fusion block and a connectionist temporal classification decoder. To better identify both short-and long-range dependencies, the recurrent layer is redesigned to capture various time-scale features with a multi-scale structure. The results show that MSRCall outperforms other basecallers in terms of both read and consensus accuracies. MSRCall is available at: https://github.com/d05943006/MSRCall. Supplementary data are available at Bioinformatics online.

Engaging Online Citizen Scientists and the Consensus Method to Monitor the Marine Biofouling Community

Citizen science is an important and useful approach to research that broadens public science engagement and expands the scale at which science can be conducted. Monitoring for marine non-native species has been repeatedly identified as a venue for citizen scientists to make substantial contributions. In this study, we evaluated the accuracy of identifications made by volunteers of marine invertebrates on the project Invader ID, hosted on the online citizen science portal Zooniverse. We tested the efficiency and accuracy of invertebrate identifications made through consensus, where more than one volunteer must agree on a final identification before it is added to the database. Using the Matthew’s Correlation Coefficient, we found that four volunteers in consensus balanced efficiency and accuracy when compared to gold standard scientist identifications. Common, large taxa were identified most accurately; Branching Bryozoa identifications made by four volunteers were 85% accurate, Solitary Tunicates 91% accurate, and Colonial Tunicates 64%. In community-based comparisons, the identity of the taxonomist (volunteer or scientist) had a small impact on overall community composition, while site and salinity gradients had a strong impact on composition. We suggest that citizen science monitoring programs focused on highly recognizable taxonomic groups, or on a few species within those groups that can provide crucial information for managers aiming to quickly identify new invasions. Moreover, long-term assessments of composition could be successfully monitored by volunteers from photographs, helping to bolster understanding of ongoing impacts of invasive species or climate change.

Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation.

Variant calling has been widely used for genotyping and for improving the consensus accuracy of long-read assemblies. Variant calls are commonly hard-filtered with user-defined cutoffs. However, it is impossible to define a single set of optimal cutoffs, as the calls heavily depend on the quality of the reads, the variant caller of choice and the quality of the unpolished assembly. Here, we introduce Merfin, a k-mer based variant-filtering algorithm for improved accuracy in genotyping and genome assembly polishing. Merfin evaluates each variant based on the expected k-mer multiplicity in the reads, independently of the quality of the read alignment and variant caller's internal score. Merfin increased the precision of genotyped calls in several benchmarks, improved consensus accuracy and reduced frameshift errors when applied to human and nonhuman assemblies built from Pacific Biosciences HiFi and continuous long reads or Oxford Nanopore reads, including the first complete human genome. Moreover, we introduce assembly quality and completeness metrics that account for the expected genomic copy numbers.

The haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar reveal novel pan-genome and allele-specific transcriptome features.

BackgroundCassava (Manihot esculenta) is an important clonally propagated food crop in tropical and subtropical regions worldwide. Genetic gain by molecular breeding has been limited, partially because cassava is a highly heterozygous crop with a repetitive and difficult-to-assemble genome.FindingsHere we demonstrate that Pacific Biosciences high-fidelity (HiFi) sequencing reads, in combination with the assembler hifiasm, produced genome assemblies at near complete haplotype resolution with higher continuity and accuracy compared to conventional long sequencing reads. We present 2 chromosome-scale haploid genomes phased with Hi-C technology for the diploid African cassava variety TME204. With consensus accuracy >QV46, contig N50 >18 Mb, BUSCO completeness of 99%, and 35k phased gene loci, it is the most accurate, continuous, complete, and haplotype-resolved cassava genome assembly so far. Ab initio gene prediction with RNA-seq data and Iso-Seq transcripts identified abundant novel gene loci, with enriched functionality related to chromatin organization, meristem development, and cell responses. During tissue development, differentially expressed transcripts of different haplotype origins were enriched for different functionality. In each tissue, 20–30% of transcripts showed allele-specific expression (ASE) differences. ASE bias was often tissue specific and inconsistent across different tissues. Direction-shifting was observed in <2% of the ASE transcripts. Despite high gene synteny, the HiFi genome assembly revealed extensive chromosome rearrangements and abundant intra-genomic and inter-genomic divergent sequences, with large structural variations mostly related to LTR retrotransposons. We use the reference-quality assemblies to build a cassava pan-genome and demonstrate its importance in representing the genetic diversity of cassava for downstream reference-guided omics analysis and breeding.ConclusionsThe phased and annotated chromosome pairs allow a systematic view of the heterozygous diploid genome organization in cassava with improved accuracy, completeness, and haplotype resolution. They will be a valuable resource for cassava breeding and research. Our study may also provide insights into developing cost-effective and efficient strategies for resolving complex genomes with high resolution, accuracy, and continuity.

Adaptive bipartite output consensus of nonlinear fractional-order multi-agent systems

A distributed control approach is proposed for a class of fractional-order multi-agent systems with unknown nonlinearities under signed bipartite digraphs. Unlike the existing results, the follower's dynamics are studied with strict-feedback fractional-order form as well as a general class of actuator faults with unknown magnitude, pattern and occurrence time is studied. To provide a simple and efficient control strategy, first a novel command fractional-order filter based on the backstepping design is proposed such that the difficulty of calculation of the fractional derivative order of the virtual control laws is removed, as well as improving the bipartite output consensus accuracy. Then, to cope with dynamic's uncertainties, the proposed method is integrated with adaptive neural approximator and minimal learning parameter scheme which reduces communication loads. Besides, a distributed fault compensation protocol based upon the proposed command fractional-order filter and relative output information of neighbours' agents is extended to ensure bipartite output consensus, without relying on any global information of the singed digraph as well as any explicit fault detection mechanism. Finally, it is guaranteed that all the error signals within the closed-loop network system are converged into adjustable compact sets around the origin. The simulation results verify the validity of the presented control approach.