Detection Method Research Articles

Toward Sensitive and Reliable Immunoassays of Marine Biotoxins: From Rational Design to Food Analysis.

Marine biotoxins are metabolites produced by algae that can accumulate in shellfish or fish and enter organisms through the food chain, posing a serious threat to biological health. Therefore, accurate and rapid detection is an urgent requirement for food safety. Although various detection methods, including the mouse bioassay, liquid chromatography-mass spectrometry, and cell detection methods, and protein phosphatase inhibition assays have been developed in the past decades, the current detection methods cannot fully meet these demands. Among these methods, the outstanding immunoassay virtues of high sensitivity, reliability, and low cost are highly advantageous for marine biotoxin detection in complex samples. In this work, we review the recent 5-year progress in marine biotoxin immunodetection technologies such as optical immunoassays, electrochemical immunoassays, and piezoelectric immunoassays. With the assistance of immunoassays, the detection of food-related marine biotoxins can be implemented for ensuring public health and preventing food poisoning. In addition, the immunodetection technique platforms including lateral flow chips and microfluidic chips are also discussed. We carefully investigate the advantages and disadvantages for each immunoassay, which are compared to demonstrate the guidance for selecting appropriate immunoassays and platforms for the detection of marine biotoxins. It is expected that this review will provide insights for the further development of immunoassays and promote the rapid progress and successful translation of advanced immunoassays with food safety detection.

Analysis of bacterial transcriptome and epitranscriptome using nanopore direct RNA sequencing.

Bacterial gene expression is a complex process involving extensive regulatory mechanisms. Along with growing interests in this field, Nanopore Direct RNA Sequencing (DRS) provides a promising platform for rapid and comprehensive characterization of bacterial RNA biology. However, the DRS of bacterial RNA is currently deficient in the yield of mRNA-mapping reads and has yet to be exploited for transcriptome-wide RNA modification mapping. Here, we showed that pre-processing of bacterial total RNA (size selection followed by ribosomal RNA depletion and polyadenylation) guaranteed high throughputs of sequencing data and considerably increased the amount of mRNA reads. This way, complex transcriptome architectures were reconstructed for Escherichia coliand Staphylococcus aureusand extended the boundaries of 225 known E. coli operons and 89 defined S. aureus operons. Utilizing unmodified in vitro-transcribed (IVT) RNA libraries as a negative control, several Nanopore-based computational tools globally detected putative modification sites in the E. coli and S. aureus transcriptomes. Combined with Next-Generation Sequencing-based N6-methyladenosine (m6A) detection methods, 75 high-confidence m6A candidates were identified in the E. coli protein-coding transcripts, while none were detected in S. aureus. Altogether, we demonstrated the potential of Nanopore DRS in systematic and convenient transcriptome and epitranscriptome analysis.

Microfluidic Chip Coupled with MALDI-TOF MS for Multitarget Detection of Allergens in Crucian Carp (Carassius auratus).

The goal of the present study was to establish a rapid, simple method for simultaneous allergy testing of sera from multiple fish-allergic patients. Sera from fish-allergic patients were pooled and used for capturing allergens in fish muscle of crucian carp (Carassius auratus), which was studied as a fish model. Sarcoplasmic proteins of crucian carp (Carassius auratus) were extracted for the analysis of allergens. Anti-human IgE antibody-functionalized magnetic beads were utilized to collect IgE antibodies from human pooled sera. The isolation of allergenic proteins was immunomagnetically performed in microfluidic channels, and the elution of the captured allergenic proteins was done with 5% (v/v) acetic acid aqueous solution. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and peptide mass fingerprinting were used for the analysis of tryptic digests of eluted proteins. Ten potential allergenic proteins were identified from crucian carp (Carassius auratus). The present protocol provides a rapid, efficient, and simple method for simultaneous detection of multiple allergens, based on multitargeted antibodies from pooled sera of allergic patients. The constructed multiple antibody-modified MBs can be applied for the deallergenicity of food matrices. The efficiency of allergen detection can be greatly improved, with promising application in allergen discovery and filtration for other muscle-based foods.

Blood CO Status Classification Using UV-VIS Spectroscopy and PSO-optimized 1D-CNN Model

Rapid and effective blood carbon monoxide (CO) assessment is of great importance, especially in estimating CO-related morbidity and instituting effective preventive measures. The conventional detection methods using CO breath analysis lack sensitivity, while collecting biological fluid samples for CO level measurement is prone to external contamination and expensive for frequent use. This study proposes a one-dimensional convolutional neural network (1D-CNN) consisting of three stacked biconvolutional layers for binary classification of blood CO status using the diffuse reflectance spectroscopy technique. Iterative particle swarm optimization (PSO) has efficiently found the best network parameters to learn important features from the reflectance spectroscopy data. The findings showed good testing accuracy, specificity, and precision of 92.9%, 90%, and 89.7%, respectively, and a high sensitivity of 96.3% in determining abnormal blood CO among smokers using the proposed CNN network. Comparisons with eight existing machine learning and deep learning models revealed the proposed method’s effectiveness in classifying blood CO status while reducing computing time by 8–13 folds. The findings of this work provide new insights that are valuable for researchers in neural network design automation, healthcare management, and skin-related research, specifically for application in nondestructive evaluation and clinical decision-making.

A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.

The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach's performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.

Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning

Low-surface-brightness (LSB) galaxies play a crucial role in our understanding of galaxy evolution and dark matter cosmology. However, efficiently detecting them in large-scale surveys is challenging, due to their dim appearance. In this study, we propose a two-step detection method based on deep learning to address this issue. First, an object detection model called GalCenterNet was designed to detect LSB galaxy candidates in astronomical images. The model was trained using a data set of 665 Sloan Digital Sky Survey (SDSS) images, which contained 667 LSB galaxies. On the test set, the model achieved an accuracy of 95.05% and a recall of 96.00%. Next, an anomaly detection technique known as Deep Support Vector Data Description was applied to identify abnormal sources, thus refining the LSB candidates. By applying the two-step detection method to SDSS images, we have obtained a sample of 37,536 LSB galaxy candidates. This wide-area sample contains diverse and abundant LSB galaxies, which are valuable for studying the properties of LSB galaxies and the role that the environment plays in their evolution. The proposed detection method enables end-to-end detection from the SDSS images to the final detection results. This approach will be further employed to efficiently identify objects in the upcoming Chinese Survey Space Telescope sky survey.

The efficacy of Hemospray in managing bleeding related to gastrointestinal tumors: systematic review and meta-analysis.

Gastrointestinal (GI) bleeding stemming from malignant tumors is increasingly recognized, due to advancements in oncology and detection methods. Traditional endoscopic hemostatic techniques have shown variable success rates in managing hemorrhagic GI neoplasms. Hemospray, an emerging endoscopic hemostatic powder, offers promise in treating upper GI bleeding, potentially extending its utility to neoplastic bleeding sites. This meta-analysis aims to evaluate Hemospray's efficacy in managing bleeding related to GI tumors. We searched Embase, Scopus, Web of Science, Medline/PubMed, and Cochrane. Inclusion criteria encompassed studies focusing on malignancy-related GI bleeding and interventions utilizing Hemospray. Comparative studies contrasted Hemospray with standard endoscopic treatments (SET), while noncomparative studies assessed Hemospray's efficacy independently. The risk of bias was assessed using appropriate tools, and statistical analyses were performed using Review Manager and open Meta analyst software. We included 19 studies in our meta-analysis. Hemospray demonstrated higher rates of immediate hemostasis compared to SET (odds ratio: 17.14, 95% confidence interval: 4.27-68.86), with consistent outcomes across studies. Rebleeding rates at 14 and 30 days were comparable between Hemospray and SET groups, suggesting similar efficacy in long-term hemostasis. Hemospray showed a significantly lower need for nonendoscopic hemostasis compared to SET (odds ratio: 0.51, 95% confidence interval: 0.30-0.87), indicating a potential reduction in supplementary interventions. Safety assessments revealed no confirmed adverse events directly linked to Hemospray. This meta-analysis highlights Hemospray's efficacy in achieving immediate hemostasis in GI tumor-related bleeding, with potential benefits in reducing supplementary interventions and improving patient outcomes. Despite comparable rebleeding rates, Hemospray emerges as a valuable adjunctive therapy in managing malignant GI bleeding.

Phase estimation via multi-photon subtraction inside the SU(1,1) interferometer

To improve the phase sensitivity, multi-photon subtraction schemes (multi-PSS) within the SU(1,1) interferometer are proposed. The input states are the coherent state and the vacuum state, and the detection method is homodyne detection. The effects of multi-photon subtraction on phase sensitivity, quantum Fisher information (QFI), and quantum Cramér-Rao bound (QCRB) are analyzed under both ideal and photon losses situations. It is shown that the internal subtraction operation can improve the phase sensitivity, which becomes better performance by increasing subtraction number. It can also efficiently improve the robustness of the SU(1,1) interferometer against internal photon losses. By comparing separatively arbitrary photon subtraction on the two-mode inside SU(1,1) interferometer, the performance differences under different conditions are analyzed, including the asymmetric properties of non-Gaussian operations on the phase precision and the QFI. Our proposed scheme represents a valuable method for achieving quantum precision measurements.

Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data

BackgroundStructural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection.ResultsThis study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines’ detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking.ConclusionsThis study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.

An Investigation of Shaft Voltage in Synchronous Generators Under SAGE and Variable Load Condition

Abstract Synchronous generators are widely used in power generation systems. Static air-gap eccentricity (SAGE) often occurs in synchronous generators due to the component wear over prolonged operation. This paper presents a comprehensive mathematical model specifically tailored for SAGE fault, incorporating for the influence of stator slotting. The study thoroughly examines the impacts of both eccentricity and varying loads on the shaft voltage using the developed model. Furthermore, a novel method for detecting SAGE is introduced, leveraging the mathematical model of shaft voltage. This detection method proves effective for identifying eccentricity in synchronous generators across different load conditions by reasonably combining shaft voltage and phase current. The mathematical model of shaft voltage and the proposed detection method are validated through three-dimensional finite-element calculations and experimental studies. The work is helpful to manage and predict the shaft voltage. This paper contributes to the prevention of shaft voltage damage and real-time monitoring of the SAGE fault in synchronous generators.

Recent Developments in Small-Molecule Fluorescent Probes for Cellular Senescence

Cellular senescence is a recently emerged research topic in modern biology. Often described as a double-edged sword, it encompasses numerous essential biological processes, including beneficial effects such as wound healing and embryonic development, as well as detrimental contributions to chronic inflammation and tumor development. Consequently, there is an increasing need to unravel the intricate networks of senescence and develop reliable detection methods to distinguish it from related phenomena. To address these challenges, a variety of detection methods have been developed. In particular, small-molecule fluorescent probes offer distinct advantages such as suitability for real-time live cell monitoring and in vivo imaging, superior tunable properties, and versatile applications. In this review, we explored recent advancements in the development of small-molecule fluorescent probes toward monitoring cellular senescence by targeting various senescence-related biological phenomena. These phenomena include the upregulation of senescence-associated enzymes, perturbation of the subcellular environment, and increased endogenous ROS levels. Moreover, multi-senescence biomarker-targeting approaches are also discussed to improve their sensitivities and specificities for the detection of cellular senescence. With recent advances in senescence probe development, current challenges in this field are also discussed to facilitate further progress.

Flood Monitoring Based on Remote Sensing of the Earth

The article deals with the problem of flooding of settlements due to floods, which causes economic damage and poses a threat to human life. Attention is paid to methods of early detection of water level rise to minimize the consequences. Modern remote sensing technologies, which complement traditional hydrological observation methods, present new opportunities for observation and forecasting of flood situations. Examples of remote sensing data and statistical data illustrate the effectiveness of modern technologies. The conclusion emphasizes the need for further improvement of flood detection technologies to enhance the effectiveness of flood risk management and ensure the safety of the population and economy of the regions.

An optimised protocol for the detection of lipofuscin, a versatile and quantifiable marker of cellular senescence.

Lipofuscin is a yellow-brown pigment typically found in the lysosomes that contains a mixture of molecules including lipids, metals and misfolded proteins. The use of Sudan black B to detect lipofuscin accumulation, a well described marker of cellular senescence and ageing, was first described in 2013 by Georgakopoulou, et al. Here, we provide an optimisation of the original protocol. Firstly, we adjusted the staining methodology for increased ease of use on cultured cells. Secondly, we show that Sudan black B-stained lipofuscin emits strong fluorescence in the far-red channel making it suitable for fluorescence microscopy detection and quantification. Moreover, we also demonstrate that this optimised protocol can be utilised in conjunction with standard immunofluorescence staining techniques, making possible the simultaneous detection of lipofuscin and other cellular proteins of interest, like additional markers of senescence. This is a significant advantage over the most commonly used method for senescence detection, based on beta galactosidase enzymatic activity. We therefore believe that these findings and the provided optimised protocol will represent a useful tool for the scientific community in the field of cellular senescence.

Earliest Metabolic Changes Associated with the Initiation of Pancreatic Cancer.

Pancreatic cancer is usually detected at a late stage, when tumors have already metastasized; therefore, it has a poor prognosis with a 5-year survival rate of 11% to 12%. A key to targeting this high mortality is to develop methods for detecting the disease at a stage in which it is still local to the pancreas. However, this needs a better understanding of the events that govern pancreatic cancer oncogenesis. In this issue of Cancer Research, Neuß and colleagues report metabolic changes associated with acinar-to-ductal metaplasia (ADM), an initiating event that leads to the formation of precursor lesions for pancreatic ductal adenocarcinoma (PDAC). Their findings reveal a switch to aerobic glycolysis, increased c-MYC signaling, and increased serine metabolism as driving factors for the ADM process. These findings are important as they demonstrate that metabolic changes that drive the proliferation and metastasis of full-blown PDAC begin in the earliest lesions. The data not only provide insights into how PDAC develops but also a potential explanation for previously described findings, such as circulating lesion cells can be detected even when no carcinoma in situ is present. In summary, this article is highly relevant for furthering our understanding of how metabolic reprogramming drives the earliest events leading to PDAC development and could lay the groundwork for developing methods for early detection or intervention. See related article by Neuß et al., p. 2297.

A Novel Voltage-Abnormal Cell Detection Method for Lithium-Ion Battery Mass Production Based on Data-Driven Model with Multi-Source Time Series Data

Before leaving the factory, lithium-ion battery (LIB) cells are screened to exclude voltage-abnormal cells, which can increase the fault rate, troubleshooting difficulty, and degrade pack performance. However, the time interval to obtain the detection results through the existing voltage-abnormal cell method is too long, which can seriously affect production efficiency and delay shipment, especially in the mass production of LIBs when facing a large number of time-critical orders. In this paper, we propose a data-driven voltage-abnormal cell detection method, using a fast model with simple architecture, which can detect voltage-abnormal cells based on the multi-source time series data of the LIB without a time interval. Firstly, our method transforms the different source data of a cell into a multi-source time series data representation and utilizes a recurrent-based data embedding to model the relation within it. Then, a simplified MobileNet is used to extract hidden feature from the embedded data. Finally, we detect the voltage-abnormal cells according to the hidden feature with a cell classification head. The experiment results show that the accuracy and average running time of our model on the voltage-abnormal cell detection task is 95.42% and 0.0509 ms per sample, which is a considerable improvement over existing methods.

Network analysis of students’ conceptual understanding of mathematical expressions for probability in upper-division quantum mechanics

One expected outcome of physics instruction is for students to be capable of relating physical concepts to multiple mathematical representations. In quantum mechanics (QM), students are asked to work across multiple symbolic notations, including some they have not previously encountered. To investigate student understanding of the relationships between expressions used in these various notations, a survey was developed and distributed to students at six different institutions. All of the courses studied were structured as “spins-first,” in which the course begins with spin-1/2 systems and Dirac notation before transitioning to include continuous systems and wave function notation. Network analysis techniques such as community detection methods were used to investigate conceptual connections between commonly used expressions in upper-division QM courses. Our findings suggest that, for spins-first students, Dirac bras and kets share a stronger identity with vectorlike concepts than are associated with quantum state or wave function concepts. This work represents a novel way of using well-developed network analysis techniques and suggests such techniques could be used for other purposes as well. Published by the American Physical Society 2024

Correlation Between Dynamic Spray Plume and Drug Deposition of Solution-Based Pressurized Metered-Dose Inhalers.

Background: The lack of visual dynamic spray characterization has made the understanding of the physical processes governing atomization and drug particle formation difficult. This study aimed to investigate the changes in the spray plume morphology and aerodynamic particle size of solution-based pressurized metered-dose inhalers (pMDIs) under different conditions to achieve better drug deposition. Methods: Solution-based pMDIs were studied, and the effects of various factors, such as propellant concentration, orifice diameters, and atomization chamber volume, on drug deposition were examined by analyzing the characteristics of spray plume and aerodynamic particle size. Results: Reducing the actuator orifice and spray area led to a concentrated spray plume and increased duration and speed. Moreover, the aerodynamic particle sizes D50 and D90 decreased, whereas D10 remained relatively unchanged. Decreasing the atomization chamber volume of the actuator led to reduced spray area and an increased duration but a decreased plume velocity. D90 exhibited a decreasing trend, whereas D10 and D50 remained relatively unchanged. Reducing the propellant concentration in the prescription, the spray area and the plume velocity first decreased and then increased. The duration initially increased and then decreased. The values of D50 and D90 showed an initial decreasing followed by an increasing trend, whereas D10 remained relatively unchanged. Conclusions: During the development process, attention should be paid to the changes in the spray area, spray angle, duration, and speed of the spray plume. This study recommended analyzing the characteristics of the spray plume and combining the data of two or more aerodynamic particle size detection methods to verify the deposition in vitro to achieve rapid screening and obtain high lung deposition invivo.

Screening of New Dendrobium officinale Strains Adapted to Karst Forest Environmental Stress Based on Electrophysiological Detection Method

At present, the main methods of breeding Dendrobium officinale (D. officinale) include introduction and domestication, selective breeding, hybrid breeding, and mutation breeding. In the process, traditional methods of field investigation of agronomic traits are often used to select resistant varieties. Although these breeding methods are effective, they have a certain degree of subjectivity and empiricism, and the breeding cycle is long. Electrophysiological instruments were used in this experiment to test the material transport and metabolic capacity of D. officinale in a karst forest epiphytic environment and to quickly evaluate the suitability of different strains of D. officinale in a drought environment. These instruments detected the data on electrophysiological information of leaves of different strains of D. officinale under long-term drought conditions, providing immediate access to the inherent electrophysiological information of the leaves of these strains. Based on the electrophysiological parameters of D. officinale leaves as defined by the inherent electrophysiological information of plants, the water metabolism, nutrient transport, and metabolic capacity in different leaves were evaluated. The key electrophysiological indexes were verified by combining the results of chlorophyll fluorescence and chlorophyll content. The results indicate the following: (1) Parameters defined based on electrophysiological information effectively characterized the differences in intracellular water utilization, the nutrient transport status, and the metabolic capacity of different D. officinale strains. (2) The intrinsic physiological resistance, intrinsic physiological reactance, active nutrient transport capacity, and passive nutrient transport capacity were closely related to the growth status and chlorophyll function of D. officinale leaves. These electrophysiological parameters could serve as critical indicators for evaluating the drought resistance of D. officinale. (3) Under severe drought stress, strain LH1 exhibited less leaf damage, adequate water/nutrient supply, vigorous life activities, and excellent drought resistance. We found that strain LH1 demonstrates better adaptation to the arid environment of karst forest lands. The electrophysiological detection method employed in this study offers a new technique for screening wild-cultivated D. officinale resistance strains. The results indicate that the real-time online leaf electrophysiological information measured by the method in this study can characterize the energy and material metabolism of crops, greatly improving the efficiency of crop-variety selection and reducing costs. These conclusions can be used to obtain real-time information on the transmission of water and nutrients within plant cells and can provide theoretical support for studying the adaptation mechanisms of crops to adverse environments.

Modal Identification Techniques for Concrete Dams: A Comprehensive Review and Application

Throughout history, the implementation of structural health monitoring systems has played a crucial role in evaluating the responses of dams to environmental and human-induced threats. By continuously monitoring structural integrity and analyzing dynamic characteristics, these systems offer a robust alternative to traditional visual inspection methods, ensuring the long-term safety of dams. This paper delves into the intricate process of operational modal analysis applied to dams, encompassing data collection, preprocessing, and the utilization of diverse modal identification techniques across both time and frequency domains. Moreover, it explores innovative approaches aimed at overcoming challenges encountered in previous methodologies. Also, the evolution of automated modal identification techniques and their application in dams are investigated. It explores the advancements in this field and their implications for enhancing the efficiency and accuracy of modal analysis processes. Furthermore, this paper evaluates the effectiveness of damage detection methods in dams based on operational modal identification.

Parallel Implementation of K-Best Quadrature Amplitude Modulation Detection for Massive Multiple Input Multiple Output Systems

Massive MIMO (Multiple Input Multiple Output) systems impose significant processing burdens along with strict latency requirements. The combination of large-scale antenna arrays and wide bandwidth requirements for next-generation wireless systems creates an exponential increase in frontend to backend data. Balancing the processing latency and reliability is critical for baseband processing tasks such as QAM detection. While linear detection algorithms have low computational complexity, their use in Massive MIMO scenario has heavy degradation in error performance. Nonlinear detection methods such as Maximum Likelihood and Sphere Decoding have good error performance, but they suffer from high, variable, and uncontrollable computational complexity. For such cases, the K-best QAM detection algorithm can provide required control over the system performance while maintaining near-ML error performance. In this paper, hard-output, as well as soft-output K-best QAM detection, is implemented in a CPU by utilizing the multiple cores combined with vector processing. Similarly, hard-output detection in a GPU is implemented by leveraging the SIMD (Single Instruction, Multiple Data) architecture and Warp-based execution model. The processing time per bit and the energy consumption per bit are compared for CPU and GPU implementations for QAM constellation density and MIMO array size. The GPU implementation shows up to 5× processing latency per bit improvement and up to 120× energy consumption per bit improvement over the CPU implementation for typical QAM constellations such as 4, 16, and 64 QAM. GPU implementation also shows up to 125× improvement over CPU implementation in energy consumption per bit for larger MIMO configurations such as 24 × 24 and 32 × 32. Finally, the soft-output detector is combined with a LDPC (Low-Density Parity Check) decoder to obtain the FER (Frame Error Rate) performance for CPU implementation. The FER is then combined with frame processing latency to form a Goodput metric to demonstrate the latency and reliability tradeoff.