Low Complexity Research Articles

Molecular Interactions between Tau Protein and TIA1: Distinguishing Physiological Condensates from Pathological Fibrils.

The interaction of tau protein with other key proteins essential for stress granule formation determines their functional and pathological impact. In a biological framework, the synergy between Alzheimer's associated tau protein and the stress granule core protein TIA1 is widely recognized. However, the molecular details of this association remain unclear. In this study, we throw light on the importance of the state in which the TIA1 exists in mediating its association with the tau protein. Investigations were carried out on the three repeat constructs of tau (K19) and different structures formed by TIA1. Specifically, the condensate formed by TIA1 full-length (TIA1-FL) protein as well as fibril formed by low complexity domain of TIA1 (TIA1-LCD). The dynamics of K19 inside TIA1-FL condensates and the aggregation kinetics of K19 in the presence of TIA1-LCD fibrils were examined using various biophysical techniques. Relaxation-based solution NMR spectroscopic investigations suggest a weak interaction with TIA1 condensates and indicated a reduction in the dynamics of K19 within these TIA1 condensates. In contrast, a significant interaction was observed between K19, and TIA1-LCD fibrils primarily mediated through 321KCGS324 and 306VQIVYKPVDLSKV317. Our findings emphasize that the interaction between Tau and TIA1 varies depending on whether TIA1 is in its physiological condensate form or its pathological fibril state.

Retraction Note: A novel approach of low complexity distributed UA algorithm is used for traffic load balancing and interference in next generation networks

Retraction Note: A novel approach of low complexity distributed UA algorithm is used for traffic load balancing and interference in next generation networks

Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques

Flotation deinking is one of the most widely used techniques for the separation of ink particles from cellulose fibers during the process of paper recycling. It is a complex process influenced by a variety of factors, and is difficult to represent and usually results in models that are inconvenient to implement and/or interpret. In this paper, a comprehensive study of several machine learning methods for the prediction of flotation deinking performance is carried out, including support vector regression, regression tree ensembles (random forests and boosting) and Gaussian process regression. The prediction relies on the development of a limited dataset that assumes representative data samples obtained under a variety of laboratory conditions, including different reagents, pH values and flotation residence times. The results obtained in this paper confirm that the machine learning methods enable the accurate prediction of flotation deinking performance even when the dataset used for training the model is limited, thus enabling the determination of optimal conditions for the paper recycling process, with only minimal costs and effort. Considering the low complexity of the Gaussian process regression compared to the aforementioned ensemble models, it should be emphasized that the Gaussian process regression gave the best performance in estimating fiber recovery (R2 = 97.77%) and a reasonable performance in estimating the toner recovery (R2 = 86.31%).

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm (Pf\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_f$$\\end{document}) of 10% while still ensuring the probability of detection (Pd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_d$$\\end{document}) of over 90%.

How Are Diagnosis-Related Groups and Staffing Allocation Systems Associated with the Complexity of Nursing Care? An Observational Study.

In Italy, Diagnosis-Related Groups (DRGs) have been adopted for hospital services reimbursement. In some Italian regions, nurse staffing allocation is purely volume-based, with different minutes/patient/day categories determined by the type of hospital ward. The Information System of Nursing Performance (SIPI) is a valid and reliable tool assessing nursing care complexity as an indicator of the actual nursing care demand. Evidence is lacking about the ability of current resource allocation methods to account for the nursing care demand. To evaluate the association between (1) DRG rates and nursing care complexity and (2) hospital ward categories of nurse staffing and nursing care complexity. All patients discharged from the medical department of an Italian hospital over a data collection period were eligible. To assess the association between nursing care complexity (SIPI) and DRGs, the distribution of the DRG rate (median and first-third quartile) was compared for cases with high or low complexity. To evaluate the association between nursing care complexity (SIPI) and nurse staffing, the frequency of high complexity within nurse staffing categories (120/180/240 min/patient/day) was compared. Because the sample was very large, methods of statistical inference were not applied, and only descriptive measures were reported. 6872 hospitalizations were included. The median DRG rate for high and low complexity admissions were very similar (EUR 3536 and EUR 3285, respectively). The proportion of admissions with high complexity decreased for wards with higher staffing allocation rates. DRG reimbursement and the nurse staffing allocation systems were ineffective in accounting for nursing care complexity. The SIPI could help identify areas requiring more financial and staffing resources for nursing care.

Intuitive-K-prototypes: A mixed data clustering algorithm with intuitionistic distribution centroid

Data sets are usually mixed with numerical and categorical attributes in the real world. Data mining of mixed data makes a lot of sense. This paper proposes an Intuitive-K-prototypes clustering algorithm with improved prototype representation and attribute weights. The proposed algorithm defines intuitionistic distribution centroid for categorical attributes. In our approach, a heuristic search for initial prototypes is performed. Then, we combine the mean of numerical attributes and intuitionistic distribution centroid to represent the cluster prototype. In addition, intra-cluster complexity and inter-cluster similarity are used to adjust attribute weights, with higher priority given to those with lower complexity and similarity. The membership and non-membership distance are calculated using the intuitionistic distribution centroid. These distances are then combined parametrically to obtain the composite distance. The algorithm is judged for its clustering effectiveness on the real UCI data set, and the results show that the proposed algorithm outperforms the traditional clustering algorithm in most cases.

A novel highly efficient alternating direction method of multipliers for large-scale trimmed concave SVM

Support vector machine (SVM) is widely utilized for classification in diverse fields thanks to its superior performance. However, when tackling large-scale SVM problems, it encounters high computational complexity. To address this issue, we develop a novel trimmed concave loss SVM model called Ltri-SVM, which achieves sparsity and robustness simultaneously. The new optimality theory for the nonconvex and nonsmooth Ltri-SVM is developed by our new constructed proximal stationary point. Based on which, a new fast alternating direction method of multipliers (ADMM) with working set and low computational complexity is established for addressing Ltri-SVM. Based on our new established optimality theory, our proposed algorithm will divides the training dataset into two distinct categories: the non-working and the working sets. In every training iteration, the algorithm modifies the parameters associated with the working set, while ensuring that the parameters belonging to the non-working set remain unchanged. This algorithm enables the updating of parameters on smaller datasets, which in turn decreases computational complexity and improves the efficiency of runtime. Further, we prove that our algorithm is global convergence. Numerical experiments have confirmed the excellent performance of our algorithm in terms of accuracy in classification, number of support vector and computational speed, surpassing nine other leading solvers. For instance, when solving the real dataset with over 107 samples, our algorithm is able to complete the task in just 18.92 s, which is much faster than other solvers that take at least 650.4 s.

A multimodal multistream multilevel fusion network for finger joint angle estimation with hybrid sEMG and FMG sensing

Finger joint angle (FJA) estimation, as a dynamic and fine-grained decoding mode, can support intuitive and natural human–machine interactions. This study is pioneering work dedicated to achieving accurate and clinically friendly FJA estimation. In this context, a novel surface electromyography and force myography-based multimodal multistream multilevel fusion network (MMMFNet) is proposed. MMMFNet utilises gate-recurrent units (GRU) networks to capture spatial dependencies among features and adopts a multilevel fusion strategy to extract more comprehensive representations from information sources than a single-level fusion. A cross-stream interaction (CSI) block is added between the GRU networks to focus on important information in the prediction and optimise the learning process and representation capability by facilitating information interaction among branch networks. The experimental results suggest that the MMMFNet has higher estimation accuracy (RMSE: 6.225±0.276 vs. 6.953±0.282, R2: 0.879±0.019 vs 0.846±0.021), shorter training time (4.146±0.074 min vs. 16.536±0.430 min), and lower computational complexity (5.4×106 FLOPs vs 8.8×106 FLOPs) than state-of-the-art fusion methods. Moreover, MMMFNet, which quantifies feature importance using attention weights, is the first explainable multimodal model for hand movement intention decoding. In summary, compared with state-of-the-art methods, MMMFNet can not only estimate FJAs more accurately but is also more clinically friendly.

Using bioinformatics to investigate functional diversity: a case study of MHC diversity in koalas.

Conservation genomics can greatly improve conservation outcomes of threatened populations, including those impacted by disease. Understanding diversity within immune gene families, including the major histocompatibility complex (MHC) and toll-like receptors (TLR), is important due to the role they play in disease resilience and susceptibility. With recent advancements in sequencing technologies and bioinformatic tools, the cost of generating high-quality sequence data has significantly decreased and made it possible to investigate diversity across entire gene families in large numbers of individuals compared to investigating only a few genes or a few populations previously. Here, we use the koala as a case study for investigating functional diversity across populations. We utilised previous target enrichment data and 438 whole genomes to firstly, determine the level of sequencing depth required to investigate MHC diversity and, secondly, determine the current level of diversity in MHC genes in koala populations. We determined for low complexity, conserved genes such as TLR genes 10 × sequencing depth is sufficient to reliably genotype more than 90% of variants, whereas for complex genes such as the MHC greater than 20 × and preferably 30 × sequencing depth is required. We used whole genome data to identify 270 biallelic SNPs across 24 MHC genes as well as copy number variation (CNV) within class I and class II genes and conduct supertype analysis. Overall, we have provided a bioinformatic workflow for investigating variation in a complex immune gene family from whole genome sequencing data and determined current levels of diversity within koala MHC genes.

A reservoir computing approach in active noise control for vehicle applications

Reservoir Computing (RC) is an alternative approach to Recurrent Neural Networks (RNN) that can avoid some shortcomings of conventional gradient decent-training that are mainly related to high computational complexity and the reduced ability to "learn" long-range dependencies. In RC the input data are transformed into patterns in a high-dimensional space by an RNN called the "reservoir", which remains unchanged during training. The desired output signal is generated as a linear combination of the neuron's signals from the input-excited reservoir. The linear part of the RC architecture, which is called the "readout" can be trained with a simple learning algorithm such as linear regression. In this work an RC architecture is proposed for the attenuation of acoustic disturbances encountered in vehicle cabins such as aircrafts and yachts. At first, a dataset of acoustic pressure measurements from real-world cabins was used to generate the readout and reservoir layers. Subsequent computer simulations demonstrated that the proposed architecture can successfully attenuate such acoustic disturbances. Finally, the performance of RC in Active Noise Cancellation task was compared with both the linear FxLMS algorithm and a conventional RNN, showing that it has several advantages regarding acoustic pressure reduction combined with relatively low computational complexity.

Stochastic weighted particle control for electrostatic particle-in-cell Monte Carlo collision simulations in an axisymmetric coordinate system

The non-uniform grids in the axisymmetric coordinate system pose a significant challenge for electrostatic particle-in-cell/Monte Carlo collision (PIC/MCC) simulations because they require numerous macroparticles to manage numerical heating around the mid-axis. To address this, we have developed a stochastic weighted particle control method that selectively samples small-weight particles, effectively controlling the particle number without inducing numerical heating. This method is based on a rejection-acceptance probability merging scheme, which is easy to implement and has a low time complexity. We have also made essential modifications, including a corrected density deposition scheme, an energy conservation scheme, and the introduction of target weights. By applying this particle control method, the number of macroparticles in the simulation can be reduced by more than one order of magnitude, significantly reducing the required computing time and storage. Furthermore, appropriately setting target weights also enables enhanced resolution of dilute regions with an acceptable increase in computational cost.

Biogeographic shifts in the microbial co-occurrence network features of three domains across complex environmental gradients in subtropical coastal waters

BackgroundBacteria, Archaea, and Microeukaryotes comprise taxonomic domains that interact in mediating biogeochemical cycles in coastal waters. Many studies have revealed contrasting biogeographic patterns of community structure and assembly mechanisms in microbial communities from different domains in coastal ecosystems; however, knowledge of specific biogeographic patterns on microbial co-occurrence relationships across complex coastal environmental gradients remains limited. Using a dense sampling scheme at the regional scale, SSU rRNA gene amplicon sequencing, and network analysis, we investigated intra- and inter-domain co-occurrence relationships and network topology-based biogeographic patterns from three microbial domains in coastal waters that show environmental gradients across the inshore-nearshore-offshore continuum in the East China Sea.ResultsOverall, we found the highest complexity and connectivity in the bacterial network, the highest modularity in the archaeal network, and the lowest complexity, connectivity, and modularity in the microeukaryotic network. Although microbial co-occurrence networks from the three domains showed distinct topological features, they exhibited a consistent biogeographic pattern across the inshore-nearshore-offshore continuum. Specifically, the nearshore zones with intermediate levels of terrestrial impacts reflected by multiple environmental factors (including water temperature, salinity, pH, dissolved oxygen, and nutrient-related parameters) had a higher intensity of microbial co-occurrence for all three domains. In contrast, the intensity of microbial co-occurrence was weaker in both the inshore and the offshore zones at the two ends of the environmental gradients. Archaea occupied a central position in the microbial inter-domain co-occurrence network. In particular, members of the Thaumarchaeota Marine Group I (MGI, now placed within the Family Nitrosopumilaceae of the Phylum Thermoproteota) appeared to be the hubs in the biogeographic shift between inter-domain network modules across environmental gradients.ConclusionsOur work offers new insights into microbial biogeography by integrating network features into biogeographic patterns, towards a better understanding of the potential of microbial interactions in shaping biogeographic patterns of coastal marine microbiota.

Design of multimodal transformable wheels for amphibious robotic vehicles

Amphibious vehicles are versatile for locomotion on both water and land; to reduce the complexities of structure and control, an amphibious vehicle is expected to use a single propulsion system without additional screw propellers or water jets; as vehicles are typically driven by wheels that are efficient for smooth land locomotion, how to make the wheels also as water propellors for multimodal transformations is meaningful, while such wheels are less explored. From this motivation, in this short article, we report the design of a multimodal transformable wheel with three driving modes (wheel mode, forward-propellor mode, and backward-propellor mode) and the integrated mode-locks for each driving mode. To achieve the multimodal transformations of the wheel, we design the integrated mode-transform mechanism and mode-lock mechanism, the two mechanical implementations are driven by only two actuators and are compact with low inertia and low control complexity, with the kinematic properties provided during the wheel transformations.

Targeting the TDP-43 low complexity domain blocks spreading of pathology in a mouse model of ALS/FTD

Abnormal cytoplasmic localization and accumulation of pathological transactive response DNA binding protein of 43 kDa (TDP-43) underlies several devastating diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP). A key element is the correlation between disease progression and spatio-temporal propagation of TDP-43-mediated pathology in the central nervous system. Several lines of evidence support the concept of templated aggregation and cell to cell spreading of pathological TDP-43. To further investigate this mechanism in vivo, we explored the efficacy of capturing and masking the seeding-competent region of extracellular TDP-43 species. For this, we generated a novel monoclonal antibody (mAb), ACI-6677, that targets the pathogenic protease-resistant amyloid core of TDP-43. ACI-6677 has a picomolar binding affinity for TDP-43 and is capable of binding to all C-terminal TDP-43 fragments. In vitro, ACI-6677 inhibited TDP-43 aggregation and boosted removal of pathological TDP-43 aggregates by phagocytosis. When injecting FTLD-TDP brain extracts unilaterally in the CamKIIa-hTDP-43NLSm mouse model, ACI-6677 significantly limited the induction of phosphorylated TDP-43 (pTDP-43) inclusions. Strikingly, on the contralateral side, the mAb significantly prevented pTDP-43 inclusion appearance exemplifying blocking of the spreading process. Taken together, these data demonstrate for the first time that an immunotherapy targeting the protease-resistant amyloid core of TDP-43 has the potential to restrict spreading, substantially slowing or stopping progression of disease.

A-005 Comparison of Performance of High Sensitive Troponin I (cTNIH) by “Point of care” (POC) and Automated cTNIH Immunoassays in Patients at Low Risk of Heart Attack in a Chilean Emergency Unit; Impact on E.R Department Efficiency

Abstract Background Most cases of death from Acute Myocardial Infarction (AMI) occur in the first hours after the manifestation of clinical symptoms, constituting 40 to 65% in the first hour and approximately 80% in the first 24 hours. For the safe, effective, and rapid diagnosis of a patient with suspected AMI, international guidelines recommend that it be done based on clinical symptoms, together with electrocardiogram and analysis of biomarkers in the context of suspected of non-ST-elevation myocardial infarction (NSTEMI). Troponins are the biomarkers of choice with level A level evidence and the ability to perform screening in patients complaining of chest pain and refer it to the correct clinical approach based on the 0h/1h algorithm. To date, the successful clinical implementation of these rapid algorithms is restricted to hospitals working with well-defined workflows and a robust central laboratory structure. The Atellica® vTLi analyzer is based on the chemiluminescence methodology for the quantification of hs-cTnI levels with low sample volume, low complexity, and may enable the rapid management of patients with chest pain and application of 0h/1h algorithm. Objectives The aim of the study is to evaluate clinical performance in effectiveness and efficiency of the hs-cTnI Atellica® vTLi point of care test to rule out acute myocardial infarction in subjects with low-risk chest pain established with a validated predictive rule, in an adult-Chilean emergency unit, compared to clinical evaluation and the commonly used ADVIA Centaur® troponin test. Methods Descriptive, observational, longitudinal study. Patients over 18 years of age, consulting with acute chest pain of at least one hour from symptom onset and a HEART SCORE less than or equal to 3 points were recruited. A venous blood sample was requested for hs-cTnI analysis by Atellica® vTLi Siemens Healthineers and ADVIA Centaur® Siemens Healthineers. The time difference between sample collection and start of sample analysis; the time difference between sample collection and release of sample results was recorded and the concordance between the two results was determined. Results 50 subjects were included in the study. The difference between blood sampling and start of sample analysis was 5 minutes with Atellica® vTLi POC and 68 minutes with ADVIA Centaur®. The concordance of hs-cTnI Atellica® vTLi POC and ADVIA Centaur® was 94% for predicting medical discharge and confirmed acute myocardial infarction. Conclusions The study findings support the safety and efficiency of the hs-cTnI POC test by providing faster results than central laboratory tests with the same analytical quality, with direct impact on reducing care times in patients consulting for low-risk chest pain.

MD-BiMamba: An aero-engine inter-shaft bearing fault diagnosis method based on Mamba with modal decomposition and bidirectional features fusion strategy

Inter-shaft bearing fault diagnosis can greatly save maintenance costs and guarantee the normal operation of aero-engines. However, existing methods are limited in their ability to handle sensor signals with high dimensional and complex noise. To tackle the above problems, we propose a Mamba network structure with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and bidirectional feature fusion for aero-engine inter-shaft bearing fault diagnosis, called MD-BiMamba. Firstly, we utilize the CEEMDAN technique to decompose the sensor signals of inter-shaft bearings and extract the intrinsic modal function by improving the noise addition method and decomposition strategy. Then, bidirectional Mamba is designed to extract the bidirectional long-range correlation of the sequence to achieve global feature extraction and fast detection of fault types with linear complexity. Moreover, an adaptive attention fusion method is proposed to achieve bidirectional feature fusion. Finally, we use the label smoothing regularization strategy to optimize the cross-entropy loss and enhance the generalization performance. Experimental results on real inter-shaft bearing datasets and noisy datasets show that the proposed method achieves 99.88 % and 95.81 % accuracy in the standard dataset and −10 dB noise environment, respectively, which is superior to other remarkable methods, in addition to achieving lower model complexity.

A lightweight visual mamba network for image recognition under resource-limited environments

The Vision Transformers (ViTs) models show great potential in recognition due to their excellent self-attention mechanisms. However, they often need more computational complexity, making achieving high performance in resource-constrained environments challenging. This paper proposes a lightweight visual model (called LightViM) to tackle these challenges, specifically devised for recognition challenges under resource-constrained environments. Considering the imperative of reducing the model’s computational complexity and resource consumption , we propose lightweight local–global feature fusion modules based on Mamba (LGF-Mamba) aimed at integrating spatially detailed local information with globally contextualized features while maintaining lower linear time complexity. In LGF-Mamba, Mamba sub-blocks are first designed to extract global information; then, for the rapid extraction of local features, LocalE module is designed and integrated into LGF-Mamba, exploring more comprehensive feature representation. This mechanism efficiently directs the network to integrate features from different spatial scales, thereby improving the recognition performance in resource-constrained environments. Experimental results indicate that, in comparison to other leading-edge lightweight methods, the proposed procedure simultaneously achieves superior recognition performance and minimal resource consumption.

High-power and ultra-wide-tunable fiber-type external-cavity diode lasers

High-power and wide-tunable fiber-type external-cavity diode lasers (FTECDLs) are of pivotal importance across various scientific and practical applications, such as polarization lidar, communication, sensing, and spectroscopy. As the output power and tuning characteristics are currently much lower than the saturation power and bandwidth of semiconductor gain media, further improvements conventionally require additional gain media in parallel and external amplifiers, resulting in lower energy efficiency and complex configurations. This study implemented energy manipulation in FTECDLs with a single gain medium, overcoming the mutual restriction between low damage threshold and laser output power. This approach makes it possible to completely exploit the intrinsic performance of the semiconductor gain medium. We demonstrated various FTECDL designs, achieving a maximum tuning range of 226.8 nm, maximum output power of 160 mW, and maximum optical signal-to-noise ratio of 86.3 dB with different configurations. These results are the highest levels ever achieved for E, S, C, L, and U bands. Additionally, the achieved polarization extinction ratio of nearly 38 dB has not been previously reported. Our work provides significant guidance for the design of traveling wave ring cavity (TWRC) lasers, including free-space light semiconductor and solid-state lasers with a TWRC.

B-192 Covid on the PCR Pro: a consumer-friendly PCR test

Abstract Background The emergence of COVID-19 highlighted the need for rapid and accurate diagnostic solutions that can be deployed on a broad scale. The complexity and long turn-around-time of PCR testing typically performed in molecular laboratories is not compatible with community-wide screening. To overcome these challenges, we present the Co-Dx PCR Pro™, a rapid and simple test platform developed for self-collection and at-home use. The instrument is controlled from the user’s smartphone or tablet and performs sample treatment, RT-PCR, and DNA melting analysis. An automatic calling algorithm provides a simple, “Detected,” or, “Not detected,” result. We demonstrate the utility of this platform using a test for the detection of SARS-COV-2. Methods A self-collected anterior nasal swab sample and 2 mLs of water are added to the collection cup. Closing the lid of the cup initiates rehydrating a dried Proteinase K mixture to digest proteins in the sample. After a brief Proteinase K heat-kill step, the instrument mixes the sample with a dried PCR master mix. Primers target two SARS-COV-2 specific genes (RdRp and E gene) and the human RNase P gene as a control. The sample undergoes an RT step followed by 45 cycles of PCR and DNA melting analysis. Detection is by an automatic calling algorithm that identifies a SARS-COV-2 amplicon melting peak at 82°C and RNase P peak at 87°C. The total runtime is less than 30 minutes. The limit of detection for the assay was assessed by contriving intact virus in negative sample matrix, which was then applied to dry swabs. In addition, frozen and banked clinical samples were tested using the Logix Smart®™ Covid-19 test as a comparator assay. A third party performed a usability assessment by having (1) naïve users follow the instructions and self-administer the test (2) expert trained users perform an independent assessment of the assay. Participants were then asked to complete a usability assessment. Results The 95% limit of detection was 2,100 copies/swab using contrived nasal swabs. Clinical samples were blinded to the operator and run on the platform. All samples resulted in a diagnosis concordant with the comparator assay. The usability assessment was performed by six naïve users and resulted in a systems usability scale score of “Best” (84.2 ± 14.3, out of 100). The two expert users determined an 80% limit of detection of 700 genome equivalents and “ease of use,” and “satisfaction,” scores of 4.5/5.0 and 5.0/5.0, respectively. Conclusions Performance of the PCR Pro platform is on par with laboratory-based PCR testing. The benefits of our assay are its speed and the low complexity platform accessible to the lay. This platform could improve access to molecular diagnostic testing, including use at home. Future expansion to include common respiratory pathogens (Flu, RSV, Strep) will increase utility. Additionally, anonymized results can be aggregated and provide a wealth of epidemiological information at the community level to better inform public health initiatives and health outcomes.

SCANet: A lightweight deep learning network for massive MIMO CSI feedback based on spatial and channel attention mechanism

Multi-user massive multiple-input multiple-output (MIMO) communication systems consume too much downlink bandwidth due to the huge channel state information (CSI) feedback, deep learning-based CSI feedback approaches fortunately can alleviate the feedback overhead while obtaining an accurate CSI. However, there is a trade-off between the high feedback performance and low computational complexity. In this paper, a low-complexity CSI feedback approach is proposed based on spatial and channel attention mechanism, namely the Spatial and Channel Attention Network (SCANet). Specifically, the spatial and channel attention mechanism makes the network’s attention mainly focus on the specific spatial regions and key feature channels. We devise a serial architecture in the encoder that composes of Spatial and Channel Attention Block (SCAB) and Encoder Transformer Block. Moreover, we design a hybrid architecture in the decoder that composes of the CNNs Block and Decoder Transformer Block. These designs enable the network to effectively extract both global and local CSI features. Computer simulations in both the indoor and outdoor scenarios show that under the same system configurations, the proposed low-complexity SCANet achieves almost the same performance as the state-of-the-art network while reducing the computational complexity by 85.76% fewer floating-point operations per second (FLOPS) on average.