Mutual Information Analysis Research Articles

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure.

BackgroundA healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis.Methods201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson’s correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups.ResultsThere was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson’s correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up.ConclusionThis study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a significant degree of network disruption in organ systems in non-survivors. Network analysis of organ systems may provide insight in developing novel prognostic models for organ allocation in patients with cirrhosis.

Influence of Regular Wave and Ship Characteristics on Mooring Force Prediction by Data-Driven Model

The study of mooring forces is an important issue in marine engineering and offshore structures. Although being widely applied in mooring system, numerical simulations suffer from difficulties in their multivariate and nonlinear modeling. Data-driven model is employed in this paper to predict the mooring forces in different lines, which is a new attempt to study the mooring forces. The height and period of regular wave, length of berth, ship load, draft and rolling period are considered as potential influencing factors. Input variables are determined using mutual information (MI) and principal component analysis (PCA), and imported to an artificial neural network (NN) model for prediction. With study case of 200 and 300 thousand tons ships experimental data obtained in Dalian University of Technology, MI is found to be more appropriate to provide effective input variables than PCA. Although the three factors regarding ship characteristics are highly correlated, it is recommended to input all of them to the NN model. The accuracy of predicting aft spring line force attains as high as 91.2%. The present paper demonstrates the feasibility of MI-NN model in mapping the mooring forces and their influencing factors.

Quantification of the Radiative Effect of Aerosol–Cloud Interactions in Shallow Continental Cumulus Clouds

Abstract The indirect radiative effect of aerosol variability on shallow cumulus clouds is realized in nature with considerable concurrent meteorological variability. Large-eddy simulations constrained by observations at a continental site in Oklahoma are performed to represent the variability of different meteorological states on days with different aerosol conditions. The total radiative effect of this natural covariation between aerosol and other meteorological drivers of total cloud amount and albedo is quantified. The changes to these bulk quantities are used to understand the response of the cloud radiative effect to aerosol–cloud interactions (ACI) in the context of concurrent processes, as opposed to attempting to untangle the effect of individual processes on a case-by-case basis. Mutual information (MI) analysis suggests that meteorological variability masks the strength of the relationship between cloud drop number concentration and the cloud radiative effect. This is shown to be mostly due to variation in solar zenith angle and cloud field horizontal heterogeneity masking the relationship between cloud drop number and cloud albedo. By combining MI and more traditional differential analyses, a framework to identify important modes of covariation between aerosol, clouds, and meteorological conditions is developed. This shows that accounting for solar zenith angle variation and implementing an albedo bias correction increases the detectability of the radiative effects of ACI in simulations of shallow cumulus.

Conserved Luminal C-Terminal Domain Dynamically Controls Interdomain Communication in Sarcolipin.

Sarcolipin (SLN) mediates Ca2+ transport and metabolism in muscle by regulating the activity of the Ca2+ pump SERCA. SLN has a conserved luminal C-terminal domain that contributes to its functional divergence among homologous SERCA regulators, but the precise mechanistic role of this domain remains poorly understood. We used all-atom molecular dynamics (MD) simulations of SLN totaling 77.5 μs to show that the N- (NT) and C-terminal (CT) domains function in concert. Analysis of the MD simulations showed that serial deletions of the SLN C-terminus do not affect the stability of the peptide nor induce dissociation of SLN from the membrane but promote a gradual decrease in both the tilt angle of the transmembrane helix and the local thickness of the lipid bilayer. Mutual information analysis showed that the NT and CT domains communicate with each other in SLN and that interdomain communication is partially or completely abolished upon deletion of the conserved segment Tyr29-Tyr31 as well as by serial deletions beyond this domain. Phosphorylation of SLN at residue Thr5 also induces changes in the communication between the CT and NT domains, which thus provides additional evidence for interdomain communication within SLN. We found that interdomain communication is independent of the force field used and lipid composition, which thus demonstrates that communication between the NT and CT domains is an intrinsic functional feature of SLN. We propose the novel hypothesis that the conserved C-terminus is an essential element required for dynamic control of SLN regulatory function.

Distinctive communication networks in inactive states of β2 -adrenergic receptor: Mutual information and entropy transfer analysis.

Mutual information and entropy transfer analysis employed on two inactive states of human beta-2 adrenergic receptor (β2 -AR) unraveled distinct communication pathways. Previously, a so-called "highly" inactive state of the receptor was observed during 1.5 microsecond long molecular dynamics simulation where the largest intracellular loop (ICL3) was swiftly packed onto the G-protein binding cavity, becoming entirely inaccessible. Mutual information quantifying the degree of correspondence between backbone-Cα fluctuations was mostly shared between intra- and extra-cellular loop regions in the original inactive state, but shifted to entirely different regions in this latest inactive state. Interestingly, the largest amount of mutual information was always shared among the mobile regions. Irrespective of the conformational state, polar residues always contributed more to mutual information than hydrophobic residues, and also the number of polar-polar residue pairs shared the highest degree of mutual information compared to those incorporating hydrophobic residues. Entropy transfer, quantifying the correspondence between backbone-Cα fluctuations at different timesteps, revealed a distinctive pathway directed from the extracellular site toward intracellular portions in this recently exposed inactive state for which the direction of information flow was the reverse of that observed in the original inactive state where the mobile ICL3 and its intracellular surroundings drove the future fluctuations of extracellular regions.

Analysis of the conditional mutual information in ballistic and diffusive non-equilibrium steady-states

The conditional mutual information (CMI) quantifies the amount of correlations shared between A and C given B. It therefore functions as a more general quantifier of bipartite correlations in multipartite scenarios, playing an important role in the theory of quantum Markov chains. In this paper we carry out a detailed study on the behavior of the CMI in non-equilibrium steady-states (NESS) of a quantum chain placed between two baths at different temperatures. These results are used to shed light on the mechanisms behind ballistic and diffusive transport regimes and how they affect correlations between different parts of a chain. We carry our study for the specific case of a 1D bosonic chain subject to local Lindblad dissipators at the boundaries. In addition, the chain is also subject to self-consistent reservoirs at each site, which are used to tune the transport between ballistic and diffusive. As a result, we find that the CMI is independent of the chain size L in the ballistic regime, but decays algebraically with L in the diffusive case. Finally, we also show how this scaling can be used to discuss the notion of local thermalization in non-equilibrium steady-states.

Distribution and diversity of glycocin biosynthesis gene clusters beyond Firmicutes.

Glycocins are the ribosomally synthesized glycosylated bacteriocins discovered and characterized in Firmicutes, only. These peptides have antimicrobial activity against several pathogenic bacteria, including Streptococcus pyogenes , methicillin-resistant Staphylococcus aureus and food-spoilage bacteria Listeria monocytogenes. Glycocins exhibit immunostimulatory properties and make a promising source of new antibiotics and food preservatives akin to Nisin. Biochemical studies of Sublancin, Glycocin F, Pallidocin and ASM1 prove that the nested disulfide-bonds are essential for their bioactivities. Using in silico approach of genome mining coupled with manual curation, here we identify 220 new putative glycocin biosynthesis gene clusters (PGBCs) spread across 153 bacterial species belonging to seven different bacterial phyla. Based on gene composition, we have grouped these PGBCs into five distinct conserved cluster Types I-V. All experimentally identified glycocins belong to Type I PGBCs. From protein sequence based phylograms, tanglegrams, global similarity heat-maps and cumulative mutual information analysis, it appears that glycocins may have originated from closely related bacteriocins, whereas recruitment of cognate glycosyltransferases (GTs) might be an independent event. Analysis further suggests that GTs may have coevolved with glycocins in cluster-specific manner to define distinctive donor specificities of GTs or to contribute to glycocin diversity across these clusters. We further identify 162 hitherto unreported PGBCs wherein the corresponding product glycocins have three or less than three cysteines. Secondary structure predictions suggest that these putative glycocins may not form di-nested disulfide-bonds. Therefore, production of such glycocins in heterologous host Escherichia coli is feasible and may provide novel antimicrobial spectrum and or mechanism of action for varied applications.

Role of Attractive Interactions in Structure Ordering and Dynamics of Glass-Forming Liquids

A key question in glass physics is what the origin of slow glassy dynamics is. The liquid structure is a natural candidate; however, an apparently severe counterexample has been known. Two model glass-forming liquids, with the standard Lennard-Jones interaction potential and its Weeks-Chandler-Andersen variation without the attractive tail, exhibit very similar structures at the two-body level but drastically different dynamical behaviors in the supercooled states. Here we look at the liquid structure through a (many-body) structural order parameter Θ characterizing the packing capability of local particle arrangements. We show that the structures of these two systems seen by Θ are actually very different at a many-body level, but, quite surprisingly, the macroscopic structure (Θ)-dynamics (τ_{α}) relationships commonly follow a Vogel-Fulcher-Tammann-like function. Furthermore, the mutual information analysis reveals strong local structure-dynamics correlations. Therefore, we conclude that attractive interactions affect the liquid structure in a nonperturbative manner, but a general structural origin of slow dynamics holds for these systems.

Power Side-Channel Attack Analysis: A Review of 20 Years of Study for the Layman

Physical cryptographic implementations are vulnerable to so-called side-channel attacks, in which sensitive information can be recovered by analyzing physical phenomena of a device during operation. In this survey, we trace the development of power side-channel analysis of cryptographic implementations over the last twenty years. We provide a foundation by exploring, in depth, several concepts, such as Simple Power Analysis (SPA), Differential Power Analysis (DPA), Template Attacks (TA), Correlation Power Analysis (CPA), Mutual Information Analysis (MIA), and Test Vector Leakage Assessment (TVLA), as well as the theories that underpin them. Our introduction, review, presentation, and survey of topics are provided for the “non expert”, and are ideal for new researchers entering this field. We conclude the work with a brief introduction to the use of test statistics (specifically Welch’s t-test and Pearson’s chi-squared test) as a measure of confidence that a device is leaking secrets through a side-channel and issue a challenge for further exploration.

A long Short-Term memory cyclic model with mutual information for hydrology forecasting: A Case study in the xixian basin

Floods result in substantial damage throughout the world every year. An accurate predictions of floods can significantly alleviate the loss of lives and properties. However, due to the complexity during flood formation, the accuracy of traditional flood forecasting models suffer from the performance degradation with the increasing of required prediction period. According to the mutual information(MI) analysis to practical hydrologic data of Xixian Basin from year 2011–2018 in China, this paper proposes a long-term cyclic hydrology prediction model with the help of an improved Long Short-Term Memory. Firstly, by flitering, classifying and MI analysis the original data, the hydrological features, e.g., rainfall, reservoir water-level and flow are extracted as the time series features of the long short-term memory cyclic(LSTMC) forecasting model. Next, the structure of the LSTMC model is trained and determined by modeling the rainfall process to reflect the long-term change of flood flow. Finally, the actual flood data is used to verify the output of our model. Compared with some traditional and machine-learning flood forecasting schemes, it can be demonstrated that our model can accurately complete the task of long and short lead time hydrology forecasting.

Enriching Topic Coherence on Reviews for Cross-Domain Recommendation

Abstract With the advent of e-commerce sites and social media, users express their preferences and tastes freely through user-generated content such as reviews and comments. In order to promote cross-selling, e-commerce sites such as eBay and Amazon regularly use such inputs from multiple domains and suggest items with which users may be interested. In this paper, we propose a topic coherence-based cross-domain recommender model. The core concept is to use topic modeling to extract topics from user-generated content such as reviews and combine them with reliable semantic coherence techniques to link different domains, using Wikipedia as a reference corpus. We experiment with different topic coherence methods such as pointwise mutual information (PMI) and explicit semantic analysis (ESA). Experimental results presented demonstrate that our approach, using PMI as topic coherence, yields 22.6% and using ESA yields 54.4% higher precision as compared with cross-domain recommender system based on semantic clustering.

Estimation of seismic quality factor in the time-frequency domain using variational mode decomposition

Seismic attenuation as represented by the seismic quality factor [Formula: see text] has a substantial impact on seismic reflection data. To effectively eliminate the interference of reflection coefficients for [Formula: see text] estimation, a new method is proposed based on the stationary convolutional model of a seismic trace using variational mode decomposition (VMD). VMD is conducted on the logarithmic spectra extracted from the time-frequency distribution of the seismic reflection data generated from the generalized S transform. For the intrinsic mode functions after VMD, mutual information and correlation analysis are used to reconstruct the signals, which effectively eliminates the influence of the reflection coefficients. The difference between the two reconstructed logarithmic spectra within the selected frequency band produces a better linear property, and it is more suitably approximated with the linear function compared to the conventional spectral-ratio method. Least-squares fitting is finally applied for [Formula: see text] estimation. Application of this method to synthetic and real data examples demonstrates the stabilization and accuracy for [Formula: see text] estimation.

Evaluation of soil moisture from CCAM-CABLE simulation, satellite-based models estimates and satellite observations: a case study of Skukuza and Malopeni flux towers

Abstract. Reliable estimates of daily, monthly and seasonal soil moisture are useful in a variety of disciplines. The availability of continuous in situ soil moisture observations in southern Africa barely exists; hence, process-based simulation model outputs are a valuable source of climate information, needed for guiding farming practices and policy interventions at various spatio-temporal scales. The aim of this study is to evaluate soil moisture outputs from simulated and satellite-based soil moisture products, and to compare modelled soil moisture across different landscapes. The simulation model consists of a global circulation model known as the conformal-cubic atmospheric model (CCAM), coupled with the CSIRO Atmosphere Biosphere Land Exchange model (CABLE). The satellite-based soil moisture data products include satellite observations from the European Space Agency (ESA) and satellite-observation-based model estimates from the Global Land Evaporation Amsterdam Model (GLEAM). The evaluation is done for both the surface (0–10 cm) and root zone (10–100 cm) using in situ soil moisture measurements collected from two study sites. The results indicate that both the simulation- and satellite-derived models produce outputs that are higher in magnitude range compared to in situ soil moisture observations at the two study sites, especially at the surface. The correlation coefficient ranges from 0.7 to 0.8 (at the root zone) and 0.7 to 0.9 (at the surface), suggesting that models mostly are in an acceptable phase agreement at the surface than at the root zone, and this was further confirmed by the root mean squared error and the standard deviation values. The models mostly show a bias towards overestimation of the observed soil moisture at both the surface and root zone, with the CCAM-CABLE showing the least bias. An analysis evaluating phase agreement using the cross-wavelet analysis has shown that, despite the models' outputs being in phase with the in situ observations, there are time lags in some instances. An analysis of soil moisture mutual information (MI) between CCAM-CABLE and the GLEAM models has successfully revealed that both the simulation and model estimates have a high MI at the root zone as opposed to the surface. The MI mostly ranges between 0.5 and 1.5 at both the surface and root zone. The MI is predominantly high for low-lying relative to high-lying areas.

Nonlinear characterization and interaction in teleconnection patterns

The complexity in atmospheric parameters not only place a limit on their predictability but also influence their mutual interaction. Our goal in this research is to investigate (i) the nonlinear characteristics, and (ii) nonlinear interaction patterns amongst twelve teleconnection indices including Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Pacific North Atlantic American Index (PNA), Quasi-biennal Oscillation (QBO), Arctic Oscillation (AO), and Western Pacific Index (WP), using tools from chaos theory. Monthly data for the indices between January 1948 and December 2016 were obtained and analyzed in this study. AMO and QBO showed the least chaotic behaviour with 0-1 test values of 0 and 0.5458. High significant correlations greater than 0.9 were observed between PNA-NAO and WP-PNA pairs. High values of recurrence rate, determinism, laminarity and Lmax suggests that AMO and QBO are periodic and stochastic signals respectively. Nonlinear interactions between pairs of teleconnection indices were investigated using interdependence measure, mutual information and cross correlation analysis. Strongest interactions were found in the PNA-NAO, WP-NAO, AO-NAO, AO-PNA, WP-AO, and WP-PNA pair of teleconnection indices.

Prediction of $${\mathrm{PM}}_{2.5}$$ concentration based on multi-source data and self-organizing fuzzy neural network

The fine particulate matter ( $$\mathrm{PM}_{2.5}$$ ) problem has become one of the biggest topics of concern in many cities, such as Beijing in China. This study attempts to employ a self-organizing fuzzy neural network (SOFNN) to predict the hourly concentration of $$\mathrm{PM}_{2.5}$$ using multi-source data. The multi-source data including meteorological data, pollutant concentration data and image data are first obtained through different methods. Then, a novel SOFNN, which is optimized by an improved gradient descent algorithm, is proposed based on the mutual information (MI) and sensitivity analysis (SA). More importantly, MI and SA are introduced to effectively adjust the structure of fuzzy neural network without changing the performance of the original network. Finally, the MI- and SA-based SOFNN is deployed to infer $$\mathrm{PM}_{2.5}$$ concentration at the study area. The experimental results show that our proposed model can achieve a more compact network structure, higher prediction performance and lower computation time as compared with state-of-the-art or popular methods.

Assessing the accuracy of direct-coupling analysis for RNA contact prediction.

Many noncoding RNAs are known to play a role in the cell directly linked to their structure. Structure prediction based on the sole sequence is, however, a challenging task. On the other hand, thanks to the low cost of sequencing technologies, a very large number of homologous sequences are becoming available for many RNA families. In the protein community, the idea of exploiting the covariance of mutations within a family to predict the protein structure using the direct-coupling-analysis (DCA) method has emerged in the last decade. The application of DCA to RNA systems has been limited so far. We here perform an assessment of the DCA method on 17 riboswitch families, comparing it with the commonly used mutual information analysis and with state-of-the-art R-scape covariance method. We also compare different flavors of DCA, including mean-field, pseudolikelihood, and a proposed stochastic procedure (Boltzmann learning) for solving exactly the DCA inverse problem. Boltzmann learning outperforms the other methods in predicting contacts observed in high-resolution crystal structures.

Data mining-based detection of the physical and chemical characteristics of Chinese medical herbs aqueous decoction in spray drying yield

Spray drying hot melt adhesive wall is mainly related to the characteristics of the material itself. The previous study of our group suggested that there was a nonlinear correlation between the physical properties and chemical compositions of Chinese Medical Herbs Aqueous (CMHDs) and the hot-melt adhesive wall of spray drying. In this study, we investigated 66 herbs from Chinese medicinal materials commonly used in Chinese pharmacopeia. Physical properties including dynamic viscosity, equilibrium surface tension, and dynamic surface tension (DST) were determined. Chemical compositions including the organic acids, low molecular weight saccharides, protein, total phenols, and tannins in their CMHDs were also analyzed. Then we conducted nonlinear-based data mining using a mutual information method based on entropy and association algorithm. Mutual information analysis showed that the score of DST10ms was higher than that of equilibrium surface tension, indicating that DST10ms was the physical property index that had the greatest influence on the hot melt adhesive wall of spray drying in this study. The higher the DST10ms, the higher the incidence of hot melt adhesive wall and the lower the yield of spray drying. The result of information entropy is shown the content of organic acids and low molecular weight saccharides among the chemical compositions directly affected the spray drying yield, which citric acid, malic acid, glucose, and fructose content had the greatest influence, and the results are in good agreement with those of previous experiments. The study also found that the water extraction liquid of fruit, flowers, seeds, and other parts of the medicine herbs has a low spray drying yield, the phenomenon of the observation is given priority to hot melt adhesive wall. The results of this study show that mutual information derived from association algorithms, based on information entropy, is useful to study correlations between parameters during the preparation of traditional Chinese medicines. Data mining has the potential to provide information for searching spray drying correlations by uncovering hidden rules between the physical and chemical properties of CMHDs, and the spray drying yield in a data set.

Comparison Study of Radiomics and Deep Learning-Based Methods for Thyroid Nodules Classification Using Ultrasound Images

Thyroid nodules have a high prevalence and a small percentage is malignant. Many non-invasive methods have been developed with the help of the Internet of Things to improve the detection rate of malignant nodules. These methods can be roughly categorized into two classes: radiomics based and deep learning based approaches. In general, convolutional neural networks based deep learning methods have achieved promising performance in many medical image analysis and classification applications; however, no existing comparison has been done between radiomics based and deep learning based approaches. Therefore, in this paper, we aim to compare the performance of radiomics and deep learning based methods for the classification of thyroid nodules from ultrasound images. On one hand, we developed a radiomics based method, which consists of extracting high throughput 302-dimensional statistical features from pre-processed images. Then dimension reduction was performed using mutual information and linear discriminant analysis respectively to achieve the final classification. On the other hand, a deep learning based method was also developed and tested by pre-training a VGG16 model with fine-tuning. Ultrasound images including 3120 images (1841 benign nodules and 1393 malignant nodules) from 1040 cases were retrospectively collected. The dataset was divided into 80% training and 20% testing data. The highest accuracies yielded on the testing data for radiomics and deep learning based methods were 66.81% and 74.69%, respectively. A comparison result demonstrated that the deep learning based method can achieve a better performance than using radiomics.

A Time Sharing Hybrid Probabilistic Shaping Scheme for Nonbinary LDPC Codes

A time sharing (TS) hybrid probabilistic shaping (HPS) scheme for nonbinary (NB) LDPC codes is proposed. For the conventional probabilistic shaping (CPS) scheme, the demodulator uses bit-metric decoding (BMD) for binary LDPC codes and NB LDPC codes. However, the demodulator for NB LDPC codes adopts symbol-metric decoding (SMD) on the AWGN channel to avoid the loss of symbol-to-bit mapping and obtain better performance than binary LDPC codes. In order to take advantage of SMD demodulation for NB LDPC codes, the NB-LDPC information symbols use constant composition distribution matcher (CCDM) to generate quadrature amplitude modulation (QAM) symbols with unequal probability distribution, while the NB-LDPC parity symbols are utilized to yield QAM symbols with approximately uniform distribution. The SMD demodulation of the proposed NB-LDPC-coded TS HPS scheme has lower demodulation complexity than the NB-LDPC-coded CPS scheme. The information-theoretical analysis of the CPS system and the TS HPS system turns out that the proposed NB-LDPC-coded TS HPS scheme is superior to the NB-LDPC-coded CPS scheme for 8QAM and 32QAM format. Simulation results verify the theoretical average mutual information (AMI) analysis and show that the proposed NB-LDPC-coded TS HPS scheme has up-to-0.24 dB and 1.10 dB performance gain compared with the NB-LDPC-coded CPS scheme for 8QAM and 32QAM format, respectively. Compared with the binary LDPC-coded regular Nyquist scheme for 8QAM and 32QAM format, the proposed NB-LDPC-coded TS HPS scheme has up-to-1.31 dB and 2.04 dB performance gain, respectively. Meanwhile, the LDPC code rate of the proposed TS HPS scheme is more flexible than the CPS scheme. Therefore, the proposed NB-LDPC-coded TS HPS scheme is more reliable, efficient, and flexible for 8QAM and 32QAM, and thus suitable for the sixth generation (6G) communication system.

Interleaver Design for Small-Coupling-Length Spatially Coupled Protograph LDPC-Coded BICM Systems Over Wireless Fading Channels

As a class of convolutional error-correction codes (ECCs), spatially coupled protograph low-density parity-check (SC-PLDPC) codes with a sufficiently large coupling length can approach the channel capacity under belief propagation (BP) decoding. However, such codes suffer from poor performance in the case of small coupling lengths. To address the above weakness, we investigate the optimization and analysis of SC-PLDPC-coded bit-interleaved coded modulation (BICM) systems over Rayleigh fading channels. Specially, inspired by the unequal-error-protection (UEP) mechanism of M-ary phase-shift keying/ quadrature amplitude modulation (M-PSK/QAM), we put forward a novel interleaver design scheme, referred to as spatial position match mapping (SPMM) scheme. Based on the mutual information (MI) analysis, the proposed interleaver can be used to significantly boost the performance of the SC-PLDPC codes in BICM systems. Furthermore, we propose a block-scheduling protograph-based extrinsic information transfer (BS-PEXIT) algorithm to estimate the convergence performance (i.e, decoding threshold) of the SPMM-aided SC-PLDPC codes in BICM scenarios. Simulation results are well consistent with the theoretical analyses, and show that the proposed SPMM-aided SC-PLDPC-coded BICM systems are superior to the state-of-the-art counterparts.