Intrinsic Complexity Research Articles

“More Than Meets the Eye”: Cryptic Diversity and Contrasting Patterns of Host-Specificity in Feather Mites Inhabiting Seabirds

Feather mites are useful models for studying speciation due to their high diversity and strong degree of host specialization. However, studies to date have focused on the evolution of higher-level mite taxa while much hidden diversity likely occurs at the level of host genera and species. In this study, we examined the diversity and evolution of feather mites infesting six sympatric seabird species from six genera, breeding in the Cape Verde archipelago. We report 32 feather mite morphospecies categorized into ten genera and three families, of which nine correspond to new, undescribed species. Molecular data corroborated morphological species descriptions, except for two morphologically-cryptic, but genetically distinct mite lineages related to Zachvatkinia oceanodromae and Laminalloptes simplex. Using these communities, we then applied a co-structure approach to test the contribution of ectosymbiont and host factors in driving feather mite evolution. Most seabird species hosted specific and unique feather mite species, even under sympatric conditions, and in general, feather mite species exhibited strong host-driven genetic structure. However, patterns of genetic differentiation were variable. That is, some mite species are more generalist than others and mite lineages/haplotypes can be shared by related seabird species. Interestingly, host-specific mites (eg., Zachvatkinia spp.) tend to display much higher intra-specific diversity compared to more generalist mites (eg., Microspalax and Plicatalloptes spp.). We discuss ectosymbiont and host life-history traits that might generate these patterns, such as host dispersal and breeding behavior and/or mite spatial and trophic specialization. Our findings highlight both the vast and largely unrecognized diversity of avian feather mites on seabirds, and the intrinsic complexity of the ecological processes underlying the evolution of these ectosymbionts.

Intrinsic Mode Function Complexity Index Using Empirical Mode Decomposition discriminates Normal Sinus Rhythm and Atrial Fibrillation on a Single Lead ECG.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia affecting approximately 3 million Americans, and is a prognostic marker for stroke, heart failure and even death. Current techniques to discriminate normal sinus rhythm (NSR) and AF from single lead ECG suffer several limitations in terms of sensitivity and specificity using short time ECG data which distorts ECG and many are not suitable for real-time implementation. The purpose of this research was to test the feasibility of discriminating single lead ECG's with normal sinus rhythm (NSR) and AF using intrinsic mode function (IMF) complexity index. 15 sets of ECG's with NSR and AF were obtained from Physionet database. Custom MATLAB® software was written to compute IMF index for each of the data set and compared for statistical significance. The mean IMF index for NSR across 15 data sets was 0.37 ± 0.08, and the mean IMF index for ECG with AF was 0.21 ± 0.07 showing robust discrimination with statistical significance (p<0.01). IMF complexity robustly discriminates single lead ECG with normal sinus rhythm and AF. Further validation of this result is required on a larger dataset. The results also motivate the use of this technique for analysis of other complex cardiac arrhythmias such as ventricular tachycardia (VT) or ventricular fibrillation (VF).

Hardware-Oriented Compression of Long Short-Term Memory for Efficient Inference

Long short-term memory (LSTM) and its variants have been widely adopted in processing sequential data. However, the intrinsic large memory requirement and high computational complexity make it hard to be employed in embedded systems. This incurs the need of model compression and dedicated hardware accelerator for LSTM. In this letter, efficient clipped gating and top- $k$ pruning schemes are introduced to convert the dense matrix computations in LSTM into structured sparse-matrix-sparse-vector multiplications. Then, mixed quantization schemes are developed to eliminate most of the multiplications in LSTM. The proposed compression scheme is well suited for efficient hardware implementations. Experimental results show that the model size and the number of matrix operations can be reduced by $32\times$ and $18.5\times$ , respectively, at a cost of less than $1\%$ accuracy loss on a word-level language modeling task.

On the Computational Complexity of Information Hiding

In this work we study the intrinsic complexity of elimination algorithms in effective algebraic geometry and we focus our attention to elimination algorithms produced within the object–oriented paradigm. To this end, we describe a new computation model called quiz game (introduced in [1]) which models the notions of information hiding (due to Parnas, see [8]) and non–functional requirements (e.g. robustness) among other important concepts in software engineering. This characteristic distinguish our model from classical computation models such as the Turing machine or algebraic models.We illustrate our computation model with a non–trivial complexity lower bound for the identity function of polynomials. We show that any object–oriented (and robust) implementation of the identity function of polynomials is necessarily inefficient compared with a trivial implementation of this function. This result shows an existing synergy between Software Engineering and Algebraic Complexity Theory. Keywords: Abstract data type, abstraction function, data structure, information hiding, lower complexity bound, non–functional requirement, quantifier elimination, quiz game, robustness, scientific computing.

Scalable data analytics using crowdsourced repositories and streams

The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master–worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances.

Exploring 3-hinge gyral folding patterns among HCP Q3 868 human subjects.

Comparison and integration of neuroimaging data from different brains and populations are fundamental in neuroscience. Over the past decades, the neuroimaging field has largely depended on image registration to compare and integrate neuroimaging data from individuals in a common reference space, with a basic assumption that the brains are similar. However, the intrinsic neuroanatomical complexity and huge interindividual cortical folding variation remain underexplored. Here we focus on a specific cortical convolution pattern, termed 3-hinge gyral folding, which is the conjunction of gyri from multiple orientations and has unique and consistent anatomically, structurally, and functionally connective patterns across subjects. By developing a novel shape descriptor and a two-stage clustering pipeline, we devise an automatic method to identify 3-hinges in the Human Connectome Project Q3 868 human brains, and further parameterize the complexity of such a pattern and quantify its regularity and variation in terms of 3-hinge number, position, and morphology. Our results not only exhibit the huge interindividual variations, but also reveal regular relationship between gyral hinges and other factors, such as their locations and cortical morphologies. It is found that "line-shape" cortices have relatively more consistent 3-hinge shape pattern distributions, and certain types of 3-hinge patterns favor particular cortical morphologies. In addition, more 3-hinges are found on "line-shape" cortices while their numbers vary more across subjects than those on "non-line-shape" cortices. This study adds new insights into a better understanding of the regularity and variability of human brain anatomy, and their functional aspects.

Core/Shell Bacterial Cables: A One-Dimensional Platform for Probing Microbial Electron Transfer.

Extracellular electron transfer (EET) from electrochemically active bacteria (EAB) plays a critical role in renewable bioelectricity harvesting through microbial fuel cells (MFC). Comprehensive interpretation and interrogation of EET mechanisms can provide valuable information to enhance MFC performance, which however are still restricted by the intrinsic complexity of natural biofilm. Here, we design core/shell EAB-encapsulating cables as a one-dimensional model system to facilitate EET studies, where the local microenvironments can be rationally controlled to establish structure-function correlations with full biological relevance. In particular, our proof-of-concept studies with Shewanella loihica PV-4 ( S. loihica) encapsulating cables demonstrate the precise modulation of fiber diameters (from 6.9 ± 1.1 to 25.1 ± 2.4 μm) and bacteria interactions, which are found to play important roles in programming the formation of different intercellular structures as revealed by in situ optical and ex situ electron microscopic studies. As-formed bacterial cables exhibit conductivity in the range of 2.5-16.2 mS·cm-1, which is highly dependent on the bacteria density as well as the nature and number of intercellular interconnections. Under electron-acceptor limited conditions, the closely contacted bacteria promote the development of high density self-assembling nanomaterials at cellular interfaces which can be directly translated to the increase of EET efficiency (16.2 mS·cm-1) as compared with isolated, remotely connected bacteria samples (6.4 mS·cm-1). Introducing exceeding concentrations of soluble electron acceptors during cell culture, however, substantially suppresses the formation of cellular interconnections and leads to significantly reduced conductivity (2.5 mS·cm-1). Frequency-dependent measurements further revealthat EET of EAB networks share similar characteristics to electron hopping in conductive polymer matrix, including dominant direct current-conduction in the low frequency region, and alternating current-induced additional electron hopping when the applied frequency is above the critical frequency (105 Hz). The current work represents a strategically new approach for noninvasively probing EET with rationally defined microenvironment and cellular interactions across a wide range of length scales, which is expected to open up new opportunities for tackling the fundamentals and implications of EET.

The Progress of Li–S Batteries—Understanding of the Sulfur Redox Mechanism: Dissolved Polysulfide Ions in the Electrolytes

AbstractRechargeable lithium–sulfur batteries have aroused great attention in recent years. Thousands of research articles are published, and among these publications, the majority are dedicated to improving the battery's performance through chemically and physically modifying the sulfur electrode, electrolytes, separator, and lithium anode. However, the single most important aspect, understanding the sulfur redox mechanism, is sparse and overwhelmed by the huge volume of work done on improving the battery's performance. Besides the intrinsic complexity of the sulfur redox chemistry, the most challenging task is to find an effective analytical technique for the quantitative and qualitative determination of the dissolved and solid polysulfides, including elemental sulfur. In this paper, the recent important research aiming to understand the redox mechanism of the sulfur electrode is reviewed in light of the unique analytical techniques used in the research. The review re‐affirms the complexity of the sulfur redox chemistry and lays the background for the future mechanistic research for Li–S batteries.

Discriminative Probabilistic Latent Semantic Analysis with Application to Single Sample Face Recognition

Face recognition is still a challenging issue due to the presence of intrinsic complexity, external variations and number limitation of training samples. In this paper, a novel face recognition method based on probabilistic latent semantic analysis (pLSA) model is developed, which mainly contains two stages: bag-of-words features extraction and semantic representation learning. In the first stage, to extract more structure information, the region-specific dictionary strategy is employed, i.e., generating a dictionary for each region. The encoded and sum-pooled features of all regions are concatenated together. In the second stage, a discriminative pLSA (DpLSA) model is presented, which initializes the word-topic distribution $$P(w|z_k)$$ by the center point of the training data from category k. As a result, the problem of how to choose appropriate number of topics in classical topic model is alleviated, and the training process of DpLSA is very fast only requiring few iterations. Moreover, the discovered topic-document distribution $$P\left( z|d\right) $$ is discriminative and semantic with the dominant topic entry corresponds to the category label of image d, which enables performing classification by $$P\left( z|d\right) $$ directly. Extensive experiments on four representative databases demonstrate that the proposed DpLSA is effective for face recognition under single training sample and possesses a certain degree of robustness to illumination, pose, as well as occlusion.

Giant pituitary adenoma: histological types, clinical features and therapeutic approaches.

Giant pituitary adenomas comprise about 6-10% of all pituitary tumors. They are mostly clinically non-functioning adenomas and occur predominantly in males. The presenting symptoms are usually secondary to compression of neighboring structures, but also due to partial or total hypopituitarism. Functioning adenomas give rise to specific symptoms of hormonal hypersecretion. The use of dopamine agonists is considered a first-line treatment in patients with giant macroprolactinomas. Somatostatin analogs can also be used as primary treatment in cases of growth hormone and thyrotropin producing giant adenomas, although remission of the disease is not achieved in the vast majority of these patients. Neurosurgical treatment, either through transsphenoidal or transcranial surgery, continues to be the treatment of choice in the majority of patients with giant pituitary adenomas. The intrinsic complexity of these tumors requires the use of different therapies in a combined or sequential way. A multimodal approach and a therapeutic strategy involving a multidisciplinary team of expert professionals form the basis of the therapeutic success in these patients.

Hydrogen sulfide pathway and skeletal muscle: an introductory review.

The presence of the H2 S pathway in skeletal muscle (SKM) has recently been established. SKM expresses the three constitutive H2 S-generating enzymes in animals and humans, and it actively produces H2 S. The main, recognized molecular targets of H2 S, that is, potassium channels and PDEs, have been evaluated in SKM physiology in order to hypothesize a role for H2 S signalling. SKM dysfunctions, including muscular dystrophy and malignant hyperthermia, have also been evaluated as conditions in which the H2 S and transsulfuration pathways have been suggested to be involved. The intrinsic complexity of the molecular mechanisms involved in excitation-contraction (E-C) coupling together with the scarcity of preclinical models of SKM-related disorders have hampered any advances in the knowledge of SKM function. Here, we have addressed the role of the H2 S pathway in E-C coupling and the relative importance of cystathionine β-synthase, cistathionine γ-lyase and 3-mercaptopyruvate sulfurtransferase in SKM diseases.

A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting

Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired point forecasting accuracy has been regarded as a challenge because of the intrinsic complexity and instability of the power load. Considering the difficulties of accurate point forecasting, interval prediction is able to tolerate increased uncertainty and provide more information for practical operation decisions. In this study, a novel hybrid system for short-term load forecasting (STLF) is proposed by integrating a data preprocessing module, a multi-objective optimization module, and an interval prediction module. In this system, the training process is performed by maximizing the coverage probability and by minimizing the forecasting interval width at the same time. To verify the performance of the proposed hybrid system, half-hourly load data are set as illustrative cases and two experiments are carried out in four states with four quarters in Australia. The simulation results verified the superiority of the proposed technique and the effects of the submodules were analyzed by comparing the outcomes with those of benchmark models. Furthermore, it is proved that the proposed hybrid system is valuable in improving power grid management.

A semi-heterogeneous approach to combining crude oil price forecasts

Crude oil price forecasting has received increased attentions due to its significant role in the global economy. Accurate crude oil price forecasts often lead to a rapid new production development with higher quality and less cost. Making such accurate forecasts, however, is challenging due to the intrinsic complexity of oil market mechanism. Many techniques have been tested in the crude oil price forecasting literature. Although forecast combination is a well-known method to improve forecast accuracy, generating forecasts using various techniques tend to be labor intensive. How to efficiently generate many individual forecasts for combination becomes a research question in crude oil price forecasting. Recently, several signal decomposition methods have been suggested for processing the oil price signals. In this paper, we propose a semi-heterogeneous approach to combining crude oil price forecasts, which interacts a set of decomposition methods with a set of forecasting techniques. We first decompose the original price series using four decomposition methods, such as Wavelet Analysis, Singular Spectral Analysis, Empirical Mode Decomposition, and Variational Mode Decomposition. We then use four different forecasting techniques, such as Autoregressive Models, Autoregressive Integrated Moving Average Models, Artificial Neural Networks, and Support Vector Regression Models, to forecast the components from each decomposition methods. Finally, we reconstruct the price forecasts from the forecasted components. This process generates 16 price forecasts in total for combination. We test the combination based on all individual forecasts, as well as a subset of the individual forecasts selected using Tabu Search. The experimental results demonstrate that the forecasting models with the addition of a decomposition technique can have an error reduction of 30.6% compared to benchmark models on average. The combined forecasts outperform the individual forecasts on average. Furthermore, comparing with the heterogeneous combination of 4 individual forecasts, the semi-heterogeneous combinations reduce the errors by 56.6% (w/o Tabu Search) and 61.6% (w/ Tabu Search).

Chemical Proteomics of Host-Microbe Interactions.

The dynamic proteome plays numerous roles in the interactions of microbes-whether they are invading pathogens or symbiotic organisms-and their hosts. Host and microbe sense, respond, and manipulate each other's biology via a multitude of mechanisms, resulting in alterations in protein expression or posttranslational modification that influence protein localization, activity, or binding partners. The intrinsic, temporal, and spatial complexity of multispecies systems makes identifying the molecular players challenging. Chemical proteomic approaches apply small molecule chemical tools to interrogate protein function, interactions or modifications. This review highlights recent advances in the application of these methods at the host-microbe interface.

The correlation between the type of forceps application and the rate of levator ani muscle avulsion: A prospective cohort study.

The association between the use of forceps and levator ani muscle (LAM) avulsion seems to be clear-cut. However, whether the lesion is due to the mechanical trauma yielded by the instrument or to the intrinsic complexity of this type of delivery, is yet to be determined. This study aims at determining the difference in LAM avulsion rate between Kielland rotational forceps and non-rotational forceps. Prospective observational study with 94 nulliparous women with forceps-assisted deliveries (FD) between July 2015 and January 2016. 3D-TpUS was performed 6 months after every patient's delivery, during which LAM avulsion, and levator hiatus area and anteroposterior and transverse diameters were assessed. A total of 89 nulliparous were studied, comprising 27 rotational-FD, and 62 non-rotational-FD. No differences in obstetric, intrapartum, or neonatal characteristics were observed between study groups. There were no statistically significant differences in the presence of avulsion between cases of rotational forceps (44.4% vs 35.5%, OR: 1.5 [0.6-3.6]), correction of asinclitism of the fetal head (34.4% vs. 40.4% OR: 0.8 [0.3-1.9]) or station (midforceps: 32.8% vs low forceps: 50.0% OR: 2.0 [0.8-5.1]). We have not observed differences in the LAM-avulsion rate between rotational forceps and non-rotational forceps performed by highly experienced personnel in instrumental deliveries.

Site-Resolved Contributions to the Magnetic-Anisotropy Energy and Complex Spin Structure of Fe/MgO Sandwiches

Fe/MgO-based Magnetic Tunnel Junctions (MTJs) are among the most promising candidates for spintronic devices due to their high thermal stability and high tunneling magnetoresistance. Despite its apparent simplicity, the nature of the interactions between the Fe and MgO layers leads to complex finite size effects and temperature dependent magnetic properties which must be carefully controlled for practical applications. In this letter, we investigate the electronic, structural and magnetic properties of MgO/Fe/MgO sandwiches using first principles calculations and atomistic spin modeling based on a fully parameterized spin Hamiltonian. We find a large contribution to the effective interfacial magnetic anisotropy from the two-ion exchange energy. Minimization of the total energy using atomistic simulations shows a surprising spin spiral ground state structure at the interface owing to frustrated ferromagnetic and antiferromagnetic interactions, leading to a reduced Curie temperature and strong layer-wise temperature dependence of the magnetization. The different temperature dependences of the interface and bulk-like layers results in an unexpected non-monotonic temperature variation of the effective magnetic anisotropy energy and temperature-induced spin-reorientation transition to an in-plane magnetization at low temperatures. Our results demonstrate the intrinsic physical complexity of the pure Fe/MgO interface and the role of elevated temperatures providing new insight when interpreting experimental data of nanoscale MTJs.

Expanding an expanded genome: long-read sequencing of Trypanosoma cruzi.

Although the genome of Trypanosoma cruzi, the causative agent of Chagas disease, was first made available in 2005, with additional strains reported later, the intrinsic genome complexity of this parasite (the abundance of repetitive sequences and genes organized in tandem) has traditionally hindered high-quality genome assembly and annotation. This also limits diverse types of analyses that require high degrees of precision. Long reads generated by third-generation sequencing technologies are particularly suitable to address the challenges associated with T. cruzi’s genome since they permit direct determination of the full sequence of large clusters of repetitive sequences without collapsing them. This, in turn, not only allows accurate estimation of gene copy numbers but also circumvents assembly fragmentation. Here, we present the analysis of the genome sequences of two T. cruzi clones: the hybrid TCC (TcVI) and the non-hybrid Dm28c (TcI), determined by PacBio Single Molecular Real-Time (SMRT) technology. The improved assemblies herein obtained permitted us to accurately estimate gene copy numbers, abundance and distribution of repetitive sequences (including satellites and retroelements). We found that the genome of T. cruzi is composed of a ‘core compartment’ and a ‘disruptive compartment’ which exhibit opposite GC content and gene composition. Novel tandem and dispersed repetitive sequences were identified, including some located inside coding sequences. Additionally, homologous chromosomes were separately assembled, allowing us to retrieve haplotypes as separate contigs instead of a unique mosaic sequence. Finally, manual annotation of surface multigene families, mucins and trans-sialidases allows now a better overview of these complex groups of genes.

Airborne Hyperspectral Evaluation of Maximum Gross Photosynthesis, Gravimetric Water Content, and CO2 Uptake Efficiency of the Mer Bleue Ombrotrophic Peatland

Peatlands cover a large area in Canada and globally (12% and 3% of the landmass, respectively). These ecosystems play an important role in climate regulation through the sequestration of carbon dioxide from, and the release of methane to, the atmosphere. Monitoring approaches, required to understand the response of peatlands to climate change at large spatial scales, are challenged by their unique vegetation characteristics, intrinsic hydrological complexity, and rapid changes over short periods of time (e.g., seasonality). In this study, we demonstrate the use of multitemporal, high spatial resolution (1 m2) hyperspectral airborne imagery (Compact Airborne Spectrographic Imager (CASI) and Shortwave Airborne Spectrographic Imager (SASI) sensors) for assessing maximum instantaneous gross photosynthesis (PGmax) in hummocks, and gravimetric water content (GWC) and carbon uptake efficiency in hollows, at the Mer Bleue ombrotrophic bog. We applied empirical models (i.e., in situ data and spectral indices) and we derived spatial and temporal trends for the aforementioned variables. Our findings revealed the distribution of hummocks (51.2%), hollows (12.7%), and tree cover (33.6%), which is the first high spatial resolution map of this nature at Mer Bleue. For hummocks, we found growing season PGmax values between 8 μmol m−2 s−1 and 12 μmol m−2 s−1 were predominant (86.3% of the total area). For hollows, our results revealed, for the first time, the spatial heterogeneity and seasonal trends for gravimetric water content and carbon uptake efficiency for the whole bog.

Introductory Remarks

In one of the most widely used definitions of qualitative research, Denzin and Lincoln (2005, p. 3) describe this practice as consisting of “a set of interpretive, material practices that make the world visible”. However, as much as qualitative research “locates the observer in the world”, it also locates the observed in research. This intrinsic complexity of qualitative research becomes even more prominent in research inquiring into such highly social and cultural phenomena as age and ageing.

A Systems Approach to Evaluate One Health Initiatives.

Challenges calling for integrated approaches to health, such as the One Health (OH) approach, typically arise from the intertwined spheres of humans, animals, and ecosystems constituting their environment. Initiatives addressing such wicked problems commonly consist of complex structures and dynamics. As a result of the EU COST Action (TD 1404) “Network for Evaluation of One Health” (NEOH), we propose an evaluation framework anchored in systems theory to address the intrinsic complexity of OH initiatives and regard them as subsystems of the context within which they operate. Typically, they intend to influence a system with a view to improve human, animal, and environmental health. The NEOH evaluation framework consists of four overarching elements, namely: (1) the definition of the initiative and its context, (2) the description of the theory of change with an assessment of expected and unexpected outcomes, (3) the process evaluation of operational and supporting infrastructures (the “OH-ness”), and (4) an assessment of the association(s) between the process evaluation and the outcomes produced. It relies on a mixed methods approach by combining a descriptive and qualitative assessment with a semi-quantitative scoring for the evaluation of the degree and structural balance of “OH-ness” (summarised in an OH-index and OH-ratio, respectively) and conventional metrics for different outcomes in a multi-criteria-decision-analysis. Here, we focus on the methodology for Elements (1) and (3) including ready-to-use Microsoft Excel spreadsheets for the assessment of the “OH-ness”. We also provide an overview of Element (2), and refer to the NEOH handbook for further details, also regarding Element (4) (http://neoh.onehealthglobal.net). The presented approach helps researchers, practitioners, and evaluators to conceptualise and conduct evaluations of integrated approaches to health and facilitates comparison and learning across different OH activities thereby facilitating decisions on resource allocation. The application of the framework has been described in eight case studies in the same Frontiers research topic and provides first data on OH-index and OH-ratio, which is an important step towards their validation and the creation of a dataset for future benchmarking, and to demonstrate under which circumstances OH initiatives provide added value compared to disciplinary or conventional health initiatives.