Superior pattern processing is the essence of the evolved human brain

Smaller Biceps Femoris Aponeurosis Size in Legs with a History of Hamstring Strain Injury

Biceps femoris long head (BFLH) aponeurosis size was compared between legs with and without prior hamstring strain injury (HSI) using two approaches: within-group (injured vs. uninjured legs of previous unilateral HSI athletes) and between-group (previously injured legs of HSI athletes vs. legs of No Prior HSI athletes). MRI scans were performed on currently healthy, competitive male athletes with Prior HSI history (n=23;≥1 verified BFLH injury; including a sub-group with unilateral HSI history; most recent HSI 1.6±1.2 years ago) and pair-matched athletes with No Prior HSI history (n=23). Anonymized axial images were manually segmented to quantify BFLH aponeurosis and muscle size. Prior unilateral HSI athletes' BFLH aponeurosis maximum width, aponeurosis area, and aponeurosis:muscle area ratio were 14.0-19.6% smaller in previously injured vs. contralateral uninjured legs (paired t-test, 0.008≤P≤0.044). BFLH aponeurosis maximum width and area were also 9.4-16.5% smaller in previously injured legs (n=28) from Prior HSI athletes vs. legs (n=46) of No Prior HSI athletes (unpaired t-test, 0.001≤P≤0.044). BFLH aponeurosis size was smaller in legs with prior HSI vs. those without prior HSI. These findings suggest BFLH aponeurosis size, especially maximum width, could be a potential cause or consequence of HSI, with prospective evidence needed to support or refute these possibilities.

Characteristic Pressure Waveforms Can Distinguish Airway Collapse Patterns in Sleep Apnea Patients: A Pilot Study

Objective

To use pharyngeal pressure recordings to distinguish different upper airway collapse patterns in obstructive sleep apnea (OSA) patients, and to assess whether these pressure recordings correlate with candidacy assessment for hypoglossal nerve stimulator (HGNS) implantation.

Study Design

Prospective case series.

Setting

Single tertiary-quaternary care academic center.

Methods

Subjects with OSA prospectively underwent simultaneous drug-induced sleep endoscopy (DISE) and transnasal pharyngeal pressure recording with a pressure-transducing catheter. Pressure was recorded in the nasopharynx and oropharynx, and endoscopic collapse patterns were classified based on site, extent, and direction of collapse. Pressure recordings were classified categorically by waveform shape as well as numerically by inspiratory and expiratory amplitudes and slopes. Waveform shape, amplitude, and slope were then compared with the endoscopic findings.

Results

Twenty-five subjects with OSA were included. Nasopharyngeal waveform shape was associated with the extent of collapse at the level of the palate (P = .001). Oropharyngeal waveform shape was associated with anatomical site of collapse (P < .001) and direction of collapse (P = .019) below the level of the palate. Pressure amplitudes and slopes were also associated with the extent of collapse at various sites. Waveform shape was also associated with favorable collapse pattern on endoscopy for HGNS implantation (P = .043), as well as surgical candidacy for HGNS (P = .004).

Conclusion

Characteristic pharyngeal pressure waveforms are associated with different airway collapse patterns. Pharyngeal pressure is a promising adjunct to DISE in the sleep surgery candidacy evaluation.

Impact of aging on vascular ion channels: perspectives and knowledge gaps across major organ systems

Individuals aged ≥65 yr will comprise ∼20% of the global population by 2030. Cardiovascular disease remains the leading cause of death in the world with age-related endothelial "dysfunction" as a key risk factor. As an organ in and of itself, vascular endothelium courses throughout the mammalian body to coordinate blood flow to all other organs and tissues (e.g., brain, heart, lung, skeletal muscle, gut, kidney, skin) in accord with metabolic demand. In turn, emerging evidence demonstrates that vascular aging and its comorbidities (e.g., neurodegeneration, diabetes, hypertension, kidney disease, heart failure, and cancer) are "channelopathies" in large part. With an emphasis on distinct functional traits and common arrangements across major organs systems, the present literature review encompasses regulation of vascular ion channels that underlie blood flow control throughout the body. The regulation of myoendothelial coupling and local versus conducted signaling are discussed with new perspectives for aging and the development of chronic diseases. Although equipped with an awareness of knowledge gaps in the vascular aging field, a section has been included to encompass general feasibility, role of biological sex, and additional conceptual and experimental considerations (e.g., cell regression and proliferation, gene profile analyses). The ultimate goal is for the reader to see and understand major points of deterioration in vascular function while gaining the ability to think of potential mechanistic and therapeutic strategies to sustain organ perfusion and whole body health with aging.

Dietary Assessment by Pattern Recognition: a Comparative Analysis

Background

Diet quality photo navigation (DQPN) is a novel dietary intake assessment tool that was developed to help address limitations of traditional tools and to easily integrate into health care delivery systems. Prevailing practice is to validate new tools against approaches that are in wide use.

Objective

This study aimed to assess 1) the validity of Diet ID in measuring diet quality, food group and nutrient intake against 2 traditional dietary assessment methods (i.e., food record [FR], food frequency questionnaire) and 2) the test reproducibility/reliability of Diet ID to obtain similar results with repeat assessments.

Methods

Using a participant-sourcing platform for online research, we recruited 90 participants, 58 of whom completed DQPN, a 3-d FR (via the Automated Self-Administered 24-hour Dietary Assessment Tool), and a food frequency questionnaire (FFQ, via the Dietary History Questionnaire III). We estimated mean nutrient and food group intake with all 3 instruments and generated Pearson correlations between them.

Results

Mean age (SD) of participants was 38 (11) y, and more than half were male (64%). The strongest correlations for DQPN when compared with the other 2 instruments were for diet quality, as measured by the Healthy Eating Index 2015; between DQPN and the FFQ, the correlation was 0.58 (P < 0.001), and between DQPN and the FR, the correlation was 0.56 (P < 0.001). Selected nutrients and food groups also showed moderate strength correlations. Test-retest reproducibility for measuring diet quality was evaluated for DQPN and showed a correlation of 0.70 (P < 0.0001).

Conclusions

The current study offers evidence that DQPN is comparable to traditional dietary assessment tools for estimating overall diet quality. This performance, plus DQPN's ease-of-use and scalability, may recommend it in efforts to make dietary assessment a universal part of clinical care.

BinaRena: a dedicated interactive platform for human-guided exploration and binning of metagenomes

BACKGROUND

Exploring metagenomic contigs and "binning" them into metagenome-assembled genomes (MAGs) are essential for the delineation of functional and evolutionary guilds within microbial communities. Despite the advances in automated binning algorithms, their capabilities in recovering MAGs with accuracy and biological relevance are so far limited. Researchers often find that human involvement is necessary to achieve representative binning results. This manual process however is expertise demanding and labor intensive, and it deserves to be supported by software infrastructure.

RESULTS

We present BinaRena, a comprehensive and versatile graphic interface dedicated to aiding human operators to explore metagenome assemblies via customizable visualization and to associate contigs with bins. Contigs are rendered as an interactive scatter plot based on various data types, including sequence metrics, coverage profiles, taxonomic assignments, and functional annotations. Various contig-level operations are permitted, such as selection, masking, highlighting, focusing, and searching. Binning plans can be conveniently edited, inspected, and compared visually or using metrics including silhouette coefficient and adjusted Rand index. Completeness and contamination of user-selected contigs can be calculated in real time. In demonstration of BinaRena's usability, we show that it facilitated biological pattern discovery, hypothesis generation, and bin refinement in a complex tropical peatland metagenome. It enabled isolation of pathogenic genomes within closely related populations from the gut microbiota of diarrheal human subjects. It significantly improved overall binning quality after curating results of automated binners using a simulated marine dataset.

CONCLUSIONS

BinaRena is an installation-free, dependency-free, client-end web application that operates directly in any modern web browser, facilitating ease of deployment and accessibility for researchers of all skill levels. The program is hosted at https://github.com/qiyunlab/binarena , together with documentation, tutorials, example data, and a live demo. It effectively supports human researchers in intuitive interpretation and fine tuning of metagenomic data. Video Abstract.

Influence of anti-obesity strategies on brain function in health and review: A review

The aim of this review was to investigate in the literature the application of strategies such as low carbohydrate diet (LCD), ketogenic diet (KD) and intermittent fasting (IF) and their effects on the CNS. We performed a narrative review of the literature. The search was specifically carried out in PubMed, selecting articles in English, which had the following keywords: obesity, central nervous system, low carb diet, ketogenic diet and intermittent fasting, using the narrative review methodology. The studies found show that the benefits of the LCD, KD and IF strategies, at the CNS level, have a strong influence on the mechanisms of hunger and satiety, as well as on the reduction of food reward and show improvement in memory and mood influenced by the interventions.

Molecular archaeology of human cognitive traits

The brains and minds of our human ancestors remain inaccessible for experimental exploration. Therefore, we reconstructed human cognitive evolution by projecting nonsynonymous/synonymous rate ratios (ω values) in mammalian phylogeny onto the anatomically modern human (AMH) brain. This atlas retraces human neurogenetic selection and allows imputation of ancestral evolution in task-related functional networks (FNs). Adaptive evolution (high ω values) is associated with excitatory neurons and synaptic function. It shifted from FNs for motor control in anthropoid ancestry (60-41 mya) to attention in ancient hominoids (26-19 mya) and hominids (19-7.4 mya). Selection in FNs for language emerged with an early hominin ancestor (7.4-1.7 mya) and was later accompanied by adaptive evolution in FNs for strategic thinking during recent (0.8 mya-present) speciation of AMHs. This pattern mirrors increasingly complex cognitive demands and suggests that co-selection for language alongside strategic thinking may have separated AMHs from their archaic Denisovan and Neanderthal relatives.

Beetle Antennae Search: Using Biomimetic Foraging Behaviour of Beetles to Fool a Well-Trained Neuro-Intelligent System

Deep Convolutional Neural Networks (CNNs) represent the state-of-the-art artificially intelligent computing models for image classification. The advanced cognition and pattern recognition abilities possessed by humans are ascribed to the intricate and complex neurological connection in human brains. CNNs are inspired by the neurological structure of the human brain and show performance at par with humans in image recognition and classification tasks. On the lower extreme of the neurological complexity spectrum lie small organisms such as insects and worms, with simple brain structures and limited cognition abilities, pattern recognition, and intelligent decision-making abilities. However, billions of years of evolution guided by natural selection have imparted basic survival instincts, which appear as an “intelligent behavior”. In this paper, we put forward the evidence that a simple algorithm inspired by the behavior of a beetle (an insect) can fool CNNs in image classification tasks by just perturbing a single pixel. The proposed algorithm accomplishes this in a computationally efficient manner as compared to the other adversarial attacking algorithms proposed in the literature. The novel feature of the proposed algorithm as compared to other metaheuristics approaches for fooling a neural network, is that it mimics the behavior of a single beetle and requires fewer search particles. On the contrary, other metaheuristic algorithms rely on the social or swarming behavior of the organisms, requiring a large population of search particles. We evaluated the performance of the proposed algorithm on LeNet-5 and ResNet architecture using the CIFAR-10 dataset. The results show a high success rate for the proposed algorithms. The proposed strategy raises a concern about the robustness and security aspects of artificially intelligent learning systems.

The transcript integrity index (TII) provides a standard measure of sperm RNA

Standardizing RNA quality is key to interpreting RNA-seq data as a compromised sample can mask the underlying biology. The challenge remains when evaluating RNA quality in samples with high RNA fragmentation. For example, programmed fragmentation and cytoplasmic expulsion, integral to sperm maturation, is a prime example of the complexities of interpreting RNA-seq data, given that fragmentation can be random and\or targeted. To meet this challenge, we developed an algorithm that accurately measures RNA quality in samples with high fragmentation, such as spermatozoa. The integrity of 1,000 previously identified abundant sperm transcripts were independently visualized and evaluated using the Transcript Integrity Index (TII) algorithm to identify intact transcripts. Full-length transcripts from visual and the TII algorithm were evaluated for testis preference in humans using the GTEx tissues database. Samples were then filtered by the Interquartile Range (IQR), identifying those in which the greatest number of transcripts failed to pass the visual or TII thresholds. Transcript lists were overlapped, forming the set of intact transcripts used as TII standards. Each sample was re-evaluated as a function of this TII set of intact transcripts, with poor quality samples identified as those failing in the largest number of transcripts. While ontologically enriched in roles related to spermatogenesis and/or fertilization, samples did not segregate based on birth outcome. The TII algorithm proved an effective means to identify samples of similar quality from sperm, a cell type enriched in biologically fragmented RNAs. The algorithm should facilitate other studies using samples compromised by high levels of RNA fragmentation, such as Formalin-Fixed Paraffin-Embedded samples. Requisite to assessing male health, TII provides a solution to the long-sought-after standard that identifies samples of similar quality.

Quantifying cilia beat frequency using high-speed video microscopy: Assessing frame rate requirements when imaging different ciliated tissues

Motile cilia are found in numerous locations throughout our body and play a critical role in various physiological processes. The most commonly used method to assess cilia motility is to quantify cilia beat frequency (CBF) via video microscopy. However, a large heterogeneity exists within published literature regarding the framerate used to image cilia motility for calculating CBF. The aim of this study was to determine the optimal frame rate required to image cilia motility for CBF assessment, and if the Nyquist theorem may be used to set this rate. One‐second movies of cilia were collected at >600 fps from mouse airways and ependyma at room‐temperature or 37°C. Movies were then down‐sampled to 30–300 fps. CBF was quantified for identical cilia at different framerates by either manual counting or automated MATLAB script. Airway CBF was significantly impaired in 30 fps movies, while ependymal CBF was significantly impaired in both 60 and 30 fps movies. Pairwise comparison showed that video framerate should be at least 150 fps to accurately measure CBF, with minimal improvement in CBF accuracy in movies >150 fps. The automated script was also found to be less accurate for measuring CBF in lower fps movies than manual counting, however, this difference disappeared in higher framerate movies (>150 fps). In conclusion, our data suggest the Nyquist theorem is unreliable for setting sampling rate for CBF measurement. Instead, sampling rate should be 3–4 times faster than CBF for accurate CBF assessment. Especially if CBF calculation is to be automated.

Predicting the number of days in court cases using artificial intelligence

Brazilian legal system prescribes means of ensuring the prompt processing of court cases, such as the principle of reasonable process duration, the principle of celerity, procedural economy, and due legal process, with a view to optimizing procedural progress. In this context, one of the great challenges of the Brazilian judiciary is to predict the duration of legal cases based on information such as the judge, lawyers, parties involved, subject, monetary values of the case, starting date of the case, etc. Recently, there has been great interest in estimating the duration of various types of events using artificial intelligence algorithms to predict future behaviors based on time series. Thus, this study presents a proof-of-concept for creating and demonstrating a mechanism for predicting the amount of time, after the case is argued in court (time when a case is made available for the magistrate to make the decision), for the magistrate to issue a ruling. Cases from a Regional Labor Court were used as the database, with preparation data in two ways (original and discretization), to test seven machine learning techniques (i) Multilayer Perceptron (MLP); (ii) Gradient Boosting; (iii) Adaboost; (iv) Regressive Stacking; (v) Stacking Regressor with MLP; (vi) Regressive Stacking with Gradient Boosting; and (vii) Support Vector Regression (SVR), and determine which gives the best results. After executing the runs, it was identified that the adaboost technique excelled in the task of estimating the duration for issuing a ruling, as it had the best performance among the tested techniques. Thus, this study shows that it is possible to use machine learning techniques to perform this type of prediction, for the test data set, with an R2 of 0.819 and when transformed into levels, an accuracy of 84%.

New Linguistic Description Approach for Time Series and its Application to Bed Restlessness Monitoring for Eldercare

Time series analysis has been an active area of research for years, with important applications in forecasting or discovery of hidden information such as patterns or anomalies in observed data. In recent years, the use of time series analysis techniques for the generation of descriptions and summaries in natural language of any variable, such as temperature, heart rate or CO2 emission has received increasing attention. Natural language has been recognized as more effective than traditional graphical representations of numerical data in many cases, in particular in situations where a large amount of data needs to be inspected or when the user lacks the necessary background and skills to interpret it. In this work, we describe a novel mechanism to generate linguistic descriptions of time series using natural language and fuzzy logic techniques. The proposed method generates quality summaries capturing the time series features that are relevant for a user in a particular application, and can be easily customized for different domains. This approach has been successfully applied to the generation of linguistic descriptions of bed restlessness data from residents at TigerPlace (Columbia, Missouri), which is used as a case study to illustrate the modeling process and show the quality of the descriptions obtained.

Classification and Treatment of Diseases in the Age of Genome Medicine Based on Pathway Pathology

The focus of pathology as a biomedical discipline is the identification of the pathomechanisms of diseases and the integration of this knowledge into routine diagnosis and classification. Standard tools are macroscopic and microscopic analysis complemented by immunohistochemistry and molecular pathology. So far, classification has been based on the paradigm of cellular pathology established by Rudolf Virchow and others more than 150 years ago, stating that diseases originate from diseased cells. This dogma is meanwhile challenged by the fact that cells can be fully reprogrammed. Many diseases are nowadays considered to originate from undifferentiated stem cells, induced into a diseased state by genetic or epigenetic alterations. In addition, the completion of the Human Genome Project, with the identification of more than 20.000 genes and a much higher number of gene variants and mutations, led to the concept that diseases are dominated by genetics/epigenetics rather than cells of origin. The axiom of cellular pathology, however, still holds true, as cells are the smallest animate units from which diseases originate. Medical doctors and researchers nowadays have to deal with a tremendous amount of data. The International Classification of Diseases will expand from 14.400 entities/codes in ICD-10 to more than 55.000 in ICD-11. In addition, large datasets generated by “genomics“, e.g., whole-genome sequencing, expression profiling or methylome analysis, are meanwhile not only applied in research but also introduced into clinical settings. It constitutes a major task to incorporate all the data into routine medical work. Pathway pathology may help solve this problem. It is based on the realization that diseases are characterized by three essential components: (i) cells of origin/cellular context and (ii) the alteration of cellular as well as (iii) molecular/signal transduction pathways. The concept is illustrated by elaborating on two key cellular pathways, i.e., the cellular senescence of normal cells and the immortality of cancer cells, and by contrasting single cell/single pathway diseases, such as mycoplasma and coughing pneumonia, with complex diseases such as cancer, with multiple cell types as well as multiple affected cellular and signaling pathways. Importantly, the concept of pathway pathology is not just intended to classify disease, but also to conceive new treatment modalities. This article is dedicated to Dr. Leonard Hayflick, who made basic discoveries in pathway pathology not only by identifying cells causing disease (Mycoplasma pneumoniae) and establishing cell strains for treating disease (WI-38 for viral vaccines), but also by first describing cellular senescence and immortality.

Role of testosterone: cortisol ratio in age- and sex-specific cortico-hippocampal development and cognitive performance

Testosterone (T) and cortisol (C) are steroid hormones that have been argued to play opposing roles in shaping physical and behavioral development in humans. While there is evidence linking T and C to different memory processes during adulthood, it remains unclear how the relative levels of T and C (TC ratio) may influence brain and behavioral development, whether they are influenced by sex of the child, and whether or not they occur as a result of stable changes in brain structure (organizational changes), as opposed to transient changes in brain function (activational changes). As such, we tested for associations among TC ratio, cortico-hippocampal structure, and standardized tests of executive, verbal, and visuo-spatial function in a longitudinal sample of typically developing 4-22-year-old children and adolescents. We found greater TC ratios to be associated with greater coordinated growth (i.e. covariance) between the hippocampus and cortical thickness in several areas primarily devoted to visual function. In addition, there was an age-related association between TC ratio and parieto-hippocampal covariance, as well as a sex-specific association between TC ratio and prefrontal-hippocampal covariance. Differences in brain structure related to TC ratio were in turn associated with lower verbal/executive function, as well as greater attention in tests of visuo-spatial abilities. These results support the notion that TC ratio may shift the balance between top-down (cortex to hippocampus) and bottom-up (hippocampus to cortex) processes, impairing more complex, cortical-based tasks and optimizing visuospatial tasks relying primarily on the hippocampus.

Exploring Sonic Hedgehog Cell Signaling in Neurogenesis: Its Potential Role in Depressive Behavior

Depression is the most prevalent form of neuropsychiatric disorder affecting all age groups globally. As per the estimation of the World Health Organization (WHO), depression will develop into the foremost reason for disability globally by the year 2030. The primary neurobiological mechanism implicated in depression remains ambiguous; however, dysregulation of molecular and signaling transductions results in depressive disorders. Several theories have been developed to explain the pathogenesis of depression, however, none of them completely explained all aspects of depressive-pathogenesis. In the current review, we aimed to explore the role of the sonic hedgehog (Shh) signaling pathway in the development of the depressive disorder and its potential as the therapeutic target. Shh signaling has a crucial function in neurogenesis and neural tube patterning during the development of the central nervous system (CNS). Shh signaling performs a basic function in embryogenesis and hippocampal neurogenesis. Moreover, antidepressants are also known to enhance neurogenesis in the hippocampus, which further suggests the potential of Shh signaling. Furthermore, there is decreased expression of a glioma-associated oncogene (Gli1) and Smoothened (Smo) in depression. Moreover, antidepressants also regulate brain-derived neurotrophic factor (BDNF) and wingless protein (Wnt) signaling, therefore, Shh may be implicated in the pathogenesis of the depressive disorder. Deregulation of Shh signaling in CNS results in neurological disorders such as depression.

Cognitive Gain or Handicap: Magical Ideation and Self-Absorption in Clinical and Non-clinical Participants

Background: This study aimed to examine magical ideation and absorption traits across non-clinical and clinical groups to determine their potential adaptive and maladaptive functions.

Method: We enrolled 760 healthy participants from neighboring communities (female = 53.2%). Moreover, we recruited 318 patients (female = 66.5%), which included 25, 183, and 110 patients with schizophrenia spectrum disorders, anxiety disorders, and mood disorders, respectively. Potentially adaptive and maladaptive sociocognitive functions were measured to determine the role of magical ideation and self-absorption in patients with psychiatric disorders.

Results: The degree of magical ideation and absorption gradually increased in the following order: anxiety disorders, mood disorders, and schizophrenia spectrum disorders. Furthermore, enhanced self-absorption-related enhanced consciousness traits were essential indicators of the presence of self-integration weakness in patients with schizophrenia spectrum disorders.

Conclusion: Magical ideation and psychological absorption may be considered as mental model construction functions, which result in both gains and handicaps in social adaptation.

Key factors in a rigorous longitudinal image-based assessment of retinopathy of prematurity

To describe a database of longitudinally graded telemedicine retinal images to be used as a comparator for future studies assessing grader recall bias and ability to detect typical progression (e.g. International Classification of Retinopathy of Prematurity (ICROP) stages) as well as incremental changes in retinopathy of prematurity (ROP). Cohort comprised of retinal images from 84 eyes of 42 patients who were sequentially screened for ROP over 6 consecutive weeks in a telemedicine program and then followed to vascular maturation or treatment, and then disease stabilization. De-identified retinal images across the 6 weekly exams (2520 total images) were graded by an ROP expert based on whether ROP had improved, worsened, or stayed the same compared to the prior week’s images, corresponding to an overall clinical “gestalt” score. Subsequently, we examined which parameters might have influenced the examiner’s ability to detect longitudinal change; images were graded by the same ROP expert by image view (central, inferior, nasal, superior, temporal) and by retinal components (vascular tortuosity, vascular dilation, stage, hemorrhage, vessel growth), again determining if each particular retinal component or ROP in each image view had improved, worsened, or stayed the same compared to the prior week’s images. Agreement between gestalt scores and view, component, and component by view scores was assessed using percent agreement, absolute agreement, and Cohen’s weighted kappa statistic to determine if any of the hypothesized image features correlated with the ability to predict ROP disease trajectory in patients. The central view showed substantial agreement with gestalt scores (κ = 0.63), with moderate agreement in the remaining views. Of retinal components, vascular tortuosity showed the most overall agreement with gestalt (κ = 0.42–0.61), with only slight to fair agreement for all other components. This is a well-defined ROP database graded by one expert in a real-world setting in a masked fashion that correlated with the actual (remote in time) exams and known outcomes. This provides a foundation for subsequent study of telemedicine’s ability to longitudinally assess ROP disease trajectory, as well as for potential artificial intelligence approaches to retinal image grading, in order to expand patient access to timely, accurate ROP screening.

Pattern Unifies Autism

Autism is a highly heterogeneous condition, genetically and phenotypically. This diversity of causation and presentation has impeded its definition, recognition, assessment, and treatment. Current diagnostic criteria for autism involve two domains, restricted interests and repetitive behavior (RRBs) and social deficits, whose relationship remains unclear. I suggest that the large suite of traits associated with autism can be usefully conceptualized under the single rubric of "pattern," a term that connects autism with basic brain and cognitive functions and structures its phenotypes within a single theoretical framework. Autism thus involves increases and enhancements to pattern perception, pattern recognition, pattern maintenance, pattern generation, pattern processing, and pattern seeking. RRBs result from increased and imbalanced pattern-related perception and cognition, and social alterations result in part from the usual lack of clear pattern in social interactions, combined with the interference of RRBs with social development. This framework has strong implications for assessment of social and non-social autism-related traits, personalized therapy, and priorities for research.

A Vector Space Model for Neural Network Functions: Inspirations From Similarities Between the Theory of Connectivity and the Logarithmic Time Course of Word Production

The present report examines the coinciding results of two study groups each presenting a power-of-two function to describe network structures underlying perceptual processes in one case and word production during verbal fluency tasks in the other. The former is theorized as neural cliques organized according to the function N = 2 ⁱ - 1, whereas the latter assumes word conglomerations thinkable as tuples following the function N = 2 ⁱ . Both theories assume the innate optimization of energy efficiency to cause the specific connectivity structure. The vast resemblance between both formulae motivated the development of a common formulation. This was obtained by using a vector space model, in which the configuration of neural cliques or connected words is represented by a N-dimensional state vector. A further analysis of the model showed that the entire time course of word production could be derived using basically one single minimal transformation-matrix. This again seems in line with the principle of maximum energy efficiency.

Microscopical discrimination of human head hairs sharing a mitochondrial haplogroup

In forensic analyses, determining the level of consensus among examiners for hair comparison conclusions and ancestry identifications is important for assessing the scientific validity of microscopical hair examinations. Here, we present data from an interlaboratory study on the accuracy of microscopical hair comparisons among a subset of experienced hair examiners currently analyzing hair in forensic laboratories across the United States. We examined how well microscopical analysis of hair can reliably be used to differentiate hair samples, many of which were macroscopically similar. Using cut hair samples, many sharing similar macroscopic and microscopic features, collected from individuals who share the same mitochondrial haplogroup as an indication of genetic relatedness, we tested multiple aspects that could impact hair comparisons. This research tested the extent to which morphological features related to ancestry and hair length influence conclusions. Microscopical hair examinations yielded accurate assessments of inclusion/exclusion relative to the reference samples among 85% of the pairwise comparisons. We found shorter hairs had reduced levels of accuracy and hairs from populations examiners were not familiar with may have impacted their ability to resolve features. The reliability of ancestry determinations is not yet clear, but we found indications that the existing categories are only somewhat related to current ethnic and genetic variation. Our results provide support for the continued utility of microscopical comparison of hairs within forensic laboratories and to advocate for a combined analytical approach using both microscopical analysis and mtDNA data on all forensic analyses of hair.

Retracted: Exploring the microbiome and mindfulness connection

Mental health and its impact on overall well-being is a topic that is at the forefront of consideration in most industrialized countries. Ironically in the expansive world of the microbiome, gut microbes are most affected by modern, fast paced, westernized lifestyles, indicating a significant correlation based on geography, and physical and mental habits. The gut–brain axis is an established axis demonstrating the effect of the gut microbiota on the biochemical processes in the brain. With the existence of mindfulness initiatives such as adoption of a ‘yogic lifestyle’ aimed at creating a sense of harmony and balance within the individual, this special report considers the available evidence base, asking whether the harmony created by adopting this lifestyle can be related to establishing harmony in the gut–brain axis.

In current times, mental health has been given much significance for the overall health of the individual. The microbiome provides a new gateway to approach mental health through the gut via the established gut–brain axis. This opinion article explores the current evidence establishing the strong gut–brain connection. We discuss the impact of lifestyle, yoga and mindfulness in balancing and creating mental health based on this data.

Dietary assessment can be based on pattern recognition rather than recall

Diet is the leading predictor of health status, including all-cause mortality, in the modern world, yet is rarely measured; whereas virtually every adult in a developed country knows their approximate blood pressure, hardly any knows their objective diet quality. Leading authorities have called for the inclusion of nutrition in every electronic health record as one of the many remedial steps required to give dietary quality the routine attention it warrants. Existing tools to capture dietary intake are based on either real-time journaling or recall. Journaling, or logging, is time and labor intensive. Recall is notoriously unreliable, as humans are notably bad at remembering detail. Even allowing for the challenge of recall, these dietary intake methods are labor and time intensive, and require analysis at the n-of-1 level. We hypothesize that dietary intake assessment can be "reverse engineered"-predicating assessment on the recognition of fully formed dietary patterns-rather than endeavoring to assemble such a representation one food, meal, dish, or day at a time. This pattern recognition-based method offers potential advantages over existing methods, including speed, efficiency, cost, and applicability. We have developed and provisionally tested such a system, and the results thus far support our hypothesis. We are convinced that leveraging pattern recognition to make dietary assessment quick, user-friendly, economical, and scalable can allow for the conversion of dietary quality into a universally measured and routinely managed vital sign. In this paper, we present the supporting case.

Facial indices in lateral cephalogram for sex prediction in Chennai population - A semi-novel study

Background:

Osteological examination is a very reliable tool to determine the sex of the individual as the consolidation of the dimorphic characteristics concludes the sex of the individual. This study was performed with lateral cephalograms, which is a vital diagnostic tool for patients undergoing orthodontic treatment. An index was formed, which could be considered as a reliable sex determinant in forensic applications.

Materials and Methods:

This pilot study was performed on samples of the Dravidian population. Two-fifty individuals, whose age ranged between 25 and 40 years, were taken (125 subjects were males and 125 subjects were females). A total of ninety-nine cephalometric variables were compared, subjected to statistical analysis and tested for significance using the t-test.

Results:

Out of a total of 99 variables tested only twenty-four variables showed statistical significance. So, these twenty-four variables were then subjected to discriminant function analysis to evaluate the effectiveness of each variable in predicting the sex of an individual Individually, Ramus length (Ramus ln), Condylion to Gnathion (Co-Gn) and ramus height showed the highest sex determining dependability of 78%. On the flipside, lower anterior facial height (LAFH), with 52%, showed the lowest consistency.

Conclusion:

From this study, it is clearly evident that cephalometric landmarks are reliable sex determinants to a good extent. All the statistically significant measurements, but one, showed acceptable percentages of reliability. This means the chosen variables can be used for the Dravidian population to robustly determine the sex of the individuals of interest.

Finding meaning in the clouds: Illusory pattern perception predicts receptivity to pseudo-profound bullshit

Previous research has demonstrated a link between illusory pattern perception and various irrational beliefs. On this basis, we hypothesized that participants who displayed greater degrees of illusory pattern perception would also be more likely to rate pseudo-profound bullshit statements as profound. We find support for this prediction across three experiments (N = 627) and four distinct measures of pattern perception. We further demonstrate that this observed relation is restricted to illusory pattern perception, with participants displaying greater endorsement of non-illusory patterns being no more likely to rate pseudo-profound bullshit statements as profound. Additionally, this relation is not a product of a general proclivity to rate all statements as profound and is not accounted for by individual differences in analytic thinking. Overall, we demonstrate that individuals with a tendency to go beyond the available data such that they uncritically endorse patterns where no patterns exist are also more likely to create and endorse false-meaning in meaningless pseudo-profound statements. These findings are discussed in the context of a proposed framework that views individuals’ receptivity to pseudo-profound bullshit as, in part, an unfortunate consequence of an otherwise adaptive process: that of pattern perception.

Exploring behavioral pattern separation and risk for emotional disorders

Pattern separation is a facet of memory encoding that facilitates the adaptive integration of old and new experiences. At the computational level, this process reduces overlap between how two entities are represented. Behaviorally, this allows for greater memory resolution while avoiding memory interference; similar entities are perceived as distinct. Poor pattern separation could contribute to psychopathology, especially anxiety, as individuals with high anxiety tend to overgeneralize their perception of threat, or have difficulty distinguishing between currently safe contexts and previously threatening ones. However, there is little empirical work examining this as a contributory mechanism of anxiety in humans. This study examines the relationship between behavioral pattern separation, anxiety, and related symptoms. Participants (N = 111) completed questionnaires assessing anxiety, depression, stress, trait worry, and state affect. They then completed the Mnemonic Similarity Task, a computerized test that serves as a putative behavioral proxy to tax and thus measure hippocampal pattern separation. Behavioral pattern separation performance alone was not predictive of high anxiety, depression, or stress. However, two significant interactions emerged. The interactions between performance and state affect, and between performance and trait worry predicted anxious and depressive symptoms. Only at higher levels of negative affect was performance predictive of symptom severity. Similarly, poor pattern separation and high trait worry together predicted the most severe symptoms. This project provides support for behavioral pattern separation as a plausible factor in anxiety and related psychopathology, particularly in combination with sensitivity to acute distress and known risk factors, such as trait worry.

Intermittent metabolic switching, neuroplasticity and brain health

During evolution, individuals whose brains and bodies functioned well in a fasted state were successful in acquiring food, enabling their survival and reproduction. With fasting and extended exercise, liver glycogen stores are depleted and ketones are produced from adipose-cell-derived fatty acids. This metabolic switch in cellular fuel source is accompanied by cellular and molecular adaptations of neural networks in the brain that enhance their functionality and bolster their resistance to stress, injury and disease. Here, we consider how intermittent metabolic switching, repeating cycles of a metabolic challenge that induces ketosis (fasting and/or exercise) followed by a recovery period (eating, resting and sleeping), may optimize brain function and resilience throughout the lifespan, with a focus on the neuronal circuits involved in cognition and mood. Such metabolic switching impacts multiple signalling pathways that promote neuroplasticity and resistance of the brain to injury and disease.

How does hormesis impact biology, toxicology, and medicine?

Hormesis refers to adaptive responses of biological systems to moderate environmental or self-imposed challenges through which the system improves its functionality and/or tolerance to more severe challenges. The past two decades have witnessed an expanding recognition of the concept of hormesis, elucidation of its evolutionary foundations, and underlying cellular and molecular mechanisms, and practical applications to improve quality of life. To better inform future basic and applied research, we organized and re-evaluated recent hormesis-related findings with the intent of incorporating new knowledge of biological mechanisms, and providing fundamental insights into the biological, biomedical and risk assessment implications of hormesis. As the literature on hormesis is expanding rapidly into new areas of basic and applied research, it is important to provide refined conceptualization of hormesis to aid in designing and interpreting future studies. Here, we establish a working compartmentalization of hormesis into ten categories that provide an integrated understanding of the biological meaning and applications of hormesis.

Systems Medicine-Complexity Within, Simplicity Without

This paper presents a brief history of Systems Theory, progresses to Systems Biology, and its relation to the more traditional investigative method of reductionism. The emergence of Systems Medicine represents the application of Systems Biology to disease and clinical issues. The challenges faced by this transition from Systems Biology to Systems Medicine are explained; the requirements of physicians at the bedside, caring for patients, as well as the place of human-human interaction and the needs of the patients are addressed. An organ-focused transition to Systems Medicine, rather than a genomic-, molecular-, or cell-based effort is emphasized. Organ focus represents a middle-out approach to ease this transition and to maximize the benefits of scientific discovery and clinical application. This method manages the perceptions of time and space, the massive amounts of human- and patient-related data, and the ensuing complexity of information.

Adaptive responses of neuronal mitochondria to bioenergetic challenges: Roles in neuroplasticity and disease resistance

An important concept in neurobiology is "neurons that fire together, wire together" which means that the formation and maintenance of synapses is promoted by activation of those synapses. Very similar to the effects of the stress of exercise on muscle cells, emerging findings suggest that neurons respond to activity by activating signaling pathways (e.g., Ca²⁺, CREB, PGC-1α, NF-κB) that stimulate mitochondrial biogenesis and cellular stress resistance. These pathways are also activated by aerobic exercise and food deprivation, two bioenergetic challenges of fundamental importance in the evolution of the brains of all mammals, including humans. The metabolic 'switch' in fuel source from liver glycogen store-derived glucose to adipose cell-derived fatty acids and their ketone metabolites during fasting and sustained exercise, appears to be a pivotal trigger of both brain-intrinsic and peripheral organ-derived signals that enhance learning and memory and underlying synaptic plasticity and neurogenesis. Brain-intrinsic extracellular signals include the excitatory neurotransmitter glutamate and the neurotrophic factor BDNF, and peripheral signals may include the liver-derived ketone 3-hydroxybutyrate and the muscle cell-derived protein irisin. Emerging findings suggest that fasting, exercise and an intellectually challenging lifestyle can protect neurons against the dysfunction and degeneration that they would otherwise suffer in acute brain injuries (stroke and head trauma) and neurodegenerative disorders including Alzheimer's, Parkinson's and Huntington's disease. Among the prominent intracellular responses of neurons to these bioenergetic challenges are up-regulation of antioxidant defenses, autophagy/mitophagy and DNA repair. A better understanding of such fundamental hormesis-based adaptive neuronal response mechanisms is expected to result in the development and implementation of novel interventions to promote optimal brain function and healthy brain aging.

Sonic Hedgehog Signaling and Hippocampal Neuroplasticity

Sonic hedgehog (Shh) is a secreted protein that controls the patterning of neural progenitor cells, and their neuronal and glial progeny, during development. Emerging findings suggest that Shh also has important roles in the formation and plasticity of neuronal circuits in the hippocampus, a brain region of fundamental importance in learning and memory. Shh mediates activity-dependent and injury-induced hippocampal neurogenesis. Activation of Shh receptors in the dendrites of hippocampal neurons engages a trans-neuronal signaling pathway that accelerates axon outgrowth and enhances glutamate release from presynaptic terminals. Impaired Shh signaling may contribute to the pathogenesis of several developmental and adult-onset neurological disorders that affect the hippocampus, suggesting a potential for therapeutic interventions that target Shh pathways.

Integrative Therapies in Anxiety Treatment with Special Emphasis on the Gut Microbiome

Over the past decade, research has shown that diet and gut health affects symptoms expressed in stress related disorders, depression, and anxiety through changes in the gut microbiota. Psycho-behavioral function and somatic health interaction have often been ignored in health care with resulting deficits in treatment quality and outcomes. While mental health care requires the professional training in counseling, psychotherapy and psychiatry, complimentary therapeutic strategies, such as attention to a nutritional and diverse diet and supplementation of probiotic foods, may be integrated alongside psychotherapy treatment models. Development of these alternative strategies is predicated on experimental evidence and diligent research on the biology of stress, fear, anxiety-related behaviors, and the gut-brain connection. This article provides a brief overview on biological markers of anxiety and the expanding nutritional literature relating to brain health and mental disorders. A case study demonstrates an example of a biopsychosocial approach integrating cognitive psychotherapy, dietary changes, and mindfulness activities, in treating symptoms of anxiety. This case study shows a possible treatment protocol to explore the efficacy of targeting the gut-brain-axis that may be used as an impetus for future controlled studies.

Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes

An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human-landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests - biodiversity value for conservation and timber production potential - with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas.

Late-onset dementia: a mosaic of prototypical pathologies modifiable by diet and lifestyle

Idiopathic late-onset dementia (ILOD) describes impairments of memory, reasoning and/or social abilities in the elderly that compromise their daily functioning. Dementia occurs in several major prototypical neurodegenerative disorders that are currently defined by neuropathological criteria, most notably Alzheimer’s disease (AD), Lewy body dementia (LBD), frontotemporal dementia (FTD) and hippocampal sclerosis of aging (HSA). However, people who die with ILOD commonly exhibit mixed pathologies that vary within and between brain regions. Indeed, many patients diagnosed with probable AD exhibit only modest amounts of disease-defining amyloid β-peptide plaques and p-Tau tangles, and may have features of FTD (TDP-43 inclusions), Parkinson’s disease (α-synuclein accumulation), HSA and vascular lesions. Here I argue that this ‘mosaic neuropathological landscape’ is the result of commonalities in aging-related processes that render neurons vulnerable to the entire spectrum of ILODs. In this view, all ILODs involve deficits in neuronal energy metabolism, neurotrophic signaling and adaptive cellular stress responses, and associated dysregulation of neuronal calcium handling and autophagy. Although this mosaic of neuropathologies and underlying mechanisms poses major hurdles for development of disease-specific therapeutic interventions, it also suggests that certain interventions would be beneficial for all ILODs. Indeed, emerging evidence suggests that the brain can be protected against ILOD by lifelong intermittent physiological challenges including exercise, energy restriction and intellectual endeavors; these interventions enhance cellular stress resistance and facilitate neuroplasticity. There is also therapeutic potential for interventions that bolster neuronal bioenergetics and/or activate one or more adaptive cellular stress response pathways in brain cells. A wider appreciation that all ILODs share age-related cellular and molecular alterations upstream of aggregated protein lesions, and that these upstream events can be mitigated, may lead to implementation of novel intervention strategies aimed at reversing the rising tide of ILODs.