Energy-Efficient Decentralized Broadcasting in Wireless Multi-Hop Networks.

Several areas of wireless networking, such as wireless sensor networks or the Internet of Things, require application data to be distributed to multiple receivers in an area beyond the transmission range of a single node. This can be achieved by using the wireless medium's broadcast property when retransmitting data. Due to the energy constraints of typical wireless devices, a broadcasting scheme that consumes as little energy as possible is highly desirable. In this article, we present a novel multi-hop data dissemination protocol called BTP. It uses a game-theoretical model to construct a spanning tree in a decentralized manner to minimize the total energy consumption of a network by minimizing the transmission power of each node. Although BTP is based on a game-theoretical model, it neither requires information exchange between distant nodes nor time synchronization during its operation, and it inhibits graph cycles effectively. The protocol is evaluated in Matlab and NS-3 simulations and through real-world implementation on a testbed of 75 Raspberry Pis. The evaluation conducted shows that our proposed protocol can achieve a total energy reduction of up to 90% compared to a simple broadcast protocol in real-world experiments.

Balrog: A universal protein model for prokaryotic gene prediction.

Low-cost, high-throughput sequencing has led to an enormous increase in the number of sequenced microbial genomes, with well over 100,000 genomes in public archives today. Automatic genome annotation tools are integral to understanding these organisms, yet older gene finding methods must be retrained on each new genome. We have developed a universal model of prokaryotic genes by fitting a temporal convolutional network to amino-acid sequences from a large, diverse set of microbial genomes. We incorporated the new model into a gene finding system, Balrog (Bacterial Annotation by Learned Representation Of Genes), which does not require genome-specific training and which matches or outperforms other state-of-the-art gene finding tools. Balrog is freely available under the MIT license at https://github.com/salzberg-lab/Balrog.

Advanced Analysis Techniques Improve Infant Bone and Body Composition Measures by Dual-Energy X-Ray Absorptiometry.

OBJECTIVE: To evaluate a novel technique designed to reduce the negative impact of motion artifacts in infant dual-energy X-ray absorptiometry (DXA) scans. STUDY DESIGN: Using cross-sectional data from a large multicenter study, we developed and tested advanced methods for infant scan analysis. Newborns (n = 750) received spine and whole-body DXA scans with up to 3 attempts to acquire a motion free scan. Precision of infant DXA was estimated from visits with multiple valid scans. Accuracy of regional reflection, fusion, and omission techniques was estimated by comparing modified scans to unmodified valid scans. The effectiveness of the acquisition and analysis protocol was represented by the reduction in rate of failure to acquire valid results from infant visits. RESULTS: For infant whole-body DXA, arm reflection and all fusion techniques caused no significant changes to bone mineral content, bone mineral density, bone area, total mass, fat mass, lean mass, and percentage fat. Leg reflection and arm/leg dual-reflection caused significant changes to total mass, but the percentage change remained small. For infant spine DXA, fusion and omission caused no significant changes. Advanced analysis techniques reduced the failure rate of whole-body scanning from 20.8% to 9.3% and the failure rate of spine scanning from 8.9% to 2.4%. CONCLUSIONS: Advanced analysis techniques significantly reduced the impact of motion artifacts on infant DXA scans. We suggest this protocol be used in future infant DXA research and clinical practice.

The Psychometric Costs of Applicants' Faking: Examining Measurement Invariance and Retest Correlations Across Response Conditions.

This study examines the stability of the response process and the rank-order of respondents responding to 3 personality scales in 4 different response conditions. Applicants to the University College of Teacher Education Styria (N = 243) completed personality scales as part of their college admission process. Half a year later, they retook the same personality scales in 1 of 3 randomly assigned experimental response conditions: honest, faking-good, or reproduce. Longitudinal means and covariance structure analyses showed that applicants' response processes could be partially reproduced after half a year, and respondents seemed to rely on an honest response behavior as a frame of reference. Additionally, applicants' faking behavior and instructed faking (faking-good) caused differences in the latent retest correlations and consistently affected measurement properties. The varying latent retest correlations indicated that faking can distort respondents' rank-order and thus the fairness of subsequent selection decisions, depending on the kind of faking behavior. Instructed faking (faking-good) even affected weak measurement invariance, whereas applicants' faking behavior did not. Consequently, correlations with personality scales-which can be utilized for predictive validity-may be readily interpreted for applicants. Faking behavior also introduced a uniform bias, implying that the classically observed mean raw score differences may not be readily interpreted.

Self-disgust in mental disorders -- symptom-related or disorder-specific?

BACKGROUND: Dysfunctional disgust experiences occur in a variety of mental disorders. Previous research focused on disgust proneness directed towards stimuli in the external environment. However, self-disgust, the devaluation of one' own physical appearance and personality (personal disgust) as well as one' own behavior (behavioral disgust) has hardly been investigated thus far, although it may play a crucial role in specific psychopathologies. METHODS: We investigated 112 patients diagnosed with different mental disorders (major depression, schizophrenia, borderline personality disorder (BPD), eating disorders, and spider phobia) and 112 matched mentally healthy individuals. Participants answered the Questionnaire for the Assessment of Self-Disgust (QASD) with two subscales 'personal disgust' and 'behavioral disgust', and the Brief Symptom Inventory (BSI) that provides an overview of patients' psychological problems and their intensity. RESULTS: Compared to healthy controls self-disgust was elevated in mental disorders. Personal disgust was more pronounced than behavioral disgust in patients, whereas there was no difference in controls. Patients with BPD and eating disorders reported the highest scores on both subscales. Findings also suggest that self-disgust is related to specific psychological problems. In mental disorders psychoticism and hostility were the best predictors for personal disgust, while anxiety and interpersonal sensitivity predicted behavioral disgust. Additionally, we found disorder-specific predictors for personal disgust (e.g., hostility in schizophrenia). Finally, traumatic events during childhood constitute a risk factor for self-disgust. CONCLUSIONS: The current study provides first evidence for the differential meaning of self-disgust for specific mental disorders and symptoms.

Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity.

Intelligence and creativity are known to be correlated constructs suggesting that they share a common cognitive basis. The present study assessed three specific executive abilities - updating, shifting, and inhibition - and examined their common and differential relations to fluid intelligence and creativity (i.e., divergent thinking ability) within a latent variable model approach. Additionally, it was tested whether the correlation of fluid intelligence and creativity can be explained by a common executive involvement. As expected, fluid intelligence was strongly predicted by updating, but not by shifting or inhibition. Creativity was predicted by updating and inhibition, but not by shifting. Moreover, updating (and the personality factor openness) was found to explain a relevant part of the shared variance between intelligence and creativity. The findings provide direct support for the executive involvement in creative thought and shed further light on the functional relationship between intelligence and creativity.

Neural efficiency as a function of task demands.

The neural efficiency hypothesis describes the phenomenon that brighter individuals show lower brain activation than less bright individuals when working on the same cognitive tasks. The present study investigated whether the brain activation-intelligence relationship still applies when more versus less intelligent individuals perform tasks with a comparable person-specific task difficulty. In an fMRI-study, 58 persons with lower (n = 28) or respectively higher (n = 30) intelligence worked on simple and difficult inductive reasoning tasks having the same person-specific task difficulty. Consequently, less bright individuals received sample-based easy and medium tasks, whereas bright subjects received sample-based medium and difficult tasks. This design also allowed a comparison of lower versus higher intelligent individuals when working on the same tasks (i.e. sample-based medium task difficulty). In line with expectations, differences in task performance and in brain activation were only found for the subset of tasks with the same sample-based task difficulty, but not when comparing tasks with the same person-specific task difficulty. These results suggest that neural efficiency reflects an (ability-dependent) adaption of brain activation to the respective task demands.

PSAURON: a tool for assessing protein annotation across a broad range of species.

Evaluating the accuracy of protein-coding sequences in genome annotations is a challenging problem for which there is no broadly applicable solution. In this manuscript we introduce PSAURON (Protein Sequence Assessment Using a Reference ORF Network), a novel software tool developed to assess the quality of protein-coding gene annotations. Utilizing a machine learning model trained on a diverse dataset from over 1000 plant and animal genomes, PSAURON assigns a score to coding DNA or protein sequence that reflects the likelihood that the sequence is a genuine protein coding region. PSAURON scores can be used for genome-wide protein annotation assessment as well as the rapid identification of potentially spurious annotated proteins. Validation against established benchmarks demonstrates PSAURON's effectiveness and correlation with recognized measures of protein quality, highlighting its potential use as a general-purpose method to evaluate gene annotation. PSAURON is open source and freely available at https://github.com/salzberg-lab/PSAURON . One-Sentence Summary: PSAURON is a machine learning-based tool for rapid assessment of protein coding gene annotation.

Novel metagenomics analysis of stony coral tissue loss disease.

Stony coral tissue loss disease (SCTLD) has devastated coral reefs off the coast of Florida and continues to spread throughout the Caribbean. Although a number of bacterial taxa have consistently been associated with SCTLD, no pathogen has been definitively implicated in the etiology of SCTLD. Previous studies have predominantly focused on the prokaryotic community through 16S rRNA sequencing of healthy and affected tissues. Here, we provide a different analytical approach by applying a bioinformatics pipeline to publicly available metagenomic sequencing samples of SCTLD lesions and healthy tissues from four stony coral species. To compensate for the lack of coral reference genomes, we used data from apparently healthy coral samples to approximate a host genome and healthy microbiome reference. These reads were then used as a reference to which we matched and removed reads from diseased lesion tissue samples, and the remaining reads associated only with disease lesions were taxonomically classified at the DNA and protein levels. For DNA classifications, we used a pathogen identification protocol originally designed to identify pathogens in human tissue samples, and for protein classifications, we used a fast protein sequence aligner. To assess the utility of our pipeline, a species-level analysis of a candidate genus, Vibrio, was used to demonstrate the pipeline's effectiveness. Our approach revealed both complementary and unique coral microbiome members compared to a prior metagenome analysis of the same dataset.

Novel metagenomics analysis of stony coral tissue loss disease.

Stony coral tissue loss disease (SCTLD) has devastated coral reefs off the coast of Florida and continues to spread throughout the Caribbean. Although a number of bacterial taxa have consistently been associated with SCTLD, no pathogen has been definitively implicated in the etiology of SCTLD. Previous studies have predominantly focused on the prokaryotic community through 16S rRNA sequencing of healthy and affected tissues. Here, we provide a different analytical approach by applying a bioinformatics pipeline to publicly available metagenomic sequencing samples of SCTLD lesions and healthy tissues from four stony coral species. To compensate for the lack of coral reference genomes, we used data from apparently healthy coral samples to approximate a host genome and healthy microbiome reference. These reads were then used as a reference to which we matched and removed reads from diseased lesion tissue samples, and the remaining reads associated only with disease lesions were taxonomically classified at the DNA and protein levels. For DNA classifications, we used a pathogen identification protocol originally designed to identify pathogens in human tissue samples, and for protein classifications, we used a fast protein sequence aligner. To assess the utility of our pipeline, a species-level analysis of a candidate genus, Vibrio, was used to demonstrate the pipeline's effectiveness. Our approach revealed both complementary and unique coral microbiome members compared to a prior metagenome analysis of the same dataset.

CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure.

CHESS 3 represents an improved human gene catalog based on nearly 10,000 RNA-seq experiments across 54 body sites. It significantly improves current genome annotation by integrating the latest reference data and algorithms, machine learning techniques for noise filtering, and new protein structure prediction methods. CHESS 3 contains 41,356 genes, including 19,839 protein-coding genes and 158,377 transcripts, with 14,863 protein-coding transcripts not in other catalogs. It includes all MANE transcripts and at least one transcript for most RefSeq and GENCODE genes. On the CHM13 human genome, the CHESS 3 catalog contains an additional 129 protein-coding genes. CHESS 3 is available at http://ccb.jhu.edu/chess .

Structure-guided isoform identification for the human transcriptome.

Recently developed methods to predict three-dimensional protein structure with high accuracy have opened new avenues for genome and proteome research. We explore a new hypothesis in genome annotation, namely whether computationally predicted structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by protein structure predictions, we evaluated over 230,000 isoforms of human protein-coding genes assembled from over 10,000 RNA sequencing experiments across many human tissues. From this set of assembled transcripts, we identified hundreds of isoforms with more confidently predicted structure and potentially superior function in comparison to canonical isoforms in the latest human gene database. We illustrate our new method with examples where structure provides a guide to function in combination with expression and evolutionary evidence. Additionally, we provide the complete set of structures as a resource to better understand the function of human genes and their isoforms. These results demonstrate the promise of protein structure prediction as a genome annotation tool, allowing us to refine even the most highly curated catalog of human proteins. More generally we demonstrate a practical, structure-guided approach that can be used to enhance the annotation of any genome.

3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources.

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.

Impact of postpartum tenofovir-based antiretroviral therapy on bone mineral density in breastfeeding women with HIV enrolled in a randomized clinical trial.

OBJECTIVES: We set out to evaluate the effect of postnatal exposure to tenofovir-containing antiretroviral therapy on bone mineral density among breastfeeding women living with HIV. DESIGN: IMPAACT P1084s is a sub-study of the PROMISE randomized trial conducted in four African countries (ClinicalTrials.gov number NCT01066858). METHODS: IMPAACT P1084s enrolled eligible mother-infant pairs previously randomised in the PROMISE trial at one week after delivery to receive either maternal antiretroviral therapy (Tenofovir disoproxil fumarate / Emtricitabine + Lopinavir/ritonavir-maternal TDF-ART) or administer infant nevirapine, with no maternal antiretroviral therapy, to prevent breastmilk HIV transmission. Maternal lumbar spine and hip bone mineral density were measured using dual-energy x-ray absorptiometry (DXA) at postpartum weeks 1 and 74. We studied the effect of the postpartum randomization on percent change in maternal bone mineral density in an intention-to-treat analysis with a t-test; mean and 95% confidence interval (95%CI) are presented. RESULTS: Among 398/400 women included in this analysis, baseline age, body-mass index, CD4 count, mean bone mineral density and alcohol use were comparable between study arms. On average, maternal lumbar spine bone mineral density declined significantly through week 74 in the maternal TDF-ART compared to the infant nevirapine arm; mean difference (95%CI) -2.86 (-4.03, -1.70) percentage points (p-value <0.001). Similarly, maternal hip bone mineral density declined significantly more through week 74 in the maternal TDF-ART compared to the infant nevirapine arm; mean difference -2.29% (-3.20, -1.39) (p-value <0.001). Adjusting for covariates did not change the treatment effect. CONCLUSIONS: Bone mineral density decline through week 74 postpartum was greater among breastfeeding HIV-infected women randomized to receive maternal TDF-ART during breastfeeding compared to those mothers whose infants received nevirapine prophylaxis.

Programming experience associated with neural efficiency during figural reasoning.

In the present study, we investigated neural processes underlying programming experience. Individuals with high programming experience might develop a form of computational thinking, which they can apply on complex problem-solving tasks such as reasoning tests. Therefore, N = 20 healthy young participants with previous programming experience and N = 21 participants without any programming experience performed three reasoning tests: Figural Inductive Reasoning (FIR), Numerical Inductive Reasoning (NIR), Verbal Deductive Reasoning (VDR). Using multi-channel EEG measurements, task-related changes in alpha and theta power as well as brain connectivity were investigated. Group differences were only observed in the FIR task. Programmers showed an improved performance in the FIR task as compared to non-programmers. Additionally, programmers exhibited a more efficient neural processing when solving FIR tasks, as indicated by lower brain activation and brain connectivity especially in easy tasks. Hence, behavioral and neural measures differed between groups only in tasks that are similar to mental processes required during programming, such as pattern recognition and algorithmic thinking by applying complex rules (FIR), rather than in tasks that require more the application of mathematical operations (NIR) or verbal tasks (VDR). Our results provide new evidence for neural efficiency in individuals with higher programming experience in problem-solving tasks.

Improved strength prediction combining clinically available measures of skeletal muscle mass and quality.

BACKGROUND: Measures of skeletal muscle function decline at a faster rate with ageing than do indices of skeletal muscle mass. These observations have been attributed to age-related changes in muscle quality, another functional determinant separate from skeletal muscle mass. This study tested the hypothesis that improved predictions of skeletal muscle strength can be accomplished by combining clinically available measures of skeletal muscle mass and quality. METHODS: The participants included 146 healthy adult (age ≥ 18 years, range 18-77 years; X ± SD 47 ± 17 years and body mass index 16.5-51.8 kg/m2 ; 27.7 ± 6.2 kg/m2 ) men (n = 60) and women (n = 86) in whom skeletal muscle mass was estimated as appendicular lean soft tissue (LST) measured by dual-energy X-ray absorptiometry and skeletal muscle quality as bioimpedance analysis-derived phase angle and B-mode-evaluated echogenicity of mid-thigh skeletal muscle. Strength of the right leg and both arms was quantified as knee isokinetic extension and handgrip strength using dynamometers. The statistical significance of adding phase angle or echogenicity to strength prediction multiple regression models that included extremity-specific LST and other covariates (e.g. age and sex) was evaluated to test the study hypothesis. RESULTS: Right leg LST mass alone was significantly (P < 0.0001) correlated with isokinetic right leg strength (R2  = 0.57). The addition of segmental phase angle measured in the right leg at 50 kHz increased the R2 of this model to 0.66 (P < 0.0001); other phase angle frequencies (5 and 250 kHz) did not contribute significantly to these models. Results were similar for both right and left arm handgrip strength prediction models. Adding age and sex as model covariates increased the R2 values of these models further (e.g. right leg strength model R2 increased to 0.71), but phase angle continued to remain a significant (all P < 0.01) predictor of extremity strength. Similarly, when predicting isokinetic right leg strength, mid-thigh skeletal muscle echogenicity added significantly (P < 0.0001) to right leg LST, increasing R2 from 0.57 to 0.64; age was a significant (P < 0.0001) covariate in this model, increasing R2 further to 0.68. CONCLUSIONS: The hypothesis of the current study was confirmed, strongly supporting and extending earlier reports by quantifying the combined independent effects of skeletal muscle mass and quality on lower-body and upper-body measures of strength. These observations provide a clinically available method for future research aimed at optimizing sarcopenia and frailty risk prediction models.

Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies.

BACKGROUND: Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status. OBJECTIVES: The objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling. METHODS: Healthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA. RESULTS: This analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female). CONCLUSIONS: 3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings.This trial was registered at clinicaltrials.gov as NCT03637855.

Digital anthropometry: a critical review.

Anthropometry, Greek for human measurement, is a tool widely used across many scientific disciplines. Clinical nutrition applications include phenotyping subjects across the lifespan for assessing growth, body composition, response to treatments, and predicting health risks. The simple anthropometric tools such as flexible measuring tapes and calipers are now being supplanted by rapidly developing digital technology devices. These systems take many forms, but excitement today surrounds the introduction of relatively low cost three-dimensional optical imaging methods that can be used in research, clinical, and even home settings. This review examines this transformative technology, providing an overview of device operational details, early validation studies, and potential applications. Digital anthropometry is rapidly transforming dormant and static areas of clinical nutrition science with many new applications and research opportunities.

Body composition by DXA.

Body composition measurements from DXA have been available since DXA technology was developed 30years ago, but are historically underutilized. Recently, there have been rapid developments in body composition assessment including the analysis and publication of representative data for the US, official usage guidance from the International Society for Clinical Densitometry, and development of regional body composition measures with clinical utility. DXA body composition is much more than whole body percent fat. In this paper celebrating 30years of DXA for body composition, we will review the principles of DXA soft tissue analysis, practical clinical and research applications, and what to look for in the future.