Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of the BBA64 Locus of Borrelia burgdorferi in Early Stages of Infectivity in a Murine Model of Lyme Disease

ABSTRACT Borrelia burgdorferi, the causative agent of Lyme disease, undergoes rapid adaptive gene expression in response to environmental signals encountered during different stages of its life cycle in the arthropod vector or the mammalian host. Among all the plasmid-encoded genes of B. burgdorferi, several linear plasmid 54 (lp54)-encoded open reading frames (ORFs) exhibit the greatest differential expression in response to mammalian host-specific temperature, pH, and other uncharacterized signals. These ORFs include members of the paralogous gene family 54 (pgf 54), such as BBA64, BBA65, and BBA66, present on lp54. In an attempt to correlate transcriptional up-regulation of these pgf 54 members to their role in infectivity, we inactivated BBA64 and characterized the phenotype of this mutant both in vitro and in vivo. There were no major differences in the protein profiles between the BBA64 mutant and the control strains, while immunoblot analysis indicated that inactivation of BBA64 resulted in increased levels of BBA65. Moreover, there was no significant difference in the ability of the BBA64 mutant to infect C3H/HeN mice compared to that of its parental or complemented control strains as determined by culturing of viable spirochetes from infected tissues. However, enumeration of spirochetes using quantitative real-time PCR revealed tissue-specific differences, suggesting a minimal role for BBA64 in the survival of B. burgdorferi in select tissues. Infectivity analysis of the BBA64 mutant suggests that B. burgdorferi may utilize multiple determinants to establish infection in mammalian hosts.
Assessing the Accuracy of Continuous Glucose Monitoring (CGM) Calibrated With Capillary Values Using Capillary or Venous Glucose Levels as a Reference

Background: Using the standard venous reference for the evaluation of continuous glucose monitoring (CGM) systems could possibly negatively affect measured CGM accuracy since CGM are generally calibrated with capillary glucose and venous and capillary glucose concentrations differ. We therefore aimed to quantify the effect of using capillary versus venous glucose reference samples on estimated accuracy in capillary calibrated CGM. Methods: We evaluated 41 individuals with type 1 diabetes mellitus (T1DM) using the Dexcom G4 CGM system over 6 days. Patients calibrated their CGM devices with capillary glucose by means of the HemoCue system. During 2 visits, capillary and venous samples were simultaneously measured by HemoCue and compared to concomitantly obtained CGM readings. The mean absolute relative difference (MARD) was calculated using capillary and venous reference samples. Results: Venous glucose values were 0.83 mmol/L (15.0 mg/dl) lower than capillary values over all glycemic ranges, P < . 0001. Below 4 mmol/l (72 mg/dl), the difference was 1.25 mmol/l (22.5 mg/dl), P = . 0001, at 4-10 mmol/l (72-180 mg/dl), 0.67 mmol/l (12.0 mg/dl), P < . 0001 and above 10 mmol/l (180 mg/dl), 0.95 mmol/l (17.1 mg/dl), P < . 0001. MARD was 11.7% using capillary values as reference compared to 13.7% using venous samples, P = . 037. Below 4 mmol/l (72 mg/dl) MARD was 16.6% and 31.8%, P = . 048, at 4-10 mmol/l (72-180 mg/dl) 12.1% and 12.6%, P = . 32, above 10 mmol/l (180 mg/dl) 8.7% and 9.2%, P = . 82. Conclusion: Using capillary glucose concentrations as reference to evaluate the accuracy of CGM calibrated with capillary samples is associated with a lower MARD than using venous glucose as the reference. Capillary glucose concentrations were significantly higher than venous in all glycemic ranges.
Landscape spatial configuration is a key driver of wild bee demographics

The majority of studies investigating the effects of landscape composition and configuration on bee populations have been conducted in regions of intensive agricultural production, ignoring regions which are dominated by seminatural habitats, such as the islands of the Aegean Archipelago. In addition, research so far has focused on the landscape impacts on bees sampled in cropped fields while the landscape effects on bees inhabiting seminatural habitats are understudied. Here, we investigate the impact of the landscape on wild bee assemblages in 66 phryganic (low scrubland) communities on 8 Aegean islands. We computed landscape metrics (total area and total perimeter–area ratio) in 4 concentric circles (250, 500, 750, and 1000 m) around the center of each bee sampling site including 3 habitat groups (namely phrygana, cultivated land, and natural forests). We further measured the local flower cover in 25 quadrats distributed randomly at the center of each sampling site. We found that the landscape scale is more important than the local scale in shaping abundance and species richness of bees. Furthermore, habitat configuration was more important than the total area of habitats, probably because it affects bees’ movement across the landscape. Phrygana and natural forests had a positive effect on bee demographics, while cultivated land had a negative effect. This demonstrates that phryganic specialists drive bee assemblages in these seminatural landscapes. This finding, together with the shown importance of landscape scale, should be considered for the management of wild bees with special emphasis placed on the spatial configuration of seminatural habitats.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classification, segmentation, and detection tasks. One deep learning technique, U-Net, has become one of the most popular for these applications. In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively. The proposed models utilize the power of U-Net, Residual Network, as well as RCNN. There are several advantages of these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architecture. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architecture with same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets such as blood vessel segmentation in retina images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models including U-Net and residual U-Net (ResU-Net).
Aircraft Radiation Shield Experiments---Preflight Laboratory Testing

In the past, measurements onboard a research Boeing 57F (RB57-F) aircraft have demonstrated that the neutron environment within the aircraft structure is greater than that in the local external environment. Recent studies onboard Boeing 737 commercial flights have demonstrated cabin variations in radiation exposure up to 30 percent. These prior results were the basis of the present study to quantify the potential effects of aircraft construction materials on the internal exposures of the crew and passengers. The present study constitutes preflight measurements using an unmoderated Cf-252 fission neutron source to quantify the effects of three current and potential aircraft materials (aluminum, titanium, and graphite-epoxy composite) on the fast neutron flux. Conclusions about the effectiveness of the three selected materials for radiation shielding must wait until testing in the atmosphere is complete; however, it is clear that for shielding low-energy neutrons, the composite material is an improved shielding material over aluminum or titanium.
Gene Conservation among Endospore-Forming Bacteria Reveals Additional Sporulation Genes in Bacillus subtilis

ABSTRACT The capacity to form endospores is unique to certain members of the low-G+C group of Gram-positive bacteria (Firmicutes) and requires signature sporulation genes that are highly conserved across members of distantly related genera, such as Clostridium and Bacillus. Using gene conservation among endospore-forming bacteria, we identified eight previously uncharacterized genes that are enriched among endospore-forming species. The expression of five of these genes was dependent on sporulation-specific transcription factors. Mutants of none of the genes exhibited a conspicuous defect in sporulation, but mutants of two, ylxY and ylyA, were outcompeted by a wild-type strain under sporulation-inducing conditions, but not during growth. In contrast, a ylmC mutant displayed a slight competitive advantage over the wild type specific to sporulation-inducing conditions. The phenotype of a ylyA mutant was ascribed to a defect in spore germination efficiency. This work demonstrates the power of combining phylogenetic profiling with reverse genetics and gene-regulatory studies to identify unrecognized genes that contribute to a conserved developmental process.
A Massively Parallel Deep Rule-Based Ensemble Classifier for Remote Sensing Scenes

In this letter, we propose a new approach for remote sensing scene classification by creating an ensemble of the recently introduced massively parallel deep (fuzzy) rule-based (DRB) classifiers trained with different levels of spatial information separately. Each DRB classifier consists of a massively parallel set of human-interpretable, transparent zero-order fuzzy IF…THEN… rules with a prototype-based nature. The DRB classifier can self-organize “from scratch” and self-evolve its structure. By employing the pretrained deep convolution neural network as the feature descriptor, the proposed DRB ensemble is able to exhibit human-level performance through a transparent and parallelizable training process. Numerical examples using benchmark data set demonstrate the superior accuracy of the proposed approach together with human-interpretable fuzzy rules autonomously generated by the DRB classifier.
Prediction of Fatigue-Creep Life Based on Crack Propagation Under 2-step Load

By taking the increment of strain energy density under tensile stress within a cycle as the damage variable of the material,and according the positive acreage enclosed by hysteresis loops,the equation of the damage variable can be educed. In view of the fact that the damage of material would result in fatigue crack propagation and fatigue crack propagation would cause the crack of specimens,J-integral is considered as the control parameter during crack growth process,crack growth is regarded as the contribution of fatigue damages as well as creep damage,the interaction of the two factors is considered,then the equation of Fatigue-Creep Life under 2-step stress load is educed; the equation is relative to loading waveform and fatigue-creep rate. The equation is applied to predict fatigue-creep life of 316 L steel,at 550 ℃ under 2-step load of strain trapezoidal wave. The prediction is proved to be satisfactory with an error factor of ± 1. 50 times.
Trace Lasso: a trace norm regularization for correlated designs

Using the l1-norm to regularize the estimation of the parameter vector of a linear model leads to an unstable estimator when covariates are highly correlated. In this paper, we introduce a new penalty function which takes into account the correlation of the design matrix to stabilize the estimation. This norm, called the trace Lasso, uses the trace norm of the selected covariates, which is a convex surrogate of their rank, as the criterion of model complexity. We analyze the properties of our norm, describe an optimization algorithm based on reweighted least-squares, and illustrate the behavior of this norm on synthetic data, showing that it is more adapted to strong correlations than competing methods such as the elastic net.
Partial Order Theory for Fast TCAM Updates

Ternary content addressable memories (TCAMs) are frequently used for fast matching of packets against a given ruleset. While TCAMs can achieve fast matching, they are plagued by high update costs that can make them unusable in a high churn rate environment. We present, in this paper, a systematic and in-depth analysis of the TCAM update problem. We apply partial order theory to derive fundamental constraints on any rule ordering on TCAMs, which ensures correct checking against a given ruleset. This theoretical insight enables us to fully explore the TCAM update algorithms design space, to derive the optimal TCAM update algorithm (though it might not be suitable to be used in practice), and to obtain upper and lower bounds on the performance of practical update algorithms. Having lower bounds, we checked if the smallest update costs are compatible with the churn rate observed in practice, and we observed that this is not always the case. We therefore developed a heuristic based on ruleset splitting, with more than a single TCAM chip, that achieves significant update cost reductions ( $1.05\sim 11.3\times $ ) compared with state-of-the-art techniques.
Assessing the Negative and Positive Emotion-Dependent Nature of Risky Behaviors Among Substance Dependent Patients

Despite literature highlighting the relevance of negative and positive emotions to risky behaviors, little research has examined the emotion-dependent context of risky behaviors. This study sought to develop and validate a comprehensive measure of the frequency and emotion-dependent context of distinct clinically relevant risky behaviors (the Risky Behavior Questionnaire [RBQ]), as well as to examine the unique relations of the RBQ Negative and Positive Scales (which assess the general tendency to engage in risky behaviors in the context of negative vs. positive emotions, respectively) to specific risky behaviors. Participants were 176 patients in a residential substance use disorder treatment facility (M age = 34.18; 65.3% White, 53.4% female). Results provided support for the construct and incremental validity (relative to extant measures of related constructs) of the RBQ Scales, as well as the differential relevance of RBQ Negative and Positive Scales to specific risky behaviors.
The Achilles Tendon in Healthy Subjects: An Anthropometric and Ultrasound Mapping Study

&NA; Ultrasonography is an inexpensive, fast, and reliable imaging technique widely used to assess the Achilles tendon. Although significant data exists regarding pathologic tendon changes, ultrasound data from healthy individuals are more limited. We aimed to better characterize ultrasound Achilles tendon measurements in healthy individuals and identify important correlating factors. The information collected included patient demographics, body habitus, activity level, foot dominance, and resting ankle angle. Ultrasound analysis was performed bilaterally on the Achilles tendons of 50 subjects using a high‐frequency transducer to measure tendon width, thickness, cross‐sectional area, and length. Males had a significantly larger mean tendon length, width, thickness, and cross‐sectional area. No statistically significant difference was found in any tendon dimension between the white and black participants. Similarly, no difference was found in any tendon parameter when comparing right versus left leg dominance. Healthy subjects had a mean ankle resting angle of 45.1° ± 24° with no statistically significant difference between right and left ankles. Considering all individuals, each tendon parameter (tendon length, width, thickness, and cross‐sectional area) correlated positively with subject height, weight, tibia length, and foot size. Only the Achilles cross‐sectional area correlated significantly with the activity level. The resting angle of the ankle correlated positively with both tendon length and thickness. In conclusion, we found significant variations in Achilles tendon anatomy in the healthy adult population. We have thoroughly characterized significant correlations between healthy tendon dimensions and various body habitus, activity levels, and ankle parameters. Greater knowledge of the normal Achilles tendon anatomy and characterization of its variations in the healthy population will potentially allow for better pathologic diagnosis and surgical repair. &NA; Level of Clinical Evidence: 2
Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes’ energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs’ election, we take nodes’ energies, nodes’ degree and neighbor nodes’ residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.
Clinical and sociodemographic variables associated with diabetes-related distress in patients with type 2 diabetes mellitus

ABSTRACT Objective: To evaluate the relation between diabetes-related distress and the clinical and sociodemographic characteristics of type 2 diabetes mellitus patients. Methods: A cross-sectional study based on a secondary analysis of data collected at a specialized care outpatient center in Brazil. Participants completed a questionnaire on sociodemographic and clinical characteristics and the Brazilian version of the Diabetes Distress Scale (B-DDS). Results: About 31% of the 130 eligible patients reported diabetes distress, and the mean B-DDS score was 2.6. Multiple regression analysis showed the B-DDS score was positively correlated with marital status (p=0.0230), use of diet and physical activities for diabetes management (p=0.0180), and use of insulin therapy (p=0.0030). The “emotional burden”, “regimen-related distress”, and “interpersonal distress” domains from B-DDS were associated with the use of insulin therapy (p=0.0010), marital status (p=0.0110), and the presence of three or more comorbidities (p=0.0175). Conclusion: These findings suggest the clinical and sociodemographic variables are relatively weak predictors of diabetes-related distress. The highest scores in the B-DDS were observed in the emotional burden domain, indicating the presence of diabetes distress among the participants of the study.
The information content of anisotropic Baryon Acoustic Oscillation scale measurements

Anisotropic measurements of the Baryon Acoustic Oscillation (BAO) feature within a galaxy survey enable joint inference about the Hubble parameter H(z) and angular diameter distance DA(z). These measurements are typically obtained from moments of the measured 2-point clustering statistics, with respect to the cosine of the angle to the line of sight mu. The position of the BAO features in each moment depends on a combination of DA(z) and H(z), and measuring the positions in two or more moments breaks this parameter degeneracy. We derive analytic formulae for the parameter combinations measured from moments given by Legendre polynomials, power laws and top-hat Wedges in μ, showing explicitly what is being measured by each in real-space for both the correlation function and power spectrum, and in redshift-space for the power spectrum. The large volume covered by modern galaxy samples means that the correlation function can be well approximated as having no correlations at different mu on the BAO scale, and that the errors on this scale are approximately independent of mu. Using these approximations, we derive the information content of various moments. We show that measurements made using either the monopole and quadrupole, or the monopole and μ2 power-law moment, are optimal for anisotropic BAO measurements, in that they contain all of the available information using two moments, the minimal number required to measure both H(z) and DA(z). We test our predictions using 600 mock galaxy samples, matched to the SDSS-III Baryon Oscillation Spectroscopic Survey CMASS sample, finding a good match to our analytic predictions. Our results should enable the optimal extraction of information from future galaxy surveys such as eBOSS, DESI and Euclid.
Systematics of Plant Pathogenic Fungi: Why It Matters.

Systematics is the study of biological diversity; it is the science that discovers, describes, and classifies all organisms and includes taxonomy, nomenclature, and phylogeny. Scientific names accurately define a set of organisms and are used to communicate about them. As systematic scientists learn more about species, scientific names change to reflect this increased knowledge. Accurately named and precisely defined species reflect what is known about their biology, host range, and geographic distribution. Online resources are available to help users determine the accepted scientific name of fungal plant pathogens. Recent advances in the systematics of fungi are discussed, including the knowledge that: (i) true Fungi are more closely related to animals than to plants; and (ii) the Oomycetes are not true Fungi; rather they are closely related to the yellow-brown algae, known as stramenopiles or Kingdom Chromista. Using molecular data, the higher level classification of true Fungi is more precisely defined.
SCD5 restored expression favors differentiation and epithelial-mesenchymal reversion in advanced melanoma

Our previous data supported a role for the Stearoyl-CoA desaturase (SCD5) in protection against malignancy, whereby it appears to functionally modify tumor stroma impairing tumor spread. SCD5 is significantly expressed in primary melanoma, but becomes barely detectable at tumor advanced stages. Looking for the regulatory mechanisms underlying SCD5 reduced expression during melanoma progression, we demonstrated a significantly lower stability of SCD5 protein as well as the direct targeting of SCD5 mRNA by the oncogenic miR-221&222 in metastatic cell lines. Moreover, our results indicated the existence of a negative feedback loop between SCD5 and miR-221&222, in good agreement with their opposite functions. Also, we showed how SCD5 re-expression and the direct supplementation of its main product oleic acid (OA) can drive advanced melanoma cell lines toward differentiation and reversion of the epithelial-mesenchymal (EMT)-like process, eventually inducing a less malignant phenotype. Indeed, SCD5 re-established the sensitivity to all-trans retinoic acid in A375M metastatic melanoma, associated with increased levels of Tyrosinase, melanin production and reduced proliferation. As evidenced by the correct modulation of some key transcription factors, SCD5 managed by favoring a partial mesenchymal-to-epithelial (MET) transition in in vitro studies. Interestingly, a more complete MET, including E-cadherin re-expression correctly localized at cell membranes, was obtained in in vivo xenograft models, thus indicating the requirement of direct contacts between tumor cells and the surrounding microenvironment as well as the presence of some essential factors for SCD5 complete function.