Miracle-dz/UniFAR-FTUs huggingface dataset:
Metamorphosis of a Scleractinian Coral in Response to Microbial Biofilms

ABSTRACT Microorganisms have been reported to induce settlement and metamorphosis in a wide range of marine invertebrate species. However, the primary cue reported for metamorphosis of coral larvae is calcareous coralline algae (CCA). Herein we report the community structure of developing coral reef biofilms and the potential role they play in triggering the metamorphosis of a scleractinian coral. Two-week-old biofilms induced metamorphosis in less than 10% of larvae, whereas metamorphosis increased significantly on older biofilms, with a maximum of 41% occurring on 8-week-old microbial films. There was a significant influence of depth in 4- and 8-week biofilms, with greater levels of metamorphosis occurring in response to shallow-water communities. Importantly, larvae were found to settle and metamorphose in response to microbial biofilms lacking CCA from both shallow and deep treatments, indicating that microorganisms not associated with CCA may play a significant role in coral metamorphosis. A polyphasic approach consisting of scanning electron microscopy, fluorescence in situ hybridization (FISH), and denaturing gradient gel electrophoresis (DGGE) revealed that coral reef biofilms were comprised of complex bacterial and microalgal communities which were distinct at each depth and time. Principal-component analysis of FISH data showed that the Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Cytophaga-Flavobacterium of Bacteroidetes had the largest influence on overall community composition. A low abundance of Archaea was detected in almost all biofilms, providing the first report of Archaea associated with coral reef biofilms. No differences in the relative densities of each subdivision of Proteobacteria were observed between slides that induced larval metamorphosis and those that did not. Comparative cluster analysis of bacterial DGGE patterns also revealed that there were clear age and depth distinctions in biofilm community structure; however, no difference was detected in banding profiles between biofilms which induced larval metamorphosis and those where no metamorphosis occurred. This investigation demonstrates that complex microbial communities can induce coral metamorphosis in the absence of CCA.
A phylogeny-free microbiome dysbiosis detection pipeline for non-invasive disease diagnosis

The microbial diversity and taxonomic profiles of human microbiome carries indicative signals associated with several complex human diseases. Methods quantifying and differentiating profiles belonging to healthy/disease microbiomes have a potential to be used as non-invasive diagnostic tools. 16S rRNA sequencing is a currently popular and feasible technology to generate the required data for such diagnostic. However, current methodology to analyze 16S rRNA data requires biologically motivated approaches including molecular database search and phylogenetic tree construction, that results in losing valuable information related to dysbiotic signals. In this study, information-theoretic measures for microbiome similarity calculation that avoid conventional projections are introduced. A computational pipeline joining the information of three novel similarity measures in an implicit feature space and consequently filtering redundant features is defined. The extracted features are finally exposed to classifiers predicting disease/healthy states. Experiments conducted on previously defined Crohn's Disease, Inflammatory Bowel Disease, Parkinson's Disease, Autistic Children, Colorectal Cancer datasets revealed that the proposed framework can achieve improved detection accuracy over conventional microbiome analysis approaches.
Assessment of binocular alignment using the three-dimensional Strabismus Photo Analyzer

Aim To evaluate the efficacy of the three-dimensional (3D) Strabismus Photo Analyzer for estimating binocular alignment using photographs. Methods Thirty-two subjects with exotropia, 30 subjects with esotropia and 38 orthotropic subjects were included. Two independent ophthalmologists examined the angle of deviation using the Krimsky test and prism and alternate cover test (PCT). Full-face photographs were obtained using an 8.2-megapixel digital single-lens reflex camera, and the images were analysed using the 3D Strabismus Photo Analyzer. The images were adjusted for age-dependent ophthalmic biometry and angle κ. The main outcome measures were inter-observer variability, test‐retest reliability and correlation between the angles of deviation measured by different methods. Results The 95% limit of agreement of inter-observer variability was ±3.5° (6.1 prism dioptres (PD)), ±3.1° (5.4 PD) and ±1.5° (2.6 PD) for the Krimsky test, PCT and the 3D Strabismus Photo Analyzer, respectively. The test‐retest reliability was ±2.8° (4.9 PD) for the 3D Strabismus Photo Analyzer versus the Krimsky test. The results of the Krimsky test and 3D Strabismus Photo Analyzer showed a strong positive correlation. Conclusions The 3D Strabismus Photo Analyzer is a simple and reliable tool for measuring ocular deviation. It has excellent agreement with the Krimsky test and substantially improved reproducibility.
Multi-class Co-training Learning for Object and Scene Recognition

It is often tedious and expensive to label large training data sets for learning-based object and scene recognition systems. This problem could be alleviated by semi-supervised learning techniques, which can automatically select more training samples from unlabel data for reducing the cost of labeling. In this paper, we proposed a multi-class co-training learning method of two different views for improving the performance of selective training samples for object and scene classification. In the co-training procedure, the classifiers are learned in two different views, respectively, and then, are used for classifying the unlabel data. At the same time, according to the confidence factor of the classified unlabel samples, we can confirm if the classifiers of the two views are enough strong for co-training or which is more stronger for co-training. Therefore, the unlabeled samples, which are classified by the strong classifier, can be chosen to label. To evaluate the performance of the proposed co-training method, two dataset (one is scene dataset, the other is object dataset) are utilized for recognition. The experimental results demonstrated that the recognition rate can be improved by co-training learning in different views, and it is also comparable with those by the art of the state algorithms.
Master Sintering Curve Formulated from Constitutive Models

The classical master sintering curve (MSC) is derived from empirical sintering model and is applicable over a range of heating rates and temperatures. For simplicity, the MSC approach was modified by assuming one dominant densification mechanism to evaluate and predict densification response. However, the concept of MSC can be extended well beyond the original formulation or the subsequent simplifications. To this effect, generalized formulations are proposed based on several constitutive equations including both grain growth and densification. These formulations can be used very effectively to obtain material properties that in turn can be used in finite-element method to improve the accuracy of the simulations.
Minimum Energy Consumption for Rate Monotonic Tasks

Limited battery power is a typical constraint in stand-alone embedded systems. One way to extend the battery lifetime is by reducing CPU power consumption. Because of the quadratic relationship between power consumption in CMOS circuits and CPU voltage, power reduction can be obtained by scaling down supply voltage, or Dynamic Voltage Scaling. However, reducing supply voltage slows down CPU speed since supply voltage has a proportional relationship with CPU frequency. On the other hand, in any real-time embedded environment (especially hard real-time), timing constraints are critical. In this paper, we focus on dynamic energy reduction of tasks scheduled by Rate Monotonic (RM) algorithm in a hard real-time embedded environment. The RM algorithm preemptively schedules any set of periodic tasks by assigning higher priorities to frequent tasks. For any periodic task set that satisfies the CPU utilization bound, we determine the provably optimal scaling of the worst-case execution time of each task that consumes minimum dynamic energy while satisfying the utilization bound. As RM algorithm is widely used, we expect this work can lead to better energy reduction management and expectations.
Improving Electrical Conductivity and Thermal Properties of Polymers by the Addition of Carbon Nanotubes as Fillers

The remarkable electrical and thermal conductivities of isolated carbon nanotubes have spurred worldwide interest in using nanotubes to enhance polymer properties. Electrical conductivity in nanotube/polymer composites is well described by percolation, where the presence of an interconnected nanotube network corresponds to a dramatic increase in electrical conductivity ranging from 10^−5 S/cm to 1 S/cm. Given the high aspect ratios and small diameters of carbon nanotubes, percolation thresholds are often reported below 1 wt% although nanotube dispersion and alignment strongly influence this value. Increases in thermal conductivity are modest (∼3 times) because the inter facial thermal re sis tance between nanotubes is considerable and the thermal conductivity of nanotubes is only 10^4 greater than the polymer, which forces the matrix to contribute more toward the composite thermal conductivity, as compared to the contrast in electrical conductivity, >10^14. The nanotube network is also valuable for improving flame-retardant efficiency by producing a protective nanotube residue. In this ar ticle, we highlight published research results that elucidate fundamental structure–property relationships pertaining to electrical, thermal, and/or flammability properties in numerous nanotube-containing polymer composites, so that specific applications can be targeted for future commercial success.
Characterization and mechanical properties of foamed poly(ɛ-caprolactone) and Mater-Bi blends using CO2 as blowing agent

Biodegradable foams are a key area of growth for packaging applications. Starch-based materials have been a successful environmentally degradable polymer. However, making foams out of these materials has been challenging due to their chemical composition and consequent low miscibility in CO2. We examine the potential for developing biodegradable foams with one component being a starch-based polymer by introducing it into polycaprolactone. Polycaprolactone has thus far shown very high-expansion ratios for foams with supercritical CO2. Blends with increasing amounts of starch-based materials were processed and foamed using isothermal treatments with supercritical CO2. Characterization of the samples was done using X-ray diffraction, differential scanning calorimetry, and scanning electron microscopy. The melting enthalpies and temperatures of the starch-based materials phase decreased with decreasing starch-based materials indicating some an influence of polycaprolactone on the starch-based materials crystallinity. Foaming, however, caused a reversal in this effect with the foamed melting points similar to the pure components. Micrographs of the samples from the scanning electron microscopy revealed that the cell size of the foams reduced with the increase in starch-based materials concentration. Mechanical tests—tensile, compression, shear, and impact—were performed on the foamed samples. The results indicate a valuable approach to foaming materials that are compostable but not CO2 miscible through blending with a highly foamable polymer such as polycaprolactone.
Cost-effective targeting of conservation investments to reduce the northern Gulf of Mexico hypoxic zone

Significance Hypoxic (low-oxygen) zones threaten an increasing number of marine ecosystems. Hypoxia in the Gulf of Mexico is the second largest in the world. The United States has a policy goal of reducing the average zone to 5,000 km2. Reductions in nutrients from cropland in the Mississippi-Atchafalaya River Basin are needed to achieve this goal. We use an integrated assessment model coupled with optimization to identify the cost-effective locations to target cropland conservation investments across the Basin’s 550 agricultural subwatersheds and to identify the nature of tradeoffs between hypoxia and costs of conservation investments. Targeted conservation practice investments are estimated to achieve the hypoxia reduction goal at the cost of $2.7 billion annually. A seasonally occurring summer hypoxic (low oxygen) zone in the northern Gulf of Mexico is the second largest in the world. Reductions in nutrients from agricultural cropland in its watershed are needed to reduce the hypoxic zone size to the national policy goal of 5,000 km2 (as a 5-y running average) set by the national Gulf of Mexico Task Force’s Action Plan. We develop an integrated assessment model linking the water quality effects of cropland conservation investment decisions on the more than 550 agricultural subwatersheds that deliver nutrients into the Gulf with a hypoxic zone model. We use this integrated assessment model to identify the most cost-effective subwatersheds to target for cropland conservation investments. We consider targeting of the location (which subwatersheds to treat) and the extent of conservation investment to undertake (how much cropland within a subwatershed to treat). We use process models to simulate the dynamics of the effects of cropland conservation investments on nutrient delivery to the Gulf and use an evolutionary algorithm to solve the optimization problem. Model results suggest that by targeting cropland conservation investments to the most cost-effective location and extent of coverage, the Action Plan goal of 5,000 km2 can be achieved at a cost of $2.7 billion annually. A large set of cost-hypoxia tradeoffs is developed, ranging from the baseline to the nontargeted adoption of the most aggressive cropland conservation investments in all subwatersheds (estimated to reduce the hypoxic zone to less than 3,000 km2 at a cost of $5.6 billion annually).
Optimal Foraging Predicts the Ecology but Not the Evolution of Host Specialization in Bacteriophages

We explore the ability of optimal foraging theory to explain the observation among marine bacteriophages that host range appears to be negatively correlated with host abundance in the local marine environment. We modified Charnov's classic diet composition model to describe the ecological dynamics of the related generalist and specialist bacteriophages φX174 and G4, and confirmed that specialist phages are ecologically favored only at high host densities. Our modified model accurately predicted the ecological dynamics of phage populations in laboratory microcosms, but had only limited success predicting evolutionary dynamics. We monitored evolution of attachment rate, the phenotype that governs diet breadth, in phage populations adapting to both low and high host density microcosms. Although generalist φX174 populations evolved even broader diets at low host density, they did not show a tendency to evolve the predicted specialist foraging strategy at high host density. Similarly, specialist G4 populations were unable to evolve the predicted generalist foraging strategy at low host density. These results demonstrate that optimal foraging models developed to explain the behaviorally determined diets of predators may have only limited success predicting the genetically determined diets of bacteriophage, and that optimal foraging probably plays a smaller role than genetic constraints in the evolution of host specialization in bacteriophages.
A Semantical Analysis of Second-Order Propositional Modal Logic

This paper is aimed as a contribution to the use of formal modal languages in Artificial Intelligence. We introduce a multi-modal version of Second-order Propositional Modal Logic (SOPML), an extension of modal logic with propositional quantification, and illustrate its usefulness as a specification language for knowledge representation as well as temporal and spatial reasoning. Then, we define novel notions of (bi)simulation and prove that these preserve the interpretation of SOPML formulas. Finally, we apply these results to assess the expressive power of SOPML.
A robust and sensitive voxel-based method for measuring cortical thickness change using fuzzy correspondence

Cortical thickness is an important surrogate biomarker for evaluating the progression of neurodegenerative diseases. We propose a new method for measuring cortical thickness changes on longitudinal magnetic resonance images (MRIs). The method is voxel-based for computational efficiency and sensitivity to subtle changes, but aims for robustness in establishing correspondences by using intuitive features that are defined on a cortical skeleton computed in each scan. In contrast to existing longitudinal methods, the proposed method does not require deformable registration. Instead, we use cortex specific matches and fuzzy correspondence, which allows a skeletal point in one scan to be partially matched to multiple points in another scan, thereby enhancing the stability of the matches. Our experiments show that the proposed method is comparable in scan-rescan reproducibility with a state-of-the-art surface-based method, and demonstrates greater sensitivity to clinically relevant changes on a dataset containing MRIs of 100 secondary progressive multiple sclerosis subjects.
Stem cell-specific activation of an ancestral myc protooncogene with conserved basic functions in the early metazoan Hydra

The c-myc protooncogene encodes a transcription factor (Myc) with oncogenic potential. Myc and its dimerization partner Max are bHLH-Zip DNA binding proteins controlling fundamental cellular processes. Deregulation of c-myc leads to tumorigenesis and is a hallmark of many human cancers. We have identified and extensively characterized ancestral forms of myc and max genes from the early diploblastic cnidarian Hydra, the most primitive metazoan organism employed so far for the structural, functional, and evolutionary analysis of these genes. Hydra myc is specifically activated in all stem cells and nematoblast nests which represent the rapidly proliferating cell types of the interstitial stem cell system and in proliferating gland cells. In terminally differentiated nerve cells, nematocytes, or epithelial cells, myc expression is not detectable by in situ hybridization. Hydra max exhibits a similar expression pattern in interstitial cell clusters. The ancestral Hydra Myc and Max proteins display the principal design of their vertebrate derivatives, with the highest degree of sequence identities confined to the bHLH-Zip domains. Furthermore, the 314-amino acid Hydra Myc protein contains basic forms of the essential Myc boxes I through III. A recombinant Hydra Myc/Max complex binds to the consensus DNA sequence CACGTG with high affinity. Hybrid proteins composed of segments from the retroviral v-Myc oncoprotein and the Hydra Myc protein display oncogenic potential in cell transformation assays. Our results suggest that the principal functions of the Myc master regulator arose very early in metazoan evolution, allowing their dissection in a simple model organism showing regenerative ability but no senescence.
Using combination of structural, feature and raster classifiers for recognition of handprinted characters

The paper presents a novel method for off- and on-line handprinted character recognition using structural patterns. A character is described as a set of structural elements such as segments, arcs, circles and points. Possible relative placement of elements is described with the help of spatial relations, which are expressed as fuzzy logic predicates. The structural pattern is matched against a character image by establishing correspondence between structural elements and parts of the image which satisfy all spatial relations. The developed matching procedure can successfully find this correspondence on broken and distorted images. The found match for the structural pattern effectively segments a character into meaningful parts, thus giving access to plenty of information about character structure and properties. This allows one to develop simple yet accurate pairwise discriminant functions for similar characters and use them to further increase recognition accuracy. The described system has been implemented and structural patterns for digits and the Cyrillic alphabet have been developed. Detailed results of experimentation are presented.
Human c-ros-1 gene homologous to the v-ros sequence of UR2 sarcoma virus encodes for a transmembrane receptorlike molecule

We isolated a human gene (designated c-ros-1) homologous to the v-ros sequence of UR2 sarcoma virus. Ten exons, 1,414 base pairs spanning 26 kilobases, contained a tyrosine kinase domain, a transmembrane domain, and a part of an extracellular domain carrying an N glycosylation site which was not acquired by UR2 sarcoma virus. The predicted structure of c-ros-1 is unique among the src family and clearly distinct from the human insulin receptor.
On the evaluation of segmentation editing tools

Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user’s subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings.
Hybrid (day-night) Routing Protocol for Mobile Ad-Hoc Networks

A Mobile Ad-Hoc Network (MANET) is a decentralized network of autonomous mobile nodes able to communicate with each other over wireless links. Routing protocols for mobile ad-hoc networks has limitations such as frequent topology changes, limited battery power, bandwidth constraint, hidden and exposed node problem, high Bit Error Rate (BER) are major problems. Both proactive and reactive routing protocols prove to be inefficient in MANET. We propose Hybrid Routing Protocol (HRP), it combines the advantages of both proactive routing protocol. The HRP performance is compared with proactive and reactive protocols. We have used the ns-2 network simulator for simulating the proposed hybrid routing protocol, and present the results of simulations of networks of 50 mobile nodes.
On Hypergraphs with Every Four Points Spanning at Most Two Triples

Let F be a triple system on an n element set. Suppose that F contains more than (1/3 ) n 3 triples, where >10 6 is explicitly defined and n is suciently large. Then there is a set of four points containing at least three triples of F . T his improves previous bounds of de Caen [1] and Matthias [7] .