Periodic training of creeping solids.
Ontology highlight
ABSTRACT:
SUBMITTER: Hexner D
PROVIDER: S-EPMC7749283 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDetection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time.ResultsA learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented.ConclusionIn comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.
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Project description:Soil creeps imperceptibly but relentlessly downhill, shaping landscapes and the human and ecological communities that live within them. What causes this granular material to 'flow' at angles well below repose? The unchallenged dogma is churning of soil by (bio)physical disturbances. Here we experimentally render slow creep dynamics down to micron scale, in a laboratory hillslope where disturbances can be tuned. Surprisingly, we find that even an undisturbed sandpile creeps indefinitely, with rates and styles comparable to natural hillslopes. Creep progressively slows as the initially fragile pile relaxes into a lower energy state. This slowing can be enhanced or reversed with different imposed disturbances. Our observations suggest a new model for soil as a creeping glass, wherein environmental disturbances maintain soil in a perpetually fragile state.
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Project description:This article investigates the mechanism behind the creeping of sodium chloride induced by additives. Here, an experimental approach is complemented with theoretical considerations to describe how creeping patterns of brine evolve and how the introduction of additives into the solution affects the morphology of the resultant crystals. We have found that these additives cause kinetic roughening and morphological instability mainly due to the reduction of surface free energy. There was also a marked increase in three-dimensional nucleation of the NaCl crystals and thus branching.
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Project description:We report an autochthonous case of oral dirofilariasis in a 46-year-old female patient exposed in South-Eastern France. The patient first presented eyelid creeping dermatitis of one-week duration, then a sub-mucosal nodule appeared in the cheek. The entire nodule was removed surgically. Histologically, the nodule appeared as inflammatory tissue in which a worm was seen. The molecular analysis, based on cox1 and 12S sequences, identified Dirofilaria repens. Ivermectin treatment was given prior to diagnosis, while taking into consideration the most common causes of creeping dermatitis, but treatment was ineffective. The oral form of dirofilariasis is uncommon and could lead to diagnostic wandering.
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Project description:Salt creeping is a ubiquitous phenomenon in which crystals precipitate far from an evaporating salt solution boundary, which constitutes a major problem in outdoor electronics, civil engineering, artworks, and agriculture. We report a novel experimental approach that allows to quantitatively describe the creeping mechanism and demonstrate its universality with respect to different salts. We show that there exists a critical contact angle below which salt creeping occurs, provided also the nucleation of multiple crystals is favored. The precipitation of new crystals happens ahead of the contact line by the meniscus that progressively advances over the crystals forming also nanometric precursor films. This enlarges the evaporative area, causing an exponential increase in the crystal mass in time. The self-amplifying process then results in a spectacular three-dimensional crystal network at macroscopic distances from the solution reservoir. These findings also allow us to control the creeping by using crystallization modifiers.
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Project description:Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (N) to maintain a sub-maximal growth rate. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the currently existing turf growth prediction model only takes temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, the data were collected from two putting greens where the turfgrass received 0 to 1,800 round/week traffic rates, various irrigation rates to maintain the soil moisture content between 9 and 29%, and N fertilization rates of 0 to 17.5 kg ha-1 applied biweekly. The RF model agreed with the actual clipping yield collected from the experimental results. The temperature and relative humidity were the most important weather factors. Including NDRE improved the prediction accuracy of the model. The highest coefficient of determination (R2) of the RF model was 0.64 for the training dataset and was 0.47 for the testing data set upon the evaluation of the model. This represented a large improvement over the existing growth prediction model (R 2 = 0.01). However, the machine-learning models created were not able to accurately predict the clipping production at other locations. Individual golf courses can create customized growth prediction models using clipping volume to eliminate the deviation caused by temporal and spatial variability. Overall, this study demonstrated the feasibility of creating machine-learning-based yield prediction models that may be able to guide N fertilization decisions on golf course putting greens and presumably other turfgrass areas.
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Project description:The formation and motion features of self interstitial atom (SIA) clusters in tungsten are studied by molecular dynamics (MD) simulations. The static calculations show that the SIA clusters are stable with binding energy over 2 eV. The SIA clusters exhibit a fast one dimensional (1D) motion along 〈111〉. Through analysis of the change of relative distance between SIAs, we find that SIAs jump in small displacements we call creeping motion, which is a new collective diffusion process different from that of iron. The potential energy surface of SIAs implicates that the creeping motion is due to the strong interaction between SIAs. These imply that several diffusion mechanism for SIA clusters can operate in BCC metals and could help us explore deep insight into the performance of materials under irradiation.
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Project description:Wildlife activity patterns tend to be defined by terms such as diurnal and nocturnal that might not fully depict the complexity of a species' life history strategy and behavior in a given system. These activity pattern categories often influence the methodological approaches employed, including the temporal period of study (daylight or nighttime). We evaluated banded mongoose (Mungos mungo) behavior in Northern Botswana through the use of remote sensing cameras at active den sites in order to characterize early morning behavior for this diurnal species. Our approach, however, provided the facility to capture unexpected nocturnal activity in a species that had otherwise only been studied during daylight hours. Camera traps were deployed for 215 trap days (24 hour data capture period) at den sites, capturing 5,472 photos over all events. Nocturnal activity was identified in 3% of trap days at study den sites with both vigilant and non-vigilant nocturnal behaviors identified. While vigilant behaviors involved troop fleeing responses, observations of non-vigilant behaviors suggest nonresident mongoose may investigate den sites of other troops during nocturnal time periods. There was no association between the occurrence of nocturnal activity and lunar phase (Fisher's exact test, n = 215, p = 0.638) and thus, increased moonlight was not identified as a factor influencing nocturnal behavior. The drivers and fitness consequences of these nocturnal activities remain uncertain and present intriguing areas for future research. Our findings highlight the need for ecological studies to more explicitly address and evaluate the potential for temporal variability in activity periods. Modifying our approach and embracing variation in wildlife activity patterns might provide new insights into the interaction between ecological phenomenon and species biology that spans the diurnal-nocturnal spectrum.
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Project description:One of the central puzzles concerning the interaction of low Reynolds number fluid transport with bacterial biomass is the formation of filamentous structures called streamers. In this manuscript, we report our discovery of a new kind of low Re bacterial streamers, which appear from pre-formed bacterial flocs. In sharp contrast to the biofilm-mediated streamers, these streamers form over extremely small timescales (less than a second). Our experiments, carried out in a microchannel with micropillars rely on fluorescence microscopy techniques to illustrate that floc-mediated streamers form when a freely-moving floc adheres to the micropillar wall and gets rapidly sheared by the background flow. We also show that at their inception the deformation of the flocs is dominated by recoverable large strains indicating significant elasticity. These strains subsequently increase tremendously to produce filamentous streamers. Interestingly, we find that these fully formed streamers are not static structures and show viscous response at time scales larger than their formation time scales. Finally, we show that such novel streamer formation can lead to rapid clogging of microfluidic devices.
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Project description:Creeping flows govern many important physiological phenomena such as elevated interstitial fluid flows in tumors, glymphatic flows in the brain, among other applications. However, few methods exist to measure such slow flows non-invasively in optically opaque biological tissues in vivo. Phase-contrast MRI is a velocimetry technique routinely used in the clinic to measure fast flows in biological tissues, such as blood and cerebrospinal fluid (CSF), in the order of cm/s. Use of this technique to encode slower flows is hampered by diffusion weighting and phase error introduced by gradient hardware imperfections. In this study, a new PC-MRI technique is developed using stimulated echo preparation to overcome these challenges. Flows as slow as 1 μm/s are measured and validated using controlled water flow through a pipe at 4.7 T. The error in measured flow rate obtained by integrating the measured velocity over the cross-sectional area of the pipe is less than 10%. The developed method was also able to capture slow natural convection flows appearing in liquids placed inside a horizontal bore magnet. Monitoring the 4D velocity vector field revealed that the natural convection flows decay exponentially with time. This method could be applied in future to study creeping flows, e.g. in tissue.
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