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Extinction, Psychological

Psychological Extinction refers to the gradual reduction or elimination of a conditioned response following the removal of the unconditioned stimulus.
This process is a fundamental principle in the field of learning and behavior modification, with applications in various areas of psychology and neuroscience.
The study of psychological extinction helps researchers understand the mechanisms of memory, emotion regulation, and the treatment of anxiety disorders.
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Most cited protocols related to «Extinction, Psychological»

1
Evaluating MEDUSA's Diversification Modeling
I compared the performance of BAMM to that of MEDUSA [20] (link), a maximum likelihood method for modeling among-lineage heterogeneity in speciation-extinction dynamics. Beginning with a constant rate birth-death process, MEDUSA uses a stepwise AIC algorithm to incrementally add rate shifts to phylogenetic trees until the addition of new partitions fails to improve the fit of the model to the data. Thus, MEDUSA is similar to the method described here in that it is explicitly designed to discover the number and location of distinct processes of speciation and extinction on phylogenetic trees. However, MEDUSA, as implemented and typically used, makes the assumption that rates of species diversification are constant in time within rate classes. This assumption has been rejected by studies across a range of taxonomic scales, from species-level phylogenies [16] (link), [17] (link), [18] (link), [26] (link) to tree-of-life scale compilations of clade age and species richness [49] (link). However, the consequences of violating this assumption for MEDUSA analyses have not been investigated.
I analyzed each of the simulated datasets described above (500 datasets under each of 6 distinct models of diversification) using MEDUSA, using the implementation of MEDUSA available in the Geiger v1.99-3 package [50] (link) for the R programming and statistical environment. Model selection used the default AICc criterion. I summarized the results of MEDUSA analyses in two ways. First, for each simulation scenario, I tabulated the distribution of “best fit” models, to assess the fraction of simulations for which MEDUSA was able to correctly estimate the number of processes in the generating model. Second, I used the same summary statistics described above for BAMM (e.g., proportional error) to compare branch-specific estimates of speciation rates under MEDUSA to the true rates under the generating model.
, & Rabosky D.L. (2014). Automatic Detection of Key Innovations, Rate Shifts, and Diversity-Dependence on Phylogenetic Trees. PLoS ONE, 9(2), e89543.
Publication 2014
Birth Extinction, Psychological Genetic Heterogeneity Trees
2
Branching Process Models for Disease Extinction
Analysis of branching process models centres on the probability generating function (pgf) of the offspring distribution, , defined for |s| ≤ 1. When R0 > 1, the long-term probability of disease extinction after introduction of a single infected individual is the unique solution of q = g(q) on the interval (0,1). For a negative binomial offspring distribution Z ≈ NegB(R0,k), the pgf is . Under population-wide control, and therefore , and the variance-to-mean ratio is 1 + (1 - c)R0/k. Under random individual-specific control, the exact pgf is with variance-to-mean ratio 1 + R0/k + cR0. This scenario can be approximated by , where is the solution to and decreases monotonically as c increases. Further details, descriptions of outbreak simulations and formal analysis of control measures are found in the Supplementary Notes.
Lloyd-Smith J.O., Schreiber S.J., Kopp P.E, & Getz W.M. (2005). Superspreading and the effect of individual variation on disease emergence. Nature, 438(7066), 355-359.
Publication 2005
Extinction, Psychological Forms Control Maritally Unattached Suby's G solution
3
Specifying Priors for Bayesian Phylogenetic Inference
A particular requirement of Bayesian phylogenetic inference is the responsibility given to users to specify a prior probability distribution on the shape of the phylogeny (node ages and branching order). This can be either a benefit or a burden, largely depending on whether an obvious prior distribution presents itself for the data at hand. For example, the coalescent prior [
56 ,
57 ] is a commonly used prior for population-level data and has been extended to include various forms of demographic functions [
58 (link),
59 (link)], sub-divided populations [
60 (link)], and other complexities. Traditional speciation models such as the Yule process [
61 ] and various birth–death models [
62 (link),
63 (link)] can also provide useful priors for species-level data. Such models generally have a number of hyperparameters (for example, effective population size, growth rate, or speciation and extinction rates), which, under a Bayesian framework, can be sampled to provide a posterior distribution of these potentially interesting biological quantities.
In some cases, the choice of prior on the phylogenetic tree can exert a strong influence on inferences made from a given dataset [
64 (link)]. The sensitivity of inference results to the prior chosen will be largely dependent on the data analyzed and few general recommendations can be made. It is, however, good practice to perform the MCMC analysis without any data in order to sample wholly from the prior distribution. This distribution can be compared to the posterior distribution for parameters of interest in order to examine the relative influence of the data and the prior (
Figure 3).
Drummond A.J., Ho S.Y., Phillips M.J, & Rambaut A. (2006). Relaxed Phylogenetics and Dating with Confidence. PLoS Biology, 4(5), e88.
Publication 2006
Biopharmaceuticals Extinction, Psychological Hypersensitivity Population Group
4
Satellite-Derived Global PM2.5 Estimation
Estimating ground-level concentrations of dry 24-hr PM2.5 (micrograms per cubic meter) from satellite observations of total-column AOD (unitless) requires a conversion factor that accounts for their spatially and temporally varying relationship:
η is a function of the factors that relates 24-hr dry aerosol mass to satellite observations of ambient AOD: aerosol size, aerosol type, diurnal variation, relative humidity, and the vertical structure of aerosol extinction (van Donkelaar et al. 2006 (link)). Following the methods of Liu et al. (2004 (link), 2007) and van Donkelaar et al. (2006) (link), we used a global 3-D CTM [GEOS-Chem; geos-chem.org; see Supplemental Material (doi:10.1289/ehp.0901623)] to calculate the daily global distribution of η.
The GEOS-Chem model solves for the temporal and spatial evolution of aerosol (sulfate, nitrate, ammonium, carbonaceous, mineral dust, and sea salt) and gaseous compounds using meteorological data sets, emission inventories, and equations that represent the physics and chemistry of atmospheric constituents. The model calculates the global 3-D distribution of aerosol mass and AOD with a transport time step of 15 min. We applied the modeled relationship between aerosol mass and relative humidity for each aerosol type to calculate PM2.5 for relative humidity values that correspond to surface measurement standards [European Committee for Standardization (CEN) 1998 ; U.S. Environmental Protection Agency 1997 ] (35% for theUnited States and Canada; 50% for Europe). We calculated daily values of η as the ratio of 24-hr ground-level PM2.5 for a relative humidity of 35% (U.S. and Canadian surface measurement gravimetric analysis standard)and of 50% (European surface measurement standard) to total-column AOD at ambient relative humidity. We averaged the AOD between 1000 hours and 1200 hours local solar time, which corresponded to the Terra overpass period. We interpolated values of η from 2° × 2.5°, the resolution of the GEOS-Chem simulation, to 0.1° × 0.1° for application to satellite AOD values.
We compared the original MODIS and MISR total-column AOD with coincident ground-based measurements of daily mean PM2.5. Canadian sites are part of the National Air Pollution Surveillance Network (NAPS) and are maintained by Environment Canada (http://www.etc.cte.ec.gc.ca/NAPS/index_e.html). The U.S. data were from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network (http://vista.cira.colostate.edu/improve/Data/data.htm) and from the U.S. Environmental Protection Agency Air Quality System Federal Reference Method sites (http://www.epa.gov/air/data/index.html). Validation of global satellite-derived PM2.5 estimates was hindered by the lack of available surface-measurement networks in many parts of the world. To supplement this lack of available surface measurements, we collected 244 annually representative, ground-based PM2.5 data from both published and unpublished field measurements outside the United States and Canada[see Supplemental Material (doi:10.1289/ehp.0901623)].
van Donkelaar A., Martin R.V., Brauer M., Kahn R., Levy R., Verduzco C, & Villeneuve P.J. (2010). Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application. Environmental Health Perspectives, 118(6), 847-855.
Publication 2010
A-factor (Streptomyces) Air Pollution Ammonium Biological Evolution Circadian Rhythms Cuboid Bone Dietary Supplements Europeans Extinction, Psychological factor A Gases Humidity Minerals N-(4-aminophenethyl)spiroperidol Nitrates Personality Inventories Sodium Chloride Sulfates, Inorganic
5
Evaluating Diversification Rate Variation
To evaluate performance of the compound Poisson process model of diversification rate variation, I simulated phylogenetic trees under six general diversification models. I first considered a simple constant-rate birth death process (model CR; 1 process), to evaluate parameter bias and the frequency of overfitting when the generating model does not include a heterogeneous mixture of processes. Given the widespread interest in identifying well-supported rate shifts and key innovations on phylogenetic trees, we are particularly interested in the frequency with which the model described here will incorrectly identify a multi-process model as having the maximum a posteriori probability, when the true generating model is a single process model. To assess whether my results were sensitive to choice of prior on the Poisson rate parameter Λ, I analyzed constant-rate phylogenies under three different prior parameterizations, corresponding to γ = 1, γ = 5, and γ = 10. All other analyses used a prior of γ = 1.0, which is conservative in the context of these analyses (see results).
I also considered a model where a pure-birth diversification process shifts to an exponential change process at some point in time (model exp2; 2 processes). Finally, I considered four variants of diversity-dependent multi-process models. In each case, I assumed that a pure-birth process at the root of the tree underwent multiple (1, 2, 3, or 4) shifts to independent and decoupled diversity-dependent speciation-extinction processes (models DD2, DD3, DD4, DD5). I conducted 500 simulations per scenario.
Each multiprocess simulation was conducted by first simulating a pure-birth phylogeny for 100 time units with λ = 0.032. I then randomly chose a time Ts on the interval (40, 95) for the occurrence of a rate shift. A shift was then assigned randomly to one of the lineages that existed at time Ts. I then sampled parameters for the new process (see below). The tree was then broken at the shift point, and a new subtree was simulated forward in time from the shift point under the new process parameters. For trees with more than two processes, this procedure was repeated until the target number of processes had been added. For the exp2 model, this consisted of sampling λ, z, and μ for the shift process uniformly on the following intervals: λ, (0.05, 0.50); z, (−0.10, 0.05); μ, (0.0, 0.45). Thus, the addition of an exponential change process could have resulted in either an increase in rates through time (if z >0) or a decrease (if z <0). For all simulations, I required that subtrees contained at least 25 and fewer than 1000 terminal taxa; any simulations failing to meet this criterion were automatically rejected.
For the diversity-dependent models, diversification dynamics followed a linear diversity-dependent model [48] . The rate of speciation was thus a function of the number of coeval lineages in the subclade, or where K is the clade-specific carrying capacity, and nt is the number of lineages in the subclade at time t. Note that the occurrence of a shift event results in a decoupling of dynamics from the parent process. To parameterize the diversity-dependent processes, I sampled λ0 from a uniform (0.05, 0.40) distribution, K from a uniform (25, 250) distribution, and μ from a uniform (0, 0.05) distribution. For the constant-rate birth-death simulations, I sampled λ from a uniform (0, 0.1) distribution and chose a corresponding relative extinction rate (μ/λ) from a uniform (0, 0.99) distribution.
Each of the 500 simulations for each of 6 simulation scenarios was thus conducted under a potentially unique speciation-extinction parameterization. The number of taxa in each simulated tree also varied among datasets. I recorded the mean rate of speciation and extinction across each branch in each simulated tree. All simulations were conducted in C++; simulated trees are available through the Dryad data repository (doi:10.5061/dryad.hn1vn).
I analyzed each of the 3000 simulated datasets using BAMM with 3 million generations of MCMC sampling. I discarded the first half of samples from each simulation of the posterior as “burn-in” and estimated the overall “best model” as the model that was sampled most frequently by the Markov chain. I computed the mean of the posterior distribution of speciation and extinction rates on each branch for each tree. I then used OLS regression to assess the relationship between branch-specific rate estimates obtained using BAMM versus the true underlying evolutionary rates. As an additional estimate of bias, I computed the proportional error [37] (link) in the estimated rates as a function of the true rates. This metric is computed as the weighted average of proportional rate differences across all N branches in the phylogeny, or where rEST and rTRUE are the estimated and true values of rates along a particular branch. A value of 2 would imply that estimated rates are, on average, equal to twice the true rate in the generating model.
, & Rabosky D.L. (2014). Automatic Detection of Key Innovations, Rate Shifts, and Diversity-Dependence on Phylogenetic Trees. PLoS ONE, 9(2), e89543.
Publication 2014
Biological Evolution Childbirth Extinction, Psychological Genes, vif Genetic Heterogeneity Genetic Processes Innovativeness Parent Plant Roots Trees

Most recents protocols related to «Extinction, Psychological»

1
Programmable Nanocomposite Tectons for Ordered Assembly
Not available on PMC !

Example 1

This example describes an exemplary nanostructure (i.e. nanocomposite tecton) and formation of a material using the nanostructure.

A nanocomposite tecton consists of a nanoparticle grafted with polymer chains that terminate in functional groups capable of supramolecular binding, where supramolecular interactions between polymers grafted to different particles enable programmable bonding that drives particle assembly (FIG. 4). Importantly, these interactions can be manipulated separately from the structure of the organic or inorganic components of the nanocomposite tecton, allowing for independent control over the chemical composition and spatial organization of all phases in the nanocomposite via a single design concept. Functionalized polystyrene polymers were made from diaminopyridine or thymine modified initiators via atom transfer radical polymerization, followed by post-functionalization to install a thiol group that allowed for particle attachment (FIG. 5). The polymers synthesized had three different molecular weights (˜3.7, ˜6.0, and ˜11.0 kDa), as shown in FIG. 6, with narrow dispersity (Ð<1.10), and were grafted to nanoparticles of different diameters (10, 15, 20, and nm) via a “grafting-to” approach.

Once synthesized, nanocomposite tectons were functionalized with either diaminopyridine-polystyrene or thymine-polystyrene were readily dispersed in common organic solvents such as tetrahydrofuran, chloroform, toluene, and N,N′-dimethylformamide with a typical plasmonic resonance extinction peak at 530-540 nm (FIG. 7A) that confirmed their stability in these different solvents. Upon mixing, diaminopyridine-polystyrene and thymine-polystyrene coated particles rapidly assembled and precipitated from solution, resulting in noticeable red-shifting, diminishing, and broadening of the extinction peak within 1-2 minutes (example with 20 nm gold nanoparticles and 11.0 kDa polymers, FIG. 7B). Within 20 minutes, the dispersion appeared nearly colorless, and large, purple aggregates were visible at the bottom of the tube. After moderate heating (˜55° C. for ˜1-2 minutes for the example in FIG. 7B), the nanoparticles redispersed and the original color intensity was regained, demonstrating the dynamicity and complete reversibility of the diaminopyridine-thymine directed assembly process. Nanocomposite tectons were taken through multiple heating and cooling cycles without any alteration to assembly behavior or optical properties, signifying that they remained stable at each of these thermal conditions (FIG. 7C).

A key feature of the nanocomposite tectons is that the sizes of their particle and polymer components can be easily modified independent of the supramolecular binding group's molecular structure. However, because this assembly process is driven via the collective interaction of multiple diaminopyridine and thymine-terminated polymer chains, alterations that affect the absolute number and relative density of diaminopyridine or thymine groups on the nanocomposite tecton surface impact the net thermodynamic stability of the assemblies. In other words, while all constructs should be thermally reversible, the temperature range over which particle assembly and disassembly occurs should be affected by these variables. To better understand how differences in nanocomposite tecton composition impact the assembly process, nanostuctures were synthesized using different nanoparticle core diameters (10-40 nm) and polymer spacer molecular weights (3.7-11.0 kDa), and allowed to fully assemble at room temperature (˜22° C.) (FIG. 8). Nanocomposite tectons were then monitored using UV-Vis spectroscopy at 520 nm while slowly heating at a rate of 0.25° C./min, resulting in a curve that clearly shows a characteristic disassembly temperature (melting temperature, Tm) for each nanocomposite tecton composition.

From these data, two clear trends can be observed. First, when holding polymer molecular weight constant, Tm increases with increasing particle size (FIG. 8A). Conversely, when keeping particle diameter constant, Tm drastically decreases with increasing polymer length (FIG. 8B). To understand these trends, it is important to note that nanocomposite tecton dissociation is governed by a collective and dynamic dissociation of multiple individual diaminopyridine-thymine bonds, which reside at the periphery of the polymer-grafted nanoparticles. The enthalpic component of nanocomposite tecton bonding behavior is therefore predominantly governed by the local concentration of the supramolecular bond-forming diaminopyridine and thymine groups, while the entropic component is dictated by differences in polymer configuration in the bound versus unbound states.

All nanocomposite tectons possess similar polymer grafting densities (i.e. equivalent areal density of polymer chains at the inorganic nanoparticle surface, FIG. 9) regardless of particle size or polymer length. However, the areal density of diaminopyridine and thymine groups at the periphery of the nanocomposite tectons is not constant as a function of these two variables due to nanocomposite tecton geometry. When increasing inorganic particle diameter, the decreased surface curvature of the larger particle core forces the polymer chains into a tighter packing configuration, resulting in an increased areal density of diaminopyridine and thymine groups at the nanocomposite tecton periphery; this increased concentration of binding groups therefore results in an increased Tm, explaining the trend in FIG. 8A.

Conversely, for a fixed inorganic particle diameter (and thus constant number of polymer chains per particle), increasing polymer length decreases the areal density of diaminopyridine and thymine groups at the nanocomposite tecton periphery due to the “splaying” of polymers as they extend off of the particle surface, thereby decreasing Tm in a manner consistent with the trend in FIG. 8B. Additionally, increasing polymer length results in a greater decrease of system entropy upon nanocomposite tecton assembly, due to the greater reduction of polymer configurations once the polymer chains are linked via a diaminopyridine-thymine bond; this would also be predicted to reduce T m. Within the temperature range tested, all samples were easily assembled and disassembled via alterations in temperature. Inorganic particle diameter and polymer length are therefore both effective handles to control nanocomposite tecton assembly behavior.

Importantly, because the nanocomposite tecton assembly process is based on dynamic, reversible supramolecular binding, it should be possible to drive the system to an ordered equilibrium state where the maximum number of binding events can occur. The particle cores and polymer ligands are polydisperse (FIG. 10) and ordered arrangements represent the thermodynamically favored state for a set of assembled nanocomposite tectons. When packing nanocomposite tectons into an ordered lattice, deviations in particle diameter would be expected to generate inconsistent particle spacings that would decrease the overall stability of the assembled structure. However, the inherent flexibility of the polymer chains should allow the nanocomposite tectons to adopt a conformation that compensates for these structural defects. As a result, an ordered nanocomposite tecton arrangement would still be predicted to be stable if it produced a larger number of diaminopyridine-thymine binding events than a disordered structure and this increase in binding events outweighed the entropic penalty of reduction in polymer chain configurations.

To test this hypothesis, multiple sets of assembled nanocomposite tectons were thermally annealed at a temperature just below their Tm, allowing particles to reorganize via a series of binding and unbinding events until they reached the thermodynamically most stable conformation. The resulting structures were analyzed with small angle X-ray scattering, revealing the formation of highly ordered mesoscale structures where the nanoparticles were arranged in body-centered cubic superlattices (FIG. 11). The body-centered cubic structure was observed for multiple combinations of particle size and polymer length, indicating that the nanoscopic structure of the composites can be controlled as a function of either the organic component (via polymer length), the inorganic component (via particle size), or both, making this nanocomposite tecton scheme a highly tailorable method for the design of future nanocomposites.

US11873396B2. Nanostructures for the assembly of materials (2024-01-16). Massachusetts Institute of Technology [US]. Inventors: Robert J. Macfarlane [US], Jianyuan Zhang [US], Peter Jeffries Santos [US], Paul Anthony Gabrys [US].
Patent 2024
chemical composition Chloroform Cuboid Bone Dimethylformamide Entropy Extinction, Psychological Gold Human Body Ligands Molecular Structure Polymerization Polymers Polystyrenes Radiography Solvents Spectrum Analysis Sulfhydryl Compounds tetrahydrofuran Thymine Toluene Vibration Vision
2
Particle Size Dependent Optical Properties
Not available on PMC !

Example 8

In order to elucidate the particle size dependence of the optical properties of nanomaterials, simulations have been performed using two models: the effective medium model (EMM) and finite element analysis+geometrical optics (FEA+GO). The simple EMM approach assumes that the nanoparticle has a single refractive index (n) and extinction ratio (k), and assumes that the particles are uniformly distributed throughout a low-refractive-index medium (FIG. 1A). The simulated spectrum (FIG. 1B) predicts a dramatic modulation of ca. 40% in the near-IR region of the electromagnetic spectrum using the optical constants of bulk VO2 in the monoclinic (M1) and tetragonal phases. The simulation assumes a constant refractive index of ca. 1.5, which is typical of polymeric media. These results underscore the need for a uniform distribution of particles within a low-refractive-index matrix to achieve the desired NIR modulation. The FEA+GO simulations allow for a more detailed elucidation of particle-size-dependent optical properties. Spectra have been simulated for a composite with a fill factor of 3.7 wt. % of spherical VO2 nanoparticles of varying diameters again assuming a temperature-independent refractive index of 1.5 for the polymeric media and the bulk optical constants for the insulating and metallic phases. As the diameter increases from 20 nm to 100 nm, the near-infrared modulation is observed to remain constant at ca. 40% (FIG. 1C). However, the visible light transmittance (at 680 nm) decreases from 80% to 68% for the low-temperature phase. When considering a composite of 100 nm long VO2 wires with varying diameters, the 50 nm and 100 nm diameter wires show a variation of ca. 45% in the near-infrared, whereas the 20 nm wires show a modulation of ca. 40% (FIG. 1D). Although the NIR modulation is slightly diminished for the 20 nm diameter nanowires, they retain superior visible light transmittance. The substantial diminution in visible light transmittance with increasing particle is derived from the scattering background contributed by larger particles. Agglomeration of particles will to first order mimic the effects of having larger particles. These simulations indicate that the viability of utilizing VO2 nanocrystals for effective thermochromic modulation will depend sensitively on their dimensions and their extent of dispersion.

US11873383B2. Thermochromic fenestration films containing vanadium dioxide nanocrystals (2024-01-16). THE TEXAS A&M UNIVERSITY SYSTEM [US]. Inventors: Sarbajit Banerjee [US], Kate E. Pelcher [US], Nathan A. Fleer [US].
Patent 2024
Cold Temperature Dietary Fiber Electromagnetics Extinction, Psychological Eye factor A Factor VII Light Light, Visible Metals Polymers Vision Volition
3
Measuring Viscosity of Antibody Solutions
Not available on PMC !

Example 4

Antibody solutions containing romosozumab PARG (SEQ ID NO: 8) C-terminal variant or wild-type romosozumab are measured using a cone and plate. The solutions are concentrated up to 120 mg/mL according to approximate volume depletion, and final concentrations are determined (±10%) using the proteins absorbance at 280 nm (after dilution to end up within 0.1-1 absorbance units (AU)) and a protein specific extinction coefficient. Viscosity analysis is performed on a Brookfield LV-DVIII cone and plate instrument (Brookfield Engineering, Middleboro, MA, USA) using a CP-40 spindle and sample cup or an ARES-G2 rheometer (TA Instruments, New Castle, DE, USA) using a TA Smart Swap 2 degree cone/plate spindle. All measurements are performed at 25° C. and controlled by a water bath attached to the sample cup. Multiple viscosity measurements were collected, manually within a defined torque range (10-90%) by increasing the RPM of the spindle. Measurements are averaged in order to report one viscosity value per sample to simplify the resulting comparison chart.

US11858983B2. C-terminal anti-sclerostin antibody variants (2024-01-02). AMGEN INC. [US]. Inventors: Zhe Huang [US], Jennitte LeAnn Stevens [US], Greg Flynn [US], Szilan Fodor [US], Mark Daris [US].
Patent 2024
Bath Extinction, Psychological Immunoglobulins Proteins Retinal Cone romosozumab Staphylococcal Protein A Technique, Dilution Torque Viscosity
4
Quantitative Esterase Activity Assay
The esterase activity was determined
spectrophotometrically by measuring the hydrolysis of 4-nitrophenyl
butyrate (pNPB) as a substrate.26 (link) The pNPB
dissolved in acetonitrile (50 mM) was added to 200 μL of Na
phosphate buffer (100 mM, pH 7.5) with 0.5% (v/v) Triton X-100 to
0.5 mM pNPB as the final concentration; 10 μL of crude extract
was used as an enzyme source. The enzymatic reaction was carried out
at 25 °C for 15 min, and the release of pNP was measured at 405
nm. Enzyme activity was calculated using the extinction coefficient
of pNP corresponding to 18.5 mM–1 cm–1. One Unit is defined as the amount of the enzyme that catalyzes
the conversion of one micromole of substrate per minute under the
specified conditions of the assay method and normalized by grams of
the fermented substrate (U/g). The statistical correlation between
groups was analyzed by Student’s t-test.
Bulgari D., Alias C., Peron G., Ribaudo G., Gianoncelli A., Savino S., Boureghda H., Bouznad Z., Monti E, & Gobbi E. (2023). Solid-State Fermentation of Trichoderma spp.: A New Way to Valorize the Agricultural Digestate and Produce Value-Added Bioproducts. Journal of Agricultural and Food Chemistry, 71(9), 3994-4004.
Publication 2023
acetonitrile Biological Assay Buffers enzyme activity Enzymes Esterases Extinction, Psychological Hydrolysis Student Triton X-100
5
Fabrication and Evaluation of Reconfigurable THz Diffractive Optics
The diffuser and the diffractive layer used for the experimental demonstration were fabricated using a 3D printer (Pr 110, CADworks3D). The 3D printing material we used in the experiments has wavelength-dependent absorption. Therefore, additional neuron height-dependent amplitude modulations were applied to the incident light, which can be formulated as alxi,yi,zi,λ=exp−2πκλhilλ where κλ is the extinction coefficient of the diffractive layer material, corresponding to the imaginary part of the complex-valued refractive index n~λ , i.e., n~λ=nλ+jκλ .
For the single-layer single-pixel diffractive model used for the experimental demonstration (Fig. 7), the diffractive layer consists of 120 × 120 diffractive neurons, each with a lateral size of 0.4 mm. The axial separation between any two consecutive planes was set to d = 20 mm. To compensate for the nonideal wavefront generated by the THz emitter, a square input aperture with a size of 8 × 8 mm2 was used as an entrance pupil to illuminate the input object, placed 20 mm away from it. The diffraction of this aperture was also included in the forward propagation model. The size of the input objects was designed as 20 × 20 mm2 (50 × 50 pixels). After being distorted by the random diffuser and modulated by the diffractive layer, the spectral power at the center region (2.4 × 2.4 mm2) of the output plane was measured to determine the class score.
To overcome potential mechanical misalignments during the experimental testing, the network was “vaccinated” with deliberate random displacements during the training stage53 (link). Specifically, a random lateral displacement Dx,Dy was added to the diffractive layer, where Dx and Dy were randomly and independently sampled, i.e., Dx~U−0.4mm,0.4mm,Dy~U−0.4mm,0.4mm where Dx and Dy are not necessarily equal to each other in each misalignment step.
A random axial displacement Dz was also added to the axial separations between any two consecutive planes. Accordingly, the axial distance between any two consecutive planes was set to d±Dz= 20 mm ±Dz , where Dz was randomly sampled as, Dz~U−0.2mm,0.2mm
In our experiments, we also measured the power spectrum of the pulsed terahertz source with only the input and output apertures present, which served as an experimental reference spectrum, Iref(λ) . Based on this, the experimentally measured power spectrum at the output single-pixel aperture of a diffractive network can be written as: si,calibrated=si,measuredIrefλi
The binary objects and apertures were all 3D-printed (Form 3B, Formlabs) and coated with aluminum foil to define the transmission areas. Apertures, objects, the diffuser, and the diffractive layer were assembled using a 3D-printed holder (Objet30 Pro, Stratasys). The setup of the THz-TDS system is illustrated in Fig. 7a. A Ti:Sapphire laser (Mira-HP, Coherent) generates optical pulses with a 135-fs pulse width and a 76-MHz repetition rate at a center wavelength of 800 nm, which pumps both a high-power plasmonic photoconductive terahertz source57 (link) and a high-sensitivity plasmonic photoconductive terahertz detector58 (link). The terahertz radiation generated by the terahertz source is collimated by a 90° off-axis parabolic mirror and illuminates the test object. After interacting with the object, the diffuser, and the diffractive neural network, the radiation is coherently detected by the terahertz detector (single-pixel). A transimpedance amplifier (DHPCA-100, Femto) converts the current signal to a voltage signal, which is then measured by a lock-in amplifier (MFLI, Zurich Instruments). By varying the optical delay between the terahertz radiation and the optical probe beam on the terahertz detector, the terahertz time-domain signal can be obtained. By taking the Fourier transform of the time-domain signal, the spectral intensity signal is revealed to calculate the class scores for each classification/inference. For each measurement, 10 time-domain traces are collected and averaged. This THz-TDS system provides a signal-to-noise ratio larger than 90 dB and a detection bandwidth larger than 4 THz.
Bai B., Li Y., Luo Y., Li X., Çetintaş E., Jarrahi M, & Ozcan A. (2023). All-optical image classification through unknown random diffusers using a single-pixel diffractive network. Light, Science & Applications, 12, 69.
Publication 2023
Aluminum Epistropheus Extinction, Psychological Hypersensitivity Light Neurons Pulses Pupil Radiation Sapphire Terahertz Radiation Transmission, Communicable Disease

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3
Uv 1800 by Shimadzu
Sourced in Japan, United States, Germany, Switzerland, China, United Kingdom, Italy, Belgium, France, India
The UV-1800 is a UV-Visible spectrophotometer manufactured by Shimadzu. It is designed to measure the absorbance or transmittance of light in the ultraviolet and visible wavelength regions. The UV-1800 can be used to analyze the concentration and purity of various samples, such as organic compounds, proteins, and DNA.
4
Nanodrop spectrophotometer by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, China, Canada, Japan, Italy, France, Australia, Poland, Belgium, Switzerland, Spain, Austria, Netherlands, Singapore, India, Ireland, Sweden, Denmark, Israel, Malaysia, Argentina, Slovakia, Finland
The NanoDrop spectrophotometer is a compact and efficient instrument designed for the measurement of small-volume samples. It utilizes a patented sample-retention technology to enable accurate and reproducible spectroscopic analyses of various biomolecules, including nucleic acids and proteins, in a simple and convenient manner.
5
Uv 1800 spectrophotometer by Shimadzu
Sourced in Japan, United States, Germany, Switzerland, Singapore, China, Malaysia, Italy
The Shimadzu UV-1800 spectrophotometer is a laboratory instrument used for the quantitative analysis of various samples. It measures the absorption of light by a sample across the ultraviolet and visible light spectrum. The instrument is designed to provide accurate and reliable results for a wide range of applications.
6
Bovine serum albumin (bsa) by Merck Group
Sourced in United States, Germany, United Kingdom, China, Italy, Japan, France, Sao Tome and Principe, Canada, Macao, Spain, Switzerland, Australia, India, Israel, Belgium, Poland, Sweden, Denmark, Ireland, Hungary, Netherlands, Czechia, Brazil, Austria, Singapore, Portugal, Panama, Chile, Senegal, Morocco, Slovenia, New Zealand, Finland, Thailand, Uruguay, Argentina, Saudi Arabia, Romania, Greece, Mexico
Bovine serum albumin (BSA) is a common laboratory reagent derived from bovine blood plasma. It is a protein that serves as a stabilizer and blocking agent in various biochemical and immunological applications. BSA is widely used to maintain the activity and solubility of enzymes, proteins, and other biomolecules in experimental settings.
7
Nanodrop 1000 by Thermo Fisher Scientific
Sourced in United States, United Kingdom, Germany, China, Japan, Australia, Canada, Italy, France, Switzerland, Ireland, Denmark, Belgium, Norway, Spain, Portugal, Jamaica, Austria, Lithuania, Singapore
The NanoDrop 1000 is a spectrophotometer designed for the quantification and analysis of small volume samples. It can measure the absorbance of samples ranging from 1 to 2 microliters in volume. The device uses innovative sample-retention technology to allow for direct measurement without the need for cuvettes or other sample vessels.
8
Nanodrop 2000 spectrophotometer by Thermo Fisher Scientific
Sourced in United States, Germany, China, United Kingdom, Japan, Canada, France, Italy, Australia, Spain, Denmark, Switzerland, Singapore, Poland, Ireland, Belgium, Netherlands, Lithuania, Austria, Brazil
The NanoDrop 2000 spectrophotometer is an instrument designed to measure the concentration and purity of a wide range of biomolecular samples, including nucleic acids and proteins. It utilizes a unique sample retention system that requires only 1-2 microliters of sample to perform the analysis.
9
Cary eclipse fluorescence spectrophotometer by Agilent Technologies
Sourced in United States, Australia, Italy, Germany, United Kingdom
The Cary Eclipse Fluorescence Spectrophotometer is a laboratory instrument designed to measure the fluorescence properties of samples. It is capable of performing excitation and emission scans, as well as time-based measurements. The instrument uses a xenon flash lamp as the light source and provides high-sensitivity detection and rapid scanning capabilities.
10
Prism 6 by GraphPad
Sourced in United States, United Kingdom, Canada, China, Germany, Japan, Belgium, Israel, Lao People's Democratic Republic, Italy, France, Austria, Sweden, Switzerland, Ireland, Finland
Prism 6 is a data analysis and graphing software developed by GraphPad. It provides tools for curve fitting, statistical analysis, and data visualization.

More about "Extinction, Psychological"

Psychological Extinction refers to the gradual reduction or elimination of a conditioned response following the removal of the unconditioned stimulus.
This process, also known as Extinction Learning or Response Extinction, is a fundamental principle in the field of learning and behavior modification, with applications in various areas of psychology and neuroscience.
The study of psychological extinction helps researchers understand the mechanisms of memory, emotion regulation, and the treatment of anxiety disorders, phobias, and addiction.
By leveraging extinction-based therapies, clinicians can help patients unlearn maladaptive behaviors and responses, leading to improved mental health and well-being.
Researchers investigating extinction processes may utilize a range of analytical tools and techniques, such as the NanoDrop 2000 and NanoDrop 1000 spectrophotometers for biomolecular quantification, the UV-1800 spectrophotometer for absorbance measurements, and the Cary Eclipse Fluorescence Spectrophotometer for fluorescence-based assays.
These instruments, combined with powerful data analysis software like Prism 6, can provide valuable insights into the underlying neurobiological and biochemical mechanisms of extinction.
PubCompare.ai's AI-driven tools can optimize your extinction research protocols by quickly identifying the best methods from published literature, preprints, and patents, while leveraging comparisons to maximize the impact of your studies.
Streamline your workflow and advance your research with PubCompare.ai's cutting-edg tools, empowering you to uncover new discoveries in the field of psychological extinction.