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Mental Orientation

Mental Orientation: The awareness of one's surroundings, situational context, and inner mental state.
This encompasses the cognitive processes that enable individuals to maintain a coherent perspective on their environment and internal experiences.
Disturbances in mental orientation can signify neurological or psychological conditions, and assessing mental orientation is a key component of clinical evaluations.
Optimizing mental orientation can enhance research productivity, focus, and overall mental well-being.

Most cited protocols related to «Mental Orientation»

An alternative robust method for Mendelian randomization with summary data has been recently proposed by Bowden et al. [2015], referred to as “MR‐Egger regression.” This approach was motivated from a method in the meta‐analysis literature for the assessment of small‐study bias (often called “publication bias”) (Egger et al., 1997). This performs a weighted linear regression of the gene‐outcome coefficients Γ^j on the gene‐exposure coefficients γ^j:
Γ^j=β0E+βEγ^j in which all the γ^j associations are orientated to be positive (the orientation of the Γ^j associations should be altered if necessary to match the orientation of the γ^j parameters), and the weights in the regression are the inverse variances of the gene‐outcome associations (σYj2). Reorientation of the variants is performed as the orientation of genetic variants is arbitrary (i.e., estimates can be presented with reference to either the major or minor allele), and different orientations of genetic variants change the estimate of the intercept, as well as the sign and magnitude of the pleiotropic effect of the genetic variant. If there is no intercept term in the regression model, then the MR‐Egger slope estimate β^E will equal the IVW estimate (Burgess et al., 2015a).
The value of the intercept term β^0E can be interpreted as an estimate of the average pleiotropic effect across the genetic variants (Bowden et al., 2015). The pleiotropic effect is the effect of the genetic variant on the outcome that is not mediated via the exposure. An intercept term that differs from zero is indicative of overall directional pleiotropy; that is, pleiotropic effects do not cancel out and the IVW estimate is biased.
MR‐Egger regression additionally provides an estimate for the true causal effect β^E that is consistent even if all genetic variants are invalid due to violation of IV3, but under a weaker assumption known as the InSIDE (instrument strength independent of direct effect) assumption. If the association of the jth genetic variant with the outcome Γj=βγj+αj, where αj is the pleiotropic (direct) effect of the variant, then the InSIDE assumption states that the pleiotropic effects αj must be distributed independently of the instrument strength parameters γj (Kolesár et al., 2014). (Formally, the consistency property holds both as the sample size and the number of instruments increases. For a fixed number of instruments, consistency only holds asymptotically if the correlation between the αj and γj parameters is zero.) The InSIDE assumption is likely to be satisfied if pleiotropic effects on the outcome are direct (i.e., not via a confounder). There is some empirical evidence supporting the proposition that genetic effects on separate exposures are independent (Pickrell, 2015). However, if the pleiotropic effects of genetic variants are all via a single confounder, then they will be correlated with instrument strength, and the InSIDE assumption will be violated.
Publication 2016
Alleles Genes Genes, vif Genetic Diversity Mental Orientation Reproduction

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Publication 2006
Anisotropy Corpus Callosum Diffusion Fibrosis Mental Orientation Population Group
Starting from a 2p atomic orbital aligned along the molecular axis, we solve the three-dimensional TDSE in single-active-electron approximation with the split-operator method on a Cartesian grid with 512 points in each dimension, a grid spacing of 0.25 a.u. and a time step of 0.02 a.u. While propagating up to a final time T = 1500 a.u., outgoing parts of the wave function are projected onto Volkov states44 (link). The potential for a single neon atom is chosen as in ref. 45 (link) but with the singularity removed using a pseudopotential46 (link) for angular momentum l = 1. The clockwise circularly polarized pulse has a 12-cycle sin2 envelope and a peak field strength of 0.096 a.u. To obtain the momentum distribution for the dimer we multiply two copies of the atomic distribution by e±ik·R/2, respectively (|R|/2 = 2.93 a.u.) and then add them coherently with an additional factor of ± 1 depending on the type of interference, gerade, or ungerade. To account for different possible orientations of the dimer with respect to the polarization plane, we vary the angle between them in 8 steps to cover a range from 0 to 45°, project the molecular photoelectron momentum distribution (PMD) onto the polarization plane and add these projections together with their geometrical weights. The PMDs are then averaged over the ATI peaks to obtain the final distributions shown in Fig. 2.
Publication 2019
Electrons Epistropheus Mental Orientation Neon Persistent Mullerian duct syndrome Pulse Rate
DSSP files are produced with the newly written DSSP 2.2.1. HSSP files are produced using HSSP 2.0. PDBFINDER files currently have version 9.0. PDBREPORTs currently are produced with WHAT_CHECK 8.4 (51 (link)), but we are planning to release version 11.0 very soon. The PDBREPORT databank will be updated accordingly. The most recent version of PDB_REDO is 5.35. PDB_REDO is under active development and the PDB_REDO databank is continuously being renewed. At the time of writing all files have been created with PDB_REDO version 5.00 or newer which means that all structure models have undergone rebuilding of side chains and flipping of peptide plane orientations when needed (52 (link)). BDB files currently are created with version 0.6.5. Most other databanks are produced using the WHAT IF software (53 (link)).
Publication 2014
Mental Orientation Peptides
As input, seqMINER supports multiple file formats popular for storage of high throughput sequencing data (BED, SAM/BAM and Bowtie). seqMINER uses two methods to quantify ChIP-seq signal depending on the type of performed analysis (Figure 2). For both methods the middle of the reference coordinate (i.e. peak) is calculated and used as reference locus in further computation. Moreover, the strand orientation of the reference feature is (by default, if the reference locus contains strand information) taken in account in order to orientate all analysed features on the same direction. For both methods, prior to analysis, all reads are extended from a user defined size (default 200 bp). For the calculation of densities over a defined window, methods are derived from the one generally used to generate density files [i.e. (18 (link)) except that tag extension is performed only on the direction of the tag (not in both strand orientations)].

Schematic representation of the algorithms for each method implemented in seqMINER. In both methods, prior to quantification, reads are extended from a user-defined value (default 200 bp). (A) Density array method: a user defined number of bins are created in a fixed window around the reference coordinate and for each bin the maximal number of overlapping reads is computed in each dataset, the collected values are pooled and submitted to clustering process and the generated clusters are visualized as heatmap. (B) Enrichment based method: the number of tags presents or overlapping a user-defined window (default 2 kb) around the reference site are counted. The values from the different datasets are computed and ploted.

For the density array method, a user defined number of bins is created over a fixed size window around the reference coordinate (sizable by the user) and for each bin the maximal number of overlapping tags is computed (Figure 2A).
To calculate a single enrichment value for a binding site, tag density is defined as the number of tags present or overlapping in user-defined window (default 2 kb) around the reference site (Figure 2B). ChIP-seq enrichments (e) are defined as e = log 2 [(foreground tags + q)/(background tags + q)]. q is defined as an empirical constant in the range of 10; foreground tags are the density value computed in the data track; background tags, the density value in the control track. q is used to lower the contribution of noise variations that is assumed to be higher at low-count levels. The use of the constant q reduces the influence of the signal variation in the noise measurement on the ratio calculation. Increasing q-value will increase the stringency of the analysis by lowering the contribution of low-density values to high-enrichment calculations.
Two normalization methods are implemented in seqMINER, namely linear and ranked-based normalization. These methods aim to lower the bias on the clustering procedure due to inter-samples intensities differences inherent to the ChIP-seq procedure. Normalized data are used only to perform the clustering step but raw intensity data are displayed in the final heatmap to recover the original patterns from the data.
Linear normalization: in each dataset, all xi values from the calculated density array are divided by the percentile P of the distribution of the all values xi > T (P is chosen by the user, default P = 75, the third quartile).
Ranked-based normalization: in each dataset, the values xi from the calculated density array are sorted in ascending order: x1 < x2 < … xi < … < xN (with N the total number of values in the density array). Then each value xi should be replaced by its rank ri, if xi > T (with T a threshold chosen by the user, default T = 10): rN = N, rN−1 = N−1,…, ri = i. All values xi ≤ T are replaced by 0, which ensures that all background values are similarly considered during the clustering process.
Publication 2010
Binding Sites Chromatin Immunoprecipitation Sequencing Mental Orientation

Most recents protocols related to «Mental Orientation»

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Example 18

A non-transitory computer readable medium storing computer readable instructions which, when executed, causes a machine to: control the operation of a plurality of illumination sources of a tissue sample wherein each illumination source is configured to emit light having a specified central wavelength; receive data from the light sensor when the tissue sample is illuminated by each of the plurality of illumination sources; calculate structural data related to a characteristic of a structure within the tissue sample based on the data received by the light sensor when the tissue sample is illuminated by each of the illumination sources; and transmit the structural data related to the characteristic of the structure to be received by a smart surgical device, wherein the characteristic of the structure is a surface characteristic or a structure composition.

While several forms have been illustrated and described, it is not the intention of the applicant to restrict or limit the scope of the appended claims to such detail. Numerous modifications, variations, changes, substitutions, combinations, and equivalents to those forms may be implemented and will occur to those skilled in the art without departing from the scope of the present disclosure. Moreover, the structure of each element associated with the described forms can be alternatively described as a means for providing the function performed by the element. Also, where materials are disclosed for certain components, other materials may be used. It is therefore to be understood that the foregoing description and the appended claims are intended to cover all such modifications, combinations, and variations as falling within the scope of the disclosed forms. The appended claims are intended to cover all such modifications, variations, changes, substitutions, modifications, and equivalents.

The foregoing detailed description has set forth various forms of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, and/or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. Those skilled in the art will recognize that some aspects of the forms disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as one or more program products in a variety of forms, and that an illustrative form of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution.

Instructions used to program logic to perform various disclosed aspects can be stored within a memory in the system, such as dynamic random access memory (DRAM), cache, flash memory, or other storage. Furthermore, the instructions can be distributed via a network or by way of other computer readable media. Thus a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), but is not limited to, floppy diskettes, optical disks, compact disc, read-only memory (CD-ROMs), and magneto-optical disks, read-only memory (ROMs), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or a tangible, machine-readable storage used in the transmission of information over the Internet via electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Accordingly, the non-transitory computer-readable medium includes any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

As used in any aspect herein, the term “control circuit” may refer to, for example, hardwired circuitry, programmable circuitry (e.g., a computer processor comprising one or more individual instruction processing cores, processing unit, processor, microcontroller, microcontroller unit, controller, digital signal processor (DSP), programmable logic device (PLD), programmable logic array (PLA), or field programmable gate array (FPGA)), state machine circuitry, firmware that stores instructions executed by programmable circuitry, and any combination thereof. The control circuit may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), an application-specific integrated circuit (ASIC), a system on-chip (SoC), desktop computers, laptop computers, tablet computers, servers, smart phones, etc. Accordingly, as used herein “control circuit” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.

As used in any aspect herein, the term “logic” may refer to an app, software, firmware and/or circuitry configured to perform any of the aforementioned operations. Software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on non-transitory computer readable storage medium. Firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in memory devices.

As used in any aspect herein, the terms “component,” “system,” “module” and the like can refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution.

As used in any aspect herein, an “algorithm” refers to a self-consistent sequence of steps leading to a desired result, where a “step” refers to a manipulation of physical quantities and/or logic states which may, though need not necessarily, take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It is common usage to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. These and similar terms may be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities and/or states.

A network may include a packet switched network. The communication devices may be capable of communicating with each other using a selected packet switched network communications protocol. One example communications protocol may include an Ethernet communications protocol which may be capable permitting communication using a Transmission Control Protocol/Internet Protocol (TCP/IP). The Ethernet protocol may comply or be compatible with the Ethernet standard published by the Institute of Electrical and Electronics Engineers (IEEE) titled “IEEE 802.3 Standard”, published in December, 2008 and/or later versions of this standard. Alternatively or additionally, the communication devices may be capable of communicating with each other using an X.25 communications protocol. The X.25 communications protocol may comply or be compatible with a standard promulgated by the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T). Alternatively or additionally, the communication devices may be capable of communicating with each other using a frame relay communications protocol. The frame relay communications protocol may comply or be compatible with a standard promulgated by Consultative Committee for International Telegraph and Telephone (CCITT) and/or the American National Standards Institute (ANSI). Alternatively or additionally, the transceivers may be capable of communicating with each other using an Asynchronous Transfer Mode (ATM) communications protocol. The ATM communications protocol may comply or be compatible with an ATM standard published by the ATM Forum titled “ATM-MPLS Network Interworking 2.0” published August 2001, and/or later versions of this standard. Of course, different and/or after-developed connection-oriented network communication protocols are equally contemplated herein.

Unless specifically stated otherwise as apparent from the foregoing disclosure, it is appreciated that, throughout the foregoing disclosure, discussions using terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

One or more components may be referred to herein as “configured to,” “configurable to,” “operable/operative to,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Those skilled in the art will recognize that “configured to” can generally encompass active-state components and/or inactive-state components and/or standby-state components, unless context requires otherwise.

The terms “proximal” and “distal” are used herein with reference to a clinician manipulating the handle portion of the surgical instrument. The term “proximal” refers to the portion closest to the clinician and the term “distal” refers to the portion located away from the clinician. It will be further appreciated that, for convenience and clarity, spatial terms such as “vertical”, “horizontal”, “up”, and “down” may be used herein with respect to the drawings. However, surgical instruments are used in many orientations and positions, and these terms are not intended to be limiting and/or absolute.

Those skilled in the art will recognize that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to claims containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that typically a disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms unless context dictates otherwise. For example, the phrase “A or B” will be typically understood to include the possibilities of “A” or “B” or “A and B.”

With respect to the appended claims, those skilled in the art will appreciate that recited operations therein may generally be performed in any order. Also, although various operational flow diagrams are presented in a sequence(s), it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently. Examples of such alternate orderings may include overlapping, interleaved, interrupted, reordered, incremental, preparatory, supplemental, simultaneous, reverse, or other variant orderings, unless context dictates otherwise. Furthermore, terms like “responsive to,” “related to,” or other past-tense adjectives are generally not intended to exclude such variants, unless context dictates otherwise.

It is worthy to note that any reference to “one aspect,” “an aspect,” “an exemplification,” “one exemplification,” and the like means that a particular feature, structure, or characteristic described in connection with the aspect is included in at least one aspect. Thus, appearances of the phrases “in one aspect,” “in an aspect,” “in an exemplification,” and “in one exemplification” in various places throughout the specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more aspects.

Any patent application, patent, non-patent publication, or other disclosure material referred to in this specification and/or listed in any Application Data Sheet is incorporated by reference herein, to the extent that the incorporated materials is not inconsistent herewith. As such, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions, statements, or other disclosure material set forth herein will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.

In summary, numerous benefits have been described which result from employing the concepts described herein. The foregoing description of the one or more forms has been presented for purposes of illustration and description. It is not intended to be exhaustive or limiting to the precise form disclosed. Modifications or variations are possible in light of the above teachings. The one or more forms were chosen and described in order to illustrate principles and practical application to thereby enable one of ordinary skill in the art to utilize the various forms and with various modifications as are suited to the particular use contemplated. It is intended that the claims submitted herewith define the overall scope.

Patent 2024
Acoustics Character Conferences DNA Chips Electricity Enzyme Multiplied Immunoassay Technique Fingers Human Body Light Medical Devices Memory Mental Orientation Ocular Refraction Physical Examination Reading Frames Surgical Instruments Teaching Tissues Transmission, Communicable Disease Vision
For the cryo-EM data collection, 2–3 μL of sample solution was applied to a holey carbon grid (Quantifoil R2/1, Mo 300 mesh; Quantifoil Micro Tools GmbH) covered with a thin amorphous carbon film at 4°C with 100% humidity. After waiting for 30 s, the excess sample solution present on the cryo-EM grids was blotted with filter papers and then, these grids were plunge-frozen into liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific). The EM grids were examined with a 300-kV Titan Krios cryo-electron microscope (Thermo Fisher Scientific) incorporating a field emission gun and a Cs-corrector (CEOS GmbH). Cryo-EM movies were recorded at a nominal magnification of x 59,000 using a Falcon 3EC direct electron detector (calibrated pixel size of 1.12 Å) (Thermo Fisher Scientific). The nominal defocus range was –1.00 to –2.75 μm. Each exposure of 48 electrons/Å2 for 2.0 s was dose-fractionated into 39 frames. The cryo-EM data collection is summarized in S1 Table. The three-dimensional (3D) capsid structure of RnMBV1 was reconstructed using RELION 3.0 [60 (link)]; the procedure of the structural analysis is summarized in S1 Fig. The movie frames were aligned and summed into a dose-weighted image using MotionCor2 software [61 (link)], and the contrast transfer function (CTF) parameters were estimated using the CTFFIND4 program [62 (link)]. The micrographs exhibiting poor power spectra (based on the extent of Thon rings) were rejected (4.5 Å resolution cutoff). To determine the 3D model of RnMBV1, 45,869 particles were automatically picked from 2,734 micrographs and then used for reference-free two-dimensional (2D) classification. Then, 39,272 particles were selected from good 2D classes (S1E Fig) and subjected to 3D classification with an icosahedral symmetry. After 3D classification, two good classes appeared (S1A Fig). The particles in class III (12,230 particles) were filled with the genome (full particles), while those in class II (14,602 particles) lacked the genome (empty class). We selected particles in the good full- and empty-particle classes separately and used them for further structural analyses. The 3D refinement and post-processing, including CTF refinement and Bayesian polishing, yielded maps of both full and empty particles at 3.2 Å resolution, which were estimated by the gold-standard Fourier shell correlation at 0.143 criterion [63 (link), 64 (link)] (S1F Fig). To determine the CrP structure, we performed focused 3D classification of the CrP trimers using a mask covering the icosahedral capsid map (S1C Fig) after the particle orientations were expanded with an icosahedral symmetry. The particle orientations in a good 3D class were selected and used for further structural 3D refinement and post-processing. The final map after the focused classification was reconstructed from 244,609 particle orientations at 3.3 Å resolution (Figs 6, S1B and S1F). The number of the CrP trimers bound to one virion particle was counted based on the classified particle orientations (Fig 7).
Publication 2023
Capsid Proteins Carbon Cryoelectron Microscopy Electrons Ethane Freezing Genome Gold Humidity Mental Orientation Microtubule-Associated Proteins Reading Frames Virion
The first level comparisons of encoding (scenes) vs. delay (scrambled scenes) between high and for low WM load were output as individual subject maps in Talairach space with 2 mm isotropic resolution and thresholded using a false discovery rate of q = 0.01. They were then imported into BESA Research 7.0 as functional activation weight maps for constrained dipole source analysis (Scherg, 1990 ).
For each participant, the scalp positions of the electrodes used in the simultaneous EEG-fMRI scanning sessions were estimated initially using an approximation of locations from a standard montage template (BESA-MRI-Standard-Electrodes) and then adjusted manually based on visual inspection of the indentation-artifacts caused by electrode on the scalp, which appeared as dips on the scalp surface reconstructions. An example of electrode locations for a single subject is shown in Supplementary Figure 2. Each participant’s T1-weighted anatomical MRI was segmented manually in BESA MRI v2.0 to create a 4-layer Finite Element Model (FEM) realistic head model to be used in the source analysis. Based on individual electrode coordinates, segmentation with anatomical landmarks transformed to Talairach Space, and fMRI statistical maps imported for each condition, BESA calculated the best fitting ellipsoid of each participant (Scherg, 1992 (link)). The fMRI-informed regional EEG source estimation with anatomical constraints approach has been documented to be a better modeling than seeding dipoles based solely on anatomical locations (Phillips et al., 2002 (link); Ahlfors and Simpson, 2004 (link); Ou et al., 2010 (link)).
Seed-based dipole fitting was based on a priori hypotheses to explain ERP changes as a function of task period and WM load. For encoding, two equivalent dipoles were fitted onto bilateral parahippocampal cortex (PHC) for each participant at low and high load WM conditions. For delay, two equivalent dipoles were fitted onto bilateral thalamus. For each participant, a time window was chosen from onset to the peak of the first Global Field Potential (GFP) peak, which is a measure for spatial standard deviation as a function of time (Strik and Lehmann, 1993 (link)). An example of a single participant’s GFP waveform is shown in Supplementary Figure 3 and an example analysis window used in the source analysis is shown in Supplementary Figure 4. During seeding of dipole locations, weighting with fMRI activation maps was initially turned off to avoid potential bias in determining the initial seed location. The dipoles were then fit onto the respective sources weighted by the fMRI statistical map activation using the RAP-MUSIC algorithm as implemented in BESA source space that estimates the dipole locations using the weighted fMRI images (Grech et al., 2008 (link)). The dipole positions were constrained to stay within the target regions, but their orientations were kept free before the fit. All the dipoles fell within the appropriate brain regions (PHC and thalamus) after the fit. An example fit with fMRI weighting for thalamus is shown in Supplementary Figure 5. The dipole positions were expressed as Talairach coordinates in units of millimeters (mm) and averaged across all subjects. The source waveforms for each participant and condition were exported and then imported for group source statistical analyses in BESA Statistics v2.0.
Publication 2023
3,5-diisopropylsalicylic acid Anatomic Landmarks Brain Cortex, Cerebral fMRI Head Mental Orientation Microtubule-Associated Proteins Reconstructive Surgical Procedures Scalp Thalamus
Using a typical minimizing algorithm such as the
simplex algorithm56 (link) to find a “best
fit” orientation,
penetration and Γ that minimizes χ2 can be
a useful analysis technique but gives no information about other potential
solutions or confidence in the fitted parameters. Calculating how
the χ2 value varies with orientation would give qualitative
information about which orientations better fit the data but cannot
easily be compared in terms of confidence. To solve this issue, Bayesian
analysis techniques such as Markov Chain Monte Carlo (MCMC) analysis
are useful for investigating the probability distribution of the parameters.
In brief, MCMC uses an iterative process to explore the parameter
space such that each step samples the posterior probability distribution
of the parameters.57 (link) With a large chain,
the distribution of the chain steps should approximate the posterior
probability distribution of the parameters and so the density of the
resulting sample points provides an estimate of the probability density
function (PDF) for the parameters. Thus, by analyzing the distribution
of the accepted θ values against ϕ values, the probability
of the orientation can be investigated, with regions of high probability
corresponding to regions of high likelihood, that is, the best fitting
regions. The PDF can be estimated by binning the points into a histogram
or calculating the kernel density estimate (KDE). A bin width of 5
degrees was used for the histograms, while KDEs were calculated using
an adaptive bandwidth diffusion technique.58 (link) Credible intervals for orientation were then found by calculating
the highest posterior density regions (HPDR) of the aforementioned
KDE, the smallest bounded area which contains the desired probability,
for example, 65% of the probability volume. As Γ and penetration
are univariate parameters distributed roughly in a Gaussian, the confidence
intervals were calculated by taking the standard deviation.
Bayesian probability analysis methods use a prior distribution,
which represents what is believed to be the most likely distribution
of the parameters. In this study, MCMC analyses were performed using
a uniform prior, to avoid making strong assumptions about the distribution
of the orientation, which were likely to be non-Gaussian. A delayed
rejection adaptive metropolis59 (link),60 algorithm was used
to improve chain convergence and exploration of the parameter space.
Due to the rotational symmetry of the problem, the algorithm was modified
to include periodic boundary conditions for θ and ϕ, allowing
the Markov Chain to wrap around the opposite limit; for example. a
step of θ = 3° could travel from θ = – 179
to θ = + 178. This change greatly improves chain mixing and
reduces autocorrelation in the results. For models of the Fab and
Fc fragments, each MCMC simulation was run for 5 repeats of 200,0000
steps with 200,000 burn-in steps, while for COE-3 due to the high
proportion of rejected steps, each MCMC simulation was run for 5 repeats
of 16,000,000 steps with 400,000 burn-in steps. Figure S5 shows the MCMC traces for the chains, while Figure S6 shows plots demonstrating the autocorrelation
of the same parameters. Figure S7 shows
the traces and autocorrelation for the measurement of 50 ppm Fc adsorbed
at the air/water interface, shown separately for reasons discussed
below in Section 3.2 and the Supporting Information.
While NR data is fitted to a single protein orientation in this
work, it is of course possible that there are multiple preferred orientations.
With sufficient neutron contrasts involving deuterated proteins or
with intelligent experimental design, it is possible to distinguish
such states. However, due to the vastly increased complexity of such
a model, this is beyond the scope of this study.
Publication 2023
Acclimatization Contrast Media Diffusion Familial Mediterranean Fever Immunoglobulins, Fab Mental Orientation Proteins Staphylococcal Protein A
We collected rhizosphere soil samples from every tree species (host plants) in each plot during the growing season (July to October 2021). First, the tree species in each plot were chosen using survey information. Then, the topsoil layer containing litter and humus was removed, and fine tertiary roots were identified using the root-seeking technique (Laliberté, 2017 (link)). Rhizosphere soil (attached to fine roots) was collected using a soft brush. We collected three to five individuals of each tree species and rhizosphere soils from each individual in three random orientations. The soil was sieved through a sieve (2 mm), freeze-dried, and stored at-40°C for molecular analysis.
Rhizosphere soil was collected in each plot for physicochemical analysis. The soil water content was calculated by subtracting the fresh weight from the dry weight (some soils dried in the oven). The remaining samples were air-dried in the shade and transferred to the Central Laboratory of the Public Technology Service Center, Xishuangbanna Tropical Botanical Garden, and the Chinese Academy of Science for measuring organic matter (OM), total carbon (TC), total nitrogen (TN), hydrolysable nitrogen (HN), total phosphorus (TP), total potassium (TK), available potassium (AK), water content, and soil pH. Detailed measurements are shown in Supplementary Tables S1, S2 (Supporting Information).
Publication 2023
Carbon Chinese Freezing Mental Orientation Nitrogen Phosphorus Plant Roots Plants Potassium Rhizosphere Trees

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More about "Mental Orientation"

Mental orientation is the awareness of one's surroundings, situational context, and inner mental state.
It encompasses the cognitive processes that enable individuals to maintain a coherent perspective on their environment and internal experiences.
Disturbances in mental orientation can signify neurological or psychological conditions, and assessing mental orientation is a key component of clinical evaluations.
Optimizing mental orientation can enhance research productivity, focus, and overall mental well-being.
Tools like PubCompare.ai can help researchers find the best protocols and products, locate protocols from literature, preprints, and patents, and use AI-driven comparisons to identify the most accurate and reproducible approaches.
This can take the guesswork out of research and improve mental clarity.
Related terms include cognitive awareness, situational awareness, mindfulness, and attentiveness.
Abbreviations like MO or MOA may also be used.
Subtopics include neurocognitive function, psychological assessment, clinical decision-making, and research optimization.
Relevant technologies like MATLAB, Gadovist, Maestro, AutoDock Tools, MAGNETOM Aera, Protein Preparation Wizard, MAGNETOM Prisma, Tim Trio, Magnevist, and MAGNETOM Avanto can provide insights into brain function, neurological conditions, and research tools to support mental orientation.
By incorporating these elements, researchers can enhance their understanding and optimization of this important cognitive process.