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Social Networks

Social Networks are interconnected groups of individuals or organizations that share ideas, information, and resources.
These networks can form organically or be purposefully constructed, and they play a crucial role in the dissemination of knowledge, the formation of communities, and the facilitation of social interactions.
Reseachers can leverage the power of social networks to enhance collaboration, accelerate the spread of scientific discoveries, and foster interdisciplinary dialogue.
By understanding the dynamics and applications of social networks, investigators can optimize their research protocols, improve reproducibility, and drive innovation in their respective fields.

Most cited protocols related to «Social Networks»

The GEPIA website is freely available to all users. It is built by the HTML5 and JavaScript libraries, including jQuery (http://jquery.com), Bootstrap (http://getbootstrap.com/) for the client-side user interface. The server-side and interactive data processing are carried out by PHP scripts (version 7.0.13). The web site automatically adjusts the look and feel according to different browsers and devices, ranging from desktop computers to tablets and smart phones. There is no login requirement for accessing any features in GEPIA.
To solve the imbalance between the tumor and normal data which can cause inefficiency in various differential analyses, we download the TCGA and GTEx gene expression data that are re-computed from raw RNA-Seq data by the UCSC Xena project based on a uniform pipeline (Figure 1). We consult with medical experts to determine the most appropriate sample grouping for tumor-normal comparisons. The datasets are stored in a MySQL relational database (version 5.7.17).
The GEPIA web server features are divided into seven major tabs: General, Differential Genes, Expression DIY, Survival, Similar Genes, Correlation and PCA, which provides key interactive functions corresponding to differential expression analysis, customizable profiling plotting, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis (Figure 2).
All plotting features in GEPIA are developed using R (version 3.3.2) and Perl (version 5.22.1) programs. The GEPIA outputs consist of plots and tables. Static visualizations are rendered as Portable Document Format (PDF), Scalable Vector Graphics (SVG) and Portable Network Graphics (PNG) images. The rotatable 3D plots are built by the plotly.js library (https://plot.ly/). Tables are generated by the DataTables (https://www.datatables.net/) JavaScript library, allowing for data querying and selection.
Publication 2017
cDNA Library Cloning Vectors Feelings Gene Expression Genes Medical Devices Neoplasms Patients RNA-Seq
To create the annotations network ClueGO provides predefined functional analysis settings ranging from general to very specific ones. Furthermore, the user can adjust the analysis parameters to focus on terms, e.g. in certain GO level intervals, with particular evidence codes or with a certain number and percentage of associated genes. An optional redundancy reduction feature (Fusion) assesses GO terms in a parent–child relation sharing similar associated genes and preserves the more representative parent or child term. The relationship between the selected terms is defined based on their shared genes in a similar way as described by Huang et al. (2007 (link)). ClueGO creates first a binary gene-term matrix with the selected terms and their associated genes. Based on this matrix, a term–term similarity matrix is calculated using chance corrected kappa statistics to determine the association strength between the terms. Since the term–term matrix is of categorical origin, kappa statistic was found to be the most suitable method. Finally, the created network represents the terms as nodes which are linked based on a predefined kappa score level. The kappa score level threshold can initially be adjusted on a positive scale from 0 to 1 to restrict the network connectivity in a customized way. The size of the nodes reflects the enrichment significance of the terms. The network is automatically laid out using the Organic layout algorithm supported by Cytoscape. The functional groups are created by iterative merging of initially defined groups based on the predefined kappa score threshold. The final groups are fixed or randomly colored and overlaid with the network. Functional groups represented by their most significant (leading) term are visualized in the network providing an insightful view of their interrelations. Also other ways of selecting the group leading term, e.g. based on the number or percentage of genes per term are provided. As an alternative to the kappa score grouping the GO hierarchy using parent–child relationships can be used to create functional groups.
When comparing two gene clusters, another original feature of ClueGO allows to switch the visualization of the groups on the network to the cluster distribution over the terms. Besides the network, ClueGO provides overview charts showing the groups and their leading term as well as detailed term histograms for both, cluster specific and common terms.
Like BiNGO, ClueGO can be used in conjuntion with GOlorize for functional analysis of a Cytoscape gene network. The created networks, charts and analysis results can be saved as project in a specified folder and used for further analysis.
Publication 2009
Child Gene Clusters Gene Regulatory Networks Genes Genes, vif Parent
Developing a comprehensive framework is more challenging than simply combining constructs from existing theories. We have carefully reviewed terminology and constructs associated with published theories for this first draft of the CFIR. In the process of standardizing terminology, we have combined certain constructs across theories while separating and delineating others to develop definitions that can be readily operationalized in implementation research studies.
We sought theories (we use the term theory to collectively refer to published models, theories, and frameworks) that facilitate translation of research findings into practice, primarily within the healthcare sector. Greenhalgh et al.'s synthesis of nearly 500 published sources across 13 fields of research culminated in their 'Conceptual model for considering the determinants of diffusion, dissemination, and implementation of innovations in health service delivery and organization' [8 (link)] and this was our starting point for the CFIR. We used a snowball sampling approach to identify new articles through colleagues engaged in implementation research and theories that cited Greenhalgh et al.'s synthesis, or that have been used in multiple published studies in health services research (e.g., the Promoting Action on Research Implementation in Health Services (PARiHS) framework [9 (link)]). We included theories related to dissemination, innovation, organizational change, implementation, knowledge translation, and research uptake that have been published in peer reviewed journals (one exception to this is Fixsen et al.'s review published by the National Implementation Research Network because of its scope and depth [10 ]). We did not include practice models such as the Chronic Care Model (CCM) because this describes a care delivery system, not a model for implementation [11 (link)]. The CFIR can be used to guide implementation of interventions that target specific components of the CCM.
With few exceptions, we limited our review to theories that were developed based on a synthesis of the literature or as part of a large study. Our search for implementation theories was not exhaustive but we did reach 'theme saturation': the last seven models we reviewed did not yield new constructs, though some descriptions were altered slightly with additional insights. We expect the CFIR to continue to evolve as researchers use the CFIR and contribute to the knowledge base.
The CFIR is a framework, which reflects a '...professional consensus within a particular scientific community. It stands for the entire constellation of beliefs, values, and techniques shared by members of that community... [and] need not specify the direction of relationships or identify critical hypotheses' [12 (link)].
It is important to note the last clause: The CFIR specifies a list of constructs within general domains that are believed to influence (positively or negatively, as specified) implementation, but does not specify the interactions between those constructs. The CFIR does provide a pragmatic organization of constructs upon which theories hypothesizing specific mechanisms of change and interactions can be developed and tested empirically.
Table 1 lists the theories we reviewed for inclusion into the CFIR. Greenhalgh et al.'s synthesis [8 (link)] was developed based on an exhaustive synthesis of a wide range of literatures including foundational work by Van de Ven, Rogers, Damanpour, and others. This body of work is an important foundation for the CFIR, though not explicitly listed in Table 1. Constructs were selected for inclusion based on strength of conceptual or evidential support in the literature for influencing implementation, high consistency in definitions, alignment with our own experience, and potential for operationalization as measures.
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Publication 2009
Anabolism Delivery of Health Care Diffusion Human Body Long-Term Care Obstetric Delivery
By default, the GeneMANIA prediction server uses one of two different adaptive network weighting methods. For longer gene lists, GeneMANIA uses the basic weighting method [called GeneMANIAEntry-1 in (10 (link)) and called ‘assigned based on query genes’ on the web site] and weights each network so that after the networks are combined, the query genes interact as much as possible with each other while interacting as little as possible with genes not in the list. GeneMANIA learns from longer gene lists, allowing a gene list-specific network weighting to be calculated. Shorter gene lists do not contain enough information for GeneMANIA to learn which networks mediate the underlying functional relationship among the genes. For short gene lists, GeneMANIA uses a similar principle to weight networks, but tries to reproduce Gene Ontology (GO) biological process co-annotation patterns rather than the gene list. This method is described in detail in (11 ). The user may choose other adaptive and non-adaptive weighting methods in the advanced options panel, found directly under the gene query text box. The two non-adaptive methods are the most conservative options and work well on small gene lists (10 (link)). These methods allow users to choose either to weight every individual network equally, or weight each class (e.g. co-expression and protein interaction) of network equally. Network weights can also be assigned based on how well they reproduce GO co-annotation patterns for that organism in the molecular function, biological process or cellular component hierarchies. Note that the annotation-based weighting may slightly inflate weights for networks on which current annotations are based or for networks that were derived based on co-annotation patterns of genes. The networks most affected by this inflation are the older, smaller scale protein and genetic interaction studies and networks classified as ‘predicted’. However, this inflation does not seem to have a large impact on weights and may be largely avoided by only using networks derived from high-throughput assays with the annotation-based schemes.
Publication 2010
Acclimatization Biological Processes Biopharmaceuticals Cellular Structures Gene Annotation Gene Regulatory Networks Genes High-Throughput Screening Proteins Reproduction
The VEP’s caches are built for each of Ensembl’s primary species (70 species as of Ensembl version 84); the files are updated in concert with Ensembl’s release cycle, ensuring access to the latest annotation data. Cache files for all previous releases remain available on Ensembl’s FTP archive site [91 ] to facilitate reproducibility. For 15 of these species there are three types of cache files: one with the Ensembl transcripts, a “refseq” one with the RefSeq transcripts, and a “merged” one that contains both. Caches for both the latest GRCh38 and previous GRCh37 (hg19) human genome builds are maintained. The human GRCh38 cache file is around 5 gigabytes in size, including transcript, regulatory, and variant annotations as well as pathogenicity algorithm predictions. Performance using the cache is substantially faster than using the database; analyzing a small VCF file of 175 variants takes 5 seconds using the cache versus 40 seconds using the public Ensembl variation database over a local network (performance can be expected to be slower when using a remote database connection).
The VEP can use FASTA format files of genomic sequence for sequence retrieval. This functionality is needed to generate HGVS notations and to quality check input variants against the reference genome. The VEP uses either an htslib-based indexer [92 ] or BioPerl’s FASTA DB interface to provide fast random access to a whole genome FASTA file. Sequence may alternatively be retrieved from an Ensembl core database, with corresponding performance penalties.
Cache and FASTA files are automatically downloaded and set up using the VEP package’s installer script, which utilizes checksums to ensure the integrity of downloaded files. The installer script can also download plugins by consulting a registry. The VEP package also includes a script, gtf2vep.pl, to build custom cache files. This requires a local GFF or general transfer format (GTF) file that describes transcript structures and a FASTA file of the genomic sequence.
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Publication 2016
GB virus C Genome Genome, Human Homo sapiens Neoplasm Metastasis Pathogenicity Patient Discharge

Most recents protocols related to «Social Networks»

Example 3

Probe Materials

A number of porous materials were tested to generate charged droplets for mass spectrometry. The materials were shaped into triangles having sharp tips and sample solution was then applied to the constructed probes. Data herein show that any hydrophilic and porous substrate could be used successfully, including cotton swab, textile, plant tissues as well as different papers. The porous network or microchannels of these materials offered enough space to hold liquid and the hydrophilic environment made it possible for liquid transport by capillary action. Hydrophobic and porous substrates could also be used successfully with properly selected hydrophobic solvents.

For further investigation, six kinds of commercialized papers were selected and qualitatively tested to evaluate their capabilities in analyte detection. Filter papers and chromatography paper were made from cellulose, while glass microfiber filter paper was made from glass microfiber. FIG. 19 shows the mass spectra of cocaine detection on those papers. The spectrum of glass fiber paper (FIG. 19E) was unique because the intensity of background was two orders of magnitude lower than other papers and the cocaine peak (m/z, 304) could not be identified.

It was hypothesized that the glass fiber paper was working on mode II and prohibiting efficient droplet generation, due to the relative large thickness (˜2 mm). This hypothesis was proved by using a thin layer peeled from glass fiber paper for cocaine detection. In that case, the intensity of the background increased and a cocaine peak was observed. All filter papers worked well for cocaine detection, (FIGS. 19A-19D). Chromatography paper showed the cleanest spectrum and relative high intensity of cocaine (FIG. 19F).

Probe Shape and Tip Angle

Many different probe shapes were investigated with respect to generating droplets. A preferred shape of the porous material included at least one tip. It was observed that the tip allowed ready formation of a Taylor cone. A probe shape of a triangle was used most often. As shown in FIGS. 25A-25C, the sharpness of the tip, the angle of the tip (FIGS. 27A-25B), and the thickness of the paper substrate could effect the spray characteristics. The device of a tube shape with multiple tips (FIG. 25-D) is expected to act as a multiple-tip sprayer, which should have improved spray efficiency. An array of micro sprayers can also be fabricated on a DBS card using sharp needles to puncture the surface (FIG. 25E).

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Patent 2024
Capillary Action Cellulose Chromatography Cocaine Gossypium Mass Spectrometry Medical Devices Needles Plant Cone Plants Punctures Solvents Tissues
Not available on PMC !

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.

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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

Example 1

An embodiment herein provides a computer-controlled marketplace network for facilitating seamless transactions among a plurality of marketplace network participant systems. The marketplace network includes a plurality of service provider systems associated with respective service provider participants and located remotely from one another physically in respective service settings that each includes one or more central servers, data stores, and cloud-based computing components for managing and processing delivery of one or more services in the service settings by the plurality of service provider systems. Each of the plurality of service provider systems are communicatively coupled to a respective merchant server. The marketplace network further includes a plurality of user systems associated with respective user participants located remotely from one another and remotely from the plurality of service provider systems and configured to generate a service request to one of the plurality of service provider systems in the marketplace network.

The marketplace server facilitates marketplace transactions digitally by executing a set of computer-executable tasks for securely processing transactional exchanges among the marketplace network participant systems, wherein the transactional exchanges include at least exchanges of ownership rights for digitally stored data at least in part owned originally by the user participants. The marketplace server includes a marketplace interaction component where the service provider participants can establish their one or more offerings digitally for the transactional exchanges. The one or more offerings are associated with respective transactional values that are predefined across the marketplace network by the respective service provider participants. The marketplace server includes a memory circuit configured to store transactional information associated with each transactional exchange of the transactional exchanges among the participants in the marketplace network. The marketplace server includes a processing circuit in communication with the memory circuit and configured to process a transactional exchange digitally and generate an ownership trail of a transacted offering when a user participant consents for data ownership transfer, exclusively or inclusively for the data at least in part, from the user participant, toward a digital purchase and delivery of the offering, wherein the data at least in part has a value of at least equal to a transactional value of the offering exchanged between the service provider participant and the user participant over the marketplace network.

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Patent 2024
Fingers Memory Obstetric Delivery TNFSF10 protein, human
Not available on PMC !

Example 2

    • Search for: Betty, “User ‘Betty’ on Social Network C”
    • Results
      • Also Known As:
        • [‘Betty’ on Social Network C]
        • [‘B123’ on Social Network D]
        • [bbb@email.com]
        • [Betty's blog at http://www.bettysblog.com]

Full text: Click here
Patent 2024
Not available on PMC !

Example 6

RF resins have been used previously to form graphene based carbon aerogels. These systems are not UV curable in the time scales necessary for PuSL (<1 min, preferably faster). Therefore a hydrogel formulation based on acrylate photocurable hydrogel was repurposed giving the fast curing ability of acrylates, with the robust aerogel integrated bridging structure afforded by RF. A unique photocured and thermally post-cured double network hydrogel was shown to exhibit highly desirable mechanical properties.

Similar to BisF/PEGDA system, it was the main concern to have the strongest gel with the least amount of polymer. The solubility of resorcinol and formaldehyde (RF) is limited in PEGDA solution and it was found increasing amounts of RF were needed in order to make a homogenous solution. For PEGDA 700, a minimum of 3 wt % RF was needed, while for PEGDA 575, 2 wt % could be used. FIG. 6 shows the difference between 3 wt % and 4 wt % RF with 20 wt % PEGDA 700 and 0.5 wt % GO.

A faster RF curing method was also tested, whereby the 4 wt % RF with PEGDA 700 was soaked in 3.0 M NaOH for 5 minutes. Concentrated base or acid causes a rapid gelation of RF, allowing us to skip the 80° C. post cure in iso-octane. The results of this experiment are shown in FIG. 7, showing mostly dense but possibly closed-cell nanoporous features.

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Patent 2024
2,2,4-trimethylpentane Acids acrylate Acrylates Carbon Cells Formaldehyde Graphene Homozygote Hydrogels poly(ethylene glycol)diacrylate Polymers Resins, Plant resorcinol

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MATLAB is a high-performance programming language and numerical computing environment used for scientific and engineering calculations, data analysis, and visualization. It provides a comprehensive set of tools for solving complex mathematical and computational problems.
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Matrigel is a solubilized basement membrane preparation extracted from the Engelbreth-Holm-Swarm (EHS) mouse sarcoma, a tumor rich in extracellular matrix proteins. It is widely used as a substrate for the in vitro cultivation of cells, particularly those that require a more physiologically relevant microenvironment for growth and differentiation.
CytoHubba is a Cytoscape plugin that provides a suite of network analysis tools to identify key nodes and interactions within a biological network. The plugin offers centrality analysis, hub detection, and bottleneck identification functionalities to help users understand the topological and functional significance of network components.
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Ingenuity Pathway Analysis (IPA) is a software tool that enables users to analyze, integrate, and understand data from -omics experiments. The core function of IPA is to provide a comprehensive database and analysis platform for identifying and visualizing biological pathways, networks, and functions relevant to experimental data.
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IPA software is a bioinformatics application developed by Qiagen. It is designed to analyze and interpret complex biological and chemical data.
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Cytoscape version 3.7.1 is a software platform for visualizing complex networks and integrating these with any type of attribute data. It provides a core set of features for network layout, analysis, and visual customization.
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Prism 8 is a data analysis and graphing software developed by GraphPad. It is designed for researchers to visualize, analyze, and present scientific data.

More about "Social Networks"

Social networks are complex interconnected systems that facilitate the exchange of information, ideas, and resources among individuals, organizations, and communities.
These networks can take many forms, from organic social media platforms to purposefully constructed collaborative research groups.
Researchers can leverage the power of social networks to enhance scientific collaboration, accelerate the dissemination of knowledge, and foster interdisciplinary dialogue.
One powerful tool for analyzing social networks is Cytoscape, a software platform that enables the visualization and analysis of complex biological and social networks.
Cytoscape can be used in conjunction with other bioinformatics tools, such as Ingenuity Pathway Analysis (IPA), to uncover the underlying relationships and patterns within social networks.
Another key aspect of social network research is understanding the dynamics of information flow and community formation.
Concepts like centrality, modularity, and network resilience, as measured by tools like CytoHubba and MATLAB, can provide valuable insights into the structure and function of social networks.
By optimizing research protocols and leveraging the power of social networks, researchers can improve the reproducibility and impact of their work.
Platforms like PubCompare.ai, which utilizes AI-driven comparisons to identify the best research protocols and products, can streamline the research process and ensure findings are reliable and reproducible.
Whether you're studying the spread of scientific discoveries, the formation of online communities, or the dynamics of interdisciplinary collaboration, understanding social networks is crucial for driving innovation and advancing knowledge in your field.
By incorporating these insights into your research strategies, you can unlock the full potential of social networks and push the boundaries of what's possible.