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Figure 1: Spreadability (plasticity) Figure-STUDIES TOWARDS OBTAINING A PHOTOPROTECTIVE DERMO-COSMETIC COMPLEX PRODUCT WITH NATURAL EXTRACTS OF INORGANIC AND ORGANIC SUBSTANCES
<p>The physical and chemical methods used to characterize the row<br> materials and the ¯nal products are as following:<br> ² to ¯nd out the amount of inorganic substances (with screening role) the<br> samples were incinerated for two hours at 800±C;<br> ² the quantity of organic substance (with the UV ¯ltering role) was deter-<br> mined by an UV-VIS spectrophotometric method;<br> ² vitamins identi¯cation was done according to the European Pharma-<br> copoeia VI-th edition;<br> ² the photoprotective ability was tested by applying a standard amount of<br> emulsion on a synthetic skin device (0,2g=cm2);<br> ² to test the microbial stability were applied the European Pharmacopoeia<br> VI-th edition stipulations;<br> ² to estimate the compatibility with the human skin a standard quantity<br> of sample (0,2g=cm2) was laid under an occlusive patch.</p>
Figure 4: Determination of hydration of the skin before applying the product-STUDIES TOWARDS OBTAINING A PHOTOPROTECTIVE DERMO-COSMETIC COMPLEX PRODUCT WITH NATURAL EXTRACTS OF INORGANIC AND ORGANIC SUBSTANCES
<p>The volunteers who applied the product on the<br> skin appreciated the sensory characteristics such as: easiness of use, pleasant<br> texture, easy absorption without oily traces, appearance, odor and pleasant<br> color (¯gures 4, 5).</p>
Figure 3: Viscosity variation depending on speed rate Table-STUDIES TOWARDS OBTAINING A PHOTOPROTECTIVE DERMO-COSMETIC COMPLEX PRODUCT WITH NATURAL EXTRACTS OF INORGANIC AND ORGANIC SUBSTANCES-
<p>Microbiological characteristics The total number of aerobic germs and fungi<br> was determined by the decimal dilution method or the multiple tube test. For<br> identifying pathogenic species we have used speci¯c tests (table6).<br> The evaluation of photodegradation was carried out through the variation<br> of photoprotective factor of the studied product kept 60 minutes under UV<br> radiation at 65±C. The tests revealed the preservation or even a slight increase<br> in the SPF. The use of screen substances in the formulation of photoprotective<br> products results in undesirable sensory characteristics: visible white ¯lm on the<br> skin surface, unpleasant sensation, dry or oily skin after application.</p>
Figure 11. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In the sentence (7) there are two clauses describing two hypothetical situations. These two<br> situations are connected by the relation COND in MultiNet representation (Figure 11).</p>
Figure 10. Comparison of MultiNet and MST representations.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Helbig [18, p. 507] defines the expression (s MCONT c) as “a specification of the<br> informational or mental content c of a mental or informational process s… By default, the second<br> argument c is assumed to be a hypothetical object or situation.”<br> MCONT relation properties are roughly equivalent to those of subject-verb complexes<br> (SVSBs) of MST. MCONT relation can generally be treated as capturing the idea behind what<br> philosophers call propositional attitudes in representational theories of mind [12] or opaque<br> contexts [10] in semantics. For example, consider the sentence in (3) rewritten below as (6) and<br> semantic representation of which is given in Figure 10.</p>
Figure 8. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Figure 8 shows the representation of the<br> first reading of the sentence (5) in MWR. The MultiNet is shown at top of the figure, and the corresponding mental space representation is shown at the bottom side. Blue circles mark<br> hypothetical or nonreal objects and situations and red circles mark real objects and situations.<br> Facticity value for John is real, for unicorn it is nonreal, and for the process of riding, marked with<br> blue broken circle, it is non-real as well.</p>
Figure 7. The mental spaces set up by the sentence If John buys the car, he will drive to Berlin.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In the sentence (4) there are three proper names which constitute elements of the base space<br> or reality space (R). The CNSB if sets up the hypothetical space (H) with elements identical to those<br> of reality space (Figure 7). Every space’s internal structure is presented in the boxes next to them.<br> In the next part, we will see how MultiNet’s built-in meaning representation mechanisms are<br> capable of representing the basic principles of mental space building outlined above.</p>
Figure 6. The mental spaces set up by the sentence Mary thinks that John smokes.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>The proper nouns Mary and John setup a base space (B). By the help of background<br> knowledge and activated frames we know that they are names of female and male humans. Not<br> having access to the previous discourse, we also consider their existence presupposed. The SVSB<br> Mary thinks that sets up a belief space (L) relative to space B (Figure 6). The identity connector<br> maintains the referential link between elements a and a ׳ both referring to the same person.</p>
Figure 9. Comparison of MultiNet and MST representations-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Second interpretation of the sentence (5) can be represented with changing John’s Facticity<br> attribute-value from [FACT = real] to [FACT = nonreal] making the whole situation and its<br> elements non-real (Figure 9).</p>
Figure 5. The mental spaces set up by the second interpretation of the sentence in the film, John is riding a unicorn.-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Second interpretation: The proper name John exists only in film space without having a<br> counterpart in base space. Therefore, the second interpretation of the sentence is: John is a<br> film character who is riding a unicorn in the film (Figure 5).</p>
Figure 4. The mental spaces set up by the first interpretation of the sentence in the film, John is riding a unicorn-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>First interpretation: The reality space (let’s call it R) contains an element a associated with<br> the proper name John. The noun phrase a unicorn introduces an element b ׳ to the film space<br> (call it F). I is the connector linking a in the space B to a ׳ in the space F (Figure 4). Since the<br> elements of both mental spaces are co-referential, this connector is an identity connector.<br> The rectangles represent the internal structure of the spaces next to them. The dashed line<br> indicates that the space F is set up in relation to R and that it is subordinate to R in<br> discourse.</p>
Figure 1. The upper ontology of sorts in MultiNet (after Helbig [17])-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>One of the distinguishing features of MultiNet is its commitment to the Cognitive Adequacy<br> requirement. According this requirement [9], semantic representations and knowledge<br> representations should be centered around concepts. Concepts2 are represented by nodes in the<br> graphical representation of the network. Every node belongs to a specific sort defined by the<br> MultiNet’s ontology of sorts (Figure 2).</p>
Figure 1. Mental space representation of "In the play, Mary is excited"-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>Thus an entity can have a variable reality status depending on the mental<br> space to which it belongs. The mental space constructed by the sentence Poirot is a Belgian<br> detective is a non-real imaginary story space of which Poirot is an element. But when we say in<br> reality, Poirot is not Belgian the constructed space is reality space in which Poirot (the actor, not<br> the character) does not have the fictional nationality.</p>
Figure 2. Semantic representation of the sentence Peter finished the discussion in MultiNet after Helbig [8, p. 447].-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>In Figure 3, semantic frame of the concept Finish realized in the form of the verb finish<br> requires two C-roles: An agent represented by the relation AGT, and an affected entity represented<br> by the relation AFF. Here agent is Peter and the affected entity is an abstract object ([SORT = ad]<br> means the concept is a dynamic abstraction).</p>
raw data nature 533:47 Fig.5a
<p>Raw data of nature paper Vol. 533:47-51 (2016) Fig.5a is provided. Fig. 5a consists of two curves (Th229 and U233). Each curve consists of 16 data points and each data point was evaluated from 300 individual image frames of 4 s exposure time. The Matlab files used for image evaluation are also contained: Run "process_images_6" on an image folder to add up all image frames. Then run "process_counts_2_clean" to obtain the background corrected number of counts in the region of interest. You will find the number in the dataset of Matlab file "nature_figure_5a" which produces the figure as presented in the paper.</p> <p>Data files Fig. 5a:</p> <p>Th229: -100V:1105_image22(2014), -90V:1105_image20(2014), -80V:1105_image18(2014), -70V:1105_image16(2014), -60V:1105_image14(2014), -50V:1105_image12(2014), -40V:1105_image10(2014), -30V:1105_image8(2014), -20V:1105_image6(2014), -10V:1105_image4(2014), -5V:1106_image9(2014), -2.5V:1106_image11(2014), -1V:1106_image13(2014), -0.5V: 1106_image15(2014), 0V:1106_image3(2014), +5V:1106_image7(2014).</p> <p>U233: -100V:1105_image21(2014), -90V:1105_image19(2014), -80V:1105_image17(2014), -70V:1105_image15(2014), -60V:1105_image13(2014), -50V:1105_image11(2014), -40V:1105_image9(2014), -30V:1105_image7(2014), -20V:1105_image5(2014), -10V:1105_image3(2014), -5V:1106_image10(2014), -2.5V:1106_image12(2014), -1V:1106_image14(2014), -0.5V:1106_image16(2014), 0V:1106_image4(2014), +5V:1106_image8(2014).</p> <p> </p><p> </p> <p></p>
raw data nature 533:47 Fig.5b
<p>Raw data of nature paper Vol. 533:47-51 (2016) Fig.5b is provided. Fig. 5b consists fo two curves (-25V and -2000V). The curve of -25V is generated from 26 data points with 300 images per data point of 4 s exposure time per image frame. Also the Matlab files used for data evaluation are contained: Run "process_images_9b_clean" on an image folder to add up all image frames. Then run "process_counts_2_clean" to obtain the background corrected number of counts in the region of interest. You will find the number in the dataset of Matlab file "nature_figure_5b" which produces the figure as presented in the paper. The -2000V curve consists of a single mass scan given as a .txt file.</p> <p>Data files Fig. 5b:</p> <p>-25V MCP surface voltage: 113.7u:0504_image1(2015), 113.9u:0504_image2(2015), 114.1u:0504_image3(2015), 114.3u:0504_image4(2015), 114.5u:0504_image5(2015), 114.7u:0504_image6(2015), 114.9u:0504_image7(2015), 115.1u:0504_image8(2015), 115.3u:0504_image9(2015), 115.5u:0504_image10(2015), 115.7u:0504_image11(2015), 115.9u:0504_image12(2015), 116.1u:0504_image13(2015), 116.3u:0505_image1(2015), 116.5u:0505_image2(2015), 116.7u:0505_image3(2015), 116.9u:0505_image8(2015), 117.1u:0505_image9(2015), 117.3u:0505_image10(2015), 117.5u:0505_image11(2015), 117.7u:0505_image12(2015), 117.9u:0505_image13(2015), 118.1u:0505_image14(2015), 118.3u:0505_image15(2015), 118.5u:0505_image16(2015), 118.7u:0505_image17(2015).</p> <p>-2000V MCP surface voltage: 0512_2+signal_19.txt (2013).</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Gotfred Kvifte, <a href="http://www.wikidata.org/entity/Q5587748">http://www.wikidata.org/entity/Q5587748</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Gunnar Soot, <a href="http://www.wikidata.org/entity/Q124540283">http://www.wikidata.org/entity/Q124540283</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Synthetic dataset for end-to-end Relation Extraction of relationships between Organisms and Natural-Products with Mixtral-8x7B-Instruct-v0.1
<p>A new synthetic dataset (training/validation) for end-to-end Relation Extraction of relationships between Organisms and Natural-Products.</p> <p>The new dataset was generated using <a href="https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1">Mixtral-8x7B-Instruct-v0.1</a>. Like the model, the produced synthetic data are also submitted to the License of the model used for generation (apache-2.0).</p> <p>The new dataset was created based on the top-1000 (per biological kingdom) LOTUS literature references extracted with the <a href="https://github.com/idiap/gme-sampler">GME-sampler</a>.</p> <p>The dataset contains 8,913 items in the training set and 344 items in the validation set.</p> <p>The dataset was generated using the same protocol as described in the article.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Wilhelm Ramm, <a href="http://www.wikidata.org/entity/Q97570173">http://www.wikidata.org/entity/Q97570173</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.