Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10,561
datasets available to search
ShareScore release 0.9.0
Dataset results
10,561 results for “mechanics”
The Mechanism of N2 Reduction Catalyzed by Fe-Nitrogenase Involves Reductive Elimination of H2
<p>Data sets for figures 2, 3, 4, 5 in comma delimited format from <em>Biochemistry </em>publication DOI: 10.1021/acs.biochem.7b01142. Headings are provided indicating the conditions and units.</p>
Figure 2. Mechanical construction of the PERSIA humanoid robots-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 2 shows one of the constructions used for our robots. Knee joints are considered to<br> bend in both directions which help faster response of the robot in backward walking. Efforts have<br> been made to hold the proportions as much as possible human like. The PERSIA robot is 38cm tall<br> and weighs about 1.6 kg. It has 18 degrees of freedom: 5 in each leg, 3 in each hand and 2 in head.<br> To facilitate exchange of the players, all robots use mechanically the same structure.</p>
Figure 5. A chromosome structure in case we have 2 visible states and 3 invisible states-Neuroevolution Mechanism for Hidden Markov Model
<p>Generating a population of size n of HMMs at random can be performed with some<br> restrictions:<br> - The weights representing the input layer in the chromosome should be always negligible as<br> initial values.<br> - The weights which are involved in summation of 1.0 in the hidden layer part of the<br> chromosome should be exactly 1.0.<br> Let us assume the following case<br> Visible states are 2 and invisible states (observations) are 3, , then we shall have a<br> chromosome as shown in Figure 5.</p>
Figure 6. Two point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
<p>Two point Crossover<br> For the two point crossover we get two parent HMMs and choose at random two cutting points for<br> the weights that have a sum of 1.0 and swap the contents between the crossing points. This is<br> illustrated in the example shown in Figure 6.</p>
Figure 4. A chromosome structure for HMM shown in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>The chromosome which represents the HMM can be extracted from its corresponding neural<br> network. The general structure of the chromosome is divided into two sections, input layer and<br> hidden layer. Each section contains many slots, and each slot represents a weight from one node in<br> that layer to a node in the next layer (from input to hidden and from hidden to output). The number<br> of slots in the input layer is the same number of input nodes in the neural network. In the hidden<br> layer, number of slots is equal to nodes in the output layer multiplied by the nodes in the hidden<br> layer.</p>
Figure 3. Neural network representation for HMM given in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>In our proposed structure, we injected a hidden layer to have a multilayer perceptron which<br> is more efficient than single layer perceptron.<br> To make this process clear, Figure 3 shows the neural networks for the HMM presented in<br> Figure 2.</p>
Figure 9. Adding-Reducing mutation.-Neuroevolution Mechanism for Hidden Markov Model
<p>Adding a small value from one weight and decrement that value to another weight. The chosen<br> weights should be involved in summation of 1.0. Figure 9 shows an example, we add 0.001 from<br> one weight and decrement the same value from another weight.</p>
Figure 1. HMM to describe a relation between the states Med. and High with the observations (invisible states) cold and hot.-Neuroevolution Mechanism for Hidden Markov Model
<p>The advantage of using this technique is that MCPRs are very useful in real time<br> applications and can be adapted over time based on the obtained experience of the networking<br> working process. Again Hewahi[6] proposed a mechanism (algorithm) to evolve and select the best<br> suitable HMM for a given problem using GA, this mechanism lacks to the training process that can<br> be of great usefulness in finding the best HMM.<br> Based on the above mentioned research, the importance of using HMM is increasing<br> rapidly.<br> Let us consider the HMM presented in Figure 1.</p>
Figure 2. HMM with weights and necessary conditions on top of edges-Neuroevolution Mechanism for Hidden Markov Model
<p>Based on the HMM structure in Figure 2, we can perform the following steps:<br> 1. Make the number of nodes of inputs in the input layer of the NN as the number of states<br> (visible states not the observations). Each input node represents one state.<br> 2. Number of nodes in the output layer in the NN is equal to the number of states and<br> observations (visible and invisible states), where each node corresponds to one state (visible<br> or invisible).<br> 3. We construct a hidden layer in NN with n number of nodes, where n is the same number of<br> nodes in the input layer.<br> 4. We make a connection from every input to every hidden layer node with a very negligible<br> weight.<br> 5. Connect every hidden node in the hidden layer to every node in the output layer.<br> 6. Assign weights from the hidden layer to output layer in a way that as every node in the<br> hidden layer corresponding to input state. The weight on top of the link between the hidden<br> node to the output node is the probability value between the states in the HMM.<br> In our proposed structure, we injected a hidden layer to have a multilayer perceptron which<br> is more efficient than single layer perceptron.<br> To make this process clear, Figure 3 shows the neural networks for the HMM presented in<br> Figure 2.</p>
Figure 11. Group mutation-Neuroevolution Mechanism for Hidden Markov Model
<p>This happens by swapping two complete groups with summation of 1.0 with the same criteria.<br> Figure 10 shows a case of this.</p>
Figure 7. One point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
This crossover is performed in the input layer part only. We choose a crossing cut point in the input layer part of the chromosome, and exchange everything before it. This is illustrated in Figure 7.
Data from: taking a shortcut: what mechanisms do fish use?
<p>Path integration is a powerful navigational mechanism whereby individuals continuously update their distance and angular vector of movement to calculate their position in relation to their departure location, allowing them to return along the most direct route even across unfamiliar terrain. While path integration has been investigated in several terrestrial animals, it has never been demonstrated in aquatic vertebrates, where movement occurs through volumetric space and sensory cues available for navigation are likely to differ substantially from those in terrestrial environments. By performing displacement experiments with <em>Lamprologus ocellatus</em>, we show evidence consistent with fish using path integration to navigate alongside other mechanisms (allothetic place cues and route recapitulation). These results indicate that the use of path integration is likely to be deeply rooted within the vertebrate phylogeny irrespective of the environment, and suggests that fish may possess a spatial encoding system that parallels that of mammals.</p>
Supplementary material from the study on survival of African swine fever virus in feed, bedding materials and mechanical vectors, and their potential role in virus transmission
<p><span>Supplementary material from the experimental studies developed to analyse the survival of African swine fever virus in feed, bedding materials and mechanical vectors, and their potential role in virus transmission. </span></p> <p> </p>
3D Printing of Double Network Granular Elastomers with Locally Varying Mechanical Properties
<p>Dataset for the article entitled "3D Printing of Double Network Granular Elastomers with Locally Varying Mechanical Properties", by Eva Baur, Benjamin Tiberghien and Esther Amstad published in Advanced Materials.</p>
Data for "Sintering activation energies of anisotropic layered and particle alumina/zirconia-based composites and their mechanical response"
<p>The dataset contains several folders that provide open data files for the manuscript "Sintering activation energies of anisotropic layered and particle alumina/zirconia-based composites and their mechanical response".</p>
Data for a publication "The role of the preparation route on microstructure and mechanical properties of AlCoCrFeNi high entropy alloy"
<p>A dataset containing data for the published article "The role of the preparation route on microstructure and mechanical properties of AlCoCrFeNi high entropy alloy".</p> <p> </p> <p>For more details, please read the <strong>README Description of data and analysis.txt</strong> file.</p> <p> </p> <p> </p>
Performance of Advanced Ambu Bag System among Adult Patients with Mechanical Ventilation: A Mixed-Effects Analysis
<p>We conducted the study at the Department of Stroke Care of the Can Tho Central General Hospital, Vietnam. There are eight intensive care beds for critical illness. The study was performed according to the Helsinki Declaration and approved by the Can Tho Central General Hospital ethics committee. All patients gave written informed consent by a legal surrogate. We enrolled patients with mechanical ventilation between November 2022 and September 2023. The inclusion criteria were: (1) patients aged 16 years and older, (2) pulse rate less than 120 times per minute, (3) systolic blood pressure from 110 to 160 mmHg, (4) peripheral oxygen saturation (SpO<sub>2</sub>) greater than 90%, (5) spontaneous breathing rate less than 28 times per minute, (6) end-tidal carbon dioxide (EtCO<sub>2</sub>) from 20 to 45 mmHg, (7) secretion required suction less than one time per hour, (8) positive end-expiratory pressure less than or equal to 5 cmH<sub>2</sub>O, (9) fraction of inspired oxygen less than or equal to 60%, (10) minute ventilation less than 15 liters per minute, (11) diameter of a tracheal or tracheostomy tube greater than or equal to 7.0 mm, (12) no usage of sedation, (13) normal ST wave in the electrocardiogram. Patients were excluded from the trial if they had one of the following conditions: (1) acute myocardial infarction, (2) acute pulmonary embolism, or (3) new dangerous arrhythmias appeared in this episode (multiform ventricular ectopy, bigeminy or trigeminy ventricular ectopy, coupled ventricular ectopy, R-on-T ventricular ectopy, high-grade atrioventricular heart block, supraventricular tachycardia, atrial fibrillation, atrial flutter, ventricular tachycardia, ventricular fibrillation), (4) using vasopressors or inotropic agents. Patients could withdraw from the study at any time without giving any reason. Besides, the patient stopped the trial of the advanced Ambu bag system immediately when one of the signs appeared, such as (1) the peripheral oxygen saturation lower than 90% prolonging more than 1 minute, (2) the end-tidal carbon dioxide greater than 45 mmHg or less than 15 mmHg prolonging more than 10 minutes, (3) pulse rate greater than 120 times per minute or less than 60 times per minute prolonging more than 10 minutes, (4) systolic blood pressure greater than 170 mmHg prolonging more than 10 minutes, (5) appearing dangerous arrhythmias, (6) progressive cognitive impairment (based on Grady coma scale), or (7) any abnormal sign that the physician evaluated the patient required respiratory support immediately with conventional mechanical ventilation.</p> <p> The following is the meaning of the variables in the study:</p> <p>age: Age of study participants.</p> <p>gender: Gender of study participants (0: Woman, 1: Man).</p> <p>day1: Day of admission to the hospital</p> <p>day2: Intervention day.</p> <p>dia1: Major disease.</p> <p>dia2: Cause of respiratory failure.</p> <p>nihss1: National Institute of Health Stroke Scale on admission</p> <p>hsg: Severity of cerebral hemorrhage (0: No hemorrhagic stroke, hi1: Scattered small petechiae, no mass effect, hi2: Confluent petechiae, no mass effect, ph1: Hematoma within infarcted tissue, occupying <30%, no substantive mass effect, ph2: Hematoma occupying 30% or more of the infarcted tissue, with obvious mass effect, 3a: Parenchymal hematoma remote from infarcted brain tissue, 3b: Intraventricular hemorrhage, 3c: Subarachnoid hemorrhage, 3d: Subdural hemorrhage)</p> <p>aspects1: Alberta stroke program early CT score of anterior circulation on CTscan</p> <p>aspect2: Alberta stroke program early CT score of anterior circulation on DWI- Diffusion-weighted Imaging.</p> <p>aspects3: Alberta stroke program early CT score of posterior circulation on CTscan</p> <p>aspects4: Alberta stroke program early CT score of posterior circulation on DWI- Diffusion-weighted Imaging.</p> <p> </p> <p>nihss2: National Institute of Health Stroke Scale before intervention</p> <p>grady: Grady coma scale before intervention</p> <p> </p> <p>rtpa: Use alteplase (0: No, 1: Yes)</p> <p>thromb: Thrombectomy (0: No, 1: Yes)</p> <p>crani: Craniectomy (0: No, 1: Yes).</p> <p>coil: Endovascular coiling (0: No, 1: Yes)</p> <p> </p> <p>mode: Ventilation mode</p> <p>mv: Mechanical ventilation (L/min)</p> <p>fio2: Fraction of inspired oxygen (%)</p> <p>peep: Positive end-expiratory pressure (cmH<sub>2</sub>O)</p> <p>sc: Static compliance (mL/cmH<sub>2</sub>O)</p> <p>alv: Pulmonary consolidation (0: No, 1: ¼ lung, 2: ½ lung, 3: ¾ lung, 4: Complete lung)</p> <p>sf: spo2/fio2 ratio.</p> <p> </p> <p>p13a: Number of pulse beats at time -13 (conventional mechanical ventilation stage)</p> <p>s13a: Systolic blood pressure at time -13 (conventional mechanical ventilation stage)</p> <p>d13a: Diastolic blood pressure at time -13 (conventional mechanical ventilation stage)</p> <p>sp13a: SpO<sub>2</sub> at time -13 (conventional mechanical ventilation stage)</p> <p>e13a: EtCO<sub>2</sub> at time -13 (conventional mechanical ventilation stage)</p> <p> </p> <p>p12a: Number of pulse beats at time -12 (conventional mechanical ventilation stage)</p> <p>s12a: Systolic blood pressure at time -12 (conventional mechanical ventilation stage)</p> <p>d12a: Diastolic blood pressure at time -12 (conventional mechanical ventilation stage)</p> <p>sp12a: SpO<sub>2</sub> at time -12 (conventional mechanical ventilation stage)</p> <p>e12a: EtCO<sub>2</sub> at time -12 (conventional mechanical ventilation stage)</p> <p> </p> <p>p11a: Number of pulse beats at time -11 (conventional mechanical ventilation stage)</p> <p>s11a: Systolic blood pressure at time -11 (conventional mechanical ventilation stage)</p> <p>d11a: Diastolic blood pressure at time -11 (conventional mechanical ventilation stage)</p> <p>sp11a: SpO<sub>2</sub> at time -11 (conventional mechanical ventilation stage)</p> <p>e11a: EtCO<sub>2</sub> at time -11 (conventional mechanical ventilation stage)</p> <p> </p> <p>p10a: Number of pulse beats at time -10 (conventional mechanical ventilation stage)</p> <p>s10a: Systolic blood pressure at time -10 (conventional mechanical ventilation stage)</p> <p>d10a: Diastolic blood pressure at time -10 (conventional mechanical ventilation stage)</p> <p>sp10a: SpO<sub>2</sub> at time -10 (conventional mechanical ventilation stage)</p> <p>e10a: EtCO<sub>2</sub> at time -10 (conventional mechanical ventilation stage)</p> <p> </p> <p>p9a: Number of pulse beats at time -9 (conventional mechanical ventilation stage)</p> <p>s9a: Systolic blood pressure at time -9 (conventional mechanical ventilation stage)</p> <p>d9a: Diastolic blood pressure at time -9 (conventional mechanical ventilation stage)</p> <p>sp9a: SpO<sub>2</sub> at time -9 (conventional mechanical ventilation stage)</p> <p>e9a: EtCO<sub>2</sub> at time -9 (conventional mechanical ventilation stage)</p> <p> </p> <p>p8a: Number of pulse beats at time -8 (conventional mechanical ventilation stage)</p> <p>s8a: Systolic blood pressure at time -8 (conventional mechanical ventilation stage)</p> <p>d8a: Diastolic blood pressure at time -8 (conventional mechanical ventilation stage)</p> <p>sp8a: SpO<sub>2</sub> at time -8 (conventional mechanical ventilation stage)</p> <p>e8a: EtCO<sub>2</sub> at time -8 (conventional mechanical ventilation stage)</p> <p> </p> <p>p7a: Number of pulse beats at time -7 (conventional mechanical ventilation stage)</p> <p>s7a: Systolic blood pressure at time -7 (conventional mechanical ventilation stage)</p> <p>d7a: Diastolic blood pressure at time -7 (conventional mechanical ventilation stage)</p> <p>sp7a: SpO<sub>2</sub> at time -7 (conventional mechanical ventilation stage)</p> <p>e7a: EtCO<sub>2</sub> at time -7 (conventional mechanical ventilation stage)</p> <p> </p> <p>p6a: Number of pulse beats at time -6 (conventional mechanical ventilation stage)</p> <p>s6a: Systolic blood pressure at time -6 (conventional mechanical ventilation stage)</p> <p>d6a: Diastolic blood pressure at time -6 (conventional mechanical ventilation stage)</p> <p>sp6a: SpO<sub>2</sub> at time -6 (conventional mechanical ventilation stage)</p> <p>e6a: EtCO<sub>2</sub> at time -6 (conventional mechanical ventilation stage)</p> <p> </p> <p>p5a: Number of pulse beats at time -5 (conventional mechanical ventilation stage)</p> <p>s5a: Systolic blood pressure at time -5 (conventional mechanical ventilation stage)</p> <p>d5a: Diastolic blood pressure at time -5 (conventional mechanical ventilation stage)</p> <p>sp5a: SpO<sub>2</sub> at time -5 (conventional mechanical ventilation stage)</p> <p>e5a: EtCO<sub>2</sub> at time -5 (conventional mechanical ventilation stage)</p> <p> </p> <p>p4a: Number of pulse beats at time -4 (conventional mechanical ventilation stage)</p> <p>s4a: Systolic blood pressure at time -4 (conventional mechanical ventilation stage)</p> <p>d4a: Diastolic blood pressure at time -4 (conventional mechanical ventilation stage)</p> <p>sp4a: SpO<sub>2</sub> at time -4 (conventional mechanical ventilation stage)</p> <p>e4a: EtCO<sub>2</sub> at time -4 (conventional mechanical ventilation stage)</p> <p> </p> <p>p3a: Number of pulse beats at time -3 (conventional mechanical ventilation stage)</p> <p>s3a: Systolic blood pressure at time -3 (conventional mechanical ventilation stage)</p> <p>d3a: Diastolic blood pressure at time -3 (conventional mechanical ventilation stage)</p> <p>sp3a: SpO<sub>2</sub> at time -3 (conventional mechanical ventilation stage)</p> <p>e3a: EtCO<sub>2</sub> at time -3 (conventional mechanical ventilation stage)</p> <p> </p> <p>p2a: Number of pulse beats at time -2 (conventional mechanical ventilation stage)</p> <p>s2a: Systolic blood pressure at time -2 (conventional mechanical ventilation stage)</p> <p>d2a: Diastolic blood pressure at time -2 (conventional mechanical ventilation stage)</p> <p>sp2a: SpO<sub>2</sub> at time -2 (conventional mechanical ventilation stage)</p> <p>e2a: EtCO<sub>2</sub> at time -2 (conventional mechanical ventilation stage)</p> <p> </p> <p>p1a: Number of pulse beats at time -1 (conventional mechanical ventilation stage)</p> <p>s1a: Systolic blood pressure at time -1 (conventional mechanical ventilation stage)</p> <p>d1a: Diastolic blood pressure at time -1 (conventional mechanical ventilation stage)</p> <p>sp1a: SpO<sub>2</sub> at time -1 (conventional mechanical ventilation stage)</p> <p>e1a: EtCO<sub>2</sub> at time -1 (conventional mechanical ventilation stage)</p> <p> </p> <p>p0a: Number of pulse beats at time 0 (conventional mechanical ventilation stage)</p> <p>s0a: Systolic blood pressure at time 0 (conventional mechanical ventilation stage)</p> <p>d0a: Diastolic blood pressure at time 0 (conventional mechanical ventilation stage)</p> <p>sp0a: SpO<sub>2</sub> at time 0 (conventional mechanical ventilation stage)</p> <p>e0a: EtCO<sub>2</sub> at time 0 (conventional mechanical ventilation stage)</p> <p> </p> <p>p1b: Number of pulse beats at time +1 (advanced Ambu bag system stage)</p> <p>s1b: Systolic blood pressure at time +1 (advanced Ambu bag system stage)</p> <p>d1b: Diastolic blood pressure at time +1 (advanced Ambu bag system stage)</p> <p>sp1b: SpO<sub>2</sub> at time +1 (advanced Ambu bag system stage)</p> <p>e1b: EtCO<sub>2</sub> at time +1 (advanced Ambu bag system stage)</p> <p> </p> <p>p2b: Number of pulse beats at time +2 (advanced Ambu bag system stage)</p> <p>s2b: Systolic blood pressure at time +2 (advanced Ambu bag system stage)</p> <p>d2b: Diastolic blood pressure at time +2 (advanced Ambu bag system stage)</p> <p>sp2b: SpO<sub>2</sub> at time +2 (advanced Ambu bag system stage)</p> <p>e2b: EtCO<sub>2</sub> at time +2 (advanced Ambu bag system stage)</p> <p> </p> <p>p3b: Number of pulse beats at time +3 (advanced Ambu bag system stage)</p> <p>s3b: Systolic blood pressure at time +3 (advanced Ambu bag system stage)</p> <p>d3b: Diastolic blood pressure at time +3 (advanced Ambu bag system stage)</p> <p>sp3b: SpO<sub>2</sub> at time +3 (advanced Ambu bag system stage)</p> <p>e3b: EtCO<sub>2</sub> at time +3 (advanced Ambu bag system stage)</p> <p> </p> <p>p4b: Number of pulse beats at time +4 (advanced Ambu bag system stage)</p> <p>s4b: Systolic blood pressure at time +4 (advanced Ambu bag system stage)</p> <p>d4b: Diastolic blood pressure at time +4 (advanced Ambu bag system stage)</p> <p>sp4b: SpO<sub>2</sub> at time +4 (advanced Ambu bag system stage)</p> <p>e4b: EtCO<sub>2</sub> at time +4 (advanced Ambu bag system stage)</p> <p> </p> <p>p5b: Number of pulse beats at time +5 (advanced Ambu bag system stage)</p> <p>s5b: Systolic blood pressure at time +5 (advanced Ambu bag system stage)</p> <p>d5b: Diastolic blood pressure at time +5 (advanced Ambu bag system stage)</p> <p>sp5b: SpO<sub>2</sub> at time +5 (advanced Ambu bag system stage)</p> <p>e5b: EtCO<sub>2</sub> at time +5 (advanced Ambu bag system stage)</p> <p> </p> <p>p6b: Number of pulse beats at time +6 (advanced Ambu bag system stage)</p> <p>s6b: Systolic blood pressure at time +6 (advanced Ambu bag system stage)</p> <p>d6b: Diastolic blood pressure at time +6 (advanced Ambu bag system stage)</p> <p>sp6b: SpO<sub>2</sub> at time +6 (advanced Ambu bag system stage)</p> <p>e6b: EtCO<sub>2</sub> at time +6 (advanced Ambu bag system stage)</p> <p> </p> <p>p7b: Number of pulse beats at time +7 (advanced Ambu bag system stage)</p> <p>s7b: Systolic blood pressure at time +7 (advanced Ambu bag system stage)</p> <p>d7b: Diastolic blood pressure at time +7 (advanced Ambu bag system stage)</p> <p>sp7b: SpO<sub>2</sub> at time +7 (advanced Ambu bag system stage)</p> <p>e7b: EtCO<sub>2</sub> at time +7 (advanced Ambu bag system stage)</p> <p> </p> <p>p8b: Number of pulse beats at time +8 (advanced Ambu bag system stage)</p> <p>s8b: Systolic blood pressure at time +8 (advanced Ambu bag system stage)</p> <p>d8b: Diastolic blood pressure at time +8 (advanced Ambu bag system stage)</p> <p>sp8b: SpO<sub>2</sub> at time +8 (advanced Ambu bag system stage)</p> <p>e8b: EtCO<sub>2</sub> at time +8 (advanced Ambu bag system stage)</p> <p> </p> <p>p9b: Number of pulse beats at time +9 (advanced Ambu bag system stage)</p> <p>s9b: Systolic blood pressure at time +9 (advanced Ambu bag system stage)</p> <p>d9b: Diastolic blood pressure at time +9 (advanced Ambu bag system stage)</p> <p>sp9b: SpO<sub>2</sub> at time +9 (advanced Ambu bag system stage)</p> <p>e9b: EtCO<sub>2</sub> at time +9 (advanced Ambu bag system stage)</p> <p> </p> <p>p10b: Number of pulse beats at time +10 (advanced Ambu bag system stage)</p> <p>s10b: Systolic blood pressure at time +10 (advanced Ambu bag system stage)</p> <p>d10b: Diastolic blood pressure at time +10 (advanced Ambu bag system stage)</p> <p>sp10b: SpO<sub>2</sub> at time +10 (advanced Ambu bag system stage)</p> <p>e10b: EtCO<sub>2</sub> at time +10 (advanced Ambu bag system stage)</p> <p> </p> <p>p11b: Number of pulse beats at time +11 (advanced Ambu bag system stage)</p> <p>s11b: Systolic blood pressure at time +11 (advanced Ambu bag system stage)</p> <p>d11b: Diastolic blood pressure at time +11 (advanced Ambu bag system stage)</p> <p>sp11b: SpO<sub>2</sub> at time +11 (advanced Ambu bag system stage)</p> <p>e11b: EtCO<sub>2</sub> at time +11 (advanced Ambu bag system stage)</p> <p> </p> <p>p12b: Number of pulse beats at time +12 (advanced Ambu bag system stage)</p> <p>s12b: Systolic blood pressure at time +12 (advanced Ambu bag system stage)</p> <p>d12b: Diastolic blood pressure at time +12 (advanced Ambu bag system stage)</p> <p>sp12b: SpO<sub>2</sub> at time +12 (advanced Ambu bag system stage)</p> <p>e12b: EtCO<sub>2</sub> at time +12 (advanced Ambu bag system stage)</p> <p> </p> <p>p13b: Number of pulse beats at time +13 (advanced Ambu bag system stage)</p> <p>s13b: Systolic blood pressure at time +13 (advanced Ambu bag system stage)</p> <p>d13b: Diastolic blood pressure at time +13 (advanced Ambu bag system stage)</p> <p>sp13b: SpO<sub>2</sub> at time +13 (advanced Ambu bag system stage)</p> <p>e13b: EtCO<sub>2</sub> at time +13 (advanced Ambu bag system stage)</p> <p> </p> <p>p14b: Number of pulse beats at time +14 (advanced Ambu bag system stage)</p> <p>s14b: Systolic blood pressure at time +14 (advanced Ambu bag system stage)</p> <p>d14b: Diastolic blood pressure at time +14 (advanced Ambu bag system stage)</p> <p>sp14b: SpO<sub>2</sub> at time +14 (advanced Ambu bag system stage)</p> <p>e14b: EtCO<sub>2</sub> at time +14 (advanced Ambu bag system stage)</p>
Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
<p>Manganese porphyrins reportedly exhibit synergic effects when combined with irradiation. However, an in-depth understanding of intratumoral heterogeneity and immune pathways, as affected by Mn porphyrins, remains limited. Here, we explored the mechanisms underlying immunomodulation of a clinical candidate, MnTnBuOE-2-PyP<sup>5+</sup> (BMX-001, MnBuOE), using single-cell analysis in murine carcinoma<em> </em>model. Mice bearing 4T1 tumors were divided into 4 groups: control, MnBuOE, radiotherapy (RT), combined MnBuOE, and radiotherapy (MnBuOE/RT). In epithelial cells, epithelial-mesenchymal transition, TNF-α signaling via NF-кB, angiogenesis, and hypoxia-related genes were significantly downregulated in the MnBuOE/RT compared to the RT. All subtypes of cancer-associated fibroblasts (CAFs) were reduced in MnBuOE and MnBuOE/RT. Inhibitory receptor-ligand interactions, in which epithelial cells and CAFs interacted with CD8+ T cells, were significantly lower in the MnBuOE/RT than in the RT. Trajectory analysis showed that DC maturation-associated markers were increased in MnBuOE/RT. M1 macrophages were significantly increased in the MnBuOE/RT compared to the RT, whereas myeloid-derived suppressor cells were decreased. CellChat analysis showed that the number of cell-cell communications was the lowest in the MnBuOE/RT. Our study is the first to provide evidence for the combined radiotherapy with a novel Mn porphyrin clinical candidate, BMX-001 from the perspective of each cell type within the tumor microenvironment.</p>
Data & Codes used in: Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)
<p>Here we provide the data, codes, and outline the procedure to reproduce the results presented in the paper "Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)" by E. Ventura-Macias, P. M. Martinez, Rubén Pérez, and J. G. Vilhena.</p> <p>The following sections contain a detailed description of the data and codes. At the end of this README, you will find instructions on how to generate the classical force-field parameters (Morse and Lennard-Jones) from the potential energy surfaces (PES) computed at the DFT level.</p> <p>The procedure is general and applies to any given pair of molecule and surface. The provided codes will allow you to swiftly generate the PES at the DFT level, fit the Lennard-Jones and Morse potentials, and test them in a LAMMPS MD simulation.</p> <p>For a thorough explanation of the procedure and the relevance of these results, please refer to the original publication (ARTICLE_DOI).</p> <p>If you find this helpful, please consider citing the article (ARTICLE_DOI).</p> <h2>Data structure</h2> <p>The data is organized in the following way:</p> <ol> <li> <p>DFT</p> <ul> <li>The equilibrium adsorption geometry of methanethiol (MTH), propanethiol (PTH), and cysteine (CYS) for the Au-mol configuration with PBE+DFT-D3.</li> <li>Potential Energy Surface (PES) computed at the DFT level (Figure 3 of the main manuscript).</li> <li>The scripts used to generate the PES.</li> </ul> </li> <li> <p>MD</p> <ul> <li>Fitting code and general instructions on how to use it.</li> <li>General input scripts used to generate MD data within LAMMPS.</li> </ul> </li> </ol> <h3>DFT Data</h3> <p>The DFT data in <code>DFT.zip</code> is organized in the following way:</p> <ul> <li> <p><code>DFT_PES/</code></p> <p>This folder contains the DFT potential energy surfaces (PES) for the interaction of the sulfur atom of methanethiolate (<code>mth</code>), propanethiolate (<code>pth</code>), and cysteine (<code>cys</code>) with the Au(111) surface and the necessary scripts to reproduce it.</p> <ul> <li> <p><code>results/</code></p> <p>The PES are given in one csv file per molecule named as <em><code>mol</code></em> + <code>_PES_S-Au111.csv</code>, where <em><code>mol</code></em> is the molecule name. Columns are as follows:</p> <table> <tbody> <tr> <td><strong>Label</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><em>i</em></td> <td>calculation number</td> </tr> <tr> <td><em>site</em></td> <td>adsorption site</td> </tr> <tr> <td><em>z</em></td> <td>distance of the S atom to the surface</td> </tr> <tr> <td><em>deltaz</em></td> <td>distance difference from the minimum energy position of the S atom</td> </tr> <tr> <td><em>pbed3</em></td> <td>PBE+D3 binding energy</td> </tr> <tr> <td><em>pbe</em></td> <td>PBE component of the binding energy</td> </tr> <tr> <td><em>d3</em></td> <td>DFT-D3 component of the binding energy</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li> <ul> <li> <p><code>mth/</code> | <code>pth/</code> | <code>cys/</code></p> <p>Each folder contains the CONTCAR (VASP) file for the optimized geometry of the molecule adsorbed on the Au(111) surface with PBE+D3.</p> </li> <li> <p><code>setup_grid.py</code></p> <p>Python script to set up the POSCAR files for the PES calculations.</p> </li> <li> <p><code>read_results.py</code></p> <p>Python script to read the results of the PES calculations.</p> </li> <li> <p><code>sub_array.sh</code></p> <p>Bash script to submit the PES calculations to an SLURM-based cluster.</p> </li> <li> <p><code>INCAR</code> | <code>KPOINTS</code> | <code>surf.CONTCAR</code></p> <p>VASP input files for the PES calculations.</p> </li> </ul> </li> </ul> <h3>MD fitting</h3> <p>The MD data in MD.zip is are organized as:</p> <ul> <li> <p><code>Fitting/</code></p> <ul> <li><code>optimize_Morse.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of Morse potential.</p> <ul> <li><code>optimize_LJ.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of LJ potential.</p> </li> <li> <p><code>Histogram/</code></p> <ul> <li><code>in.test</code></li> </ul> <p>LAMMPS input script to extract an XY file for the position of the S atom in an NVT simulation.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>sheng.eam</code></li> </ul> <p>EAM potential file in case Au dynamics are wished to be included.</p> </li> <li> <p><code>Single_point_scan/</code></p> <ul> <li><code>in.scan</code></li> </ul> <p>LAMMPS input script to perform single-point energy scan of a given molecule.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>launch.sh</code></li> </ul> <p>Launches the single point scan.</p> <ul> <li><code>plot_scan.py</code></li> </ul> </li> <li> <p><code>Molecules</code></p> <p>LAMMPS sample geometries for the 3 molecules.</p> </li> </ul> <h2>Steps to reproduce the results</h2> <p>The PES calculations were performed using the VASP code and two Python scripts for setup and results parsing.</p> <h3>Requirements</h3> <ul> <li>VASP (tested with version 5.4.4) <ul> <li><code>PAW_PBE</code> pseudopotentials set version 5.4</li> </ul> </li> <li>Python3 with packages: <ul> <li>ASE (Atomic Simulation Environment)</li> <li>Numpy</li> <li>Pandas</li> <li>matplotlib (optional)</li> </ul> </li> </ul> <h3>Steps</h3> <p>Each molecule has its own directory with the necessary files to reproduce the results. The following steps are for the methanethiolate molecule (<code>mth</code>).</p> <ol> <li>Set up the grid of points for the PES calculations by running the <code>setup_grid.py</code> script. It will create a subfolder <code>run/</code> inside the molecule's directory with the POSCAR files for each point in the grid.</li> </ol> <blockquote> <p>python setup_grid.py mth</p> </blockquote> <ol> <li> <p>Create the corresponding <code>POTCAR</code> file and place it in the molecule's directory.</p> </li> <li> <p>Change the <code>sub_array.sh</code> script to match the number of calculations in the array numbers and the MOL variable.</p> </li> </ol> <pre><code>#SBATCH --array=1-number of calculations MOL=mth</code></pre> <ol> <li>Submit the calculations to a SLURM-based cluster by running the <code>sub_array.sh</code> script from the <code>DFT_PES</code> directory.</li> </ol> <blockquote> <p>sbatch sub_array.sh</p> </blockquote> <ol> <li>After the calculations are finished, run the <code>read_results.py</code> script to parse the results and generate the PES csv files. It takes the arguments <code>--surf</code> and <code>--mol</code> to specify the surface and molecule PBE+D3 and D3 total energies.</li> </ol> <blockquote> <p>python read_results.py mth --surf -128.5228 -17.7147 --mol -22.6026 -0.0083</p> </blockquote> <h3>Reference values for the PBE+D3 and D3 surface and molecule energies</h3> <table> <tbody> <tr> <td> </td> <td><strong>Surface</strong></td> <td> </td> <td><strong>Molecule</strong></td> <td> </td> </tr> <tr> <td><strong>Molecule</strong></td> <td><strong>PBE+D3<br></strong></td> <td><strong>D3</strong></td> <td><strong>PBE+D3</strong></td> <td><strong>D3</strong></td> </tr> <tr> <td>mth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-22.6026</td> <td>-0.0083</td> </tr> <tr> <td>pth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-55.8615</td> <td>-0.0086</td> </tr> <tr> <td>cys</td> <td>-128.5228</td> <td>-17.7147</td> <td>-74.0338</td> <td>-0.1611</td> </tr> </tbody> </table> <h3>Fitting procedure</h3> <p>Both the .xyz and .csv files should be located at the same folder as the script. Then, simply run the code (<code>mth</code> is used as an example):</p> <blockquote> <p>python optimize_Morse.py mth</p> </blockquote> <p>or</p> <blockquote> <p>python optimize_LJ.py mth</p> </blockquote> <p>The script will print the optimized parameters: [De, re, α] or [ϵ, σ] for Morse or LJ respectively. It will also plot a fitting plot and a birdview of the resulting PES.</p> <h3>Using the potential. Histogram example</h3> <p>The code will run for the optimized <code>mth</code> Morse parameters and extract a <code>occ.lammpstrj</code> containing the (x,y) positions of the S atom throughout the NVT simulation. Note that only Au-S interaction is included. Start as:</p> <blockquote> <p>lmp -in in.test</p> </blockquote> <p>This is easily adaptable to other routines or molecules and is thought to be a generic LAMMPS starting input.</p> <h3>Single-point energy scan</h3> <p>Go to the 2C) folder and launch the scan with:</p> <blockquote> <p>./launch.sh</p> </blockquote> <p>This will create a folder named <code>fine_scan</code> containing 128 folders. Each folder is assigned to an (x,y) position. Then, inside each folder, a single-point energy evaluation is performed at various Z heights around the absolute minima.</p> <p>The <code>in.scan</code> file should be modified accordingly with the appropriate potentials. It is set to perform the scan with the optimized Morse potential by default.</p> <p>The output is gathered in the <code>E_readout.dat</code> folder with the following structure (all energies in eV):</p> <table> <tbody> <tr> <th>Total Energy</th> <th>Intramolecular energy</th> <th>Au-mol vdW interaction energy</th> <th>Au-S interaction energy</th> </tr> </tbody> <tbody> <tr> <td>-133.62</td> <td>0.123112</td> <td>-0.2403</td> <td>-1.31765</td> </tr> <tr> <td>-133.721</td> <td>0.123112</td> <td>-0.28-401</td> <td>-1.37853</td> </tr> </tbody> </table> <p>Therefore, the total adsorption energy will be the sum of the last two columns.</p> <p>Energies are ordered in increasing Z for the same (x,y) point. That is, the first 12 lines correspond to 12 heights of the starting (x,y) coordinate, the next 12 lines to heights at the second (x,y) configuration and so on.</p> <p>The python script <code>plot_scan.py</code> may be used to plot the results. The <code>E_readout.dat</code> file and <code>.csv</code> must be in the same folder.</p> <blockquote> <p>python plot_scan.py mth</p> </blockquote>
Data on quantum mechanics and electro-magnetic excitation of particles involved in combustion process
<p><span>Distribution of average free electron energy in the body and the closest vicinity of the high frequency spark, arc and corona gaseous discharge</span></p>
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.