Contrast Mechanism Research Articles

Cognitive and sensory expectations independently shape musical expectancy and pleasure.

Expectation is crucial for our enjoyment of music, yet the underlying generative mechanisms remain unclear. While sensory models derive predictions based on local acoustic information in the auditory signal, cognitive models assume abstract knowledge of music structure acquired over the long term. To evaluate these two contrasting mechanisms, we compared simulations from four computational models of musical expectancy against subjective expectancy and pleasantness ratings of over 1000 chords sampled from 739 US Billboard pop songs. Bayesian model comparison revealed that listeners' expectancy and pleasantness ratings were predicted by the independent, non-overlapping, contributions of cognitive and sensory expectations. Furthermore, cognitive expectations explained over twice the variance in listeners' perceived surprise compared to sensory expectations, suggesting a larger relative importance of long-term representations of music structure over short-term sensory-acoustic information in musical expectancy. Our results thus emphasize the distinct, albeit complementary, roles of cognitive and sensory expectations in shaping musical pleasure, and suggest that this expectancy-driven mechanism depends on musical information represented at different levels of abstraction along the neural hierarchy. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

R3-Noria-methanesulfonate: A Molecular Cage with Superior Hyperpolarized Xenon-129 MRI Contrast.

Hyperpolarized (HP) xenon-129 (129Xe) magnetic resonance imaging (MRI) has the potential to be used as a molecular imaging modality. For this purpose, numerous supramolecular cages have been developed and evaluated in the past. Herein, we report a novel and unique macrocycle that can be successfully utilized for xenon MRI, the resorcinarene trimer methanesulfonate (R3-Noria-MeSO3H). This molecule is capable of two different contrast mechanisms for xenon-MRI, resulting from an increase in the effective spin-spin relaxation and hyperpolarized chemical exchange saturation transfer (HyperCEST). We have demonstrated a superior negative contrast caused by R3-Noria-MeSO3H on HP 129Xe MRI at 3.0 T as well as HyperCEST imaging of the studied macrocycle. Additionally, we have found that the complex aggregation behaviors of R3-Noria-methanesulfonate and its impact on xenon-129 relaxivity are an area for future study.

Investigating Selective Neuronal Vulnerability in Alzheimer’s and Parkinson’s Disease with Induced Pluripotent Stem Cells and Pre‐formed Fibrils

AbstractBackgroundAlzheimer’s and Parkinson’s disease feature progressive neurodegeneration associated with the accumulation of protein aggregates in a remarkably regionally selective manner. The cortical neurons that are relatively vulnerable in Alzheimer’s Disease (AD) are only affected late in Parkinson’s Disease (PD), whereas midbrain dopaminergic neurons exhibit striking vulnerability in PD, but are relatively spared in AD. Rodent and human post mortem studies have posited a role for cell autonomous mechanisms such as autophagy or calcium buffering deficits driving this, but having a live human cell model that can replicate the phenomenon of selective neuronal vulnerability can help to better determine common and contrasting disease mechanisms and identify therapeutic targets.MethodHere, we used induced pluripotent stem cell (iPSC) derived neurons as they offer a rare opportunity to examine cell autonomous vulnerability in live human cells. iPSCs from patients with AD‐related presenilin‐1 mutations (n = 3), PD‐related leucine rich repeat kinase 2 mutations (G2019S n = 3, R1441C n = 3), and isogenic corrected (n = 3) and healthy controls (n = 4) have been differentiated into both cortical neurons and midbrain dopaminergic neurons to enable comparison of vulnerability phenotypes in different neuronal subtypes from the same patient.ResultAD cortical neurons insulted with alpha‐synuclein pre‐formed fibrils (PFFs) have impaired neurite outgrowth, reduced synaptic density, and extensive aggregate formation. Meanwhile, PFF insulted PD cortical neurons exhibit normal neurite outgrowth and relatively little aggregation, whereas PD dopamine neurons readily produce aggregates.ConclusionThese preliminary results show relative vulnerability of AD and resilience of PD cortical neurons to alpha synuclein aggregates for the first time. This suggests the selective vulnerability to proteinopathy exhibited in these diseases may be replicated by the iPSC neuronal model, and additionally supports the notion that cell intrinsic factors may partly determine this vulnerability.

Contrasting response mechanisms and ecological stress of net primary productivity in sub-humid to arid transition regions: a case study from the Loess Plateau, China

Contrasting response mechanisms and ecological stress of net primary productivity in sub-humid to arid transition regions: a case study from the Loess Plateau, China

Non-destructive analysis of Pb-acid battery positive plates, based on neutron tomography

Notwithstanding the in-depth understanding of lead-acid battery degradation processes developed in a time-honored field of science, there is still wide scope for knowledge-based technological advancements, in particular, targeting positive plate (PP) durability. Non-destructive imaging of the internal morphology, structure and chemistry of these components, enabled by neutron-based methods, is capable of disclosing otherwise inaccessible observables, that can form the factual basis for new pathways to device improvement. In this study, we propose a post mortem investigation of as-formed and electrochemically aged PPs, centered on neutron tomography and complemented by x-ray radiography and scanning-electron microscopy. These complementary imaging methods, thriving on different contrast mechanisms, allow 2D/3D position-sensitive morphological analyses of the nature and evolution of the positive active material (PAM) and of the spines of the PP, including: morphology variation, crack location, porosity distribution and identification of hydrogenated compounds. Specifically, in this work, we have pinpointed the differences in degradation modes—resulting from the application of a standard ageing protocol—of PPs fabricated in two technologically alternative ways: punching and gravity-casting, showing the superiority of the formed in terms of electrode stability upon charging abuse conditions.

Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation

Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code at https://github.com/SUSTechBruce/RESTC-Source-code .

Transform, contrast and tell: Coherent entity-aware multi-image captioning

Coherent entity-aware multi-image captioning aims to generate coherent captions for neighboring images in a news document. There are coherence relationships among neighboring images because they often describe same entities or events. These relationships are important for entity-aware multi-image captioning, but are neglected in entity-aware single-image captioning. Most existing work focuses on single-image captioning, while multi-image captioning has not been explored before. Hence, this paper proposes a coherent entity-aware multi-image captioning model by making use of coherence relationships. The model consists of a Transformer-based caption generation model and two types of contrastive learning-based coherence mechanisms. The generation model generates the caption by paying attention to the image and the accompanying text. The caption-caption coherence mechanism aims to render entities in the caption of the image be also in captions of neighboring images. The caption-image-text coherence mechanism aims to render entities in the caption of the image be also in the accompanying text. To evaluate coherence between captions, two coherence evaluation metrics are proposed. The new dataset DM800K is constructed that has more images per document than two existing datasets GoodNews and NYT800K, and is more suitable for multi-image captioning. Experiments on three datasets show the proposed captioning model outperforms 7 baselines according to BLUE, Rouge, METEOR, and entity precision and recall scores. Experiments also show that the generated captions are more coherent than that of baselines according to caption entity scores, caption Rouge scores, the two proposed coherence evaluation metrics, and human evaluations.

Automated Detection of Corneal Edema With Deep Learning-Assisted Second Harmonic Generation Microscopy

Second Harmonic Generation Microscopy (SHG) is widely acknowledged as a valuable non-linear optical imaging tool, its contrast mechanism providing the premises to non-invasively identify, characterize, and monitor changes in the collagen architecture of tissues. However, the interpretation of SHG data can pose difficulties even for experts histopathologists, which represents a bottleneck for the translation of SHG-based diagnostic frameworks to clinical settings. The use of artificial intelligence methods for automated SHG analysis is still in an early stage, with only few studies having been reported to date, none addressing ocular tissues yet. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We observe that Deep Learning models building on different architectures provide complementary results for the classification of cornea SHG images and demonstrate an AU-ROC = 0.98 for their joint use. These results have potential to be extrapolated to other diagnostics scenarios, such as automated extraction of hydration level of cornea, or identification of corneal edema causes, and thus pave the way for novel methods for precision diagnostics of the cornea with Deep-Learning assisted SHG imaging.

In vivo optical coherence tomography attenuation imaging of the breast surgical cavity using a handheld probe

Approximately 20–30% of breast-conserving surgery (BCS) patients require follow-up re-excision surgery due to inadequate cancer resection. To aid cancer detection and resection, we present a handheld optical coherence tomography (OCT) probe and attenuation imaging method to enable in vivo imaging of the surgical cavity during BCS. The attenuation imaging method incorporates three-dimensional correction of system factors in the probe, namely, confocal function and sensitivity roll-off function, demonstrating accurate and repeatable attenuation imaging of phantoms, validated using the Mie scattering model. To assess the imaging performance of the probe prior to in vivo imaging, ex vivo attenuation imaging of breast specimens from eight mastectomy patients was performed and the results compared to those acquired using a benchtop imaging system, showing consistent tissue contrast in attenuation images. In vivo imaging of breast tissue in the surgical cavity of seven patients was subsequently performed during BCS. The results demonstrate the potential of attenuation imaging to delineate cancer in vivo in the surgical cavity. Attenuation imaging provides an additional tissue contrast mechanism to complement OCT imaging, potentially contributing to more complete identification and resection of cancer during BCS.

Geometry and formation mechanism of tension gashes and their implication on the hydrocarbon accumulation in the deep-seated strata of sedimentary basin: A case from Shunnan area of Tarim Basin

With the theoretical and technological developments related to cratonic strike-slip faults, the Shuntuoguole Low Uplift in the Tarim Basin has attracted considerable attention recently. Affected by multi-stage tectonic movements, the strike-slip faults have controlled the distribution of hydrocarbon resources owing to the special fault characteristics and fault-related structures. In contrast, the kinematics and formation mechanism of strike-slip faults in buried sedimentary basins are difficult to investigate, limiting the discussion of these faults and hydrocarbon accumulation. In this study, we identified the characteristics of massive sigmoidal tension gashes (STGs) that formed in the Shunnan area of the Tarim Basin. High-resolution three-dimensional seismic data and attribute analyses were used to investigate their geometric and kinematic characteristics. Then, the stress state of each point of the STGs was calculated using seismic curvature attributes. Finally, the formation mechanism of the STGs and their roles in controlling hydrocarbon migration and accumulation were discussed. The results suggest that: (1) the STGs developed in the Shunnan area have a wide distribution, with a tensile fault arranged in an en échelon pattern, showing an S-shaped bending. These STGs formed in multiple stages, and differential rotation occurred along the direction of strike-slip stress during formation. (2) Near the principal displacement zone of the strike-slip faults, the stress value of the STGs was higher, gradually decreasing at both ends. The shallow layer deformation was greater than the deep layer deformation. (3) STGs are critical for connecting source rocks, migrating oil and gas, sealing horizontally, and developing efficient reservoirs. This study not only provides seismic evidence for the formation and evolution of super large STGs, but also provides certain guidance for oil and gas exploration in this area.

Regulatory mechanisms and cell membrane properties of Candida glycerinogenes differ under 2-phenylethanol addition or fermentation conditions.

The biosynthesis of 2-phenylethanol (2-PE) at high yields and titers is often limited by its toxicity. In this study, we describe the molecular mechanisms of 2-PE tolerance in the multi-stress tolerant industrial yeast, Candida glycerinogenes. They were different under 2-PE addition or fermentation conditions. After extracellular addition of 2-PE, C. glycerinogenes cells became rounder and bigger, which reduced specific surface area. However, during 2-PE fermentation C. glycerinogenes cells were smaller, which increased specific surface area. Other differences in the tolerance mechanisms were studied by analyzing the composition and molecular parameters of the cell membrane. Extracellular 2-PE stress resulted in down-regulation of transcriptional expression of unsaturated fatty acid synthesis genes. This raised the proportion of saturated fatty acids in the cell membrane, which increased rigidity of the cell membrane and reduced 2-PE entry to the cell. However, intracellular 2-PE stress resulted in up-regulation of transcriptional expression of unsaturated fatty acid synthesis genes, and increased the proportion of unsaturated fatty acids in the cell membrane; this in turn enhanced flexibility of the cell membrane which accelerated efflux of 2-PE. These contrasting mechanisms are mediated by transcriptional factors Hog1 and Swi5. Under 2-PE addition, C. glycerinogenes activated Hog1 and repressed Swi5 to upregulate erg5 and erg4 expression, which increased cell membrane rigidity and resisted 2-PE import. During 2-PE fermentation, C. glycerinogenes activated Hog1 and repressed Swi5 to upregulate 2-PE transporter proteins cdr1 and Acyl-CoA desaturase 1 ole1 to increase 2-PE export, thus reducing 2-PE intracellular toxicity. The results provide new insights into 2-PE tolerance mechanisms at the cell membrane level and suggest a novel strategy to improve 2-PE production by engineering anti-stress genes.

Direct x-ray scattering signal measurements in edge-illumination/beam-tracking imaging and their interplay with the variance of the refraction signals

X-ray dark-field or ultra-small angle scatter imaging has become increasingly important since the introduction of phase-based x-ray imaging and is having transformative impact in fields such as in vivo lung imaging and explosives detection. Here, we show that dark-field images acquired with the edge-illumination method (either in its traditional double mask or simplified single mask implementation) provide a direct measurement of the scattering function, which is unaffected by system-specific parameters such as the autocorrelation length. We show that this is a consequence both of the specific measurement setup and of the mathematical approach followed to retrieve the dark-field images. We show agreement with theoretical models for datasets acquired both with synchrotron and laboratory x-ray sources. We also introduce a new contrast mechanism, the variance of refraction, which is extracted from the same dataset and provides a direct link with the size of the scattering centers. We show that this can also be described by the same theoretical models. We study the behavior of both signals vs key parameters such as x-ray energy and scatterer radius. We find this allows quantitative and direct scattering measurements during imaging, with implications in all fields where dark-field imaging is used.

Contrasting mechanisms of nutrient mobilization in rhizosphere hotspots driven by straw and biochar amendment

Straw return strategies are widely used green management practices that can alter soil organic matter transformation and dynamics through changes in microbial community structure and functions. How the exogenous input of organic materials of contrasting qualities affects the composition of dominant taxa, growth, and microbial functional properties related to nutrient acquisition in space remains unclear. In this study, we investigated the hotsopts and kinetics of C- and N-acquiring hydrolases, microbial growth, and bacterial community structure in maize rhizosphere hotspots after the addition of straw and straw-derived biochar using soil zymography, substrate-induced respiration and high-throughput sequencing. Compared with no amendment and maize straw-derived biochar, straw addition increased the growing biomass and microbial specific growth rate by 1.2–1.6 and 1.7–2.0-fold, respectively, indicating the relative dominance of fast-growing r-strategists. This corresponds to an increased relative abundance of the keystone taxa Firmicutes and their gene copies encoding β-1,4-glucosidase (BG) and β-N-acetylglucosaminidase (NAG). The potential activity and affinity (Vmax and Km) of BG increased 2.2 and 1.8 times, respectively, and those of NAG increased 4.0 and 2.0 times, respectively. In contrast, the relative abundance of Actinobacteria belonging to K-strategists increased in the biochar-amended soil. This resulted in slower growth and retarded enzymatic activity than the straw return treatment. Biochar enhanced the root biomass by 31% and increased the rhizosphere hotspot extents of BG and NAG by 26% and 47%, respectively. The highest robustness and modularity of the co-occurrence network indicated a more stable network with biochar input. In summary, the addition of straw accelerated rhizosphere nutrient cycling by triggering microbial growth, especially fast-growth r-strategists (Firmicutes), and synthesizing a large number of enzymes. In contrast, the addition of biochar increased rhizosphere nutrient mobilization by expanding the extent of rhizosphere hotspots to mobilize nutrients from a larger soil volume. This suggests that there are different strategies for nutrient mobilization in the rhizosphere with contrasting exogenous C addition.

GAN inversion-based semi-supervised learning for medical image segmentation

The crux of medical image segmentation stems from learning pixel-wise semantic consistency from a mount of labeled samples through exhaustive annotation. Most existing methods rely heavily on labeled image pairs and are optimized in a fully supervised manner, often resulting in unsatisfactory results due to limited paired training samples. In this paper, we propose a GAN inversion-based semi-supervised learning framework InvSSL for medical image segmentation, which generates corresponding variants from labeled images by GAN inversion. Specifically, it first trains a StyleGAN as an inversion generator which is able to generate samples with the same distribution as training samples, and then obtains variant samples from a training sample by performing GAN inversion to strengthen the segmentation performance in a semi-supervised learning manner. In particular, inversion generator inverts a training sample into the latent space and adds interference to the latent codes to evolve variant samples. To model high-level consistency between pixels and decouple semantic components in the latent space, our InvSSL designs a multi-level dense contrastive learning mechanism. Extensive experiments on lung segmentation and skin lesion segmentation demonstrate that our InvSSL outperforms state-of-the-art methods for medical image segmentation. Our code will be released at https://github.com/funkdub/InvSSL.

Recharge and vulnerability assessment of groundwater resources in North west India: Insights from isotope-geospatial modelling approach

Recent studies indicate dwindling groundwater quantity and quality of the largest regional aquifer system in North West India, raising concern over freshwater availability to about 182 million population residing in this region. Widespread agricultural activities have resulted severe groundwater pollution in this area, demanding a systematic vulnerability assessment for proactive measures. Conventional vulnerability assessment models encounter drawbacks due to subjectivity, complexity, data-prerequisites, and spatial–temporal constraints. This study incorporates isotopic information into a weighted-overlay framework to overcome the above-mentioned limitations and proposes a novel vulnerability assessment model. The isotope methodology provides crucial insights on groundwater recharge mechanisms (18O and 2H) and dynamics (3H) - often ignored in vulnerability assessment. Isotopic characterisation of precipitation helped in establishing Local Meteoric Water Line (LMWL) as well as inferring contrasting recharge mechanisms operating in different aquifers. Shallow aquifer (depth < 60 m) showed significant evaporative signature with evaporation loss accounting up to 18.04% based on Rayleigh distillation equations. Inter-aquifer connections were apparent from Kernel Density Estimate (KDE) and isotope correlations. A weighted overlay isotope-geospatial model was developed combining 18O, 3H, aquifer permeability, and water level data. The central and northern parts of study area fall under least (0.29%) and extremely (1.79%) vulnerable zones respectively, while majority of the study area fall under moderate (42.71%) and highly vulnerable zones (55.20%). Model validation was performed using groundwater NO3– concentration, which showed an overall accuracy up to 82%. Monte Carlo Simulation (MCS) was performed for sensitivity analysis and permeability was found to be the most sensitive input parameter, followed by 3H, 18O, and water level. Comparing the vulnerability map with Land Use Land Cover (LULC) and population density maps helped in precisely identifying the high-risk sites, warranting a prompt attention. The model developed in this study integrates isotopic information with vulnerability assessment and resulted in model output with good accuracy, scientific basis, and widespread relevance, which highlights its crucial role in formulating proactive water resource management plans, especially in less explored data-scarce locations.

Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follow a semi-supervised learning manner, which notably limits their wide use in reality since labels are usually scarce in real applications. Recently, contrastive learning, a self-supervised method, becomes one of the most exciting learning paradigms and shows great potential when there are no labels. In this paper, we study the problem of self-supervised HGNNs and propose a novel co-contrastive learning mechanism for HGNNs, named HeCo. Different from traditional contrastive learning which only focuses on contrasting positive and negative samples, HeCo employs cross-view contrastive mechanism. Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously. Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views. This enables the two views to collaboratively supervise each other and finally learn high-level node embeddings. Moreover, to further boost the performance of HeCo, two additional methods are designed to generate harder negative samples with high quality. The essence of HeCo is to make positive samples from different views close to each other by cross-view contrast, and learn the factors invariant to two proposed views. However, besides the invariant factors, view-specific factors complementally provide the diverse structure information between different nodes, which also should be contained into the final embeddings. Therefore, we need to further explore each view independently and propose a modified model, called HeCo++. Specifically, HeCo++ conducts hierarchical contrastive learning, including cross-view and intra-view contrasts, which aims to enhance the mining of respective structures. Extensive experiments conducted on a variety of real-world networks show the superior performance of the proposed methods over the state-of-the-arts.

Selective Adsorption of Organic Micro-Pollutants by Smectite Clays Revealed from Atomistic Simulations.

In this study, atomistic simulations were carried out to study the difference in the adsorption process between two similar molecules, diazepam and oxazepam, on Na+-montmorillonite. Kinetic and XRD measurements showed a contrasting adsorption mechanism of these two molecules, differing only by the presence/absence of methyl and hydroxyl groups, with a larger adsorption amount and intercalation for the oxazepam. The structural characterization of these molecules was investigated through DFT calculations and showed the vicinity of hydroxyl and carbonyl groups for only the chair conformation of oxazepam compared to the boat conformation. Classical molecular dynamics simulations of diazepam and the two forms of oxazepam on the external surface of Na+-montmorillonite highlighted the better coordination of the oxazepam-chair conformation, compared to its boat counterpart and diazepam. This has been confirmed through DFT calculations, from which a coordination energy that is greater by 10 kcal·mol-1 is observed. This strongly suggests that the experimentally observed intercalation of oxazepam occurs only in the chair form because of the strong coordination with the Na+ cation present in the Na-Mt interlayer. Classical MD simulations of the intercalated oxazepam chair molecule in the Na-Mt interlayer allowed the evaluation of the interlayer spacing d001, which was in very good agreement with the experimental XRD measurement.

Chemical-Quantum Analysis Of The Anticancer Potential Of Rhodionin Present In The Plant Rhodiola Rosea Vs. Amino Acids Of The Human Body

Rhodiola rosea (R. rosea) extract has molecular contrast mechanisms, which have normal physiological functions. Extracts of R. rosea have anticancer potentials. Rhodiodin (RDN) is a flavonoid compound found in Rhodiola plants. The aim was to analyze the anticancer potential of rodionin present in the R. rosea plant by quantum chemistry. Hyperchem software was used as a quantum chemistry simulator. The fundamental basis of quantum calculations was the electron transfer coefficient (ETC) theory. We can see the ETCs ordered according to the quantum well. The substance lies at the bottom of the quantum well. This situation indicates that the probability of oxidative interactions occurring is very high. We found that the RDN is a potent oxidant of AAs in the human body; for this reason, it has potential as an anticancer chemotherapeutic agent.

Mechanistic insights highlight regioselective Cobalt-catalyzed [3+3] annulation of anilides with benzylallenes

C-H annulation of anilide with benzylallene under the catalysis of Co(III)-complex has been studied theoretically by employing density functional theory (DFT) to find out the plausible mechanistic path. Our findings reveal that the C-H activation prefers the formation of a four-membered cobalta benzoazetidine intermediate rather than six-membered 1,2,4-cobalta benzoxazine intermediate to generate the product. This fact reveals a new mechanism in contrast to the mechanism proposed earlier. The metal mediated annulation of various anilides include six steps - coordination of substrate with catalyst, N-H metalation, C-H metalation, insertion of benzylallene, proton abstraction and migration insertion. Moreover, it reveals regioselectivity of cobalt for allene insertion. The effect of substituents on the mechanism has also been studied and the high energetic span obtained for this catalytic cycle implies that very high temperature is required for this reaction. It is consistent with the experimental result.

Configuration of drive mechanisms with fewer wires in a surgical robot considering operation trajectory and delay

ABSTRACT Surgical robots have contributed greatly to the development of minimally invasive surgery. Surgical robots mostly use a wire cable drive mechanism because wire has high responsiveness, however, the wires often loosen or break, resulting in high maintenance costs. In contrast, flexible-shaft, timing-belt, and pneumatic-actuator mechanisms have low maintenance costs but lengthen the mechanical delay. In this study, we derived an appropriate configurations of drive mechanisms with fewer wires for each joint of a surgical robot, considering the delay of the mechanisms. We assumed the configuration of the four drive mechanisms at six joints of the surgical robot. Using a virtual surgical simulator, we measured the operation trajectory of a robot in virtual space without delay. From this operation trajectory, the trajectory of the robot’s tip was calculated by simulating the delay for 4096 configurations of the drive mechanisms. We compared the suturing accuracy of the robot with and without delay for all combinations and determined the four configurations whose accuracy was within requirements. Finally, we conducted a verification experiment to compare theoretical and experimental values. The small relative errors validate the feasibility of the method proposed for designing the configuration of drive mechanisms in a surgical robot.