bluuebunny/crossref_metadata_2025_split huggingface dataset:
Controllable assembly of skeletal muscle-like bundles through 3D bioprinting
Authors: Fan Tingting , Wang Shuo , Jiang Zongmin , Ji Shen , Cao Wenhua , Liu Wenli , Ji Yun , Li Yujing , Shyh-Chang Ng , Gu Qi 
Published: November 2021

Abstract
3D printing is an effective technology for recreating skeletal muscle tissue in vitro. To achieve clinical skeletal muscle injury repair, relatively large volumes of highly aligned skeletal muscle cells are required; obtaining these is still a challenge. It is currently unclear how individual skeletal muscle cells and their neighbouring components co-ordinate to establish anisotropic architectures in highly homogeneous orientations. Here, we demonstrated a 3D printing strategy followed by sequential culture processes to engineer skeletal muscle tissue. The effects of confined printing on the skeletal muscle during maturation, which impacted the myotube alignment, myogenic gene expression, and mechanical forces, were observed. Our findings demonstrate the dynamic changes of skeletal muscle tissue during in vitro 3D construction and reveal the role of physical factors in the orientation and maturity of muscle fibres.
Study on the linkages between microstructure and permeability of porous media using pore network and BP neural network
Authors: Liu Hao , Xu Ying , Wang Chengyong , Ding Feng , Xiao Haishan 
Published: November 2021

Abstract
In applying porous media air bearings (PMABs), designing the pore microstructure of porous media to obtain the desired permeability is challenging. The key parameters in this design are to map the pore microstructure characteristics to permeability and adapt to manufacturing process with the characteristics. For this purpose, a framework is proposed to characterize pore microstructure with morphology descriptor and predict permeability. 3D digital images of porous media are obtained using X-ray micro-computed tomography and various image construction techniques. The complex pore microstructure of porous media is represented with a pore network. Permeability is calculated based on the pore network. Sixteen pore microstructure morphology descriptors are initially calculated to characterize pore microstructure. A back-propagation neural network (BPNN) is built to learn the correlation between morphology descriptors and permeability. Pearson correlation coefficient (PCC) and feature importance scores of morphology descriptors are obtained based on the dataset and trained BPNN. The results demonstrate that the prediction performance of BPNN is excellent. The following six morphology descriptors (porosity, coordination number, average pore diameter, average throat diameter, average pore throat ratio, average throat length) are reserved to characterize pore microstructure. Finally, two types of pore microstructure are designed with the help of knowledge obtained by this research.
Thermal analysis of Kevlar/basalt reinforced hybrid polymer composite
Authors: Ramesh V , Anand P 
Published: November 2021

Abstract
Use of lightweight composite materials in automobile applications such as doors, bonnets, and bumpers and also the utilization of composite materials in building insulations require superior mechanical and thermal properties. This study attempts to determine the thermal conductivity, linear thermal expansion coefficient, heat deflection temperature and thermo gravimetric analysis of hybrid composite containing reinforcement fibers stacked in seven different combinations in an epoxy matrix as per ASTM standards. Each composite contained two different fibre materials, i.e., Kevlar and basalt. The study revealed that the stacked layers of basalt fibers had more influence on the thermal properties. It was observed that the hybrid composite made of least quantity layers of Kevlar and most of basalt exhibited the maximum thermal conductance of 0.219 W mK−1, while with vice versa laminate developed 0.191 W mK−1 which was least thermal conductance. The composition prepared by made Kevlar as core layer and basalt as its outer layers exhibited coefficient of linear thermal expansion above 11.5 × 10−6/°C. Maximum decomposition weight loss of 76.92% occurred in the composition prepared by keeping basalt as core and Kevlar as outer layer. The differential thermal graph showed that the said hybrid composite exhibited the peak decomposition rate of 1wt.%/°C. The thermal properties of the laminate prepared by keeping two layers of Kevlar sandwiched between the basalt were excellent when compared to other six hybrid composites investigated in this study.
Dye extracted from Bael leaves as a photosensitizer in dye sensitized solar cell
Authors: Alhorani Samah , Kumar Sarvesh , Genwa Mahaveer , Meena P L 
Published: November 2021

Abstract
This study has explored a new plant source, Bael tree leaves, as an efficient dye extraction towards green energy harvesting through dye-sensitized solar cells (DSSCs). The photosensitizers, photo-absorption, bandgap, and ionic conductivity characteristics of the extracted dye were determined using thin-layer chromatography (TLC), ultraviolet-visible spectroscopy, Tauc plot, and conductivity meter, respectively. Chlorophyll is the main constituent in the extracted dye confirmed by TLC analysis. An optimum concentration (0.2 g ml−1) with ionic conductivity of 455 μS cm−1 of the dye was used as a photoactive layer in DSSC, demonstrating power densities of 1.345 μW m−2 and 8.078 μW m−2 under the illumination of the LED lamp (1555 lx) and tungsten bulb (1926 lx), respectively. Additional parameters, including fill factor (0.26), ideality factor (1.25), characteristic resistance (309 Ω), series resistance (313 Ω), and shunt resistance (662 Ω) of the fabricated DSSC under tungsten illumination reveal that the novel Bael tree leaves-based dye can harvest green energy efficiently through DSSCs.
Setup of high resolution thermal expansion measurements in closed cycle cryostats using capacitive dilatometers
Authors: Rajak Neeraj K , Kondedan Neha , Jan Husna , Dilshah U Muhammed , Navya S D , Kaipamangalath Aswathy , Ramavarma Manoj , Bansal Chandrahas , Jaiswal-Nagar Deepshikha 
Published: November 2021

Abstract
We present high resolution thermal expansion measurement data obtained with high relative sensitivity of ΔL/L = 10−9 and accuracy of ± 2 % using closed cycle refrigerators employing two different dilatometers. Experimental details of the set-up utilizing the multi-function probe integrated with the cold head of two kinds of closed cycle refrigerators, namely, pulse tube and Gifford-McMahon cryocoolers, has been described in detail. The design consists of decoupling the bottom sample puck and taking connections from the top of the multi-function probe to mitigate the vibrational noise arising from the cold heads, using which smooth and high quality thermal expansion data could be obtained. It was found that dilatometer#2 performs a better noise mitigation than dilatometer#1 due to the constrained movement of the spring in dilatometer#2. This was confirmed by finite element method simulations that were performed for understanding the spring movement in each dilatometer using which the effect of different forces/pressures and vibrations on the displacement of the spring was studied. Linear thermal expansion coefficient α obtained using both dilatometers was evaluated using derivative of a polynomial fit. The resultant α obtained using dilatometer#2 and either of the closed cycle cryostats on standard metals silver and aluminium showed excellent match with published values obtained using wet cryostats. Finally, thermal expansion measurements is reported on single crystals of two high temperature superconductors YBa2Cu3−x
Al
x
O6+δ
and Bi2Sr2CaCu2O8+x
along the c-axis with very good match found with published data obtained earlier using wet liquid helium based cryostats.
Effects of cobalt deposition and pretreatment process on electrical properties of products
Authors: Xu Shaohui , Miao Haisheng , Zhang Jiandong 
Published: November 2021

Abstract
The preferable conditions for formation of high quality CoSi2 films and effect of process parameters on properties of products were investigated. The pretreatment should not only remove the natural oxide layer completely, but also could not damage Si substrate. The good static random access memory (SRAM) proportion of products is high when pretreatment thickness is 20 Å, reached 96.5%. The radio frequency (RF) bias power process parameter should also take an optimal value. When RF bias power is 150 W, the good SRAM proportion of products is greater than 98%. The 100 Å Co can just completely react with Si substrate after twice annealing (500 °C 30 s and 750 °C 30 s), and if it exceeds 100 Å, Co will be residual. Decreasing Co thickness leads to contact resistance (RC) increase whatever in N-well or P-well. The overall standby current (Isb) of product is least when Co thickness is 80 Å. Finally, the products achieved good electrical properties when Co thickness is 80 Å, pretreatment thickness is 20 Å and RF bias power is 150 W.
Magnetic excitation of high-Q resonance with split-ring resonators
Authors: Yan Jing-Chun , Zhang Shi-Qian , Zhang Yong , Wang Yu-Lin , Huang Cheng-Ping 
Published: November 2021

Abstract
Planar split-ring resonators (SRRs) with broken symmetry, excited by the electric field of incident wave, have been widely used to realize the high-Q resonance. In this paper, we report by theory and experiment an alternative scheme to induce the SRR-based high-Q resonance. The proposed scheme utilizes a two-dimensional array of vertical SRRs with vertical air gaps, which enables the excitation of narrow resonance with magnetic field and strong enhancement of local electromagnetic fields. The working mechanism correlates with the strong directional dependence of the dipole radiation (i.e., the elimination of electric-dipole radiation of the SRRs in the propagation direction), rather than the destructive interference due to the structural symmetry breaking. The dependence of Q factor on the structural parameters has also been studied theoretically, suggesting that a Q factor more than 2000 can be achieved. The results may be useful for designing narrow-band filters and sensors in the microwave or THz regime.
Supercritical dynamics at the edge-of-chaos underlies optimal decision-making
Authors: Amil Adrián F , Verschure Paul F M J 
Published: November 2021

Abstract
Critical dynamics, characterized by scale-free neuronal avalanches, is thought to underlie optimal function in the sensory cortices by maximizing information transmission, capacity, and dynamic range. In contrast, deviations from criticality have not yet been considered to support any cognitive processes. Nonetheless, neocortical areas related to working memory and decision-making seem to rely on long-lasting periods of ignition-like persistent firing. Such firing patterns are reminiscent of supercritical states where runaway excitation dominates the circuit dynamics. In addition, a macroscopic gradient of the relative density of Somatostatin (SST+) and Parvalbumin (PV+) inhibitory interneurons throughout the cortical hierarchy has been suggested to determine the functional specialization of low- versus high-order cortex. These observations thus raise the question of whether persistent activity in high-order areas results from the intrinsic features of the neocortical circuitry. We used an attractor model of the canonical cortical circuit performing a perceptual decision-making task to address this question. Our model reproduces the known saddle-node bifurcation where persistent activity emerges, merely by increasing the SST+/PV+ ratio while keeping the input and recurrent excitation constant. The regime beyond such a phase transition renders the circuit increasingly sensitive to random fluctuations of the inputs—i.e., chaotic—, defining an optimal SST+/PV+ ratio around the edge-of-chaos. Further, we show that both the optimal SST+/PV+ ratio and the region of the phase transition decrease monotonically with increasing input noise. This suggests that cortical circuits regulate their intrinsic dynamics via inhibitory interneurons to attain optimal sensitivity in the face of varying uncertainty. Hence, on the one hand, we link the emergence of supercritical dynamics at the edge-of-chaos to the gradient of the SST+/PV+ ratio along the cortical hierarchy, and, on the other hand, explain the behavioral effects of the differential regulation of SST+ and PV+ interneurons by acetylcholine in the presence of input uncertainty.
How is the Behavior Change Technique Content of the NHS Diabetes Prevention Program Understood by Participants? A Qualitative Study of Fidelity, With a Focus on Receipt
Authors: Miles Lisa M , Hawkes Rhiannon E , French David P 
Published: November 2021

Abstract

Background
The National Health Service (NHS) Diabetes Prevention Program (DPP) is a nationally implemented behavioral intervention for adults at high risk of developing Type 2 diabetes in England, based on a program specification that stipulates inclusion of 19 specific behavior change techniques (BCTs). Previous work has identified drift in fidelity from these NHS England specifications through providers’ program manuals, training, and delivery, especially in relation to BCTs targeting self-regulatory processes.

Purpose
This qualitative study investigates intervention receipt, i.e., how the self-regulatory BCT content of the NHS-DPP is understood by participants.

Methods
Twenty participants from eight NHS-DPP locations were interviewed; topics included participants’ understanding of self-monitoring of behavior, goal setting, feedback, problem solving, and action planning. Transcripts were analyzed thematically using the framework method.

Results
There was a wide variation in understanding among participants for some BCTs, as well as between BCTs. Participants described their understanding of “self-monitoring of behaviors” with ease and valued BCTs focused on outcomes (weight loss). Some participants learned how to set appropriate behavioral goals. Participants struggled to recall “action planning” or “problem solving” or found these techniques challenging to understand, unless additional support was provided (e.g., through group discussion).

Conclusions
Participants’ lack of understanding of some self-regulatory BCTs is consistent with the drift across fidelity domains previously identified from NHS design specifications. Behavioral interventions should build-in necessary support for participants to help them understand some BCTs such as action planning and problem solving. Alternatively, these self-regulatory BCTs may be intrinsically difficult to use for this population.
Generating tertiary protein structures via interpretable graph variational autoencoders
Authors: Guo Xiaojie , Du Yuanqi , Tadepalli Sivani , Zhao Liang , Shehu Amarda 
Published: November 2021

Abstract

Motivation
Modeling the structural plasticity of protein molecules remains challenging. Most research has focused on obtaining one biologically active structure. This includes the recent AlphaFold2 that has been hailed as a breakthrough for protein modeling. Computing one structure does not suffice to understand how proteins modulate their interactions and even evade our immune system. Revealing the structure space available to a protein remains challenging. Data-driven approaches that learn to generate tertiary structures are increasingly garnering attention. These approaches exploit the ability to represent tertiary structures as contact or distance maps and make direct analogies with images to harness convolution-based generative adversarial frameworks from computer vision. Since such opportunistic analogies do not allow capturing highly structured data, current deep models struggle to generate physically realistic tertiary structures.

Results
We present novel deep generative models that build upon the graph variational autoencoder framework. In contrast to existing literature, we represent tertiary structures as ‘contact’ graphs, which allow us to leverage graph-generative deep learning. Our models are able to capture rich, local and distal constraints and additionally compute disentangled latent representations that reveal the impact of individual latent factors. This elucidates what the factors control and makes our models more interpretable. Rigorous comparative evaluation along various metrics shows that the models, we propose advance the state-of-the-art. While there is still much ground to cover, the work presented here is an important first step, and graph-generative frameworks promise to get us to our goal of unraveling the exquisite structural complexity of protein molecules.

Availability and implementation
Code is available at https://github.com/anonymous1025/CO-VAE.

Supplementary information
Supplementary data are available at Bioinformatics Advances online.
GLYCO: a tool to quantify glycan shielding of glycosylated proteins
Authors: Lee Myungjin , Reveiz Mateo , Rawi Reda , Kwong Peter D , Chuang Gwo-Yu 
Published: November 2021

Abstract

Motivation
Glycans play important roles in protein folding and cell–cell interactions—and, furthermore, glycosylation of protein antigens can dramatically impact immune responses. While there have been attempts to quantify the glycan shielding or coverage of a protein surface, none of the publicly available tools analyzes glycan shielding computationally at an atomistic level.

Results
Here, we developed an in silico approach, GLYCO (GLYcan COverage), to quantify the glycan shielding of a protein surface. The software provides insights into glycan-dense/sparse regions of the entire protein surface or a subset of the protein surface. GLYCO calculates glycan shielding from a single coordinate file or from multiple coordinate files, for instance, as obtained from molecular dynamics simulations or by nuclear magnetic resonance spectroscopy structure determination, enabling analysis of glycan dynamics. Overall, GLYCO provides fundamental insights into the glycan shielding of glycosylated proteins.

Availability and implementation
GLYCO is freely available at GitHub (https://github.com/myungjinlee/GLYCO).

Supplementary information
Supplementary data are available at Bioinformatics online.
SBGNview: towards data analysis, integration and visualization on all pathways
Authors: Dong Xiaoxi , Vegesna Kovidh , Brouwer Cory , Luo Weijun 
Published: November 2021

Abstract

Summary
Pathway analysis is widely used in genomics and omics research, but the data visualization has been highly limited in function, pathway coverage and data format. Here, we develop SBGNview a comprehensive R package to address these needs. By adopting the standard SBGN format, SBGNview greatly extend the coverage of pathway-based analysis and data visualization to essentially all major pathway databases beyond KEGG, including 5200 reference pathways and over 3000 species. In addition, SBGNview substantially extends or exceeds current tools (esp. Pathview) in both design and function, including standard input format (SBGN), high-quality output graphics (SVG format) convenient for both interpretation and further update, and flexible and open-end workflow for iterative editing and interactive visualization (Highlighter module). In addition to pathway analysis and data visualization, SBGNview provides essential infrastructure for SBGN data manipulation and processing.

Availability and implementation
The data underlying this article are available as part of the SBGNview package is available on both GitHub and Bioconductor: https://github.com/datapplab/SBGNview, https://bioconductor.org/packages/SBGNview.

Supplementary information
Supplementary data are available at Bioinformatics online.
DeepSTF: A Deep Spatial–Temporal Forecast Model of Taxi Flow
Authors: Lv Zhiqiang , Li Jianbo , Dong Chuanhao , Xu Zhihao 
Published: November 2021

AbstractTaxi flow forecast is significant for planning transportation and allocating basic transportation resources. The flow forecast in the urban adjacent area is different from the fixed-point flow forecast. Their data are more complex and diverse, which make them more challenging to forecast. This paper introduces a deep spatial–temporal forecast (DeepSTF) model for the flow forecasting of urban adjacent area, which divides the urban into grids and makes it have a graph structure. The model builds a spatial–temporal calculation block, which uses graph convolutional network to extract spatial correlation feature and uses two-layer temporal convolutional networks to extract time-dependent feature. Based on the theory of dilation convolution and causal convolution, the model overcomes the under-fitting phenomenon of other models when calculating with rapidly changing data. In order to improve the accuracy of prediction, we take weather as an implicit factor and let it participate in the feature calculation process. A comparison experiment is set between our model and the seven existing traffic flow forecast models. The experimental results prove that the model has better the capabilities of long-term traffic prediction and performs well in various evaluation indicators.
Percutaneous treatment of a huge patent ductus venosus and severe portal vein hypoplasia using a Figulla Flex II atrial septal defect occluder in a 2-year-old infant: a case report
Authors: Kardos Marek , Kitzmueller Erwin , Olejnik Peter , Michel-Behnke Ina 
Published: November 2021

Abstract

Background
Intra- or extrahepatic porto-caval shunts (PCSs) can account for multiorgan dysfunction with pulmonary arterial hypertension and portosystemic encephalopathy as the most serious consequences of bypass of the hepatic circulation. The ductus venosus (DV) represents a rare foetal PCS and might be persistently patent in newborns after birth. Treatment strategies include surgical ligation and percutaneous device closure. The degree of portal vein hypoplasia limits therapy making liver transplantation the only option in some of them.

Case summary
In a newborn female patient a huge persistently patent DV, known already prenatally, resulted in severe secondary portal vein hypoplasia. She presented with hyperammonaemia, elevated liver enzymes, and pulmonary hypertension. With only diminutive portal venous branches and exceedingly high portal venous pressures during test-occlusion of the DV, shunt closure was not possible. At the age of 2 years more favourable portal venous pressures allowed transcatheter device closure with a nitinol atrial septal defect occlusion device. Pulmonary artery pressures and ammonia levels normalized after the procedure without any signs of portal hypertension.

Discussion
The case highlights the importance of meticulous imaging using balloon occlusion angiography of PCSs like the DV, to search for intrahepatic portal veins. Moreover, portal vein pressure during test-occlusion can identify patients amenable for surgical or endovascular shunt closure. Occlusion devices licensed for other indications like atrial septal defect closure can be used safely in huge PCS vessels in a one-step or staged procedure. Optimal timing of the intervention should be tailored to the patient’s needs