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To identify the valuable predictors of grade≥2 radiation pneumonitis (RP) in patient treated with radiotherapy after pneumonectomy for non-small cell lung cancer (NSCLC); and to construct a nomogram predicting the incidence of grade≥2 RP in such patients.We reviewed 82 patients with NSCLC received radiotherapy after pneumonectomy from 2008 to 2018. The endpoint was grade≥2 RP. Univariate and multivariate regression analysis were conducted to evaluate significant factors of grade≥2 RP. Receiver operating characteristic (ROC) curve was used to establish optimal cutoff values and the nomogram was built to make the predictive model visualized. Descriptive analysis was performed on 5 patients with grade 3 RP.A total of 22(26.8%) patients developed grade 2 RP and 5(6.1%) patients were grade 3 RP. V5, V10, V20, V30, MLD, PTV, and PTV/TLV were associated with the occurrence of grade≥2 RP in univariate analysis, while none of the clinical factors was significant; V5(OR,1.213;95%CI,1.099-1.339; P0.001) and V20(OR,1.435;95%CI,1.166-1.765; P=0.001) were the independent significant predictors by multivariate analysis and were included in the nomogram. The ROC analysis for the cutoff values for predicting grade≥2 RP were V523% (AUC=0.819, sensitivity:0.701, specificity:0.832) and V208% (AUC=0.812, sensitivity:0.683, specificity:0.811). Additionally, grade≥3 RP did not occur when V530%, V2013% and MLD751.2cGy, respectively.Our study showed that V5 and V20 were independent predictors for grade≥2 RP in NSCLC patients receiving radiotherapy after pneumonectomy. Grade 3 RP did not occur whenV530%, V2013% and MLD751.2cGy, respectively. In addition, patient underwent right pneumonectomy may have a lower tolerance to radiation compared to left pneumonectomy.

The anisotropy of mechanical strength on a 3D printed model can be controlled in a multi-axis 3D printing system as materials can be accumulated along dynamically varied directions. In this paper, we present a new computational framework to generate specially designed layers and toolpaths of multi-axis 3D printing for strengthening a model by aligning filaments along the directions with large stresses. The major challenge comes from how to effectively decompose a solid into a sequence of strength-aware and collision-free working surfaces. We formulate it as a problem to compute an optimized governing field together with a selected orientation of fabrication setup. Iso-surfaces of the governing field are extracted as working surface layers for filament alignment. Supporting structures in curved layers are constructed by extrapolating the governing field to enable the fabrication of overhangs. Compared with planar-layer based Fused Deposition Modeling (FDM) technology, models fabricated by our method can withstand up to 6 . 35× loads in experimental tests.

Neutron–gamma discrimination (NGD) for gadolinium-containing scintillators is a challenging issue which prevents them from being used in highly efficient neutron detectors with high signal-to-noise ratios. Pulse-shape based digital-signal processing has been selected to tackle this issue, with an emphasis on the dimensionality reduction of the raw data and capability to mitigate the influence of noise, unwanted variations, and outliers on the accuracy of classification. We present two graph-embedded non-negative matrix factorization digital classifiers in which an event graph (or a network) is introduced to find a low-dimensional structure hidden in the high-dimensional experimental data. Utilizing energy-independent normalized features of waveforms and a graph composed of mixed-source reference events, the smooth or sparse low-dimensional representation of detector signals from a Ce:Gd3Al2Ga3O12 scintillator leads to a high thermal neutron detection efficiency (77%–80%) and a high NGD ratio (neutron–gamma efficiency ratio, ∼109) simultaneously. Moreover, excellent discrimination between neutron and ambient background events has been achieved. The proposed graph-embedded algorithms may be applied not only for thermal-neutron position-sensitive detectors and single-channel detectors but also for other radiation detectors that need excellent particle (or crystal) discrimination capabilities and high detection efficiencies in many applications, including medical imaging, nondestructive testing, and security scans.

The fabrication flexibility of 3D printing has sparked a lot of interest in designing structures with spatially graded material properties. In this paper, we propose a new type of density graded structure that is particularly designed for 3D printing systems based on filament extrusion. In order to ensure high-quality fabrication results, extrusion-based 3D printing requires not only that the structures are self-supporting, but also that extrusion toolpaths are continuous and free of self-overlap. The structure proposed in this paper, called CrossFill, complies with these requirements. In particular, CrossFill is a self-supporting foam structure, for which each layer is fabricated by a single, continuous and overlap-free path of material extrusion. Our method for generating CrossFill is based on a space-filling surface that employs spatially varying subdivision levels. Dithering of the subdivision levels is performed to accurately reproduce a prescribed density distribution. We demonstrate the effectiveness of CrossFill on a number of experimental tests and applications., Comment: Submission to Symposium on Solid and Physical Modeling 2019

International audience; We present an inverse design tool for fabric formwork - a process where flat panels are sewn together to form a fabric container for casting a plaster sculpture. Compared to 3D printing techniques, the benefit of fabric formwork is its properties of low-cost and easy transport. The process of fabric formwork is akin to molding and casting but having a soft boundary. Deformation of the fabric container is governed by force equilibrium between the pressure forces from liquid fill and tension in the stretched fabric. The final result of fabrication depends on the shapes of the flat panels, the fabrication orientation and the placement of external supports. Our computational framework generates optimized flat panels and fabrication orientation with reference to a target shape, and determines effective locations for external supports. We demonstrate the function of this design tool on a variety of models with different shapes and topology. Physical fabrication is also demonstrated to validate our approach.

We tackle the problem of dense reconstruction with a practical system, in which near point lighting (NPL) is employed. Different from the conventional formulation of photometric stereo (PS) that assumes parallel lighting, PS under the NPL condition is a nonlinear problem as the local surface normals are coupled with its distance to the camera as well as the light sources. After obtaining the locations of point lights by a calibration process, we develop a new framework to solve this nonlinear reconstruction problem via mesh deformation, in which each facet is corresponding to a pixel in the image captured by the camera. In our framework, mesh deformation is decoupled into an iteration of interlaced steps of local projection and global blending. Experimental results verify that our method can generate accurate estimation of surface shape under NPL in a few iterations. Besides, this approach is robust to errors on the positions of light sources and is easy to be implemented.

Study question How do endometrium OCT image characteristics during peri-implantation window correlate with histomorphometry and associate with implantation outcomes in women with reproductive failure? Summary answer Endometrium OCT intensity correlated with stromal cell density and gland size. Endometrium with recurrent implantation failure had low OCT intensity but reversed in successful implantation. What is known already OCT is a non-invasive imaging technique using low energy near-infrared light to capture micrometer-scale resolution images from optical scattering media. An image produced by OCT resembles tissue architecture observed in histology, so OCT imaging has been regarded as “optical biopsy”. Our previous findings demonstrated OCT is better than ultrasound to identify endometrial pathology. We also showed association of OCT signal with microvessel density in peri-implantation endometrium. However, other histomorphometry were not evaluated. It is still unclear whether endometrium OCT image characteristics are different in reproductive failure and can predict implantation outcomes. Study design, size, duration This was a prospective study conducted at teaching hospital of The Chinese University of Hong Kong from Jan 2018 to Dec 2019. 46 infertile women with or without recurrent miscarriage (RM) and implantation failure (RIF) were recruited in this study. Endometrium OCT imaging and subsequent biopsy were performed on the seventh day after luteal hormone surge (LH + 7) in natural cycle prior to the consecutive natural conception or embryo transfer (ET) cycle. Participants/materials, setting, methods At least 5 systematic random endometrium OCT images from uterine fundus, body and lower segment of each subject were included for intensity analysis by two independent observers. OCT intensity of each image was classified as low, moderate, high based on optical range and then average OCT intensity in each uterine region was calculated for group comparison. Endometrium glandular epithelial, stromal, endothelial, uNK cells were defined by standard H&E and specific immunostaining for histomorphometry and correlation. Main results and the role of chance OCT intensity significantly correlated with endometrial cell and gland parameters regardless classifications of reproductive failure and implantation outcome. Higher OCT intensity indicated higher stromal cell density, gland to stromal (G/S) ratio and average gland area, but fewer microvessel and uNK cells. None of the endometrium histomorphometry were significantly different among different reproductive failure types and implantation outcomes, suggesting it may not be sensitive enough to detect the abnormal histological features. However, OCT intensity was significantly lower in the uterine fundus and body of RIF group than in that of infertile and RM groups. There was no significant difference of OCT intensity in the lower part of the endometrium among three groups. It indicates that OCT intensity is a sensitive marker to differentiate endometrium with RIF from the endometrium with other conditions and also endometrium with RIF is characterized with less stromal cells and smaller glands. Compared with infertile group with unsuccessful implantation, OCT intensity was higher in all three parts of the uterus from the infertile group with successful implantation, but the results were not statistically different. The results further implied that endometrial cells and gland size may potentially contribute to the endometrium receptivity for implantation. Limitations, reasons for caution Current endometrium OCT imaging depth is within 3mm, change beyond this thickness is inaccessible but still the most important layer for implantation. This is a pilot and small study with lack of normal fertile control. Endometrium OCT imaging in the same natural conception or ET cycle will be more accurate. Wider implications of the findings: OCT imaging could be used as a potential noninvasive modality to evaluate peri-implantation window endometrium. It enables real-time and in-situ visualization of endometrium structure and pathology with no additional biopsy risk and examination delay. Larger clinical trials are needed to confirm its clinical applications and utilities. Trial registration number CREC 2016.160

Study question To compare the changing peripheral levels of inflammation-related cytokine profile during a 9-day period after blastocyst transfer between women who did and did not conceive. Summary answer:Successful implantation is associated with transient increase in serum pro-inflammatory cytokine profile followed by a switch to anti-inflammatory cytokine profile prior to confirmation of pregnancy.What is known already: Immunomodulation is thought to be important for the prevention of rejection of the implanting semi-allograft embryo and successful establishment of pregnancy. A successful pregnancy is characterized by a dominance of anti-inflammatory cytokine profile in the peripheral blood in the first and second trimesters of pregnancy. It is achieved by a complex interplay between various immune cells and cytokines at the fetal-maternal interface, among which the key-players are interleukine–10 (IL–10) and transforming growth factor-β1 (TGF-β1). The circulating inflammatory response in the first few days after embryo transfer to the pathophysiology of implantation failure remains unclear. Study design, size, duration: This prospective observational and longitudinal study on 47 women with infertility was performed in an in vitro fertilization unit from December 2018 to August 2019. The amounts of a range of cytokines was measured on serial blood samples obtained during a 9-day period after blastocyst transfer. Participants/materials, setting, methods Serial blood samples were obtained on the day of embryo transfer, and 3, 6, and 9 days afterward for measurement of serum interferon gamma (IFN-γ), tumor necrosis factor alpha, interleukin (IL)–2, IL–4, IL–10, IL–12, IL–13, IL–17, IL–18, and IL–22 using cytometric bead arrays; transforming growth factor beta 1 (TGF-β1) was measured using commercial enzyme-linked immunosorbent assay kits. Main results and the role of chance The cytokine profile was similar between the women who conceived and those who did not on the day of blastocyst transfer. In women who conceived, IFN-γ and IL–17 (pro-inflammatory cytokines) exhibited a transient and significant increase on day 3 after blastocyst transfer, which decreased to the baseline levels by day 6. Meanwhile, IL–10 (anti-inflammatory cytokine) was increased significantly on days 6 and 9, and TGF-β1 (anti-inflammatory cytokine) was increased significantly on day 9 after blastocyst transfer. In women who did not conceive, there was a more pronounced increase in IFN-γ and IL–17 (pro-inflammatory cytokines) on day 3, which was sustained on days 6 and 9 without a switch to an anti-inflammatory cytokine profile. Among women who conceived after blastocyst embryo transfer, there was a transient and modest increase in serum pro-inflammatory cytokine profile (IFN-γ and IL–17) 3 days after blastocyst transfer, which was followed by a switch to anti-inflammatory cytokine profile (increase IL–10 and TGF-β1) by 6 days after blastocyst transfer and the latter increase was sustained 9 days after blastocyst transfer, when pregnancy was confirmed. Limitations, reasons for caution This is an observational study on peripheral blood cytokine levels, so it is not possible to draw conclusions if the implantation failure is due primarily to failure of the embryo to elicit a trigger for the switch or failure of maternal response to a normal trigger released by the embryo. Wider implications of the findings: The characteristic change in peripheral cytokine profile during successful implantation, well before confirmation of pregnancy, may provide an opportunity to develop serum biomarkers to monitor implantation and to understand the mechanism of its failure, especially in women who experience recurrent implantation failure after IVF. Trial registration number Not applicable

Study question Whether the changing peripheral levels of Tim–3/Galectin–9 (Gal–9) and PD–1/PL–1 over 4 weeks after ET in ongoing pregnancies is different from pregnancies destined to miscarry. Summary answer A significant and sustained increase of Tim–3 in pNK cells was observed in pregnancies which were ongoing but not in pregnancies which later miscarried. What is known already The importance of maternal immune adaptation and tolerance to the implanting embryo, an allograft, has been extensively investigated for decades. Immune checkpoint molecules, like T-cell immunoglobulin mucin–3 (Tim–3) and programed cell death–1 (PD–1), are co-stimulatory receptors negatively regulating immune responses. During pregnancy, Tim–3 and PD–1 are expressed by several immune cells in the decidua and participate in the maternal-fetal immune interactions to mediate maternal immune tolerance through binding to their ligands Gal–9 and progressed death-ligand 1 (PD-L1) produced by trophoblast and immune cells. In addition to the implantation site, Tim–3 and PD–1 expressions in peripheral lymphocytes are modified during pregnancy. Study design, size, duration A prospective observational study includes 81 women who achieved ongoing pregnancy and 17 women who suffered from miscarriage after single day–5 blastocyst transfer. All the subjects were recruited from November 2018 to January 2020 in a university teaching hospital. Participants/materials, setting, methods Women undergoing single blastocyst transfer after in-vitro-fertilization (IVF) or intracytoplasmic sperm injection (ICSI) treatment were recruited on the day of ET following informed, written consent. They had serial blood samples taken on the day of ET, and 4, 5, 6 and 7 weeks of gestation for measurement of (1) membranous Tim–3 and PD–1 expression on various peripheral lymphocytes by flow cytometry; and (2) serum concentrations of ligands Gal–9 and PD-L1 by ELISA. Main results and the role of chance The comparisons between two groups showed there was no significant difference between the 2 groups in baseline levels among all the parameters measured. In women who achieved ongoing pregnancy, a significant and sustained increase of Tim–3 in either peripheral NK (pNK) subsets was observed at 4-week, 5-week, 6-week and 7-week gestations compared to the baseline (Tim–3+CD56dimNK 39.14±1.51%, 41.14±1.62%, 41.34±1.94%, and 41.69±2.12% vs. 30.27±1.49%; Tim–3+CD56brihgtNK cells, 24.54±1.71%, 25.43±1.54%, 27.26±1.88% and 24.70±1.64% vs. 19.08±1.13%), and the concentration of serum PD-L1 was significantly increased at 6-week and 7-week gestations (48.33±17.78 pg/ml, 52.53±20.60 pg/ml) when compared to the day of blastocyst transfer (41.40±16.01 pg/ml). The expressions of Tim–3 in T, NKT cells and PD–1 in NK, T, NKT cells were not significantly changed across the 5 time points. In women who conceived but later miscarried, all the parameters examined from 4–7 weeks of gestation were not significantly different when compared with the baseline measurement. The only measurement which showed a significant difference between the 2 groups and across all time points after ET was the proportion of Tim–3+CD56dimNK cells which was significantly higher in women who achieved ongoing pregnancies compared with women who destined to miscarry from 4 to 7 weeks of gestation. Limitations, reasons for caution It is uncertain if the observation would be different between miscarriage associated with aneuploid embryo or euploid embryo as we had not been able to obtain karyotyping result in most of the miscarriage cases. Wider implications of the findings: Our preliminary observation suggests that the proportion of Tim–3+pNK cells as early as 4-week gestation could be a potential immuno-bio-marker to predict if a pregnancy is likely to progress normally or result in a miscarriage. Clearly, the finding in this study needs to be confirmed in a larger cohort study Trial registration number not applicable.

Real-time proprioception is a challenging problem for soft robots, which have almost infinite degrees-of-freedom in body deformation. When multiple actuators are used, it becomes more difficult as deformation can also occur on actuators caused by interaction between each other. To tackle this problem, we present a method in this paper to sense and reconstruct 3D deformation on pneumatic soft robots by first integrating multiple low-cost sensors inside the chambers of pneumatic actuators and then using machine learning to convert the captured signals into shape parameters of soft robots. An exterior motion capture system is employed to generate the datasets for both training and testing. With the help of good shape parameterization, the 3D shape of a soft robot can be accurately reconstructed from signals obtained from multiple sensors. We demonstrate the effectiveness of this approach on two designs of soft robots -- a robotic joint and a deformable membrane. After parameterizing the deformation of these soft robots into compact shape parameters, we can effectively train the neural networks to reconstruct the 3D deformation from the sensor signals. The sensing and shape prediction pipeline can run at 50Hz in real-time on a consumer-level device., 8 pages, 10 figures

In this paper, we present a new computational pipeline for designing and fabricating 4D garments as knitwear that considers comfort during body movement. This is achieved by careful control of elasticity distribution to reduce uncomfortable pressure and unwanted sliding caused by body motion. We exploit the ability to knit patterns in different elastic levels by single-jersey jacquard (SJJ) with two yarns. We design the distribution of elasticity for a garment by physics-based computation, the optimized elasticity on the garment is then converted into instructions for a digital knitting machine by two algorithms proposed in this paper. Specifically, a graph-based algorithm is proposed to generate knittable stitch meshes that can accurately capture the 3D shape of a garment, and a tiling algorithm is employed to assign SJJ patterns on the stitch mesh to realize the designed distribution of elasticity. The effectiveness of our approach is verified on simulation results and on specimens physically fabricated by knitting machines.

We present a field-based method of toolpath generation for 3D printing continuous fibre reinforced thermoplastic composites. Our method employs the strong anisotropic material property of continuous fibres by generating toolpaths along the directions of tensile stresses in the critical regions. Moreover, the density of toolpath distribution is controlled in an adaptive way proportionally to the values of stresses. Specifically, a vector field is generated from the stress tensors under given loads and processed to have better compatibility between neighboring vectors. An optimal scalar field is computed later by making its gradients approximate the vector field. After that, isocurves of the scalar field are extracted to generate the toolpaths for continuous fibre reinforcement, which are also integrated with the boundary conformal toolpaths in user selected regions. The performance of our method has been verified on a variety of models in different loading conditions. Experimental tests are conducted on specimens by 3D printing continuous carbon fibres (CCF) in a polylactic acid (PLA) matrix. Compared to reinforcement by load-independent toolpaths, the specimens fabricated by our method show up to 71.4% improvement on the mechanical strength in physical tests when using the same (or even slightly smaller) amount of continuous fibres.

Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning-based approach to efficiently generate RGB-D images with completed information in high resolution. To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules—the completion module, the refinement module, and the super-resolution module. The completion module is based on an architecture of encoder–decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural network. The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions. For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for upsampling. Benefited from the adaptive convolution operators proposed in this article, our results outperform the existing deep-learning-based approaches for RGB-D image complete and super-resolution. As an end-to-end approach, high-fidelity RGB-D images can be generated efficiently at the rate of 22 frames/s. Note to Practitioners —With the development of consumer-level RGB-D cameras, industries have started to employ these low-cost sensors in many robotic and automation applications. However, images generated by consumer-level RGB-D cameras are generally in low resolution. Moreover, the depth images often have incomplete regions when the surface of an object is transparent, highly reflective, or beyond the distance of sensing. With the help of our method, engineers are able to “repair” the images captured by consumer-level RGB-D cameras in high efficiency. As the typical deep-learning networks are employed in this approach, the proposed approach fits well with the GPU-based hardware architecture of deep-learning computation—therefore, it potentially can be integrated into the hardware of RGB-D cameras.

3D printing techniques such as Fused Deposition Modeling (FDM) have enabled the fabrication of complex geometry quickly and cheaply. High stiffness parts are produced by filling the 2D polygons of consecutive layers with contour-parallel extrusion toolpaths. Uniform width toolpaths consisting of inward offsets from the outline polygons produce over- and underfill regions in the center of the shape, which are especially detrimental to the mechanical performance of thin parts. In order to fill shapes with arbitrary diameter densely the toolpaths require adaptive width. Existing approaches for generating toolpaths with adaptive width result in a large variation in widths, which for some hardware systems is difficult to realize accurately. In this paper we present a framework which supports multiple schemes to generate toolpaths with adaptive width, by employing a function to decide the number of beads and their widths. Furthermore, we propose a novel scheme which reduces extreme bead widths, while limiting the number of altered toolpaths. We statistically validate the effectiveness of our framework and this novel scheme on a data set of representative 3D models, and physically validate it by developing a technique, called back pressure compensation, for off-the-shelf FDM systems to effectively realize adaptive width., 16 pages, 20 figures, submitted to Computer-Aided Design journal

Alternative neutron detectors are actively being researched to replace 3He gas detectors for neutron scattering and homeland security. Neutron scintillators for this purpose must have high light output, short decay time, low sensitivity to gamma-rays, and possibly be non-hygroscopic. Gd-containing oxide scintillators are excellent at detecting gamma-rays, but their thermal-neutron detectability has not been proven yet, because of similar pulse-shapes and pulse-heights generated under thermal neutrons and gamma irradiation. Here we show that a non-negative matrix factorization (NMF) algorithm for digital signal processing greatly improves neutron-gamma discrimination ratio (the ratio of neutron and gamma detection efficiencies) of a Gd-containing scintillator, Ce:Gd 3 Al 2 Ga 3 O 12 (GAGG). In the case of Ce:GAGG as a gamma detector, better discrimination against ambient background events was observed compared to results from a traditional pulse-height method. It was also found that the secondary integration area (S 2 ) of a waveform in the time window of 0-50 ns is an energy measure of gamma-rays. Therefore, Gd-containing scintillators are potentially applicable in not only neutron detection but also in gamma detection with higher signal-to-noise ratio.

For grasping (unknown) objects, soft pneumatic actuators are primarily designed to bend towards a specific direction. Due to the flexibility of material and structure, soft actuators are also prone to out-of-plane deformations including twisting and sidewards bending, especially if the loading is asymmetric. In this paper, we demonstrate the negative effects of out-of-plane deformation on grasping. A structural design is proposed to reduce this type of deformation and thus improve grasping stability. Comparisons are first performed on soft pneumatic actuators with the same bending stiffness but different resistances to out-of-plane deformation, which is realized by changing the cross-section of the inextensible layer. To reduce out-of-plane deformation, a stiffening structure inspired by spatial flexures is integrated into the soft actuator. The integrated design is 3D printed using a single material. Physical experiments have been conducted to verify the improved grasping stability.

Recent work has demonstrated that the interior material layout of a 3D model can be designed to make a fabricated replica satisfy application-specific demands on its physical properties, such as resistance to external loads. A widely used practice to fabricate such models is by layer-based additive manufacturing (AM), which however suffers from the problem of adding and removing interior supporting structures. In this paper, we present a novel method for generating application-specific infill structures on rhombic cells so that the resultant structures can automatically satisfy manufacturing requirements on overhang-angle and wall-thickness. Additional supporting structures can be avoided entirely in our framework. To achieve this, we introduce the usage of an adaptive rhombic grid, which is built from an input surface model. Starting from the initial sparse set of rhombic cells, via numerical optimization techniques an objective function can be improved by adaptively subdividing the rhombic grid and thus adding more walls in cells. We demonstrate the effectiveness of our method for generating interior designs in the applications of improving mechanical stiffness and static stability.

We present an evolution method for designing the styling curves of garments. The procedure of evolution is driven by aesthetics-inspired scores to evaluate the quality of styling designs, where the aesthetic considerations are represented in the form of streamlines on human bodies. A dual representation is introduced in our platform to process the styling curves of designs, based on which robust methods for realizing the operations of evolution are developed. Starting from a given set of styling designs on human bodies, we demonstrate the effectiveness of set evolution inspired by aesthetic factors. The evolution is adaptive to the change of aesthetic inspirations. By this adaptation, our platform can automatically generate new designs fulfilling the demands of variations in different human bodies and poses.

Western commercial pig breeds have been intensively selected, resulting in a sizeable, rapid and efficient accretion of muscle but a reduction in meat quality. When compared with Western commercial pig breeds, Chinese indigenous pig breeds exhibited slower growth rates and reduced lean meat content but superior perceived meat quality. To study the factors that determine meat quality, we examined piglets of one Western commercial breed (Yorkshire) and one Chinese indigenous breed (Wannanhua) and sequenced the longissimus dorsi muscle using RNA-sequencing (RNA-seq). We analyzed their transcriptomes, focusing on identifying candidate genes that may influence porcine muscle growth, meat quality and adipose deposition. Gene Ontology functional enrichment and pathway enrichment analyses identified differentially expressed genes (DEGs) primarily associated with glycolytic metabolism, biological processes of muscle development and signaling pathways related to fatty acid metabolism, growth and carcass traits. This finding suggests that the DEGs may play important roles in determining meat quality traits. Quantitative real-time reverse transcription polymerase chain reaction confirmed the differential expression of 12 selected DEG. This study identified a number of novel candidate genes for porcine meat quality and carcass traits that merit further investigation to elucidate the molecular mechanisms responsible for muscle growth and fat deposition.

The layout and development of tropical agriculture is very important to the construction of Hainan Free Trade Port (HNFTP). Only by reasonably planning to the agricultural layout, the agricultural development could be promoted and met the needs of developing Hainan Free Trade Port (HNFTP). To scientifically and effectively plan Hainan’s agricultural spatial layout, this research thinks about the agricultural spatial layout of developed areas as a reference could be taken into consideration, and combine with the local characteristics, formulate guidelines, overall planning, and specific implementation.

Data-driven methods for modeling the realistic shapeof 3D human bodies need to access datasets that contain a large amount of 3D human models. We develop a method based on sparse representation in this paper to represent 3D human models as signals of patches. Unlike the general mesh compression approaches, all mesh models used in a data-driven human modeling framework have the same mesh connectivity. By using this property, we segment a human model into patches containing the same number of vertices. L 0 -learning algorithm is selected to train an overcomplete dictionary matrix, which in turn introduces sparse representation of the dataset. Patch signals of individual human models can then be extracted by using the dictionary matrix. With the ease of balance control between sparsity and accuracy that is featured by the chosen learning algorithm, a representation with high compression ratio and low shape-approximation error can be determined. The results have been compared with the widely used statistic representation based on principal component analysis (PCA) to verify the effectiveness of our approach. Moreover, the method for using sparse representation in the regression-based statistical modeling of 3D human models has been presented at the end of the paper.

This paper introduces a novel data-driven approach based on subjective constraints and feature learning for training perceptual models of preference. Fuzzy evaluation is applied to describe the subjective opinions from a large set of data collected from user study. Combined with the objective attributes of the training models and the subjective preferences, an optimization method is developed successfully for training and learning perceptual models. Two applications are given in details for the selection of “best” viewpoint of 3D objects and the optimized direction of 3D printing, which verify the effectiveness of our approach. This work also demonstrate a good human-computer interaction practice that draws supporting knowledge from both the machine side and the human side.

Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper, we propose a novel proprioception method for soft actuators during real-time interaction with previously unknown objects. First, we design a color-based sensing structure that instantly translates the inflation of a bellow into changes in color, which are subsequently detected by a miniaturized color sensor. The color sensor is small and, thus, multiple of them can be integrated into soft pneumatic actuators to reflect local deformations. Second, we make use of a feed-forward neural network to reconstruct a multivariate global shape deformation from local color signals. Our results demonstrate that deformations of the actuator during interaction, including sigmoid-like shapes, can be accurately reconstructed. The accurate shape sensing represents a significant step toward closed-loop control of soft robots in unstructured environments.

Tangible interaction with customized products integrating sensors and actuators recently grows into an interdisciplinary research area in computer graphics and human-robot interaction (e.g., [Groeger et al. 2016; Yu et al. 2018]). In this paper, we introduce tangible interaction into 3D printed modular robots. Our user study demonstrates that interacting with our robots can effectively improve human spatial ability, which plays an important role during a person's development in science, technology, engineering or math (STEM).

International audience; This paper presents a new method to fabricate 3D models on a robotic printing system equipped with multi-axis motion. Materials are accumulated inside the volume along curved tool-paths so that the need of supporting structures can be tremendously reduced - if not completely abandoned - on all models. Our strategy to tackle the challenge of tool-path planning for multi-axis 3D printing is to perform two successive decompositions, first volume-to-surfaces and then surfaces-to-curves. The volume-to-surfaces decomposition is achieved by optimizing a scalar field within the volume that represents the fabrication sequence. The field is constrained such that its iso-values represent curved layers that are supported from below, and present a convex surface affording for collision-free navigation of the printer head. After extracting all curved layers, the surfaces-to-curves decomposition covers them with tool-paths while taking into account constraints from the robotic printing system. Our method successfully generates tool-paths for 3D printing models with large overhangs and high-genus topology. We fabricated several challenging cases on our robotic platform to verify and demonstrate its capabilities.

Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a "positive feedback loop" effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration. The fundamental attributes and challenges/barriers of Additive Manufacturing (AM).The evolution of research on AM with a focus on engineering capabilities.The affordances enabled by AM such as geometry, material and tools design.The developments in industry, intellectual property, and education-related aspects.The important future trends of AM technologies.

This paper introduces a perceptual model for determining 3D printing orientations. Additive manufacturing methods involving low-cost 3D printers often require robust branching support structures to prevent material collapse at overhangs. Although the designed shape can successfully be made by adding supports, residual material remains at the contact points after the supports have been removed, resulting in unsightly surface artifacts. Moreover, fine surface details on the fabricated model can easily be damaged while removing supports. To prevent the visual impact of these artifacts, we present a method to find printing directions that avoid placing supports in perceptually significant regions. Our model for preference in 3D printing direction is formulated as a combination of metrics including area of support, visual saliency, preferred viewpoint and smoothness preservation. We develop a training-and-learning methodology to obtain a closed-form solution for our perceptual model and perform a large-scale study. We demonstrate the performance of this perceptual model on both natural and man-made objects.

In layer-based additive manufacturing (AM), supporting structures need to be inserted to support the overhanging regions. The adding of supporting structures slows down the speed of fabrication and introduces artifacts onto the finished surface. We present an orientation-driven shape optimizer to slim down the supporting structures used in single material-based AM. The optimizer can be employed as a tool to help designers to optimize the original model to achieve a more self-supported shape, which can be used as a reference for their further design. The model to be optimized is first enclosed in a volumetric mesh, which is employed as the domain of computation. The optimizer is driven by the operations of reorientation taken on tetrahedra with ‘facing-down’ surface facets. We formulate the demand on minimizing shape variation as global rigidity energy. The local optimization problem for determining a minimal rotation is analyzed on the Gauss sphere, which leads to a closed-form solution. Moreover, we also extend our approach to create the functions of controlling the deformation and searching for optimal printing directions.

To solve the problem of generating segmentations of meaningful parts from scanned models with freeform surfaces, we explore a compact shape prior-based segmentation approach in this paper. Our approach is inspired by an observation that a variety of natural objects consist of meaningful components in the form of compact shape and these components with compact shape are usually separated with each other by salient features. The segmentation for multiregions is performed in two phases in our framework. First, the segmentation is taken in low-level with the help of discrete Morse complex enhanced by anisotropic filtering. Second, we extract components with compact shape by using agglomerative clustering to optimize the normalized cut metric, in which the affinities of boundary compatibility, 2D shape compactness and 3D shape compactness are incorporated. The practical functionality of our approach is proved by applying it to the application of customized dental treatment.

Designing cooling channels for the thermoplastic injection process is a very important step in mold design. A conformal cooling channel can significantly improve the efficiency and the quality of production in plastic injection molding. This paper introduces an approach to generate spiral channels for conformal cooling. The cooling channels designed by our algorithms has very simple connectivity and can achieve effective conformal cooling for the models with complex shapes. The axial curves of cooling channels are constructed on a free-form surface conformal to the mold surface. With the help of boundary-distance maps, algorithms are investigated to generate evenly distributed spiral curves on the surface. The cooling channels derived from these spiral curves are conformal to the plastic part and introduce nearly no reduction at the rate of coolant flow. Therefore, the channels are able to achieve uniform mold cooling. Moreover, by having simple connectivity, these spiral channels can be fabricated by copper duct bending instead of expensive selective laser sintering.

Offsetting-based hollowing is a solid modeling operation widely used in 3D printing, which can change the model's physical properties and reduce the weight by generating voids inside a model. However, a hollowing operation can lead to additional supporting structures for fabrication in interior voids, which cannot be removed. As a consequence, the result of a hollowing operation is affected by these additional supporting structures when applying the operation to optimize physical properties of different models. This paper proposes a support-free hollowing framework to overcome the difficulty of fabricating voids inside a solid. The challenge of computing a support-free hollowing is decomposed into a sequence of shape optimization steps, which are repeatedly applied to interior mesh surfaces. The optimization of physical properties in different applications can be easily integrated into our framework. Comparing to prior approaches that can generate support-free inner structures, our hollowing operation can reduce more volume of material and thus provide a larger solution space for physical optimization. Experimental tests are taken on a number of 3D models to demonstrate the effectiveness of this framework.

Wireframe models are becoming a popular option in 3D printing. Generating sparse wireframe models using classic mesh simplification methods leads to models that require a lot of support structures in the layer-upon-layer additive process. In this paper we present a mesh simplification method that takes into account the overhang angle. Specifically, we propose a metric for self-supportability. By combining this novel metric together with the classic error metrics for mesh simplification, our method generates sparse wireframe models that need much less supports. Moreover, the operations of vertex position optimization and edge flipping are used to further increase self-supportability of the wireframe models. We demonstrate the effectiveness of the proposed method on a number of 3D models.

Amyloid precursor protein (APP) is a highly conserved integral membrane protein extensively expressed in various types of cells. Previously we found that overexpression of APP in patients with AML1/ETO-positive acute myeloid leukemia (AML) associated with a higher incidence of extramedullary infiltrationin and indicate a poor prognosis. In this study, we attempted to define the roles of APP in AML1/ETO-positive leukemia cells. Western blotting and qRT-PCR analysis showed that protein levels of APP are significantly higher in Kasumi-1, a t(8;21)/ AML1/ETO-positive M2-type AML cell line. Stable knockdown of APP by lentivirus-based RNA interference (RNAi) dramatically impaired colony-formation and migration ability of Kasumi-1 cells, whereas APP knockdown had very little effect on cell viability, apoptosis, cell cycle and differentiation. We further explored whether the pan-histone deacetylase inhibitor panobinostat could deplete the protein levels of APP in Kasumi-1 cells. Treatment with panobinostat caused depletion of APP in Kasumi-1 cells. These findings indicate that overexpression of APP is involved in promoting proliferation and migration of AML1/ETO-positive leukemia cells and can be inhibited by panobinostat, which provide an attractive prospect for treatment of AML1/ETO-positive AML.

This paper introduces a novel method for designing personalized orthopedic casts which are aware of thermal-comfort while satisfying mechanical requirements. Our pipeline starts from thermal images taken by an infrared camera, by which the distribution of thermal-comfort sensitivity is generated on the surface of a 3D scanned model. We formulate a hollowed Voronoi tessellation pattern to represent the covered region for a web-like cast design. The pattern is further optimized according to the thermal-comfort sensitivity calculated from thermal images. Working together with a thickness variation method, we generate a solid model for a personalized cast maximizing both thermal comfort and mechanical stiffness. To demonstrate the effectiveness of our approach, 3D printed models of personalized casts are tested on body parts of different individuals.

3D printed electronics introduces new opportunities in product design. On the other side, it also brings challenges regarding many aspects in the design and manufacturing process of prototypes/products. In this paper, based on our preliminary investigations, we summarize the opportunities of using 3D printed electronics in product design as: 1) it offers designers more freedom in their designs; 2) it promotes miniaturization of design; 3) it accelerates the design and manufacturing process; 4) it is able to improve the efficiency of producing customized/personalized mechatronic/electronic products and 5) it improves the sustainability in the product design and manufacturing process. Motivated by those opportunities, we conducted four case studies regarding four key aspects of 3D printed electronics: the conductive materials, geometric modelling, multiphysics simulation and manufacturing tools. Based on the findings in those case studies, we identified the challenges in 3D printed electronics and highlight the future works which may provide a better support to the needs of product designers.

This paper presents a robotic system — RoboFDM that targets at printing 3D models without support-structures, which is considered as the major restriction to the flexibility of 3D printing. The hardware of RoboFDM consists of a robotic arm providing 6-DOF motion to the platform of material accumulation and an extruder forming molten filaments of polylactic acid (PLA). The fabrication of 3D models in this system follows the principle of fused decomposition modeling (FDM). Different from conventional FDM, an input model fabricated by RoboFDM is printed along different directions at different places. A new algorithm is developed to decompose models into support-free parts that can be printed one by one in a collision-free sequence. The printing directions of all parts are also determined during the computation of model decomposition. Experiments have been successfully taken on our RoboFDM system to print general freeform objects in a support-free manner.

In this paper, we present an optimization framework for automating the shape customization of human-centric products, which can be mounted on or embedded in human body (such as exoskeletal devices and implants). This kind of products needs to be customized to fit the body shapes of users. At present, the design customization for freeform objects is often taken in an interactive manner that is inefficient. We investigate a method to automate the procedure of customization. Major difficulty in solving this problem is caused by the not freely changed shape of components. They should be selected from a series of standardized shapes. Different from the existing approaches that allow fabricating all components by customized production, we develop a new method to generate customized products by using as-many-as-possible standardized components. Our work is based on a mixed-integer shape optimization framework.

Swine influenza virus (SIV), one of the most important zoonotic agents, is associated with major public health concerns. The current study was conducted to investigate the role of p38 mitogen-activated protein kinase (p38 MAPK) in the regulation of the inflammatory response to acute lung injury (ALI) induced by SIV of H9N2 subtype (H9N2-SIV) in mice. For this purpose, BALB/c mice were intranasally infected with 20 LD(50) of H9N2-SIV (infected group), while non-infected mice served as control (control group). To assess the effect of p38 MAPK, its specific inhibitor SB203580 was employed followed by SIV infection (SB group). At various times after infection, mouse lungs were subjected to pathological and histological observations and detection of inflammatory cytokines tumor necrosis factor α (TNF)-α, interleukin (IL)-1β, IL-6, and IL-10 and phosphorylated p38 MAPK. The obtained results showed obvious inflammatory responses, injury and raised levels of inflammatory cytokines and phosphorylated p38 MAPK in the lungs of virus-infected mice. In the mice inoculated with the virus alone, the level of phosphorylated p38 MAPK increased from day 2 and peaked at day 6 post infection (p.i.). However, SB203580 caused lower increases in inflammatory cytokines and phosphorylated p38 MAPK and a milder lung injury. These findings indicate that the activation of p38 MAPK upregulated the inflammatory responses to H9N2-SIV-induced ALI, increased its severity and promoted the production of inflammatory cytokines.

In this paper a non-layer-based additive manufacturing (AM) process named computer numerically controlled (CNC) accumulation process is presented for applications such as plastic part repairing and modification. To facilitate the AM process, a novel three-dimensional (3D) laser scanning system based on a micro-electo-mechanical system (MEMS) device is developed for in situ scanning of inserted components. The integration of the scanning system in the CNC accumulation process enables the building-around-inserts with little human efforts. A point processing method based on the algebraic point set surface (APSS) fitting and layered depth-normal image (LDNI) representation is developed for converting the scanning points into triangular meshes. The newly developed 3D scanning system is compact with sufficient accuracy for the CNC accumulation process. Based on the constructed base surface model, data processing operations including multi-axis tool path planning and motion control are also investigated. Multiple test cases are performed to illustrate the capability of the integrated CNC accumulation process on addressing the requirements of building-around-inserts.

Purpose – Given an intersection-free mesh surface S, the paper introduces a method to thicken S into a solid H located at one side of S. By such a surface-to-solid conversion operation, industrial users are able to fabricate a designed (or reconstructed) surface by rapid prototyping. Design/methodology/approach – The paper first investigates an implicit representation of the thickened solid H according to an extension of signed distance function. After that, a partial surface reconstruction algorithm is proposed to generate the boundary surface of H, which retains the given surface S on the resultant surface. Findings – Experimental tests show that the thickening results generated by the method give nearly uniform thickness and meanwhile do not present shape approximation error at the region of input surface S. These two good properties are important to the industrial applications of solid fabrication. Research limitations/implications – The input polygonal model is assumed to be intersection-free, where models containing self-intersection will lead to invalid thickening results. Originality/value – A novel robust operation is to convert a freeform open surface into a solid by introducing no shape approximation error. A new implicit function gives a compact mathematical representation, which can easily handle the topological change on the thickened solids. A new polygonization algorithm generates faces for the boundary of thickened solid meanwhile retaining faces on the input open mesh.

PurposeMost layer‐based rapid prototyping systems use polygonal models as input. In addition, the input polygonal models need to be manifold and water‐tight; otherwise the built objects may have defects or the building process may fail in some cases. This paper aims to present a regulation method of an arbitrarily complex polygonal model for rapid prototyping and manufacturing applications.Design/methodology/approachThe method is based on a semi‐implicit representation of a solid model named the layered depth‐normal images (LDNI), which sparsely encodes the shape boundary of a polygonal model in three orthogonal directions. In the method, input polygonal models or parametric equations are first converted into LDNI models. A regulation operator based on the computed LDNI models is presented. A volume tiling technique is developed for very complex geometries and high accuracy requirements. From the processed LDNI model, an adaptive contouring method is presented to construct a cell representation that includes both uniform and octree cells. Finally, two‐manifold and water‐tight polygonal mesh surfaces are constructed from the cell representation.FindingsThe LDNI‐based mesh regulation operation can be robust due to its simplicity. The accuracy of the generated regulated models can be controlled by setting LDNI pixel width. Parallel computing techniques can be employed to accelerate the computation in the LDNI‐based method. Experimental results on various CAD models demonstrate the effectiveness and efficiency of our approach for complex geometries.Research limitations/implicationsThe input polygonal model is assumed to be closed in our method. The regulated polygonal model based on our method may have a big file size.Originality/valueA novel mesh regulation method is presented in this paper. The method is suitable for rapid prototyping and manufacturing applications by achieving a balance between simplicity, robustness, accuracy, speed and scalability. This research contributes to the additive manufacturing development by providing a digital data preparation method and related tools.

Self-reconfigurable modular robots (SRRobot) that can change their shape and function in different environments according to different tasks have caught a lot of attention recently. Most existing prototypes use professional electronic components with relatively expensive cost and high barrier of fabrication. In this paper, we present a low-cost SRRobot with double-cube modules. Our system is easy-to-build even for novices as all electric components are off-the-shelf and the structural components in plastics are made by 3D printing. To have a better design of interior structures, we first construct a design space for all feasible solutions that satisfy the constraints of fabrication. Then, an optimized solution is found by an objective function incorporating the factors of space utilization, structural sound-ness and assembly complexity. Thirty EasySRRobot modules are manufactured and assembled. The functionality of our algorithm is demonstrated by comparing an optimized interior design with other two feasible designs and realizing different motions on an EasySRRobot with four modules.

Digital Light Processing (DLP) is a 3D printing technology that uses ultraviolet to solidify photopolymer resin. It has been widely used for its efficiency in printing speed and simplicity in hardware setup [Pan et al. 2011]. However, to ensure a layer of model be completely solidified, the working area is restricted by the performance of projectors in terms of light intensity -- i.e., only a very small region can be solidified by each projection. This limits the usage of DLP-based 3D printing in practical applications having large models. Although prior effort was made to decompose a large 3D model into smaller parts to fit into the working envelope, the seams between assembled parts can harm both the mechanical strength and the aesthetic appearance on the resultant model.

We present a delta DLP 3D printer with large size in this paper. Compared with traditional DLP 3D printers that use a low-cost off-the-shelf consumer projector and a single vertical carriage, the platform of our delta DLP 3D printer can also move horizontally in the plane. We show that this structure allows the printer to have a larger printing area than the projection area of a projector. Our system can print 3D models much larger than traditional DLP 3D printers. The major challenge to realize delta 3D printing with large size comes from how to partition an arbitrary planar polygonal shape (possibly with holes or multiple disjoint polygons) into a minimum number of rectangles with fixed size, which is NP-hard. We propose a simple yet efficient approximation algorithm to solve this problem. The time complexity of our algorithm is O(n3 log n), where n is the number of edges in the polygonal shape. A physical prototype system is built and several large 3D models with complex geometric structures have been printed as examples to demonstrate the effectiveness of our approach.

Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve real-time imitation with small latency, we present a framework in this paper to reconstruct motion on humanoids based on sparsely sampled correspondence. The imitation problem is formulated as finding the projection of a point from the configuration space of a human's poses into the configuration space of a humanoid. An optimal projection is defined as the one that minimizes a back-projected deviation among a group of candidates, which can be determined in a very efficient way. Benefited from this formulation, effective projections can be obtained by using sparse correspondence. Methods for generating these sparse correspondence samples have also been introduced. Our method is evaluated by applying the human's motion captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be realized and used in the example application of tele-operation., 8 pages, 8 figures, technical report

Three high-resolution positioning methods based on the FluoroBancroft linear-algebraic method [S. B. Andersson, Opt. Express 16, 18714 (2008)] are proposed for wavelength-shifting fiber (WLSF) neutron detectors. Using a Gaussian or exponential-decay light-response function, the non-linear relation of photon-number profiles vs. x-pixels was linearized and neutron positions were determined. After taking the super-Poissonian photon noise into account, the proposed algorithms give an average of 0.03-0.08 pixel position error much smaller than that (0.29 pixel) from a traditional maximum photon algorithm (MPA). The new algorithms result in better detector uniformity, less position misassignment (ghosting), better spatial resolution, and an equivalent or better instrument resolution in powder diffraction than the MPA. These improvements will facilitate broader applications of WLSF detectors at time-of-flight neutron powder diffraction beamlines, including single-crystal diffraction and texture analysis.