Flow diverters treatment planning of small- and medium-sized intracranial saccular aneurysms on the internal carotid artery via constraint-based virtual deployment.

PURPOSE: The internal carotid artery (ICA) is a region with a high incidence for small- and medium-sized saccular aneurysms. However, the treatment relies heavily on the surgeon's experience to achieve optimal outcome. Although the finite element method (FEM) and computational fluid dynamics can predict the postoperative outcomes, due to the computational complexity of traditional methods, there is an urgent need for investigating the fast but versatile approaches related to numerical simulations of flow diverters (FDs) deployment coupled with the hemodynamic analysis to determine the treatment plan. METHODS: We collected the preoperative and postoperative data from 34 patients (29 females, 5 males; mean age 55.74 ± 9.98 years) who were treated with a single flow diverter for small- to medium-sized intracranial saccular aneurysms on the ICA. The constraint-based virtual deployment (CVD) method is proposed to simulate the FDs expanding outward along the vessel centerline while be constrained by the inner wall of the vessel. RESULTS: The results indicate that there were no significant differences in the reduction rates of wall shear stress and aneurysms neck velocity between the FEM and methods. However, the solution time of CVD was greatly reduced by 98%. CONCLUSION: In the typical location of small- and medium-sized saccular aneurysms, namely the ICA, our virtual FDs deployment simulation effectively balances the computational accuracy and efficiency. Combined with hemodynamics analysis, our method can accurately represent the blood flow changes within the lesion region to assist surgeons in clinical decision-making.

Deep local-to-global feature learning for medical image super-resolution.

Medical images play a vital role in medical analysis by providing crucial information about patients' pathological conditions. However, the quality of these images can be compromised by many factors, such as limited resolution of the instruments, artifacts caused by movements, and the complexity of the scanned areas. As a result, low-resolution (LR) images cannot provide sufficient information for diagnosis. To address this issue, researchers have attempted to apply image super-resolution (SR) techniques to restore the high-resolution (HR) images from their LR counterparts. However, these techniques are designed for generic images, and thus suffer from many challenges unique to medical images. An obvious one is the diversity of the scanned objects; for example, the organs, tissues, and vessels typically appear in different sizes and shapes, and are thus hard to restore with standard convolution neural networks (CNNs). In this paper, we develop a dynamic-local learning framework to capture the details of these diverse areas, consisting of deformable convolutions with adjustable kernel shapes. Moreover, the global information between the tissues and organs is vital for medical diagnosis. To preserve global information, we propose pixel-pixel and patch-patch global learning using a non-local mechanism and a vision transformer (ViT), respectively. The result is a novel CNN-ViT neural network with Local-to-Global feature learning for medical image SR, referred to as LGSR, which can accurately restore both local details and global information. We evaluate our method on six public datasets and one large-scale private dataset, which include five different types of medical images (i.e., Ultrasound, OCT, Endoscope, CT, and MRI images). Experiments show that the proposed method achieves superior PSNR/SSIM and visual performance than the state of the arts with competitive computational costs, measured in network parameters, runtime, and FLOPs. What is more, the experiment conducted on OCT image segmentation for the downstream task demonstrates a significantly positive performance effect of LGSR.

Risk prediction and analysis of gallbladder polyps with deep neural network.

The aim of this study is to analyze the risk factors associated with the development of adenomatous and malignant polyps in the gallbladder. Adenomatous polyps of the gallbladder are considered precancerous and have a high likelihood of progressing into malignancy. Preoperatively, distinguishing between benign gallbladder polyps, adenomatous polyps, and malignant polyps is challenging. Therefore, the objective is to develop a neural network model that utilizes these risk factors to accurately predict the nature of polyps. This predictive model can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. A retrospective study was done on patients who had cholecystectomy surgeries at the Department of Hepatobiliary Surgery of the Second People's Hospital of Shenzhen between January 2017 and December 2022. The patients' clinical characteristics, lab results, and ultrasonographic indices were examined. Using risk variables for the growth of adenomatous and malignant polyps in the gallbladder, a neural network model for predicting the kind of polyps will be created. A normalized confusion matrix, PR, and ROC curve were used to evaluate the performance of the model. In this comprehensive study, we meticulously analyzed a total of 287 cases of benign gallbladder polyps, 15 cases of adenomatous polyps, and 27 cases of malignant polyps. The data analysis revealed several significant findings. Specifically, hepatitis B core antibody (95% CI -0.237 to 0.061, p < 0.001), number of polyps (95% CI -0.214 to -0.052, p = 0.001), polyp size (95% CI 0.038 to 0.051, p < 0.001), wall thickness (95% CI 0.042 to 0.081, p < 0.001), and gallbladder size (95% CI 0.185 to 0.367, p < 0.001) emerged as independent predictors for gallbladder adenomatous polyps and malignant polyps. Based on these significant findings, we developed a predictive classification model for gallbladder polyps, represented as follows, Predictive classification model for GBPs = -0.149 * core antibody - 0.033 * number of polyps + 0.045 * polyp size + 0.061 * wall thickness + 0.276 * gallbladder size - 4.313. To assess the predictive efficiency of the model, we employed precision-recall (PR) and receiver operating characteristic (ROC) curves. The area under the curve (AUC) for the prediction model was 0.945 and 0.930, respectively, indicating excellent predictive capability. We determined that a polyp size of 10 mm served as the optimal cutoff value for diagnosing gallbladder adenoma, with a sensitivity of 81.5% and specificity of 60.0%. For the diagnosis of gallbladder cancer, the sensitivity and specificity were 81.5% and 92.5%, respectively. These findings highlight the potential of our predictive model and provide valuable insights into accurate diagnosis and risk assessment for gallbladder polyps. We identified several risk factors associated with the development of adenomatous and malignant polyps in the gallbladder, including hepatitis B core antibodies, polyp number, polyp size, wall thickness, and gallbladder size. To address the need for accurate prediction, we introduced a novel neural network learning algorithm. This algorithm utilizes the aforementioned risk factors to predict the nature of gallbladder polyps. By accurately identifying the nature of these polyps, our model can assist patients in making informed decisions regarding their treatment and management strategies. This innovative approach aims to improve patient outcomes and enhance the overall effectiveness of care.

PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography.

Cardiovascular disease, primarily caused by atherosclerotic plaque formation, is a significant health concern. The early detection of these plaques is crucial for targeted therapies and reducing the risk of cardiovascular diseases. This study presents PlaqueNet, a solution for segmenting coronary artery plaques from coronary computed tomography angiography (CCTA) images. For feature extraction, the advanced residual net module was utilized, which integrates a deepwise residual optimization module into network branches, enhances feature extraction capabilities, avoiding information loss, and addresses gradient issues during training. To improve segmentation accuracy, a depthwise atrous spatial pyramid pooling based on bicubic efficient channel attention (DASPP-BICECA) module is introduced. The BICECA component amplifies the local feature sensitivity, whereas the DASPP component expands the network's information-gathering scope, resulting in elevated segmentation accuracy. Additionally, BINet, a module for joint network loss evaluation, is proposed. It optimizes the segmentation model without affecting the segmentation results. When combined with the DASPP-BICECA module, BINet enhances overall efficiency. The CCTA segmentation algorithm proposed in this study outperformed the other three comparative algorithms, achieving an intersection over Union of 87.37%, Dice of 93.26%, accuracy of 93.12%, mean intersection over Union of 93.68%, mean Dice of 96.63%, and mean pixel accuracy value of 96.55%.

Automatic segmentation of white matter hyperintensities and correlation analysis for cerebral small vessel disease.

Objective: Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume. Methods: This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively. Result: Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively. Conclusion: The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research.

Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention.

PURPOSE: Segmentation of liver vessels from CT images is indispensable prior to surgical planning and aroused a broad range of interest in the medical image analysis community. Due to the complex structure and low-contrast background, automatic liver vessel segmentation remains particularly challenging. Most of the related researches adopt FCN, U-net, and V-net variants as a backbone. However, these methods mainly focus on capturing multi-scale local features which may produce misclassified voxels due to the convolutional operator's limited locality reception field. METHODS: We propose a robust end-to-end vessel segmentation network called Inductive BIased Multi-Head Attention Vessel Net(IBIMHAV-Net) by expanding swin transformer to 3D and employing an effective combination of convolution and self-attention. In practice, we introduce voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels and adopt multi-scale convolutional operators to gain local spatial information. On the other hand, we propose the inductive biased multi-head self-attention which learns inductively biased relative positional embedding from initialized absolute position embedding. Based on this, we can gain more reliable queries and key matrices. RESULTS: We conducted experiments on the 3DIRCADb dataset. The average dice and sensitivity of the four tested cases were 74.8[Formula: see text] and 77.5[Formula: see text], which exceed the results of existing deep learning methods and improved graph cuts method. The Branches Detected(BD)/Tree-length Detected(TD) indexes also proved the global/local feature capture ability better than other methods. CONCLUSION: The proposed model IBIMHAV-Net provides an automatic, accurate 3D liver vessel segmentation with an interleaved architecture that better utilizes both global and local spatial features in CT volumes. It can be further extended for other clinical data.

Enhancing the depth perception of DSA images with 2D-3D registration.

Objective: Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D-3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images. Methods: To make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D-3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm. Result: In this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI). Conclusion: The experimental results in this study show that in the 2D-3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.

Configuration-based Optimization for Virtual Hand Haptic Simulation.

Interacting with virtual objects via haptic feedback using the user's hand directly (virtual hand haptic interaction) provides a natural and immersive way to explore the virtual world. It remains a challenging topic to achieve 1 kHz stable virtual hand haptic simulation with no penetration amid hundreds of hand-object contacts. In this paper, we advocate decoupling the high-dimensional optimization problem of computing the graphic-hand configuration, and progressively optimizing the configuration of the graphic palm and fingers, yielding a decoupled-and-progressive optimization framework. We also introduce a method for accurate and efficient hand-object contact simulation, which constructs a virtual hand consisting of a sphere-tree model and five articulated cone frustums, and adopts a configuration-based optimization algorithm to compute the graphic-hand configuration under non-penetration contact constraints. Experimental results show both high update rate and stability for a variety of manipulation behaviors. Non-penetration between the graphic hand and complex-shaped objects can be maintained under diverse contact distributions, and even for frequent contact switches. The update rate of the haptic simulation loop exceeds 1 kHz for the whole-hand interaction with about 250 contacts.

Versatile cutting fracture evolution modeling for deformable object cutting simulation.

BACKGROUND AND OBJECTIVES: Soft body cutting simulation is the core module of virtual surgical training systems. By making full use of the powerful computing resources of modern computers, the existing methods have already met the needs of real-time interaction. However, there is still a lack of high realism. The main reason is that most current methods follows the "Intersection-IS-Fracture" mode, namely cutting fracture occurs as long as the cutting blade intersects with the object. To model real-life cutting phenomenon considering deformable objects' fracture resistance, this paper presents a highly realistic virtual cutting simulation algorithm by introducing an energy-based cutting fracture evolution model. METHODS: We design the framework based on the co-rotational linear FEM model to support large deformations of soft objects and also adopt the composite finite element method (CFEM) to balance between simulation accuracy and efficiency. Then, a cutting plane constrained Griffth's energy minimization scheme is proposed to determine when and how to generate a new cut. Moreover, to provide the contact effect before the fracture occurs, we design a material-aware adaptation scheme that can guarantee indentation consistent with the cutting tool blade and visually plausible indentation-induced deformation to avoiding large computational effort. RESULTS AND CONCLUSION: The experimental results demonstrate that the proposed algorithm is feasible for generating highly realistic cutting simulation results of different objects with various materials and geometrical characteristics while introducing a negligible computational cost. Besides, for different blade shapes, the proposed algorithm can produce highly consistent indentation and fracture. Qualitative evaluation and performance analysis indicate the versatility of the proposed algorithm.

The human malaria parasite genome is configured into thousands of coexpressed linear regulatory units.

The human malaria parasite Plasmodium falciparum thrives in radically different host environments in mosquitoes and humans, with only a limited set of transcription factors. The nature of regulatory elements or their target genes in the P. falciparum genome remains elusive. Here, we found that this eukaryotic parasite uses an efficient way to maximally use genetic and epigenetic regulation to form regulatory units (RUs) during blood infections. Genes located in the same RU tend to have the same pattern of expression over time and are associated with open chromatin along regulatory elements. To precisely define and quantify these RUs, a novel hidden Markov model was developed to capture the regulatory structure in a genome-wide fashion by integrating expression and epigenetic evidence. We successfully identified thousands of RUs and cross-validated with previous findings. We found more genes involved in red blood cell (RBC) invasion located in the same RU as the PfAP2-I (AP2-I) transcription factor, demonstrating that AP2-I is responsible for regulating RBC invasion. Our study has provided a regulatory mechanism for a compact eukaryotic genome and offers new insights into the in vivo transcriptional regulation of the P. falciparum intraerythrocytic stage.

MMTLNet: Multi-Modality Transfer Learning Network with adversarial training for 3D whole heart segmentation.

The accurate whole heart segmentation (WHS) of multi-modality medical images including magnetic resonance image (MRI) and computed tomography (CT) plays an important role in many clinical applications, such as accurate preoperative diagnosis planning and intraoperative treatment. Considering that the shape information of each component of the whole heart is complementary, we can extract multi-modality features and obtain the final segmentation results by fusing MRI and CT images. In this paper, we proposed a multi-modality transfer learning network with adversarial training (MMTLNet) for 3D multi-modality whole heart segmentation. Firstly, the network transfers the source domain (MRI domain) to the target domain (CT domain) by reconstructing the MRI images with a generator network and optimizing the reconstructed MRI images with a discriminator network, which enables us to fuse the MRI images with CT images to fully utilize the useful information from images in multi-modality for segmentation task. Secondly, to retain the useful information and remove the redundant information for accurate segmentation, we introduce the spatial attention mechanism into the backbone connection of UNet network to optimize the feature extraction between layers, and add channel attention mechanism at the jump connection to optimize the information extracted from the low-level feature map. Thirdly, we propose a new loss function in the adversarial training by introducing a weighted coefficient to distribute the proportion between Dice coefficient loss and generator loss, which can not only ensure the images to be correctly transferred from MRI domain to CT domain, but also achieve accurate segmentation with the transferred domain. We extensively evaluated our method on the data set of the multi-modality whole heart segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The dice values of whole heart segmentation are 0.914 (CT images) and 0.890 (MRI images), which are both higher than the state-of-the-art.

Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation.

It is a challenge to automatically and accurately segment the liver and tumors in computed tomography (CT) images, as the problem of over-segmentation or under-segmentation often appears when the Hounsfield unit (Hu) of liver and tumors is close to the Hu of other tissues or background. In this paper, we propose the spatial channel-wise convolution, a convolutional operation along the direction of the channel of feature maps, to extract mapping relationship of spatial information between pixels, which facilitates learning the mapping relationship between pixels in the feature maps and distinguishing the tumors from the liver tissue. In addition, we put forward an iterative extending learning strategy, which optimizes the mapping relationship of spatial information between pixels at different scales and enables spatial channel-wise convolution to map the spatial information between pixels in high-level feature maps. Finally, we propose an end-to-end convolutional neural network called Channel-UNet, which takes UNet as the main structure of the network and adds spatial channel-wise convolution in each up-sampling and down-sampling module. The network can converge the optimized mapping relationship of spatial information between pixels extracted by spatial channel-wise convolution and information extracted by feature maps and realizes multi-scale information fusion. The proposed ChannelUNet is validated by the segmentation task on the 3Dircadb dataset. The Dice values of liver and tumors segmentation were 0.984 and 0.940, which is slightly superior to current best performance. Besides, compared with the current best method, the number of parameters of our method reduces by 25.7%, and the training time of our method reduces by 33.3%. The experimental results demonstrate the efficiency and high accuracy of Channel-UNet in liver and tumors segmentation in CT images.

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).

RIANet: Recurrent interleaved attention network for cardiac MRI segmentation.

BACKGROUND: Segmentation of anatomical structures of the heart from cardiac magnetic resonance images (MRI) has a significant impact on the quantitative analysis of the cardiac contractile function. Although deep convolutional neural networks (ConvNets) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing deep ConvNets to precisely and automatically segment multiple heart structures from cardiac MRI. This paper presents a novel recurrent interleaved attention network (RIANet) to comprehensively tackle this issue. METHOD: The proposed RIANet can efficiently reuse parameters to encode richer representative features via introducing a recurrent feedback structure, Clique Block, which incorporates both forward and backward connections between different layers with the same resolution. Further, we integrate a plug-and-play interleaved attention (IA) block to modulate the information passed to the decoding stage of RIANet by effectively fusing multi-level contextual information. In addition, we improve the discrimination capability of our RIANet through a deep supervision mechanism with weighted losses. RESULTS: The performance of RIANet has been extensively validated in the segmentation contest of the ACDC 2017 challenge held in conjunction with MICCAI 2017, with mean Dice scores of 0.942 (left ventricular), 0.923 (right ventricular) and 0.910 (myocardium) for cardiac MRI segmentation. Besides, we visualize intermediate features of our RIANet using guided backpropagation, which can intuitively depict the effects of our proposed components in feature representation. CONCLUSION: Experimental results demonstrate that our RIANet have achieved competitive segmentation results with fewer parameters compared with the state-of-the-art approaches, corroborating the effectiveness and robustness of our proposed RIANet.

Assessing performance of augmented reality-based neurosurgical training.

This paper presents a novel augmented reality (AR)-based neurosurgical training simulator which provides a very natural way for surgeons to learn neurosurgical skills. Surgical simulation with bimanual haptic interaction is integrated in this work to provide a simulated environment for users to achieve holographic guidance for pre-operative training. To achieve the AR guidance, the simulator should precisely overlay the 3D anatomical information of the hidden target organs in the patients in real surgery. In this regard, the patient-specific anatomy structures are reconstructed from segmented brain magnetic resonance imaging. We propose a registration method for precise mapping of the virtual and real information. In addition, the simulator provides bimanual haptic interaction in a holographic environment to mimic real brain tumor resection. In this study, we conduct AR-based guidance validation and a user study on the developed simulator, which demonstrate the high accuracy of our AR-based neurosurgery simulator, as well as the AR guidance mode's potential to improve neurosurgery by simplifying the operation, reducing the difficulty of the operation, shortening the operation time, and increasing the precision of the operation.

Altered expression of K13 disrupts DNA replication and repair in Plasmodium falciparum.

BACKGROUND: Plasmodium falciparum exhibits resistance to the artemisinin component of the frontline antimalarial treatment Artemisinin-based Combination Therapy in South East Asia. Millions of lives will be at risk if artemisinin resistance (ART-R) spreads to Africa. Single non-synonymous mutations in the propeller region of PF3D7_1343700,"K13" are implicated in resistance. In this work, we use transcriptional profiling to characterize a laboratory-generated k13 insertional mutant previously demonstrated to have increased sensitivity to artemisinins to explore the functional role of k13. RESULTS: A set of RNA-seq and microarray experiments confirmed that the expression profile of k13 is specifically altered during the early ring and early trophozoite stages of the mutant intraerythrocytic development cycle. The down-regulation of k13 transcripts in this mutant during the early ring stage is associated with a transcriptome advance towards a more trophozoite-like state. To discover the specific downstream effect of k13 dysregulation, we developed a new computational method to search for differential gene expression while accounting for the temporal sequence of transcription. We found that the strongest biological signature of the transcriptome shift is an up-regulation of DNA replication and repair genes during the early ring developmental stage and a down-regulation of DNA replication and repair genes during the early trophozoite stage; by contrast, the expressions of housekeeping genes are unchanged. This effect, due to k13 dysregulation, is antagonistic, such that k13 levels are negatively correlated with DNA replication and repair gene expression. CONCLUSION: Our results support a role for k13 as a stress response regulator consistent with the hypothesis that artemisinins mode of action is oxidative stress and k13 as a functional homolog of Keap1 which in humans regulates DNA replication and repair genes in response to oxidative stress.

Unraveling the Plasmodium vivax sporozoite transcriptional journey from mosquito vector to human host.

Malaria parasites transmitted by mosquito bite are remarkably efficient in establishing human infections. The infection process requires roughly 30 minutes and is highly complex as quiescent sporozoites injected with mosquito saliva must be rapidly activated in the skin, migrate through the body, and infect the liver. This process is poorly understood for Plasmodium vivax due to low infectivity in the in vitro models. To study this skin-to-liver-stage of malaria, we used quantitative bioassays coupled with transcriptomics to evaluate parasite changes linked with mammalian microenvironmental factors. Our in vitro phenotyping and RNA-seq analyses revealed key microenvironmental relationships with distinct biological functions. Most notable, preservation of sporozoite quiescence by exposure to insect-like factors coupled with strategic activation limits untimely activation of invasion-associated genes to dramatically increase hepatocyte invasion rates. We also report the first transcriptomic analysis of the P. vivax sporozoite interaction in salivary glands identifying 118 infection-related differentially-regulated Anopheles dirus genes. These results provide important new insights in malaria parasite biology and identify priority targets for antimalarial therapeutic interventions to block P. vivax infection.

Magnetic Levitation Haptic Augmentation for Virtual Tissue Stiffness Perception.

Haptic-based tissue stiffness perception is essential for palpation training system, which can provide the surgeon haptic cues for improving the diagnostic abilities. However, current haptic devices, such as Geomagic Touch, fail to provide immersive and natural haptic interaction in virtual surgery due to the inherent mechanical friction, inertia, limited workspace and flawed haptic feedback. To tackle this issue, we design a novel magnetic levitation haptic device based on electromagnetic principles to augment the tissue stiffness perception in virtual environment. Users can naturally interact with the virtual tissue by tracking the motion of magnetic stylus using stereoscopic vision so that they can accurately sense the stiffness by the magnetic stylus, which moves in the magnetic field generated by our device. We propose the idea that the effective magnetic field (EMF) is closely related to the coil attitude for the first time. To fully harness the magnetic field and flexibly generate the specific magnetic field for obtaining required haptic perception, we adopt probability clouds to describe the requirement of interactive applications and put forward an algorithm to calculate the best coil attitude. Moreover, we design a control interface circuit and present a self-adaptive fuzzy proportion integration differentiation (PID) algorithm to precisely control the coil current. We evaluate our haptic device via a series of quantitative experiments which show the high consistency of the experimental and simulated magnetic flux density, the high accuracy (0.28 mm) of real-time 3D positioning and tracking of the magnetic stylus, the low power consumption of the adjustable coil configuration, and the tissue stiffness perception accuracy improvement by 2.38 percent with the self-adaptive fuzzy PID algorithm. We conduct a user study with 22 participants, and the results suggest most of the users can clearly and immersively perceive different tissue stiffness and easily detect the tissue abnormality. Experimental results demonstrate that our magnetic levitation haptic device can provide accurate tissue stiffness perception augmentation with natural and immersive haptic interaction.

Uncovering the essential genes of the human malaria parasite Plasmodium falciparum by saturation mutagenesis.

Severe malaria is caused by the apicomplexan parasite Plasmodium falciparum. Despite decades of research, the distinct biology of these parasites has made it challenging to establish high-throughput genetic approaches to identify and prioritize therapeutic targets. Using transposon mutagenesis of P. falciparum in an approach that exploited its AT-rich genome, we generated more than 38,000 mutants, saturating the genome and defining mutability and fitness costs for over 87% of genes. Of 5399 genes, our study defined 2680 genes as essential for optimal growth of asexual blood stages in vitro. These essential genes are associated with drug resistance, represent leading vaccine candidates, and include approximately 1000 Plasmodium-conserved genes of unknown function. We validated this approach by testing proteasome pathways for individual mutants associated with artemisinin sensitivity.

Animating Wall-Bounded Turbulent Smoke via Filament-Mesh Particle-Particle Method.

Turbulent vortices in smoke flows are crucial for a visually interesting appearance. Unfortunately, it is challenging to efficiently simulate these appealing effects in the framework of vortex filament methods. The vortex filaments in grids scheme allows to efficiently generate turbulent smoke with macroscopic vortical structures, but suffers from the projection-related dissipation, and thus the small-scale vortical structures under grid resolution are hard to capture. In addition, this scheme cannot be applied in wall-bounded turbulent smoke simulation, which requires efficiently handling smoke-obstacle interaction and creating vorticity at the obstacle boundary. To tackle above issues, we propose an effective filament-mesh particle-particle (FMPP) method for fast wall-bounded turbulent smoke simulation with ample details. The Filament-Mesh component approximates the smooth long-range interactions by splatting vortex filaments on grid, solving the Poisson problem with a fast solver, and then interpolating back to smoke particles. The Particle-Particle component introduces smoothed particle hydrodynamics (SPH) turbulence model for particles in the same grid, where interactions between particles cannot be properly captured under grid resolution. Then, we sample the surface of obstacles with boundary particles, allowing the interaction between smoke and obstacle being treated as pressure forces in SPH. Besides, the vortex formation region is defined at the back of obstacles, providing smoke particles flowing by the separation particles with a vorticity force to simulate the subsequent vortex shedding phenomenon. The proposed approach can synthesize the lost small-scale vortical structures and also achieve the smoke-obstacle interaction with vortex shedding at obstacle boundaries in a lightweight manner. The experimental results demonstrate that our FMPP method can achieve more appealing visual effects than vortex filaments in grids scheme by efficiently simulating more vivid thin turbulent features.