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Objective. Parotid gland (PG) is a radiosensitive organ, and xerostomia (XS) is a key factor affecting patients’ life quality after conventional radiotherapy for head and neck tumors. In this study, dosimetry analysis was performed on PG stem cell preservation in intensity-modulated radiotherapy (IMRT) for nasopharyngeal carcinoma (NPC). Methods. All clinical data of 80 NPC patients diagnosed pathologically in the Radiotherapy Department of Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University from August 2017 to September 2019 were retrospectively analyzed. Patients were assigned to a regular group and a restricted group according to different IMRT plans, in which a dose limitation for the parotid duct was added in the restricted group in addition to the conventional plan used in the regular group to minimize the parotid duct radiation dose. The differences in planning target volume (PTV) dose distribution, organ at risk (OAR) dose, and dose to the PG and its ducts were compared between the two groups. Results. Significantly higher mean irradiation doses of the brainstem, mandible, and oral cavity were determined in the restricted group compared with the regular group ( P > 0.05 ), but there was no significant difference in the mean dose of other OARs irradiated ( P > 0.05 ). As compared to the irradiation of bilateral PGs, no statistical differences were found in the mean irradiation dose and V30 between regular and restricted groups ( P > 0.05 ), but lower V20 and higher V45 were determined in the restricted group ( P < 0.05 ). The mean irradiation dose, V15, V20, and V26 of bilateral parotid ducts were lower in the restricted group as compared to the regular group ( P < 0.05 ). Conclusion. IMRT for NPC can effectively reduce the mean irradiation dose and play a PG stem cell preservation role by giving specific dose limitation conditions to the parotid duct area without affecting PTV dose distribution and OAR irradiation dose, which has certain feasibility.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. However, there is a lack of adequate means of treatment prognostication for HCC. Pyroptosis is a newly discovered way of programmed cell death. However, the prognostic role of pyroptosis in HCC has not been thoroughly investigated. Here, we generated a novel prognostic signature to evaluate the prognostic value of pyroptosis-related genes (PRGs) using the data from The Cancer Genome Atlas (TCGA) database. The accuracy of the signature was validated using survival analysis through the International Cancer Genome Consortium cohort ( n = 231 ) and the First Affiliated Hospital of Wenzhou Medical University cohort ( n = 180 ). Compared with other clinical factors, the risk score of the signature was found to be associated with better patient outcomes. The enrichment analysis identified multiple pathways related with pyroptosis in HCC. Furthermore, drug sensitivity testing identified six potential chemotherapeutic agents to provide possible treatment avenues. Interestingly, patients with low risk were confirmed to be associated with lower tumor mutation burden (TMB). However, patients at high risk were found to have a higher count of immune cells. Consensus clustering was performed to identify two main molecular subtypes (named clusters A and B) based on the signature. It was found that compared with cluster B, better survival outcomes and lower TMB were observed in cluster A. In conclusion, signature construction and molecular subtype identification of PRGs could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC treatment.

Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain–computer interface (BCI). Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments. Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments. Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies and exhibits a high rate of relapse and unfavorable outcomes. Ferroptosis, a relatively recently described type of cell death, has been reported to be involved in cancer development. However, the prognostic value of ferroptosis-related genes (FRGs) in AML remains unclear. In this study, we found 54 differentially expressed ferroptosis-related genes (DEFRGs) between AML and normal marrow tissues. 18 of 54 DEFRGs were correlated with overall survival (OS) (P

Background: In recent studies, the role of modification patterns of N6-methyladenosine (m6A) regulators in tumor microenvironment (TME) cell infiltration in the tumor microenvironment has been identified. However, the role of 5-methylcytosine (m5C) modification patterns in TME cell infiltration remains to be discovered. Methods: In this study, 660 bladder cancer patients were enrolled to comprehensively evaluate the m5C modification pattern based on 13 m5C regulators, and to explore the association between m5C modification pattern and TME cell infiltration status. Finally, PRINCIPAL component analysis (PCA) algorithm constructed m5Cscore, which was used to evaluate and quantify individual m5C modification patterns. Results: Our study identified three distinct m5C modification patterns in bladder cancer. Studies showed that TME cell infiltration characteristics under these three modification patterns were highly associated with three tumor immunophenotypes (immune-inflamed, immune-excluded, immune-desert). In addition, m5C methylation modification patterns were proved to be able to predict tumor disease progression, genetic variation and prognosis of patients. The individualized scoring system – m5Cscore, which was based on m5C methylation modification, showed great differences in tumor mutation burden (TMB), clinical characteristics, immune checkpoint and immunotherapy. The low m5Cscore group was characterized by matrix activation and lack of effective immune cell infiltration, indicating the immune-excluded phenotype. The higher m5Cscore group showed fewer PD-L1 mutations, which were associated with a promoted response to PD-L1/PD-1 immunotherapy and a better prognosis. Conclusions: This work confirmed the extensive regulatory mechanisms of the m5C modification pattern in the tumor microenvironment. The m5C modification pattern functioned as an important element of forming the heterogeneity and complexity of TME landscape. Understanding and in-depth evaluation of individual m5C modification patterns would help us understand the TME landscape and guide better immunotherapy strategies.

A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multilevel Regression and Poststratification (MRP), a model-based approach, is gaining traction against the traditional weighted approach for survey estimates. MRP estimates are susceptible to bias if there is an underlying structure that the methodology does not capture. This work aims to provide a new framework for specifying structured prior distributions that lead to bias reduction in MRP estimates. We use simulation studies to explore the benefit of these prior distributions and demonstrate their efficacy on non-representative US survey data. We show that structured prior distributions offer absolute bias reduction and variance reduction for posterior MRP estimates in a large variety of data regimes., Comment: Minor revision. Added plots showing share of simulations where structured priors outperformed baseline priors in MRP

An error-related potential (ErrP) occurs when people’s expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain–computer interfaces.

Recently published research shows that Lomax distribution exhibits compressibility in Lorentz curves. In this paper, we address the problem of signal reconstruction in the high noise level and phase error environments in a Bayesian framework of Lomax prior distribution. Furthermore, from the perspective of improving sparsity and compressibility of the signal constraints, a novel reconstruction model deducted from Lomax-prior-based Bayesian compressed sensing (LomaxCS) is proposed. The LomaxCS improves the accuracy of existing Bayesian compressed sensing signal reconstruction methods and enhances the robustness against Gauss noise and phase errors. Compared with the conventional models, the proposed LomaxCS model still reveals the general profile of the signal in the worst conditions. The experimental results demonstrate that the proposed algorithm can achieve substantial improvements in terms of recovering signal quality and robustness; meanwhile, it brings an evident application prospect.

Self-assembly of cyclohexyl cyclic (alkyl)(amino)carbenes (cyCAAC) can be realized and reversibly switched from a close-packed trimer phase to a chainlike dimer phase, enabled by the ring-flip of the cyclohexyl wingtip. Multiple methods including scanning tunneling microscopy (STM), X-ray photoelectron spectroscopy (XPS) and density functional theory (DFT) calculations identified a distinct isomer (axial or equatorial chair conformer) in each phase, and consequently support the conclusion regarding the determination of molecular surface geometry on the self-assembly of cyCAAC. Moreover, various substrates such as Ag (111) and Cu (111) are tested to elucidate the importance of cyCAAC-surface interactions on cyCAAC based nanopatterns. These investigations of patterned surfaces prompted a deep understanding of cyCAAC binding mode, surface geometry and reversible self-assembly, which are of paramount significance in the areas of catalysis, biosensor design and surface functionalization.

Transition metal dichalcogenides like 2H-NbSe_{2} in their two-dimensional (2D) form exhibit Ising superconductivity with the quasiparticle spins being firmly pinned in the direction perpendicular to the basal plane. This enables them to withstand exceptionally high magnetic fields beyond the Pauli limit for superconductivity. Using field-angle-resolved magnetoresistance experiments for fields rotated in the basal plane we investigate the field-angle dependence of the upper critical field (H_{c2}), which directly reflects the symmetry of the superconducting order parameter. We observe a sixfold nodal symmetry superposed on a twofold symmetry. This agrees with theoretical predictions of a nodal topological superconducting phase near H_{c2}, together with a nematic superconducting state. We demonstrate that in NbSe_{2} such unconventional superconducting states can arise from the presence of several competing superconducting channels.

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies and exhibits a high rate of relapse and unfavorable outcomes. Ferroptosis, a relatively recently described type of cell death, has been reported to be involved in cancer development. However, the prognostic value of ferroptosis-related genes (FRGs) in AML remains unclear. In this study, we found 54 differentially expressed ferroptosis-related genes (DEFRGs) between AML and normal marrow tissues. Eighteen of these 54 DEFRGs were correlated with overall survival (OS) (P

Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma and has poor prognosis in the locally advanced stage. Ferroptosis, a relatively new type of cell death, has gained significant attention in recent years. This study aimed to explore the prognostic value of ferroptosis-related genes (FRGs) in ccRCC. In this study, 50 differentially expressed FRGs between ccRCC and adjacent normal kidney tissues were identified, 26 of them correlated with overall survival (OS) (P <0.05). Eight optimal FRGs were selected by Lasso regression and multivariate Cox regression analysis, and used to construct a new prognostic risk signature to predict the prognosis of ccRCC patients. In addition, the signature passed the validation of prognostic survival analyses by a significant margin, and the risk score was identified as an independent prognostic marker via Cox regression analyses. Further studies indicated that the signature was significantly correlated with immune cell infiltration. Moreover, the levels of eight FRGs were examined in ccRCC. Collectively, the 8-FRG prognostic risk signature helps the clinicians predict the prognosis and OS of the patients, and standardize prognostic assessments.

Processing of mesoscale structures of soft matter and liquid is of great importance in both science and engineering. In this work, we introduce the concept of laser-assisted micromachining to this field and inject a certain number of microdroplets into a preselected location on the surface of a liquid crystal drop through laser irradiation. The impact of laser energy on the triggered injection is discussed. The sequentially injected microdroplets are spontaneously captured by the defect ring in the host drop and transported along this defect track as micro-cargos. By precisely manipulating the laser beam, the tailored injection of droplets is achieved, and the injected droplets self-assemble into one necklace ring within the host drop. The result provides a bottom-up approach for the in-situ and three-dimensional microfabrication of droplet structure of soft matter using a laser beam, which may be applicable in the development of optical and photonic devices.

The kagome lattice Co3Sn2S2 exhibits the quintessential topological phenomena of a magnetic Weyl semimetal such as the chiral anomaly and Fermi-arc surface states. Probing its magnetic properties is crucial for understanding this correlated topological state. Here, using spin-polarized scanning tunneling microscopy/spectroscopy (STM/S) and non-contact atomic force microscopy (nc-AFM) combined with first-principle calculations, we report the discovery of localized spin-orbit polarons (SOPs) with three-fold rotation symmetry nucleated around single S-vacancies in Co3Sn2S2. The SOPs carry a magnetic moment and a large diamagnetic orbital magnetization of a possible topological origin associated relating to the diamagnetic circulating current around the S-vacancy. Appreciable magneto-elastic coupling of the SOP is detected by nc-AFM and STM. Our findings suggest that the SOPs can enhance magnetism and more robust time-reversal-symmetry-breaking topological phenomena. Controlled engineering of the SOPs may pave the way toward practical applications in functional quantum devices., Kagome lattice material Co3Sn2S2 is identified as a magnetic Weyl semimetal and its magnetic properties are less studied. Here, the authors observe localized spin-orbit polarons nucleated around single S-vacancies carrying a large diamagnetic orbital magnetism in Co3Sn2S2.

With the gradual commercialization of 5G, antenna equipment that can meet the requirements of multiple frequency hands is particularly important. For this reason, this paper proposes and designs a three-hand L-type monopole antenna suitable for 4G/5G communication. The antenna consists of three branches, including rectangular parasitic patches, floor and feeder lines. The antenna size is 16 x 30mm. Three L-shaped branches generate three resonance frequencies, covering the 2.6/ 3.5/4.8 frequency band. After testing, the impedance bandwidths of the three frequency bands are 180MHz, 250MHz and 1130MHz, and the antenna also has good omnidirectional characteristics and good impedance matching.

Peripheral nerve regeneration following injury is often incomplete, resulting in significant personal and socioeconomic costs. Nerve conduits have been developed to complement or replace autologous nerve grafts. In this study, repairing injured peripheral nerves in rats is investigated by fabricating poly(D, l -lactic acid) (PDLLA)/β-tricalcium phosphate (β-TCP) nerve conduits filled by injectable chitosan (CS)-hyaluronic acid (HA) hydrogels featuring sustained release of nerve growth factor (NGF). All CS-HA hydrogels have porosities higher than 80%. The mass loss of the CS-HA hydrogels reaches 70% in 8 weeks. In vitro experiments suggest that the CS-HA/NGF hydrogels are suitable for the adhesion, spreading, and differentiation of neuronal cells, which also allow for sustained release of NGF. In vivo studies on the repair of 10-mm sciatic nerve defects in rats are used to assess nerve repair rate through walking track analysis, gastrocnemius weight analysis, and histological analysis. It is shown that the PDLLA/β-TCP nerve conduits laden with CS-HA/NGF hydrogels have a significant positive effect on the axon regeneration and myelination, compared to the PDLLA/β-TCP hollow nerve conduits and the autograft group. These results show that the CS-HA/NGF injectable hydrogel can effectively promote nerve regeneration making it a good candidate in the field of neural tissue engineering.

A multifunctional drug delivery system (GNRs@mSiO2-HA-RGD) was developed by conjugating targeting ligand hyaluronic acid (HA) and RGD with mesoporous silica- coated gold nanorods (GNRs@mSiO2) for dual-targeted chemo-photothermal therapy. The physiochemical properties of the prepared nanoparticles were characterized by FTIR, UV-vis spectra, and 1H NMR. Doxorubicin hydrochloride (DOX), an anticancer drug, was used as the model drug to investigate the drug loading, in vitro drug release profiles and cytotoxicity. The experimental results show that DOX-GNRs@mSiO2-HA-RGD is synthesized with a mean diameter of 116 nm and a sufficient load capacity of about 19.8%. It also has pH-enzyme sensitive and NIRtriggered drug release manner. Cellular uptake indicates that DOX-GNRs@mSiO2-HA-RGD exhibits a higher cellular uptake via CD44 receptor and integrin receptor mediated endocytosis compared with the GNRs@ mSiO2 modified with one receptor or no receptor. In comparison with chemotherapy or photothermal therapy alone, DOX-GNRs@mSiO2-HA-RGD displayes the synergistic effects and achieves a higher therapeutic efficacy. It can be expected that DOX-GNRs@mSiO2-HA-RGD is a potential dual-targeted chemo-photothermal therapeutic platform for effective cancer treatment.

Co3Sn2S2 has attracted a lot of attention for its multiple novel physical properties, including topological nontrivial surface states, anomalous Hall effect, and anomalous Nernst effect. Vacancies, which play important roles in functional materials, have attracted increasing research attention. In this paper, by using density functional theory calculations, we first obtain band structures and magnetic moments of Co3Sn2S2 with exchange-correlation functionals at different levels. It is found that the generalized gradient approximation gives the positions of Weyl points consistent with experiments in bulk Co3Sn2S2. We then investigate the electronic structures of defects on surfaces with S and Sn terminations which have been observed in experiments. The results show that the single sulfur vacancy on the S-terminated surface introduces localized bond states inside the bandgap near the Fermi level. For di- and tri-sulfur vacancies, the localized defect states hybridize with neighboring ones, forming bonding states as well as anti-bonding states. The Sn vacancy on the Sn-terminated surface also introduces localized bond states, which are merged with the valence bands. These results provide a reference for future experimental investigations of vacancies in Co3Sn2S2.

As robots begin to provide daily assistance to individuals in human environments, their end-users, who do not necessarily have substantial technical training or backgrounds in robotics or programming, will ultimately need to program and "re-task" their robots to perform a variety of custom tasks. In this work, we present PATI---a Projection-based Augmented Table-top Interface for robot programming---through which users are able to use simple, common gestures (e.g., pinch gestures) and tools (e.g., shape tools) to specify table-top manipulation tasks (e.g., pick-and-place) for a robot manipulator. PATI allows users to interact with the environment directly when providing task specifications; for example, users can utilize gestures and tools to annotate the environment with task-relevant information, such as specifying target landmarks and selecting objects of interest. We conducted a user study to compare PATI with a state-of-the-art, standard industrial method for end-user robot programming. Our results show that participants needed significantly less training time before they felt confident in using our system than they did for the industrial method. Moreover, participants were able to program a robot manipulator to complete a pick-and-place task significantly faster with PATI. This work indicates a new direction for end-user robot programming.

During the casting of high aluminum steel, the dramatic increase in the Al2O3/SiO2 ratio is inevitable, resulting in significant changes of the crystallization behavior, which would result in heat transfer and lubrication problems. Crystallization products and structure characterization of glassy CaO-SiO2-based mold fluxes with different Al2O3/SiO2 ratios were experimentally investigated using a differential scanning calorimetry technique and Raman spectroscopy. With increasing Al2O3/SiO2 ratios, the following results were obtained. The crystallization temperature and the crystallization products are changed. With increasing Al2O3/SiO2 ratios from 0.088 to 0.151, the crystallization temperature first increases greatly from 1152 °C to 1354 °C, and then moderately increases. The crystallization ability of the mold flux is strengthened. The species of the precipitated crystalline phase change from two kinds, i.e., Ca4Si2O7F2 and Ca2SiO4, to four kinds, i.e., Ca4Si2O7F2, Ca2SiO4, 2CaO·Al2O3·SiO2 and Ca12Al14O32F2, the crystallization ability of Ca4Si2O7F2 is gradually attenuated, but other species show the opposite trend. The results of Raman spectroscopy indicate that Al3+ mainly acts as a network former by the information of [AlO4]-tetrahedral structural units, which can connect with [SiO4]-tetrahedral by the formation of new bridge oxygen of Al–O–Si linkage, but there is no formation of Al–O–Al linkage. The linkage of Al–O–Si increases and that of Si–O–Si decreases. The polymerization degree of the network and the average number of bridging oxygens decrease. Further, the relatively strong Si–O–Si linkage gradually decreases and the relatively weak Al–O–Si gradually increases. The change of the crystalline phase was interpreted from the phase diagram and structure.

Quantitative Finance is a field that replies on data analysis and big data enabling software to discover market signals. In this, a decisive factor is the speed that concerns execution speed and software development speed. So, an efficient software plays a key role in helping trading firms. Inspired by this, we present a novel software: QuantCloud to integrate a parallel Python system with a C++-coded Big Data system. C++ is used to implement this big data system and Python is used to code the user methods. The automated parallel execution of Python codes is built upon a coprocess-based parallel strategy. We test our software using two popular algorithms: moving-window and autoregressive moving-average (ARMA). We conduct an extensive comparative study between Intel Xeon E5 and Xeon Phi processors. The results show that our method achieved a nearly linear speedup for executing Python codes in parallel, prefect for today's multicore processors.

Introduction The association between non-alcoholic fatty liver disease (NAFLD) and Type 2 diabetes (T2DM) and obesity is well established yet current guidelines in the United Kingdom do not recommend screening for NAFLD these groups. Moreover, metabolic clinics have little hepatology support and few formalised NAFLD management pathways, Resulting in missed opportunities to diagnose, stage and treat NAFLD. We sought to determine the scale of clinically significant NAFLD in our T2DM and obese populations. Method T2DM or obese patients attending metabolic clinic over a 3 month period were included. Fibrosis risk was assessed via a 2-step pathway. First, NAFLD fibrosis score (NFS) was calculated, followed by Fibroscan for those with indeterminate/high (‘abnormal’) NFS scores; Fibroscan readings of >8 kPa were considered abnormal. Results 89 people were screened. We excluded those with both normal liver function (LFT) and ultrasound (n=11), previously diagnosed liver disease (n=3) or insufficient data to calculate the NFS score (n=43), leaving 32 patients (20 T2DM; 12 obese) of whom the majority were middle-aged males with median age 53 [28–75] and BMI 38 [22.1–68]. Most of those with NAFLD had normal LFT (25/32; 78.2%; p The 27/32 patients with abnormal score were invited for Fibroscan of whom 70.4% attended. Median Result was 7.05 kPa [2.8–26.3], with a non-significant trend to higher readings in obesity vs. T2DM (7.7 vs 6.6 kPa; p=0.29). 36.84% (7/19) had abnormal Fibroscan Result. ALT was significantly higher in those with abnormal Fibroscan (mean 66.9 vs. 18.83; p=0.43; 95% CI 33.76–62.32) but importantly, 42.9% (3/7) of those with abnormal Fibroscan had completely normal LFT. Conclusion NAFLD was common in the cohort, usually undiagnosed and frequently associated with abnormal NFS and Fibroscan despite normal LFT, suggesting there is a sizeable population in metabolic services with potentially significant liver disease. We only included those with proven steatosis and sufficient data to calculate NFS, therefore the true prevalence of significant fibrosis is likely to be greater. Although biopsy has not yet been performed, abnormal 2-step non-invasive assessment alone mandates specialist input and as such active NAFLD screening in these groups should be considered.

A radical relay strategy is described to synthesize functionalized β-amino alcohols. This strategy is enabled by photoredox-catalyzed and nitrogen-centered radical-triggered cascade reactions of styrenes (or phenylacetylenes), enol derivatives, and O-acyl hydroxylamines in DMSO. The broad synthetic application of this method is demonstrated by the reaction of structurally diverse reaction components, including complex molecular scaffolds. Multiple functional groups of the resultant highly functionalized β-amino alcohol derivatives facilitate their further transformations.

Cloud computing and big data technologies are converging to offer a cost-effective delivery model for cloud-based big data analytics. Though impacts of size and scaling of big data on cloud have been extensively studied, the effects of complexity of underlying analytic methods on cloud performance have received less attention. This paper will develop and evaluate a computationally intensive statistical methodology to perform inference in the presence of both non-Gaussian data and missing data. Two well-established statistical approaches, bootstrap and multiple imputations (MI), will be combined to form the methodology. Bootstrap is a computer-based nonparametric resampling procedure that involves randomly selecting data many thousands of times to construct an empirical distribution, which is then used to construct confidence intervals for significance tests. This statistical technique enables scientists who conduct studies on data with known non-normality to obtain higher quality significance tests than is possible with a traditional asymptotic, normal-theory based significance test. However, the bootstrapping procedure only works when no data are missing or the data are missing completely at random (MCAR). Missing data can lead to biased estimates when the MCAR assumption is violated. It is unclear how to best implement a bootstrapping procedure in the presence of missing data. The proposed methods will provide guidelines and procedures that will enable researchers to use the technique in all areas of health, behavior and developmental science in which a study has missing data and cannot rely on parametric inference. Either bootstrapping or MI can be computationally expensive, and combining these two can lead to further computation costs in the cloud. Using carefully constructed simulation examples, we demonstrate that it is feasible to implement the proposed methodology in a high performance Knights Landing platform. However, the computation costs are substantial even with small data size. Further studies are needed to study the effects of optimizing the implementation and its performance with big data.

Using genetic data to infer relatedness has been crucial for genetics studies for decades. In a previously published paper together with the KING software, we demonstrated that the kinship coefficient, a measure of relatedness between a pair of individuals, can be accurately estimated using their genome-wide SNP data, without estimating the allele frequencies at each SNP in the whole dataset. The computational efficiency of this algorithm has been substantially improved in the second generation of KING. Three levels of computational speed-up are implemented in KING 2.0, including: 1) bit-level parallelism; 2) multiple-core parallelism using OpenMP; and 3) a multi-stage procedure to eliminate unrelated or distantly related pairs of individuals. The efficient implementation in KING 2.0 allows instant relationship inference in a matter of seconds in a typical dataset (with 10,000s individuals). To demonstrate the computational performance and scalability of KING 2.0, we use the Knights Landing platform to infer relatedness in a dataset consisting of 303,750 individuals each typed at 168,749 autosome SNPs. The computational time to identify all first-degree relatives by scanning 46 billion pairs of individuals is ∼10 minutes using 256 threads, a noticeable speed-up comparing to the general-purpose CPU. Algorithm improvement in the second generation of KING and the use of the latest computing system such as the Knights Landing platform makes it feasible for researchers to infer relatedness in their genetic datasets in the largest size up-to-date on a single computer.

Finding the best model to reveal potential relationships of a given set of data is not an easy job and often requires many iterations of trial and errors for model sections, feature selections and parameters tuning. This problem is greatly complicated in the big data era where the I/O bottlenecks significantly slowed down the time needed to finding the best model. In this article, we examine the case of Box-Cox transformation when assumptions of a regression model are violated. Specifically, we construct and compute a set of summary statistics and transformed the maximum likelihood computation into a per-role operational fashion. The innovative algorithms reduced the big data machine learning problem into a stream based small data learning problem. Once the Box-Cox information array is obtained, the optimal power transformation as well as the corresponding estimates of model parameters can be quickly computed. To evaluate the performance, we implemented the proposed Box-Cox algorithms on QCT developer cloud. Our results showed that by leveraging both the algorithms and the QCT cloud technology, find the fittest model from 101 potential parameters is much faster than the conventional approach.

Aiming at the problem of segmented weak orthogonal matching pursuit (SWOMP) imaging blur in the process of through-wall radar (TWR) imaging, a dynamic threshold weak orthogonal matching pursuit algorithm (DWOMP) is introduced in this paper, which can significantly improve the imaging performance. Firstly, the TWR compressed sensing simulation model and over-complete dictionary are established by using Chirp signal radar echo data. Then the specific process of DWOMP algorithm is proposed and used to reconstruct multi-sparse target scenes. Finally, the DWOMP algorithm is compared with Basis Pursuit (BP) algorithm and SWOMP algorithm through simulation experiments. The simulation results show that in the same experimental conditions, the imaging time of DWOMP algorithm is about 3/5 of that of BP algorithm, and the imaging resolution of DWOMP algorithm is better than that of SWOMP algorithm.

This paper presents constraint generalized orthogonal matching pursuit (C-gOMP) approach to tackle the problem of clutter mitigation for radar imaging in the compressive sensing context. The generalized orthogonal matching pursuit algorithm (gOMP) in radar imaging process is susceptible to environmental noise interference. Therefore, a particular constraint condition is added to the recovery signal, and the noise component is restrained after the greedy iteration. The C-gOMP algorithm uses the cost function to impose more constraints on each coefficient to ensure the convergence of the whole function. To further analyze the performance of C-gOMP algorithm, the effectiveness of this algorithm is demonstrated by simulations. The results show that the proposed approach is very obvious at suppressing unwanted clutter and enhancing the desired targets, and C-gOMP algorithm brings an evident performance improvement and application prospect.

Through eigenanalysis of communication matrices, we develop a new objective function formulation for mapping tasks to parallel computers with cellular networks. This new formulation significantly speeds up the solution process through consideration of the symmetries in the supply matrix of a network and a transformation of the demand matrix of any application. The extent of the speedup is not easily obtainable through analytical means for most production networks. However, numerical experiments of mapping wave equations on 2D mesh onto 3D torus networks by simulated annealing demonstrate a far superior convergence rate and quicker escape from local minima with our new formulation than with the standard graph theory-based one.