Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety of Overlapping Surgery at a High-volume Referral Center.

The authors’ utilized data from their institution’s participation in the University HealthSystem Consortium (UHC) to compare 10,765 overlapping inpatient surgical procedures with 16,111 nonoverlapping procedures. Overlapping surgical procedures were associated with a lower risk of mortality and no difference in length of stay or HCAHPS Summary Star Score. The flaw in the authors’ analysis of their UHC data is that they have failed to incorporate outpatient surgical procedures in their definition of overlap. That is, on any given day, a surgeon may perform procedures on both inpatients and outpatients, and these procedures may overlap. By focusing solely on the inpatient procedures, the authors have misclassified some procedures as nonoverlapping, when, in fact, they overlap with outpatient procedures. The impact of this misclassification is hard to predict. There are a number of additional concerns related to the use of the American College of Surgeons—National Surgical Quality Improvement Program (ACSNSQIP) database for confirmatory analyses. The first is that, as noted above, the UHC data were limited to inpatients, whereas the ACSNSQIP included both inpatients and outpatients. This makes it a questionable confirmatory data source. Additionally, some patients are likely to exist in both data sets, further calling into question the ACS-NSQIP as an independent source for confirmation of the UHC results. Another concern relates to the fact that institutions can select cases to be submitted to the ACS-NSQIP; it is not a random or complete group of cases performed. Lastly, certain findings from the ACS-NSQIP data do not confirm the analysis of the UHC data, such as increased mortality, length of stay, and decreased HCAHPS scores with overlapping surgery, all findings opposite of those in the UHC data. The challenge of assessing the potential impact overlapping surgery has upon patient safety is vital to our profession. There are numerous challenges to analyzing the outcomes from the practice of overlapping surgery. A few of these were highlighted by Hyder et al, including a lack of transferability of findings to other institutions and differences amongst individual surgeons. Other challenges include identifying particular procedures, surgical subspecialties, or individual surgeons where overlapping surgery might increase complications. In addition, heterogeneity between datasets, lack of standardization in reporting practices, potential impact of skill level of the primary or assisting surgeon make analysis of this practice exceedingly difficult. We applaud Hyder et al for their contribution and echo their recommendation for continued dialogue and research. We hope the aforementioned article and also this letter serve as inspiration for such.
Shape memory nanocomposite of poly(L-lactic acid)/graphene nanoplatelets triggered by infrared light and thermal heating

In this study, the effect of graphene nanoplatelets (GNPs) on the shape memory properties of poly(L-lactic acid) (PLLA) was studied. In addition to thermal activation, the possibility of infrared actuating of thermo-responsive shape mem- ory PLLA/GNPs nanocomposite was investigated. The incorporated GNPs were expected to absorb infrared wave's energy and activate shape memory PLLA/GNPs. Different techniques such as differential scanning calorimetry (DSC), wide-angle X-ray diffraction (WAXD), field emission gun scanning electron microscope (FEG-SEM) and dynamic mechanical thermal analysis (DMTA) were used to characterize samples. DSC and WAXD results indicated that GNPs augmented crystallinity due to nucleating effect of graphene particles. GNPs improved both thermal and infrared activating shape memory properties along with faster response. Pure shape memory PLLA was slightly responsive to infrared light and its infrared actuated shape recovery ratio was 86% which increased to more than 95% with loading of GNPs. Drastic improvement in the crystallinity was obtained in nanocomposites with lower GNPs contents (0.5 and 1 wt%) due to finer dispersion of graphene which resulted in more prominent mechanical and shape memory properties enhancement. Infrared activated shape memory PLLA/GNPs nanocomposites can be developed for wireless remote shape control of smart medical and bio-systems.
Attention Cropping: A Novel Data Augmentation Method for Real-world Plant Species Identification

This paper investigates the issue of realistic plant species identification. The recognition is close to the condition of a real-world scenario and the dataset is at a large-scale level. A novel data augmentation method is proposed. The image is cropped in terms with visual attention. Different from general way, the cropping is implemented before the image is resized and fed to convolutional neural networks in our proposed method. To deal with the challenge of distinguishing target from complicated background, a method of multiple saliency detections is introduced. Extensive experiments are conducted on both traditional and specific datasets for real-world identification. We introduce the concept of complexity of image background to describe the background complicated rate. Experiments demonstrate that multiple saliency detections can generate corresponding coordinates of the interesting regions well and attention cropping is an efficient data augmentation method. Results show that our method can provide superior performance on different types of datasets. Compared with the precision of methods without attention cropping, the results with attention cropping data augmentation achieve substantial improvement.
Clinical presentation of CIED infection following initial implant versus reoperation for generator change or lead addition

Objective To explore differences in clinical manifestations and outcomes in those patients who develop infection after undergoing initial implantation versus reoperation. Methods We compared cases of cardiac implantable electronic device (CIED) infection based on initial implantation versus reoperation from 11 centres. Results There were 432 patients with CIED infection, 178 occurring after initial device placement and 254 after repeat reoperation. No differences were seen in age, sex or device type. Those with infection after initial implant had a higher Charlson Comorbidity Score (median 3 (IQR 2–6) vs 2 (IQR 1–4), p<0.001), shorter time since last procedure (median 8.9 months (IQR 0.9–33.3) vs 19.5 months (IQR 1.1–62.9), p<0.0001) and fewer leads (2.0±0.6vs 2.5±0.9, p<0.001). Pocket infections were more likely to occur after a reoperation (70.1%vs48.9%, p<0.001) and coagulase negative staphylococci (CoNS) was the most frequently isolated organism in this group (p=0.029). In contrast, initial implant infections were more likely to present with higher white cell count (10.5±5.1 g/dL vs 9.5±5.4 g/dL, p=0.025), metastatic foci of infection (16.9%vs8.7%, p=0.016) and sepsis (30.9%vs19.3%, p=0.006). There were no differences in in-hospital (7.9%vs5.2%, p=0.31) or 6-month mortality (21.9%vs14.0%, p=0.056). Conclusions CIED infections after initial device implant occur earlier, more aggressively, and often due to Staphylococcus aureus. In contrast, CIED infections after reoperation occur later, are due to CoNS, and have more indolent manifestations with primary localisation to the pocket.
Back to the Drawing Board - Bringing Security Constraints in an Architecture-centric Software Development Process

Today, security is still poorly considered in early phases of software engineering. Architects and software engineers still lack knowledge about architectural security design as well as implementing it compliantly. However, a software system that is not designed for security or does not adhere to this design can hardly meet its security requirements. In this paper, we present an approach we are working on. The approach consists of two parts: Firstly, we improve the architecture’s security level through model transformation. Secondly, we derive rules and constraints from the secured architecture in order to check the implementation’s conformance. Through these activities we aim to support architects and software developers in building a secure software system. We plan to evaluate our approach in industrial case studies.
Computational Approaches to tRNA-Derived Small RNAs

tRNA-derived small RNAs (tDRs) are a group of small, non-coding RNAs derived from transfer RNAs (tRNAs). They can be classified as tRNA halves and tRNA-derived small RNA fragments (tRFs). Accumulating experimental evidence suggests their functional roles in cells and in various biological processes. Advances in next-generation sequencing (NGS) techniques allow a large amount of small RNA deep-sequencing data to be generated. To investigate tDRs from these data, software to identify tDRs and databases to retrieve or manage tDR data have been devised. In this review, we summarized the tools and databases for tDR identification and collection, with the aim of helping researchers choose the best tools for their analysis and inspiring the invention or improvement of tools in the field.
LC-MS-based lipidomics to examine acute rat pulmonary responses after nano- and fine-sized ZnO particle inhalation exposure

Abstract Zinc oxide (ZnO) nano- and fine-sized particles are associated with respiratory toxicity in humans, but the underlying molecular mechanisms remain unclear. Our previous nuclear magnetic resonance-based metabolomic study demonstrated that changes in phosphorylcholine-containing lipids (PC-CLs) in the respiratory system were associated with ZnO particle-induced respiratory toxicity. However, the details of the lipid species associated with adverse effects and possible biomarker signatures have not been identified. Thus, a liquid chromatography-mass spectrometry (LC-MS)-based lipidomics platform was applied to examine the alterations of PC-CL species in the lungs of rats treated with a series of concentrations of nano-sized (35 nm) or fine-sized (250 nm) ZnO particles via inhalation. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and the Mann–Whitney U (MWU) test with false discovery rate (FDR) control were conducted to explore the perturbed lipid species and to discriminate a potential pulmonary biomarker signature after ZnO particle exposure. The PCA and PLS-DA models revealed that the fine-sized ZnO particle-treated groups and the high-concentration nano-sized group were separated from the control groups as well as from the low and moderate nano-sized groups. The results from the MWU test further suggested that after FDR adjustment, numerous PC-CL species were altered in the high-concentration and moderate-concentration fine-sized groups. Furthermore, our results suggested that lipids involved in anti-oxidation, membrane conformation, and cellular signal transduction were altered in response to ZnO-induced oxidative stress and inflammation. One lipid, PC(18:0/18:1), exhibited good performance (AUC > 0.8) of discriminative ability in distinguishing ZnO particle exposure from the control. These findings not only provide a foundation for the exploration of possible ZnO particle-mediated mechanisms but also suggest a lipid biomarker for ZnO particle exposure.
Pointwise adaptation via stagewise aggregation of local estimates for multiclass classification

We consider a problem of multiclass classification, where the training sample $S_n = \{(X_i, Y_i)\}_{i=1}^n$ is generated from the model $\mathbb p(Y = m | X = x) = \theta_m(x)$, $1 \leq m \leq M$, and $\theta_1(x), \dots, \theta_M(x)$ are unknown Lipschitz functions. Given a test point $X$, our goal is to estimate $\theta_1(X), \dots, \theta_M(X)$. An approach based on nonparametric smoothing uses a localization technique, i.e. the weight of observation $(X_i, Y_i)$ depends on the distance between $X_i$ and $X$. However, local estimates strongly depend on localizing scheme. In our solution we fix several schemes $W_1, \dots, W_K$, compute corresponding local estimates $\widetilde\theta^{(1)}, \dots, \widetilde\theta^{(K)}$ for each of them and apply an aggregation procedure. We propose an algorithm, which constructs a convex combination of the estimates $\widetilde\theta^{(1)}, \dots, \widetilde\theta^{(K)}$ such that the aggregated estimate behaves approximately as well as the best one from the collection $\widetilde\theta^{(1)}, \dots, \widetilde\theta^{(K)}$. We also study theoretical properties of the procedure, prove oracle results and establish rates of convergence under mild assumptions.
Guided Medical Data Segmentation Using Structure-Aligned Planar Contours

OF THE DISSERTATION Guided Medical Data Segmentation Using Structure-Aligned Planar Contours by Michelle Holloway Doctor of Philosophy in Computer Science Washington University in St. Louis, December 2016 Research Advisor: Professor Tao Ju Segmentation of 3D/4D biological images is a critical step for a wide range of applications such as treatment planning, quantitative analysis, virtual simulations, and rendering visualizations. Automatic segmentation methods are becoming more reliable, but many experts still rely on manual intervention which makes segmentation a time and resource intensive bottleneck. Marking boundary contours in 3D images can be difficult when images are often noisy or the delineation of biological tissue is unclear. Non-parallel contours can be more accurate and reduce the amount of marking necessary, but require extra effort to ensure boundary consistency and maintain spatial orientation. This dissertation focuses three problems that pertain to drawing non-parallel contour networks and generating a segmentation surface from those networks. First a guided structure-aligned segmentation system is detailed that utilizes prior structure knowledge from past segmentations of similar data. It employs a contouring protocol to aid in navigating the volume data and support using arbitrarily-oriented contouring planes placed to capture or follow the global structure shape. A user study is provided to test how xii well novices perform segmentation using this system. The following two problems then aim to improve different aspects of this system. A new deformation approach to reconstruction is discussed which deforms previous segmentation meshes to fit protocol drawn contours from new data instances in order to obtain accurate segmentations that have the correct topology and general shape and preserves fine details. The focus is on the problem of finding a correspondence between a mesh and a set of contours describing a similar shape. And finally, a new robust algorithm that resolves inconsistencies in contour networks is detailed. Inconsistent contours are faster and less demanding to draw, and they allow the segmenter to focus on drawing boundaries and not maintaining consistency. However, inconsistency is detrimental to most reconstruction algorithms, so the network must be fixed as a post process after drawing.
Optimization of Multicast Source-Routing Based on Bloom Filter

In multicast source-routing scheme, packet encodes the generated multicast tree at the header, and can be delivered successfully by checking the encoded tree. Traditional multicast source-routing uses a single Bloom filter (BF) to represent the multicast tree, which incurs high false positive rate and inefficient space utilization. In this letter, we propose two multicast source-routing using multiple BFs (MSRM), which generate multiple BFs and each BF only records one sub-tree of the multicast tree. In addition, we optimize the parameter settings with a given constraint on the number of false positives. The evaluation results show that MSRM gains great performance.
The RNA-Seq-based high resolution gene expression atlas of chickpea (Cicer arietinum L.) reveals dynamic spatio-temporal changes associated with growth and development.

Chickpea is one of the world's largest cultivated food legumes and is an excellent source of high-quality protein to the human diet. Plant growth and development are controlled by programmed expression of a suite of genes at the given time, stage, and tissue. Understanding how the underlying genome sequence translates into specific plant phenotypes at key developmental stages, information on gene expression patterns is crucial. Here, we present a comprehensive Cicer arietinum Gene Expression Atlas (CaGEA) across different plant developmental stages and organs covering the entire life cycle of chickpea. One of the widely used drought tolerant cultivars, ICC 4958 has been used to generate RNA-Seq data from 27 samples at 5 major developmental stages of the plant. A total of 816 million raw reads were generated and of these, 794 million filtered reads after quality control (QC) were subjected to downstream analysis. A total of 15,947 unique number of differentially expressed genes across different pairwise tissue combinations were identified. Significant differences in gene expression patterns contributing in the process of flowering, nodulation, and seed and root development were inferred in this study. Furthermore, differentially expressed candidate genes from "QTL-hotspot" region associated with drought stress response in chickpea were validated.
Systematically Monitoring Social Media: The case of the German federal election 2017

It is a considerable task to collect digital trace data at a large scale and at the same time adhere to established academic standards. In the context of political communication, important challenges are (1) defining the social media accounts and posts relevant to the campaign (content validity), (2) operationalizing the venues where relevant social media activity takes place (construct validity), (3) capturing all of the relevant social media activity (reliability), and (4) sharing as much data as possible for reuse and replication (objectivity). This project by GESIS - Leibniz Institute for the Social Sciences and the E-Democracy Program of the University of Koblenz-Landau conducted such an effort. We concentrated on the two social media networks of most political relevance, Facebook and Twitter.
Light Illumination Induced Photoluminescence Enhancement and Quenching in Lead Halide Perovskite

It has been found that light illumination (soaking) can significantly change the optical and electronic properties of perovskites and the performance of perovskite solar cells. Here using steady state and time-resolved photoluminescence (PL) techniques, we investigate systematically the effect of light illumination on a well encapsulated CH3NH3PbI3 perovskites. Under a continuous constant illumination at low intensity, the PL exhibits either enhancement or constant; dependent on the detrapping rate and density of the defect states in the perovskite which in turn is determined by fabrication methods. Defect curing is attributed to the origin of the PL enhancement. Under a continuous higher intensity illumination, PL quenching is observed with different thresholds relevant to the sample fabrication. This is ascribed to mobile ion accumulation resulting in increased electron/ hole non-radiative recombination. We confirmed the PL enhancement and quenching under continuous illumination (light soaking) partly originate from the intrinsic contribution of the perovskite, closely correlated to the defect density therefore the fabrication. These findings provide novel insight into the carrier dynamics correlated to the defect trapping and mobile ions which helps to understand the instability of perovskite solar cells.
Using Binary Classifiers to Augment Stereo Vision for Enhanced Autonomous Robot Navigation ; CU-CS-1027-07

Autonomous robot navigation in unstructured outdoor environments is a challenging area of active research. At the core of this navigation task lies the concept of identify ing safe, traversable paths which allow the robot to progress to ward a goal. Stereo vision is frequently exploited for autonomou s navigation, but has limitations in terms of its density and accuracy in the far field. This paper describes image classification techniques which augment near field stereo to identi fy safe terrain and obstacles in the far field. Machine Learning classification techniques using appearance-based feat ures appear well suited to the task of far-field obstacle detectio n, where stereo vision fails. In particular, binary classifiers are appropriate for this task and have performance characteristics suitable for real-time navigation systems. In this paper, we examine the use of stereo vision to identify obstacles and sa fe terrain in the near field, then using the appearance of these identified regions from the image to classify the remaining far field regions. We rigorously evaluate five binary classifiers as applied to the problem for logged image and navigation data and report on their performance. We also perform live experiments on a DARPA LAGR robot and show that the use of image classification techniques to augment stereo vision re sults in an enhanced navigational capability in the far field.
Hole‐Programmed Superfast Multistep Folding of Hydrogel Bilayers

Two important aspects of actuation behavior of stimuli‐responsive hydrogels are the complexity of the shape change and its speed. Here, it is shown that varying the shape of simple polymer bilayers can result in very complex and very fast spontaneous folding. The complexity and high folding rate arise from the choice of the shape and from the presence of inhomogeneous swelling within the thermoresponsive layer entrapped between the top hydrophobic layer and the substrate. In contrast to homogeneous swelling of a freestanding bilayer, which leads to a gradual increase of curvature throughout the whole bilayer, inhomogeneous swelling first results in complete rolling of the periphery of the film, which changes its mechanical properties and affects the subsequent morphing process. Further swelling of the thermoresponsive layer generates more stress that builds up until a buckling threshold is overcome, allowing very fast switching from the flat edge‐rolled configuration into a folded one. The research demonstrates how the introduction of holes into actuating bilayers gives rise not only to a novel geometric control over the folding fate of the films but also adds the ability to tune the rate of folding, through the careful selection of hole size, location, and shape.
Multi-Parameter-Setting Based on Data Original Distribution for DENCLUE Optimization

DENCLUE is a typical density-based clustering method, and it also is an important pattern classification analysis technique. In this clustering method, the propriety of parameters’ values greatly influences the quality of distinguishing. Accordingly, how to choose an appropriate value of every parameter is a problem that is worth studying. The problem is focused in this paper, and a method which is very different from previous ones is presented. The highlight of the method is that the selection of parameters no longer depends on personal experience but on data original distribution. More specifically, smoothing parameter <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> is more or less proportional to the average value of distance between two arbitrary data points; step size <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> is adapted according to the density of data points in the hill-climbing progress; noise threshold <inline-formula> <tex-math notation="LaTeX">$\xi $ </tex-math></inline-formula> is replaced by <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> and total number of data points. Compared with the original DENCLUE and an improved DENCLUE proposed in our previous research, the optimized algorithm can bring better clustering from experiments. In addition, due to the adaptiveness of <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>, the method will become less complex.