Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender Differences in Receipt of National Institutes of Health R01 Grants Among Junior Faculty at an Academic Medical Center: The Role of Connectivity, Rank, and Research Productivity.

OBJECTIVE To determine whether there were gender differences in likelihood of receiving a first National Institutes of Health (NIH) R01 award among 5445 instructors and assistant professors at Harvard Medical School (HMS). MATERIALS AND METHODS Data on R01 award principal investigators were obtained from NIH ExPORTER and linked with faculty data. Using Cox proportional hazard regression, we examined the association of gender with receipt of first R01 award between 2008 and 2015 accounting for demographics, research productivity metrics, and professional characteristics. RESULTS Compared to males, females had fewer publications, lower h-index, smaller coauthor networks and were less likely to be assistant professors (p < 0.0001). Four hundred and thirteen of 5445 faculty (7.6%) received their first R01 award during the study period. There was no gender difference in receipt of R01 awards in age-adjusted (hazard ratio [HR]: 0.87, 95% confidence interval [CI]: 0.70-1.08) or multivariable-adjusted models (HR: 1.07, 95% CI: 0.86-1.34). Compared to white males, there was a nonsignificant 10%, 18%, and 30% lower rate of R01 receipt among white, Asian or Pacific Islander, and underrepresented minority females, respectively. These differences were eliminated in the multivariable-adjusted model. Network reach, age, HMS start year, h-index, academic rank, previous K award, terminal degree, and HMS training were all significant predictors of receiving an R01 award. CONCLUSIONS A relatively small proportion of HMS junior faculty obtained their first NIH R01 award during the study period. There was no significant gender difference in likelihood of award. However, we are unable to distinguish faculty that never applied from those who applied and were not successful.
Putting the Pieces Back Together Again: Contest Webs for Large-Scale Problem Solving

A key issue, whenever people work together to solve a complex problem, is how to divide the problem into parts done by different people and combine the parts into a solution for the whole problem. This paper presents a novel way of doing this with groups of contests called contest webs. Based on the analogy of supply chains for physical products, the method provides incentives for people to (a) reuse work done by themselves and others, (b) simultaneously explore multiple ways of combining interchangeable parts, and (c) work on parts of the problem where they can contribute the most. The paper also describes a field test of this method in an online community of over 50,000 people who are developing proposals for what to do about global climate change. The early results suggest that the method can, indeed, work at scale as intended.
SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer’s attention. Android allows downloading and installation of apps from other unofficial market places. This gives malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of malware analysis and detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from malware. However, the existing research clearly lags in detecting malware efficiently and accurately. For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid malware detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid malware detection model for Android operating systems is developed, that can provide high detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high malware detection accuracy by ensuring the efficiency in terms of power and storage consumption.
The role of β2 integrins and lipopolysaccharide‐binding protein in the phagocytosis of dead Neisseria meningitidis

Phagocytosis of microbial pathogens is essential for the host immune response to infection. Our previous work has shown that lipooligosaccharide (LOS) expression on the surface of Neisseria meningitidis (Nm) is essential for phagocytosis, but the receptor involved remained unclear. In this study, we show that human CR3 (CD11b/CD18) and CR4 (CD11c/CD18) are phagocytic receptors for Nm as illustrated by the capacity of CR3‐ and CR4‐transfected Chinese hamster ovary (CHO) cells to facilitate Nm uptake. A CR3‐signalling mutant failed to internalize Nm, showing that the ability of CR3 to signal is essential for phagocytosis. Internalization of Nm by CR3‐transfected CHO cells could be inhibited by the presence of CR3‐specific antibodies. Furthermore, dendritic cells from leukocyte adhesion deficiency‐1 patients, who have diminished expression of β2 integrins, showed markedly reduced phagocytosis of Nm. The CR3‐mediated phagocytosis required the presence of lipopolysaccharide‐binding protein (LBP). Furthermore, the expression of LOS by Nm was essential for LBP binding and phagocytosis via CR3. These results reveal a critical role of CR3 and LBP in the phagocytosis of Nm and provide important insights into the initial interaction meningococci have with the immune system.
Dual inhibition of Wnt and Yes‐associated protein signaling retards the growth of triple‐negative breast cancer in both mesenchymal and epithelial states

Triple‐negative breast cancer (TNBC), the most refractory subtype of breast cancer to current treatments, accounts disproportionately for the majority of breast cancer‐related deaths. This is largely due to cancer plasticity and the development of cancer stem cells (CSCs). Recently, distinct yet interconvertible mesenchymal‐like and epithelial‐like states have been revealed in breast CSCs. Thus, strategies capable of simultaneously inhibiting bulk and CSC populations in both mesenchymal and epithelial states have yet to be developed. Wnt/β‐catenin and Hippo/YAP pathways are crucial in tumorigenesis, but importantly also possess tumor suppressor functions in certain contexts. One possibility is that TNBC cells in epithelial or mesenchymal state may differently affect Wnt/β‐catenin and Hippo/YAP signaling and CSC phenotypes. In this report, we found that YAP signaling and CD44high/CD24−/low CSCs were upregulated while Wnt/β‐catenin signaling and ALDH+ CSCs were downregulated in mesenchymal‐like TNBC cells, and vice versa in their epithelial‐like counterparts. Dual knockdown of YAP and Wnt/β‐catenin, but neither alone, was required for effective suppression of both CD44high/CD24−/low and ALDH+ CSC populations in mesenchymal and epithelial TNBC cells. These observations were confirmed with cultured tumor fragments prepared from patients with TNBC after treatment with Wnt inhibitor ICG‐001 and YAP inhibitor simvastatin. In addition, a clinical database showed that decreased gene expression of Wnt and YAP was positively correlated with decreased ALDH and CD44 expression in patients’ samples while increased patient survival. Furthermore, tumor growth of TNBC cells in either epithelial or mesenchymal state was retarded, and both CD44high/CD24−/low and ALDH+ CSC subpopulations were diminished in a human xenograft model after dual administration of ICG‐001 and simvastatin. Tumorigenicity was also hampered after secondary transplantation. These data suggest a new therapeutic strategy for TNBC via dual Wnt and YAP inhibition.
Smooth Loss Functions for Deep Top-k Classification

The top-k error is a common measure of performance in machine learning and computer vision. In practice, top-k classification is typically performed with deep neural networks trained with the cross-entropy loss. Theoretical results indeed suggest that cross-entropy is an optimal learning objective for such a task in the limit of infinite data. In the context of limited and noisy data however, the use of a loss function that is specifically designed for top-k classification can bring significant improvements. Our empirical evidence suggests that the loss function must be smooth and have non-sparse gradients in order to work well with deep neural networks. Consequently, we introduce a family of smoothed loss functions that are suited to top-k optimization via deep learning. The widely used cross-entropy is a special case of our family. Evaluating our smooth loss functions is computationally challenging: a na\"ive algorithm would require $\mathcal{O}(\binom{n}{k})$ operations, where n is the number of classes. Thanks to a connection to polynomial algebra and a divide-and-conquer approach, we provide an algorithm with a time complexity of $\mathcal{O}(k n)$. Furthermore, we present a novel approximation to obtain fast and stable algorithms on GPUs with single floating point precision. We compare the performance of the cross-entropy loss and our margin-based losses in various regimes of noise and data size, for the predominant use case of k=5. Our investigation reveals that our loss is more robust to noise and overfitting than cross-entropy.
Understanding Environment-Adaptive Force Control of Series Elastic Actuators

Series elastic actuators (SEAs) have become fundamental components in robots that physically interact with the surrounding world and with humans. Force control of SEAs is indeed an active area of research. This paper refines and improves the stability analysis of the environment-adaptive force controller we previously proposed and proves asymptotic convergence in several cases of practical interest. In particular, we theoretically motivate observations that are not adequately explained or covered in our previous work. The analysis reveals an interesting generalization property of the approach achieved by using a simple and generic model to account for very different environments including stiff contacts, purely inertial loads, and soft materials. This is allowed by model parameters with interchangeable physical meaning: the same parameter describes different physical properties or variables, depending on the kind of interacting environment. In this light, the proposed adaptive controller adapts not only to parameters values, but also to parameter meaning. The analysis also shows that as the environment stiffness decreases, the convergence precision may degrade and a sliding-mode robustification is proposed to overcome the issue. Simulations and experiments are conducted to validate the control convergence in different environments, showing agreement with the theoretical expectation.
On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimization

We adopt and analyze a synchronous K-step averaging stochastic gradient descent algorithm which we call K-AVG for solving large scale machine learning problems. We establish the convergence results of K-AVG for nonconvex objectives. Our analysis of K-AVG applies to many existing variants of synchronous SGD. We explain why the K-step delay is necessary and leads to better performance than traditional parallel stochastic gradient descent which is equivalent to K-AVG with $K=1$. We also show that K-AVG scales better with the number of learners than asynchronous stochastic gradient descent (ASGD). Another advantage of K-AVG over ASGD is that it allows larger stepsizes and facilitates faster convergence. On a cluster of $128$ GPUs, K-AVG is faster than ASGD implementations and achieves better accuracies and faster convergence for training with the CIFAR-10 dataset.
Influencing organisational culture to improve hospital performance in care of patients with acute myocardial infarction: a mixed-methods intervention study

Background Hospital organisational culture affects patient outcomes including mortality rates for patients with acute myocardial infarction; however, little is known about whether and how culture can be positively influenced. Methods This is a 2-year, mixed-methods interventional study in 10 US hospitals to foster improvements in five domains of organisational culture: (1) learning environment, (2) senior management support, (3) psychological safety, (4) commitment to the organisation and (5) time for improvement. Outcomes were change in culture, uptake of five strategies associated with lower risk-standardised mortality rates (RSMR) and RSMR. Measures included a validated survey at baseline and at 12 and 24 months (n=223; average response rate 88%); in-depth interviews (n=393 interviews with 197 staff); and RSMR data from the Centers for Medicare and Medicaid Services. Results We observed significant changes (p<0.05) in culture between baseline and 24 months in the full sample, particularly in learning environment (p<0.001) and senior management support (p<0.001). Qualitative data indicated substantial shifts in these domains as well as psychological safety. Six of the 10 hospitals achieved substantial improvements in culture, and four made less progress. The use of evidence-based strategies also increased significantly (per hospital average of 2.4 strategies at baseline to 3.9 strategies at 24 months; p<0.05). The six hospitals that demonstrated substantial shifts in culture also experienced significantly greater reductions in RSMR than the four hospitals that did not shift culture (reduced RSMR by 1.07 percentage points vs 0.23 percentage points; p=0.03) between 2011–2014 and 2012–2015. Conclusions Investing in strategies to foster an organisational culture that supports high performance may help hospitals in their efforts to improve clinical outcomes.
High episiotomy rate protects from obstetric anal sphincter ruptures: A birth register-study on delivery intervention policies in Finland

Aim: To assess the impact of hospital episiotomy policy on obstetric and anal sphincter rupture (OASR, n = 2448) rates and risks among singleton vaginal deliveries in Finland between 1997 and 2007. Methods: An observational, retrospective, population-based register study. All 424,297 women in hospitals with more than 1000 deliveries annually, were divided into three groups based on the episiotomy rate quartiles for 11 years and separated on the basis of whether the women were primiparous or multiparous. The lowest and the highest quartiles were compared against the hospitals with intermediate episiotomy rates, comprising the two quartiles around the median. Stepwise logistic regression analysis was used to adjust significant risk factors. Results: The annual range of episiotomy varied from 11 to 94% in primiparous women, and from 1 to 46% in multiparous women. After adjustment the risk of OASR appears to be 39% lower (OR 0.61, 95% CI 0.52—0.90) in primiparous and 45% lower (OR 0.55, 95% CI 0.42—0.72) in multiparous women delivered in the highest quartile hospitals. At an individual level, episiotomy was a protective factor (OR 0.82, 95% CI 0.75—0.91) in primiparous women, but increased the risk by 2.36-fold in multiparous women (OR 2.36, 95% CI 1.86—2.84). Conclusions: The results suggest that high episiotomy rate provided protection from OASR among both groups of women. Among the multiparous women, the 2.4-fold risk of OASR related to episiotomy at an individual level might be explained by confounding by indication, since episiotomy was performed more often to women at a high risk of OASR.
Learning Important Features Through Propagating Activation Differences

The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL, code: http://goo.gl/RM8jvH.
Fast flow-based algorithm for creating density-equalizing map projections

Significance Geographic maps are a popular means to visualize spatial statistics. Conventionally, each map region is displayed with an area proportional to the actual land area. But equal-area maps can grossly misrepresent demographic data: Densely populated cities should be given more prominence than large, but sparsely populated territories. Cartograms solve this problem by rescaling map regions in proportion to, for example, population or gross domestic products. Until now, it has generally been cumbersome or slow to calculate map projections for contiguous cartograms. Here we describe and benchmark a fast flow-based algorithm that computes cartograms in a matter of seconds, yet maintains the strengths of previous methods—a development which may lead to a more widespread adoption of cartograms. Cartograms are maps that rescale geographic regions (e.g., countries, districts) such that their areas are proportional to quantitative demographic data (e.g., population size, gross domestic product). Unlike conventional bar or pie charts, cartograms can represent correctly which regions share common borders, resulting in insightful visualizations that can be the basis for further spatial statistical analysis. Computer programs can assist data scientists in preparing cartograms, but developing an algorithm that can quickly transform every coordinate on the map (including points that are not exactly on a border) while generating recognizable images has remained a challenge. Methods that translate the cartographic deformations into physics-inspired equations of motion have become popular, but solving these equations with sufficient accuracy can still take several minutes on current hardware. Here we introduce a flow-based algorithm whose equations of motion are numerically easier to solve compared with previous methods. The equations allow straightforward parallelization so that the calculation takes only a few seconds even for complex and detailed input. Despite the speedup, the proposed algorithm still keeps the advantages of previous techniques: With comparable quantitative measures of shape distortion, it accurately scales all areas, correctly fits the regions together, and generates a map projection for every point. We demonstrate the use of our algorithm with applications to the 2016 US election results, the gross domestic products of Indian states and Chinese provinces, and the spatial distribution of deaths in the London borough of Kensington and Chelsea between 2011 and 2014.
In Vitro Activity of Delafloxacin and Microbiological Response against Fluoroquinolone-Susceptible and Nonsusceptible Staphylococcus aureus Isolates from Two Phase 3 Studies of Acute Bacterial Skin and Skin Structure Infections

ABSTRACT Delafloxacin is an investigational anionic fluoroquinolone antibiotic with broad-spectrum in vitro activity, including activity against Gram-positive organisms, Gram-negative organisms, atypical organisms, and anaerobes. The in vitro activity of delafloxacin and the percent microbiological response in subjects infected with fluoroquinolone-susceptible and nonsusceptible Staphylococcus aureus isolates were determined from two global phase 3 studies of delafloxacin versus vancomycin plus aztreonam in patients with acute bacterial skin and skin structure infections (ABSSSI). Patients from 23 countries, predominately the United States but also Europe, South America, and Asia, were enrolled. The microbiological intent-to-treat (MITT) population included 1,042 patients from which 685 S. aureus isolates were submitted for identification and susceptibility testing per CLSI guidelines at the central laboratory (JMI Laboratories, North Liberty, IA). The comparator fluoroquinolone antibiotics included levofloxacin and ciprofloxacin. Nonsusceptibility to these antibiotics was determined using CLSI breakpoints. S. aureus isolates were 33.7% levofloxacin nonsusceptible (LVX-NS). The delafloxacin MIC90 values against levofloxacin-nonsusceptible S. aureus, methicillin-resistant S. aureus (MRSA), and methicillin-susceptible S. aureus isolates were all 0.25 μg/ml. Delafloxacin demonstrated high rates of microbiological response against LVX-NS isolates as well as isolates with documented mutations in the quinolone resistance-determining region (QRDR). S. aureus was eradicated or presumed eradicated in 98.4% (245/249) of delafloxacin-treated patients. Similar eradication rates were observed for delafloxacin-treated subjects with levofloxacin-nonsusceptible S. aureus isolates (80/81; 98.8%) and MRSA isolates (70/71; 98.6%). Microbiological response rates of 98.6% were observed with delafloxacin-treated subjects with S. aureus isolates with the S84L mutation in gyrA and the S80Y mutation in parC, the most commonly observed mutations in global phase 3 studies. The data suggest that delafloxacin could be a good option for the treatment of infections caused by S. aureus isolates causing ABSSSI, including MRSA isolates, where high rates of ciprofloxacin and levofloxacin nonsusceptibility are observed. (The phase 3 studies described in this paper have been registered at ClinicalTrials.gov under identifiers NCT01984684 and NCT01811732.)
Polymeric microcapsules with switchable mechanical properties for self-healing concrete: synthesis, characterisation and proof of concept

Microcapsules, with sodium silicate solution as core, were produced using complex coacervation in a double, oil-in-water-in oil, emulsion system. The shell material was a gelatin–acacia gum crosslinked coacervate and the produced microcapsules had diameters ranging from 300 to 700 μm. The shell material designed with switchable mechanical properties. When it is hydrated exhibits soft and ‘rubbery’ behaviour and, when dried, transitions to a stiff and ‘glassy’ material. The microcapsules survived drying and rehydrating cycles and preserved their structural integrity when exposed to highly alkaline solutions that mimic the pH environment of concrete. Microscopy revealed that the shell thickness of the microcapsules varies across their perimeter from 5 to 20 μm. Thermal analysis showed that the produced microcapsules were very stable up to 190 °C. Proof of concept investigation has demonstrated that the microcapsules successfully survive and function when exposed to a cement-based matrix. Observations showed that the microcapsules survive mixing with cement and rupture successfully upon crack formation releasing the encapsulated sodium silicate solution.