Notes

A new device for localised dynamics-driven activation in Bruton's tyrosine kinase.
Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a long history, recent advances have greatly improved their performance in computer vision, natural language processing, etc. From the statistical and scientific perspective, it is natural to ask What is deep learning? What are the new characteristics of deep learning, compared with classical methods? What are the theoretical foundations of deep learning? To answer these questions, we introduce common neural network models (e.g., convolutional neural nets, recurrent neural nets, generative adversarial nets) and training techniques (e.g., stochastic gradient descent, dropout, batch normalization) from a statistical point of view. Along the way, we highlight new characteristics of deep learning (including depth and over-parametrization) and explain their practical and theoretical benefits. We also sample recent results on theories of deep learning, many of which are only suggestive. While a complete understanding of deep learning remains elusive, we hope that our perspectives and discussions serve as a stimulus for new statistical research.The rise of network data in many different domains has offered researchers new insight into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using covariates as a first step. We provide a discussion on existing statistical and computational methods for edge esitimation and subsequent statistical inference problems in these two types of biological networks.The COVID-19 pandemic affects the mental health status of perinatal women, which makes it important to gain insight into and to effectively measure specific stressors of the COVID-19 pandemic. Therefore, we aimed to develop a COVID-19 Perinatal Perception Questionnaire (COVID19-PPQ). In-depth interviews were conducted during the first national lockdown period with pregnant women, new mothers and perinatal healthcare professionals, resulting in (a) a 27-item pregnancy and (b) a 21-item postpartum scale. Explorative factor analyses (EFA) in sample Ia (N = 154) and Ib (N = 90), and confirmatory factor analyses (CFA) in sample IIa (N = 113) and IIb (N = 81) were conducted to test the psychometric properties of both scales. For the pregnancy scale, EFA suggested a three-factor solution (risk of infection, contact, future), which was confirmed by CFA and resulted in a final nine-item scale. For the postpartum scale, a three-factor solution (first postpartum week, COVID-19 measures, fear for infection) was suggested by EFA and confirmed by CFA, resulting in a final ten-item scale. Symptoms of depression and pregnancy-specific distress were significantly correlated with the pregnancy (sub)scale(s), while symptoms of postpartum depression and anxiety showed significant correlations with the COVID-19 measures and fear for infection subscale. The COVID19-PPQ seems to be a valid instrument for assessment of perinatal COVID-19-related stress perception, showing adequate psychometric properties for both the pregnancy and postpartum scale. Future research should examine the use of this instrument in clinical practice during new episodes of the COVID-19 pandemic.This study investigated the extent to which problem behaviors were factors associated with response to a year-long multicomponent reading intervention for fourth- and fifth-grade students with reading difficulties. Students scoring ≤85 standard score on the Test of Silent Reading Efficiency and Comprehension (n = 108), a reading fluency and comprehension screener measure, were randomized to the researcher-provided treatment condition (n = 55) or the business-as-usual comparison condition (n = 53). Results indicated that problem behaviors were associated with lower reading comprehension outcomes. Findings also suggested that students with higher levels of overall problem behaviors and externalizing behaviors in the treatment condition outperformed similar students in the comparison condition on the Gates-MacGinitie Reading Test (p less then .05). Future research is needed on how to best identify, develop, and adapt effective interventions for students with reading difficulties and problem behaviors within school-wide response to intervention frameworks.This article examines the anexo's use of Latino culture and shared experiences to promote recovery and its appeal to 1.5- and second-generation Latinos. Anexos are grassroots recovery groups with origins in Mexico that offer a residential Alcoholics Anonymous program in Latino communities. Data were gathered from a two-year (2014-2016) ethnographic study of anexos in Northern California and were analyzed thematically. Despite having access to publicly funded treatment, many 1.5- and second-generation Latinos accessed anexos based on cultural familiarity, shared experiences, and a desire to recuperate cultural practices lost during their substance use.The purpose of this study was to determine which patient- or surgery-related factors are predictive of need for perioperative transfusion to avoid obtaining unnecessary pre-operative type and screens (T&S). We conducted an observational retrospective cohort study of 1200 women ≥ 18 years old undergoing gynecologic surgery for benign, possibly benign, or malignant indications on a gynecologic oncology service at a university medical center from 2009-2016. A logistic regression model was used to examine patient-related and surgery-related variables predictive of outcome of transfusion. Independent variables included patient demographics, comorbidities, and surgical indication surgical route, and surgical type. Dependent variable was transfusion outcome (T&S only, conversion to type and cross (T&C), or transfusion). Eight hundred ninety-nine (74.9%) women underwent pre-operative T&S, of which 118 (9.8%) were converted to T&C, and 80 (6.7%) received a transfusion of blood or blood products. Cancer indication, major surgery, and preoperative hematocrit less than 36% were significantly associated with need for transfusion (P = 0.002, P less then 0.0001, P less then 0.0001, respectively). Patients with a benign indication undergoing minor procedures and with normal preoperative hematocrit are least likely to require transfusion.Missing data is a common, difficult problem for network studies. Unfortunately, there are few clear guidelines about what a researcher should do when faced with incomplete information. We take up this problem in the third paper of a three-paper series on missing network data. Here, we compare the performance of different imputation methods across a wide range of circumstances characterized in terms of measures, networks and missing data types. We consider a number of imputation methods, going from simple imputation to more complex model- based approaches. Overall, we find that listwise deletion is almost always the worst option, while choosing the best strategy can be difficult, as it depends on the type of missing data, the type of network and the measure of interest. We end the paper by offering a set of practical outputs that researchers can use to identify the best imputation choice for their particular research setting.Homophily, or the tendency for social contact to occur among those who are similar, plays a crucial role in structuring our social networks. Most previous work considers whether homophily shapes the patterns of all social ties, regardless of their frequency of interaction or level of intimacy. As complex network data become increasingly available, however, researchers need to evaluate whether homophily operates differently for ties defined by strong versus weak measures of strength. Raf inhibitor Here, I take this approach by first defining two variants of homophily (1) strong tie homophily, or the tendency for ties with high measures of strength to cluster together similar peers, and (2) weak tie homophily, or the tendency for ties with low edge weights to connect same-attribute actors. Then, I apply valued ERGMs to demonstrate the utility of differentiating between the two variants across simulated and observed networks. In most networks, I find that there are observable differences in the magnitude of strong versus weak tie homophily. Additionally, when there are low levels of clustering on the attribute of interest, distinguishing between strong and weak tie homophily can reveal that these processes operate in opposite directions. Since strong and weak ties carry substantively different implications, I argue that differentiating between the two homophily variants has the potential to uncover novel insights on a variety of social phenomena.The shocking severity of the Covid-19 pandemic has woken up an unprecedented interest and accelerated effort of the scientific community to model and forecast epidemic spreading to find ways to control it regionally and between regions. Here we present a model that in addition to describing the dynamics of epidemic spreading with the traditional compartmental approach takes into account the social behaviour of the population distributed over a geographical region. The region to be modelled is defined as a two-dimensional grid of cells, in which each cell is weighted with the population density. In each cell a compartmental SEIRS system of delay difference equations is used to simulate the local dynamics of the disease. The infections between cells are modelled by a network of connections, which could be terrestrial, between neighbouring cells, or long range, between cities by air, road or train traffic. In addition, since people make trips without apparent reason, noise is considered to account for them to carry contagion between two randomly chosen distant cells. Hence, there is a clear separation of the parameters related to the biological characteristics of the disease from the ones that represent the spatial spread of infections due to social behaviour. We demonstrate that these parameters provide sufficient information to trace the evolution of the pandemic in different situations. In order to show the predictive power of this kind of approach we have chosen three, in a number of ways different countries, Mexico, Finland and Iceland, in which the pandemics have followed different dynamic paths. Furthermore we find that our model seems quite capable of reproducing the path of the pandemic for months with few initial data. Unlike similar models, our model shows the emergence of multiple waves in the case when the disease becomes endemic.Eccrine poroma is a rare tumor arising from sweat glands with common location being soles and palms. We are reporting a case of 70-year male patient with large lower lid mass lesion. Owing to its location and history of growth, malignancy was suspected. Biopsy proved it to be eccrine poroma which is a benign lesion. Complete excision with lid reconstruction was done. Eccrine poroma, though rare, should be kept in the differential diagnosis of eyelid tumors. Owing to the risk of malignant transformation and difficulty in clinical differentiation between poroma and porocarcinoma, wide excision should be done.
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