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* Context.--Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. We previously demonstrated a CNN that differentiates reactive gastropathy, Helicobacter pylori gastritis (HPG), and normal gastric mucosa. Objective.--To determine whether a CNN can differentiate the following 2 gastric inflammatory patterns: autoimmune gastritis (AG) and HPG. Design.--Gold standard diagnoses were blindly established by 2 gastrointestinal (GI) pathologists. One hundred eighty-seven cases were scanned for analysis by HALO-AI. All levels and tissue fragments per slide were included for analysis. The cases were randomized, 112 (60%; 60 HPG, 52 AG) in the training set and 75 (40%; 40 HPG, 35 AG) in the test set. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The test set was blindly reviewed by pathologists with different levels of GI pathology expertise as follows: 2 GI pathologists, 2 general surgical pathologists, and 2 residents. Each pathologist rendered their preferred diagnosis, HPG or AG. Results.--At the HALO-AI AD percentage cutoff of 50% or more, the CNN results were 100% concordant with the gold standard diagnoses. On average, autoimmune gastritis cases had 84.7% HALO-AI autoimmune gastritis AD and HP cases had 87.3% HALO-AI HP AD. The GI pathologists, general anatomic pathologists, and residents were on average, 100%, 86%, and 57% concordant with the gold standard diagnoses, respectively. Conclusions.--A CNN can distinguish between cases of HPG and autoimmune gastritis with accuracy equal to GI pathologists., Deep learning is a new and emerging paradigm of artificial intelligence (AI) research. Convolutional neural networks (CNN) are an algorithmic form of deep learning that are highly accurate in image-based [...]

* Context.--The adoption of digital capture of pathology slides as whole slide images (WSI) for educational and research applications has proven utility. Objective.--To compare pathologists' primary diagnoses derived from WSI versus the standard microscope. Because WSIs differ in format and method of observation compared with the current standard glass slide microscopy, this study is critical to potential clinical adoption of digital pathology. Design.--The study enrolled a total of 2045 cases enriched for more difficult diagnostic categories and represented as 5849 slides were curated and provided for diagnosis by a team of 19 reading pathologists separately as WSI or as glass slides viewed by light microscope. Cases were reviewed by each pathologist in both modalities in randomized order with a minimum 31-day washout between modality reads for each case. Each diagnosis was compared with the original clinical reference diagnosis by an independent central adjudication review. Results.--The overall major discrepancy rates were 3.64% for WSI review and 3.20% for manual slide review diagnosis methods, a difference of 0.44% (95% CI, -0.15 to 1.03). The time to review a case averaged 5.20 minutes for WSI and 4.95 minutes for glass slides. There was no specific subset of diagnostic category that showed higher rates of modality-specific discrepancy, though some categories showed greater discrepancy than others in both modalities. Conclusions.--WSIs are noninferior to traditional glass slides for primary diagnosis in anatomic pathology., Histopathology interpretation remains an essential, foundational method for disease diagnosis in medical practice. (1) Standard histopathology assessment is initiated through interpretation of hematoxylin and eosin--stained glass microscope slides prepared from [...]

Context.--Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched. Objective.--To investigate the use of DL for nonneoplastic gastric biopsies. Design.--Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion. Results.--For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/ specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%). Conclusions.--A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis. (Arch Pathol Lab Med. 2020;144:370-378; doi: 10.5858/arpa.2019-0004-OA), The recent approval of whole-slide images for primary diagnosis has rapidly accelerated the adoption of digital pathology in diagnostic settings. In addition, there are now large, publicly available data sets [...]

* Context.--Metastatic mucinous tumors present a diagnostic challenge for pathologists as tumor histomorphology is often nonspecific and optimal immunoprofiles are still under investigation. Objective.--To present a head-to-head comparison of special AT-rich sequence-binding protein 2 (SATB2) and caudal type homeobox 2 (CDX2) expression in a diverse array of primary mucinous tumors. Design.--SATB2 and CDX2 immunohistochemical stains were performed on whole sections from 44 mucinous colorectal carcinomas and 175 noncolorectal mucinous tumors. A nuclear scoring system measuring intensity (0-3+) and percentage staining (0 = Results.--SATB2 demonstrated acceptable accuracy at low to moderate expression levels (H-scores of 1-4). With these H-score cutoffs, overall accuracy was greater than 90%. In contrast, CDX2's accuracy rivaled that of SATB2 only at an H-score of 5 (89.0%), as its specificity suffered at lower expression levels ( 3 and CDX2 = 5). Conclusions.--SATB2 is a more accurate marker of colorectal origin across a variety of expression levels compared with CDX2 when applied to mucinous tumors from a host of primary sites. However, these stains are near equivalent when each is interpreted at its optimal expression level. (Arch Pathol Lab Med. 2019;143:1119-1125; doi: 10.5858/arpa.2018-0337-OA), Metastatic mucinous tumors pose a unique diagnostic challenge for the surgical pathologist as their morphologic and clinical characteristics are often nonspecific. The colorectum is the most common site of origin. [...]

Objectives: The presence of nodal metastases in patients with papillary thyroid carcinoma (PTC) has both staging and treatment implications. However, lymph nodes are often not removed during thyroidectomy. Prior work has demonstrated the capability of artificial intelligence (AI) to predict the presence of nodal metastases in PTC based on the primary tumor histopathology alone. This study aimed to replicate these results with multi-institutional data. Methods: Cases of conventional PTC were identified from the records of 2 large academic institutions. Only patients with complete pathology data, including at least 3 sampled lymph nodes, were included in the study. Tumors were designated "positive" if they had at least 5 positive lymph node metastases. First, algorithms were trained separately on each institution's data and tested independently on the other institution's data. Then, the data sets were combined and new algorithms were developed and tested. The primary tumors were randomized into 2 groups, one to train the algorithm and another to test it. A low level of supervision was used to train the algorithm. Board-certified pathologists annotated the slides. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic curves and the Youden J statistic were used for primary analysis. Results: There were 420 cases used in analyses, 45% of which were negative. The best performing single institution algorithm had an area under the curve (AUC) of 0.64 with a sensitivity and specificity of 65% and 61% respectively, when tested on the other institution's data. The best performing combined institution algorithm had an AUC of 0.84 with a sensitivity and specificity of 68% and 91% respectively. Conclusion: A convolutional neural network can produce an accurate and robust algorithm that is capable of predicting nodal metastases from primary PTC histopathology alone even in the setting of multi-institutional data. [ABSTRACT FROM AUTHOR]

Finding patterns and making conjectures are important thinking skills for students at all levels of mathematics education. Both the Common Core State Standards for Mathematics and the National Council of Teachers of Mathematics speak to the importance of these thought processes. NCTM suggests that students should be able to recognize reasoning and proof as fundamental aspects of mathematics, make and investigate mathematical conjectures, develop and evaluate mathematical arguments and proofs, and select and use various types of reasoning and methods of proof. CCSS states that students should "make conjectures and build a logical progression of statements to explore the truth of their conjectures" (CCSSI 2010, p. 6). The activity presented in this article makes such reasoning accessible to high school students with some previous study of trigonometry. In this activity, students use technology to graph parametric equations, make conjectures based on their graphs, modify their conjectures on the basis of evidence, and find counterexamples or investigate methods of proof. Students will be left with some open-ended questions to engage them in investigations beyond the classroom. Students learn to plot parametric curves, including those involving sine and cosine. After exploring several of these curves, students will conjecture about parameters that will make a curve appear to fill in the area of a square. Students deal with the idea of spacefilling curves from a naive perspective; it is important to note that none of the curves encountered in this activity actually fill space.

Transferrin receptor (TFRC) is the major mediator for iron entry into a cell. Under excessive iron conditions, TFRC is expected to be reduced to lower iron uptake and toxicity. However, the mechanism whereby TFRC expression is maintained at high levels in iron‐enriched cancer cells and the contribution of TFRC to cancer development are enigmatic. Here the work shows TFRC is induced by adenomatous polyposis coli (APC) gene loss‐driven β‐catenin activation in colorectal cancer, whereas TFRC‐mediated intratumoral iron accumulation potentiates β‐catenin signaling by directly enhancing the activity of tankyrase. Disruption of TFRC leads to a reduction of colonic iron levels and iron‐dependent tankyrase activity, which caused stabilization of axis inhibition protein 2 (AXIN2) and subsequent repression of the β‐catenin/c‐Myc/E2F Transcription Factor 1/DNA polymerase delta1 (POLD1) axis. POLD1 knockdown, iron chelation, and TFRC disruption increase DNA replication stress, DNA damage response, apoptosis, and reduce colon tumor growth. Importantly, a combination of iron chelators and DNA damaging agents increases DNA damage response and reduces colon tumor cell growth. TFRC‐mediated iron import is at the center of a novel feed‐forward loop that facilitates colonic epithelial cell survival. This discovery may provide novel strategies for colorectal cancer therapy. [ABSTRACT FROM AUTHOR]

The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images. Index Terms--Texture, supervised learning, cue combination, natural images, ground truth segmentation data set, boundary detection, boundary localization.

Although attachment theory has long posited a link between early experiences of care and children's prosocial behavior, investigations of this association have not embraced the multifaceted nature of prosociality. This study is the first to assess associations between child attachment and independent observations of helping, sharing, and comforting. Attachment quality in 3- to 5-year-old children (N = 137) was linked to all three prosocial behaviors. Additionally, bifactor analyses revealed distinct associations between attachment and children's general prosocial dispositions and their specific abilities to meet the unique challenges of helping and, marginally, comforting. These findings underscore the importance of considering multiple explanations for links between attachment and prosocial behavior and provide novel insights into sources of variation in children's prosociality. [ABSTRACT FROM AUTHOR]

Research indicates that dispositional attachment security fosters empathy, and that short-term increases in security ("security priming") increase empathy and willingness to help others. In two experiments, we examined effects of recalling one's own vulnerability ( feeling hurt by a relationship partner) and security priming on empathy for a recipient in need. In Study 1, the recipient was a middle-aged homeless woman (low similarity to participants); in Study 2, the recipient was a college-aged woman whose boyfriend had been unfaithful (high similarity to participants). In both studies, hurt feelings influenced participants' empathy, but the nature of the influence varied as a function of target-participant similarity. In Study 1, hurt feelings decreased empathy and increased caregiving avoidance. In Study 2, hurt feelings not only increased empathy but also aroused caregiving anxiety. Furthermore, security priming weakened defensive barriers against helping: In Study 1, it reduced caregiving avoidance, and in Study 2, it reduced caregiving anxiety. [ABSTRACT FROM AUTHOR]

Although evidence shows that attachment insecurity and disorganization increase risk for the development of psychopathology (Fearon, Bakermans-Kranenburg, van IJzendoorn, Lapsley, & Roisman, 2010; Groh, Roisman, van IJzendoorn, Bakermans-Kranenburg, & Fearon, 2012), implementation challenges have precluded dissemination of attachment interventions on the broad scale at which they are needed. The Circle of Security–Parenting Intervention (COS-P; Cooper, Hoffman, & Powell, 2009), designed with broad implementation in mind, addresses this gap by training community service providers to use a manualized, video-based program to help caregivers provide a secure base and a safe haven for their children. The present study is a randomized controlled trial of COS-P in a low-income sample of Head Start enrolled children and their mothers. Mothers (N = 141; 75 intervention, 66 waitlist control) completed a baseline assessment and returned with their children after the 10-week intervention for the outcome assessment, which included the Strange Situation. Intent to treat analyses revealed a main effect for maternal response to child distress, with mothers assigned to COS-P reporting fewer unsupportive (but not more supportive) responses to distress than control group mothers, and a main effect for one dimension of child executive functioning (inhibitory control but not cognitive flexibility when maternal age and marital status were controlled), with intervention group children showing greater control. There were, however, no main effects of intervention for child attachment or behavior problems. Exploratory follow-up analyses suggested intervention effects were moderated by maternal attachment style or depressive symptoms, with moderated intervention effects emerging for child attachment security and disorganization, but not avoidance; for inhibitory control but not cognitive flexibility; and for child internalizing but not externalizing behavior problems. This initial randomized controlled trial of the efficacy of COS-P sets the stage for further exploration of “what works for whom” in attachment intervention. [ABSTRACT FROM AUTHOR]

The article describes an activity program for secondary education which teaches mathematics students various aspects including parametric curves plotting, using technology to graph parametric equations to fill space, and mathematical conjecture about parameters.

Blood cultures are often obtained in postoperative patients to rule out bloodstream infections. Our study objectives were to determine the efficacy of blood cultures in postoperative patients with suspected sepsis and to determine variables predisposing patients to positive cultures. This was a retrospective study including patients with blood cultures drawn from January to March 2009 at our institution. We recorded demographics, presence of fever (temperature 101.5°F or higher), elevated white blood cell count (12,000/µL or greater), central line, diabetes, intensive care unit admission, postoperative day of blood draw, National Research Council surgical wound classification, and pre- or postoperative antibiotics. Blood cultures were drawn from 150 patients undergoing surgery within 30 days prior. Sixteen had positive cultures and nine were true-positives (6.3%). There was no statistical difference (P > 0.05) between patients with positive and negative cultures except that those with negative cultures were more likely to have received preoperative antibiotics (P = 0.0186). Blood cultures are invasive, expensive tests with low yield. We recommend that blood cultures be drawn in patients not receiving preoperative antibiotics who have undergone surgery more than 4 days before culture. [ABSTRACT FROM AUTHOR]

Summary Differential scanning calorimetry (DSC) and nuclear magnetic resonance (NMR) techniques were used to obtain evidence whether a glassy state exists within fully hydrated dormant Bacillus subtilis spores. DSC thermograms of hydrated dormant spores contained two major transitions; one at ∼60 °C assigned to be associated with the initial ‘activation’ stage for germination, and the other at ∼120 °C assigned to be associated with the ‘inactivation’ process (i.e. killing of the spores). The peak at ∼60 °C had characteristics which were all consistent with at least some region of the compartmentalised structure of fully hydrated dormant spores being in a glassy state. Solid (CPMAS) and liquid state (SPMAS) mobility resolved 13C NMR spectroscopy studies on the calcium dipicolinic acid (CaDPA) resonances, which are only present in the central protoplast of bacterial spores, showed it to be present in an amorphous solid-like environment. The DSC and NMR results were both found to be consistent with the hypothesis that the dormancy of bacterial spores is due to at least the central protoplast region being maintained in a low moisture content glassy state. [ABSTRACT FROM AUTHOR]

Matching visual patterns that appear scaled, rotated, and deformed with respect to each other is a challenging problem. We propose a linear formulation that simultaneously matches feature points and estimates global geometrical transformation in a constrained linear space. The linear scheme enables search space reduction based on the lower convex hull property so that the problem size is largely decoupled from the original hard combinatorial problem. Our method therefore can be used to solve large scale problems that involve a very large number of candidate feature points. Without using prepruning in the search, this method is more robust in dealing with weak features and clutter. We apply the proposed method to action detection and image matching. Our results on a variety of images and videos demonstrate that our method is accurate, efficient, and robust. [ABSTRACT FROM AUTHOR]