Long-term Disease Behavior and Surgical Intervention Analysis in Hospitalized Patients with Crohn's Disease in China: A Retrospective Cohort Study

Background There is lack of real-world data for disease behavior and surgery of Crohn’s disease (CD) from large-scale Chinese cohorts. Methods Hospitalized patients diagnosed with CD in our center were consecutively included from January 2000 to December 2018. Disease behavior progression was defined as the initial classification of B1 to the progression to B2 or B3. Clinical characteristics including demographics, disease classification and activity, medical therapy, development of cancers, and death were collected. Results Overall, 504 patients were included. Two hundred and thirty one (45.8%) patients were initially classified as B1; 30 (13.0%), 71 (30.7%), and 95 (41.1%) of them had disease progression at the 1-year follow-up, 5-year follow-up, and overall, respectively. Patients without location transition before behavior transition were less likely to experience behavior progression. However, patients without previous exposure to a corticosteroid, immunomodulator, or biological agent had a greater chance of experiencing behavior progression. When the long-term prognosis was evaluated, 211 (41.9%) patients underwent at least one CD-related surgery; 108 (21.4%) and 120 (23.8%) of these patients underwent surgery before and after their diagnosis, respectively. An initial classification as B1, no behavior transition, no surgery prior to diagnosis, and previous corticosteroid exposure during follow-up were associated with a lower risk of undergoing surgery. Conclusions This study depicts the clinical features and factors associated with behavior progression and surgery among hospitalized CD patients in a Chinese center. Behavior progression is associated with a higher probability of CD-related surgery, and strengthened therapies are necessary for them in the early phase.

Authentication of Transylvanian Spirits Based on Isotope and Elemental Signatures in Conjunction with Statistical Methods

The potential association between stable isotope ratios of light elements and mineral content, in conjunction with unsupervised and supervised statistical methods, for differentiation of spirits, with respect to some previously defined criteria, is reviewed in this work. Thus, based on linear discriminant analysis (LDA), it was possible to differentiate the geographical origin of distillates in a percentage of 96.2% for the initial validation, and the cross-validation step of the method returned 84.6% of correctly classified samples. An excellent separation was also obtained for the differentiation of spirits producers, 100% in initial classification, and 95.7% in cross-validation, respectively. For the varietal recognition, the best differentiation was achieved for apricot and pear distillates, a 100% discrimination being obtained in both classifications (initial and cross-validation). Good classification percentages were also obtained for plum and apple distillates, where models with 88.2% and 82.4% in initial and cross-validation, respectively, were achieved for plum differentiation. A similar value in the cross-validation procedure was reached for the apple spirits. The lowest classification percent was obtained for quince distillates (76.5% in initial classification followed by 70.4% in cross-validation). Our results have high practical importance, especially for trademark recognition, taking into account that fruit distillates are high-value commodities; therefore, the temptation of “fraud”, i.e., by passing regular distillates as branded ones, could occur.

Improved Fusion of Spatial Information into Hyperspectral Classification through the Aggregation of Constrained Segment Trees: Segment Forest

Compared with traditional optical and multispectral remote sensing images, hyperspectral images have hundreds of bands that can provide the possibility of fine classification of the earth’s surface. At the same time, a hyperspectral image is an image that coexists with the spatial and spectral. It has become a hot research topic to combine the spatial spectrum information of the image to classify hyperspectral features. Based on the idea of spatial–spectral classification, this paper proposes a novel hyperspectral image classification method based on a segment forest (SF). Firstly, the first principal component of the image was extracted by the process of principal component analysis (PCA) data dimension reduction, and the data constructed the segment forest after dimension reduction to extract the non-local prior spatial information of the image. Secondly, the images’ initial classification results and probability distribution were obtained using support vector machine (SVM), and the spectral information of the images was extracted. Finally, the segment forest constructed above is used to optimize the initial classification results and obtain the final classification results. In this paper, three domestic and foreign public data sets were selected to verify the segment forest classification. SF effectively improved the classification accuracy of SVM, and the overall accuracy of Salinas was enhanced by 11.16%, WHU-Hi-HongHu by 15.89%, and XiongAn by 19.56%. Then, it was compared with six decision-level improved space spectrum classification methods, including guided filtering (GF), Markov random field (MRF), random walk (RW), minimum spanning tree (MST), MST+, and segment tree (ST). The results show that the segment forest-based hyperspectral image classification improves accuracy and efficiency compared with other algorithms, proving the algorithm’s effectiveness.

A Visual Mining Approach to Improved Multiple-Instance Learning

Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, transforming an MIL problem into standard supervised learning. Visualization can be a useful tool to assess learning scenarios by incorporating the users’ knowledge into the classification process. Considering that multiple-instance learning is a paradigm that cannot be handled by current visualization techniques, we propose a multiscale tree-based visualization called MILTree to support MIL problems. The first level of the tree represents the bags, and the second level represents the instances belonging to each bag, allowing users to understand the MIL datasets in an intuitive way. In addition, we propose two new instance selection methods for MIL, which help users improve the model even further. Our methods can handle both binary and multiclass scenarios. In our experiments, SVM was used to build the classifiers. With support of the MILTree layout, the initial classification model was updated by changing the training set, which is composed of the prototype instances. Experimental results validate the effectiveness of our approach, showing that visual mining by MILTree can support exploring and improving models in MIL scenarios and that our instance selection methods outperform the currently available alternatives in most cases.

WOWEN SEXUAL DESIRES EXPERIENCES DURING HONEYMOON PERIODS

Background: Honeymoon is a vacation trip that is usually done by newly married couples to celebrate their wedding. The most beautiful moment awaited by newlywed couples, where everything still looks beautiful and sweet like honey. This study aimed to explore married women’s experience of their sexual desire during their honeymoon periods. Methods: The researcher conducted this qualitative study on six eligible married women who met the requirements for reproductive age. Data were collected using semi structured–interviews and analyzed using thematic methods. All of the participants in this study were obtained through purposive sampling. After being conducted, each interview was transcribed verbatim and read several times to achieve the sense of the whole and then, the key terms were highlighted as codes. After the initial classification of the codes, categories and themes gradually appeared. Results: a theme was found and divided into two categories:1) passionate and emotional sexual desires;2) Spontaneous and sensitive sexual desires Conclusions: During their honeymoon period, the majority of women experienced sexual desire that is spontaneous, sensitive or easily rises when stimulated, hence its getting more excited, and often this sexual desire even being so selfish and emotional, especially at their 'first night'.Keywords: experiences, honeymoon, qualitative research, sexual desire, women

Deep Vision for Breast Cancer Classification and Segmentation

(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.

A preliminary classification of different tasks that make use of uncertainty visualizations

Individuals often struggle with tasks that involve uncertainty. Uncertainty visualizations are a type of cognitive aid that provides uncertainty information to help people with performing these tasks. However, the literature has shown that uncertainty visualizations differ in the extent they improve individuals’ task performance. We hypothesize that differences in the tasks can account for some of this variability. In this study, we aimed to create an initial classification of task types based on studies on uncertainty visualizations by reviewing a diverse set of recent research involving uncertainty visualizations. We classified the experimental tasks found in these papers into four groups: uncertainty assessment, forecasting, decision making, and metacognition. Then, we reviewed the result of the experiments in terms of the similarities and differences in the use of uncertainty visualizations within and between tasks. This classification serves as a starting point for further research into the effective design of visualizations of uncertainty.

A preliminary classification of different tasks that make use of uncertainty visualizations

Individuals often struggle with tasks that involve uncertainty. Uncertainty visualizations are a type of cognitive aid that provides uncertainty information to help people with performing these tasks. However, the literature has shown that uncertainty visualizations differ in the extent they improve individuals’ task performance. We hypothesize that differences in the tasks can account for some of this variability. In this study, we aimed to create an initial classification of task types based on studies on uncertainty visualizations by reviewing a diverse set of recent research involving uncertainty visualizations. We classified the experimental tasks found in these papers into four groups: uncertainty assessment, forecasting, decision making, and metacognition. Then, we reviewed the result of the experiments in terms of the similarities and differences in the use of uncertainty visualizations within and between tasks. This classification serves as a starting point for further research into the effective design of visualizations of uncertainty.

Comparison of Roll and Pitch Among Patients with Vertical and Horizontal Skeletal Patterns Using Cant-O-Meter: A New Gyroscopic Device for Measuring Occlusal Cant

In Orthodontics, initial classification of malocclusions was based on planar malocclusions in the anteroposterior, transverse and vertical planes that were based only on translation of the jaws in space. In 2007, Ackermann and Proffit introduced rotational components—roll, pitch, and yaw—analogous to the position of the airplane in space. These rotations can result in canting of the occlusal plane. There are no quantitative methods available in the literature for a precise estimation of the occlusal cant. Qualitative evaluation of occlusal cant is subjective and is associated with inter-individual variations. This article describes an indigenously devised simple chairside device that can quantify cant of the occlusal plane in terms of the roll and pitch in degrees. There is accurate quantification of cant, which can be used effectively in many clinical scenarios.

Reassessing the Net Benefit of Cancer Drugs With Evolution of Evidence Using the ASCO Value Framework

Background: Regulatory approval of oncology drugs is often based on interim data or surrogate endpoints. However, clinically relevant data, such as long-term overall survival and quality of life (QoL), are often reported in subsequent publications. This study evaluated the ASCO-Value Framework (ASCO-VF) net health benefit (NHB) at the time of approval and over time as further evidence arose. Methods: FDA-approved oncology drug indications from January 2006 to December 2016 were reviewed to identify clinical trials scorable using the ASCO-VF. Subsequent publications of clinical trials relevant for scoring were identified (until December 2019). Using ASCO-defined thresholds (≤40 for low and ≥45 for substantial benefit), we assessed changes in classification of benefit at 3 years postapproval. Results: Fifty-five eligible indications were included. At FDA approval, 40.0% were substantial, 10.9% were intermediate, and 49.1% were low benefit. We then identified 90 subsequent publications relevant to scoring, including primary (28.9%) and secondary endpoint updates (47.8%), safety updates (31.1%), and QoL reporting (47.8%). There was a change from initial classification of benefit in 27.3% of trials (10.9% became substantial, 9.1% became low, and 7.3% became intermediate). These changes were mainly due to updated hazard ratios (36.4%), toxicities (56.4%), new tail-of-the-curve bonus (9.1%), palliation bonus (14.5%), or QoL bonus (18.2%). Overall, at 3 years postapproval, 40.0% were substantial, 9.1% were intermediate, and 50.9% were low benefit. Conclusions: Because there were changes in classification for more than one-quarter of indications, in either direction, reassessing the ASCO-VF NHB as more evidence becomes available may be beneficial to inform clinical shared decision-making. On average, there was no overall improvement in the ASCO-VF NHB with longer follow-up and evolution of evidence.