Complete Information Research Articles

The complete chloroplast genome sequence and phylogenetic relationship analysis of Eomecon chionantha, one species unique to China.

Eomecon chionantha Hance, an endemic species in China, has a long medical history in Chinese ethnic minority medicine and is known for its anti-inflammatory and analgesic effects. However, studies of E. chionantha are lacking. In this study, we investigated the characteristics of the E. chionantha chloroplast genome and determined the taxonomic position of E. chionantha in Papaveraceae via phylogenetic analysis. In addition, we determined molecular markers to identify E. chionantha at the molecular level by comparing the chloroplast genomes of E. chionantha and its closely related species. The complete chloroplast genomic information indicated that E. chionantha chloroplast DNA (178,808bp) contains 99 protein-coding genes, 8 rRNAs, and 37 tRNAs. Meanwhile, we were able to identify a total of 54 simple sequence repeats through our analysis. Our findings from the phylogenetic analysis suggest that E. chionantha shares a close relationship with four distinct species, namely Macleaya microcarpa, Coreanomecon hylomeconoides, Hylomecon japonica, and Chelidonium majus. Additionally, using the Kimura two-parameter model, we successfully identified five hypervariable regions (ycf4-cemA, ycf3-trnS-GGA, trnC-GCA-petN, rpl32-trnL-UAG, and psbI-trnS-UGA). To the best of our knowledge, this is the first report of the complete chloroplast genome of E. chionantha, providing a scientific reference for further understanding of E. chionantha from the perspective of the chloroplast genome and establishing a solid foundation for the future identification, taxonomic determination and evolutionary analysis of this species.

Changes in Sleep Patterns, Genetic Susceptibility, and Incident Cardiovascular Disease in China

The associations of changes in sleep patterns with incident cardiovascular disease (CVD) are not fully elucidated, and whether these associations are modified by genetic susceptibility remains unknown. To investigate the associations of 5-year changes in sleep patterns with incident CVD and whether genetic susceptibility modifies these associations. This prospective cohort study of the Dongfeng-Tongji cohort was conducted from 2008 to 2018 in China. Eligible participants included those with complete sleep information at baseline survey (2008-2010) and the first follow-up survey (2013); participants who had no CVD or cancer in 2013 were prospectively assessed until 2018. Statistical analysis was performed in November 2023. Five-year changes in sleep patterns (determined by bedtime, sleep duration, sleep quality, and midday napping) between 2008 and 2013, and polygenic risk scores (PRS) for coronary heart disease (CHD) and stroke. Incident CVD, CHD, and stroke were identified from 2013 to 2018. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) and 95% CIs. Among 15 306 individuals (mean [SD] age, 65.8 [7.4] years; 8858 [57.9%] female and 6448 male [42.1%]), 5474 (35.78%) had persistent unfavorable sleep patterns and 3946 (25.8%) had persistent favorable sleep patterns. A total of 3669 incident CVD cases were documented, including 2986 CHD cases and 683 stroke cases, over a mean (SD) follow-up of 4.9 (1.5) years. Compared with those with persistent unfavorable sleep patterns, individuals with persistent favorable sleep patterns over 5 years had lower risks of incident CVD (HR, 0.80; 95% CI, 0.73-0.87), CHD (HR, 0.84; 95% CI, 0.76-0.92), and stroke (HR, 0.66; 95% CI, 0.54-0.82) in the subsequent 5-year period. No significant effect modification by PRS was observed for sleep pattern change and CHD or stroke risk. However, sleep pattern changes and PRS were jointly associated with the CHD and stroke risk in a dose-dependent manner, with the lowest risk being among those with persistent favorable sleep patterns combined with low PRS (HR for CHD, 0.65; 95% CI, 0.52-0.82 and HR for stroke, 0.48; 95% CI, 0.29-0.79). In this cohort study of middle-aged and older Chinese adults, individuals with persistent favorable sleep patterns had a lower CVD risk, even among those with higher genetic risk. These findings highlight the importance of maintaining favorable sleep patterns for CVD prevention.

Critical importance of correctly defining and reporting secondary endpoints when assessing the ethics of research biopsies.

This article reviews the implementation challenges to the American Society of Clinical Oncology's ethical framework for including research biopsies in oncology clinical trials. The primary challenges to implementation relate to the definitions of secondary endpoints, the scientific and regulatory framework, and the incentive structure that encourages inclusion of biopsies. Principles of research stewardship require that the clinical trials community correctly articulate the scientific goals of any research biopsies, especially those that are required for the patient to enroll on a trial and receive an investigational agent. Furthermore, it is important to sufficiently justify the characterization of secondary (as distinguished from exploratory) endpoints, protect the interest of research participants, and report accurate and complete information to ClinicalTrials.gov and the published literature.

Digital transformation and port operations: Optimal investment under incomplete information

Port digital transformation requires substantial investments. However, the benefits after transformation are difficult to accurately predict due to incomplete information available to ports. Balancing digital investments with benefits poses a key challenge for ports. We propose a game model based on revenue sharing between the port and the technology company to determine the digital level in equilibrium under incomplete information. Our study reveals that the incomplete information influences digital level of ports. When information is incomplete and ambiguous, the digital level of ports may be lower. However, significant ambiguous does not necessarily decrease the digital level due to risk pooling and agent cost effects. Additionally, revenue sharing reduces the incentive for technology company to withhold private information, and specific sharing ratios are identified. The study also finds that under incomplete information, digital level of port initially increases and then decreases with increasing optimism or the ratio of port's retained revenue. By considering these factors, our study provides guidance for ports in optimal investment strategies for digital transformation.

An “Agile” project planning course: Learning by doing in process engineering education

Process engineering education requires a comprehensive foundation and practical application. To bridge the gap between theoretical education and market requirements, a "Project Planning Course” has been offered since 2018 as part of the MSc specialization in Chemical Engineering at the FHNW School of Life Sciences. The course didactics combines the principles of an “agile” teaching mindset and problem-based learning, which optimally support the experience of this module. Students had to work on unresolved real-world problems, make decisions based on incomplete information, and present their work in a board meeting role play with board members from industry. These situations represent typical real-world challenges for future chemical engineers. The results show that most of the students learned to cope with the unconventional teaching methodology. The students’ evaluations of the module have been very positive, especially the fact that the active participation of the students triggers the actual learning process - which means that the essential learning goal has been achieved.

Multi-gene phylogeny of the primary freshwater crab genus Ptychophallus Smalley, 1964 (Pseudothelphusidae: Ptychophallinae) from the Neotropical region

The diversity of primary freshwater crabs of Central America is underestimated and poorly known, specially when considering both Atlantic and Pacific drainages distribution. Among the family Pseudothelphusidae Ortmann, 1893, the genus Ptychophallus Smalley, 1964 was recently revised using morphological data and encompassing 14 valid species, which are distributed exclusively in Costa Rica and Panama. Despite the informative scenario constructed with this previous study, some uncertainties remained such as those related to the populations distributed along different drainages and the lack of complete information regarding the phylogenetic relationships among the species that belong to this genus. Our phylogenetic reconstruction based on multigenes revealed four lineages. Ptychophallus costaricensis, P. colombianus, P. lavallensis, P. montanus, and P. tumimanus are well-established species, while P. paraxanthusi, P. exilipes and P. tristani were recovered as non-monophyletic in some relationships. Ptychophallus tristani and P. paraxanthusi were determined to be close relatives one with other, but with some representatives showing no clear correspondence and positioning in the molecular phylogeny, which raised doubts about the validity as separate species and/or the accuracy of identifications for some GenBank sequences. Ptychophallus tumimanus and P. montanus are morphologically very similar, but they are positioned in distinct clades. The genetic results confirmed a previous proposal that P. barbillaensis is a junior synonym of P. uncinatus, and P. bilobatus is closely related to P. uncinatus. The findings revealed the presence of one peculiar lineage with one species from Costa Rica compound by the resurrected P. campylus and another species from Costa Rica, originally identified as “P. cf. exilipes”; its morphology, however, did not match with any other analyzed species, which requires further analysis. Finally, possible misidentification in some sequences deposited in the GenBank were detected and should be reviewed. The reconstructed molecular phylogeny for the genus that inhabits both Atlantic and Pacific drainages of Central America provides a better understanding of the knowledge on the evolution of freshwater crabs and represents key information that will serve as baseline for further taxonomic studies on the cryptic biodiversity of this group.

Three-way decision method based on triangular norms in incomplete information systems and its applications in medical diagnosis

Three-way decision, as an extension of traditional two-way decision, was proposed by Yao in 2009, which can effectively avoid unnecessary losses caused by incorrect decisions in the decision-making process. Meanwhile, incomplete hybrid information systems represent the database of the relationship between objects and attributes, which refers to a system with multiple data and missing data. In this paper, based on the fact that many existing literatures involving incomplete hybrid information systems did not fully consider the impact of different conditional attributes on decision attributes and lacked effective aggregation methods to integrate weights and distances, we propose a new three-way decision method to deal with incomplete hybrid information systems with the help of triangular norms. First, in incomplete hybrid information systems, we redefine the distance between two objects based on conditional attributes and give the calculation formula of different data attributes in conditional attributes. At the same time, we define a new weight calculation method based on the relationship between conditional attributes and decision attributes. And then, by the distance between different conditional attributes and their corresponding weights, the hybrid distance is obtained using triangular norms. Furthermore, we use the hybrid distance to get the tolerance relation on the target set of an incomplete hybrid information system, thereby, using this tolerance relation, we get a new decision theoretic rough set model and the corresponding decision rules. Finally, by two experiments involving medical diagnosis, we demonstrate that our model has better classification ability, lower misclassification rate, and better stability compared to other corresponding models, thereby confirming that the proposed model provides a new and effective method for handling incomplete hybrid information systems in practical applications.

Convex set-oriented singular value decomposition with bounded uncertainties

Singular value decomposition (SVD) has played a crucial role in mathematics and applied mathematics, particularly in the field of practical engineering technology, which is almost indispensable in these fields. However, incomplete information caused by uncertainties from multiple sources and fluctuations in inherent properties during engineering practice can seriously interfere with obtaining singular values. Based on the convex set uncertainty theory, a novel convex set-oriented singular value decomposition (CSSVD) is proposed to overcome the limitations of using the nominal SVD method. The CSSVD aims to accurately and efficiently calculate the uncertainty bounds of singular values and vectors through the bounds and correlations of initial uncertainty parameters. Traditional probability methods rely on a sufficient sample size, and when the sample size is insufficient, accurate results cannot be obtained. However, the CSSVD method is based on set theory and can still demonstrate excellent performance in scenarios with small samples. This study investigates the detailed derivation steps of the proposed CSSVD method, taking into consideration properties such as normalization, orthogonality, and Monte Carlo simulation (MCS) to verify the accurate bounds of convex sets. The proposed method is finally applied to several numerical examples to demonstrate its superiority. This includes verifying singular values and vectors, handling high-order matrices, matrices with close singular values, and rectangular matrices. An application of image denoising is also used to evaluate the effectiveness of the proposed method.

Effects of incomplete information on risk management

Recent research shows that while the creditors’ interests determine an issuer’s hedging policy, creditors observe incomplete information regarding the cash flow. This study examines hedging effects with incomplete information through a real-option model, thereby deriving associated implications. We show that the difference in firm values between hedged and unhedged policies increases with more incomplete information. The higher the hedging efficiency, the larger the difference in hedging values across the degrees of incomplete information. The hedging effect decreases with shareholders’ bargaining power in renegotiations. Our findings imply that creditors are more likely to impose hedging requirements when facing more incomplete information.

Gender bias in customer perceptions: The case of agro-input dealers in Uganda

CONTEXTFaced with incomplete and imperfect information, economic actors rely predominantly on perceptions and often base decisions on heuristics prone to bias. Gender bias in perceptions favoring men has been found in a variety of settings and may be an important reason why some sectors remain dominated by men and gender gaps in terms of benefits persist. In modernizing food supply chains in a patriarchal context such as the maize sub-sector in Uganda, this may result in women facing significant barriers to entry. OBJECTIVEUsing a unique dataset of ratings of agro-input dealers provided by smallholder farmers in their vicinity, we test if farmers perceive male-managed agro-input shops differently than agro-input shops managed by women. METHODSWe use a dyadic dataset of farmer-dealer links to explicitly control for quality differences between male- and female-managed agro-input shops and use the fact that a farmer has generally rated more than one agro-input to account for farmer-level heterogeneity using fixed-effects regression. RESULTS AND CONCLUSIONSWe find that farmers rate male-managed agro-input outlets higher on a range of attributes related to the dealership in general, as well as on the quality of inputs sold by the dealer. After controlling for both dealer and farmer level confounders, we conclude that gender bias in customer perceptions persists. SIGNIFICANCEOur results suggest a comparative disadvantage and an important entry barrier for female agro-input dealers. The gender bias may also affect social outcomes like women's capabilities, aspirations, and empowerment in seed systems but also impairs development at more aggregate levels: as a considerable share of agro-input shops is managed by women, this finding may impose challenges for varietal turnover, hindering agricultural productivity, food security, and rural transformation. Policies and interventions designed to challenge gender norms and customs are needed to correct this bias.

A model fidelity check in cooperative learning research in physical education

ABSTRACT Background Fidelity is an essential component of research, theory and practice in pedagogical models in physical education [Hastie, P. A., and A. Casey. 2014. “Fidelity in Models-based Practice Research in Sport Pedagogy: A Guide for Future Investigations.” Journal of Teaching in Physical Education 33 (3): 422–431. https://doi.org/10.1123/jtpe.2013-0141]. Reporting fidelity data has been recognized as a way to know whether student learning outcomes can be attributed to the practical architectures of pedagogical models [Casey, A., and D. Kirk. 2020. Models-based in Physical Education. London: Routledge]. However, research that examines the effectiveness of pedagogical models rarely reports this valuable information about the authenticity of implementation [Casey, A., V. Goodyear, and B. Dyson. 2015. “Model Fidelity and Students’ Responses to an Authenticated Unit of Cooperative Learning.” Journal of Teaching in Physical Education 34 (4): 642–660. https://doi.org/10.1123/jtpe.2013-0227]. To date, no overall data have been published on how researchers reported fidelity data in cooperative learning literature in physical education. Therefore, it seems necessary to examine whether researchers have attended to fidelity to offer insights about the quality of the model delivery. Purpose To conduct a model fidelity check in cooperative learning research in physical education using checklists. Method First, a literature review was conducted to capture intervention studies focusing on cooperative learning in physical education. Second, a quality assessment was performed to obtain a global methodological score for the selected studies. Third, a model fidelity check was implemented to examine the quality of model delivery. Findings A total of 28 studies (11 qualitative, 13 quantitative, and 4 mixed methods) fulfilled the inclusion criteria and were selected. Regarding checklist-1, only 10 studies (35.7%) reported the complete information of all items. Most presented a rich description of the curricular elements of the unit (71.4%) and a detailed description of the program context (68.7%). Information concerning teacher expertise (60.7%) and students’ previous experience (46.4%) was the lowest reported. Data from the validation of model implementation was reported at 50% (14 studies). Regarding checklist-2, no study reported the assessment of all five components of fidelity of implementation. More than 90% reported information about program adherence to intervention components (model features, lesson plans, learning tasks and teacher/students’ behaviors) and duration. Less than 50% of the studies reported information related to program differentiation (41.1%), delivery quality (35.2%) and participant responsiveness (11.7%). Conclusion Model fidelity remains a prominent theme in cooperative learning research in physical education. Findings, at least as captured in this study, indicated a notable lack of attention to and reporting of model fidelity in this research area. It is highly recommended to report information on model fidelity to enhance its validity with trustworthy guidelines. Therefore, documenting meaningful information about model fidelity is required to bridge the gap between planning and delivery. Additional research is needed to gain a better understanding of how the model is delivered.

Learning-Based Hierarchical Decision-Making Framework for Automatic Driving in Incompletely Connected Traffic Scenarios.

The decision-making algorithm serves as a fundamental component for advancing the level of autonomous driving. The end-to-end decision-making algorithm has a strong ability to process the original data, but it has grave uncertainty. However, other learning-based decision-making algorithms rely heavily on ideal state information and are entirely unsuitable for autonomous driving tasks in real-world scenarios with incomplete global information. Addressing this research gap, this paper proposes a stable hierarchical decision-making framework with images as the input. The first step of the framework is a model-based data encoder that converts the input image data into a fixed universal data format. Next is a state machine based on a time series Graph Convolutional Network (GCN), which is used to classify the current driving state. Finally, according to the state's classification, the corresponding rule-based algorithm is selected for action generation. Through verification, the algorithm demonstrates the ability to perform autonomous driving tasks in different traffic scenarios without relying on global network information. Comparative experiments further confirm the effectiveness of the hierarchical framework, model-based image data encoder, and time series GCN.

Neuron image segmentation based on convolution and BN fusion and multi-input feature fusion

The segmentation of neuronal morphology in electron microscopy images is crucial for the analysis and understanding of neuronal function. However, most of the existing segmentation methods are not suitable for challenging datasets where the neuronal structure is contaminated by noise or has interrupted parts. In this paper, we propose a segmentation method based on deep learning to determine the location information of neurons and reduce the influence of image noise in the data. Specifically, we adapt our neuron dataset based on UNet by using convolution with BN fusion and multi-input feature fusion. The method is named REDAFNet. The model simplifies the model structure and enhances the generalization ability by fusing the convolution layer and BN layer. The noise interference in the data was reduced by multi-input feature fusion, and the ability to understand and express the data was enhanced. The method takes a neuron image as input and its pixel segmentation map as output. Experimental results show that the segmentation accuracy of the proposed method is 91.96%, 93.86% and 80.25% on the ISBI2012 dataset, U-RISC retinal neuron dataset and N2DH-GOWT1 stem cell dataset, respectively. Compared with the existing segmentation methods, the proposed method can extract more complete feature information and achieve more accurate segmentation.

Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder

AimWe investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK. MethodsWe used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used. ResultsSCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified. LimitationsThe combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information. ConclusionOur study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.

Association between predicted fat mass, predicted lean mass, predicted percent fat and type 2 diabetes mellitus in Japanese adults: a retrospective study.

Type 2 diabetes mellitus (T2DM) is known to have obesity as a risk factor. Body mass index cannot distinguish between lean mass and fat mass. We aimed to examine the association between predicted fat mass, predicted lean mass, predicted percent fat and risk of T2DM in Japanese adults. We also explored whether these three new parameters could predict T2DM better than other obesity markers. This present study is a secondary data analysis. The study enrolled 20,944 Japanese individuals who participated in the NAGALA medical assessment program between 2004 and 2015. 15,453 participants who are eligible and have complete information were included to our analysis. Through the use of Kaplan-Meier curve, restricted cubic spline and univariate and multivariate Cox regression analysis, the relationship between predicted fat mass, predicted lean mass, predicted percent fat and T2DM risk was examined. The area under the curve method was used to assess the differences between these markers of obesity. A total of 373 cases of T2DM occurred over a median time of 5.4 years. In the male group, we found a U-shaped connection between predicted fat mass, predicted lean mass, and T2DM onset (p value, non-linearity < 0.05). A linear relationship was found between predicted percent fat and T2DM onset. The linear relationship was also found in the female group for predicted fat mass, and predicted percent fat. And for women, predicted lean mass was not an independent predictor. The area under the curve (AUC) for predicted fat mass, predicted lean mass, predicted percent fat in men was 0.673 (95%CI: 0.639 ~ 0.707), 0.598 (95%CI: 0.561 ~ 0.635), 0.715 (95%CI: 0.684 ~ 0.745), respectively. In males, WHtR was the strongest predictor (AUC 0.7151, 95%CI: 0.684 ~ 0.746), followed by predicted percent fat (AUC 0.7150, 95%CI: 0.684 ~ 0.745). In the females, WHtR was also the strongest predictor (AUC 0.758, 95%CI: 0.703 ~ 0.813), followed by body mass index (AUC 0.757, 95%CI: 0.704 ~ 0.811) and predicted percent fat (AUC 0.742, 95%CI: 0.687 ~ 0.798). Predicted fat mass, predicted lean mass, predicted percent fat were strongly connected with an increased risk for developing T2DM in Japanese, particularly in males. WHtR and predicted percent fat had a slightly better discrimination than other common obesity indicators in males. In the females, predicted fat mass and predicted percent fat were associated with T2DM risk, WHtR and body mass index had the slightly higher predictive power.

Rough Neutrosophic Shapley Weighted Einstein Averaging Aggregation Operator and its Application in Multi-Criteria Decision-Making Problem

The study of aggregation operators has played a crucial role in various decision-making methods. The primary function of the aggregation operator is to combine multiple numbers into a single value. While Einstein operators offer a compact notation and can handle complex and large datasets, they do not consider the interaction involved in determining the criteria weights or account for imprecise and indeterminate data. To overcome this limitation, this paper introduces an improved aggregation operator, the rough neutrosophic Shapley weighted Einstein averaging aggregation operator. The rough neutrosophic sets offer a method for effectively managing the fuzziness and uncertainty that commonly occur in real-world scenarios and the Shapley fuzzy measure helps us understand the importance or value of different elements in each scenario. This operator combines the Shapley fuzzy measure with Einstein operators under rough neutrosophic sets, which are an effective tool for handling incomplete, indeterminate, and inconsistent information. The proposed operator satisfies essential algebraic properties such as idempotency, boundedness, and monotonicity. This paper also presents a decision-making methodology based on the proposed operator, with attribute values derived from the rough neutrosophic set. Finally, the applicability of the suggested aggregation operator is illustrated with a numerical example.

Uterine Inflammatory Myofibroblastic Tumors: p16 as a Surrogate for CDKN2A Deletion and Predictor of Aggressive Behavior.

Uterine inflammatory myofibroblastic tumors (IMTs) are rare mesenchymal neoplasms of uncertain malignant potential. Aside from the recently described risk stratification score, which has not been validated by other studies, and rare reports of aberrant p16 expression in malignant tumors, there are no criteria to reliably predict behavior. Herein, we evaluated the clinicopathologic features and p16 expression patterns in 30 IMTs, with genomic profiling performed in a subset (13 malignant, 3 benign). Fifteen patients had malignant IMTs, defined by extrauterine disease at diagnosis (n=5) or recurrence (n=10; median: 24 mo). Patients ranged from 8 to 65 (median: 51) years and tumors from 6 to 22 (median: 12.5) cm. In primary tumors (n=13), infiltrative borders were noted in 10, moderate/severe cytologic atypia in 9, tumor cell necrosis in 7, and lymphovascular invasion in 6, while mitoses ranged from 0 to 21 (median: 7) per 10 high-power fields. In contrast, 15 patients with benign IMTs ranged from 28 to 65 (median: 44) years, with follow-up of 18 to 114 (median: 41) months. Tumors ranged from 1.9 to 8.5 (median: 5.5) cm, 2 demonstrated infiltrative borders, and 1 had moderate cytologic atypia. No other high-risk histologic features were observed. Application of the previously described clinicopathologic risk stratification score in all primary IMTs with complete data (n=18) classified 8 as high-risk (all malignant), 8 as intermediate-risk (3 malignant, 5 benign), and 2 as low-risk (benign). p16 was aberrant in all malignant IMTs, with <1% expression noted in 10, overexpression (>90%) in 4, and subclonal loss in 1; all benign tumors had patchy staining (20% to 80%; median 50%). Molecular analysis detected CDKN2A deletions in 8 of 9 tumors with <1% p16 expression, while the other harbored a TERT promoter mutation. TERT promoter mutations were also identified in 2 of 3 IMTs with p16 overexpression. Neither of these alterations was detected in the 3 sequenced benign IMTs. Thus, we recommend performing p16 on all uterine IMTs, which, combined with the risk stratification score, is a promising and cost-effective tool for predicting CDKN2A status and outcome in these patients. It may be particularly useful for tumors with incomplete information for risk stratification (ie, morcellated tumors) and for further stratifying intermediate-risk IMTs when sequencing is unavailable.

Black-box adversarial transferability: An empirical study in cybersecurity perspective

The rapid advancement of artificial intelligence within the realm of cybersecurity raises significant security concerns. The vulnerability of deep learning models in adversarial attacks is one of the major issues. In adversarial machine learning, malicious users try to fool the deep learning model by inserting adversarial perturbation inputs into the model during its training or testing phase. Subsequently, it reduces the model confidence score and results in incorrect classifications. The novel key contribution of the research is to empirically test the black-box adversarial transferability phenomena in cyber attack detection systems. It indicates that the adversarial perturbation input generated through the surrogate model has a similar impact on the target model in producing the incorrect classification. To empirically validate this phenomenon, surrogate and target models are used. The adversarial perturbation inputs are generated based on the surrogate-model for which the hacker has complete information. Based on these adversarial perturbation inputs, both surrogate and target models are evaluated during the inference phase. We have done extensive experimentation over the CICDDoS-2019 dataset, and the results are classified in terms of various performance metrics like accuracy, precision, recall and f1-score. The findings indicate that any deep learning model is highly susceptible to adversarial attacks, even if the attacker does not have access to the internal details of the target model. The results also indicate that white-box adversarial attacks have a severe impact compared to black-box adversarial attacks. There is a need to investigate and explore adversarial defence techniques to increase the robustness of the deep learning models against adversarial attacks.

A defect opening profile reconstruction method based on multidirectional magnetic flux leakage detection

Magnetic flux leakage (MFL) is a popular technique for detecting defects. The opening profile reconstruction is one of the most popular concerns. The commonly used unidirectional magnetization-based methods easily lead to incomplete MFL information to reconstruct the opening profile. The superimposition of MFL signals from different directions would cause information cancellation, making it difficult for the existing data processing methods to reconstruct. To solve the issues, this study constructs a model-based method to inverse defect opening profiles. It builds the forward equations from one magnetic permeability to multidirectional MFL fields. Then the pseudo-inversion with Tikhonov regularization is applied as the inversion algorithm. The algorithm associates the forward equations and finds the solution that can fulfill them simultaneously. Such a method can combine multidirectional MFL information without cancellation. It reduces the ill-posed problems that exist in unidirectional inversion and makes the reconstructed profile complete and accurate. The comparison experiments have been carried out, and the results have proved the high accuracy of the proposed method.

Individualized center-based analysis of urinary and sexual functional outcome after radical prostatectomy based on the prostate cancer outcome study: a post hoc pathway to patient outcome measurement analysis for quality improvement.

We evaluate differences of patient-reported outcome measurements (PROM) based urinary continence and sexual function 12 months after radical prostatectomy (RPE) based on perioperative, surgical, and patient-specific characteristics in a large European academic urology center. All men enrolled in the Prostate Cancer Outcome Study (PCO) study who were treated with RPE between 2017 and 2021 completed EPIC-26 information surveys before and 12 months after RPE. Survey data were linked to clinical data of our institution. Logistic regression analyses were performed to examine the correlation between individual surgeons, patient characteristics, patient clinical data, and their urinary continence and sexual function. In total, data of 429 men were analyzed: unstratified mean (SD) EPIC-26 domain score for urinary function decreased from 93.3 (0.7) to 60.4 (1.5) one year after RPE, respectively for sexual function from 64.95 (1.6) to 23.24 (1.1). Patients with preoperative adequate urinary function (EPIC-26 score > 80) reported significantly different mean urinary function scores between 53.35 (28.88) and 66.25 (25.15), p= 0.001, stratified by surgeons experience. On binary logistic regression analyses, only nerve sparing techniques (OR: 1,83, 95% CI: 1.01;3.21; p = 0.045) and low body mass index (OR: 0.91, CI: 0.85;0.99, p= 0.032) predicted adequate postoperative urinary function. The results show how using provider-specific data from a larger cohort study enables to develop institution-specific analysis for functional outcomes after RPE. These models can be used for internal quality improvement as well as enhanced and provider-specific patient communication and shared decision making.