Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study.

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

Characterisation of glucose-induced protein fragments among the order Enterobacterales using matrix-assisted laser desorption ionization-time of flight mass spectrometry.

To characterise the glucose-induced protein fragments by MALDI-TOF MS analysis, we compared data for samples from Escherichia coli cultured in media with or without glucose. Characteristic peaks were observed in the presence of glucose, and MS/MS revealed Asr-specific fragments. The amino acid sequences of the fragments suggested sequence-specific proteolysis. Blast-analysis revealed that numerous Enterobacterales harbored genes encoding Asr as well as E. coli. Here, we analysed 32 strains from 20 genera and 25 species of seven Enterobacterales families. We did not detect changes in the mass spectra of four strains of Morganellaceae lacking asr, whereas peaks of Asr-specific fragments were detected in the other 28 strains. We therefore concluded that the induction of Asr production in the presence of glucose is common among the Enterobacterales, except for certain Morganellaceae species. In members of family Budviciaceae, unfragmented Asr was detected. Molecular genetic information suggested that the amino acid sequences of Asr homologs are diverse, with fragments varying in number and size, indicating that Asr may serve as a discriminative biomarker for identifying Enterobacterales species.

Respiratory rate­oxygenation (ROX) index for predicting high-flow nasal cannula failure in patients with and without COVID-19.

BACKGROUND: The predictive value of the respiratory rate­oxygenation (ROX) index for a high-flow nasal cannula (HFNC) in patients with COVID-19 with acute hypoxemic respiratory failure (AHRF) may differ from patients without COVID-19 with AHRF, but these patients have not yet been compared. We compared the diagnostic accuracy of the ROX index for HFNC failure in patients with AHRF with and without COVID-19 during acute emergency department (ED) visits. METHODS: We performed a retrospective analysis of patients with AHRF treated with an HFNC in an ED between October 2020 and April 2022. The ROX index was calculated at 1, 2, 4, 6, 12, and 24 h after HFNC placement. The primary outcome was the failure of the HFNC, which was defined as the need for subsequent intubation or death within 72 h. A receiver operating characteristic (ROC) curve was used to evaluate discriminative power of the ROX index for HFNC failure. RESULTS: Among 448 patients with AHRF treated with an HFNC in an ED, 78 (17.4%) patients were confirmed to have COVID-19. There was no significant difference in the HFNC failure rates between the non-COVID-19 and COVID-19 groups (29.5% vs. 33.3%, p = 0.498). The median ROX index was higher in the non-COVID-19 group than in the COVID-19 group at all time points. The prognostic power of the ROX index for HFNC failure as evaluated by the area under the ROC curve was generally higher in the COVID-19 group (0.73-0.83) than the non-COVID-19 group (0.62-0.75). The timing of the highest prognostic value of the ROX index for HFNC failure was at 4 h for the non-COVID-19 group, whereas in the COVID-19 group, its performance remained consistent from 1 h to 6 h. The optimal cutoff values were 6.48 and 5.79 for the non-COVID-19 and COVID-19 groups, respectively. CONCLUSIONS: The ROX index had an acceptable discriminative power for predicting HFNC failure in patients with AHRF with and without COVID-19 in the ED. However, the higher ROX index thresholds than those in previous publications involving intensive care unit (ICU) patients suggest the need for careful monitoring and establishment of a new threshold for patients admitted outside the ICU.

Self-reported mental health difficulties were of limited use when screening for psychiatric diagnoses in adults born small for gestational age at term.

AIM: Being born small for gestational age (SGA) at term increases the risk of adverse health outcomes. We examined whether self-reported mental health differed between adults born SGA and non-SGA at term and could be used to screen for psychiatric diagnoses. METHODS: We used the Strengths and Difficulties Questionnaire to gather data from 68 participants born SGA and 88 non-SGA controls at a mean age of 26.5 years. Group differences were analysed by linear regression. We calculated the area under the curve and the sensitivity, specificity and predictive values for psychiatric diagnoses. RESULTS: The mean total difficulties score was 1.9 (95% confidence interval 0.4-3.5) points higher for participants born SGA. They also reported more internalising and emotional problems (p < 0.05). The areas under the curve were 0.82 and 0.68 in the SGA and control groups, respectively. Among participants born SGA, the 90th percentile cut-off had a sensitivity of 0.38, a specificity of 0.93 and positive and negative predictive values of 0.75 and 0.71. The 80th percentile cut-off had higher sensitivity and lower specificity. CONCLUSION: Adults born SGA reported more mental health difficulties than non-SGA controls. The low sensitivity using the 90th percentile cut-off suggests that a lower cut-off should be considered.

Surprising and novel multivariate sequential patterns using odds ratio for temporal evolution in healthcare.

BACKGROUND: Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of measures of interest for patterns are focused on reducing the number of patterns and quantifying their relevance (utility/usefulness). However, although the temporal dimension plays a key role in medical records, few efforts have been made to extract temporal knowledge about the patient's evolution from multivariate sequential patterns. METHODS: In this paper, we propose a method to extract a new type of patterns in the clinical domain called Jumping Diagnostic Odds Ratio Sequential Patterns (JDORSP). The aim of this method is to employ the odds ratio to identify a concise set of sequential patterns that represent a patient's state with a statistically significant protection factor (i.e., a pattern associated with patients that survive) and those extensions whose evolution suddenly changes the patient's clinical state, thus making the sequential patterns a statistically significant risk factor (i.e., a pattern associated with patients that do not survive), or vice versa. RESULTS: The results of our experiments highlight that our method reduces the number of sequential patterns obtained with state-of-the-art pattern reduction methods by over 95%. Only by achieving this drastic reduction can medical experts carry out a comprehensive clinical evaluation of the patterns that might be considered medical knowledge regarding the temporal evolution of the patients. We have evaluated the surprisingness and relevance of the sequential patterns with clinicians, and the most interesting fact is the high surprisingness of the extensions of the patterns that become a protection factor, that is, the patients that recover after several days of being at high risk of dying. CONCLUSIONS: Our proposed method with which to extract JDORSP generates a set of interpretable multivariate sequential patterns with new knowledge regarding the temporal evolution of the patients. The number of patterns is greatly reduced when compared to those generated by other methods and measures of interest. An additional advantage of this method is that it does not require any parameters or thresholds, and that the reduced number of patterns allows a manual evaluation.

Inadequate Classification of Poor Response After Total Knee Arthroplasty: A Comparative Analysis of 15 Definitions Using Data From the Dutch Arthroplasty Register and the Osteoarthritis Initiative Database.

BACKGROUND: Variations in defining poor response to total knee arthroplasty (TKA) impede comparisons of response after TKA over time and across hospitals. This study aimed to compare the prevalence, overlap, and discriminative accuracy of 15 definitions of poor response after TKA using 2 databases. METHODS: Data of patients one year after primary TKA from the Dutch Arthroplasty Register (n = 12,275) and the Osteoarthritis Initiative database (n = 204) were used to examine the prevalence, overlap (estimated by Cohen's kappa), and discriminative accuracy (sensitivity, specificity, positive predictive value, negative predictive value, and Youden index) of 15 different definitions of poor response after TKA. In the absence of a gold standard for measuring poor response to TKA, the numeric rating scale satisfaction (≤ 6 'poor responder') and the global assessment of knee impact (dichotomized: ≥ 4 'poor responder') were used as anchors for assessing discriminative accuracy for the Dutch Arthroplasty Register and Osteoarthritis Initiative dataset, respectively. These anchors were chosen based on a prior qualitative study that identified (dis)satisfaction as a central theme of poor responses to TKA by patients and knee specialists. RESULTS: The median (25th to 75th percentile) prevalence of poor responders in the examined definitions was 18.5% (14.0 to 25.5%), and the median Cohen's kappa for the overlap between pairs of definitions was 0.41 (0.32 to 0.59). Median (25th to 75th percentile) sensitivity was 0.45 (0.39 to 0.54), specificity was 0.86 (0.82 to 0.94), positive predictive value was 0.45 (0.34 to 0.62), negative predictive value was 0.89 (0.87 to 0.89), and the Youden index was 0.36 (0.20 to 0.43). CONCLUSIONS: This study found a lack of overlap between different definitions of poor response to TKA. None of the examined definitions adequately classified poor responders to TKA. In contrast, the absence of a poor response could be classified with confidence.

Classification of Corrosion Severity in SPCC Steels Using Eddy Current Testing and Supervised Machine Learning Models.

Steel Plate Cold-Rolled Commercial (SPCC) steel is known to have long-term durability. However, it still undergoes corrosion when exposed to corrosive environments. This paper proposes an evaluation method for assessing the corrosion level of SPCC steel samples using eddy current testing (ECT), along with two different machine learning approaches. The objective is to classify the corrosion of the samples into two states: a less corroded state (state-1) and a highly corroded state (state-2). Generative and discriminative models were implemented for classification. The generative classifier was based on the Gaussian mixture model (GMM), while the discriminative model was based on the logistic regression model. The features used in the classification models are the peaks of the perturbated magnetic fields at two different frequencies. The performance of the classifiers was evaluated using metrics such as absolute error, accuracy, precision, recall, and F1 score. The results indicate that the GMM model is more conducive to categorizing states with higher levels of corrosion, while the logistic regression model is helpful in estimating states with lower levels of corrosion. Meanwhile, high classification accuracy can be achieved based on both methods using eddy current testing.

Duplex-Hierarchy Representation Learning for Remote Sensing Image Classification.

Remote sensing image classification (RSIC) is designed to assign specific semantic labels to aerial images, which is significant and fundamental in many applications. In recent years, substantial work has been conducted on RSIC with the help of deep learning models. Even though these models have greatly enhanced the performance of RSIC, the issues of diversity in the same class and similarity between different classes in remote sensing images remain huge challenges for RSIC. To solve these problems, a duplex-hierarchy representation learning (DHRL) method is proposed. The proposed DHRL method aims to explore duplex-hierarchy spaces, including a common space and a label space, to learn discriminative representations for RSIC. The proposed DHRL method consists of three main steps: First, paired images are fed to a pretrained ResNet network for extracting the corresponding features. Second, the extracted features are further explored and mapped into a common space for reducing the intra-class scatter and enlarging the inter-class separation. Third, the obtained representations are used to predict the categories of the input images, and the discrimination loss in the label space is minimized to further promote the learning of discriminative representations. Meanwhile, a confusion score is computed and added to the classification loss for guiding the discriminative representation learning via backpropagation. The comprehensive experimental results show that the proposed method is superior to the existing state-of-the-art methods on two challenging remote sensing image scene datasets, demonstrating that the proposed method is significantly effective.

A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis.

Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of "few data, poor information", this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.

Assessment of Temporal Somatosensory Discrimination in Females with Fibromyalgia: Reliability and Discriminative Ability of a New Assessment Tool.

We assessed the test-retest reliability and discriminative ability of a somatosensory temporal discrimination (SSTD) assessment tool for fibromyalgia syndrome (FMS) and determined if pain-related variables were associated with SSTD performance. Twenty-five women with FMS and twenty-five asymptomatic women were assessed during two sessions 7 to 10 days apart. The proportion of correct responses (range 0-100) was calculated. Sociodemographic information was collected for both groups. The participants with FMS also completed the widespread pain index and the Brief Pain Inventory. Test-retest reliability was verified by calculating intraclass correlation coefficients. Discriminative ability was verified by a between-group comparison of scores using a t-test. Associations between SSTD score and pain variables were tested using Pearson or Spearman correlation coefficients. The test-retest reliability of the SSTD score was excellent (ICC > 0.9, CI: 0.79-0.96) for the asymptomatic group and good for the FMS group (ICC: 0.81, 95% CI: 0.62-0.91). The median (Q1-Q3) test session SSTD score differed significantly between the FMS 84.1 (71-88) and the asymptomatic 91.6 (83.4-96.1) groups (p < 0.001). Only pain duration was associated with the SSTD score. In conclusion, the new SSTD test seems reliable for people with FMS and is discriminative. Further studies should examine its sensitivity to change and correlations with other SSTD tests.

A demonstration of incorporating discriminative stimuli into an AAC device during functional communication training.

Functional communication training (FCT) is an effective intervention for teaching communication responses and reducing challenging behavior. One limitation of FCT is that frequent reinforcement may be impractical or impossible in many situations. Recently, Mitteer et al. published a tutorial in the journal AAC that provided video models on how to implement an empirically supported strategy for thinning reinforcement during FCT, known as FCT with discriminative stimuli, when teaching with an augmentative and alternative communication (AAC) device. However, no study has empirically evaluated the approach described in that tutorial. This paper details a case study using several single-case experimental designs to teach a non-vocal autistic adult who did not use speech to communicate requests only when reinforcement was signaled to be available by the color of the AAC icons. We demonstrated the efficacy of this approach with unique pairs of discriminative stimuli for tangible and edible items, thinning reinforcement for each stimulus class independently. We then rapidly transferred stimulus control to new icons and integrated both classes of stimuli into a single AAC grid. This first demonstration of embedding discriminative stimuli into an AAC device represents a promising advancement for individuals who do not use speech and may not readily respond to delay or denial cues.

Episodic Visual Hallucinations, Inference and Free Energy.

Understandings of how visual hallucinations appear have been highly influenced by generative approaches, in particular Friston's Active Inference conceptualization. Their core proposition is that these phenomena occur when hallucinatory expectations outweigh actual sensory data. This imbalance occurs as the brain seeks to minimize informational free energy, a measure of the distance between predicted and actual sensory data in a stationary open system. We review this approach in the light of old and new information on the role of environmental factors in episodic hallucinations. In particular, we highlight the possible relationship of specific visual triggers to the onset and offset of some episodes. We use an analogy from phase transitions in physics to explore factors which might account for intermittent shifts between veridical and hallucinatory vision. In these triggered forms of hallucinations, we suggest that there is a transient disturbance in the normal one-to-one correspondence between a real object and the counterpart perception such that this correspondence becomes between the real object and a hallucination. Generative models propose that a lack of information transfer from the environment to the brain is one of the key features of hallucinations. In contrast, we submit that specific information transfer is required at onset and offset in these cases. We propose that this transient one-to-one correspondence between environment and hallucination is mediated more by aberrant discriminative than by generative inference. Discriminative inference can be conceptualized as a process for maximizing shared information between the environment and perception within a self-organizing nonstationary system. We suggest that generative inference plays the greater role in established hallucinations and in the persistence of individual hallucinatory episodes. We further explore whether thermodynamic free energy may be an additional factor in why hallucinations are temporary. Future empirical research could productively concentrate on three areas. Firstly, subjective perceptual changes and parallel variations in brain function during specific transitions between veridical and hallucinatory vision to inform models of how episodes occur. Secondly, systematic investigation of the links between environment and hallucination episodes to probe the role of information transfer in triggering transitions between veridical and hallucinatory vision. Finally, changes in hallucinatory episodes over time to elucidate the role of learning on phenomenology. These empirical data will allow the potential roles of different forms of inference in the stages of hallucinatory episodes to be elucidated.

Supervised Manifold Learning Based on Multi-Feature Information Discriminative Fusion within an Adaptive Nearest Neighbor Strategy Applied to Rolling Bearing Fault Diagnosis.

Rolling bearings are a key component for ensuring the safe and smooth operation of rotating machinery and are very prone to failure. Therefore, intelligent fault diagnosis research on rolling bearings has become a crucial task in the field of mechanical fault diagnosis. This paper proposes research on the fault diagnosis of rolling bearings based on an adaptive nearest neighbor strategy and the discriminative fusion of multi-feature information using supervised manifold learning (AN-MFIDFS-Isomap). Firstly, an adaptive nearest neighbor strategy is proposed using the Euclidean distance and cosine similarity to optimize the selection of neighboring points. Secondly, three feature space transformation and feature information extraction methods are proposed, among which an innovative exponential linear kernel function is introduced to provide new feature information descriptions for the data, enhancing feature sensitivity. Finally, under the adaptive nearest neighbor strategy, a novel AN-MFIDFS-Isomap algorithm is proposed for rolling bearing fault diagnosis by fusing various feature information and classifiers through discriminative fusion with label information. The proposed AN-MFIDFS-Isomap algorithm is validated on the CWRU open dataset and our experimental dataset. The experiments show that the proposed method outperforms other traditional manifold learning methods in terms of data clustering and fault diagnosis.

Sensory Evaluation through RATA and Sorting Task of Commercial and Traditional Panettones Sold in Peru.

In Peru, the consumption of panettone has increased, highlighting the importance of its sensory aspect, quality and price for its acceptance. This study evaluated sensory, physicochemical, texture and color attributes in commercial and traditional panettones. The RATA descriptive test and the discriminative sorting task were used, with 168 and 92 consumers, respectively. In addition, acceptability and purchase intention were evaluated. Significant differences were found between the samples; the traditional panettone showed lower weight, pH and fat content. Regarding the color of the crust and crumb, differences were also observed between both types. Regarding texture, traditional panettone showed less hardness and chewiness compared to commercial ones. The sorting method allowed us to differentiate the samples, where consumers differentiated the traditional panettone from the commercial ones, although within the commercial ones, they also found differences. The RATA test showed a similar behavior, traditional panettones were described as spongy, with fruits and a strong smell, unlike the commercial ones characterized as greasy, brown and fibrous. It is concluded that sensory methods are useful to understand the quality of panettone along with the physicochemical parameters, which influence consumer preferences according to the sensory characteristics and the quality of the ingredients.

Stimulus control of habits: Evidence for both stimulus specificity and devaluation insensitivity in a dual-response task.

Goal-directed and habitual actions are clearly defined by their associative relations. Whereas goal-directed control can be confirmed via tests of outcome devaluation and contingency-degradation sensitivity, a comparable criterion for positively detecting habits has not been established. To confirm habitual responding, a test of control by the stimulus-response association is required while also ruling out goal-directed control. Here we describe an approach to developing such a test in rats using two discriminative stimuli that set the occasion for two different responses that then earn the same outcome. Performance was insensitive to outcome devaluation and showed stimulus-response specificity, indicative of stimulus-controlled behavior. The reliance of stimulus-response associations was further supported by a lack of sensitivity during the single extinction test session used here. These results demonstrate that two concurrently trained responses can come under habitual control when they share a common outcome. By reducing the ability of one stimulus to signal its corresponding response-outcome association, we found evidence for goal-directed control that can be dissociated from habits. Overall, these experiments provide evidence that tests assessing specific stimulus-response associations can be used to investigate habits.

Relations between tactile sensitivity of the finger, arm, and cheek skin over the lifespan showing decline only on the finger.

Touch sensitivity generally declines with age, contributing to loss of manual dexterity and tactile function. We investigated how touch changes over the lifespan, using different tests and on three body sites. We used a classical test of force detection sensitivity, where calibrated monofilaments were applied passively to the right index finger pad, forearm, and cheek. In addition, at the index, we used an active touch spatial discrimination task, developed by our group. Spatial discrimination was estimated through participants' ability to evaluate the distance between parallel bands printed on acrylic plates. Data were collected from 96 healthy women, aged 20-75 years. Force detection and tactile spatial discrimination on the index deteriorated significantly with age; however, no change was found for tactile detection on the forearm or cheek. Tactile detection on the cheek remained remarkably highly sensitive throughout life. There was a significant positive relationship between force detection and spatial discrimination on the index. Further, force detection on the forearm was significantly associated with detection on the index and cheek. Our results suggest a decrease in touch perception with age on the index finger pad, yet a preservation of tactile sensitivity in hairy skin. This opens discussion about the impact of daily activities upon the glabrous hand skin and on the function of hairs in tactile sensitivity. We highlight the need for new methods in evaluating tactile sensitivity on hairy skin.

The Ability of Physical Performance Measures to Identify Fall Risk in Older Adults Living With Dementia: A Systematic Review and Meta-Analysis.

OBJECTIVES: To determine whether physical performance measures commonly used in clinical settings can discriminate fallers from nonfallers and predict falls in older adults with dementia. DESIGN: Systematic review and meta-analysis. SETTING AND PARTICIPANTS: Older adults with dementia residing in the community, hospitals, and residential care facilities. METHODS: MEDLINE, Embase, PsycINFO, CINAHL, SPORTDiscus, the Cochrane Library, and the PEDro databases were searched from inception until December 27, 2023 (PROSPERO registration number: CRD42022303670). Retrospective or prospective studies that evaluated the associations between physical performance measures and falls in older adults with dementia were included. A random effects model was used to calculate the standardized mean difference (SMD) and 95% CI for each physical performance measure between fallers and nonfallers. Sensitivity analyses were conducted on the longitudinal studies to determine the ability of physical performance measures to predict future falls. RESULTS: Twenty-eight studies were included in this review (n = 3542). The 5-time chair stand test [SMD = 0.23 (0.01, 0.45)], the Berg Balance Scale [SMD = -0.52 (-0.87, -0.17)], postural sway when standing on the floor [SMD = 0.25 (0.07, 0.43)] and on a foam surface [SMD = 0.45 (0.25, 0.66)], and the Short Physical Performance Battery total score [SMD = -0.46 (-0.66, -0.27)] could discriminate fallers from nonfallers. Sensitivity analyses showed that gait speed could predict future falls in longitudinal cohort studies [SMD = -0.29 (-0.49, -0.08)]. Subgroup analyses showed that gait speed [SMD = -0.21 (-0.38, -0.05)] and the Timed Up and Go test [SMD = 0.54 (0.16, 0.92)] could identify fallers staying in residential care facilities or hospitals. CONCLUSIONS AND IMPLICATIONS: The 5-time chair stand test, the Berg Balance Scale, postural sway when standing on the floor and a foam surface, and the Short Physical Performance Battery can be used to predict falls in older adults with dementia. Gait speed and the Timed Up and Go test can be used to predict falls in institutionalized older adults with dementia. Clinicians are recommended to use these physical performance measures to assess fall risk in older adults with dementia.

An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators.

OBJECTIVE: To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovarian cancer. METHODS: Patients with ovarian cancer clearly diagnosed in our hospital were collected as a case group (n = 185), and three groups of patients with other malignant otolaryngology tumors (n = 138), patients with benign otolaryngology diseases (n = 339) and those with normal physical examination (n = 92) were used as an overall control group. In this paper, a fully automated segmentation network for magnetic resonance images of ovarian cancer is proposed to improve the reproducibility of tumor segmentation results while effectively reducing the burden on radiologists. A pre-trained Res Net50 is used to the three edge output modules are fused to obtain the final segmentation results. The segmentation results of the proposed network architecture are compared with the segmentation results of the U-net based network architecture and the effect of different loss functions and region of interest sizes on the segmentation performance of the proposed network is analyzed. RESULTS: The average Dice similarity coefficient, average sensitivity, average specificity (specificity) and average hausdorff distance of the proposed network segmentation results reached 83.62%, 89.11%, 96.37% and 8.50, respectively, which were better than the U-net based segmentation method. For ROIs containing tumor tissue, the smaller the size, the better the segmentation effect. Several loss functions do not differ much. The area under the ROC curve of the machine learning diagnostic model reached 0.948, with a sensitivity of 91.9% and a specificity of 86.9%, and its diagnostic efficacy was significantly better than that of the traditional way of detecting CA125 alone. The model was able to accurately diagnose ovarian cancer of different disease stages and showed certain discriminative ability for ovarian cancer in all three control subgroups. CONCLUSION: Using machine learning to integrate multiple conventional test indicators can effectively improve the diagnostic efficacy of ovarian cancer, which provides a new idea for the intelligent auxiliary diagnosis of ovarian cancer.

How does the primate brain combine generative and discriminative computations in vision?

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of "inference" in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes that give rise to it. In this conception, vision inverts a generative model through an interrogation of the sensory evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision.

MoS2-Based Sensor Array for Accurate Identification of Cancer Cells with Ensemble-Modified Aptamers.

In this work, we present an array-based chemical nose sensor that utilizes a set of ensemble-modified aptamer (EMAmer) probes to sense subtle physicochemical changes on the cell surface for cancer cell identification. The EMAmer probes are engineered by domain-selective incorporation of different types and/or copies of positively charged functional groups into DNA scaffolds, and their differential interactions with cancer cells can be transduced through competitive adsorption of fluorophore-labeled EMAmer probes loaded on MoS2 nanosheets. We demonstrate that this MoS2-EMAmer-based sensor array enables rapid and effective discrimination among six types of cancer cells and their mixtures with a concentration of 104 cells within 60 min, achieving a 94.4% accuracy in identifying blinded unknown cell samples. The established MoS2-EMAmer sensing platform is anticipated to show significant promise in the advancement of cancer diagnostics.