A preliminary examination of gender confusions in the charters of St Gall

Conventional flood control engineering techniques and approaches that have been traditionally been applied are now challenged by the public and no longer readily available as floodplain management tools. Increased awareness and concern regarding environmental degradation has resulted in protection of the riparian and river ecosystems. A variety of drainage related projects are required to provide habitat mitigation as a part of regulatory permit requirements. Natural floodplains typically provide the most appropriate location for constructing habitat replacement since this is a natural riparian ecosystem, especially in semi-arid areas. Incorporating environmental features into an active floodplain requires an understanding of the natural physical processes and stream mechanics, along with commonly encountered failure problems. General planning guidelines and a formalized design process adopt fundamental procedures to enhance the preservation of the natural floodplain characteristics. The recommendations include typical design considerations and site selection techniques addressing many primary concerns. Recent technology utilizing computer graphic visualizations facilitates the design development process and assists in the consensus building process with the community. Applying multi-disciplined planning guidelines to develop habitat mitigation within active floodplains assists in developing an implementable and successful long-term floodplain management project which addresses flood protection as well as providing environmental opportunities.

Deciphering the brain’s structure-function relationship is key to understanding the neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, is a classic example of primate neocortex structure-function organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence across higher-level cortex is debated, particularly regarding complex tuning in natural image space. However, a key hurdle in identifying columns is characterizing the complex, nonlinear tuning of neurons to high-dimensional sensory inputs. Building on prior findings of topological organization for features like color and orientation, we investigate functional clustering in macaque visual area V4 in non-parametric natural image space, using large-scale recordings and deep learning–based analysis. We combined linear probe recordings with deep learning methods to systematically characterize the tuning of >1,200 V4 neurons using in silico synthesis of most exciting images (MEIs), followed by in vivo verification. Single V4 neurons exhibited MEIs containing complex features, including textures and shapes, and even high-level attributes with eye-like appearance. Neurons recorded on the same silicon probe, inserted orthogonal to the cortical surface, often exhibited similarities in their spatial feature selectivity, suggesting a degree of functional organization along the cortical depth. We quantified MEI similarity using human psychophysics and distances in a contrastive learning-derived embedding space. Moreover, the selectivity of the V4 neuronal population showed evidence of clustering into functional groups of shared feature selectivity. These functional groups showed parallels with the feature maps of units in artificial vision systems, suggesting potential shared encoding strategies. These results demonstrate the feasibility and scalability of deep learning–based functional characterization of neuronal selectivity in naturalistic visual contexts, offering a framework for quantitatively mapping cortical organization across multiple levels of the visual hierarchy.

The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole‐brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all‐relevant feature selection procedure within cross‐validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.

Simple SummaryThe acute oral mucositis (AOM) is a prevalent side effect of radiation therapy for nasopharyngeal carcinoma (NPC). Severe AOM could impair the survival and quality of life for NPC patients. Accurate method to predict the incidence of severe AOM can aid clinicians in adjusting the treatment plan to improve the outcomes for NPC patients. We integrated multi-modalities, multi-omics and multi-regions data with two methods, integrate the original data or combine data after feature selection. The performance of models using each data integration method with different modalities, types of data and VOIs were analyzed. We developed a best-performing model with mean AUC at 0.81 ± 0.10 to predict the incidence of severe AOM for NPC patients following radiation therapy. (1) Background: Acute oral mucositis is the most common side effect for nasopharyngeal carcinoma patients receiving radiotherapy. Improper or delayed intervention to severe AOM could degrade the quality of life or survival for NPC patients. An effective prediction method for severe AOM is needed for the individualized management of NPC patients in the era of personalized medicine. (2) Methods: A total of 242 biopsy-proven NPC patients were retrospectively recruited in this study. Radiomics features were extracted from contrast-enhanced CT (CECT), contrast-enhanced T1-weighted (cT1WI), and T2-weighted (T2WI) images in the primary tumor and tumor-related area. Dosiomics features were extracted from 2D or 3D dose-volume histograms (DVH). Multiple models were established with single and integrated data. The dataset was randomized into training and test sets at a ratio of 7:3 with 10-fold cross-validation. (3) Results: The best-performing model using Gaussian Naive Bayes (GNB) (mean validation AUC = 0.81 ± 0.10) was established with integrated radiomics and dosiomics data. The GNB radiomics and dosiomics models yielded mean validation AUC of 0.6 ± 0.20 and 0.69 ± 0.14, respectively. (4) Conclusions: Integrating radiomics and dosiomics data from the primary tumor area could generate the best-performing model for severe AOM prediction.

The necessity of lymphadenectomy remains controversial in patients with early ovarian cancer. This study aimed to estimate the prevalence of lymph node metastasis (LNM) in patients with apparent local epithelial ovarian cancer (EOC) and to develop a risk model for the prediction of LNM in local EOC with machine learning algorithms. A cohort of 4110 patients who underwent lymphadenectomy with apparent local EOC were retrospectively evaluated. In the model development, a least absolute shrinkage and selection operator (LASSO)-derived Cox regression with internal 10-fold cross-validation was used to identify the risk factors of LNM. Clinical performance was assessed using the decision curve analysis, and a nomogram-based analysis was used to identify the risk factors for LNM. The incidence rate of LNM was rare in our cohort, at 7.4% (213/2885) in the primary set and 4.8% (59/1225) in the validation set. After feature selection, 10-fold internal validation, and external validation, our risk model performed well in both the discrimination (area under the curve=0.790 in the primary set and area under the curve=0.752 in the validation set) and the Hosmer-Lemeshow tests (P=0.873 in the primary set and P=0.380 in the validation set). Additionally, decision curve analysis demonstrated the superiority of our model for clinical interventions in daily practice. LNM is rare in patients with local EOC. In our study, which is based on machine learning algorithms, we developed a prediction model to identify patients with local EOC who could benefit from lymphadenectomy.

Diabetes, which substantially affects the people lives as well as the healthcare systems worldwide, has become the primary area of concern. The purpose of this study is to predict the length of hospital stay among diabetic patients using machine learning (ML), deep learning (DL), along with electronic health record (EHR) data. This work evaluated several models: XGBoost, Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network with LSTM (RNN-LSTM), and an ensemble model combining GAT and CNN in terms of performance metrics used, such as MAE, MSE, and R². Among all the models tested, MLP using the top 20 features performed the best, recording an MAE of 0.37 and an R² of 0.62. The model emphasized certain critical aspects concerning feature selection and dimensionality reduction, which enhanced the algorithm's accuracy, especially in cases with numerous redundant or verbose variables. More generally, these findings point to the potential that predictive modeling and artificial intelligence have in optimizing the allocation of hospital resources, reducing the costs of health care, and improving patient outcomes, thereby paving the way forward in the development of some clinical decision support tools to manage inpatient diabetic care.

The research to identify the relationship between neural activity underlying motor tasks such as motor imagery, execution, planning and observation has been of significant interest, since it is an important aspect in improving our understanding of neuromotor networks. Neuroimaging literature suggests that the central mechanism in all these tasks is the activation of the primary motor areas of the brain. To study correlates of these tasks, the researchers often employ neural data recorded from the subjects as they perform motor tasks using their dominant hand. In this study, we investigate the distinction between EEG spectral features during unilateral right and unilateral left, imagined and executed hand motor tasks. The analysis is conducted for the data from each hand separately, with the objective to identify whether the EEG activity that distinguished unilateral motor tasks is distinct for each hand. Further, a novel approach of selection of multichannel EEG spectral features is proposed and supervised classification based on support vector machines is employed to classify unilateral motor imagination and motor execution tasks using the selected features. Grand averages of spectral features, over 10 subjects, are studied and it is observed that EEG activity during unilateral motor tasks are both spatially localized and lateralized. Using the proposed pattern recognition model, accuracies of 82.4 % and 83.1 % with a reduction in 15-20 % features for right and left unilateral motor tasks respectively.

How does brain activity in distributed semantic brain networks evolve over time, and how do these regions interact to retrieve the meaning of words? We compared spatiotemporal brain dynamics between visual lexical and semantic decision tasks (LD and SD), analysing whole-cortex evoked responses and spectral functional connectivity (coherence) in source-estimated electroencephalography and magnetoencephalography (EEG and MEG) recordings. Our evoked analysis revealed generally larger activation for SD compared to LD, starting in primary visual area (PVA) and angular gyrus (AG), followed by left posterior temporal cortex (PTC) and left anterior temporal lobe (ATL). The earliest activation effects in ATL were significantly left-lateralised. Our functional connectivity results showed significant connectivity between left and right ATL, PTC and right ATL in an early time window, as well as between left ATL and IFG in a later time window. The connectivity of AG was comparatively sparse. We quantified the limited spatial resolution of our source estimates via a leakage index for careful interpretation of our results. Our findings suggest that the different demands on semantic information retrieval in lexical and semantic decision tasks first modulate visual and attentional processes, then multimodal semantic information retrieval in the ATLs and finally control regions (PTC and IFG) in order to extract task-relevant semantic features for response selection. Whilst our evoked analysis suggests a dominance of left ATL for semantic processing, our functional connectivity analysis also revealed significant involvement of right ATL in the more demanding semantic task. Our findings demonstrate the complementarity of evoked and functional connectivity analysis, as well as the importance of dynamic information for both types of analyses.

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features—one that enhances and one that suppresses activity—around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models—deep learning-based predictive models of biological neurons—we were able to systematically identify each neuron’s preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

SummaryModels of the cerebral cortex propose a canonical columnar architecture preserved across the brain. Evidence for this model primarily stems from observations in primary sensory areas, such as the visual cortex, where neurons in an anatomical column respond preferentially to the same stimulus features. Similar columnar feature selectivity has been described for visual area V2, visual area V3 [9, 10], the middle temporal visual area (area V5), and inferotemporal cortex. The fourth visual area (V4) features color selective neurons, organized in clusters across its surface. However, whether these color-sensitive neurons are organized in a columnar fashion is debated and uncertain due to technical limitations. Here, we utilize laminar multielectrode recordings along V4 columns to resolve rival hypotheses. Linear electrode arrays were introduced orthogonally into area V4, spanning all cortical layers. Monkeys viewed color stimuli presented to the visual receptive field of the column of neurons. We found that preference for a specific color was conserved along depth but varied across columns. Interestingly, while color preference (e.g. red or green) was consistent along depth, color selectivity was significantly greater in upper layers relative to the input layer and was weakest in the lower layers. These findings demonstrate that area V4 is organized in functional columns like other visual areas.

The primary goal of this work was to apply data-driven machine learning regression to assess if resting state functional connectivity (rs-FC) could estimate measures of behavioral domains in stroke subjects who completed brain-computer interface (BCI) intervention for motor rehabilitation. The study cohort consisted of 20 chronic-stage stroke subjects exhibiting persistent upper-extremity motor deficits who received the intervention using a closed-loop neurofeedback BCI device. Over the course of this intervention, resting state functional MRI scans were collected at four distinct time points: namely, pre-intervention, mid-intervention, post-intervention and 1-month after completion of intervention. Behavioral assessments were administered outside the scanner at each time-point to collect objective measures such as the Action Research Arm Test, Nine-Hole Peg Test, and Barthel Index as well as subjective measures including the Stroke Impact Scale. The present analysis focused on neuroplasticity and behavioral outcomes measured across pre-intervention, post-intervention and 1-month post-intervention to study immediate and carry-over effects. Rs-FC, changes in rs-FC within the motor network and the behavioral measures at preceding stages were used as input features and behavioral measures and associated changes at succeeding stages were used as outcomes for machine-learning-based support vector regression (SVR) models. Potential clinical confounding factors such as age, gender, lesion hemisphere, and stroke severity were included as additional features in each of the regression models. Sequential forward feature selection procedure narrowed the search for important correlates. Behavioral outcomes at preceding time-points outperformed rs-FC-based correlates. Rs-FC and changes associated with bilateral primary motor areas were found to be important correlates of across several behavioral outcomes and were stable upon inclusion of clinical variables as well. NIH Stroke Scale and motor impairment severity were the most influential clinical variables. Comparatively, linear SVR models aided in evaluation of contribution of individual correlates and seed regions while non-linear SVR models achieved higher performance in prediction of behavioral outcomes.

MRI radiomics in overall survival prediction of local advanced cervical cancer patients tread by adjuvant chemotherapy following concurrent chemoradiotherapy or concurrent chemoradiotherapy alone

Dynamics and Hierarchical Encoding of Non-compact Acoustic Categories in Auditory and Frontal Cortex.