Molecules are essential building blocks of life and their different conformations (i.e., shapes) crucially determine the functional role that they play in living organisms. Cryogenic Electron Microscopy (cryo-EM) allows for acquisition of large image datasets of individual molecules. Recent advances in computational cryo-EM have made it possible to learn latent variable models of conformation landscapes. However, interpreting these latent spaces remains a challenge as their individual dimensions are often arbitrary. The key message of our work is that this interpretation challenge can be viewed as an Independent Component Analysis (ICA) problem where we seek models that have the property of identifiability. That means, they have an essentially unique solution, representing a conformational latent space that separates the different degrees of freedom a molecule is equipped with in nature. Thus, we aim to advance the computational field of cryo-EM beyond visualizations as we connect it with the theoretical framework of (nonlinear) ICA and discuss the need for identifiable models, improved metrics, and benchmarks. Moving forward, we propose future directions for enhancing the disentanglement of latent spaces in cryo-EM, refining evaluation metrics and exploring techniques that leverage physics-based decoders of biomolecular systems. Moreover, we discuss how future technological developments in time-resolved single particle imaging may enable the application of nonlinear ICA models that can discover the true conformation changes of molecules in nature. The pursuit of interpretable conformational latent spaces will empower researchers to unravel complex biological processes and facilitate targeted interventions. This has significant implications for drug discovery and structural biology more broadly. More generally, latent variable models are deployed widely across many scientific disciplines. Thus, the argument we present in this work has much broader applications in AI for science if we want to move from impressive nonlinear neural network models to mathematically grounded methods that can help us learn something new about nature.

UNASSIGNED: To investigate how passive hyperthermia affect the resting-state functional brain activity based on an acute mouse model after heat stress exposure.
UNASSIGNED: Twenty-eight rs-fMRI data of C57BL/6J male mice which weighing about 24 ∼ 29 g and aged 12 ∼ 16 weeks were collected. The mice in the hyperthermia group (HT, 40 °C ± 0.5 °C, 40 min) were subjected to passive hyperthermia before the anesthesia preparation for scanning. While the normal control group (NC) was subjected to normothermia condition (NC, 20 °C ± 2 °C, 40 min). After data preprocessing, we performed independent component analysis (ICA) and region of interested (ROI)-ROI functional connectivity (FC) analyses on the data of both HT (n = 13) and NC (n = 15).
UNASSIGNED: The group ICA analysis showed that the HT and the NC both included 11 intrinsic connectivity networks (ICNs), and can be divided into four types of networks: the cortical network (CN), the subcortical network (SN), the default mode network (DMN), and cerebellar networks. CN and SN belongs to sensorimotor network. Compared with NC, the functional network organization of ICNs in the HT was altered and the overall functional intensity was decreased. Furthermore, 13 ROIs were selected in CN, SN, and DMN for further ROI-ROI FC analysis. The ROI-ROI FC analysis showed that passive hyperthermia exposure significantly reduced the FC strength in the overall brain represented by CN, SN, DMN of mice.
UNASSIGNED: Prolonged exposure to high temperature has a greater impact on the overall perception and cognitive level of mice, which might help understand the relationship between neuronal activities and physiological thermal sensation and regulation as well as behavioral changes.

OBJECTIVE: Cantu Syndrome (CS), a multisystem disease with a complex cardiovascular phenotype, is caused by GoF variants in the Kir6.1/SUR2 subunits of ATP-sensitive potassium (KATP) channels, and is characterized by low systemic vascular resistance, as well as tortuous, dilated vessels, and decreased pulse-wave velocity. Thus, CS vascular dysfunction is multifactorial, with both hypomyotonic and hyperelastic components. To dissect whether such complexities arise cell-autonomously within vascular smooth muscle cells (VSMCs), or as secondary responses to the pathophysiological milieu, we assessed electrical properties and gene expression in human induced pluripotent stem cell-derived VSMCs (hiPSC-VSMCs), differentiated from control and CS patient-derived hiPSCs, and in native mouse control and CS VSMCs.
RESULTS: Whole-cell voltage-clamp of isolated aortic and mesenteric arterial VSMCs isolated from wild type (WT) and Kir6.1[V65M] (CS) mice revealed no clear differences in voltage-gated K+ (Kv) or Ca2+ currents. Kv and Ca2+ currents were also not different between validated hiPSC-VSMCs differentiated from control and CS patient-derived hiPSCs. While pinacidil-sensitive KATP currents in control hiPSC-VSMCs were consistent with those in WT mouse VSMCs, they were considerably larger in CS hiPSC-VSMCs. Under current-clamp conditions, CS hiPSC-VSMCs were also hyperpolarized, consistent with increased basal K conductance, and providing an explanation for decreased tone and decreased vascular resistance in CS. Increased compliance was observed in isolated CS mouse aortae, and was associated with increased elastin mRNA expression. This was consistent with higher levels of elastin mRNA in CS hiPSC-VSMCs, suggesting that the hyperelastic component of CS vasculopathy is a cell-autonomous consequence of vascular KATP GoF.
CONCLUSIONS: The results show that hiPSC-VSMCs reiterate expression of the same major ion currents as primary VSMCs, validating the use of these cells to study vascular disease. Results in hiPSC-VSMCs derived from CS patient cells suggest that both the hypomyotonic and hyperelastic components of CS vasculopathy are cell-autonomous phenomena driven by KATP overactivity within VSMCs.

BACKGROUND: The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpose, the present study aims to classify individuals with PTSD versus healthy control.
METHODS: The resting-state functional MRI (rs-fMRI) scans of 19 PTSD and 24 healthy control male subjects have been used to identify the activation pattern in most affected brain regions using group-level independent component analysis (ICA) and t-test. To classify PTSD-affected subjects from healthy control six machine learning techniques including random forest, Naive Bayes, support vector machine, decision tree, K-nearest neighbor, linear discriminant analysis, and deep learning three-dimensional 3D-CNN have been performed on the data and compared.
RESULTS: The rs-fMRI scans of the most commonly investigated 11 regions of trauma-exposed and healthy brains are analyzed to observe their level of activation. Amygdala and insula regions are determined as the most activated regions from the regions-of-interest in the brain of PTSD subjects. In addition, machine learning techniques have been applied to the components extracted from ICA but the models provided low classification accuracy. The ICA components are also fed into the 3D-CNN model, which is trained with a 5-fold cross-validation method. The 3D-CNN model demonstrated high accuracies, such as 98.12%, 98.25 %, and 98.00 % on average with training, validation, and testing datasets, respectively.
CONCLUSIONS: The findings indicate that 3D-CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.

Structural differences along the hippocampal long axis are believed to underlie meaningful functional differences. Yet, recent data-driven parcellations of the hippocampus subdivide the hippocampus into a 10-cluster map with anterior-medial, anterior-lateral, and posteroanterior-lateral, middle, and posterior components. We tested whether task and experience could modulate this clustering using a spatial learning experiment where male and female participants were trained to virtually navigate a novel neighborhood in a Google Street View-like environment. Participants were scanned while navigating routes early in training and after a 2 week training period. Using the 10-cluster map as the ideal template, we found that participants who eventually learn the neighborhood well have hippocampal cluster maps consistent with the ideal-even on their second day of learning-and their cluster mappings do not deviate over the 2 week training period. However, participants who eventually learn the neighborhood poorly begin with hippocampal cluster maps inconsistent with the ideal template, though their cluster mappings may become more stereotypical after the 2 week training. Interestingly this improvement seems to be route specific: after some early improvement, when a new route is navigated, participants\' hippocampal maps revert back to less stereotypical organization. We conclude that hippocampal clustering is not dependent solely on anatomical structure and instead is driven by a combination of anatomy, task, and, importantly, experience. Nonetheless, while hippocampal clustering can change with experience, efficient navigation depends on functional hippocampal activity clustering in a stereotypical manner, highlighting optimal divisions of processing along the hippocampal anterior-posterior and medial-lateral axes.

BACKGROUND: Complete mesocolic excision (CME) and central vascular detachment are very important procedures in surgery for colorectal cancer. Preoperative and intraoperative assessments of the anatomy of major colorectal vessels are necessary to avoid massive bleeding, especially in endoscopic surgery. A case with a rare anomaly in which the middle colic artery (MCA) and ileocolic artery (ICA) had a common trunk is reported.
METHODS: The patient was a 73-year-old woman diagnosed with ascending colon cancer on colonoscopy. Preoperative abdominal contrast-enhanced computed tomography confirmed that the MCA and ICA had a common trunk. She underwent laparoscopic ileocecal resection for the ascending colon cancer with D3 lymph node dissection. Intraoperative indocyanine green fluorescence imaging was conducted. After confirming vessel bifurcation, the ICA was dissected at the distal end of the MCA bifurcation. The patient has been followed as an outpatient, with no signs of recurrence as of 2 years postoperatively.
CONCLUSIONS: A case of an ascending colon cancer with a unique vascular bifurcation pattern was presented. Preoperative and intraoperative evaluations of the major colorectal vessels are very important for preventing perioperative and postoperative complications.

This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF\'s mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.

The Myelin Regulator Factor (MYRF) is a master regulator governing myelin formation and maintenance in the central nervous system. The conservation of MYRF across metazoans and its broad tissue expression suggest it has functions extending beyond the well-established role in myelination. Loss of MYRF results in developmental lethality in both invertebrates and vertebrates, and MYRF haploinsufficiency in humans causes MYRF-related Cardiac Urogenital Syndrome, underscoring its importance in animal development; however, these mechanisms are largely unexplored. MYRF, an unconventional transcription factor, begins embedded in the membrane and undergoes intramolecular chaperone mediated trimerization, which triggers self-cleavage, allowing its N-terminal segment with an Ig-fold DNA-binding domain to enter the nucleus for transcriptional regulation. Recent research suggests developmental regulation of cleavage, yet the mechanisms remain enigmatic. While some parts of MYRF\'s structure have been elucidated, others remain obscure, leaving questions about how these motifs are linked to its intricate processing and function.

Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate - 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is - 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.

The electrophysiological basis of resting-state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data-driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase-amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group-specific fMRI-RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI-RSN than the two existing approaches. Importantly, with this approach, all networks-except for the visual network-had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI-RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.