Structural Alignment Research Articles

Enhancing Video-Language Representations With Structural Spatio-Temporal Alignment.

While pre-training large-scale video-language models (VLMs) has shown remarkable potential for various downstream video-language tasks, existing VLMs can still suffer from certain commonly seen limitations, e.g., coarse-grained cross-modal aligning, under-modeling of temporal dynamics, detached video-language view. In this work, we target enhancing VLMs with a fine-grained structural spatio-temporal alignment learning method (namely Finsta). First of all, we represent the input texts and videos with fine-grained scene graph (SG) structures, both of which are further unified into a holistic SG (HSG) for bridging two modalities. Then, an SG-based framework is built, where the textual SG (TSG) is encoded with a graph Transformer, while the video dynamic SG (DSG) and the HSG are modeled with a novel recurrent graph Transformer for spatial and temporal feature propagation. A spatial-temporal Gaussian differential graph Transformer is further devised to strengthen the sense of the changes in objects across spatial and temporal dimensions. Next, based on the fine-grained structural features of TSG and DSG, we perform object-centered spatial alignment and predicate-centered temporal alignment respectively, enhancing the video-language grounding in both the spatiality and temporality. We design our method as a plug&play system, which can be integrated into existing well-trained VLMs for further representation augmentation, without training from scratch or relying on SG annotations in downstream applications. On 6 representative VL modeling tasks over 12 datasets in both standard and long-form video scenarios, Finsta consistently improves the existing 13 strong-performing VLMs persistently, and refreshes the current state-of-the-art end task performance significantly in both the fine-tuning and zero-shot settings.

Protein structure alignment by reseek improves sensitivity to remote homologs.

Recent breakthroughs in protein fold prediction from amino acid sequences have unleashed a deluge of new structures, presenting new opportunities and challenges to bioinformatics. Reseek is a novel protein structure alignment algorithm based on sequence alignment where each residue in the protein backbone is represented by a letter in a "mega-alphabet" of 85,899,345,920 (∼1011) distinct states. Reseek achieves substantially improved sensitivity to remote homologs compared to state-of-the-art methods including DALI, TMalign and Foldseek, with comparable speed to Foldseek, the fastest previous method. Scaling to large databases of AI-predicted folds is analyzed. Foldseek E-values are shown to be under-estimated by several orders of magnitude, while Reseek E-values are in good agreement with measured error rates. https://github.com/rcedgar/reseek. Supplementary data are available at Bioinformatics online.

UCBShift 2.0: Bridging the Gap from Backbone to Side Chain Protein Chemical Shift Prediction for Protein Structures.

Chemical shifts are a readily obtainable NMR observable that can be measured with high accuracy, and because they are sensitive to conformational averages and the local molecular environment, they yield detailed information about protein structure in solution. To predict chemical shifts of protein structures, we introduced the UCBShift method that uniquely fuses a transfer prediction module, which employs sequence and structure alignments to select reference chemical shifts from an experimental database, with a machine learning model that uses carefully curated and physics-inspired features derived from X-ray crystal structures to predict backbone chemical shifts for proteins. In this work, we extend the UCBShift 1.0 method to side chain chemical shift prediction to perform whole protein analysis, which, when validated against well-defined test data shows higher accuracy and better reliability compared to the popular SHIFTX2 method. With the greater abundance of cleaned protein shift-structure data and the modularity of the general UCBShift algorithms, users can gain insight into different features important for residue-specific stabilizing interactions for protein backbone and side chain chemical shift prediction. We suggest several backward and forward applications of UCBShift 2.0 that can help validate AlphaFold structures and probe protein dynamics.

Assessment of genetic diversity and heterotic alignment of CIMMYT and IITA maize inbred lines adapted to sub‐Saharan Africa

AbstractDespite the breeding efforts by many institutions, maize (Zea mays L.) productivity in sub‐Saharan Africa is still low. A limited number of productive maize hybrids have been developed partly due to a lack of knowledge on the diversity and heterotic relationship of the germplasm, especially in public breeding programs. Understanding the extent of diversity, structure, and heterotic grouping of available maize germplasm originating from different breeding programs is important to enhance long‐term genetic gain in hybrid maize breeding programs by optimizing heterotic pools using modern breeding tools. Information about the genetic structure of the available germplasm could help breeders design effective breeding strategies to improve yield. This study was conducted to determine the genetic diversity, population structure, and heterotic alignment among 187 elite maize inbred lines from the IITA (International Institute of Tropical Agriculture) and CIMMYT (International Maize and Wheat Improvement Center) maize breeding programs. The inbred lines were genotyped with 9857 Diversity Array Technology sequencing based single nucleotide polymorphism markers. Hierarchical clustering revealed three major groups, with some subgroups consistent with the selection history, and pedigree of the inbred lines. Three broad groups were detected: two consisting of CIMMYT lines and a mixed group consisting of both CIMMYT and IITA inbred lines. The STRUCTURE analysis revealed six subpopulations (fixation index value = 0.58), which depicts a moderate genetic diversity among the materials. Population 2 comprises the highest number of genotypes (102) from both programs. More than 89% of the elite lines had homozygosity exceeding 95%, with the remaining lines requiring further inbreeding through repeated self‐pollination. There was inconsistency in the predetermined heterotic groups' alignment between CIMMYT and IITA elite inbred lines. Analysis of molecular variance revealed that 96% of the total variation was accounted for by differences within groups, with the remaining 4% representing the variation between groups. This suggests that the two programs can benefit from germplasm exchange for the improvement of maize productivity.

REDalign: accurate RNA structural alignment using residual encoder-decoder network

BackgroundRNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of O(L6)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(L^6)$$\\end{document} for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities.ResultsIn this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency.ConclusionREDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics.

Biophysics of ACL injuries

Anterior Cruciate Ligament (ACL) injuries rank among the most prevalent and severe types of injuries, significantly impacting both athletes and non-athletes alike. These injuries not only result in immediate physical impairment, such as intense pain, substantial swelling, and a marked loss of mobility, but also carry long-term health consequences that can alter a person’s quality of life. Chronic pain, persistent instability, and an increased risk of developing osteoarthritis are among the lasting effects that can follow an ACL injury. An in-depth understanding of the biophysics behind ACL injuries is paramount for devising effective prevention and treatment protocols. Biophysics, which combines principles from physics with biological systems, provides crucial insights into the mechanical and structural integrity of the ACL and its susceptibility to injury under various conditions. This systematic review aims to collate and synthesize the current knowledge surrounding the biophysical mechanisms that underlie ACL injuries. The review encompasses a range of factors, including the biomechanical forces that place stress on the ligament, anatomical structures that may predispose individuals to injury, and physiological conditions that affect ligament health and resilience. Each of these factors plays a crucial role in the incidence and severity of ACL injuries. Biomechanical forces, for example, can involve sudden changes in direction or impact during physical activity, leading to excessive stress on the ACL. Anatomical factors might include variations in bone structure or ligament alignment that inherently increase the risk of injury. Additionally, physiological conditions such as muscle strength, flexibility, and overall ligament health can influence the likelihood and extent of an ACL injury. The findings of this review underscore the necessity of adopting integrated approaches in both injury prevention and rehabilitation. Such approaches must consider the multifaceted nature of ACL injuries, involving not only mechanical and anatomical aspects but also physiological and possibly even genetic factors. By emphasizing a multi-faceted understanding, interventions can be more effectively tailored to address the complex interplay of elements that contribute to ACL injuries. This holistic approach can lead to better outcomes for those at risk of or recovering from ACL injuries, enhancing the efficacy of prevention strategies and rehabilitation protocols.

RepeatsDB in 2025: expanding annotations of structured tandem repeats proteins on AlphaFoldDB.

RepeatsDB (URL: https://repeatsdb.org) stands as a key resource for the classification and annotation of Structured Tandem Repeat Proteins (STRPs), incorporating data from both the Protein Data Bank (PDB) and AlphaFoldDB. This latest release features substantial advancements, including annotations for over 34000 unique protein sequences from >2000 organisms, representing a fifteenfold increase in coverage. Leveraging state-of-the-art structural alignment tools, RepeatsDB now offers faster and more precise detection of STRPs across both experimental and predicted structures. Key improvements also include a redesigned user interface and enhanced web server, providing an intuitive browsing experience with improved data searchability and accessibility. A new statistics page allows users to explore database metrics based on repeat classifications, while API enhancements support scalability to manage the growing volume of data. These advancements not only refine the understanding of STRPs but also streamline annotation processes, further strengthening RepeatsDB's role in advancing our understanding of STRP functions.

Structural Analysis of Virus Regulatory N6-Methyladenosine (m6A) Machinery of the Black Flying Fox (Pteropus alecto) and the Egyptian Fruit Bat (Rousettus aegyptiacus) Shows Evolutionary Conservation Amongst Mammals

Background: N6-methyladenosine (m6A) is an abundant RNA epitranscriptomic modification in eukaryotes. The m6A machinery includes cellular writer, eraser and reader proteins that regulate m6A. Pteropus alecto (P. alecto) (the Australian black flying fox) and Rousettus aegyptiacus (R. aegyptiacus) (the Egyptian fruit bat) are bats associated with several viral zoonoses yet neglected in the field of m6A epigenetics studies. Objectives: This study utilises various bioinformatics and in silico tools to genetically identify, characterise and annotate the m6A machinery in P. alecto and R. aegyptiacus. Methods: A range of bioinformatic tools were deployed to comprehensively characterise all known m6A-associated proteins of P. alecto and R. aegyptiacus. Results: Phylogenetically, the m6A fat mass and obesity-associated protein (FTO) eraser placed the order Chiroptera (an order including all bat species) in a separate clade. Additionally, it showed the lowest identity matrices in P. alecto and R. aegyptiacus when compared to other mammals (74.1% and 72.8%) and Homo sapiens (84.0% and 76.1%), respectively. When compared to humans, genetic loci-based analysis of P. alecto and R. aegyptiacus showed syntenic conservation in multiple flanking genes of 8 out the 10 m6A-associated genes. Furthermore, amino acid alignment and protein tertiary structure of the two bats’ m6A machinery demonstrated conservation in the writers but not in erasers and readers, compared to humans. Conclusions: These studies provide foundational annotation and genetic characterisation of m6A machinery in two important species of bats which can be exploited to study bat–virus interactions at the interface of epitranscriptomics.

Improving the catalytic performance of carbonyl reductase based on the functional loops engineering.

Vibegron functions as a potent and selective β3-adrenergic receptor agonist, with its chiral precursor (2S,3R)-aminohydroxy ester (1b) being crucial to its synthesis. In this study, loop engineering was applied to the carbonyl reductase (EaSDR6) from Exiguobacterium algae to achieve an asymmetric reduction of the (rac)-aminoketone ester 1a. The variant M5 (A138L/A190V/S193A/Y201F/N204A) was obtained and demonstrated an 868-fold increase in catalytic efficiency (kcat/Km = 260.3 s-1 mM-1) and a desirable stereoselectivity (>99% enantiomeric excess, e.e.; >99% diastereomeric excess, d.e.) for the target product 1b in contrast to the wild-type EaSDR6 (WT). Structural alignment with WT indicated that loops 137-154 and 182-210 potentially play vital roles in facilitating catalysis and substrate binding. Moreover, molecular dynamics (MD) simulations of WT-1a and M5-1a complex illustrated that M5-1a exhibits a more effective nucleophilic attack distance and more readily adopts a pre-reaction state. The interaction analysis unveiled that M5 enhanced hydrophobic interactions with substrate 1a on cavities A and B while diminishing unfavorable hydrophilic interactions on cavity C. Computational analysis of binding free energies indicated that M5 displayed heightened affinity towards substrate 1a compared to the WT, aligning with its decreased Km value. Under organic-aqueous biphasic conditions, the M5 mutant showed >99% conversion within 12 h with 300 g/L substrate 1a (highest substrate loading as reported). This study enhanced the catalytic performance of carbonyl reductase through functional loops engineering and established a robust framework for the large-scale biosynthesis of the vibegron intermediate.

Understanding the impact of deep structures on the hydrological setting of the Eastern Bahira Basin in Morocco using combined geophysical analysis of gravity, seismic, and electrical resistivity data

In arid regions like the Eastern Bahira Basin, communities mainly rely on groundwater for drinking and irrigation. However, efficiently managing these vital resources requires a deep understanding of the underlying aquifers’ structure and identifying the most suitable areas for exploitation. This presents a significant challenge for the success of water supply and irrigation programs in the Eastern Bahira Basin. This study is based on an integrative approach, combining Electrical Resistivity Tomography data, with the compilation and reinterpretation of pre-existing seismic, gravimetric and vertical electrical sounding data. This approach is based on compiling old gravimetric data and applying advanced processing techniques to determine the horizontal gradient maxima, which helps highlight the major structural alignments in the basin. Furthermore, the approach utilizes seismic data in order to enhance understanding of the deep structure of the basin, reinterpreting it in light of recent drilling data. The interpretation of the gravimetric and seismic data has also been validated by the results of vertical electrical soundings and electrical tomography that we recently acquired in the Eastern Bahira basin. The outcomes of this research provide new insights into the deep structure of the Eastern Bahira Basin and suggest the most promising hydrogeological prospects, thereby contributing to the success of the ongoing drinking water supply and irrigation program in the Eastern Bahira Basin.

Deep coupled registration and segmentation of multimodal whole-brain images.

Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requirements are typically modeled as two distinct tasks: Registration and segmentation. However, prevailing methods, fail to fully explore and utilize the inherent correlation and complementarity between the two tasks. Furthermore, variations in brain anatomy, brightness and texture pose another formidable challenge in designing multi-modal similarity metrics. A high-throughput approach capable of overcomingthe bottleneck of multi-modal similarity metric design, while effective leveraging the highly correlated and complementary nature of two tasks is highly desirable. We introduce a deep learning framework for joint registration and segmentation of multi-modal brain images. Under this framework, registration and segmentation tasks are deeply coupled and collaborated at two hierarchical layers. In the inner layer, we establish a strong feature-level coupling between the two tasks by learning a unified common latent feature representation. In the outer layer, we introduce a mutually supervised dual-branch network to decouple latent features and facilitate task-level collaboration between registration and segmentation. Since the latent features we designed are also modality-independent, the bottleneck of designing multi-modal similarity metric is essentially addressed. Another merit offered by this framework is the interpretability of latent features, which allows intuitive manipulation of feature learning, thereby further enhancing network training efficiency and the performance of both tasks. Extensive experiments conducted on both multi-modal and mono-modal datasets of mouse and human brains demonstrate the superiority of our method. The code is available at https://github.com/tingtingup/DCRS. Supplementary data are available at Bioinformaticsonline.

Sequence-Similar Protein Domain Pairs With Structural or Topological Dissimilarity.

For a variety of applications, protein structures are clustered by sequence similarity, and sequence-redundant structures are disregarded. Sequence-similar chains are likely to have similar structures, but significant structural variation, as measured with RMSD, has been documented for sequence-similar chains and found usually to have a functional explanation. Moving two neighboring stretches of backbone through each other may change the chain topology and alter possible folding paths. The size of this motion is compatible to a variation in a flexible loop. We search and find domains with alternate chain topology in CATH4.2 sequence families relatively independent of sequence identity and of structural similarity as measured by RMSD. Structural, topological, and functional representative sets should therefore keep sequence-similar domains not just with structural variation but also with topological variation. We present BCAlign that finds Alignment and superposition of protein Backbone Curves by optimizing a user chosen convex combination of structural derivation and derivation between the structure-based sequence alignment and an input sequence alignment. Steric and topological obstructions from deforming a curve into an aligned curve are then found by a previously developed algorithm. For highly sequence-similar domains, sequence-based structural alignment better represents the chains motion and generally reveals larger structural and topological variation than structure-based does. Fold-switching protein pairs have been reported to be most frequent between X-ray and NMR structures and estimated to be underrepresented in the PDB as the alternate configuration is harder to resolve. Here we similarly find chain topology most frequently altered between X-ray and NMR structures.

Enhancing Underwater Images through Multi-Frequency Detail Optimization and Adaptive Color Correction

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality.

Self-powered γ-In2Se3/p-Si heterojunction for photodetection: exploring humidity and light intensity dependent photoresponse

In this study, high-quality γ-In2Se3 thin films were successfully deposited on p-Si substrates via the RF sputtering technique. Structural characterization using XRD and Raman spectroscopy confirmed the formation of the hexagonal γ-phase of In2Se3 film. FESEM analysis revealed the presence of small, homogeneous, and well-defined grains in the prepared γ-In2Se3 film. EDS analysis confirmed the stoichiometric composition (∼ 2:3) of the In2Se3 films. XPS further validated the presence of In, Se, and Si elements in the deposited films. Band gap analysis using UV-visible spectroscopy yielded a value of 1.96 eV for the γ-In2Se3 film. Integration of γ-In2Se3 with p-type Si enhanced photoresponsivity of 47.9 mA/W, photosensitivity of 282, and photo detectivity of 8.45 × 1010 Jones. The self-powered γ-In2Se3/p-Si heterojunction-based photodetector exhibited rapid rise time attributed to the type II band alignment structure, facilitating efficient electron-hole pair separation and minimizing recombination. Furthermore, humidity and light intensity-dependent photodetector properties of γ-In2Se3/p-Si heterojunction photodetector were also investigated. An increase in photocurrent from 271 to 291 µA was observed with rising humidity levels, indicating the device’s sensitivity to humidity variations. Furthermore, light intensity dependence studies revealed a linear relationship between photocurrent and incident light intensity, demonstrating the device’s reliable response across various illumination levels.

Nanoscale View of Alignment and Domain Growth in a Hexagonal Columnar Liquid Crystal.

Highly ordered liquid crystalline (LC) phases have important potential for organic electronics. We studied the molecular alignment and domain structure in a columnar LC thin film with nanometer resolution during in situ heating using four-dimensional scanning transmission electron microscopy (4D STEM). The initial disordered vapor-deposited LC glass thin film rapidly ordered at its glass transition temperature into a hexagonal columnar phase with small (<10 nm), well-aligned, planar domains (columns oriented parallel to the surface). Upon further heating, the domains coarsen via bulk diffusion, then the film crystallizes, then finally transforms back to an LC phase at an even higher temperature. The LC phase at high temperature shows straight columns of molecules, which we attribute to structure inherited from the intermediate crystalline phase. Nanoscale 4D STEM offers direct insight into the mechanisms of domain reorganization, and intermediate crystallization is a potential approach to manipulate orientational order and texture at the nano- to mesoscale in LC thin films.

An ultrasensitive flexible force sensor with nature-inspired minimalistic architecture to achieve a detection resolution and threshold of 1 mN for underwater applications

Highly sensitive flexible force sensors enable precise detection of underwater signals for monitoring biological activity, environmental conditions, and vehicle movement. Multilayer stack assembly and micro/nano structure array are often seen in most force/pressure sensors which are toughly hard to control the interlayer spacing and micro/nano structures alignment precisely, resulting in poor consistency and stability. Herein, we first reported a new force sensor with a nature-inspired minimalistic architecture, addressing such issues in an elegant and surprising approach by using a single-layer arched functional membrane with one microgroove. Inspired by the scorpions’ slit sensilla and mantis’ campaniform sensilla, a highly sensitive and waterproof flexible force sensor was fabricated. It is demonstrated that the force sensor has a sensitivity of 27.6 N−1, a high force resolution (1 mN), a fast response time of 70 ms, excellent stability over 5000 cycles and linearity (0.996), and a small force detection limit (≤ 1 mN), showing great potential in underwater environment sensing and motion monitoring of vehicles. This novel but minimalistic architecture provides a new direction in the development of sensors with advanced performance.

Two medicinal molecules Hydralazine and Isoniazid have the potential to control wheat crown rot by combing with a N-acetyltransferase FDB2

Fusarium pseudograminearum is the main pathogen that causes wheat crown rot (WCR), causing serious harm to wheat production. Wheat secretes Benzoxazolinones (Bxs) as fungicidins to prevent F. pseudograminearum infection. Fusarium Detoxification of Bx 2 (FDB2) can degrade Bx to non-fungitoxic N-(2-hydroxyphenyl) malonamic acid. Therefore, FDB2 may be a potential drug target for WCR.In the present study, the structure of FDB2 was determined using the molecular replacement method. The overall FDB2 structure displayed a typical N-acetyltransferase (NAT1) conformation. Unlike other NAT1s, the active site cleft is divided into two parts by a long loop (A135MSPYPDVRKNQA147). Hydralazine, Isoniazid, and 2,4′-dibromoacetanilide were screened out as potential inhibitors of FDB2 by structure alignment. Affinity measurements by MST showed that FDB2 prefers to combine Isoniazid and Hydralazine rather than its natural substrate, 2-aminophenol. Wheat seedling infection assays showed that Isoniazid and Hydralazine suppress F. pseudograminearum invasion in wheat. Our study found that Hydralazine and Isoniazid have the potential to control WCR. This article provides a new idea for the application of medicine, which has serious adverse effects, on plant disease control to reduce research costs and make obsolete drugs shine with vitality.

Multiscale and multimodal signatures of structure-function coupling variability across the human neocortex

The relationship between the brain's structural wiring and its dynamic activity is thought to vary regionally, implying that the mechanisms underlying structure-function coupling may differ depending on a region's position within the brain's hierarchy. To better bridge the gap between structure and function, it is crucial to identify the factors shaping this regionality, not only in terms of how static functional connectivity aligns with structure, but also regarding the time-domain variability of this interplay. Here we map structure - function coupling and its time-domain variability and relate them to the heterogeneity of the cortex. We show that these two properties split the cortical landscape into two districts anchored to the opposite ends of the brain's hierarchy. By looking at statistical relationships with layer-specific gene transcription, T1w/T2w ratio, and synaptic density, we show that macro-scale structure-function coupling may be rooted in the brain's microstructure and meso-scale laminar specialization. Finally, we demonstrate that a lower and more variable alignment of function and structure may bestow the emergence of unique functional dynamics.

Electromagnetic neuronavigation in neuroendoscopy. Navigation proposal for the LOTTA ventriculoscope. Technical note

Background and objectiveNeuronavigation in ventriculoscopy has been described in several papers. However, there are different ventriculoscopes and navigation systems. Because of these different combinations, it is difficult to find detailed navigation protocols for each ventriculoscope. We describe, step by step, a simple method to navigate both the trajectory to reach the ventricular system and the intraventricular work for the LOTTA ventriculoscope. MethodsWe used a rigid ventriculoscope (LOTTA, KarlStorz) with an electromagnetic stylet (S8-StealthSystem, Medtronic) as the main navigation tool. The protocol is based on a 3D printed trocar or alternatively, on a modification of the original trocar for extraventricular phase navigation and a modified pediatric nasogastric tube for intraventricular phase navigation. ResultsThe protocol can be set up in less than 10 min. The extraventricular part is navigated by inserting the electromagnetic stylet inside the 3D printed trocar or inside the original modified trocar. Intraventricular navigation is performed by combining a modified pediatric nasogastric tube with the electromagnetic stylet inside the working channel of the endoscope. The most critical point is to obtain a blunt, bloodless approach to the ventricle and to achieve perfect alignment of all target structures by means of previously planned pure straight trajectories. ConclusionsThis protocol is easy to set up, avoids rigid head fixation, bulky optical navigation accessories, while allows continuous navigation of both parts of the surgery. Since we have implemented this protocol, we have seen a significant improvement in both simple and complex neuroendoscopy procedures as the surgery is dramatically simplified.

Physical and electrochemical performances of novel tellurium composite films as anode applications in energy storage systems

Portable electronic devices and electric cars use lithium-ion batteries, but clumping lithium alloys limit their lifespan. Due to their strong electronic conductivity, volumetric capacity, and high energy density, researchers are conducting research on electrochemical metal cells utilizing tellurium for high-performance batteries. Theoretically, lithium-tellurium batteries can improve energy densities three times more than lithium-ion batteries. However, metal-tellurium faces challenges such as low rate capability, unclear redox reactions, intermediate dissolution, and electrode volume changes. This study explores the enhancement of the energy storage capacity of next-generation batteries by fabricating coated electrode films as novel anodes from tellurium, silicon, and graphene. Physical, thermal, and morphological analysis of composite material are investigated by XRD, TEM, TGA, DSC, SEM, UV, and XPS analyses, revealing its rigidity as well as durability through its crystal structure alignment and thermal stability. In electrochemical analysis (CV) at various scan rates, samples that exhibit consistent and high specific capacity (Cp) values at different scan speeds (25, 50, and 100 mV/s) indicate excellent ability to store and maintain charge. Decreasing Cp values with increasing scan rates indicate that the speed of cycling limits the charge transfer kinetics and electrode performance. In EIS, the charge transfer resistances (Rct) for the pure Te, Te + Gr, Te + Si + Gr, and Te + Si samples are 759.07 Ω, 4.21 Ω, 36.39 Ω, and 164.90 Ω, respectively. The Te + Gr sample has the lowest Rct, indicating the best charge transfer efficiency at the electrode contact, whereas the Te + Si + Gr sample has a comparatively lower Rct, indicating better charge transfer kinetics. The combined result exhibits the synergistic impact of tellurium, silicon, and graphene in enhancing the energy storage capacity of future batteries across the industry.