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1. Total disc replacement alters the biomechanics of cervical spine based on sagittal cervical alignment: A finite element study

   https://doi.org/10.4103/jcvjs.jcvjs_21_22  

   Mumtaz, Muzammil; Mendoza, Justin; Tripathi, Sudharshan; Kelkar, Amey; Nishida, Norihiro; Sahai, Ashish; Goel, Vijay K (January 2022, Journal of Craniovertebral Junction and Spine)

   IntroductionThe correlation between cervical alignment and clinical outcome of total disc replacement (TDR) surgery is arguable. We believe that this conflict exists because the parameters that influence the biomechanics of the cervical spine are not well understood, specifically the effect of TDR on different cervical alignments. Methods:A validated osseo-ligamentous model from C2-C7 was used in this study. The C2-C7 Cobb angle of the base model was modified to represent: lordotic (−10°), straight (0°), and kyphotic (+10°) cervical alignment. The TDR surgery was simulated at the C5-C6 segment. The range of motion (ROM), intradiscal pressure, annular stresses, and facet loads were computed for all the models. Results:The ROM results demonstrated kyphotic alignment after TDR surgery to be the most mobile when compared to intact base model (41% higher in flexion–extension, 51% higher in lateral bending, and 27% higher in axial rotation) followed by straight and lordotic alignment, respectively. The annular stresses for the kyphotic alignment when compared to intact base model were higher at the index level (33% higher in flexion–extension and 48% higher in lateral bending) compared to other alignments. The lordotic model demonstrated higher facet contact forces at the index level (75% higher in extension than kyphotic alignment, 51% higher in lateral bending than kyphotic alignment, and 78% higher in axial rotation than kyphotic alignment) when compared among the three alignment models. Conclusion:Preoperative cervical alignment should be an integral part of surgical planning for TDR surgery as different cervical alignments may significantly alter the postsurgical outcomes. 

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2. P‐188: Cellulose Nanocrystals as an Alignment Layer for Liquid‐Crystal Displays in a Circular Economy

   https://doi.org/10.1002/sdtp.18667  

   Zhang, Ye; Doherty, James L; Franklin, Aaron; Kymissis, loannis (June 2025, SID Symposium Digest of Technical Papers)

   We report a Liquid Crystal Display (LCD) structure employing cellulose nanocrystals (CNC) as a recyclable, non‐toxic alignment layer in a twisted nematic configuration The CNC alignment layer, fabricated via spin coating and mechanical rubbing, demonstrated comparable performance to polyimide in transparency, threshold voltage, response speed, and liquid crystal alignment This work demonstrates the viability of CNC alignment layers, advancing LCD technology toward circular economy principles. 

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3. EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment

   https://doi.org/10.1186/s13015-023-00247-x  

   Shen, Chengze; Liu, Baqiao; Williams, Kelly P; Warnow, Tandy (December 2023, Algorithms for Molecular Biology)

   Abstract BackgroundAdding sequences into an existing (possibly user-provided) alignment has multiple applications, including updating a large alignment with new data, adding sequences into a constraint alignment constructed using biological knowledge, or computing alignments in the presence of sequence length heterogeneity. Although this is a natural problem, only a few tools have been developed to use this information with high fidelity. ResultsWe present EMMA (Extending Multiple alignments using MAFFT--add) for the problem of adding a set of unaligned sequences into a multiple sequence alignment (i.e., a constraint alignment). EMMA builds on MAFFT--add, which is also designed to add sequences into a given constraint alignment. EMMA improves on MAFFT--add methods by using a divide-and-conquer framework to scale its most accurate version, MAFFT-linsi--add, to constraint alignments with many sequences. We show that EMMA has an accuracy advantage over other techniques for adding sequences into alignments under many realistic conditions and can scale to large datasets with high accuracy (hundreds of thousands of sequences). EMMA is available athttps://github.com/c5shen/EMMA. ConclusionsEMMA is a new tool that provides high accuracy and scalability for adding sequences into an existing alignment. 

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4. Environmental averaging

   https://doi.org/10.4171/EMSS/81  

   Shvydkoy, Roman (September 2024, EMS Surveys in Mathematical Sciences)

   Many classical examples of models of self-organized dynamics, including the Cucker–Smale, Motsch–Tadmor, multi-species, and several others, include an alignment force that is based upon density-weighted averaging protocol. Those protocols can be viewed as special cases of “environmental averaging”. In this paper we formalize this concept and introduce a unified framework for systematic analysis of alignment models.A series of studies are presented including the mean-field limit in deterministic and stochastic settings, hydrodynamic limits in the monokinetic and Maxwellian regimes, hypocoercivity and global relaxation for dissipative kinetic models, several general alignment results based on chain connectivity and spectral gap analysis. These studies cover many of the known results and reveal new ones, which include asymptotic alignment criteria based on connectivity conditions, new estimates on the spectral gap of the alignment force that do not rely on the upper bound of the macroscopic density, uniform gain of positivity for solutions of the Fokker–Planck-alignment model based on smooth environmental averaging. As a consequence, we establish unconditional relaxation result for global solutions to the Fokker–Planck-alignment model, which presents a substantial improvement over previously known perturbative results. 

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5. BANANA: when Behavior ANAlysis meets social Network Alignment

   https://doi.org/10.24963/ijcai.2020/200  

   Ren, Fuxin; Zhang, Zhongbao; Zhang, Jiawei; Su, Sen; Sun, Li; Zhu, Guozhen; Guo, Congying (July 2020, International Joint Conference on Artificial Intelligence)

   null (Ed.)

   Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively. 

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