Local Translation Research Articles

Writing of Business Ethnography: Reflections on Fieldwork and Theory

The writing of business ethnography has expanded from the study of small social units such as factories to the discussion of transnational markets, transnational business networks and global finance in under the context of globalization. A holistic anthropological perspective and use of ethnography research have played important roles in the development of business anthropology. The research tradition and orientation of anthropology not only determine research perspectives and approaches in business ethnography, they also influence the theoretical construction of business anthropology. The study of business ethnography in China provides not only local interpretations of the theory and practice of business anthropology, but also opportunities to reflect on the use of ethnography for such studies.

A synodal campus ministry in the Roman Catholic Diocese of Kalookan context

Questions about what walking a synodal path means for the Church in different local contexts undoubtedly occupy a central place in academic and pastoral discussions regarding the currently convoked synod on synodality. A particular concern is how the youth can be actively involved in this process. The purpose of this paper is to examine the role of the campus ministry in living out and actively participating in the process of synodality in response to Pope Francis’ call for a Synodal Church. Vatican documents will be analyzed and compared with local Church and university documents for this study. The result of this study reveals that despite the shared vision of inclusivity in terms of ‘journeying together,’ the local interpretation of synodality in the Philippines takes a different route. Moreover, Campus Ministry programmes that could animate the youth to be engaged in the synodal process also need to provide appropriate avenues for the praxis of the synodal vision from the perspective of both the Vatican and the local context. Since the Church is both global and local, or ‘glocal’ in nature, this paper proposes an assimilation of the global and the local Church vision in navigating the synodal processes in the local context. The direction in which the Campus Ministry must walk must also be further identified. Thus, this study probes into the Synodal Campus Ministries in the Philippines. Researchers’ investigation goes deeper, however, in a specific context of De La Salle Araneta University or its Campus Ministry.

Characterization of a lipid-based jumbo phage compartment as a hub for early phage infection

Viral genomes are most vulnerable to cellular defenses at the start of the infection. A family of jumbo phages related to phage ΦKZ, which infects Pseudomonas aeruginosa, assembles a protein-based phage nucleus to protect replicating phage DNA, but how it is protected prior to phage nucleus assembly is unclear. We find that host proteins related to membrane and lipid biology interact with injected phage protein, clustering in an early phage infection (EPI) vesicle. The injected virion RNA polymerase (vRNAP) executes early gene expression until phage genome separation from the vRNAP and the EPI vesicle, moving into the nascent proteinaceous phage nucleus. Enzymes involved in DNA replication and CRISPR/restriction immune nucleases are excluded by the EPI vesicle. We propose that the EPI vesicle is rapidly constructed with injected phage proteins, phage DNA, host lipids, and host membrane proteins to enable genome protection, early transcription, localized translation, and to ensure faithful genome transfer to the proteinaceous nucleus.

Ribosome-Associated Vesicles promote activity-dependent local translation.

Local protein synthesis in axons and dendrites underpins synaptic plasticity. However, the composition of the protein synthesis machinery in distal neuronal processes and the mechanisms for its activity-driven deployment to local translation sites remain unclear. Here, we employed cryo-electron tomography, volume electron microscopy, and live-cell imaging to identify Ribosome-Associated Vesicles (RAVs) as a dynamic platform for moving ribosomes to distal processes. Stimulation via chemically-induced long-term potentiation causes RAV accumulation in distal sites to drive local translation. We also demonstrate activity-driven changes in RAV generation and dynamics in vivo, identifying tubular ER shaping proteins in RAV biogenesis. Together, our work identifies a mechanism for ribosomal delivery to distal sites in neurons to promote activity-dependent local translation.

Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study.

This study aimed to develop and evaluate machine-learning models for predicting the onset of overweight in adolescents aged 14‒17, utilizing easily collectible personal information. This study was a one-year prospective cohort study. Baseline data were collected through anthropometric measurements and questionnaires, and the incidence of overweight was calculated one year later via anthropometric measurements. Predictive factors were selected through univariate analysis. Six machine-learning models were developed for predicting the onset of overweight. The SHapley Additive exPlanations (SHAP) was used for global and local interpretation of the models. Out of 1,241 adolescents, 204 (16.4%) were identified as overweight after one year. Nineteen features were associated with the overweight incidence in univariable analysis. Participants were randomly divided into a training group and a testing group in a 7:3 ratio. The Light Gradient Boosting Machine (LGBM) algorithm achieved outperformed other models, achieving the following metrics: Accuracy (0.956), Recall (0.812), Specificity (0.983), F1-score (0.855), AUC (0.961). Importance ranking revealed that the top 11 minimal feature set can maintain the stability of model performance. The onset of overweight in adolescents was accurately predicted using easily collectible personal information. The LGBM-based model exhibited superior performance. Oversampling technique notably improved model performance. The model interpretation technique provided innovative strategies for managing adolescent overweight/obesity.

Cross-Border E-commerce Product Introduction: Needs and Characteristics for Localized Machine Translation

With the development of science and technology and the rise of international trade, cross-border e-commerce platforms play an important role in international trade. While machine translation plays a crucial role in the introduction of goods in cross-border e-commerce, many machine translations have limitations, such as inaccurate descriptions and not conforming to Internet trends and cultural conflicts. Therefore, this study aims to investigate the characteristics of localization translation and apply them to the establishment of a machine translation corpus. By conducting questionnaires, this paper analyses the translation characteristics and user needs of cross-border e-commerce product introductions. The results found that product introduction translation needs to have the following characteristics: accurate description, comprehensive description, vivid description, concise description, and cross-cultural homogeneity. Based on the analysis, this study raises credible and valid suggestions and provides insights into building a machine translation corpus.

TRANSLATION STRATEGIES IN ENGLISH-UKRAINIAN RENDERING OF DUAL-NUMBER QUANTIFI

The article deals with translation strategies implemented in English-Ukrainian rendering of dualnumber quantifiers ‘both,’ ‘either,’ ‘neither’ that occur in modern fantasy fiction. The study aims to identify local and global translation strategies and methods of their adequate rendering at the post-translation stage of generalizing the translator’s experience. The research objectives are: 1) to compile a complete register of the lexemes under investigation based on J.K. Rowling’s “Harry Potter and the Prisoner of Azkaban” and its authorized Ukrainian translation produced by V. Morozov.; 2) to determine their semantic and functional characteristics and direct translation equivalents; 3) to identify their semantic roles and syntactic functions in the source text; 4) to specify the types of grammatical shifts and methods of their rendering into Ukrainian; 5) to identify and analyze the translation strategies applied by the translator to reach equivalence and adequacy of translation. The research material comprises 96 dual-number quantifiers used in the original text and their translations in the corresponding Ukrainian text fragments. In the course of the study, the following methods were employed: 1) entire sampling to compile a register of the lexemes and accomplish the source and target text levelling; 2) dictionary entries’ analysis to establish the lexical-semantic and functional properties of the quantifiers and their direct Ukrainian translation equivalents; 3) contextual analysis and immediate constituents’ analysis to identify their semantic roles and syntactic functions in the source text; 4) comparative-contrastive translation transformational analysis to specify the translation methods and types of grammatical shifts applied in their rendering; 5) to identify and analyze the translation strategies implemented by the translator; 6) quantity calculations to determine the frequencies of the analyzed phenomena. As a result, based on dictionary entries’ analysis, the main functional-semantic classes of the quantifiers have been found to be the determiner, pronoun, conjunction and pronominal adverb. By means of immediate constituents’ and contextual analysis, 17 pure and blended semantic roles of the lexemes’ referents in ‘state of affairs’ situations have been identified, the most frequent being the agent (36.4%), locative (14.6%) and experiencer (11.4%). By way of sentence parsing, 19 combinations of the semantic roles and syntactic functions have been established, the most frequent one being the agent subject (37.5%), the others showing frequencies around 10%. With the aid of translation transformational analysis, the most common methods of rendering the dual-number quantifiers into Ukrainian have been found to be omission (62.5%), equivalent (literal) translation (21.9%) and lexical substitution (15.6%). Correspondingly, the main translation strategies, including ST-oriented and TToriented ones, have been proved to be 1) communicative translation (68.8%) by omission with emphasis decrease, related and unrelated-word paraphrase, clause/sentence structure change and explicitness decrease, and by equivalent with emphasis change and clause/sentence structure change and/or addition; 2) literal translation (15.6%) including direct translation (4.2%) by pure equivalent and oblique translation (11.4%) by blended equivalent. i.e. with emphasis change and/or clause/sentence change; 3) semantic translation (15.6%) involving synonymy, emphasis change, phrase structure change, modulation, clause/ sentence structure change and explicitness decrease, 4) domestication (100%) incorporated in the above three strategies, all applied to ensure equivalence and adequacy of the translation.

A novel transformer-based neural network under model interpretability for improving wheat yield estimation using remotely sensed multi-variables

Timely and accurate yield prediction before wheat harvest is of great significance for food policy formulation and national economic development. Deep learning method gains importance on crop yield estimation and growth monitoring with the rapid development of deep learning models combined with remotely sensed data. However, the problems of nonlinear parameter optimization and long-term dependence in time series data have restricted the improvement of yield estimation accuracy. Transformer model completely abandons the traditional deep learning architecture and is gradually applied to time series tasks due to the advantage in modeling long-term dependence. This study introduced a novel transformer-based deep learning framework to estimate winter wheat yield in the Guanzhong Plain utilizing remotely sensed multi-variables, namely leaf area index (LAI), fraction of photosynthetically active radiation (FPAR) and vegetation temperature condition index (VTCI). The proposed model, called SSA-LSTM-transformer (SLTF), was developed for dealing with above problems under the automatic optimization capability of the sparrow search algorithm (SSA) and the long-term memory advantage of the long short-term memory (LSTM) structure. The findings demonstrated that the SLTF model achieved better estimation accuracy at the county level (R2 = 0.72, RMSE = 488.68 kg/ha) compared with the single transformer model (R2 = 0.61, RMSE = 573.99 kg/ha). The analysis on SLTF model’s performance at sampling sites over different disasters suggested that the model can effectively learn the effects of diseases, lodge and pests on crop yield estimation, which had good generalization ability at all sampling sites (R2 = 0.45, RMSE = 738.63 kg/ha). The Shapley Additive exPlanations (SHAP) approach was employed to evaluate the relative importance of remotely sensed multi-variables to yield and to understand how each input variable influences the estimated yield for global interpretability and local interpretability of the SLTF model. It was found that FPAR played the largest roles in yield estimation with the highest importance value, and FPAR and LAI from late April to late May and VTCI from late March to mid-April were regarded as important features for the estimated yield with high importance value. In conclusion, our findings indicated the potential of SLTF model for winter wheat yield estimation, which contributes to promoting further application of remotely sensed technology for agricultural production.

Employing machine learning (ML) and explainable artificial intelligence (XAI) to predict and explain suicidal ideation among patients with prostate cancer.

e23086 Background: It is anticipated that implementing machine learning (ML) strategies will impact the clinical decision-making process in the future. This study aimed to compare the accuracy of various machine learning algorithms in predicting suicidal ideation in patients who were receiving treatment for prostate cancer (PC). Further, to predict suicidal ideation and explain the burden of illness (BOI) by utilizing ML algorithms and XAI-based interpretability. Methods: We analyzed the United States 2017 National Inpatient Sample database for patients hospitalized for PC using linear and non-linear ML methods. Using techniques such as forward selection (FS) and backward elimination (BE), Random Forest (RF), decision trees, Multivariate Adaptive Regression Splines, and Gradient Boosting Machine (GBM), we determined subsets and features. We used linear and non-linear MLs-- Lasso, Ridge, RF, and Neural Networks (NN). A 70% and 30% partitioning was performed on the training and test datasets. The performance of the model was tested using discrimination (C-statistics), the Receiver-Operating Characteristics (ROC) curve, and the Hosmer-Lemeshow tests on both the training data and the test data. Several XAI approaches, including permutation significance, global surrogate marker, feature interpretation, interactivity, and local interpretability, were also utilized to examine the BOI (Length of stay (LOS)) among PC cancer cohort with suicide ideation. Results: We identified 680 patients with suicidal ideation among 208,730 PC patients. The most important variable derived through FS and BE were depression, drug abuse, psychiatric disorder, alcohol dependence, race, Medicaid beneficiaries, cardiac arrhythmias, metastasis, weight loss, congestive heart failure, and un-complicated hypertension, further confirmed through the GBM and other methods. There was successful documentation of demographic, socioeconomic, and clinical characteristics through feature selection approaches. Linear models, Lasso, and Ridge demonstrated an excellent area under the ROC for all cohorts ( > 0.9 in train and test datasets). On the other hand, tree-based models, such as RF, performed poorly (AUC 0.72 for train and 0.66 for test). The findings of the XAI methodologies showed that among PC suicide ideation, higher BOI was associated with patients staying in low-income neighborhoods, Whites, Medicaid beneficiaries, and those with weight loss. Conclusions: We showcase the increasing capability of predictive analytics, particularly XAI, in predicting suicidal ideation. The combination of the vast amount of data generated by the digitalization of healthcare systems with the advanced processing power of modern servers enables the development of machine learning-based care pathways, which has the potential to revolutionize clinical decision-making in cancer.

Axonal endoplasmic reticulum tubules control local translation via P180/RRBP1-mediated ribosome interactions

Local mRNA translation in axons is critical for the spatiotemporal regulation of the axonal proteome. A wide variety of mRNAs are localized and translated in axons; however, how protein synthesis is regulated at specific subcellular sites in axons remains unclear. Here, we establish that the axonal endoplasmic reticulum (ER) supports axonal translation in developing rat hippocampal cultured neurons. Axonal ER tubule disruption impairs local translation and ribosome distribution. Using nanoscale resolution imaging, we find that ribosomes make frequent contacts with axonal ER tubules in a translation-dependent manner and are influenced by specific extrinsic cues. We identify P180/RRBP1 as an axonally distributed ribosome receptor that regulates local translation and binds to mRNAs enriched for axonal membrane proteins. Importantly, the impairment of axonal ER-ribosome interactions causes defects in axon morphology. Our results establish a role for the axonal ER in dynamically localizing mRNA translation, which is important for proper neuron development.

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer. The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process. Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer. The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.

#2695 A Polycystin-1-Bicaudal C1 complex regulates focal adhesion function through integrin beta-1, F-actin organisation and local translation in HKE cells

Abstract Background and Aims Variants in Bicc1, result in a recessive murine PKD phenotype (bpk, jcpk) by uncertain mechanisms. In a recent study, we reported genetic and biochemical evidence of novel interactions between PKD1, PKD2 and BICC1 in Xenopus, mouse and man suggesting that BICC1 is a modifier of the ADPKD phenotype (submitted). In this study, our major aim was to investigate the molecular mechanisms underlying cystogenesis that result from loss of the normal interaction between the PKD1 protein, Polycystin-1 (PC1), a cell surface receptor and Bicaudal C1 (BICC1), an RNA-binding protein. Methods Isogenic PKD1 and BICC1 CRISP-Cas9 KO cells were generated from a conditionally-immortalised wild-type human proximal tubular line (UCL93); RNA-seq was performed in PKD1 and BICC1 KO cells to identify commonly altered genes and pathways; MS pull-down was conducted in HEK293 cells with BICC1-HA to identify BICC1 interacting proteins; cellular phenotypes in normal and KO cells were assessed through adhesion and migration assays, alongside Traction Force Microscopy (TFM); proximal ligation assays (PLA) to confirm protein-protein interactions; single-molecule FISH (smFISH) to localize mRNA transcripts; puromycin labeling with a proximity ligation assay (Puro-PLA) to detect sites of translation of specific mRNAs. Results Consistent with a functional interaction, loss of PC1 resulted in lower BICC1 levels and vice-versa in single KO (SKO) cells. In migrating cells, BICC1 was expressed at the leading edge where it co-localised with β1-integrin (ITGB1) by PLA, suggesting enrichment at focal adhesion sites (FA). BICC1 KO cells had no cilia phenotype but instead displayed striking disorganization of stress fibres and aster-like structures, features which could be completely rescued by lentiviral Bicc1-HA re-expression. Gene set enrichment and pathway analysis confirmed that F-actin organization and actin-associated proteins (AAP) were commonly dysregulated in SKO cells. BICC1 was also found to interact ITGB1, several components of an integrin-associated complex and proteins involved in actin turnover by MS pull-down. Both SKO cells showed major abnormalities of cell attachment, detachment and directional migration indicative of functional alterations in cell-matrix and cell-cell adhesion. We confirmed that FA structure and function were altered by reduced co-localisation of several AAPs (including TNS1 and FBLIM1) and by TFM. BICC1 directly interacted with F-actin and FBLIM1 in pull-down assays and with FBLIM1 by RNA-IP. Finally, we found evidence of FBLIM1 mRNA by smFISH and of local FBLIM1 translation (Puro-PLA) at FA sites in wild-type cells, both of which were markedly reduced in SKO cells. Conclusion Our data reveal the key roles for actin dynamics and local translation as novel mechanisms regulated by PC1 and BICC1 in the pathogenesis of ADPKD. We demonstrate that BICC1 and PC1 are co-regulated, forming a PC1-BICC1 complex critical for maintaining the structural integrity and function of FAs in normal kidney epithelial cells. Loss of this interaction results in major alterations in cell adhesion, migration and stress fibre organisation, features typical of the ADPKD cellular phenotype. BICC1 plays a dual role in this process via protein-protein interactions with ITGB1, F-actin and other AAPs, and through protein-RNA interactions regulating the local translation of key AAPs (FBLIM1) at FA sites.

How do local governments respond to central mandate in affordable housing policy? A qualitative comparative analysis of forty-one Chinese cities

Countries in developed and developing worlds have faced growing challenges to solve affordable housing shortages for lower-income households, prompting the adoption of various types of mandates to compel local implementation of affordable housing policy. A large volume of literature has focused on whether and how such top-down mandate indeed improves local supply of affordable housing. Insufficient understanding has been provided regarding the multi-faceted nature of local strategic responses to a top-down housing mandate. This research addresses this intellectual gap with an empirical study of local strategic behavior in response to the affordable housing mandate that was announced by the central government during the Twelfth Five-Year Plan (2011–2015). Through systematic content analysis of local news reports on affordable housing from 41 Chinese cities from various provinces, we describe the multiple-dimensional characteristics of local policy actions as cities were compelled to complied with the mandate. We further employ a fuzzy-set qualitative comparative analysis method to investigate how top-down political pressure combines with local policy interpretation, resource mobilization capacity, and local public pressure lead to more diverse policy actions in affordable housing. This research enriches the understanding of how city governments respond to top-down housing mandate, as well as offers valuable policy implications for affordable housing policy.

Water thieves

The use of tropes is a defining feature of resource regulation and is fundamental to both the way legal theories have evolved and to the practical, localized interpretations and implementations of law. This article offers a discursive study of the "water thief" trope in order to clarify the ways in which language users are able to perform and construe broader notions about individual and communal rights to water. I underline the palpable shift from a conventional law-based account of theft to a logic incorporating key characteristics of criminality and immorality. The study builds on critical discourse analysis to show how the water thief trope creates a new water world in which "water thieves," although technically not criminals under formal law, are created and made to matter.

Risk factors and their association network for young adults' suicidality: a cross-sectional study.

Understanding the intricate influences of risk factors contributing to suicide among young individuals remains a challenge. The current study employed interpretable machine learning and network analysis to unravel critical suicide-associated factors in Chinese university students. A total of 68,071 students were recruited between Sep 2016 and Sep 2020 in China. Students reported their lifetime experiences with suicidal thoughts and behaviors, categorized as suicide ideation (SI), suicide plan (SP), and suicide attempt (SA). We assessed 36 suicide-associated factors including psychopathology, family environment, life events, and stigma. Local interpretations were provided using Shapley additive explanation (SHAP) interaction values, while a mixed graphical model facilitated a global understanding of their interplay. Local explanations based on SHAP interaction values suggested that psychoticism and depression severity emerged as pivotal factors for SI, while paranoid ideation strongly correlated with SP and SA. In addition, childhood neglect significantly predicted SA. Regarding the mixed graphical model, a hierarchical structure emerged, suggesting that family factors preceded proximal psychopathological factors, with abuse and neglect retaining unique effects. Centrality indices derived from the network highlighted the importance of subjective socioeconomic status and education in connecting various risk factors. The proximity of psychopathological factors to suicidality underscores their significance. The global structures of the network suggested that co-occurring factors influence suicidal behavior in a hierarchical manner. Therefore, prospective prevention strategies should take into account the hierarchical structure and unique trajectories of factors.

Shallow defects identification for urban roads using interpretable dynamic broad network

Deep learning is extensively utilised in transport geotechnical engineering. However, deep architectures have large computational costs and update times, while failing to understand decisions. To this regard, we propose an interpretable dynamic broad network combined with ground-penetrating radar for internal defect identification in roadbeds. The method is more suitable for feature characterisation of two-dimensional data and satisfies incremental updates. The test results indicated that the proposed method has an average recognition accuracy of 0.9124 for the four types of internal defects in roadbeds. Compared to the other four classical machine learning methods, it balances training efficiency and recognition accuracy. Robustness analysis results demonstrated that the method is noise-resistant. However, comprehending the recognition results of intelligent algorithms is a key topic. Local interpretation approach is introduced to quantify the feature importance that affects the decision of the model. Based on the feature importance calculation, it is possible to distinguish between positive and negative regions in one sample that influence the decision of the detection model. These interpretative analyses can assist us in better understanding the reasons for decisions generated by the detection model that provide technical support for subsequent enhancements.

Explainable machine learning models for defects detection in industrial processes

Machine learning algorithms in non-linear pattern recognition for defect detection in manufacturing processes are increasingly prevalent in the context of Industry 4.0. This scenario enables factories to develop methodologies for monitoring and controlling the quality of their products, obtaining better operational efficiency indicators, and offering increasingly competitive products in the market. Explainable Artificial Intelligence can facilitate understanding how the models make decisions and assist in tracking anomaly points. This paper proposes an explainability framework for machine learning models in defect detection. This work applied the multinational industry’s approach to the tire manufacturing process. Models such as random forest, gradient boosting decision tree, light gradient boosting machine, logistic regression, support vector machine, and multi-layer perceptron were considered to evaluate the performance of tires in compliance with production standards. Statistical methodologies had to be adopted to deal with the small number of samples training the machine learning models. The random forest and logistic regression models achieved the best performances with an accuracy of 92% and 96%, respectively. The Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations methods were used to identify the relevant process variables. The approach presents the global and local interpretations of the results, allowing for a deeper understanding of how process variables discriminate the fault. The proposed framework determines reference values for each selected process variable for tire manufacturing in compliance with quality standards. This approach provides a relevant tool for quality control management in tire manufacturing industries.

Localized translation and sarcomere maintenance requires ribosomal protein SA in mice.

Cardiomyocyte sarcomeres contain localized ribosomes, but the factors responsible for their localization and the significance of localized translation are unknown. Using proximity labeling, we identified Ribosomal Protein SA (RPSA) as a Z-line protein. In cultured cardiomyocytes, the loss of RPSA led to impaired local protein translation and reduced sarcomere integrity. By employing CAS9 expressing mice along with adeno-associated viruses expressing CRE recombinase and single-guide RNAs targeting Rpsa, we knocked out Rpsa in vivo and observed mis-localization of ribosomes and diminished local translation. These genetic mosaic mice with Rpsa knockout in a subset of cardiomyocytes developed dilated cardiomyopathy, featuring atrophy of RPSA-deficient cardiomyocytes, compensatory hypertrophy of unaffected cardiomyocytes, left ventricular dilation, and impaired contractile function. We demonstrate that RPSA C-terminal domain is sufficient for localization to the Z-lines and that if the microtubule network is disrupted RPSA loses its sarcomeric localization. These findings highlight RPSA as a ribosomal factor essential for ribosome localization to the Z-line, facilitating local translation and sarcomere maintenance.

Does Quantum Mechanics Require "Conspiracy"?

Quantum states containing records of incompatible outcomes of quantum measurements are valid states in the tensor-product Hilbert space. Since they contain false records, they conflict with the Born rule and with our observations. I show that excluding them requires a fine-tuning to an extremely restricted subspace of the Hilbert space that seems "conspiratorial", in the sense that (1) it seems to depend on future events that involve records (including measurement settings) and on the dynamical law (normally thought to be independent of the initial conditions), and (2) it violates Statistical Independence, even when it is valid in the context of Bell's theorem. To solve the puzzle, I build a model in which, by changing the dynamical law, the same initial conditions can lead to different histories in which the validity of records is relative to the new dynamical law. This relative validity of the records may restore causality, but the initial conditions still must depend, at least partially, on the dynamical law. While violations of Statistical Independence are often seen as non-scientific, they turn out to be needed to ensure the validity of records and our own memories and, by this, of science itself. A Past Hypothesis is needed to ensure the existence of records and turns out to require violations of Statistical Independence. It is not excluded that its explanation, still unknown, ensures such violations in the way needed by local interpretations of quantum mechanics. I suggest that an as-yet unknown law or superselection rule may restrict the full tensor-product Hilbert space to the very special subspace required by the validity of records and the Past Hypothesis.

Chloroplast biogenesis involves spatial coordination of nuclear and organellar gene expression in Chlamydomonas.

The localization of translation can direct the polypeptide product to the proper intracellular compartment. Our results reveal translation by cytosolic ribosomes on a domain of the chloroplast envelope in the unicellular green alga Chlamydomonas (Chlamydomonas reinhardtii). We show that this envelope domain of isolated chloroplasts retains translationally active ribosomes and mRNAs encoding chloroplast proteins. This domain is aligned with localized translation by chloroplast ribosomes in the translation zone, a chloroplast compartment where photosystem subunits encoded by the plastid genome are synthesized and assembled. Roles of localized translation in directing newly synthesized subunits of photosynthesis complexes to discrete regions within the chloroplast for their assembly are suggested by differences in localization on the chloroplast of mRNAs encoding either subunit of the light-harvesting complex II or the small subunit of Rubisco. Transcription of the chloroplast genome is spatially coordinated with translation, as revealed by our demonstration of a subpopulation of transcriptionally active chloroplast nucleoids at the translation zone. We propose that the expression of chloroplast proteins by the nuclear-cytosolic and organellar genetic systems is organized in spatially aligned subcompartments of the cytoplasm and chloroplast to facilitate the biogenesis of the photosynthetic complexes.