bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social Decision Preferences for Close Others are Embedded in Neural and Linguistic Representations
Authors: Guassi Moreira João F. , Esparza L. Concepción , Silvers Jennifer A. , Parkinson Carolyn 
Published: July 2024

AbstractHumans frequently make decisions that impact close others. Prior research has shown that people have stable preferences regarding such decisions and maintain rich, nuanced mental representations of their close social partners. Yet, if and how such mental representations shape social decisions preferences remains to be seen. Using a combination of functional magnetic resonance imaging (fMRI) and natural language processing (NLP), this study investigated how neural and linguistic representations of close others influence social decision-making. Participants nominated a parent and a friend, rated their characteristics, and made hypothetical social decisions while undergoing fMRI. Neural representations of parents and friends, relative to the self, predicted social decision preferences. Specifically, greater neural similarity between self and parent in the temporoparietal junction (TPJ) and nucleus accumbens (NAcc) was associated with a preference for parents, while greater self-friend similarity in the medial prefrontal cortex (mPFC) predicted friend-preference. Additionally, linguistic analysis of written descriptions of close others revealed that social decision preferences could be reliably predicted from semantic features of the text. The convergence of neural and linguistic data further strengthened the prediction of social decision preferences. These findings elucidate the neural and linguistic underpinnings of social decision-making, emphasizing the critical role of mental representations in guiding choices involving familiar others.
Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation
Authors: Sun Eric D. , Zhou Olivia Y. , Hauptschein Max , Rappoport Nimrod , Xu Lucy , Negredo Paloma Navarro , Liu Ling , Rando Thomas A. , Zou James , Brunet Anne 
Published: July 2024

AbstractOld age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain aging is complex and accompanied by many cellular changes2–20. However, the influence that aged cells have on neighboring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in aging tissues have not yet been developed. Here, we generate spatiotemporal data at single-cell resolution for the mouse brain across lifespan, and we develop the first machine learning models based on spatial transcriptomics (‘spatial aging clocks’) to reveal cell proximity effects during brain aging and rejuvenation. We collect a single-cell spatial transcriptomics brain atlas of 4.2 million cells from 20 distinct ages and across two rejuvenating interventions—exercise and partial reprogramming. We identify spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Using spatial aging clocks and deep learning models, we find that T cells, which infiltrate the brain with age, have a striking pro-aging proximity effect on neighboring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating effect on neighboring cells. By developing computational tools to identify mediators of these proximity effects, we find that pro-aging T cells trigger a local inflammatory response likely via interferon-γ whereas pro-rejuvenating neural stem cells impact the metabolism of neighboring cells possibly via growth factors (e.g. vascular endothelial growth factor) and extracellular vesicles, and we experimentally validate some of these predictions. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.
Conditional Sequence-Structure Integration: A Novel Approach for Precision Antibody Engineering and Affinity Optimization
Authors: Jamialahmadi Benyamin , Chamankhah Mahmood , Kohandel Mohammad , Ghodsi Ali 
Published: July 2024

AbstractAntibodies, or immunoglobulins, are integral to the immune response, playing a crucial role in recognizing and neutralizing external threats such as pathogens. However, the design of these molecules is complex due to the limited availability of paired structural antibody-antigen data and the intricacies of structurally non-deterministic regions. In this paper, we introduce a novel approach to designing antibodies by integrating structural and sequence information of antigens. Our approach employs a protein structural encoder to capture both sequence and conformational details of antigen. The encoded antigen information is then fed into an antibody language model (aLM) to generate antibody sequences. By adding cross-attention layers, aLM effectively incorporates the antigen information from the encoder. For optimal model training, we utilized the Causal Masked Language Modeling (CMLM) objective. Unlike other methods that require additional contextual information, such as epitope residues or a docked antibody framework, our model excels at predicting the antibody sequence without the need for any supplementary data. Our enhanced methodology demonstrates superior performance when compared to existing models in the RAbD benchmark for antibody design and SKEPMI for antibody optimization.
A critical role of protein damage response in mediating cancer drug resistance
Authors: Shao Fangyuan , Li Jie , Xiao Hao , Li Ling , Li Bo , Chen YuJun , Chu Xiangpeng , Ran Maoxin , Tang Dongyang , Peng Yuzhong , Huang Yujian , Wang Lijian , Shi Yanxia , Shao Nan , Miao Kai , Zhang Changhua , Lin Ying , Yan Jun , Tam Kin Yip , Xu Xiaoling , Deng Chu-Xia 
Published: July 2024

AbstractMultidrug resistance (MDR) frequently occurs during cancer therapy and remains a major obstacle for the cure of most cancers(1, 2). Drug resistance could exist intrinsically or be acquired by drug treatment(3–5), yet factors that regulate the resistance remain elusive. Here, we show that most anticancer drugs damage neosynthesized proteins prior to reaching their canonic targets and elicit profound cytotoxicity, which is largely compensated by protein damage response (PDR). We demonstrate that the PDR includes damage recognition and clearance that are mainly mediated by ubiquitin and proteasome systems, although some other factors, including cellular ATP levels and proliferation status, are also involved. We show that cancer stem cells (CSCs), which have lower protein synthesis, and drug resistance acquired cells (DRAC), which have higher proteasome activity, are more resistant than other cells. We further demonstrate that ATP promotes protein synthesis and suppresses proteasome activity, thus, increasing mitochondrial ATP production by PDK1 inhibition and using proteasome inhibitor to block protein damage clearance render CSCs and DRACs more vulnerable to anticancer drugs. Thus, patients with drug-resistant cancers and treatment-naïve patients with low ATP levels and/or high proteasome activity can be identified and subtyped, and therapies containing PDK1-I and/or proteasome-I may be effective options for these patients.
A viscoelastic-plastic deformation model of hemisphere-like tip growth in Arabidopsis zygotes
Authors: Kang Zichen , Nonoyama Tomonobu , Ishimoto Yukitaka , Matsumoto Hikari , Nakagawa Sakumi , Ueda Minako , Tsugawa Satoru 
Published: July 2024

AbstractPlant zygote cells exhibit tip growth, producing a hemisphere-like tip. To understand how this hemisphere-like tip shape is formed, we revisited a viscoelastic-plastic deformation model that enabled us to simultaneously evaluate the shape, stress, and strain of Arabidopsis (Arabidopsis thaliana) zygote cells undergoing tip growth. Altering the spatial distribution of cell wall extensibility revealed that cosine-type distribution and growth in a normal direction to the surface creates a stable hemisphere-like tip shape. Assuming these as constraints for cell elongation, we determined the best-fitting parameters for turgor pressure and wall extensibility to computationally reconstruct an elongating zygote that retained its hemisphere-like shape using only cell contour data, leading to formulation of non-dimensional growth parameters. Our computational results demonstrate the different morphologies in elongating zygotes through effective non-dimensional parameters.
Oscillations create groove: A reservoir model for learning complex drumming performances
Authors: Kawai Yuji , Fujii Shinya , Asada Minoru 
Published: July 2024

AbstractMusical performances, particularly drumming, intricately balance rhythmic patterns and creative expression, all heavily influenced by the concept of “groove.” This study explored how the brain learns and internalizes complex rhythms and grooves from a computational perspective. The cerebellum and basal ganglia play key roles in rhythm perception, production, and timing. We utilized an oscillation-driven reservoir computing model, which is a simplified recurrent neural network designed for timing learning, to acquire complex rhythms. The model was trained to replicate the drumming style of Jeff Porcaro by learning his hi-hat patterns. Analyses revealed that the model’s outputs, including fluctuations in interbeat timings and amplitudes, were similar to the original drumming. Next, it was trained to generate multidimensional drum kit performances for various genres (funk, jazz, samba, and rock). Model outputs had timing deviation and audio features related to a sense of groove, similar to the original performances. By incorporating the oscillations of multiple frequencies into the reservoir, the model reproduced fluctuations of timing intervals (microtiming) and amplitudes, as well as audio features of each musical genre. Our results indicated that oscillation-driven reservoir computing can successfully replicate the timing and rhythmic complexity of professional drumming, suggesting that it is a common computational principle for motor timing and rhythm generation. Furthermore, this approach offers insights into the neural underpinnings of musical groove, shedding light on how the brain processes and reproduces intricate rhythmic patterns.Author summaryDrumming is a sophisticated art form combining rhythmic precision and creative flair, encapsulated by the elusive quality of “groove.” Understanding how the brain generates these complex rhythms can provide insights into both musical and neural functions. In this study, we employed oscillation-driven reservoir computing to model the principal neural processes involved in learning and generating complex drum rhythms in the cerebellum and basal ganglia. We trained the model using the renowned drummer Jeff Porcaro’s hi-hat patterns and expanded its capabilities to produce multi-instrument drum performances. By introducing oscillations of different frequencies to reservoir computing, we enhanced the reservoir’s complex dynamics to create dynamic, non-repetitive, fluctuating time intervals and amplitudes of skilled drumming perfromances. Our findings demonstrate that this computational approach can emulate the nuanced microtiming and audio features essential for skilled drumming, shedding light on the potential neural mechanisms underlying skilled musical performances.
RAB12-LRRK2 Complex Suppresses Primary Ciliogenesis and Regulates Centrosome Homeostasis in Astrocytes
Authors: Li Xingjian , Zhu Hanwen , Huang Bik Tzu , Li Xianting , Kim Heesoo , Tan Haiyan , Zhang Yuanxi , Choi Insup , Peng Junmin , Xu Pingyi , Sun Ji , Yue Zhenyu 
Published: July 2024

SummaryLeucine-rich repeat kinase 2 (LRRK2) phosphorylates a subset of RAB GTPases, and the phosphorylation levels are elevated by Parkinson’s disease (PD)-linked mutations of LRRK2. However, the precise function of the specific RAB GTPase targeted by LRRK2 signaling in the brain remains to be elucidated. Here, we identify RAB12 as a robust LRRK2 substrate in the mouse brains through phosphoproteomics profiling and solve the structure of RAB12-LRRK2 protein complex through Cryo-EM analysis. Mechanistically, RAB12 cooperates with LRRK2 to inhibit primary ciliogenesis and regulate centrosome homeostasis in astrocytes through enhancing the phosphorylation of RAB10 and recruiting Rab interacting lysosomal protein like 1 (RILPL1), while the functions of RAB12 require a direct interaction with LRRK2 and LRRK2 kinase activity. Furthermore, the ciliary deficits and centrosome alteration caused by the PD-linked LRRK2-G2019S mutation are prevented by the deletion ofRab12in astrocytes. Thus, our study reveals a physiological function of the RAB12-LRRK2 complex in regulating ciliogenesis and centrosome homeostasis. The RAB12-LRRK2 structure offers a guidance in the therapeutic development of PD by targeting the RAB12-LRRK2 interaction.
Application of Digital Tools and Artificial Intelligence to the Myasthenia Gravis Core Examination
Authors: Garbey Marc , Lesport Quentin , Girma Helen , Öztosen Gülṣen , Abu-Rub Mohammed , Guidon Amanda C. , Juel Vern , Nowak Richard , Soliven Betty , Aban Inmaculada , Kaminski Henry J. 
Published: July 2024

BackgroundAdvances in video image analysis and artificial intelligence provide the opportunity to transform the approach to patient evaluation through objective digital evaluation.ObjectivesWe assessed ability to quantitate Zoom video recordings of a standardized neurological examination the myasthenia gravis core examination (MG-CE), which had been designed for telemedicine evaluations.MethodsWe used Zoom (Zoom Video Communications) videos of patients with myasthenia gravis undergoing the MG-CE. Computer vision in combination with artificial intelligence methods were used to build algorithms to analyze videos with a focus on eye or body motions. For the assessment of examinations involving vocalization, signal processing methods were developed, including natural language processing. A series of algorithms were built that could automatically compute the metrics of the MG-CE.ResultsFifty-one patients with MG with videos recorded twice on separate days and 15 control subjects were assessed once. We were successful in quantitating lid, eye, and arm positions and as well as well as develop respiratory metrics using breath counts. Cheek puff exercise was found to be of limited value for quantitation. Technical limitations included variations in illumination, bandwidth, and recording being done on the examiner side, not the patient.ConclusionsSeveral aspects of the MG-CE can be quantitated to produce continuous measures via standard Zoom video recordings. Further development of the technology offer the ability for trained, non-physician, health care providers to perform precise examination of patients with MG outside the clinic, including for clinical trials.Plain Language SummaryAdvances in video image analysis and artificial intelligence provide the opportunity to transform the approach to patient evaluation. Here, we asked whether video recordings of the typical telemedicine examination for the patient with myasthenia gravis be used to quantitate examination findings? Despite recordings not made for purpose, we were able to develop and apply computer vision and artificial intelligence to Zoom recorded videos to successfully quantitate eye muscle, facial muscle, and limb fatigue. The analysis also pointed out limitations of human assessments of bulbar and respiratory assessments. The neuromuscular examination can be enhanced by advance technologies, which have the promise to improve clinical trial outcome measures as well as standard care.
Tandem standing duration is a rapid, sensitive and specific test of Parkinson’s Disease subtype
Authors: Hosli Sarah , Ciocca Matteo , Hadi Zaeem , Molloy Sophie , Tai Yen , Seemungal Barry M 
Published: July 2024

AbstractBackgroundParkinson’s Disease (PD) patients with postural instability and gait disorder (PIGD) subtype are at increased risk for falls compared to the tremor-dominant subtype. We aimed to establish an easy clinical balance tool to rapidly and reliably identify PIGD patients, potentially important for directing healthcare resources or research phenotyping.Methods45 consecutive patients with PD completed clinical testing including Romberg, tandem stance, single leg stance, 360° turning and 10-meter walking. MDS-UPDRS part II and III, collected as part of regular follow-up, was used to classify disease subtype. Multinominal logistic regression models were fitted to find optimal subtype predictors and compared using receiver operating characteristic (ROC) curves.ResultsUnassisted tandem stand duration and time to turn 360° were significantly different between PIGD and tremor dominant subtypes. Both tandem standing and 360° turning showed very high predictive accuracy to predict PD subtype with an area under the ROC curve (AUC) of 86.6% and 88% respectively, which increased to 91.4% by combining both measures. Optimal cut-off values for identifying PD subtypes were tandem standing less than 20s and 360° turning longer than 6.5s.ConclusionTandem stand duration and 360° turning are easy to apply clinical tests that rapidly identify PD patients with PIGD subtype with high sensitivity and specificity. These findings may be useful in the clinic to identify PD patients’ current falls risk or screening for research studies.Plain Language SummaryBalance problems and falls are common in late-stage Parkinson’s Disease, affecting nearly 70% of patients 10 years post-diagnosis. In contrast, Parkinson’s patients who complain mainly of shaking (tremor) are less liable to fall. We set out to find an easy and reliable bedside test to distinguish patients at risk of falls with early Parkinson’s. This is important so that resources can be targeted to patients in need of support such as physiotherapy and fall prevention. 45 patients with Parkinson’s disease participated in this study and completed a battery of balance tests completed within the time of their regular follow-up appointment. We found that tandem standing duration – a test where patient stand still in the heel-to-toe position – and time taken to complete a full circle, were highly reliable in detecting patients with balance and gait problems. Specifically, patients with balance and gait problems were unable to tandem stand for more than 20 seconds and took more than 6.5 seconds to turn a full circle. Together, these two tests that take a minute to complete in the clinic, and may help improve the care for patients with Parkinson’s as a quick screening tool to identify Parkinson’s disease at risk of falls.
Effect of disclosing a polygenic risk score for coronary heart disease on adverse cardiovascular events: 10-year follow-up of the MI-GENES randomized clinical trial
Authors: Naderian Mohammadreza , Hamed Marwan E. , Vaseem Ali A. , Norland Kristjan , Dikilitas Ozan , Teymourzadeh Azin , Bailey Kent R. , Kullo Iftikhar J. 
Published: July 2024

AbstractBackgroundThe MI-GENES clinical trial (NCT01936675), in which participants at intermediate risk of coronary heart disease (CHD) were randomized to receive a Framingham risk score (FRSg, n=103), or an integrated risk score (IRSg, n=104) that additionally included a polygenic risk score (PRS), demonstrated that after 6 months, participants randomized to IRSghad higher statin initiation and lower low-density lipoprotein cholesterol (LDL-C).ObjectivesIn a post hoc 10-year follow-up analysis of the MI-GENES trial, we investigated whether disclosure of a PRS for CHD was associated with a reduction in adverse cardiovascular events.MethodsParticipants were followed from randomization beginning in October 2013 until September 2023 to ascertain adverse cardiovascular events, testing for CHD, and changes in risk factors, by blinded review of electronic health records. The primary outcome was the time from randomization to the occurrence of the first major adverse cardiovascular event (MACE), defined as cardiovascular death, non-fatal myocardial infarction, coronary revascularization, and non-fatal stroke. Statistical analyses were conducted using Cox proportional hazards regression and linear mixed-effects models.ResultsWe followed all 203 participants who completed the MI-GENES trial, 100 in FRSgand 103 in IRSg(mean age at the end of follow-up: 68.2±5.2, 48% male). During a median follow-up of 9.5 years, 9 MACEs occurred in FRSgand 2 in IRSg(hazard ratio (HR), 0.20; 95% confidence interval (CI), 0.04 to 0.94;P=0.042). In FRSg, 47 (47%) underwent at least one test for CHD, compared to 30 (29%) in IRSg(HR, 0.51; 95% CI, 0.32 to 0.81;P=0.004). IRSgparticipants had a longer duration of statin therapy during the first four years post-randomization and a greater reduction in LDL-C for up to 3 years post-randomization. No significant differences between the two groups were observed for hemoglobin A1C, systolic and diastolic blood pressures, weight, and smoking cessation rate during follow-up.ConclusionsThe disclosure of an IRS that included a PRS to individuals at intermediate risk for CHD was associated with a lower incidence of MACE after a decade of follow-up, likely due to a higher rate of initiation and longer duration of statin therapy, leading to lower LDL-C levels.
The SAP-LAP and IRP approaches for formation of policy framework of electric vehicles and its charging infrastructure in India
Authors: Ruchita  , Shankar Ravi 
Published: July 2024

Purpose
In today’s environment, electric vehicles (EVs) industries and services provided to consumers are facing many challenges. The public in large numbers is not adopting Electric Vehicles because of the unavailability of suitable EVs and not getting proper charging infrastructure to the consumers. The purpose of this study and research work is to analyze the condition of this industry, its charging infrastructure and E-government policies. Based on the above analysis, frameworks/models are to be designed to assist organizations in formulating strategies for the EV industry and providing proper infrastructure to the consumer.

Design/methodology/approach
The Variables for the EV and EVsCI case study are identified from an exhaustive literature review, stakeholders’ perspectives, experts’ opinions, existing EV policies, future policy blueprints and data available for this industry, applying the Situation Actor Process-Learning Action Performance (SAP-LAP) methodology. After that, the Interpretive Ranking Process (IRP) is integrated with the SAP-LAP approach to develop models.

Findings
The rankings of Actors Versus Processes and Actions Versus Performances have been obtained and then models have been developed. These models can serve policymakers in framing and implementing effective policies.

Originality/value
Although the SAP-LAP approach is an innovative approach for identifying variables, however, this approach lacks perfect interactive relationships among the variables. It may lead to imperfect decision-making. To avoid this, the IRP approach is integrated with the SAP-LAP approach which provides more accurate models for analysis and providing recommendations to government and organizations.