bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differences Between Employed and Retired Older Adults in Information and Communication Technology Use and Attitudes
Authors: Schuster Amy M , Cotten Shelia R 
Published: September 2022

Abstract
Older adults, 65 years and older, are a heterogeneous group in terms of their information and communication technology (ICT) use. Few studies, however, have examined how ICT use patterns may vary between employed older adults versus those who are no longer in the workforce. We expand research on this topic by examining whether variations in ICT use, as well as attitudes toward ICTs and online self-efficacy, exist between these 2 groups. Key outcomes include online self-efficacy, attitudes towards new technology, length of time online, average daily hours online, and number of ICTs used. Data were collected in 2017 through an online survey from a national sample of U.S. older adults (N = 1,142) matched with the demographic characteristics of the 2010 U.S. Census for individuals aged 65 and older based on age, race, sex, and education. Data were analyzed using OLS regression, binary logistic regression, and mediation analysis. Employed older adults, compared to retired older adults, had higher online self-efficacy, more positive attitudes towards new technology, spent more time online (years), and were more likely to use 2 or more ICTs. Being retired was associated with fewer years online, which was mediated by more negative attitudes toward technology and less online self-efficacy. Longitudinal research is needed which examines the types of technologies used in the workforce by older adults, the exposure to others’ uses of technologies, and how use changes over time—all of which may potentially affect older adults’ technology skills and attitudes.
Conjugation of bisphosphonates with scorpion venom enhanced apoptotic activities in pancreatic cancer cells
Authors: Alhakamy Nabil A. , Al-Rabia Mohammed W. , Rizg Waleed Y. , Alghaith Adel F. , Ahmed Osama A. A. , Fahmy Usama A. 
Published: September 2022

AbstractBackgroundIn recent times, prevalence and the incidence of pancreatic cancer (PC) have increased exponentially. Among of the major issues of PC are the asymptotic manifestation that delays diagnosis, and scarcity of therapeutic means.MethodologyTo provide an effective therapeutic approach, current study was executed, in which an optimized alendronate sodium (ALS) and scorpion venom (SV) nanoconjugate (ALS-SV nanoconjugate) was selected via experimental design applying size and zeta potential as the selected factors. Additionally, efficacy against PANC1 cells was determined by analysis of IC50, cell cycle, annexin V staining, Bcl-2, Bax, p53, etc.ResultsThe particle size of the optimized nanoconjugate was found to be 28.4± 1.2 nm and the zeta potential to be +26.2± 0.8 mV. The cytotoxicity in terms of IC50 ALS-SV nanoconjugate was found better (2.73 ± 0.2 µg/mL) than ALS alone (22.3 ± 0.2 µg/mL) and SV alone (6.23 ± 0.4 µg/mL). Besides, outcomes of cell cycle analysis exhibited maximum efficacy for nanoconjugate in the G2-M phase. Annexin V staining, Bax, Bcl-2, p53, and caspase 3 estimation also showed superior apoptotic activity for ALS-SV nanoconjugate. However, as compared to ALS alone and SV alone, nanoconjugate showed elevated expression levels of TNF-α. The apoptotic activity was enhanced by the ALS-SV nanoconjugate as confirmed by MMP analysis.ConclusionAccordingly, the current study revealed an enhancement in the anti-PC effects of the ALS-SV nanoconjugate that is considered as a novel approach against PC.
Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer
Authors: Hany Dina , Zoetemelk Marloes , Bhattacharya Kaushik , Nowak-Sliwinska Patrycja , Picard Didier 
Published: September 2022

AbstractBreast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, remains standard therapy for ERα+ breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα+ breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα+/HER2-/PI3Kα- mutant subtype of breast cancer. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.
MAM-STAT3-induced upregulation of mitochondrial Ca+2 causes immunosenescence in patients with type A mandibuloacral dysplasia
Authors: Padhiar Arshad Ahmed , Yang Xiaohong , Li Zhu , Liao Jinqi , Ali Ilyas , Shu Wei , Chishti AA , He Liangge , Alam Gulzar , Faqeer Abdullah , Zhou Yan , Zhang Shuai , Wang Ting , Liu Tao , Zhou Meiling , Wang Gang , Zou Xuenong , Zhou Guangqian 
Published: September 2022

AbstractHomozygous lamina/c p.R527C mutations result in severe mandibuloacral dysplasia (MAD) and progeroid syndrome, but the underlying molecular pathology remains unknown. Here, we report on three patients with MAD, all displaying severe systemic inflammaging and characterized the major molecular pathways involved in the manifestation of this disease. Analysis of induced pluripotent stem cell (IPSC)-derived mesenchymal stem cells (MAD-iMSCs) obtained from the patients revealed that increased mitochondrial Ca+2 loading was the root cause of lost mitochondrial membrane potential, abnormal fission/fusion and fragmentation, which then participated in inflammaging by inducing the inflammasome. These alterations in Ca+2 homeostasis were mediated by signal transducer and activator of transcription 3 (STAT3), which is located on the mitochondrial associated membrane (MAM). STAT3 function could be rescued by treatment with clinically-approved IL-6 blockers, or by correction of R527C mutations. In addition, extracellular vesicles (EVs) obtained from MAD-iMSCs displayed reduced immunomodulatory function, being unable to rescue bleomycin-induced lung fibrosis and triggering mitochondrial dysfunction, senescence, and fibrosis in healthy cells. Our results provide new insights into the pathology of complex lamin-associated MAD with systemic immunosenescence, and suggest that targeting defective mitochondrial Ca+2 homeostasis may represent a promising novel therapy for this condition.
Modeling the biomechanics of cells on microcarriers in a stirred-tank bioreactor
Authors: Camphuijsen Jaro , Rivera Fernando J. Cantarero , Potter Greg , Clark Chris , Chen Jiajia , Kahan Simon , Aguilar Boris 
Published: September 2022

AbstractHighly productive and efficient growth of biomass in bioreactors is an essential bioprocess outcome in many industrial applications. In the nascent cultivated meat industry, large-scale biomass creation will be critical given the size of demand in the conventional meat and seafood sectors. However, there are many challenges that must be overcome before cultivated meat and seafood become commercially viable including cost reductions of cell culture media, bioprocess design innovation and optimization, and scaling up in the longer term. Computational modelling and simulation can help to address many of these challenges, and can be a far cheaper and faster alternative to performing physical experiments. Computer modelling can also help researchers pinpoint system interactions that matter, and guide researchers to identify those parameters that should be changed in later designs for eventual optimization. In this work, a computational model that combines agent-based modeling and computational fluid dynamics was developed to study biomass growth as a function of the operative conditions of stirred-tank bioreactors. The focus was to analyze how the mechanical stress induced by rotor speed can influence the growth of cells attached to spherical microcarriers. The computer simulation results reproduced observations from physical experiments that high rotor speeds reduce cell growth rates and induce cell death under the high mechanical stresses induced at these stir speeds. Moreover, the results suggest that modeling both cell death and cell quiescence are required to recapitulate these observations from physical experiments. These simulation outcomes are the first step towards more comprehensive models that, in combination with experimental observations, will improve our knowledge of biomass production in bioreactors for cultivated meat and other industries.
Community-driven shark monitoring for informed decision making: A case study from Fiji
Authors: Ward-Paige CA , Sykes H , Osgood GJ , Brunnschweiler J 
Published: September 2022

AbstractContextGlobally, more than 121 million people enjoy nature-based marine tourism, making it one of the largest marine industries. Ocean degradation threatens this industry and management has not kept pace to ensure long-term sustainability. In response, some individuals within the industry are taking it upon themselves to monitor the ocean and provide the data needed to assist management decisions. Fiji is one such place.AimsBetween 2012 and 2016, 39 Fijian dive operators, in collaboration with eOceans, conducted the Great Fiji Shark Count (GFSC) to document sharks on their dives.MethodsUsing 146,304 shark observations from 30,668 dives, we document spatial and temporal patterns of eleven shark species at 592 sites.Key resultsSharks were observed on 13,846 dives (45% of recorded dives) at 441 (74%) sites. Generally, our results matched those from other, more limited surveys, including from BRUVs. We found high variability in shark presence, species richness, and relative abundance through space and time. One trend was surprising: the most common species, Whitetip Reef Shark, decreased over the study period at eastern sites and increased at western sites — the cause is currently unknown.ConclusionsOur results can guide management and conservation needs, future scientific questions, and provide a baseline for future assessments.ImplicationsThis study demonstrates the value of longitudinal observation data that includes absences for describing marine fauna, and confirms the capacity of stakeholders to document the ocean. It also points the direction for broadscale participatory science methodologies to track the ocean.
A genome-scale atlas reveals complex interplay of transcription and translation in an archaeon
Authors: Lorenzetti Alan P. R. , Kusebauch Ulrike , Zaramela Lívia S. , Wu Wei-Ju , de Almeida João P. P. , Turkarslan Serdar , de Lomana Adrián L. G. , Gomes-Filho José V. , Vêncio Ricardo Z. N. , Moritz Robert L. , Koide Tie , Baliga Nitin S. 
Published: September 2022

ABSTRACTThe scale of post-transcriptional regulation and the implications of its interplay with other forms of regulation on environmental acclimation is underexplored for organisms of the domain Archaea. Here, we have investigated the scale of post-transcriptional regulation in the extremely halophilic archaeon Halobacterium salinarum NRC-1 by integrating transcriptome-wide locations of transcript processing sites (TPS) and SmAP1 binding, genome-wide locations of antisense RNAs (asRNAs), and consequences of RNase_2099C knockout on differential expression of all genes. This integrated analysis has discovered that 54% of all protein-coding genes in the genome of this haloarchaeon are likely targeted by multiple mechanisms for putative post-transcriptional processing and regulation, with about 20% of genes likely regulated by combinatorial schemes involving SmAP1, asRNAs, and RNase_2099C. Comparative analysis of mRNA levels (RNA-Seq) and protein levels (SWATH-MS) for 2,579 genes over four phases of batch culture growth in complex medium has generated additional evidence for conditional post-transcriptional regulation of 7% of all protein-coding genes. We demonstrate that post-transcriptional regulation may act to fine-tune specialized and rapid acclimation to stressful environments, e.g., as a switch to turn on gas vesicle biogenesis to promote vertical relocation in anoxic conditions and to modulate frequency of transposition by IS elements of the IS200/IS605, IS4, and ISH3 families. Findings from this study are provided as an atlas in a public web resource (https://halodata.systemsbiology.net).IMPORTANCEWhile the transcriptional regulation landscape of archaea has been extensively investigated, we currently have limited knowledge about post-transcriptional regulation and its driving mechanisms in this domain of life. In this study, we collected and integrated omics data from multiple sources and technologies to infer post-transcriptionally regulated genes and the putative mechanisms modulating their expression at the protein level in Halobacterium salinarum NRC-1. The results suggest that post-transcriptional regulation may drive environmental acclimation by regulating hallmark biological processes. To foster discoveries by other research groups interested in the topic, we extended our integrated data to the public in the form of an interactive atlas (https://halodata.systemsbiology.net).
Statistical inference of the rates of cell proliferation and phenotypic switching in cancer
Authors: Gunnarsson Einar Bjarki , Foo Jasmine , Leder Kevin 
Published: September 2022

AbstractRecent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.
Therapeutic Potential of Blocking GAPDH Nitrosylation with CGP3466b in Experimental Autoimmune Encephalomyelitis
Authors: Godfrey Wesley H. , Hwang Soonmyung , Gharibani Payam , Abramson Efrat , Kornberg Michael D. 
Published: September 2022

AbstractMultiple sclerosis (MS) is a neuroinflammatory disease of the central nervous system (CNS). Although classically considered a demyelinating disease, neuroaxonal injury occurs in both the acute and chronic phases and represents a pathologic substrate of disability not targeted by current therapies. Nitric oxide (NO) generated by CNS macrophages and microglia contributes to neuroaxonal injury in all phases of MS, but candidate therapies that prevent NO-mediated injury have not been identified. Here, we demonstrate that the multifunctional protein glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is robustly nitrosylated in the CNS in the experimental autoimmune encephalomyelitis (EAE) mouse model of MS. GAPDH nitrosylation is blocked in vivo with daily administration of CGP3466b, a CNS-penetrant compound with an established safety profile in humans. Consistent with the known role of nitrosylated GAPDH (SNO-GAPDH) in neuronal cell death, blockade of SNO-GAPDH with CGP3466b attenuates neurologic disability and reduces axonal injury in EAE independent of effects on the immune system. Our findings suggest that SNO-GAPDH contributes to neuroaxonal injury during neuroinflammation and identify CGP3466b as a candidate neuroprotective therapy in MS.
Excitation creates a distributed pattern of cortical suppression due to varied recurrent input
Authors: O’Rawe Jonathan F , Zhou Zhishang , Li Anna J , LaFosse Paul K , Goldbach Hannah C , Histed Mark H 
Published: September 2022

SummaryDense local, recurrent connections are a major feature of cortical circuits, yet how they affect neurons’ responses is unclear, with some studies reporting weak recurrent effects, some amplification, and others showing instead local suppression. Here, we show that optogenetic input to mouse V1 excitatory neurons generates salt-and-pepper patterns of both excitation and suppression. Responses in individual neurons are not strongly predicted by that neuron’s direct input. A balanced-state network model reconciles a set of diverse observations: the observed dynamics, suppressed responses, decoupling of input and output, and long tail of excited responses. The model shows recurrent excitatory-excitatory connections are strong and also variable across neurons. Together, these results demonstrate that excitatory recurrent connections can have major effects on cortical computations, by shaping and changing neurons’ responses to input.
Toward a generalizable deep CNN for neural drive estimation across muscles and participants
Authors: Wen Yue , Kim Sangjoon J. , Avrillon Simon , Levine Jackson T. , Hug François , Pons José L. 
Published: September 2022

AbstractHigh-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to online decomposition, from isometric to dynamic contractions, leading to a wide range of neural-machine interface applications. However, current online methods need offline retraining when applied to the same muscle on a different day or to a different person, which limits their applications in a real-time neural-machine interface. We proposed a deep convolutional neural network (CNN) framework for neural drive estimation, which captures general spatiotemporal properties of motor unit action potentials to generalize its application without retraining to HD-EMG data recorded in separate sessions, muscles, and participants. We recorded HD-EMG signals from the vastus medialis and vastus lateralis muscles while participants performed isometric contractions during two sessions separated by approximately 20 months. We identified motor unit spike trains from HD-EMG signals using a blind source separation (BSS) method, and then used the cumulative spike train (CST) of these motor units and the HD-EMG signals to train and validate the deep CNN. On average, the correlation coefficients between CST from BSS and that from deep CNN were 0.977±0.007 for leave-one-out across-sessions-and-muscles validation and 0.985±0.005 for leave-one-out across-participants validation. When trained with more than four datasets, the performance of deep CNN saturated at 0.979±0.001 for cross validations across muscles, sessions, and participants. Therefore, we can conclude that the deep CNN is generalizable across the afore-mentioned conditions without retraining. We could potentially generate a robust deep CNN to estimate neural drive to muscles for neural-machine interfaces.
Lysyl oxidase-dependent subendothelial matrix stiffening promotes RAGE-mediated retinal endothelial activation in diabetes
Authors: Chandrakumar Sathishkumar , Tierno Irene Santiago , Agarwal Mahesh , Matisioudis Nikolaos , Kern Timothy S. , Ghosh Kaustabh 
Published: September 2022

AbstractEndothelial cell (EC) activation is a crucial determinant of retinal vascular inflammation associated with diabetic retinopathy (DR), a major microvascular complication of diabetes. We previously showed that, similar to abnormal biochemical factors, aberrant mechanical cues in the form of lysyl oxidase (LOX)-dependent subendothelial matrix stiffening also contribute significantly to retinal EC activation in diabetes. Yet, how LOX is itself regulated and precisely how it mechanically controls retinal EC activation in diabetes is poorly understood. Here we show that high glucose-induced LOX upregulation in human retinal ECs (HRECs) is mediated by proinflammatory RAGE (receptor for advanced glycation end products/AGEs). HRECs treated with methylglyoxal (MGO), an active precursor to the AGE MG-H1, exhibited LOX upregulation that was blocked by a RAGE inhibitor, thus confirming the ability of RAGE to promote LOX expression. Crucially, as a downstream effector of RAGE, LOX was found to mediate both the proinflammatory and matrix remodeling effects of MGO/RAGE, primarily through its ability to crosslink/stiffen matrix. Finally, using decellularized HREC-derived matrices and a mouse model of diabetes, we demonstrate that LOX-dependent matrix stiffening feeds back to enhance RAGE, thereby achieving its autoregulation and proinflammatory effects. These fresh insights into the regulation and proinflammatory role of LOX-dependent mechanical cues may help identify new therapeutic targets to block AGE/RAGE signaling in DR.
From sequence to function through structure: deep learning for protein design
Authors: Ferruz Noelia , Heinzinger Michael , Akdel Mehmet , Goncearenco Alexander , Naef Luca , Dallago Christian 
Published: September 2022

AbstractThe process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be leveraged to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.AvailabilitypLM generated and UniRef50 sampled sequence sets and predictions are available at http://data.bioembeddings.com/public/design. Code-base and Notebooks for analysis are available at https://github.com/hefeda/PGP. An online version of Table 1 can be found at https://github.com/hefeda/design_tools.