bluuebunny/crossref_metadata_2025_split huggingface dataset:
Temporal variation in lymphocyte proteomics
Authors: McCown Michaela A. , Allen Carolyn , Machado Daniel D. , Boekweg Hannah , Liang Yiran , Nwosu Andikan J. , Kelly Ryan T. , Payne Samuel H. 
Published: July 2021

AbstractChronic Lymphocytic Leukemia (CLL) is a slow progressing disease, characterized by a long asymptomatic stage followed by a symptomatic stage during which patients receive treatment. While proteomic studies have discovered differential pathways in CLL, the proteomic evolution of CLL during the asymptomatic stage has not been studied. In this pilot study, we show that by using small sample sizes comprising ~145 cells, we can detect important features of CLL necessary for studying tumor evolution. Our small samples are collected at two time points and reveal large proteomic changes in healthy individuals over time. A meta-analysis of two CLL proteomic papers showed little commonality in differentially expressed proteins and demonstrates the need for larger control populations sampled over time. To account for proteomic variability between time points and individuals, large control populations sampled at multiple time points are necessary for understanding CLL progression. Data is available via ProteomeXchange with identifier PXD027429.
A machine-learning approach for detection of local brain networks and marginally weak signals identifies novel AD/MCI differentiating connectomic neuroimaging biomarkers
Authors: Li Yanming , Kang Jian , Wu Chong , Dinov Ivo D. , Hu Jinxiang , Chalise Prabhakar , Mahnken Jonathan D. ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: July 2021

AbstractIntroductionA computationally fast machine learning method is introduced for uncovering the wholebrain voxel-level connectomic spectra that differentiates different status of Alzheimer’s disease (AD). The method is applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Fluorinefluorodeoxyglucose Positron Emission Tomography (FDG-PET) imaging and clinical data and identified novel AD/MCI differentiating connectomic neuroimaging biomarkers.MethodsA divide-and-conquer algorithm is introduced for detect informative local brain networks at voxel level and whole-brain scale. The connection information within the local networks is integrated into the node voxels, which makes detection of the marginally weak signals possible. Prediction accuracy is significantly improved by incorporating the local brain networks and marginally weak signals.ResultsBrain connectomic structures differentiating AD and mild cognitive impairment (MCI), AD and healthy, and MIC and healthy were discovered. We identified novel AD/MCI-associated neuroimaging biomarkers by integrating local brain networks and marginally weak signals. For example, networkbased signals in paracentral lobule (p-value=6.1e-5), olfactory cortex (p-value=4.6e-5), caudate nucleus (1.8e-3) and precentral gyrus (1.8e-3) are informative in differentiating AD and MCI. Connections between calcarine sulcus and lingual gyrus (p-value=0.049), between parahippocampal gyrus and Amygdala (p-value=0.025), between rolandic opercula and insula lobes (p-values=0.0028 and 0.0026). An overall prediction accuracy of 95.3% was achieved by integrating the selected local brain networks and marginally weak signals, compared to 84.0% by not considering the inter-voxel connections and using marginally strong signals only.Conclusion(i) The connectomic structures differentiating AD and MCI are significantly different to that differentiating MCI and healthy, which may indicate different neuronal etiology for AD and MCI. (ii) Many neuroimaging biomarkers exert their effects on the outcome diseases through their connections to other markers. Integrating such connections can help identify novel neuroimaging biomarkers and improve disease prediction accuracy.
Outer hair cell electro-mechanical power conversion
Authors: Rabbitt Richard D. 
Published: July 2021

AbstractOuter hair cells are the cellular motors in the mammalian inner ear responsible for sensitive high-frequency hearing. Motor function requires expression of the protein prestin in the OHC lateral membrane and mechano-electrical transduction in the apical hair bundle. In the present report, electrical power consumption and mechanical power output are determined using previously published voltage clamp data from isolated OHCs and membrane patches. Results reveal that power output by the prestin-motor complex peaks at a best frequency much higher than implied by the low-pass character of nonlinear capacitance, and much higher than the whole-cell resistive-capacitive corner frequency. High frequency power output is enabled by a −90° shift in the phase of prestin-dependent electrical charge displacement, manifested electrically as emergence of imaginary-valued nonlinear capacitance.
Virtual screening for small molecule pathway regulators by image profile matching
Authors: Rohban Mohammad H. , Fuller Ashley M. , Tan Ceryl , Goldstein Jonathan T. , Syangtan Deepsing , Gutnick Amos , DeVine Ann , Nijsure Madhura P. , Rigby Megan , Sacher Joshua R. , Corsello Steven M. , Peppler Grace B. , Bogaczynska Marta , Boghossian Andrew , Ciotti Gabrielle E. , Hands Allison T. , Mekareeya Aroonroj , Doan Minh , Gale Jennifer P. , Derynck Rik , Turbyville Thomas , Boerckel Joel D. , Singh Shantanu , Kiessling Laura L. , Schwarz Thomas L. , Varelas Xaralabos , Wagner Florence F. , Kafri Ran , Eisinger-Mathason T.S. Karin , Carpenter Anne E. 
Published: July 2021

AbstractIdentifying chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual profile-based screening approach instead identifies compounds that match to pathways based on phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes, and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.One sentence summaryIf a genetic perturbation impacts cell morphology, a computational query can reveal compounds whose morphology “matches”.
A small-molecule fluorescent probe for live-cell imaging of endocytosis
Authors: Seino Ryo , Iwashita Hidefumi , Takahashi Masataka , Ezoe Takatoshi , Ishiyama Munetaka , Ueno Yuichiro 
Published: July 2021

AbstractEndocytosis involves plasma membrane-derived vesicles for the recycling of intra- and extracellular components. Increasing evidence suggests that endocytosis is related to maintaining intracellular homeostasis and defense against disease. Consequently, investigation of the endocytic pathway attracts considerable scientific interest. This study reports live-cell imaging of endocytosis using the newly-developed fluorescent probe ECGreen. We demonstrate that ECGreen is not membrane permeable and its fluorescence signal increases in acidic conditions. Because of these characteristics, ECGreen remains on the plasma membrane, and then shows increased fluorescence when it is internalized into the acidic vesicles formed in the endocytic process. ECGreen allows direct observation of the internalized vesicle; it is a valuable new probe for endocytic imaging.
Neural-specific alterations in glycosphingolipid biosynthesis and cell signaling associated with two human ganglioside GM3 Synthase Deficiency variants
Authors: Dookwah Michelle , Wagner Shannon K. , Ishihara Mayumi , Yu Seok-Ho , Ulrichs Heidi , Kulik Michael J. , Zeltner Nadja , Dalton Stephen , Strauss Kevin A. , Aoki Kazuhiro , Steet Richard , Tiemeyer Michael 
Published: July 2021

ABSTRACTGM3 Synthase Deficiency (GM3SD) is a neurodevelopmental disorder resulting from pathogenic variants in the ST3GAL5 gene, which encodes GM3 synthase, a glycosphingolipid (GSL)-specific sialyltransferase. This enzyme adds a single α3-linked sialic acid to the terminal galactose of lactosylceramide (LacCer) to produce the monosialylated ganglioside GM3. In turn, GM3 is extended by other glycosyltransferases to generate nearly all the complex gangliosides enriched in neural tissue. Pathogenic mechanisms that account for neural phenotypes associated with GM3SD are not known. To explore how loss of GM3 impacts neural-specific glycolipid glycosylation and cell signaling, GM3SD patient fibroblasts bearing one of two different ST3GAL5 variants were reprogrammed to induced pluripotent stem cells (iPSCs) and then differentiated to neural crest cells (NCCs). GM3 and GM3-derived gangliosides were undetectable in iPSCs and NCCs from both variants, while LacCer precursor levels were elevated compared to wildtype (WT). NCCs of both variants synthesized elevated levels of neutral lacto- and globo-series, as well as minor alternatively sialylated, GSLs compared to WT. Shifts in ceramide profiles associated with iPSC and NCC GSLs were also detected in GM3SD variants. Altered GSL profiles in the GM3SD cells were accompanied by dynamic changes in the cell surface proteome, protein O-GlcNAcylation, and receptor tyrosine kinase abundance. GM3SD cells also exhibited increased apoptosis and sensitivity to erlotnib, an inhibitor of epidermal growth factor receptor signaling. Pharmacologic inhibition of O-GlcNAcase increased protein O-GlcNAcylation and significantly rescued baseline and erlotnib-induced apoptosis. Collectively, these findings indicate broad effects on cell signaling during differentiation of GM3SD patient-derived iPSCs to NCCs. Thus, human GM3SD cells provide a novel platform to investigate structure/function relationships that connect GSL diversity to cell signaling, cell survival, and neural differentiation.
Habit formation viewed as structural change in the behavioral network
Authors: Yamada Kota , Toda Koji 
Published: July 2021

AbstractHabit formation is a process in which an action becomes involuntary. While goal-directed behavior is driven by its consequences, habits are elicited by a situation rather than its consequences. Existing theories have proposed that actions are controlled by corresponding two distinct systems. Although canonical theories based on such distinctions are starting to be challenged, a few theoretical frameworks that implement goal-directed behavior and habits within a single system. Here, we propose a novel theoretical framework by hypothesizing that behavior is a network composed of several responses. With this framework, we have shown that the transition of goal-directed actions to habits is caused by a change in a single network structure. Furthermore, we confirmed that the proposed network model behaves in a manner consistent with the existing experimental results reported in animal behavioral studies. Our results revealed that habit could be formed under the control of a single system rather than two distinct systems. By capturing the behavior as a single network change, this framework provides a new perspective on studying the structure of the behavior for experimental and theoretical research.Author summaryTo obtain the desired consequences, organisms need to respond based on the knowledge of the consequences obtained by the response and the change in the environment caused by it. Such a process is called goal-directed behavior, which is flexible, but requires high computational cost. Once the same response is repeatedly performed under the same environment, the response becomes automatic, and transforms into a habit. In the canonical views, such a change from goal-directed response to habit was explained by the associative structures between the corresponding systems, goal-directed, and habit systems. However, the dichotomy in the mechanisms of behavior between goal-directed responses and habits has recently been challenged. Here, we show that, instead of assuming two explicitly distinguished mechanisms as in the canonical views, behavior is regarded as a network consisting of multiple responses, and that changes in the structure of the network cause two behavioral features, goal-directed behavior and habit. The transition from goal-directed behavior to habit has been operationally defined by sensitivity to the reward obtained by the response. We replicate such an experimental paradigm in the simulation and show that the behavioral network model can reproduce the empirical results on habit formation obtained from animal experiments. Our results demonstrate that habit formation can be explained in terms of changes in the network structure of behavior without assuming explicitly distinct systems and thus, provide a new theoretical framework to study the psychological, biological, and computational mechanisms of the behavior.
Improving energy efficiency of HVAC systems in buildings: a review of best practices
Authors: Simpeh Eric Kwame , Pillay Jon-Patrick George , Ndihokubwayo Ruben , Nalumu Dorothy Julian 
Published: July 2021

PurposeHeating, ventilation and air-conditioning (HVAC) systems account for approximately half of all energy usage in the operational phase of a building's lifecycle. The disproportionate amount of energy usage in HVAC systems against other utilities within buildings has proved a huge cause for alarm, as this practice contributes significantly to global warming and climate change. This paper reviews the status and current trends of energy consumption associated with HVAC systems with the aim of interrogating energy efficiency practices for improving HVAC systems' consumption in buildings in the context of developing countries.Design/methodology/approachThe study relied predominantly on secondary data by analysing the relevant body of literature and proposing conceptual insights regarding best practices for improving the energy efficiency of HVAC systems in buildings. The systematic review of the literature (SLR) was aided by the PRISMA guiding principle. Content analysis technique was adopted to examine germane scholarly articles and finally grouped them into themes.FindingsBased on the SLR, measures for enhancing the energy efficiency of HVAC systems in buildings were classified based on economic considerations ranging from low-cost measures such as the cost of tuning the system, installing zonal control systems, adopting building integrated greenery systems and passive solar designs to major approaches such as HVAC smart technologies for energy management which have multi-year pay-back periods. Further, it was established that practices to improve energy efficiency in buildings range from integrated greening system into buildings to HVAC system which are human-centred and controlled to meet human modalities.Practical implicationsThere is a need to incorporate these energy efficiency practices into building regulations or codes so that built environment professionals would have a framework within which to design their buildings to be energy efficient. This energy efficient solution may serve as a prerequisite for newly constructed buildings.Originality/valueTo this end, the authors develop an integrated optimization conceptual framework mimicking energy efficiency options that may complement HVAC systems operations in buildings.
The relationship between cross-border acquisitions and R&D investments by Indian firms – substituting or complementing
Authors: Singal Ajay Kumar , Ahsan Faisal Mohammad 
Published: July 2021

PurposeEmerging economy firms seek strategic assets through cross-border acquisitions (CBAs) to upgrade their capabilities. The paper explores the relation between emerging economy firms' investments in CBAs and subsequent investments in domestic R&D. It investigates the underlying mechanism that links a firm's decision to pursue CBAs and the outcomes from the CBAs. The main idea behind the study is that firms have higher possibility of creating value from cross-border acquisitions when they simultaneously invest in domestic R&D though both investments are constrained by financial and managerial resources.Design/methodology/approachThe hypotheses are tested on a panel data set of 296 Indian firms over a period of 13 years (2003–2015). The authors use a two-stage Heckman procedure for testing their hypotheses. In the first stage, a probit model predicts the probability of a firm being a cross-border acquirer. The second stage model is estimated by a pooled-data GLS (generalized least squares) regression technique.FindingsThe authors find a nonlinear (inverted U-shaped) relationship between firm's investments in CBAs and domestic R&D. This suggests a complementary relation between investments in CBAs and a firm's domestic R&D at lower levels of investments in CBAs. At higher levels of investments in CBAs, CBA investments begin to substitute for firm's domestic R&D investments. For firms with higher international product-market experience and those operating in the hi-tech industry, the relationship between investments in CBAs and domestic R&D is complementary even at higher levels of CBA investments.Originality/valueThe study highlights the role of an emerging market firm's investment in domestic R&D as a link between the decision to invest in CBAs and related outcomes thereof. Emerging market firms face resource constraints while pursuing simultaneous investments in CBAs and R&D, but investment in R&D is essential for realizing the acquisition objectives. The authors also establish the significance of industry context and experiential learning in deciding the allocation of resources between CBAs and internal R&D.
Leadership styles and organisational innovation in Vietnam: does employee creativity matter?
Authors: Nguyen Nhat Tan , Hooi Lai Wan , Avvari Mohan V. 
Published: July 2021

PurposeThis paper aims to look into the role of transformational leadership and transactional leadership as predictors of employee creativity and organisational innovation. Employee creativity is examined as a potential mediator in the leadership styles–organisational innovation relationships.Design/methodology/approachA cross-sectional, quantitative design was adopted and structural equation modelling (SEM) techniques were used to analyse data collected from 369 employees working in 39 public coffee enterprises in Vietnam.FindingsTransformational leadership and transactional leadership were significant predictors of employee creativity and organisational innovation. Specifically, transformational leadership was instrumental to employee creativity and organisational innovation while transactional leadership was detrimental to these two variables. Additionally, employee creativity partially mediated the relationships between the two leadership styles and organisational innovation.Practical implicationsResults of this study benefit the management of organisations and policy makers by providing an insight of which leadership style will effectively suit public enterprises to promote employee creativity and foster organisational innovation.Originality/valueWhile there is a lack of studies investigating organisational innovation in organisational methods and that the interrelationships between leadership styles, employee creativity and organisational innovation are not fully understood, this study pioneers in examining relationships between leadership styles and organisational innovation that is being mediated by employee creativity. Figuring out that organisational innovation is more likely to be fostered by the positive influence of leadership behaviours and the improvement of employee creativity, in particular, the significant role of employee creativity represents important contributions of the current study.
Impeller in suction pipe Thai Phaya-Nakh for agriculture
Authors: Noosomton Jaruphant 
Published: July 2021

Purpose
The suction pipes are important in agriculture and are used widely in water management and agricultural–mechanical industry in ASEAN. Thus, this paper aims to present design of new impeller in suction pipe and include shape blade impeller to optimize for suction head, which has been higher than efficiency local-type by the performance. It mostly depends on the hydrodynamic characteristics, e.g. lift, drag and ratio, which is known as the “Thai Phaya-Nakh pipe”.

Design/methodology/approach
By approach NACA methodology and use applied technique: leading edge of blade, skew-line, cambered-line, developed area and advanced number etc., for analyzing data which the result of CFD simulation.

Findings
The models were tested in field by using motor at rotation speed 1500 rpm and found that the summarized average suction efficiency of the new impeller was estimated to be 72%, which has been greater than that of the local-type impeller with an average suction efficiency of 28% to 2.6 times. In addition, the amount of required electrical energy was reduced by 18%.

Originality/value
Then after analyzing the data from the static pressure distribution flow rate of impeller models, it is found that the new curved impeller has higher flow rate than the local type impeller. Thus, this study suggests the shape new impeller has higher flow rate than the local type impeller.
Consequential factors of Big Data's Analytics Capability: how firms use data in the competitive scenario
Authors: Contreras Pinochet Luis Hernan , Amorim Guilherme de Camargo Belli , Lucas Júnior Durval , Souza Cesar Alexandre de 
Published: July 2021

PurposeThe article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.Design/methodology/approachThe research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.FindingsThe results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.Practical implicationsThe proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.Originality/valueThe contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.