Miracle-dz/UniFAR-FTUs huggingface dataset:
Variability in the Aerobic Fitness-Related Dependence on Respiratory Processes During Muscle Work Is Associated With the ACE-I/D Genotype

Background The efficiency of aerobic energy provision to working skeletal muscle is affected by aerobic fitness and a prominent insertion/deletion polymorphism in the angiotensin-converting enzyme (ACE-I/D) gene for the major modulator of tissue perfusion. We assessed whether variability in the fitness state is dependent on the contribution of multiple aspects of oxygen transport to the development of muscle power, and the respective control coefficients, are associated with the ACE-I/D genotype. Methods Twenty-five women and 19 men completed a ramp test of cycling exercise to exhaustion during which serial steps of oxygen transport [oxygen uptake (L O2 min−1) (VO2), minute ventilation in (L min−1) (VE), cardiac output in equivalents of L min−1 (Q), arterial oxygen saturation (SpO2), muscle oxygen saturation (SmO2), and total hemoglobin concentration (g dL−1) (THb) in Musculus vastus lateralis and Musculus gastrocnemius, respiration exchange ratio (RER)], blood lactate and glucose concentration, were continuously monitored. The contribution/reliance of power output (PO) on the parameters of oxygen transport was estimated based on the slopes in Pearson's moment correlations (|r| > 0.65, p < 0.05) vs. power values over the work phase of the ramp test, and for respective fractional changes per time (defining control coefficients) over the rest, work, and recovery phase of the ramp test. Associations of variability in slopes and control coefficients with the genotype and aerobic fitness were evaluated with ANOVA. Results All parameters characterizing aspects of the pathway of oxygen, except THb, presented strong linear relationships [(|r| > 0.70) to PO]. Metabolic efficiency was 30% higher in the aerobically fit subjects [peak oxygen uptake (mL O2 min−1) (VO2peak) ≥ 50 ml min−1 kg−1], and energy expenditure at rest was associated with the fitness state × ACE-I/D genotype, being highest in the fit non-carriers of the ACE D-allele. For VO2, VE, and RER the power-related slopes of linear relationships during work demonstrated an association with aerobic fitness, being 30–40% steeper in the aerobically fit than unfit subjects. For VE the power-related slope also demonstrated an association with the ACE-I/D genotype. For increasing deficit in muscle oxygen saturation (DSmO2) in Musculus vastus lateralis (DSmO2 Vas), the power-related slope was associated with the interaction between aerobic fitness × ACE-I/D genotype. Conclusion Local and systemic aspects of aerobic energy provision stand under influence of the fitness state and ACE-I/D genotype. This especially concerns the association with the index of the muscle's mitochondrial respiration (SmO2) which compares to the genetic influences of endurance training.
The Impact of Financial Redundancy on Corporate Social Responsibility Performance: Evidence From Chinese Listed Firms

This study examines the impact of financial redundancy on corporate social responsibility (CSR) based on a sample of Chinese listed firms from 2010 to 2020. The results indicate that financial redundancy has a significant positive effect on CSR. However, financially redundant resources are not balanced in terms of how they encourage firms to undertake different dimensions of social responsibility; specifically, firms actively take social responsibility toward shareholders and the public but take less responsibility for employees and the environment. The incentive for firms with financially redundant resources to promote CSR initiatives is attributable to their high level of social awareness and pursuit of reputation. Consistent with their motives, our economic consequence analysis reveals that the incremental effect of CSR driven by financial redundancy improves corporate reputation but has no enhancement effect on corporate performance. Finally, our extended analysis reveals that the relative impact of financial redundancy on CSR depends on several organizational variables that influence a firm’s preferences for CSR investments. The positive impact of financial redundancy on CSR is stronger among firms with high managerial career concerns and firms in regions with high market competition. This research provides a necessary structure for future CSR studies to follow. By delving deeply into the relationship between financial redundancy and CSR, it enables scholars to better address the critical management question of whether wealthy firms do more good for society compared to those that are less wealthy.
The Combination of a Novel GLUT1 Inhibitor and Cisplatin Synergistically Inhibits Breast Cancer Cell Growth By Enhancing the DNA Damaging Effect and Modulating the Akt/mTOR and MAPK Signaling Pathways

Breast cancer is the most prevalent cancer and the second leading cause of cancer death in women. Cisplatin is a commonly used chemotherapeutic drug for breast cancer treatment. Owing to serious side effects, the combination of cisplatin with other drugs is an effective strategy to simultaneously reduce side effects and increase the anticancer efficacy. GLUT1 is an emerging target for cancer treatment since cancer cells usually consume more glucose, a phenomenon called the Warburg effect. In this study, we found that the combination of cisplatin and a novel GLUT1 inhibitor #43 identified from our previous high-throughput screening exerted a synergistic anticancer effect in MCF-7 and MDA-MB-231 breast cancer cells. Mechanism studies in MCF-7 cells revealed that combination of cisplatin and #43 significantly induced apoptosis, intracellular reactive oxygen species, and loss of mitochondrial membrane potential. Furthermore, #43 enhanced the DNA damaging effect of cisplatin. Akt/mTOR downstream signaling and the ERK signaling pathway usually involved in cell growth and survival were inhibited by the combination treatment. On the other hand, phosphorylation of p38 and JNK, which may be associated with apoptosis, was induced by the combination treatment. Altogether, our data indicate that oxidative stress, DNA damage, the Akt/mTOR and MAPK signaling pathways, and apoptosis may be involved in the synergism of cisplatin and #43 in breast cancer cells.
Sub-Lethal Concentrations of Graphene Oxide Trigger Acute-Phase Response and Impairment of Phase-I Xenobiotic Metabolism in Upcyte® Hepatocytes

The impact of graphene oxide on hepatic functional cells represents a crucial evaluation step for its potential application in nanomedicine. Primary human hepatocytes are the gold standard for studying drug toxicity and metabolism; however, current technical limitations may slow down the large-scale diffusion of this cellular tool for in vitro investigations. To assess the potential hepatotoxicity of graphene oxide, we propose an alternative cell model, the second-generation upcyte® hepatocytes, which show metabolic and functional profiles akin to primary human hepatocytes. Cells were acutely exposed to sub-lethal concentrations of graphene oxide (≤80 μg/ml) for 24 h and stress-related cell responses (such as apoptosis, oxidative stress, and inflammatory response) were evaluated, along with a broad investigation of graphene oxide impact on specialized hepatic functions. Results show a mild activation of early apoptosis but not oxidative stress or inflammatory response in our cell model. Notably, while graphene oxide clearly impacted phase-I drug-metabolism enzymes (e.g., CYP3A4, CYP2C9) through the inhibition of gene expression and metabolic activity, conversely, no effect was observed for phase-II enzyme GST and phase-III efflux transporter ABCG2. The GO-induced impairment of CYP3A4 occurs concomitantly with the activation of an early acute-phase response, characterized by altered levels of gene expression and protein production of relevant acute-phase proteins (i.e., CRP, Albumin, TFR, TTR). These data suggest that graphene oxide induces an acute phase response, which is in line with recent in vivo findings. In conclusion, upcyte® hepatocytes appear a reliable in vitro model for assessing nanomaterial-induced hepatotoxicity, specifically showing that sub-lethal doses of graphene oxide have a negative impact on the specialized hepatic functions of these cells. The impairment of the cytochrome P450 system, along with the activation of an acute-phase response, may suggest potential detrimental consequences for human health, as altered detoxification from xenobiotics and drugs.
Comparative Genomic Analysis of Type VII Secretion System in Streptococcus agalactiae Indicates Its Possible Sequence Type-Dependent Diversity

Streptococcus agalactiae causes sepsis and meningitis in neonates, presenting substantial clinical challenges. Type VII secretion system (T7SS), an important secretion system identified in Mycobacterium sp. and Gram-positive bacteria, was recently characterized in S. agalactiae and considered to contribute to its virulence and pathogenesis. In the present study, 128 complete genomic sequences of S. agalactiae were retrieved from GenBank to build a public dataset, and their sequences, capsular types, and clonal complexes were determined. Polymerase chain reaction (PCR) screening and genomic sequencing were conducted in an additional clinical dataset. STs and capsular types were determined using PCR. Eleven different types of T7SS were detected with similarities in gene order but differences in gene content. Strains with incomplete T7SS or lack of T7SS were also identified. Deletion, insertion, and segmentation of T7SS might be related to insertion sequences. The genetic environment of T7SS in S. agalactiae was also investigated and different patterns were identified downstream the T7SS, which were related to the diversity of T7SS putative effectors. The T7SS demonstrated possible sequence type (ST)-dependent diversity in both datasets. This work elucidated detailed genetic characteristics of T7SS and its genetic environment in S. agalactiae and further identified its possible ST-dependent diversity, which gave a clue of its mode of transmission. Further investigations are required to elucidate the underlying mechanisms and its functions.
Engagement in Digital Health App-Based Prevention Programs Is Associated With Weight Loss Among Adults Age 65+

Background Digital health programs have been shown to be feasible and effective for the prevention of chronic diseases such as diabetes. Contrary to expectations, findings also suggest that older adults have higher levels of engagement with digital health programs than younger adults. However, there is a paucity of research examining outcomes among older adults in digital health programs and whether higher engagement is related to better outcomes. Methods We examined weight loss outcomes for 538 users aged 65 and older participating in one of two app-based prevention programs called the Diabetes Prevention Program and the Prevention Program, respectively. Both programs were available on a single artificial intelligence (AI)-powered digital health platform and shared a common goal of weight loss. We also examined the relationship between key engagement metrics (i.e., conversing with the AI-powered coach, weigh-ins, and initiating educational lessons early in the program) and weight loss outcomes. Results The average weight loss of all enrollees having a weight measurement after after the 9th week was 4.51%, and the average weight loss of the Diabetes Prevention Program enrollees meeting a minimum engagement level was 8.56%. Greater weight loss was associated with a greater number of days with AI-powered coaching conversations (p = 0.03), more weigh-ins (p = 0.00), and early educational lesson initiation (p = 0.02). Conclusions Digital health programs powered by AI offer a promising solution for health management among older adults. The results show positive health outcomes using app-based prevention programs, and all three engagement metrics were independently associated with weight loss.
Synergistic Interaction of Low Salinity Stress With Vibrio Infection Causes Mass Mortalities in the Oyster by Inducing Host Microflora Imbalance and Immune Dysregulation

A sudden drop in salinity following extreme precipitation events usually causes mass mortality of oysters exposed to pathogens in ocean environment. While how low salinity stress interacts with pathogens to cause mass mortality remains obscure. In this study, we performed an experiment by low salinity stress and pathogen infection with Vibrio alginolyticus to investigate their synergistic effect on the mortality of the Pacific oyster toward understanding of the interaction among environment, host, and pathogen. We showed that low salinity stress did not significantly affect proliferation and virulence of V. alginolyticus, but significantly altered microbial composition and immune response of infected oysters. Microbial community profiling by 16S rRNA amplicon sequencing revealed disrupted homeostasis of digestive bacterial microbiota with the abundance of several pathogenic bacteria being increased, which may affect the pathogenesis in infected oysters. Transcriptome profiling of infected oysters revealed that a large number of genes associated with apoptosis and inflammation were significantly upregulated under low salinity, suggesting that low salinity stress may have triggered immune dysregulation in infected oysters. Our results suggest that host-pathogen interactions are strongly affected by low salinity stress, which is of great significance for assessing future environmental risk of pathogenic diseases, decoding the interaction among environment, host genetics and commensal microbes, and disease surveillance in the oyster.
Mapping Pitch Accents to Memory Representations in Spoken Discourse Among Chinese Learners of English: Effects of L2 Proficiency and Working Memory

We examined L2 learners’ interpretation of pitch accent cues in discourse memory and how these effects vary with proficiency and working memory (WM). One hundred sixty-eight L1-Chinese participants learning L2-English listened to recorded discourses containing pairs of contrastive alternatives and then took a later recognition memory test. Their language proficiency and WM were measured through standard tests and the participants were categorized into low, medium, advanced, and high advanced language proficiency groups. We analyzed recognition memory task performance using signal detection theory to tease apart response bias (an overall tendency to affirm memory probes) from sensitivity (the ability to discern whether a specific probe statement is true). The results showed a benefit of contrastive L + H* pitch accents in rejecting probes referring to items unmentioned in a discourse, but not contrastive alternatives themselves. More proficient participants also showed more accurate memory for the discourses overall, as well as a reduced overall bias to affirm the presented statements as true. Meanwhile, that the benefit of L + H* accents in rejecting either contrast probes or unmentioned probes was modulated for people with greater working memory. Participants with higher WM were quite sure that it did not exist in the memory trace as this part of discourse wasn’t mentioned. The results support a contrast-uncertainty hypothesis, in which comprehenders recall the contrast set but fail to distinguish which is the correct item. Further, these effects were influenced by proficiency and by working memory, suggesting they reflect incomplete mapping between pitch accent and discourse representation.
User-Defined Operators: Efficiently Integrating Custom Algorithms into Modern Databases

In recent years, complex data mining and machine learning algorithms have become more common in data analytics. Several specialized systems exist to evaluate these algorithms on ever-growing data sets, which are built to efficiently execute different types of complex analytics queries. However, using these various systems comes at a price. Moving data out of traditional database systems is often slow as it requires exporting and importing data, which is typically performed using the relatively inefficient CSV format. Additionally, database systems usually offer strong ACID guarantees, which are lost when adding new, external systems. This disadvantage can be detrimental to the consistency of the results. Most data scientists still prefer not to use classical database systems for data analytics. The main reason why RDBMS are not used is that SQL is difficult to work with due to its declarative and set-oriented nature, and is not easily extensible. We present User-Defined Operators (UDOs) as a concept to include custom algorithms into modern query engines. Users can write idiomatic code in the programming language of their choice, which is then directly integrated into existing database systems. We show that our implementation can compete with specialized tools and existing query engines while retaining all beneficial properties of the database system.
Networks of networks

A recent review on networks by Elizabeth Quill (ScienceNews, 182(6), 22 September 2012, p. 18) provides some cautionary but also promising views about developments in the theory of networks and especially networks of networks. Potential scenarios range from total catastrophe to the provision of a better understanding of how networks of networks function - for example, how the circulatory system, the respiratory system and the musculoskeletal system might interact with one another in combatting disease. For my own part, the recent announcements on the positive medical outcomes from the 'placebo effect' suggests this is a problem looking for a solution in networks of networks analysis.
Fixing Continuous Integration Tests From Within the IDE With Contextual Information

The most common reason for Continuous Integration (CI) builds to break is failing tests. When a build breaks, a developer often has to scroll through hundreds to thousands of log lines to find which test is failing and why. Finding the issue is a tedious process that relies on a developer's experience and increases the cost of software testing. We investigate how presenting different kinds of contextual information about CI builds in the Integrated Development Environment (IDE) impacts the time developers take to fix a broken build. Our IntelliJ plugin TESTAXIS surfaces additional information such as a unique view of the code under test that was changed leading up to the build failure. We conduct a user experiment and show that TESTAXIS helps developers fix failing tests 13.4% to 48.6% faster. The participants found the features of TESTAXIS useful and would incorporate it in their development workflow to save time. With TESTAXIS we set an important step towards removing the need to manually inspect build logs and bringing CI build results to the IDE, ultimately saving developers time.
Physical Layer Data Manipulation Attacks on the CAN Bus

—The Controller Area Network (CAN) bus standard is the most common in-vehicle network that provides communication between Electronic Control Units (ECUs). CAN messages lack authentication and data integrity protection mechanisms and hence are vulnerable to attacks, such as impersonation and data injection, at the digital level. The physical layer of the bus allows for a one-way change of a given bit to accommodate prioritization; viz . a recessive bit ( 1 ) may be changed to a dominant one ( 0 ). In this paper, we propose a physical-layer data manipulation attack wherein multiple compromised ECUs collude to cause 0 → 1 (i.e., dominant to recessive) bit-flips, allowing for arbitrary bit-flips in transmitted messages. The attack is carried out by inducing transient voltages in the CAN bus that are heightened due to the parasitic reactance of the bus and non-ideal properties of the line drivers. Simulation results indicate that, with more than eight compromised ECUs, an attacker can induce a sufficient voltage drop to cause dominant bits to be flipped to recessive ones.
Euler: Detecting Network Lateral Movement via Scalable Temporal Link Prediction

Lateral movement is a key stage of system compromise used by advanced persistent threats. Detecting it is no simple task. When network host logs are abstracted into discrete temporal graphs, the problem can be reframed as anomalous edge detection in an evolving network. Research in modern deep graph learning techniques has produced many creative and complicated models for this task. However, as is the case in many machine learning fields, the generality of models is of paramount importance for accuracy and scalability during training and inference. In this article, we propose a formalized approach to this problem with a framework we call Euler. It consists of a model-agnostic graph neural network stacked upon a model-agnostic sequence encoding layer such as a recurrent neural network. Models built according to the Euler framework can easily distribute their graph convolutional layers across multiple machines for large performance improvements. Additionally, we demonstrate that Euler-based models are as good, or better, than every state-of-the-art approach to anomalous link detection and prediction that we tested. As anomaly-based intrusion detection systems, our models efficiently identified anomalous connections between entities with high precision and outperformed all other unsupervised techniques for anomalous lateral movement detection. Additionally, we show that as a piece of a larger anomaly detection pipeline, Euler models perform well enough for use in real-world systems. With more advanced, yet still lightweight, alerting mechanisms ingesting the embeddings produced by Euler models, precision is boosted from 0.243, to 0.986 on real-world network traffic.
Navigating Compliance with Data Transfers in Federated Data Processing

In an increasingly digital world, compliance with data regulations play an important role. More and more individuals are rapidly getting concerned with the way their data is being stored and processed by organizations. Therefore, it is crucial that data processing be subjected to regulatory obligations at its core. Yet, achieving compliance with data regulations requires the entire data processing pipeline to be revisited to embrace data policies as ﬁrst-class citizens. In this paper, we present our work on novel systems and methods for federated data processing, where the processing of geo-distributed data is subjected to data transfer regulations. We showcase our work on compliant geo-distributed data processing and present research challenges and opportunities for a federated data processing system to make compliance truly its ﬁrst-class citizens.
Soda Taxes and Dynamic Advertising

Soda taxes are commonly used to reduce sugar consumption. The effects of taxes on prices and consumption have been widely studied, however there is less work on the impact of taxes on firms’ strategic choices over advertising spending. We study how soda taxes affect both pricing and advertising strategies of firms in a dynamic oligopoly game. We use rich longitudinal data to estimate an equilibrium model of the cola market. We exploit an institutional feature of the advertising market that involves cola firms delegating decisions over advertising spots to agencies. This allows us to model the rich impacts of advertising on demand, while solving the otherwise intractable dynamic equilibrium impact of the introduction of a tax. We find that the dynamic competition game between the main players (Coca Cola and Pepsi) results in asymmetric responses in advertising spending, with both firms reducing advertising expenditure in response to a sugary soda tax.