Oakland University

Copper is an essential nutrient for sustaining vital cellular processes spanning respiration, metabolism, and proliferation. However, loss of copper homeostasis, particularly misregulation of loosely bound copper ions which are defined as the labile copper pool, occurs in major diseases such as cancer, where tumor growth and metastasis have a heightened requirement for this metal. To help decipher the role of copper in the etiology of cancer, we report a histochemical activity-based sensing approach that enables systematic, high-throughput profiling of labile copper status across many cell lines in parallel. Coppermycin-1 reacts selectively with Cu(I) to release puromycin, which is then incorporated into nascent peptides during protein translation, thus leaving a permanent and dose-dependent marker for labile copper that can be visualized with standard immunofluorescence assays. We showcase the utility of this platform for screening labile Cu(I) pools across the National Cancer Institute’s 60 (NCI-60) human tumor cell line panel, identifying cell types with elevated basal levels of labile copper. Moreover, we use Coppermycin-1 to show that lung cancer cells with heightened activation of nuclear factor-erythroid 2-related factor 2 (NRF2) possess lower resting labile Cu(I) levels and, as a result, have reduced viability when treated with a copper chelator. This work establishes that methods for labile copper detection can be used to assess cuproplasia, an emerging form of copper-dependent cell growth and proliferation, providing a starting point for broader investigations into the roles of transition metal signaling in biology and medicine.

Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel’s CA time-trace. This study introduces an unsupervised feature engineering technique (Kohonen-Self-Organizing-Map (K-SOM)) to estimate the voxel-wise probability of each nested model. Sixty-six immune-compromised-RNU rats were implanted with human U-251 N cancer cells, and DCE-MRI data were acquired from all the rat brains. The time-trace of change in the longitudinal-relaxivity (ΔR1) for all animals’ brain voxels was calculated. DCE-MRI pharmacokinetic (PK) analysis was performed using NMS to estimate three model regions: Model-1: normal vasculature without leakage, Model-2: tumor tissues with leakage without back-flux to the vasculature, Model-3: tumor vessels with leakage and back-flux. Approximately two hundred thirty thousand (229,314) normalized ΔR1 profiles of animals’ brain voxels along with their NMS results were used to build a K-SOM (topology-size: 8 × 8, with competitive-learning algorithm) and probability map of each model. K-fold nested-cross-validation (NCV, k = 10) was used to evaluate the performance of the K-SOM probabilistic-NMS (PNMS) technique against the NMS technique. The K-SOM PNMS’s estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC = 0.774 [CI: 0.731–0.823], and 0.866 [CI: 0.828–0.912] for Models 2 and 3, respectively) to their respective NMS regions. The mean-percent-differences (MPDs, NCV, k = 10) for the estimated permeability parameters by the two techniques were: -28%, + 18%, and + 24%, for vp, Ktrans, and ve, respectively. The KSOM-PNMS technique produced microvasculature parameters and NMS regions less impacted by the arterial-input-function dispersion effect. This study introduces an unsupervised model-averaging technique (K-SOM) to estimate the contribution of different nested-models in PK analysis and provides a faster estimate of permeability parameters.

Humans may play a key role in providing small prey mammals spatial and temporal refuge from predators, but few studies have captured the heterogeneity of these effects across space and time. Global COVID‐19 lockdown restrictions offered a unique opportunity to investigate how a sudden change in human presence in a semi‐urban park impacted wildlife. Here, we quantify how changes in the spatial distributions of humans and natural predators influenced the landscape of fear for the California ground squirrel (Otospermophilus beecheyi) in a COVID‐19 pandemic (2020) and non‐COVID (2019) year. We used a structural equation modeling approach to explore the direct and indirect effects of human presence, predator presence, and habitat features on foraging that reflected fear responses (e.g., giving‐up densities [GUDs], number of foragers, and average food intake rate while at food patches). In 2019, humans and dogs had moderate effects on GUDs; squirrels were less fearful (lower GUDs) in areas frequently visited by humans and dogs, but the effects of raptors were weak. In contrast, in 2020, the effects of humans and dogs on GUDs were weak; squirrels were more fearful of high raptor activity, open sky, and ground cover. In both years, squirrels farthest from refuge were the most risk‐averse. Overall, our analyses revealed an increase in perceived risk from natural predators in 2020 associated with a change in the concentration of human presence. Thus, risk‐sensitive foraging was dynamic across space and time, depending on a complex interplay among human and dog activity, natural predators, and microhabitat features. Our findings elucidate the myriad ways humans directly and indirectly influence animal perception of safety and danger.

Middle-age and older runners demonstrate differences in running biomechanics compared with younger runners. Female runners demonstrate differences in running biomechanics compared with males, and females experience hormonal changes during menopause that may also affect age-related changes in running biomechanics. The purpose of this study was to determine the relationship between age and running biomechanics in healthy female recreational runners. Fifty-two participants (ages 27–65 y) ran on an instrumented treadmill at 2 different self-selected speeds: easy pace and 5 km race pace. Lower-extremity kinematic and kinetic variables were calculated from 14 consecutive strides. Linear regression was used to determine the relationship between age and lower-extremity running biomechanics, controlling for self-selected running speed. There was a negative relationship between age and easy pace ( R = −.49, P < .001) and age and 5 km race pace ( R = −.43, P = .001). After controlling for self-selected running speed, there were no significant relationships between age and running biomechanics for either running speed. Several biomechanical variables were moderately to strongly correlated with running speed. Running speed should be considered when investigating age-related differences in running biomechanics.

Building upon recent developments in production function identification and decomposition methods, this paper investigates the sources of output and productivity growth among China’s listed manufacturing companies from 2000 to 2022. While previous studies on China’s manufacturing have predominantly focused on the period preceding 2007, our study extends the analysis to a broader timeframe and divide it into four sub-periods to accommodate diverse economic conditions and varying growth rates. We provide new insights into the Chinese economy during a period marked by gradual economic transformation. Specifically, we first decompose industry output growth into factor deepening and firm productivity progress within each sub-period. To account for heterogeneity across firms in terms of production technology and sources of growth, we employ a nonparametric production function and decompose firm output growth at both the mean and different quantiles of the output distribution. We find that increased materials usage and productivity growth are primary growth drivers. However, the contribution of productivity experiences a significant decline, particularly in recent years and among median-sized and large firms. Furthermore, we examine China’s industry aggregate productivity growth and its origins among state-invested, foreign-invested, and domestic private firms. Our findings suggest that reforms among state firms are the largest contributor to industry productivity growth before the 2008 financial crisis, whereas productivity progress of domestic private firms emerges as the sole significant driver in recent years. Additionally, there is no evidence of improvements in output reallocation efficiency within China’s manufacturing sector throughout our sample period.

As a unique nonlinear optical material, Ba3(ZnB5O10)PO4 (BZBP) boasts a range of distinctive properties, including low anisotropic thermal expansivity, high specific heat, minimal walk‐off effect, large acceptance angle, non‐hygroscopicity, and high conversion efficiency. These features position BZBP as a highly promising candidate for crucial components in ultraviolet (UV) laser systems. Notably, all previous studies have been conducted under ambient pressures. In this research, synchrotron X‐ray diffraction and Raman spectroscopy are employed to investigate BZBP's behavior under extreme conditions. The findings revealed that BZBP remains exceptionally stable up to 43 Gigapascals (GPa), significantly extending its application range from ambient to high‐pressure environments. This stability enhancement opens new avenues for utilizing BZBP in optical systems designed to function under extreme conditions. Additionally, the study determined BZBP's bulk modulus (110 GPa) and linear compressibility along each lattice axis. Theoretical computations are used to assign the Raman modes, characterize their corresponding lattice vibrations, validate the experimental results, and elucidate the mechanisms underlying the material's remarkable stability.

Lymphedema is localized swelling due to lymphatic system dysfunction, often affecting arms and legs due to fluid accumulation. It occurs in 20% to 94% of patients within 2 to 5 years after breast cancer treatment, with around 20% of women developing breast cancer-related lymphedema (BCRL). This condition involves the accumulation of protein-rich fluid in interstitial spaces, leading to symptoms like swelling, pain, and reduced mobility that significantly impact quality of life. The early diagnosis of lymphedema helps mitigate the risk of deterioration and prevent its progression to more severe stages. Healthcare providers can reduce risks through exercise prescriptions and self-manual lymphatic drainage techniques. Lymphedema diagnosis currently relies on physical examinations and limb volume measurements, but challenges arise from a lack of standardized criteria and difficulties in detecting early stages. Recent advancements in computational imaging and decision support systems have improved diagnostic accuracy through enhanced image reconstruction and real-time data analysis. The aim of this comprehensive review is to provide an in-depth overview of the research landscape in computational diagnostic techniques for lymphedema. The computational techniques primarily include imaging-based, electrical, and machine learning approaches, which utilize advanced algorithms and data analysis. These modalities were compared based on various parameters to choose the most suitable techniques for their applications. Lymphedema detection faces challenges like subtle symptoms and inconsistent diagnostics. The research identifies Bioimpedance Spectroscopy (BIS), Kinect sensor and Machine Learning integration as the promising modalities for early lymphedema detection. BIS can effectively identify lymphedema as early as four months post-surgery with sensitivity of 44.1% and specificity of 95.4% in diagnosing lymphedema whereas in Machine learning, Artificial Neural Network (ANN) achieved an impressive average cross-validation accuracy of 93.75%, with sensitivity at 95.65% and specificity at 91.03%. Machine learning and imaging can be integrated into clinical practice to enhance diagnostic accuracy and accessibility.

Due to the increasing aging population, the number of people affected by neurodegenerative diseases is expected to grow in the coming years, causing a high cost of elderly care. However, early detection of these diseases can slow down the deterioration of patients’ conditions. This paper focuses on detecting behavioral abnormalities through continuous monitoring of the daily activities of elderly people in smart homes. Human Activity Recognition (HAR) is an area that has been extensively explored in the past few years. However, there is a lack of focused work that leverages AI-driven techniques to identify unusual behaviors due to neurological disorders. In this work, we propose a framework that uses a novel deep-learning sequential model for predicting daily activities using smartphone data and an ontology-based behavioral abnormality detection system. Our knowledge-driven technique caters to multiple abnormal behavioral symptoms related to Alzheimer’s disease and can incorporate any updates in the daily schedule of end users. We use the latest MARBLE dataset (released in 2021) for multi-occupant scenarios and validate our solutions using multiple datasets. Our personalized HAR model is able to achieve accuracy up to 96% even with a new user and is capable of detecting a number of behavioral abnormalities using a rule-based engine.

Background Adults with ADHD benefit from treatment with extended-release (ER) formulations that provide symptom control for the entire day. Some patients are advised to supplement their extended-release medication with an immediate-release (IR) medication later in the day if they need to prolong its effects. Given that several FDA-approved ER formulations are available and many individual patient variables may affect efficacy, the purpose of this study was to identify reliable predictors of the tendency for patients to supplement their daily ER medication with an IR medication. Methods This retrospective study analyzed data from medical treatment records of adults with ADHD who received at least one ER psychostimulant (amphetamine or methylphenidate preparations) for at least six months between November 2022 and June 2024 (N = 417). Data from their intake evaluations, pre-visit measures of depression, anxiety, and ADHD via validated self-report scales, and post-visit clinician evaluations were compiled from their electronic medical records and the Qualtrics API. The association between Dyanavel XR, IR supplementation, and patient variables were investigated by backward stepwise linear regressions modeled using the variable groupings: (1) side effects reported at baseline, (2) side effects reported after 90 days, and (3) change in depression, anxiety, and ADHD symptoms from baseline to 90 days using assessment scale scores. Results Compared to the other amphetamine and methylphenidate ER medications, only Dyanavel XR resulted in lower IR supplementation at 90 days. This relationship held when controlling for baseline IR use. Regardless of whether patients supplemented with an IR, they demonstrated improved ADHD symptoms as measured by the ADHD Symptom and Side Effect Tracking (ASSET) scale after 90 days (d = 0.68 in patients with IR, d = 0.39 in patients without IR). Dyanavel XR was significantly associated with reduced IR supplementation at 90 days compared to the pooled group of patients taking other ER medications (χ² = 4.320, Nagelkerke R² = 0.039, p = .038). The CGI-I score at baseline was also significantly associated with supplementation at 90 days (r = .14, p = .010). No other baseline variable was independently associated with IR supplementation. Along with being on Dyanavel XR, improved ADHD and anxiety symptom presentation from the baseline to the 90-day visit predicted reduced IR supplementation (ASSET change: t = 2.377, p = .018; GAD-2 change: t = -2.543, p = .011; Dyanavel XR: t = -2.112, p = .035). Conclusion These analyses support Dyanavel XR as a monotherapy for the daily management of ADHD in adults compared with other ER medications. Considering its tendency to reduce IR supplementation and its relationship with improved ADHD and anxiety symptoms, Dyanavel XR may simplify treatment regimens and improve outcomes. Clinical trial number Not applicable.

Land use change threatens global biodiversity and compromises ecosystem functions, including pollination and food production. Reduced taxonomic α‐diversity is often reported under land use change, yet the impacts could be different at larger spatial scales (i.e., γ‐diversity), either due to reduced β‐diversity amplifying diversity loss or increased β‐diversity dampening diversity loss. Additionally, studies often focus on taxonomic diversity, while other important biodiversity components, including phylogenetic diversity, can exhibit differential responses. Here, we evaluated how agricultural and urban land use alters the taxonomic and phylogenetic α‐, β‐, and γ‐diversity of an important pollinator taxon—bees. Using a multicontinental dataset of 3117 bee assemblages from 157 studies, we found that taxonomic α‐diversity was reduced by 16%–18% in both agricultural and urban habitats relative to natural habitats. Phylogenetic α‐diversity was decreased by 11%–12% in agricultural and urban habitats. Compared with natural habitats, taxonomic and phylogenetic β‐diversity increased by 11% and 6% in urban habitats, respectively, but exhibited no systematic change in agricultural habitats. We detected a 22% decline in taxonomic γ‐diversity and a 17% decline in phylogenetic γ‐diversity in agricultural habitats, but γ‐diversity of urban habitats was not significantly different from natural habitats. These findings highlight the threat of agricultural expansions to large‐scale bee diversity due to systematic γ‐diversity decline. In addition, while both urbanization and agriculture lead to consistent declines in α‐diversity, their impacts on β‐ or γ‐diversity vary, highlighting the need to study the effects of land use change at multiple scales.

Introduction Despite the health benefits of performing regular muscle-strengthening exercise (MSE), prevalence rates of meeting the public health guidelines for MSE remain low. Understanding barriers to MSE participation may aid in health promotion efforts and intervention design. Thus, the purpose of this study was to develop and test the psychometric properties of the Perceived Environment and Muscle Strengthening Exercise Questionnaire (PEMSE-Q), a tool that measures environmental effects on MSE. Methods The questionnaire included 77 items measuring physical environment, home MSE equipment accessibility, and MSE social support. This study was conducted in a combined national sample of two independent groups using an online research participant recruitment tool, Prolific: 1) healthy adults ( n = 237 (female: n = 125; male: n = 111; intersex: n = 1), mean age ± standard deviation (SD) = 36.1 ± 10.7 yr) and 2) type 2 diabetes (T2D) ( n = 221 (female: n = 122; male: n = 99), mean age ± SD = 46.5 ± 10.9 yr). The factor structure, internal consistency, test–retest reliability, criterion validity, construct validity, and concurrent validity of PEMSE-Q were determined using exploratory factor analyses, Cronbach’s alpha, intraclass correlation coefficients (ICC), regression analyses, chi-square tests of association and logistic regression, and Pearson correlation and logistic regression, respectively. Results PEMSE-Q had good to excellent test–retest reliability (ICC = 0.83–0.94), acceptable criterion validity evidence (all P values <0.001, standard error = 0.033–12.043), moderate concurrent validity ( r = 0.449–0.469), and apparent construct validity for public MSE facilities ( χ ² = 5.991, P = 0.01). Conclusion PEMSE-Q is a valid and reliable tool for assessing MSE home equipment and accessibility, convenient public MSE facilities, and social support. This tool could be used by researchers, public health professionals, and clinicians to aid in identifying barriers and facilitators to MSE participation.

Diazotrophic cyanobacteria can overcome nitrogen (N)‐limitation by fixing atmospheric N2; however, this increases their energetic, iron, molybdenum, and boron costs. It is unknown how current and historic N‐supplies affect cyanobacterial elemental physiology beyond increasing demands for elements involved in N‐fixation. Here, we examined the changes in pigment concentrations, N‐storage, and the ionome (i.e., multivariate elemental composition) of the freshwater diazotroph Dolichospermum flosaquae adapted to an N‐gradient for two temporal scales: 27 days and 45 months. We found short‐term adaptation of Dolichospermum to low N‐supply decreased pigment concentrations, N‐storage, N:carbon (C), and increased boron:C, calcium:C, and magnesium:C than high N‐supply adapted populations. Dolichospermum adapted to low N‐supplies for 45 months had higher pigment concentrations, N‐storage, and lower boron:C, calcium:C, magnesium:C, and phosphorus:C than the short‐term adapted populations when grown in low N‐supplies. Our results highlight the connections between the ionome and physiology, identifying the previously unrecognised roles of elements that can be used to advance physiological patterns.

Digital evidence processing can be more critical in the heterogeneous Internet-of-Things (IoT) paradigm due to the lack of transparency, trust, integrity, and immutability. Researchers have conducted a substantial range of research over the past few decades to improve the digital chain of custody and forensic investigation. In a digital forensic investigation, preserving the digital evidence chain of custody with end-to-end security, integrity, privacy, traceability, access control, provenance, transparency, and evidence verification presents several challenges. The present paper proposes a blockchain-based model for a smart IoT environment to address these challenges. The primary goal of this study is to ensure the integrity, confidentiality, trust, and privacy of vital digital evidence throughout the forensic investigation. We only insert or delete digital evidence after verifying the user's authentication. We have utilized a consortium blockchain-based secure system that harnesses the benefits of an interplanetary file system (IPFS) to enhance the security and integrity of digital evidence management and chain-of-custody. The proposed mechanism can achieve security, integrity, access control, immutability, and trust for a digital chain-of-custody. The integration of the Ethereum blockchain-based test network and IPFS facilitates the development, testing, and deployment of decentralized applications (dApps) with enhanced security, efficiency, and decentralization. We deployed smart contracts on Metamask and tested them on the Sepolia test network. The Ethereum network records different gas fees and transaction fees based on various timestamps, gas prices, and traffic. In different numbers and timestamps, gas prices vary between 2.50000001 Gwei and 13.559962651 Gwei. Compared to many other transactions, the reduction ranges from 10 to 20%. The proposed model provides the highest throughput, approximately 5% more transactions per second, as well as the lowest latency.

Ocean acidification, driven by rising atmospheric carbon dioxide levels, poses a significant threat to the health of marine ecosystems, particularly in the Pacific Ocean. This study employs a multi-variate hybrid machine learning approach to predict future pH trends within the Pacific and to analyze the influence of key biogeochemical drivers on these trends. Hybrid models, strategically combining the strengths of individual algorithms, were developed for predicting several ocean acidification parameters. A performance analysis demonstrated the superior accuracy of hybrid models compared to their counterparts. The predicted pH trends reveal a concerning shift towards increased acidity within the Pacific Ocean, highlighting the urgency of understanding and mitigating its impacts. In-depth analysis was conducted to identify the relative influence of key biogeochemical factors on the changing pH dynamics. This research aims to provide crucial insights for developing targeted mitigation strategies and protecting the vulnerable ecosystems of the Pacific Ocean from the escalating consequences of ocean acidification.

An in situ polymerization method fabricated the electrically conductive magnetic epoxy nanocomposites with magnetite@polyaniline. With the introduction of polyaniline on the magnetite nanoparticles, the structural integrity of the synthesized epoxy nanocomposites was enhanced with the bridging effect of the polyaniline. Specifically, compared with pure epoxy, the tensile strength was improved to 82.2 MPa when 1.0 wt% magnetite@polyaniline was added to the epoxy matrix. The enhanced mechanical property is due to the enhanced interfacial interaction. With further increasing particle loading to 30.0 wt%, glass transition temperature (Tg) was decreased to 85.4 °C, which is related to the enlarged free volume between epoxy chains. The saturation magnetization of 30.0 wt% magnetite@polyaniline/epoxy composites was 12.79 emu/g. Moreover, with the assistance of magnetite@polyaniline, the thermal stability was enhanced compared with pure epoxy. The electromagnetic wave absorption of the unique magnetite@polyaniline/epoxy nanocomposites was also studied. When the content of magnetite@polyaniline reached 30.0 wt%, the reflection loss even reached − 35.9 dB. This work guides the fabrication of multifunctional epoxy nanocomposites with comprehensive electrical, magnetic, and mechanical properties. Graphical abstract Magnetite epoxy nanocomposites with polyaniline as coupling agent with enhanced electromagnetic wave absorption performance

Three studies were conducted to investigate if four and five year old children recognize that kinship relationships are determined by biological associations and not environmental conditions. All three studies employed the “switched-at-birth” task. Study 1 investigated if children and adults recognize who the biological parents and siblings are. Study 2 examined preschoolers’ and adults’ recognition of who the biological parents and siblings are when step parents and step siblings were introduced into the family. Study 3 examined if children and adults extend their knowledge of kinship relationships to non-human creatures. For Studies 1 and 2, results indicated that preschoolers and adults have a robust and accurate biological model of kinship for both biological parents and sibling relationships. However in Study 3, preschoolers had a more difficult time recognizing biological sibling relationships than biological parent relationships in the presence of step parents and step siblings for non-human biological creatures. In totality, these results suggest that even young children (like adults) have a robust theory of kinship when reasoning about human relationships. However children’s model of kinship is fragile and still developing when reasoning and extending their knowledge about humans to non-human species.