A professor at a “research” university is expected to contribute to Research, Teaching, and Service. Tenure and promotions are supposed to rest on sufficient contributions in all these areas. Traditionally, they have been separate spheres of activity, but the online media are changing rapidly, and we think will have the ultimate effect of leading to an alignment-a ”syzygy’-of this trinity into a single integrated and global fabric of scientific communication and education. This will be a fine advancement for science and scholarship, but administrators and reviewers will have to adapt to this changing reality by learning new ways to assess impact when making funding and professional advancement decisions.

A document by The Winnower. Click on the document to view its contents.

Time travel has captured the public imagination for much of the past century, but little has been done to actually search for time travelers. Here, three implementations of Internet searches for time travelers are described, all seeking a prescient mention of information not previously available. The first search covered prescient content placed on the Internet, highlighted by a comprehensive search for specific terms in tweets on Twitter. The second search examined prescient inquiries submitted to a search engine, highlighted by a comprehensive search for specific search terms submitted to a popular astronomy web site. The third search involved a request for a direct Internet communication, either by email or tweet, pre-dating to the time of the inquiry. Given practical verifiability concerns, only time travelers from the future were investigated. No time travelers were discovered. Although these negative results do not disprove time travel, given the great reach of the Internet, this search is perhaps the most comprehensive to date.

The HeLa cell line, named after the patient Henrietta Lacks, was one the first human cell lines to be used for tissue culture. In the decades since its origin, it has become a feature in labs across the world. Despite its longevity, repeated detection of most HeLa marker chromosomes have lead the scientific community to view HeLa as a stable cell line and its corresponding findings to be reproducible. However, to date no investigation has examined other meaningful aspects of HeLa's genomic variability, such has whether HeLa's modal chromosome number remains constant. Considering the importance of a cell line's karyotype with respect to reproducibility, I sought to examine the stability of HeLa's karyotype by examining HeLa's modal chromosome number as reported in the literature.

ABSTRACT BACKGROUND AND PURPOSE Enhancing the precision of drug-drug interaction (DDI) prediction is essential for mitigating potential drug interactions and enhancing drug safety and efficacy. This study aims to investigate the impact of in vitro and in silico approaches for calculating the fraction metabolized by CY3A4 (fm) on DDI prediction accuracy and identified the most effective method for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models. EXPERIMENTAL APPROACH Both in vitro and in silico methods were utilized to determine fm values for 33 approved drugs, or fm values were assumed to be 100%. These fm values were then integrated into PBPK models. Subsequently, the PBPK models were combined with a PBPK model of ketoconazole to predict potential DDIs. Finally, the accuracy of these predictions was assessed. KEY RESULTS The integration of in vitro fm had remarkable precision in predicting CmaxR of 31 drugs and accurately predicting AUCRs of 28 drugs out of 33 drugs, both within 2 times of the measured values. However, using 100% fm and in silico fm resulted in lower prediction accuracy that was comparable to each other. CONCLUSIONS AND IMPLICATIONS Our study highlights the importance of incorporating in vitro fm data into PBPK models to improve the accuracy of predicting DDIs. While in silico fm may have some potential, its influence on predictions appears to be limited. Additionally, our findings suggest that drugs with high Clliver levels (>15 L·h-1) and high fm (>75%) are particularly susceptible to the impact of CYP3A4 inhibitor ketoconazole.

Scientists, institutions and journals have been increasingly evaluated statistically, by metrics that focus on the number of published reports rather than on their content, raising a concern that this approach interferes with the progress of biomedical research. To offset this effect, we propose to use the R-factor, a metric that indicates whether a report or its conclusions have been verified.

Good lattice point (GLP) sets are many of points uniformly distributed over the domain of interest and thus have good space-filling property. GLP sets are frequently used in many applications, such as quasi-Monte Carlo, dimensional integral, structural reliability evaluation and other areas. However, as the number of dimensions and points increases, the amount of calculation of GLP sets also increases and could not get a better generating vector to obtain the good point sets especially in high-dimensional space. To handle this problem, a new method is proposed for GLP sets in high-dimensional space, which has a great advantage and low-discrepancy compared with existing GLP method. Firstly, changing the generating vector of GLP sets based on the existing theories and making it more suitable high-dimensional space. Secondly, selecting a prime p as the number of points and getting all of primitive roots of p. Then, the phenomenon of the same discrepancy in the set of points produced by different primitive root is theoretically derived, to reduce the range of primitive roots and obtaining the point sets for each primitive root in the new range. Next, substituting the primitive roots in new range into the proposed method and getting point set. Finally, viewing discrepancy as an index, the point set with the lowest discrepancy is selected as the GLP set in the high-dimensional space. Two numerical examples are studied to validate the proposed method, the first numerical example indicates that the proposed method is of low-discrepancy and efficiency for high-dimensional GLP sets; The second numerical example shows that the point set generated by the method proposed in this paper is suitable for solving fatigue reliability evaluation of stayed cables and the results are consistent with the accuracy of Monte Carlo simulation.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases, and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity, and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation, and quantitation workflows.

The prediction of remaining useful life (RUL) of lithium-ion batteries takes a critical effect in the battery management system, and precise prediction of RUL guarantees the secure and reliable functioning of batteries. For the difficult problem of selecting the parameter kernel of the training data set of the RUL prediction model constructed based on the support vector regression model, an intelligent gray wolf optimization algorithm is introduced for optimization, and owing to the premature stagnation and multiple susceptibility to local optimum problems of the gray wolf algorithm, a differential evolution strategy is introduced to propose a hybrid gray wolf optimization algorithm based on differential evolution to enhance the original gray wolf optimization. The variance and choice operators of differential evolution are designed to sustaining the diversity of stocks, and then their crossover operations and selection operators are made to carry out global search to enhance the prediction of the model and realize exact forecast of the remaining lifetime. Experiments on the NASA lithium-ion battery dataset demonstrate the effectiveness of the proposed RUL prediction method. Empirical tests demonstrate that the maximum average absolute value error of the prediction of the fusion algorithm on the battery dataset is limited to within 1%, which reflects the high accuracy prediction capability and strong robustness.

This paper presents the state space modeling and simulation of different micro electrostatic energy harvesters – in plane overlap, in plane gap closing and their compound structures – with freedom of movement in x, x-y and x-y-z axis. The structures are specially designed for energy harvesting of vibrations whose frequencies are lower than 100 Hz to be suitably applied for heartbeat energy harvesting. The detail models of both electrical and mechanical subsystems including stopper function, motion drag, parasitic capacitors and multi directional transducing capacitors are developed in the format of state space equations. To reach the optimal heartbeat energy harvesting, the system behavior is simulated under different parameters by numerical solution of nonlinear state space equation set.

Biologists have long been interested in whether different species share similar patterns of growth. Using 8,794,737 measurements for 285 species from the U.S. Forest Inventory and Analysis database, we test several predictions emerging from a recently published model for plant growth and allometry. We show that the model’s predicted curvature for intraspecific relationships between height, DBH and biomass is found in 88.1% of examined cases, and empirical slopes fall as predicted between the elastic similarity and flow similarity predictions in 71.1% of cases. We also find a strong size dependence in observed intraspecific allometric exponents, with large species, particularly gymnosperms, converging near the expectation for elastic similarity, and the central tendency among small species approaching the expectations for flow similarity in most cases. Our results support the idea that differences in growth patterns across plant species depend on plant size and their attendant hydraulic and/or biomechanical demands.

Animal movement is a fundamental driver of disease spread. We show that an outbreak of high pathogenicity avian influenza (HPAI) is coincident with unprecedented behavioural changes in GPS tracked Northern gannets. Previously characterised by strong fidelity to their nest sites and foraging areas (2015 – 2019; n = 120), breeding gannets tracked before, during and after the 2022 outbreak showed half of ten birds stopped transmitting and most likely died, while the survivors instigated unusual long-distance movements. Two adults visited one - three other gannetries – the first such incidence of prospecting in this age class. Our findings suggest the HPAIV outbreak triggered changes in space use patterns of possibly infected individuals that amplified the epidemiological connectivity among colonies and may generate super-spreader events that accelerate disease transmission across the metapopulation. Such self-propagating transmission from and towards high density animal aggregations may explain the rapid pan-European spread of HPAIV in the gannet.

A document by Sinead Wright. Click on the document to view its contents.

DNA damage occurs throughout life from a variety of sources, and it is imperative to repair damage in a timely manner to maintain genome stability. Thus, DNA repair mechanisms are a fundamental part of life. Nucleotide Excision Repair (NER) plays an important role in the removal of bulky DNA adducts, such as cyclobutane pyrimidine dimers (CPDs) from ultraviolet (UV) light or DNA crosslinking damage from platinum-based chemotherapeutics, such as cisplatin. A main component for the NER pathway is transcription factor IIH (TFIIH), a multifunctional, 10-subunit protein complex with crucial roles in both transcription and NER. In transcription, TFIIH is a component of the pre-initiation complex (PIC) and is important for promoter opening and the phosphorylation of RNA Polymerase II (RNA Pol II). During repair, TFIIH is important for DNA unwinding, recruitment of downstream repair factors, and verification of the bulky lesion. Several different disease states can arise from mutations within subunits of the TFIIH complex. Most strikingly are Xeroderma Pigmentosum (XP), XP combined with Cockayne Syndrome (CS), and Trichothiodystrophy (TTD). Here, we summarize the recruitment and functions of TFIIH in the two NER subpathways, global genomic (GG-NER) and transcription-coupled NER (TC-NER). We will also discuss how TFIIH’s roles in the two subpathways lead to different genetic disorders.

Robotic concrete extrusion is a novel additive manufacturing process (3D concrete printing) and is part of a continuously digitally controlled value chain. According to the state of the art, concrete is considered to be an isotropic material due to the manufacturing process. However, for the additive manufacturing process, the isotropic approach hast to be reconsidered due to the layered structure. It can be assumed that due to the layered structure, the material properties vary depending on the deposition direction and the geometry of the layers. The aim of the work was to record the material-technical characteristics of extruded elements manufactured according to standards in comparison with concrete recipes. Process-related influences on the mechanical parameters of additively manufactured concrete elements were examined and evaluated in more detail. Based on the findings obtained, the dimensioning, design and measurement of components can be carried out and thus guidelines for components can be derived. With these derived guidelines, the material utilization and economic efficiency can be improved.

Background and Purpose Doxorubicin is a broad-spectrum antineoplastic agent; however, however, its genotoxic/cytotoxic effects limit its clinical application. Dimethyl fumarate (DMF) is an FDA-approved oral drug shown to have antioxidant, anti-inflammatory and antimutagenic effects via activating Nrf2 antioxidant pathway. The present study aimed to investigate the possible protective effect of DMF against doxorubicin-induced chromosomal and DNA damage in rat bone marrow cells. Experimental Approach Wistar Albino rats of both sexes were administered DMF orally (15mg/kg once daily for 14 days) alone or with doxorubicin which was injected as a single dose (90 mg/kg at day 14) to induce toxicity. The blood samples were collected 24 hours after doxorubicin’s injection from all groups to measure the serum levels of MDA, GSH, SOD, and GPx1 and bone marrow was harvested to assess chromosomal aberration, micronucleus, and comet assays. Key Results The rats in the doxorubicin-only group exhibited a significant decrease in mitotic index and depleted GSH and antioxidants enzymes serum levels with a significant elevation in MDA serum level, % DNA in Tail, micronucleus appearance and chromosomal aberrations compared to the control group; DMF pretreatment prior to doxorubicin exposure, significantly-reduced % DNA in Tail, micronucleus appearance, and chromosomal aberrations, improved mitotic index, restored GSH level and antioxidant enzymes activity compared doxorubicin-only group. Conclusion and Implication This study revealed that DMF alone has no DNA-damaging or clastogenic activities; DMF has protective effects against the genotoxicity induced by doxorubicin; thus, DMF might be a potential chemoprotective agent against doxorubicin-induced toxicity in cancer chemotherapy

Recently, some studies demonstrated the association between reactivation of Cytomegalovirus and increase of morbi-mortality in immunocompetent patients admitted to ICUs. To determine the associated factors and consequences of CMV reactivation among Moroccan patients hospitalized in a medical ICU, a prospective and monocentric study was carried out on 72 immunocompetent CMV-seropositive patients admitted to the Medical ICU. The monitoring of CMV by PCR in plasma was carried out at admission and then weekly. The CMV reactivation was occurred in 25 (34.7%) patients. Age of patients (37.7 vs 49.4, p=0.001), median duration of hospitalization before ICU admission (1 vs 4, p=0.006), lower lymphocyte count (1452.98 vs 992.4, p=0.019), increased urea (4.8 vs 13.1, p=0.001) levels and use of oxygen therapy (15 vs 16, p=0.009) were significantly more observed in patients with CMV reactivation upon admission. In term of consequence, the patients with CMV reactivation had a significantly longer length of stay (15 vs 18, p=0.002). In these patients, a last CMV viral load ≥ 3 Log IU/ml is associated with increased risks of mortality. These results demonstrated the association of CMV reactivation with increased morbidity in immunocompetent patients hospitalized in a medical ICU.

Background: Due to the recency of the postbiotic field, there are no head-to-head postbiotic studies investigating its biotherapeutic potential for atopic dermatitis (AD). No network meta-analysis (NMA) has yet been conducted to synthesize relevant studies to compare postbiotic interventions for AD. Objective: To assess the comparative efficacy and safety of postbiotic strains for treating pediatric AD. Methodology: This is an NMA of randomized controlled studies that evaluate postbiotics in treating pediatric AD. Systematic search of databases and registers from inception to November 30, 2022. Three authors independently performed the search, screening, appraisal using the Cochrane risk of bias tool version 2 and data extraction. Data analysis was done using STATA14 software. Results: There were 9 studies that evaluated 8 postbiotic preparations. Lactobacillus rhamnosus IDCC 3201 (LR) ranked highest in the efficacy outcome. Compared to placebo, LR may be effective in reducing symptoms of atopic dermatitis both in the main analysis (SMD -0.53, 95%CI -1.02 to -0.04) and sensitivity analysis involving studies that used SCORAD (MD -5.52, 95% CI -10.46 to -0.58), based on low-certainty evidence. Based on moderate-certainty evidence, LR probably does not increase the risk of adverse events (RR 0.97, 95% CI 0.79 to 1.21). Although Lactobacillus paracasei GM080 (LP2) ranked highest in the safety outcome, it may not reduce AD symptoms compared to placebo (SMD -0.03, 95% CI -0.37 to 0.32) based on low-certainty evidence. Conclusion: LR showed significant benefits for children with AD, based on low-certainty evidence. Further investigation on LR is recommended.

This paper is concerned with the tracking control problem for a class of networked systems subject to denial-of-service (DoS) attacks using reinforcement learning methods. Taking the effects of DoS attacks into consideration, a novel value function is proposed, which considers the cost of the control input, external disturbance and tracking error. Then, using the structure of the value function, the tracking Bellman equation and Hamilton function are defined. By employing the Bellman optimality theory, the optimal control strategy and the game algebraic Riccati equation (GARE) are solved with the Hamilton function. Next, the desired tracking performance is guaranteed as the solution of the GARE is found. Furthermore, an attacks-based Q-learning algorithm is projected to find the solution to the optimal tracking problem without the system dynamics and the convergence of the Q-learning algorithm is given. Finally, the F-404 aircraft engine system is given to verify the effectiveness of the proposed control strategy.

In the centralized communication and distributed control of hybrid microgrid, a hierarchical event triggering control strategy with the participation of the microgrid central control (MGCC) is presented to solve the problem that the control method depends heavily on the communication network. It can effectively reduce the communication load of the distributed cluster and ensure the reliability of the power network. This strategy divides the control system of the hybrid microgrid into two layers, the bottom layer is the local control layer, which uses distributed collaborative control. The local controller collects data information from adjacent measurement units, controls update output status in place, and achieves the distributed autonomous operation of the hybrid microgrid. In addition, an event triggering strategy is introduced in the upper control layer of the microgrid, which coordinates MGCC to obtain global information of the hybrid microgrid, thereby issuing predefined control instructions to local controllers to achieve flexible dispatch of the hybrid microgrid, especially in response to power grid emergencies. Finally, the hybrid microgrid model is built using MATLAB and simulated to verify the effectiveness of the proposed control strategy.

Background: Long-term changes in medication dispensings post cystic fibrosis transmembrane conductance regulator (CFTR) modulator initiation have not been described. We investigated changes in dispensings among people with Cystic Fibrosis (PwCF) following modulator initiation, using national prescription claims data in Australia. Methods: Using a 10% sample of the Australian Pharmaceutical Benefits Scheme (PBS) data between 2013-2022, linear regression was used to analyse dispensings in PwCF who initiated any modulator (cases) and matched PwCF controls not dispensed a modulator. The difference in mean number of total monthly dispensings pre- and post-modulator initiation was analysed, with separate analyses by medication class. Results: A total of 247 cases were matched 1:1 to controls (case and control median age 21 years (IQR: 13-32), 55.1% male). Immediately after modulator initiation, the mean number of dispensings was 0.9 higher in the modulator group, but then decreased to the level of controls after approximately 5 years. After 7.5 years, cases had decreased opioids compared to the pre-modulator period (β-coefficient: -0.00131, 95% CI: -0.00164, -0.00097) whilst controls did not (β: -0.00014, 95% CI: -0.00042, 0.00014). Over the same time period controls had an increase in psychotropics (β: 0.00389, 95% CI:0.00295, 0.00484) whilst cases remained stable (β: -0.00014, 95% CI: -0.0006, 0.00031). Women’s health medications increased in cases (β:0.00026, 95% CI:0.0001, 0.00042) but decreased in controls (β:-0.00044, 95% CI:-0.00063, -0.00025). Conclusions: Modulator initiation in PwCF was associated with decreased dispensings of opioids and psychotropics, and increased dispensings of women’s health medications, suggesting improved patient outcomes across multiple clinical domains .

The ease with which mobile money is used to facilitate cross-border payments presents a global threat to law enforcement in the fight against laundering and terrorist financing. This paper aims to use machine learning classifiers to predict transactions flagged as a fraud in mobile money transfers. Data for this paper came from real-time transactions that stimulate a  well-known mobile transfer fraud scheme. This paper uses logistic regression as the baseline model and compares it with ensembles and gradient descent models. The results indicate that the established logistic regression model did not perform too poorly compared to the other models. The random forest classifier had the most outstanding performance among all measures. The amount of money transferred was the top feature to predict money laundering transactions through mobile money transfers. These findings suggest that more research is needed to improve the logistic regression model. The random forest classifier should be further explored as a potential tool for law enforcement and financial institutions to detect money laundering activities in mobile money transfers.

Objectives/background: Sleep laboratory polysomnography (PSG) is the gold standard for obstructive sleep apnea (OSA) diagnosis in infants, but its access remains limited. Another simple and widely used tool, oximetry-capnography can provide information on the presence of desaturations and alveolar hypoventilation. However, its reliability is debated. This study aimed at examining its use in determining OSA severity in infants. Patients/methods: This retrospective study was conducted in a sleep unit in a tertiary hospital, in infants < 4 months old with clinical signs of OSA or Pierre Robin Sequence (PRS) who underwent a one-night PSG coupled with oximetry-capnography. Results: Among the 78 infants included (median [IQR] age: 61 [45-89] days at PSG), 44 presented with PRS, and 34 presented with isolated airway obstruction. The clinical, sleep and respiratory characteristics were not significantly different between the two subgroups. In the entire cohort, 63.5% had severe OSA. Median OAHI was 14.5/h [7.4-5.9], Spo2 was 97.4% [96.5-98.1], and PtCO2 was 41.1 mmHg [38.3-44.9]. The optimal threshold to predict OAHI > 10/h was 6/h for OD3% (sensitivity 95.7%, specificity 51.9%) and 2/h for OD4% (sensitivity 95.7%, specificity 48.1%). Conclusion: Whereas transcutaneous capnography does not appear to be sufficient in predicting severe OSA in infants < 4 months old with PRS or clinical signs of OSA, oximetry may be a useful alternative for the screening of severe OSA in infants in the absence of PSG.

Thermal conductivity of soil is one of the phenomenon which measure the amount of heat passing per cross-sectional area. Thermal conductivity of soils depends on various factors. In this study the influence of presence of Microorganisms on enhancement of thermal conductivity of soils is determined experimentally. In this research, soil samples from four different regions namely Dholera, Navsari, Sabarmati River and Madhavpur of Gujarat, India, were taken. The aim of study was to develop experimental model to measure the enhancement of thermal conductivity of soil by microorganisms. The laboratory model which measures thermal conductivity of soil is based on guarded hot plate method. The colonies of microorganisms available in soil were studied by serial dilution and spread plate technique. The influence of microorganisms on soil structure after the treatment was analysed by microscope. Four types of microorganisms were introduced in the soil and the thermal conductivity was recorded after one week of curing period. The microorganisms which gave better result of thermal conductivity were selected for further study. Thereafter, thermal conductivity of sample was measured for curing period of 7, 14, 21 and 28 d. The results of thermal conductivity and soil aggregations were compared after pre and post treatment.