The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

Verification of inclusivity of molecular detection methods gives indications on the reliability of viral infections diagnosis because of the tendency of viral pathogens to undergo sequence variation. This study was aimed to select inclusive probe based reverse transcription quantitative PCR (RT-qPCR) assays for the diagnosis of the most widespread and detrimental viruses infecting honeybees, namely the acute bee paralysis virus (ABPV), the black queen cell virus (BQCV), the chronic paralysis bee virus (CBPV), the deformed wing virus variants A (DWVA) and B (DWVB), and the sacbrood virus (SBV). Therefore, detection methods previously described were re-evaluated in silico for specificity and inclusivity. Based on this evaluation, selected methods were modified or new ones were designed and tested in duplex RT-qPCR reactions. The limits of detection (LODs), effect of multiplexing on sensitivity and viral RNA quantification potential in bees and hive debris were assessed. This study made available diagnostic assays able to detect an increased number of virus variants compared to previously described tests and two viral pathogens in a single PCR reaction.

This paper details the comprehensive activities conducted in a laboratory setting to assess the Structural Health Monitoring (SHM) of prefabricated building envelopes. Aimed at enhancing the structural evaluation of buildings, the focus of this research lies in employing nondestructive sensors for SHM with the objective to advance in the understanding and assessment of facade conditions. The paper outlines the methodologies employed, starting with a modal analysis to define the structural vibration's modal frequency. Subsequently, tests were defined and executed to validate building envelope compliance in case of stress typical of high-rise building. Throughout these tests, continuous monitoring of the façade was conducted using both conventional (accelerometers, extensimeter washers) and non-conventional sensors (Fiber Optic Sensing) The gathered data underwent thorough processing and analysis to provide an overall perspective on the structure's behavior during the stress test. The investigation aimed to identify whether specific physical phenomena correlate with distinct signals and to evaluate if continuous façade monitoring over the building's lifespan aids in preventing potential structural damage. Moreover, two modal analyses were conducted to compare final data and frequencies with earlier data, enabling the detection of any structural alterations. The results from compliance testing and structural analysis yielded noteworthy insights contributing to the evolving field of structural health monitoring, offering valuable findings that advance our ability to proactively manage and maintain the integrity of building structures.

A method based on ultra performance liquid chromatography with diode array detector (UPLC-DAD) was developed for quantitative analysis of five active compounds and chemical fingerprint analysis of Rosa rugosa. Ten batches of Rosa rugosa collected from different plantations in the Xinjiang region of China were used to establish the fingerprint. The feasibility and advantages of the used UPLC fingerprint were verified for its similarity evaluation by systematically comparing chromatograms with professional analytical software recommended by State Food and Drug Administration (SFDA) of China. In quantitative analysis, the five compounds showed good regression (R²=0.999 5) within the test ranges and the recovery of the method was in the range of 94.2–103.8%. The similarities of the fingerprints of 10 batches of the samples were more than 0.981. The developed UPLC fingerprint method is simple, reliable and validated for the quality control and identification of Rosa rugosa. Additionally, simultaneous quantification of five major bioactive ingredients in the Rosa rugosa samples was conducted to interpret the consistency of the quality test. The results indicated that the UPLC fingerprint as a characteristic distinguishing method combining similarity evaluation and quantification analysis, can be successfully used to assess the quality and to identify the authenticity of Rosa rugosa.

The performance of the presurgical test (preSurg) for temporal lobe epilepsy (TLE) was evaluated via video-electroencephalography (VEEG), electroencephalography (EEG), 99mTc-HmPAO single-photon emission tomography (SPECT) and 1.5 T magnetic resonance imaging (MRI) in a group of 112 men (37.0 ± 1.1 years) and 106 women (39.7 ± 1.1 years) operated on for TLE. The epileptic zone (EZ) was adequately identified to determine whether the patient reached an Engel I grade (EI) at least one year postop. Accuracy was evaluated by the coefficient α, ranging from 3 (when result = EZ) to 2 (result in the same hemisphere as the EZ), 1 (noninformative result) or 0 (EZ in the contralateral hemisphere). The simplicity of diagnosis was defined as the number of preSurg surgeries needed to identify the EZ. EI was obtained in 85.8% of patients even though 42.2% of patients had noninformative MRI results. For preSurg α was (mean ± SEM) = VEEG (2.83 ± 0.04) > MRI (2.14 ± 0.08) > EEG (1.87 ± 0.09) > SPECT (1.71 ± 0.09). The accuracy (combination of sensitivity and specificity) was calculated as follows: VEEG (0.797) > MRI (0.518) > EEG (0.446) > SPECT (0.360). The likelihood positive ratio was greater, and the likelihood negative ratio was lower for VEEG. The most relevant factor for the regression model was VEEG (3.106) > MRI (2.558) > EEG (1.905), and the factors were not significantly related to SPECT. EZs in EI patients with low simplicity were identified mainly by VEEG. A very good postoperative outcome can be obtained even in TLE patients with no lesions on MRI. The VEEG is the most reliable preSurg test and may be the only reliable test for patients with very low simplicity (i.e., difficult).single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.

In this work, we designed a MEMS device which allows simultaneous direct measurement of mechanical properties during deformation under external stress and characterization of the evolution of microstructure of nanomaterials within a transmission electron microscope. This MEMS device makes it easy to establish the correlation between microstructure and mechanical properties of nanomaterials. The device uses piezoresistive sensors to qualitatively measure the force and displacement of nanomaterials, e.g., in wire and thin plate forms. The device has a theoretical displacement resolution of 0.19 nm and a force resolution of 2.1 μN. The device has a theoretical displacement range limit of 2.74 μm and a load range limit of 27.75 mN.

: Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. Integration of a Mohr-Coulomb element with the Burgers model in series serves to characterise the yield creep behaviour of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilising randomised creep parameters, and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally.

Cognitive impairment is a condition which affects aging patient populations and is becoming more prevalent as the geriatric population expands. It is categorized by the extent of cognitive decline, memory loss, functional abilities, and the capability to perform daily tasks. Based on the degree of impairment, it is classified as mild cognitive impairment or dementia. Considering the debilitating nature of cognitive decline and its impact on quality of life, prevention is key. In addition, given the gradual progression of the underlying causes of cognitive decline, early diagnosis and appropriate management has the potential to offer clinical advantages and better patient outcomes. Management options discussed vary from pharmaceutical regimens to neurosurgical interventions, with differing outcomes based on etiology of cognitive impairment.

To address the insufficient mechanical properties of calcined phosphogypsum, this research aims to investigate the utilization of basalt fiber and calcium carbonate whiskers as reinforcing agents. The study examines their impact on the flexural and compressive strength, toughness, water resistance, and tensile strength of calcined phosphogypsum. In the individual tests, basalt fibers with different lengths (3 mm, 6 mm, 9 mm, and 18 mm) were added at dosages of 0%, 0.5%, 1.0%, and 1.5% respectively. The findings demonstrate that basalt fiber effectively enhances the flexural and compressive strength, toughness, and tensile strength of calcined phosphogypsum, albeit potentially compromising water resistance. Among the various fiber lengths, the 6 mm fibers exhibit the most advantageous influence on the performance of calcined phosphogypsum. A subsequent test was conducted to investigate the impact of cross-scale fibers on the properties of calcined phosphogypsum by mixing 6 mm basalt fibers and calcium carbonate whiskers. The results illustrate that calcium carbonate whisker refines the pores, thereby improving the flexural strength and toughness of calcined phosphogypsum. Furthermore, it compensates for the water resistance limitations associated with the sole utilization of basalt fiber while further augmenting the tensile strength and strain capacity. However, it should be noted that increasing the dosage of both calcium carbonate whiskers and basalt fibers concurrently leads to increased porosity of phosphogypsum and reduced compressive strength.

Exposure to malachite green (MG) may pose great health risks to humans, thus it is of prime importance to develop fast and robust methods to quantitatively screen the presence malachite green in environment. Herein application of extractive electrospray ionization mass spectrometry (EESI-MS) has been extended to the trace detection of MG within lake water and aquiculture water, due to the intensive use of MG as biocide in fisheries. This method has the advantage of obviating offline liquid-liquid extraction or tedious matrix separation prior to the measurement of malachite green in native aqueous medium. The experimental results indicate that the extrapolated detection limit for MG was ~3.8 ug L-1 (S/N=3) in lake water samples and ~ 0.5 ug L-1 in ultrapure water under optimized experimental conditions. The signal intensity of MG showed good linearity over the concentration range of 10–5000 ug L-1. Measurement of practical water samples fortified with MG at 0.01 and 0.1 mg L-1 gave a good validation of the established calibration curve. The average recoveries of malachite green in lake water and Carassius carassius fish farm effluent water were 114.9% (6.6% RSD) and 85.4% (9.2% RSD), respectively. Overall, the established EESI-MS/MS method has been demonstrated suitable for sensitive and rapid (<2 min per sample) quantitative detection of malachite green in various aqueous media, indicating its potential for online real-time monitoring of real life samples.

Frontotemporal lobar degeneration (FTLD) belongs to a heterogeneous group of highly complex neurodegenerative diseases and represents the second cause of presenile dementia in individuals under 65. Frontotemporal-TDP is a subgroup of frontotemporal dementia characterized by the aggregation of abnormal protein deposits, predominantly transactive response DNA-binding protein 43 (TDP-43), in the frontal and temporal brain regions. These deposits lead to progressive degeneration of neurons resulting in cognitive and behavioral impairments. Limbic age-related encephalopathy (LATE) pertains to age-related cognitive decline primarily affecting the limbic system which is crucial for memory, emotions, and learning. Though distinct, emerging research suggests a potential overlap in pathogenic processes, with some cases of limbic encephalopathy displaying TDP-43 pathology. Genetic factors play a pivotal role in both disorders. Mutations in various genes, such as progranulin (GRN) and chromosome 9 open reading frame 72 (C9orf72), have been identified as causative in frontotemporal-TDP. Similarly, specific genetic variants have been associated with an increased risk of developing LATE. Understanding these genetic links provides crucial insights into disease mechanisms and the potential for targeted therapies.

This study provides a detailed investigation into the pathways through which microplastics from urban sources are transported to marine ecosystems, their distribution across different environments, and their impacts on marine life. Utilizing a combination of water and sediment sampling across urban, riverine, and marine environments, the research quantitatively assesses the concentration and types of microplastics present, and evaluates the ecological consequences of their accumulation in marine ecosystems. The findings reveal a significant concentration of microplastics in all examined environments, with the highest levels detected in urban areas, closely linked to industrial activities and inadequate waste management practices. The study documents a variety of microplastic polymers, with polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) being the most prevalent. A substantial impact on marine organisms was observed, including evidence of microplastic ingestion across a wide range of species and indications of stress and physical damage to coral reefs. Seasonal variations indicate that microplastic concentrations peak during periods of high rainfall, suggesting urban runoff as a critical pathway for microplastic transport. The study concludes with recommendations for improving waste management practices, enhancing public awareness and engagement in plastic recycling efforts, and implementing policies aimed at reducing the production and use of single-use plastics. This research underscores the urgent need for coordinated actions to mitigate the pervasive issue of microplastic pollution, highlighting the role of urban management and policy interventions in safeguarding marine ecosystems.

Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD) patients using neuropsychological testing. The study involved 71 post-COVID patients, with matched control groups: recovered COVID-19 individuals without complications (n=18) and individuals without prior COVID-19 history (n=19). A post-COVID depression group (PCD, n=25) was identified based on psychiatric diagnosis, a comparison group (noPCD, n=46) included participants with neurological COVID-19 complications, excluding clinical depression. The PCD patients showed significantly less scores in the MoCA test, decreased immediate memory recall in the Word Memory Test, decreased processing speed and higher accuracy in the Trail Making Test, and near to significant worse executive control and processing speed in the Stroop task compared to controls and the noPCD patients. The number of post-COVID symptoms negatively correlated with immediate word memory recall and processing speed among all post-COVID patients. In PCD patients, negative correlations between number of post-COVID symptoms and delayed recall, between time after recovery and immediate recall, and positive correlation between the number of acute symptoms and processing speed in the incongruent condition of the Stroop task were found. No differences between groups in Sniffin’s stick olfactory test was found. Overall, our study revealed cognitive impairment in PCD patients similar to those in major depressive disorder.

Aim: The aim of this study was to investigate if tests used to assess core stability could be used to determine success in physiological tests applied to assess dynamic balance and agility for a young active population. Methods: Pearson's r correlation coefficient was used to assess the relationship between the core stability tests and the dynamic balance and agility tests. Evaluation of the tests was established using Cronbach's coefficient of variance as part of intra-rater reliability tests. An analysis of 18 active college aged students was conducted (males: n= 13, females: n= 5). The mean ± SD age for males was 19.2 years ± 3.22 years and for females was 19.4 years ± 1.14 years. Conclusion: The results indicate that there is no significant relationship between tests that assess core stability and tests conducted to assess dynamic balance in active young adults. With the exception of the abdominal flexion test, no significant relationship exists between the remaining core stability tests and agility T-Test. Core stability is not a determinant of balance and agility.

The aim of the article is to verify differences in the perception of selected factors of sustainability among small and medium-sized enterprises (SMEs) in Central Europe. The subjects of analysis are factors such as corporate social responsibility (CSR), business risk sources (market, financial, personnel, and operational), reputation and social media, national support, social changes, and crisis events in business. The questionnaire was completed by 1,090 SMEs from the business environments of Visegrad Group countries. Statistical hypotheses were verified using Z-scores for two population properties. The most negatively perceived factors among owners and managers in the SME segment are national support and legislative changes. The most positively perceived factor is reputation and social media. The country of doing business is a significant factor. The perception of crisis events in business is most negative among Czech SMEs. More than 80% of Hungarian SMEs perceive their companies as sustainable. In comparison with Slovak and Polish SMEs, Hungarian perceptions are more positive in the context of sustainability. The country of doing business is the strongest factor in evaluating CSR indicators. The positive perception of CSR in Czech SMEs (43.9%) is lower than in other countries.

The devastating growth in the worldwide frequency of neurocognitive disorders and its allied difficulties such as decline in memory, spatial competency, and ability to focus poses a significant psychological public health problem. Inhibitor of Differentiation (ID) proteins are members of a family of helix-loop-helix (HLH) transcription factors. ID proteins have been demonstrated to be involved in neurodevelopmental & depressive diseases and thus may influence neurocognitive deficiencies due to environmental exposure. Previously, it has been demonstrated that environmental factors such as estrogenic endocrine disruptors (EEDs) have played an essential role in the influence of various neurocognitive disorders such as Alzheimer’s, Dementia, and Parkinson’s disease. Based on this increasing number of reports, we consider the impact of these environmental pollutants on ID proteins. Better understanding of how these ID proteins by which EED exposure can affect neurocognitive disorders in populations will prospectively deliver valuable information in the impediment and regulation of these diseases linked with environmental factor exposure.

Cranberries are rich in bioactive constituents reported to influence a variety of health benefits, ranging from improved immune function and decreased infections to reduced cardiovascular disease and more recently cancer inhibition. A review of cranberry research targeting cancer revealed positive effects of cranberries or cranberry derived constituents against 17 different cancers utilizing a variety of in vitro techniques; whereas, in vivo studies supported the inhibitory action of cranberries toward cancers of the esophagus, stomach, colon, bladder, prostate, glioblastoma and lymphoma. Mechanisms of cranberry-linked cancer inhibition include cellular death induction via apoptosis, necrosis and autophagy; reduction of cellular proliferation; alterations in reactive oxygen species; and modification of cytokine and signal transduction pathways. Given the emerging positive preclinical effects of cranberries, future clinical directions targeting cancer or premalignancy will be considered

There is a need to find methods to intensify the anaerobic digestion process. One possibility is the use of pre-treatment techniques. Many laboratory tests confirm their effectiveness, but in most cases, there is no verification work carried out on industrial plants. The aim of the research carried out under laboratory conditions and on a large scale was to determine the technological and energy efficiency of the use of hydrodynamic cavitation in the pre-treatment of a waste mixture from dairy farms. It has been shown that hydrodynamic cavitation significantly increases the concentration of organic compounds in the dissolved phase. In the most effective variants, the increase in the content of these indicators was over 90% for both COD and TOC. The degree of solubilisation achieved was 49±2.6% for COD and almost 52±4.4% for TOC. Under laboratory conditions, the highest effects of anaerobic digestion were achieved after 10 minutes of pre-treatment. The amount of biogas was on average 367±18 mL/gCOD and the amount of methane 233±13 mL/gCOD. Further large-scale optimisation trials showed that after 8 minutes of hydrodynamic cavitation, the biogas yield was 327±8 L/kgCOD with a CH4 content of 62.9±1.9%. With this variant, the net energy yield was 66.4±2.6 kWh/day, a value that was 13.9% higher than the original variant with 10 minutes of disintegration and 3.1% higher than the variant without pre-treatment.

New antimicrobial approaches are essential to counter antimicrobial resistance. The drug development pipeline is exhausted with emergence of resistance resulting in unsuccessful trials. The lack of an effective drug developed from the conventional drug portfolio has mandated the introspection into the list of potentially effective unconventional alternate antimicrobial molecules. Alternate therapies, that are clinically explicable forms include monoclonal antibodies, antimicrobial peptides, aptamers and phages. Clinical diagnostics optimizes the drug delivery. In the era of diagnostic-based applications, it is logical to draw diagnostic based treatment for infectious diseases. Selection criteria of alternate therapeutics in infectious disease include detection, monitoring of response and resistance mechanism identification. Integrating these diagnostic applications is disruptive to the traditional therapeutic development. The challenges and mitigation methods need to be noted. Applying the goals of clinical pharmacokinetics that include enhancing efficacy and decreasing toxicity of drug therapy this review analyses the strong correlation of alternate antimicrobial therapeutics in infectious diseases. The relationship between drug concentration and the resulting effect defined by the pharmacodynamic parameters are also analyzed. This review analyzes the perspectives of aligning diagnostic initiatives into the use of alternate therapeutics with a particular focus on companion diagnostic applications in infectious diseases.

There has been a growing interest in incorporating psychological and neuroscientific knowledge about the development of cognitive functions in educational policies and academic practices. In this paper, we argue that the current knowledge about the interactions between these functions and their neurodevelopmental characteristics should also be considered in order to develop practices that could be better suited to pupils depending on their age. To facilitate this, we review current neuroscientific knowledge on the competitive interactions between two neural circuitry underlying distinct learning functions, their developmental trajectories and how they are linked to other functions such as cognitive control. The incorporation of this knowledge in education could help improve academic outcomes.

Academic performance and capabilities were greatly influenced by creativity. It has also contributed to comprehending academic skill development independent of motivation and intelligence. This study examines the relationship between student creativity and academic skills. This is the systematic literature review for 2016 to 2022. The main criteria for including an article in the research were that it was written in English, that it was pertinent to the topic of the study, that its sample consisted of students aged 5 to 16 with regular, typical development, that it was a descriptive, exploratory, or longitudinal study, and that it was accessible to the public. Seven articles were included in the study following a critical and systematic analysis and evaluation. As the results indicate, creativity and its characteristics correlate substantially with academic skills such as reading, comprehension, academics, word processing, and written assignments. Additionally, creativity contributed considerably to children’s academic performance, both currently and in the future. The present study concludes that creativity is essential for enhancing and promoting children’s academic performance.

Background: There is now evidence that schizophrenia and deficit schizophrenia are neuro-immune conditions and that oxidative stress toxicity (OSTOX) may play a pathophysiological role. Aims of the study: To compare OSTOX biomarkers and antioxidant (ANTIOX) defenses in deficit versus non-deficit schizophrenia. Methods: We examined lipid hydroperoxides (LOOH), malondialdehyde (MDA), advanced oxidation protein products (AOPP), sulfhydryl (-SH) groups, paraoxonase 1 (PON1) activity and PON1 Q192R genotypes, total radical-trapping antioxidant parameter (TRAP) as well as immune biomarkers in patients with deficit (n=40) and non-deficit (n=40) schizophrenia and healthy controls (n=40). Results: Deficit schizophrenia is characterized by significantly increased levels of AOPP and lowered -SH, and PON1 activity, while no changes in the OSTOX/ANTIOX biomarkers were found in non-deficit schizophrenia. An increased OSTOX/ANTIOX ratio was significantly associated with deficit versus non-deficit schizophrenia (Odds ratio=3.15, p<0.001). Partial least squares analysis showed that 47.6% of the variance in a latent vector extracted from psychosis, excitation, hostility, mannerism, negative symptoms, psychomotor retardation, formal thought disorders, and neurocognitive test scores was explained by LOOH+AOPP, PON1 genotype + activity, CCL11, tumor necrosis factor (TNF)-α, IgA responses to neurotoxic tryptophan catabolites (TRYCATs), whereas -SH groups and IgM responses to MDA showed indirect effects mediated by OSTOX and neuro-immune biomarkers. Discussion: Our findings indicate that with increasing overall severity of schizophrenia, neuro-immune and neuro-oxidative (especially protein oxidation indicating chlorinative stress) toxicities become more prominent and together with lowered antioxidant defenses and impairments in innate immunity-associated resilience against neurotoxic processes shape a distinct nosological entity, namely deficit schizophrenia.

Eating disorder (ED) symptoms often co-occur with nonsuicidal self-injury (NSSI). This comorbidity is consistent with evidence that trait negative urgency increases risk for both of these phenomena. We previously found that impaired late-stage negative emotional response inhibition (i.e., negative emotional action termination or NEAT) might represent a neurocognitive mechanism for heightened negative urgency among people with NSSI history. The current study evaluated whether relations between negative urgency and ED symptoms similarly reflect deficits in this neurocognitive process. One hundred and five community adults completed an assessment of ED symptoms, negative urgency, and an emotional response inhibition task. Results indicated that, contrary to predictions, negative urgency and NEAT contributed independent variance to the prediction of ED symptoms, while controlling for demographic covariates and NSSI history. Worse NEAT was also uniquely associated with restrictive eating, after accounting for negative urgency. Our findings suggest that difficulty inhibiting ongoing motor responses triggered by negative emotional reactions (i.e., NEAT) may be a shared neurocognitive characteristic of ED symptoms and NSSI. However, negative urgency and NEAT dysfunction capture separate variance in the prediction of ED-related cognitions and behaviors, distinct from the pattern of results we previously observed in NSSI.

Wastewater-based epidemiology (WBE) has emerged as a key method for continuous monitoring of COVID-19 prevalence including circulating SARS-CoV-2 lineages. WBE addresses limitations of traditional clinical COVID-19 surveillance such as clinical test availability, fluctuating testing rates and increased reliance on rapid antigen tests. Our study in Perth, Western Australia, found a significant positive correlation between SARS-CoV-2 concentrations in wastewater and clinical PCR positivity rates (rs = 0.772; p < 0.001) over an 18-month period that included four successive COVID-19 waves. A strong positive correlation was apparent between the proportions of SARS-CoV-2 lineages in wastewater and clinical cases within the same region (rs = 0.728; p < 0.001), including earlier detection of Omicron and recombinant lineages in wastewater before clinical case confirmation. The successful integration of WBE with healthcare data underscores its critical role in enhancing public health decision-making and pandemic management. This approach not only demonstrates the value of WBE in current global health surveillance efforts but also highlights the potential of WBE to address future public health challenges, as a comprehensive disease monitoring and response approach.

Background. Amyotrophic Lateral Sclerosis (ALS) is a devastating disease involving motor neuron degeneration. The few drugs approved for treatment have at most a marginal benefit, and death usually occurs 2-5 years after diagnosis. Methods. A thorough manual examination of the relevant literature, covering over 35,000 papers. Results. Two major phenomena that are generally not known to clinicians were found. First, insulin signaling is impaired in ALS even in patients not diagnosed with diabetes (DB). Almost all studies that have explicitly tested insulin function in non-DB ALS patients using glucose tolerance tests (18 out of 20, 1964-2022, different groups) have found it to be impaired. Second, there is strong evidence for excessive insulin-independent glucose uptake (IIGU) in ALS. In addition, (i) early/late diabetes are associated with increased/decreased risk, respectively; (ii) insulin-based diabetes drugs are protective in ALS in large retrospective human studies; and (iii) strong animal and human evidence shows that insulin opposes all of the major pathological processes in ALS. Conclusion. Most ALS patients have insulin impairment, yet this is commonly not diagnosed, likely because excessive IIGU normalizes glucose levels. The impairment promotes disease progression. Late diabetes is associated with decreased risk because high glucose levels indicate non-excessive IIGU, and because diabetes drugs are protective. Insulin-based treatment (e.g., GLP1 agonists, insulin) is beneficial and can be disease-modifying in ALS and in frontotemporal dementia variants comorbid with ALS. ALS patients should be routinely tested for insulin function and treated if test results are positive.

The main foundations of the standard $\Lambda $CDM model of cosmology are that: 1) The redshifts of the galaxies are due to the expansion of the Universe plus peculiar motions; 2) The cosmic microwave background radiation and its anisotropies derive from the high energy primordial Universe when matter and radiation became decoupled; 3) The abundance pattern of the light elements is explained in terms of primordial nucleosynthesis; and 4) The formation and evolution of galaxies can be explained only in terms of gravitation within a inflation+dark matter+dark energy scenario. Numerous tests have been carried out on these ideas and, although the standard model works pretty well in fitting many observations, there are also many data that present apparent caveats to be understood with it. In this paper, I offer a review of these tests and problems, as well as some examples of alternative models.

Augmented and Virtual Reality have been experiencing a rapidly growth in recent years, but there is not still a deep knowledge on their capabilities and where they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of the Microsoft's HoloLens 2 (Augmented Reality device) and HTC Vive (Virtual Reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, while in HTC Vive, the object tracked was the system's hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimetre scale, while the HoloLens 2 revealed not so accurate measures (around 2 centimetres). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user's hand and not an inherit controller made a huge impact. The results were considered a significant step for the on going project of developing a human-robot interface to program by demonstration an industrial robot using Extended Reality, which shows great potential to succeed based on this data.

An extensive review of literature simulations of polymer melts is given, considering results that test aspects of the Rouse model in the melt. We focus on the mean-square amplitudes < (X_p(0))^{2} > and time correlation functions < X_p(0) X_p(t) > of the Rouse modes $X_p(t)$. Contrary to the Rouse model: (i) Mean-square Rouse mode amplitudes < (X_p(0))^2> do not scale as sin^{-2}(p \pi/2N), N being the number of beads in the polymer. For small p (say, p <= 3) < (X_p(0))^2> scales with p as p^{-2}$; for larger p it scales as p^{-3}. (ii) Rouse mode time correlation functions < X_p(t) X_p(0) > do not decay with time as exponentials; they instead decay as stretched exponentials exp(-a t^b)$. b depends on p, typically with a minimum near N/2 or N/4. (iii) Polymer bead displacements are not described by independent Gaussian random processes. (iv) For p not equal to q, < X_p(t) X_{q}(0) > is sometimes non-zero. (v) The response of a polymer coil to a shear flow is a rotation, not the affine deformation predicted by Rouse. Simulations thus conclusively demonstrate that the Rouse model is invalid in polymer melts. We also briefly consider the Kirkwood-Riseman polymer model.

Persistent land subsidence or uplift can cause serious latent hazards to urban infrastructure. To thoroughly investigate the causes and impacts of land surface deformation, this study examines the case of Wuxi City, focusing on the land fluctuation patterns in the region following the enforcement of the groundwater abstraction prohibition policy in 2005. This research utilizes the PS-InSAR technique to quantify land surface deformations in the Wuxi region from November 2015 to June 2023 and analyzes 100 SAR images to obtain comprehensive spatiotemporal evolution data of the land surface. Furthermore, this study conducts an in-depth analysis of the effects of various factors, including groundwater, precipitation, and soil geology, on the characteristics of land surface deformation. It pioneers the application of the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model to analyze the time-series displacement in the study area and innovatively introduces the "amplitude factor" index to assess land surface fluctuations. This study investigates the recent land surface deformation patterns and mechanisms in Wuxi City. By integrating the "amplitude factor", it provides a scientific foundation for future urban development and disaster mitigation strategies in Wuxi City, offering valuable insights for related research.

Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏ lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

This study introduces and demonstrates a comprehensive, accurate, unbiased approach to robust quantitative comparison of Electronic Nicotine Delivery Systems (ENDS) appropriate for establishing substantial equivalence (or lack thereof) between tobacco products. The approach is demonstrated across a family of thirteen pen- and pod-style ENDS products. Methods employed consist of formulating a robust emissions surface regression model, quantifying the empirical accuracy of the model as applied to each product, evaluating relationships between product design characteristics and maximum emissions characteristics, and presenting results in formats useful to researchers, regulators, and consumers. Results provide a response surface to characterize emissions (total particulate matter and constituents thereof) from each ENDS appropriate for use in a computer model and for conducting quantitative exposure comparisons between products. Results demonstrate that emissions vary as a function of puff duration, flow rate, E-Liquid composition, and device operating power. Further, results indicate that regulating design characteristics of ENDS devices and consumables may not achieve desired public health outcomes; it is more effective to regulate maximum permissible emissions directly. Three emissions outcome measures (yield per puff, mass concentration and constituent mass ratio) are recommended for adoption as standard quantities for reporting by manufacturers and research laboratories. The approach provides a means of (a) quantifying and comparing maximal emissions from ENDS products spanning their entire operating envelope, (b) comparative evaluation of ENDS devices and consumable design characteristics, and (c) establishing comparative equivalence of maximal emissions from ENDS. A consumer-oriented product emissions dashboard is proposed for comparative evaluation of ENDS exposure potential. Maximum achievable power dissipated in the coil of ENDS is identified as a potentially effective regulatory parameter.

Although the neurofilament light chain (NfL) is used as a biomarker for neurodegenerative decline, its use in surgery- and anaesthesia-induced acute cognitive dysfunction remains controversial. We aimed to synthesise the evidence to explore the potential of NfL as an established biomarker for perioperative neurocognitive disorders (PND). We systematically searched the PubMed, EMBASE, MEDLINE, Cochrane Library electronic databases, and Cochrane Central Registry of Clinical Trials. Our results show that CSF NfL levels positively correlated with blood NfL levels (r = 0.63; 95% confidence interval [CI], 0.42-0.77). The pooled standardised mean difference (SMD) showed a significantly higher preoperative CSF NfL levels (SMD = 0.27; 95% CI, 0.07-0.47) and both pre- and postoperative blood NfL levels (SMD = 0.53, 95% CI, 0.40-0.66 and SMD = 0.58, 95% CI, 0.43-0.73) in the postoperative delirium (POD) group. Postoperative blood NfL levels were significantly elevated in both POD group (SMD = 0.49; 95% CI, 0.34-0.64) and no-POD group (SMD = 0.67; 95% CI, 0.53-0.81). Our meta-analysis indicates that NfL may serve as a potential biomarker for POD, and elevated preoperative blood NfL levels could be a risk factor for POD. Further studies are needed to confirm the association of CSF/blood NfL levels with other PND types.

In the 21st century, the effects of HIV-associated neurocognitive disorders (HAND) have been significantly reduced in individuals due to the development of antiretroviral therapies (ARTs). However, the growing epidemic of polysubstance use (PSU) has led to concern for the effects of PSU on HIV-seropositive individuals. To effectively treat individuals affected by HAND, it is critical to understand the biological mechanisms affected by PSU including identification of novel markers. To fill this important knowledge gap, we used an in vivo HIV-Transgenic (HIV-Tg) animal model, to investigate the effects of the combined use of chronic methamphetamine (METH) and oxycodone (oxy). RNA-Seq analysis on the striatum - a brain region that is primarily targeted by both HIV and drugs of abuse - identified key differentially expressed markers post-METH and oxy exposure. Furthermore, ClueGO analysis and Ingenuity Pathway Analysis (IPA) revealed crucial molecular and biological functions associated with ATP-activated adenosine receptor, neuropeptide hormone activity, and the oxytocin signaling pathway to be altered between the different treatment groups. The current study further reveals the harmful effects of chronic PSU and HIV infection that can subsequently impact neurological outcomes in polysubstance users with HAND.

Background: Patients undergoing brain tumor resection experience changes to their neurocognitive abilities, many of which can be difficult to predict. We hypothesized that changes in brain connectivity could predict changes in neurocognitive functioning, demonstrating the potential for brain connectivity aware surgical planning to provide enhanced outcomes for patients. Methods: Patients underwent functional and diffusion MR scanning and neuropsychological testing before tumor resection and two weeks post-resection. Using this functional and diffusion imaging, we measured changes in the topology of the functional and structural graph networks, respectively. From the neuropsychological testing scores, we derived a composite score that describes a patient’s overall level of neurocognitive functioning. We then used a multiple linear regression model to test if structural and functional connectivity measures could predict changes in composite scores. Results: Twenty-one subjects completed imaging and neuropsychological evaluation both before and after surgery. The multiple linear regression model showed that changes in functional local efficiency were inversely correlated with changes in composite score (p

There is an important complementarity between experimental methods for the study of high-temperature viscoelasticity in the time and frequency domains, that has not always been fully exploited. Here we show that parallel processing of forced-oscillation data and microcreep records, involving consistent use of either Andrade or extended Burgers creep-function models, yields a robust composite modulus-dissipation dataset spanning a broader range of periods than either technique alone. In fitting this dataset, the alternative Andrade and extended Burgers models differ in their partitioning of strain between the anelastic and viscous contributions. The extended Burgers model is preferred because it involves a finite range of anelastic relaxation times, and accordingly a well-defined anelastic relaxation strength. The new strategy offers the prospect of better constraining the transition between transient and steady-state creep, or equivalently, between anelastic and viscous behaviour.

The paper presents construction, laboratory tests as well as the first field application of a new fiber-optic rotational seismograph. The system based on fiber-optic gyroscope (FOG) with determined Angle Random Walk of the order of 10-8 rad/Sqrt(s) and a few rad/s maximum detectable amplitude of rotation in the frequency range from DC to 328.12 Hz. It has been designed for rotational seismology area of interest. This work also presents exemplary relevant measurements which were conducted using a set of two devices installed in the geophysical observatory in Książ, Poland.

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Many of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical measures; but none measure has been developed to statistically quantify the noise in an arranged vector posterior a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, to assess this problem.

In this review, we present the latest exclusion limits obtained with astroparticles for Lorentz Invariance Violation (LIV) in the photon sector. We discuss the techniques known as energy-dependent time delay or time lag, subluminal pair production threshold shift, superluminal photon decays and superluminal photon splitting. Perspectives for future results in LIV with the next generation of experiments are also addressed.

The up-to-date challenge towards clean road transport are non-exhaust emissions. Important advances regarding measurement systems, including well defined characterisation techniques, as well as regulation, will happen in the next few years. In this work we present detailed results of particle emissions from a test bed for wheel suspension and brakes, consisting of aerosol (size distribution, particle number (PN) and mass (PM)) and electron microscopy measurements (EM) under different load conditions. Standard tyres and brakes from series production were tested with a high-load driving cycle while particle measurements were conducted by gravimetric measurements and with a TSI SMPS, a TSI APS and a GRIMM OPS. Furthermore, samples were analyzed by electron microscopy. A bimodal particle size distribution (PSD) was obtained with the SMPS, with peaks at 20nm and around 400nm. EM analysis of &gt;1400 single particles from the electrostatic sampler matches the PSD results. EM analysis also showed ultrafine particles, mainly from O, Fe, Si, Ba, Mg and S and also fractal particles with high C fractions. Our results suggest, in agreement with previously published literature, that particulate emissions are related to brake disc temperature and occur in significant amounts above a threshold temperature.

How language mediates emotional perception and experience is poorly understood. The present event-related potential (ERP) study examined the explicit and implicit processing of emotional speech to differentiate the relative influences of communication channel, emotion category and task type in the prosodic salience effect. Thirty participants (15 women) were presented with spoken words denoting happiness, sadness and neutrality in either the prosodic or semantic channel. They were asked to judge the emotional content (explicit task) and speakers’ gender (implicit task) of the stimuli. Results indicated that emotional prosody (relative to semantics) triggered larger N100 and P200 amplitudes with greater delta, theta and alpha inter-trial phase coherence (ITPC) values in the corresponding early time windows, and continued to produce larger LPC amplitudes and faster responses during late stages of higher-order cognitive processing. The relative salience of prosodic and semantics was modulated by emotion and task, though such modulatory effects varied across different processing stages. The prosodic salience effect was reduced for sadness processing and in the implicit task during early auditory processing and decision-making but reduced for happiness processing in the explicit task during conscious emotion processing. Additionally, across-trial synchronization of delta, theta and alpha bands predicted the ERP components with higher ITPC values significantly associated with stronger N100, P200 and LPC enhancement. These findings reveal the neurocognitive dynamics of emotional speech processing with prosodic salience tied to stage-dependent emotion- and task-specific effects, which can reveal insights to research reconciling language and emotion processing from cross-linguistic/cultural and clinical perspectives.

Immunoglobulin G (IgG) adopts a modular multidomain structure that mediates antigen recognition and effector functions, such as complement-dependent cytotoxicity. IgG molecules are self-assembled into a hexameric ring on antigen-containing membranes, recruiting the complement component, C1q. To provide deeper insights into the initial step of the complement pathway, we report a high-speed atomic force microscopy study for quantitative visualization of the interaction between IgG and the C1 complex composed of C1q, C1r, and C1s. Results showed that C1q in the C1 complex is restricted regarding internal motion and has a stronger binding affinity for on-membrane IgG assemblages than C1q alone, presumably because of smaller conformational entropy loss upon binding. Furthermore, we visualized a 1:1 stoichiometric interaction between C1/C1q and an IgG variant that lacks the entire CH1 domain in the absence of antigen. In addition to the canonical C1q-binding site on Fc, their interactions are mediated through a secondary site on the CL domain that is cryptic in the presence of the CH1 domain. Our findings offer clues for novel-modality therapeutic antibodies.

Background: Cognitive for patients with neurocognitive disorders are mostly measured by Montreal Cognitive Assessment (MoCA) and Visual Cognitive Assessment Test (VCAT) as screening tools. These cognitive scores are usually left skewed and the results of association analysis might not be robust. This study aims to study the distribution of the cognitive outcomes and to discuss potential solutions. Materials and Methods: The inverse transformed cognitive outcomes are modelled using different statistical distributions. Robustness of the proposed models are checked under different scenarios: with intercept only, models with covariate, with and without bootstrapping. Results: Main results were based on VCAT score, and validated via MoCA score. Findings suggested that the inverse transformation method improves modelling the cognitive scores compared to the conventional methods using the original cognitive scores. Association of baseline characteristics (age, gender and years of education) and the cognitive outcomes were reported as estimates and 95% confidence intervals. Bootstrap methods improved the estimate precision and the bootstrapped standard errors of the estimates are more robust. Cognitive outcomes are widely analyzed using linear regression models with default normal distribution as a conventional method. We compared the results of our suggested models with the normal distribution under various scenarios. Goodness-of-fit measurements were compared between proposed models and conventional methods. Conclusions: The findings support the use of the inverse transformation method to model the cognitive outcomes instead of the original cognitive scores for early stage neurocognitive disorders where the cognitive outcomes are left skewed.

HIV-associated neurocognitive disorder (HAND) is an array of neurocognitive changes associated with HIV infection, and the roles of microRNAs in HAND are not completely revealed yet. Based on published data and publicly available databases, we constructed an integrated miRNA-mRNA network involved in HAND. Bioinformatics analyses, including gene ontology, network analysis, and KEGG pathway analysis, were applied for further study of the network and the genes of the network. The axon guidance KEGG pathway, three genes NTNG1, EFNB2, CXCL12, and 17 miRNAs which regulates them, are spotlighted in our study. This study provides new perspectives to the knowledge of miRNAs’ roles in the process of HAND, and our findings provided potential therapeutic targets and clues of HAND.

The energy E takes the place of the gravitational mass m_G to produce gravity [1]. We wonder whether the inertial mass m_i in the inertial force is also replaced by E. The experiments to measure the inertial force and weight of a carrier are presented. Now we can test the principle of equivalence and study foundations of physics in condensed matter.

There is an increasing need for quantitative rockfall hazard and risk assessment that requires a precise definition of the terms and concepts used for this particular type of landslide. This paper suggests to use terms that appear to be the more logic and explicit as possible, and describes methods to derive some of the main hazard and risk descriptors. The terms and concepts presented concern the rockfall process (failure, propagation, fragmentation, modelling) and the hazard and risk descriptors, distinguishing the cases of localized hazards and diffused hazards. For a localized hazard, the failure probability of the considered rock compartment in a given period of time has to be assessed and the probability for a given element at risk to be impacted with a given energy must be derived combining the failure probability, the propagation probability and the exposure of the element. For a diffuse hazard that is characterized by a failure frequency, the number of rockfalls reaching the element at risk per unit of time and with a given energy (reach frequency) can be derived. However, when the element at risk is not replaced or repaired, the probability that it is impacted by at least one rockfall must be considered.

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. We also ask the question, "Do different input classes have a relationship or are they unrelated?" For instance, could there be an overlapping set of highly active neurons to identify different classes? Could there be a set of neurons that are useful for one input class whereas misleading for a different one? Intuition answers these questions positively, implying the existence of a structured set of neurons inclined to a particular class. Knowing this structure has significant values; it provides a principled way for identifying redundancies across the classes. Here the interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. We also propose an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image. Our relevance score verifies the interpretability scores in terms of neurons tuned to a particular concept/class. Further, we perform network ablation and measure the performance of the network based on our approach.

Statistical inferences is regarded as the general criteria for statistical conclusion and drawing generalizations. However, most of the inferential statistical tools are based on strong assumptions which create a strict limitations on their use and application. Analysis of variance (ANOVA) is one of such statistics. In this article, eight (8) new ANOVA-like methodologies were proposed, in alternative to one-way ANOVA, based on the assumptions of statistical mirroring. Methods validation in comparison with one-way ANOVA was designed to assess the suitability and statistical power of the new proposals as an alternative methods, using different sets of logically generated multivariate datasets with different problems and statistical complications. The results of comparisons validate that the eight (8) proposed ANOVA-like methodologies were suitable alternatives to ANOVA, in the sense that they require no normality assumption to be meet, used different ways to compare the data with different statistical elements rather than depending on only variance, efficient with negative values, and results interpretation is easier.

The Medi-test system we developed was motivated by the large number of resources available for the medical domain, as well as the number of tests needed in this field (during and after the medical school) for evaluation, promotion, certification, etc. Generating questions to support learning and user interactivity has been an interesting and dynamic topic in NLP since the availability of e-book curricula and e-learning platforms. Current e-learning platforms offer increased support for student evaluation, with an emphasis in exploiting automation in both test generation and evaluation. In this context, our system is able to evaluate a student’s academic performance for the medical domain. Using as input medical reference texts and supported by a specially designed medical ontology, Medi-test generates different types of questionnaires for Romanian language. The evaluation includes 4 types of questions (multiple-choice, fill in the blanks, true/false and match), can have customizable length and difficulty and can be automatically graded. A recent extension of our system also allows for the generation of tests which include images. We evaluated our system with a local testing team, but also with a set of medicine students, and user satisfaction questionnaires showed that the system can be used to enhance learning.

The study of the development of the vertebrate retina can be addressed from several perspectives: from purely qualitative to a more quantitative approach that takes into account its spatiotemporal features, its three dimensional structure and also the regulation and properties at the systems level. Here we review the ongoing transition towards a full four-dimensional characterization of the developing vertebrate retina, focusing on the challenges at the experimental, image acquisition, image processing and quantification. Using the developing zebrafish retina, we illustrate how quantitative data extracted from these type of highly dense three-dimensional tissues depends strongly on the image quality, image processing and algorithms used to segment and quantify. Therefore, we propose that the scientific community that focuses on developmental systems could strongly benefit from a more detailed disclosure of the tools and pipelines used to process and analyze images from biological samples.

Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and their informal caregivers: (1) Background: It is important to get a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences, based on trustable information.; (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized findings; (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed the identification of current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.

Ground penetrating radar (georadar, GPR), widely used in geology, archeology and road construction, is an efficient tool for searching and studying subsurface objects and structures. When planning GPR missions, it is necessary to predict the resolution of the device and the achievable probing depth. The article discusses the methods and results of assessing these characteristics of the Loza-V and Loza-N radars obtained in the course of archaeological and geographical expedition works.

Background and objectives: The fact that the results of troponin and Nt-proBNP interfere from biotin caused some commercial firms to update their measurement methods. In particular, the clinical incompatibility of cardiac test results may affect the risk of morbidity and mortality. The aim of this study is to investigate the interference effects of 7 different contrast agents on cardiac markers (Troponin-I, Nt-proBNP, Mass CK-MB, CK, AST, LDH) and coagulation tests (PT, APTT). Materials and Methods: Seven different contrast medias were added into control materials by using interference protocol. Concentration of PT, APTT, CK, AST, LDH, Mass CK-MB, Troponin-I, Nt-proBNP were measured by Sysmex CS-2100, Abbott c16000, Siemens Centaur XP and AFİAS-6 analyzer. The amount of deviations from target values were calculated. Results: 7 different contrast medias caused negative interference in troponin levels between 57.43% and 62.87%. It was found that different contrast medias produced false negativity in the Nt-proBNP test ranging from 6.11% to 96.01%. Enzymes and coagulation tests have been less affected. Conclusions: Different contrast medias may cause false negative cTnI and Nt-proBNP. Therefore, the contrast medias which causes the least interference should be preferred. The results of samples taken in the first hour of contrast imaging should be interpreted with care.

Yijing possesses inherent principles and logic, and it has achieved consensus as an auxiliary decision-making function. Research on Yijing has been validated in the field of superpsychology, primarily focusing on individuals. However, the universal value of Yijing extends beyond individual divination or telepsychological induction, and there are untapped scientific potential values associated with it. Consequently, many scholars studying Yijing, including those in Chinese medicine, attempt to decipher its internal scientific mechanism through quantitative methods. In summary, scientific quantitative analysis of Yijing can be categorized into several approaches. These include quantification based on Yijing itself (binary and hexagram order), as well as quantification based on mathematical statistics, probability, and calculus. Another approach involves quantification based on expert knowledge, as well as the quantification of timewave curves and Yi-globle. Significant progress has been made in all of these quantitative research areas within the scientific paradigm. Nevertheless, future quantification of Yijing may explore rule-based evolutionary analysis, incorporating artificial intelligence technology, and conduct quantitative analysis based on the evolutionary process and results. There is even potential to deepen this research with tools such as ChatGPT. Additionally, the focus of Yijing divination may shift from individual events to group events, accompanied by space-time modeling and analysis through the construction of a new coordinate system.

Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subject. MM manifestations are usually known with the abbreviation CRAB (hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions). In particular, the extent of the bone disease is negatively related to a decreased patients’ quality of life and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially CT and MRI, is becoming crucial from the clinical viewpoint to separate asymptomatic from symptomatic MM patients and the detection of focal lytic lesion in these imaging data is becoming relevant even when no clinical symptoms are present. Therefore, radiology is pivotal in the staging and accurate management of patients with MM even in early phases of the disease. In this review we describe the opportunities offered by quantitative imaging and radiomics in multiple myeloma. At present time there is still high variability in the choice between various imaging methods to study MM patients and high variability in image interpretation with suboptimal agreement among readers even in tertiary centres. Therefore, the potential of medical imaging for patients affected by MM is still to be completely unveiled. In the next years, new insights to study MM with medical imaging will derive from artificial intelligence (AI) and radiomics usage in different bone lesions and from the wide implementations of quantitative methods to report CT and MRI. Eventually, medical imaging data can be integrated with the patient's outcomes with the purpose to find radiological biomarkers for predicting the disease prognostic flow and its therapeutic response.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an es-sential biomarker for directly assessing tissue viability and function in neurologic disorders. For quantitative mapping of OEF, an integrative model of quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) was recently pro-posed. However, QQ assumes Gaussian noise in both susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low sig-nal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic noise in mGRE complex signals. The proposed mcQQ was implemented using a deep learning framework (mcQQ-NET) and compared with the existing deep learning-based QQ (QQ-NET) in simulations, ischemic stroke patients, and healthy subjects. Both mcQQ-NET and QQ-NET used identical training and testing datasets and schemes for a fair comparison. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the sub-acute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaf-fected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR. Therefore, mcQQ-NET improves OEF accuracy by better reflecting realistic data noise characteristics.

This study considers the values of the credit payments that are made at random values for both payment size and number. The simultaneous existence of the two random variables increases the total value of the volatility of the payment process. The dependence for the simultaneous influence of the two random variables is derived by applying conditional probabilistic formulations. Formal relations apply to animal husbandry credit payments. The credit policy was analyzed for the cases of regular payments and with a stochastic number of payments. A recommendation for the upper level of credit payments is proposed based on a model predictive approach using historical payment data. A credit management algorithm is derived, in which decision-making for credit payments is formally derived and numerically discussed. The application of derived quantitative relations is empirically applied to real data on animal husbandry. The recommendations from the dependencies obtained allow a reduction in the mean values of credit payments, which correspond to a reduction in the credits used for business management, without a change in business policy.

Metabolomics is a powerful set of methods that uses analytical techniques to identify and quantify metabolites in biological samples, providing a snapshot of the metabolic state of a biological system. In medicine, metabolomics can help diagnose diseases, reveal molecular basis of a disease, and monitor treatment responses, while in agriculture, it can improve crop yields and plant breeding. However, animal metabolomics faces several challenges due to the complexity and diversity of animal metabolomes, the lack of standardized protocols, and the difficulty in interpreting metabolomic data. The current dataset includes quantitative metabolomic profiles of eye lens tissues from 26 bird species (111 specimens) that can aid researchers in developing new experiments, mathematical models, and integrations with other omics data. The dataset includes raw 1H NMR spectra, protocols for sample preparation, and data preprocessing with the final table containing information on 89 reliably identified and quantified metabolites. The dataset is quantitative, making it relevant for supplementing with new specimens or comparison groups, followed by data mining and expected new interpretations. It was obtained from bird specimens collected in compliance with ethical standards and reveals potential differences in metabolic pathways due to phylogenetic difference or environmental exposure.

This paper presents the goals and components of a quantitative energy balance assessment framework to define PEDs flexibly in three important contexts: the context of the district's density and RES potential, the context of a district's location, induced mobility and the context of the dis-trict's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition: system boundaries, balancing weights and balance targets and argue how they can be linked to the definition goals in detail. In particular we specify three levels of system boundaries and argue their individual necessity: operation, including everyday mobili-ty, including embodied energy and emissions. It argues that all three pillars of PEDs, energy effi-ciency, onsite renewables and energy flexibility can be assessed with the single metric of a prima-ry energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the sur-rounding energy system, which we identify as a "context factor". Three examples of such context factors, each corresponding to the balance target of one of the previously defined system bounda-ries operation, mobility and embodied emissions are presented: Density (as a context of opera-tion), sectoral energy balances and location (as a context for mobility) and an outlook of a person-al emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

Tandem mass tag (TMT)-based proteomics facilitate multiplexing in mass spectrometry (MS)-based quantification and identification of proteins and their post-translational modifications. The use of TMT isobaric tags can enable multiplexing of up to 18 samples using commercially available kits. A single TMT experiment can quantify proteome, serine, threonine phosphorylation, and tyrosine phosphorylation. Of note, tyrosine phosphorylation is of low abundance, and identification/quantification can be improved using two complementary strategies. First, by employing SH2 superbinder which increases the number of identified sites. The SH2 Superbinder is more cost-effective than the commonly used phosphotyrosine antibodies. Second, by employing phosphotyrosine booster strategy, a pervanadate-treated channel to boost the signal of low-abundant phosphotyrosine. Noteworthy, pervanadate boost increases the likelihood of low abundant peptide to be selected for MS2, and facilitating the detection of > 6000 proteins, 10,000 unique pS/T and 1000 unique pY sites.

The PACS (Picture Archiving and Communication System) market is heterogenous with dozens of PACS providers having deployed installations in healthcare facilities. The DICOM query and retrieve interfaces provided by PACS have multiple variations, related to the implemented SOP (Service Object Pair) Class UID (Unique Identifier), transfer syntaxes, extended negotiations, matching attributes and matching types. These variations make any integration of a new DICOM consumer with a PACS complex and time consuming. As there is no collective information describing the various PACS query and retrieve capabilities, application developers and healthcare facilities lack a method to evaluate the PACS capabilities and classify its functionalities. GE designed a method to evaluate the PACS capabilities in terms of query retrieve functionalities. Our aim is to analyze several PACS in test and production environments, using our method to provide the DICOM object consumers a macroscopic knowledge of query/retrieve capabilities of PACS functionalities and its different variations. Our evaluation can also be used by PACS and VNA (Vendor Neutral Archive) developers to evaluate their query/retrieve capabilities, for quality improvement purpose.

Various designs of spherical robots using barycenter offset mechanisms were previously reported. This kind of robots offers advantages such as turning in placeor moving in any direction at any time. Yet, the approaches resulting in a balanced robot, which could roll with reduced energy costs once accelerated, are scarce. We introduce anovel robot of this type. The feasibility of the approach is verified on a cylindrical robot. This paper presents an open-source, remotely controlled, robotic cylinder. The robot uses a specifically designed mechanism to displace a mass in its center, which causes the cylinder to roll forwards or backwards. The movement characteristics of the cylinder, as well as the mathematical and physical limitations of the mechanism were studied. An intuitive user interface allows the robot to be controlled through WiFi. This robot was manufactured only with readily available and inexpensive materials, which would make it a suitable platform for education, typically in the fields of physics and engineering.

A numerical mechanism connecting ice algal ecodynamics with the buildup of organic macromolecules is tested within modeled pan-Arctic brine channels. The simulations take place offline in a reduced representation of sea ice geochemistry. Physical driver quantities derive from the global sea ice code CICE, including snow cover, thickness and internal temperature. The framework is averaged over ten boreal biogeographic zones. Computed nutrient-light-salt limited algal growth supports grazing, mortality and carbon flow. Vertical transport is diffusive but responds to pore structure. Simulated bottom layer chlorophyll maxima are reasonable, though delayed by about a month relative to observations due to uncertainties in snow variability. Upper level biota arise intermittently during flooding events. Macromolecular concentrations are tracked as proxy proteins, polysaccharides, lipids and refractory humics. The fresh biopolymers undergo succession and removal by bacteria. Baseline organics enter solely through cell disruption, so that the internal carbon content is initially biased low. By including exudation, agreement with dissolved organic or individual biopolymer data is achieved given strong release coupled to light intensity. Detrital carbon then reaches hundreds of micromolar, sufficient to support structural changes to the ice matrix.

Archaeology is often argued to provide a unique long-term perspective on humans that can be utilised for effective policy-making, for example in discussions of resilience and sustainability. However, the specific archaeological evidence for resilient/sustainable systems is rarely explored, with these terms used simply to describe a community that survived a particular shock. In this study, a set of 74 case studies of papers discussing archaeological evidence for resilience/sustainability are identified and analysed using bibliometric methods. Variables from the papers are also quantified to assess patterns and provide a review of current knowledge. A great variety of scales of analysis, case study locations, stressors, resilient/sustainable characteristics, and archaeological evidence types are present. Climate change was the most cited stressor (n=40) and strategies relating to natural resources were common across case studies, especially subsistence adaptations (n=35), other solutions to subsistence deficiencies (n=23), and water management (n=23). Resilient/sustainable characteristics were often in direct contrast to one-another, suggesting the combination of factors is more important than each factor taken individually. Further quantification of well-defined variables within a formally-produced framework is required to extract greater value from archaeological case studies of resilience/sustainability.

The analysis of drugs is a priority in public health, particularly antibiotics which develop resistance due, among other things, to bad dosages or poor quality of the active substance. In this study, we evaluated eight antibiotics available in health centers in Djibouti through qualitative and quantitative analysis. The tests were based on high-purity reference products. In qualitative analysis by HPLC, only amoxicillin presented two impurity peaks. This observation is found in the dosage of this active ingredient with a value of 83 ±0.6 mg instead of the 500 mg indicated on the label. It will be necessary to diversify the sample’s provenance to seek the origin of this degradation.

Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61- 0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97(p-value &lt; 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.

So-called ‘zero’ or ‘null’ tenses have often been characterized as functionally deficient forms, deprived of any inherent content. In this paper, we will focus on the semantics of a morpho-phonologically null inflectional verbal paradigm in Anindilyakwa (Groote Eylandt, N.T., Australia, which is both temporally and aspectually underspecified. Through a quantitative corpus study conducted in the paper, we establish that ‘zero inflection’ in this language, contra prior works on such tenses in general (e.g. Bybee 1990) and in Anindilyakwa in particular (Bednall 2019), presents various degrees of sensitivity to traditional Vendlerian aspectual parameters. We show that while telicity is not a significant predictor for the temporal interpretation of zero-inflected Anindilyakwa verbs, and dynamicity is a good but not very good predictor, only a very broad opposition between change-of-state (including qua boundedness) and non-change-of-state, or perfective/imperfective, gives very significant biases towards past vs. present anchoring. We also show that atomic telicity is the only categorical Aktionsart predictor for temporal anchoring in this context correctly predict the temporal anchoring of such verbs, and stativity is not biased towards present interpretations, thereby questioning currently received typological theories of the semantics of so-called ‘zero-tenses’ / aspectuo-temporally underspecified tenses.

A precise understanding of the genes and associated genomes of SARS-CoV2 is important for various reasons such as discovering origin of the virus and virulence and so on. A thorough descriptive understanding of the SARS-CoV2 genomes and other coronavirus of the beta-coronavirus genus is primarily important. In this article, a set of ten genomes of four CoVs and their associated genes are considered for this present study. A spatial representations of nucleotide bases including purine-pyrimidine representations of the different genes of the corresponding genomes are quantified using Hurst exponent, Shannon entropy and density estimation of different nucleotides including GC content, in order to draw a comparison and contrast among the ten genomes of different types of CoVs which include MERS, SARS-CoV, HKU1 (Human Coronavirus) and associated their genes.

How much is the h-index of an editor of a well ranked journal improved due to citations which occur after his/her appointment? Scientific recognition within academia is widely measured nowadays by the number of citations or h-index. Our dataset is based on a sample of four editors from a well ranked journal (impact factor – IF – greater than 2). The target group consists of two editors who seem to benefit by their position through an increased citation number (and subsequently h-index) within journal. The total amount of citations for the target group is bigger than 600. The control group is formed by another set of two editors from the same journal whose relations between their positions and their citation records remain neutral. The total amount of citations for the control group is more than 1200. The timespan for which citations’ pattern has been studied is 1975-2015. Previous coercive citations for a journal benefit (increase its IF) has been signaled. To the best of our knowledge, this is a pioneering work on coercive citations for personal (editors’) benefit. Editorial teams should be aware about this type of potentially unethical behavior and act accordingly.

Background: First-line biological diagnosis of malaria in Mali is based on the use of rapid diagnostic tests (RDT), which detect in the blood the Histidin Rich Protein 2 antigen, specific to Plasmodium falciparum. Our study, based on a real-time polymerase chain reaction (qPCR) gold-standard diagnosis, aims to evaluate the performance of RDTs used in Mali and to describe the distribution of Plasmodium species in each administrative regions of Mali; Methods: We collected RDT cassettes in 47 sites of the nine regions of Mali. We randomly selected 150 malaria-negative and up to 30 malaria-positive RDTs from each regions. DNA was extracted from the RDTs’ nitrocellulose strip and then assayed with a pan-Plasmodium qPCR. Positive samples were then analyzed with qPCRs specific for P. falciparum, P. malariae, P. vivax, and P. ovale; Results: Of the 1,389 tested by qPCR RDTs, 258 (18.6%) were positive for Plasmodium spp. P.falciparum was the most predominant (97.3%). However, the prevalence of P.vivax reach 21.1% in the Ménaka region in the north; Conclusion: Overall, RDT diagnostic indices are adequate for the biological diagnosis of malaria in Mali. Our finding support the adjustment of RDTs to the local epidemiology.

In only a few months after initial discovery in Wuhan, China, SARS-CoV-2 and the associated COVID-19 disease has become a global pandemic causing significant mortality and morbidity. In the absence of vaccines and effective therapeutics, reliable serological testing can be a key element of public health policy to control further spread of the disease and gradually ease quarantine measures. However, prior to launch of large-scale seroprevalence studies to assess herd immunity, it is critical to understand the limits and potential of current SARS-CoV-2 serological tests on the market. In this study, we provide an overview of serological testing and conduct a systematic review of independent evaluations of SARS-CoV-2 serological tests performance. Our findings show significant variability in the accuracy of marketed tests and highlight several lab-based and point-of-care rapid diagnostic tests with high performance level in detecting SRAS-CoV-2 specific antibodies. The findings of this review highlight the need for ongoing independent evaluations of commercialized COVID-19 diagnostic tests.

In social & health sciences, many statistical procedures and estimation techniques rely on the underlying distributional assumption of normality of the data. Non-normality may lead to incorrect statistical inferences. This study evaluates the performance of selected normality tests on the stringency framework for the skewed alternative space. Stringency concept allows us to rank the tests uniquely. Bonett & Seier test (T_w) turns out to be the best statistics for slightly skewed alternatives and the Anderson-Darling (AD), Chen-Shapiro (CS), Shapiro-Wilk (W) and Bispo, Marques, & Pestana, (BCMR) statistics are the best choices for moderately skewed alternative distributions. Maximum loss of Jarque-Bera (JB) and its robust form (RJB), in terms of deviations from the power envelope, is greater than 50% even for large sample sizes which makes them less attractive in testing the hypothesis of normality against the moderately skewed alternatives. On balance, all selected normality tests except T_w and COIN performed exceptionally well against the highly skewed alternative space.

Increased gut permeability (leaky gut) with increased translocation of Gram-negative bacteria plays a role in the gut-brain axis through effects on systemic immune-inflammatory processes. Deficit schizophrenia is characterized by an immune-inflammatory response combined with a deficit in natural IgM antibodies to oxidative specific epitopes (OSEs), which are a first line defense against bacterial infections. This study measured plasma IgA/IgM responses to 5 Gram-negative bacteria in association with IgM responses to malondialdehyde (MDA) and azelaic acid in 80 schizophrenia patients (40 with the deficit syndrome and 40 without) and in 38 healthy controls.Deficit schizophrenia was characterized by significantly increased IgA responses to Hafnei alvei, Pseudomonas aeruginosa, Morganella morganii and Klebsiella pneumoniae as compared with non-deficit schizophrenia. The presence of deficit schizophrenia was highly predicted by increased IgA responses to Pseudomonas putida and IgM responses to all 5 Gram-negative bacteria and lowered natural IgM to MDA and azelaic acid with a bootstrap area under the ROC curve of 0.960 (2000 random curves). A large proportion of the variance (41.5%) in the PANSS negative score was explained by the regression on IgA responses to K. pneumoniae and IgM responses to the 5 enterobacteria coupled with lowered IgM antibodies to azelaic acid. There were significant associations between IgA levels to Gram-negative bacteria and Mini Mental State Examination, Boston naming test, Verbal Fluency and Word List Memory test scores.These findings provide further evidence that deficit schizophrenia is a distinct phenotype of schizophrenia, which is characterized by an increased impact of Gram-negative commensal bacteria coupled with a deficit in natural IgM, pointing to aberrations in B1 cells. It is concluded that increased bacterial translocation and deficits in the compensatory immune-regulatory system (CIRS) may drive negative symptoms and neurocognitive impairments, which are hallmarks of deficit schizophrenia.

Quantitative differential phase contrast (qDPC) imaging has become an important method of optical measurement and life science research in microscopy because of its high reconstruction resolution and non-invasive, high-contrast and quantitative imaging of biological samples. Despite the continuous development of the principle and algorithm, the frame rate of the existing qDPC algorithm is still much lower than that of camera acquisition, so it is hardly applied to real-time image the fast-moving biological samples. In this paper, based on color-coded multiplexing strategy, a compact real-time quantitative phase imaging system is designed to realize multi-mode imaging. The system employs a programmable LED array to illuminate directly, and the phase reconstruction algorithm is deployed in the graphics processing unit (GPU) of the laptop to accelerate the calculation. The system can achieve high-speed quantitative phase imaging of non-stained biological samples, and the frame rate can reach 60fps. The device has the advantages of compact structure, low cost and portability. Thus, it is suitable for mobile medical applications.

To evaluate the behaviour of the relevant microbial populations during stretched cheese production, the quantitative microbiological analysis was performed during the critical steps of the preparation. The obtained data distributions proved statistically significant increases in all indicators, on average by 4.55 ± 0.64 log CFU/g of presumptive lactococci counts, 4.06 ±0.61 of lactobacilli, 1.53 ± 0.57 log CFU/g of coliforms, 2.42 ± 0.67 log CFU/g of Escherichia coli, 1.53 ± 0.75 log CFU/g of yeasts and moulds, and 0.99 ± 0.27 log CFU/g of presumptive Staphylococcus aureus, from the early stage of milk coagulation until curd ripening (0–24 h). The following steaming/stretching process caused reductions in viable counts with the most significant inactivation effect on coliform bacteria, including E. coli (-4.0 ± 1.0 log CFU/g). Total viable counts and yeasts and moulds showed 2 and almost 3 log reduction (-2.2 ± 1.1 log CFU/g and -2.6 ± 0.9 log CFU/g), respectively. The lowest decreases in presumptive S. aureus counts were estimated at the level of -1.50 ± 0.64 log CFU/g. The counts of yeasts and moulds showed the best indicatory function during the entire storage period of vacuum-packaged cheeses at 6 °C.

Plant breeding conventionally depends on genetic variability available in a species to improve a particular trait in the crop. However, epigenetic diversity may provide an additional tier of variation. The recent advent of epigenome technologies has elucidated the role of epigenetic variation in shaping phenotype. Further, the development of epigenetic recombinant inbred lines (epi-RILs) in the model species such as Arabidopsis has enabled accurate genetic analysis of epigenetic variation. Subsequently, mapping of epigenetic quantitative trait loci (epiQTL) allowed association between epialleles and phenotypic traits. Thus, quantitative epigenetics provides ample opportunities to dissect the role of epigenetic variation in trait regulation, which can be eventually utilized in crop improvement programs. Moreover, locus-specific manipulation of DNA methylation by epigenome-editing tools such as clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) can facilitate epigenetic based molecular breeding of important crop plants.

Background: Schizophrenia and treatment-resistant schizophrenia (TRS) are associated with aberrations in immune-inflammatory pathways. Increased High Mobility Group Protein 1 (HMGB1), an inflammatory mediator, and Dickkopf-Related Protein (DKK1), a Wnt/β-catenin signaling antagonist, affect the blood-brain-barrier and induce neurotoxic effects and neurocognitive deficits.Aim of the study: The present study aims to examine HMGB1 and DDK1 in non-responders to treatments with antipsychotics (NRTT, n=60), partial RTT (PRTT, n=55) and healthy controls (n=43) in relation to established markers of schizophrenia including IL-6, IL-10 and CLL11 (eotaxin); and to delineate whether these proteins are associated with the schizophrenia symptom subdomains and neurocognitive impairments.Results: HMGB1, DKK1, IL-6 and CCL11 were significantly higher in schizophrenia patients than in controls. DKK1 and IL-6 were significantly higher in NRTT than in PRTT and controls while IL-10 was higher in NRTT than in controls. Binary logistic regression analysis showed that schizophrenia was best predicted by increased DDK1 and HMGB1 while NRTT (versus PRTT) was best predicted by increased IL-6 and CCL11 levels. A large part of the variance in psychosis, hostility, excitation, mannerism and negative (PHEMN) symptoms, and formal thought disorders was explained by HMGB1, IL-6, and CCL11 while most neurocognitive functions were predicted by HMGB1, DDK1 and CCL11. Conclusion: The neurotoxic effects of HMGB1, DKK1, IL-6 and CCL11 including effects on the blood-brain-barrier and the Wnt/β-catenin signaling pathway may cause impairments in executive functions, and working, episodic and semantic memory and explain, in part, PHEMN symptoms and a non-response to treatment with antipsychotic drugs.

Abstract: Development of the fast neutron radiography with higher penetration capability in Non-Destructive Testing (NDT) techniques is rapidly. The detection efficiency and the imaging spatial resolution are the main problem of the radiography detector for many fast neutron imaging systems. In this paper, a detector composed of pixelated EJ200 scintillator array，an 16 × 16 silicon-photomultipliers (SiPM) array, and a capacitive multiplexing network readout electronics is designed. Using Monte-Carlo simulations，the main parameters of the detector was optimization. Then the detector prototype was fabricated and tested at a 14 MeV D-T neutron source. The preliminary test results showed that the prototype has spatial resolution of 1.26 mm. Using pixelated scintillator structure，the conflict between spatial resolution and detect efficiency could improved. Moreover, SiPMs offer a flexibility for the expansion of application field of the imaging system due to their excellent photon detection performance, relatively low price and joint possibility for large area.

COVID-19, caused by the SARS-CoV-2 virus, has spread around the world and killed around 6.9 million people. Rapid and accurate diagnosis is essential for preventing and controlling the disease, reducing transmission and consequently saving lives. RT-PCR is the gold standard test used to detect the disease. However, the test is expensive and the result is time-consuming, which makes mass testing difficult, especially in countries with limited resources. In addition, the test has high analytical specificity and low diagnostic sensitivity, which leads to false-negative results. Several studies in the literature report the presence of hematological and biochemical alterations in infected patients and use these alterations with machine learning algorithms to help diagnose the disease. Therefore, this article presents the results obtained by different neural network architectures based on Adaptive Resonance Theory (ART) for the diagnosis of COVID-19. The study was conducted in two distinct stages: the first consisted of selecting the best ART network among several, using three open-access datasets and comparing the results with the literature. In the second stage, the chosen model was tested on a dataset containing patients from various hospitals in four countries. In addition, the model was subjected to external validation, including data from a country not present during the training and adjustment of the model, in order to validate the robustness and generalization capacity of the model. The results obtained by the ART networks in this study are promising, outperforming not only classical models, but also the deep learning models often used in the literature. Validation on data from different countries strengthens the model’s reliability and effectiveness.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

Optical characterization and thermal aging tests are performed on a sputter-deposited coating, consisting of SiO₂/Cr₂O₃/Cr/Cr₂O₃ layers, designed and developed as a selective solar absorber to be used for unconcentrated solar thermal applications. Both measurements are performed by using a home-made apparatus, which mimics a flat plate collector under high vacuum. A Performance Criterion (PC_η(T)), based on absorber efficiency is proposed, and a forecast of service lifetime is obtained. As a result of the thermal aging tests, the selective solar absorber under study appears to be highly efficient at mid temperatures (up to 573 K) and thermally stable at temperatures (up to 690 K).

Brucellosis is one of the most common etiologies of laboratory-acquired infections worldwide, and handling of living brucellae should be performed in a Class II biological safety cabinet. The low infecting dose, multiple portals of entry to the body, the great variety of potentially contaminated specimens, and the unspecific clinical manifestations of human infections facilitate the unintentional transmission of brucellae to laboratory personnel. Work accidents such as spillage of culture media cause only a small minority of exposures, whereas >80% of events result from unfamiliarity with the phenotypic features of the genus, misidentification of isolates, and unsafe laboratory practices such as aerosolization of bacteria and working on an open bench without protective goggles or gloves. Although the bacteriological diagnosis of brucellae by traditional methods is simple, the Gram stain and the biochemical profile of the organism, as determined by commercial kits, can be misleading, resulting in inadvertent exposure and contagion. The use of novel identification technologies is not hazard-free. The MALDI-TOF technology requires an initial bacterial inactivation step, while the instruments’ reference database may misidentify Brucella as belonging to other Gram-negative species. The rapid identification by the FISH method mistakes brucellar isolates for members of the closely related Ochrobactrum genus.

An experimental series of shearing tests with water infiltration were performed on compacted unsaturated soil to simulate the behavior of shallow slope failures. Soil samples were compacted at moisture contents from dry to wet of optimum moisture content with the degree of saturation varying from 24.0% to 59.5% while maintaining the degree of compaction at 80%. Two series of shearing with infiltration tests were performed in this study. In Series-I, just before the start of shearing, matric suction was decreased by increasing pore water pressure to start water infiltration i.e. shearing is carried simultaneously with water infiltration. In Series-II, the soil was first sheared with drained pore air and undrained pore water to pre-defined value of deviatoric stress, after which matric suction was decreased by increasing pore water pressure to start water infiltration and shearing is performed by keeping deviatoric stress constant on the specimen. The test results showed that the decrease in matric suction has an effect on the volume of infiltrated water and degree of saturation. The soil slopes compacted on the dry side of optimum moisture content showed better performance than other soils, they require more decrease in matric suction to start water infiltration and showed higher deviatoric stress. In addition to this, water infiltration alone can cause the failure of shallow slopes without having to have any further loading.

According to the requirement of 20kg load of casting barrel, we design a three-dimensional model of the robot and analyze the three stages of liquid level change in the casting barrel during the casting process. Through the analysis, it is determined that the time and angle function is an implicit function, and it is impossible to find the analytical expression of time about angle. The kinetic function of the casting barrel is simulated by MATLAB, and it is found that the function image has mutation points, and when the original function is fitted and approximated by fifth-degree polynomial interpolation, the fifth-degree polynomial function image of the time versus the angular acceleration has a large amplitude of oscillation, especially the mutation points. A PID algorithm based on the discretization of the time and acceleration function is introduced for correction, and the results show that this method effectively reduces the oscillation amplitude of the image and improves the coincidence with the original function.

This study aims to investigate a friction stir welded joint between steel and aluminum alloy. Friction stir welding is nowadays one of the most interesting joining technique due to the possibility to connect materials and thicknesses that are difficult or impossible to weld with traditional techniques. The main advantage is that Materials are not affected by thermal cycle problems during solidification and cooling and the absence of fumes and pollution during the process favors the quality of the welded joint. The life of metal joints could be greatly reduced in a corrosive environment, since the less noble material will tend to increase its corrosion rate while the nobler one will reduce its electrochemical dissolution. Accelerated ageing tests (i.e. salt fog test) are used to estimate the lifetime of metal joints in highly aggressive environments. The aim of the present work is to evaluate the durability at long ageing time in salt spray test (according to ASTM B117) of carbon steel/aluminum alloy joints, obtained by FSW. In this first part, mechanical tests results are reported. A deep metallographic and chemical investigation are going to be reported in the part 2.

Hydraulic fracturing is an efficient method to develop oil/gas resources economically. Temporary plugging and diverting fracturing (TPDF) can generate diversion fractures perpendicular to the initial fractures and enhance the stimulated area. The aperture of the diversion fractures determines its conductivity and the oil/gas production. This work established a fluid-solid fully coupled simulation model to investigate the fracture aperture influenced by various factors during TPDF. The factors include the permeability of the tight plug, the length of the tight plug, Young’s modulus, rock tensile strength, in-situ stress contrast, the leak-off coefficient of the fracture surface, and fluid injection rate. Results show that the aperture of the previous fracture can be enlarged, and the aperture of the diversion fracture can be decreased by the tight plug. The aperture at the diversion fracture mouth is much smaller than that along the diversion fracture. Reservoirs with low Young’s modulus and high rock tensile strength can generate the diversion fracture with a wider aperture. Moreover, increasing the fluid injection rate can effectively increase the fracture mouth aperture. In this way, the risk of screenout can be lowered. This work is beneficial for the design of the TPDF and ensures safe construction.

We provide quantitative results from GIS-based modelling of urban emission functions for a range of representative low- and mid-rise locations, ranging from individual streets to residential communities within cities as well as entire towns and city regions with the aim of whether lantern photometry or built environment has the dominant effect on light pollution. We demonstrate the scalability of our work by providing results for the largest urban area modelled to date, comprising the central 117 km2 area of Dublin City and containing nearly 42,000 public lights. Our results show a general similarity in the shape of the azimuthally-averaged emission function for all areas examined, with differences in total light output distribution depending primarily on the nature of the lighting and, to a smaller extent, on the obscuring environment including seasonal foliage effects. A comparison with global satellite observations shows that they are consistent with the deduced angular emission function for other low-rise areas worldwide. We further validate our approach by comparing results for a range of urban locations by the close agreement observed in a detailed comparison of with calibrated imagery from the International Space Station. To our knowledge, this is the first such detailed quantitative verification of light loss calculations.

Accompanied by increasing population growth and urban sprawl, most coastal cities are unprecedentedly vulnerable to climate change and its impacts, such as sea level rise, increasing extreme storm events, and coastal flooding. Coastal resilience and sustainable development are antidotes to vulnerability; they aim to enhance the adaptive capability of absorbing disturbances and resisting uncertainty. This study explores building a quantitative assessment framework to measure resilience and provide an objective and comparable method to understand the strengths and weaknesses in a given region. The proposed 25 resilience indicators incorporate the aspects of essential livelihood protection, infrastructure and natural resource maintenance, emergency facilities and institutions, floodplain management regulations, and adaptive planning process. Each indicator is assigned the resilience quality that includes robustness, resourcefulness, redundancy, and rapidity. The aggregated resilience quality scoring reflects the systematic performance of the city to cope with the coastal hazards. The innovative part of this framework is combining hazard mitigation measures, climate adaptation strategies, and sustainable development goals together to achieve a comprehensive assessment method. In the case of New Haven, the resilience assessment is taken as a practical monitoring tool and decision-making support.

The increasing prevalence of Metabolic Syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of Metabolic Syndrome, thereby addressing a significant concern in public health.

In this paper, we propose a method for the generation of simulation test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtual simulation scenarios and functional scenario libraries for autonomous driving, which can be used for simulation verification of different ADAS functions. Firstly, the design operation domain (ODD) of the functional scenario is selected and the weight values of the ODD elements taken are calculated; then a combination test algorithm based on parameter weights is improved to generate autonomous driving virtual simulation test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm; then the traffic participant elements under each test case are Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (HSIC) and its d-variate version (d-HSIC), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (CPT) based on the Hilbert-Schmidt conditional independence criterion (HSCIC), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socio-economic data.

Accumulating evidence suggests that TNF-α-mediated immune-neurotoxicity contributes to cognitive impairments and the overall severity of schizophrenia (OSOS). There are no data whether peripheral IL-6 and IL-4 may affect the phenome of schizophrenia above and beyond the effects of TNF-α and whether those cytokines are regulated by lowered natural IgM to malondialdehyde (MDA) and paraoxonase 1 enzyme activity. We assessed the aforementioned biomarkers in schizophrenia patients with (n=40) and without (n=40) deficit schizophrenia and 40 healthy controls. Deficit schizophrenia was best predicted by a combination of increased IL-6 and PON1 status (QQ genotype and lowered CMPAase activity) and lowered IgM to MDA. Partial Least Squares bootstrapping shows that 41.0% of the variance in negative symptoms, psychosis, hostility, excitation, mannerism, psychomotor retardation, and formal thought disorders was explained by increased TNF-α and PON1 status (QQ genotype and lowered CMPAase activity), lowered IL-4 and IgM to MDA as well as male sex and lowered education. We found that 47.9% of the variance in verbal fluency, word list memory, true recall, Mini-Mental State Examination, and executive functions was predicted by increased TNF-α and lowered IL-4, IgM to MDA and education. In addition, both TNF-α and IL-4 levels were significantly associated with lowered IgM to MDA, while TNF-α was correlated with PON1 status. These data provide evidence that the symptomatic (both the deficit subtype and OSOS) and cognitive impairments in schizophrenia are to a large extent mediated by the effects of immune-mediated neurotoxicity as well as lowered regulation by the innate immune system.

Consumers often cite cognitive improvements as reasons for making dietary changes or using dietary supplements, a motivation that if leveraged could greatly enhance public health. However, rarely is it considered whether standardized cognitive tests that are used in nutrition research are aligned to outcomes of interest to the consumer. This knowledge gap presents a challenge to the scientific substantiation of nutrition-based cognitive health benefits. Here we combined qualitative consumer research and expert group methodology to (1) understand how consumers comprehend the potential benefits of nutrition for brain health, and (2) determine the alignment between consumers' articulated brain benefits and validated tests and tools. We derived a ‘Consumer Taxonomy of Cognitive and Affective Health in Nutrition Research’ which describes the cognitive and affective structure from the consumers' perspective. Experts agreed that validated tests exist for some consumer benefits including focused attention, sustained attention, episodic memory, energy levels, and anxiety. Prospective memory, flow, and presence represented novel benefits that require the development and validation of new tests and tools. Closing the gap between science and consumers, and fostering co-creative approaches to nutrition research, are critical to the development of products and dietary recommendations that support realizable cognitive benefits that benefit public health.

Flammability was evaluated and the results showed the values of critical flux at extinguishment (CFE) as 49.5kW/㎡ on average, total heat release (Qt) as 0.21MJ on average, and peak heat release rate (Qp) as 0.66kW on average, which satisfied all of the thresholds presented by the IMO. Therefore, flame-retardant plywood, of which the flame-retardant performance was evaluated, was determined to be an applicable finishing material that satisfies fire safety standards.

Natural products are considered an essential source of chemical compounds with relevant biological activities. Important antibiotics, anti-virals, and anticancer agents within healthcare facilities have been isolated from them. In recent decades, the study of alkaloids and the modulation of the immune response has aroused interest. The survey of SAR alkaloids with immunomodulatory effects is of great importance since structural modifications impact the effectiveness of the immunomodulatory activity, thus promoting the rational design of more effective and specific drugs. This review exhibits the main alkaloids with an immunomodulatory effect and their relationship with SAR studies, particularly in activating leukocytes and secretion of molecules with critical inflammatory activities such as cytokines, chemokines, and nitric oxide. These studies optimize the properties of alkaloids and allow a deeper understanding of the mechanisms of action at a molecular level, which is fundamental for the innovation of immunological therapies.

In his Cynefin classification of system behaviours, Snowden (2007), posits that his Simple (Clear ) and even Complicated system categories are sufficiently understandable to be amenable to the traditional methods of system analysis and modelling. Here, relationships between entities in the system are clear and unambiguous and they behave predictably. So, we can build mathematical “models” to describe how the system is expected to function in different situations. Leaving aside his fourth category, Chaotic, as too hard for the moment, this paper focusses on the third category, Complex systems. Yaneer Bar-Yam [1998] defined "complex systems" as systems that "have multiple interacting components, whose collective behaviour cannot be simply inferred from the behaviour of components." So, the traditional system modelling approach of decomposition into components and attempting to build up a picture of system performance from individual pieces, alone, is no longer appropriate for this class. The problem is that most of the methods we currently employ, and particularly the methods that attempt to make the case that these systems are safe, (such as Fault trees, FMEA. and Root Cause Analysis), rely on this decomposition into components and attempt to make the parts more reliable individually. Even the more systemic approaches, such as Reason’s (1990) Swiss Cheese Model, and Leveson’s System Theoretic Analysis (STPA) (2004), encourage adding layers of protection, barriers, strengthened control loops and better safety critical systems, in the hope that they are making the systems safer. But without a valid model to test the effectiveness of these add-ons, we cannot be sure that they will not have an opposite effect, of making the systems even more complex, unreliable and unpredictable. For complex systems, the careful gathering and documentation of system components and their relationships is still a necessary first step, but we need an additional capacity to model these complex systems to test out and prove they work safely and successfully in the real world. (not just draw fixed component relationships as system “models” (wiring diagrams)) In the last 10 years, an original approach developed by Hollnagel, (2012), to model systems as sets of interactive, interdependent “functions”, (abstracted from and independent of component details), has now been developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, such as MBSE( Goldberg (1994)), and can pull it all together in a dynamic system model from which analysts can discern how it really works in practice, and predict the emergent behaviours characteristic of complex systems. The paper describes how this methodology developed and how it is currently used to model real situations, to analyse incidents and anticipate events.

In 2020, the pandemic caused by the Coronaviruses (CoV) that are a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV2). The Coronavirus disease (COVID-19) is a new strain that was discovered in 2019 and has not been previously identified in humans. It is the high time to investigate the quantitative and/or qualitative genomic informations of the virus SARS-CoV2 in order to strengthen the healthcare facility to fight against this viral disease. In this article, a through quantitative understanding of the purine and pyrimidine spatial distribution/organization of all 89 complete sequences of SARS-CoV (available as on date in the NCBI virus database, is made using different parameters such as fractal dimension, Hurst exponent, Shannon entropy and GC content of the nucleotide sequences of the genome of SARS-CoV2. Also a cluster among all the the SARS-CoV sequences of nucleotide have been made based on their phylogeny made through their closeness (Hamming distance) based on respective purine-pyrimidine distribution.

Background: Plasma metabolites are associated with cognitive and physical function in the elderly. Because cerebral small vessel disease (SVD) and neurodegeneration are common causes of cognitive and physical function decline, the primary objective of this study was to investigate the associations of six plasma metabolites (two plasma phosphatidylcholines [PCs]: PC aa C36:5 and PC aa 36:6 and four sphingomyelins [SMs]: SM C26:0, SM [OH] C22:1, SM [OH] C22:2, SM [OH] C24:1) with magnetic resonance imaging (MRI) features of cerebral SVD and neurodegeneration in older adults. Methods: This study included 238 older adults in the Atherosclerosis Risk in Communities study at the fifth exam. Multiple linear regression was used to assess the association of each metabolite (log-transformed) in separate models with MRI measures except lacunar infarcts, for which binary logistic regression was used. Results: Higher concentrations of plasma PC aa C36:5 had adverse associations with MRI features of cerebral SVD (odds ratio of 1.69 [95% confidence interval: 1.01, 2.83] with lacunar infarct, and beta of 0.16 log [cm³] [0.02, 0.30] with log [White Matter Hyperintensities (WMH) volume]) while higher concentrations of 3 plasma SM (OH)s were associated with higher total brain volume (beta of 12.0 cm³ [5.5, 18.6], 11.8 cm³ [5.0, 18.6], and 7.3 cm³ [1.2, 13.5] for SM [OH] C22:1, SM [OH] C22:2, and SM [OH] C24:1, respectively). Conclusions: This study identified associations between certain plasma metabolites and brain MRI measures of SVD and neurodegeneration in older adults, particularly higher SM (OH) concentrations with higher total brain volume.

This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens to understand how fish adapt to environmental changes. Utilizing simulations rooted in a random walk model for fish movement, we delve into entropy fluctuations under varying environmental conditions, including responses to feeding and external stimuli. Our findings underscore the utility of information entropy in capturing the intricacies of fish behavior, particularly highlighting the synchrony in collective actions and adaptations to environmental shifts. This research not only broadens our comprehension of fish behavior but also paves the way for its application in fields like aquaculture and resource management. Through our analysis, we discovered that smaller grid sizes in simulations capture detailed local fluctuations, while larger grids elucidate general trends, pinpointing a 2.5m grid as optimal for our study. Moreover, changes in swimming speeds and behavioral adaptations during feeding were quantitatively analyzed, with results illustrating significant behavior modifications. Additionally, employing a Gaussian Mixture Model helped to clarify the nuanced changes in fish behavior in response to altered light conditions, demonstrating the layered complexity of fish responses to environmental stimuli. This investigation confirms the efficacy of information entropy as a robust metric for evaluating fish shoal behavior, offering a fresh methodology for ecological and environmental studies, with promising implications for sustainable management practices.

We describe a competitive colorimetric assay that enables rapid and sensitive detection of galactose and reduced nicotinamide adenine dinucleotide (NADH) via colorimetric readouts and demon-strates its usefulness for monitoring NAD+-driven enzymatic reactions. We present a sensitive plasmonic sensing approach for assessing galactose concentration and the presence of NADH using galactose dehydrogenase immobilized gold nanostars (AuNS-PVP-GalDH). The AuNS-PVP-GalDH assay remains turquoise blue in the absence of galactose and NADH; however, as galactose and NADH concentrations grow, the reaction well color changes to a characteristic red color in the presence of an alkaline environment and metal ions catalyst (detection solution). As a result, when galactose is sensed in the presence of H2O2, the colored response of the AuNS-PVP-GalDH assay transforms from turquoise blue to light pink, then to wine red in a con-centration-dependent manner discernible to the human eye. This competitive AuNS-PVP-GalDH assay could be a viable analytical tool for rapid and convenient galactose quantification in re-source-limited areas.