Fronto-parietal activity changes associated with changes in working memory load: Evidence from simultaneous electroencephalography and functional near-infrared spectroscopy analysis.

Working memory (WM) involves the capacity to maintain and manipulate information over short periods. Previous research has suggested that fronto-parietal activities play a crucial role in WM. However, there remains no agreement on the effect of working memory load (WML) on neural activities and haemodynamic responses. Here, our study seeks to examine the effect of WML through simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). In this study, a delay change detection task was conducted on 23 healthy volunteers. The task included three levels: one item, three items and five items. The EEG and fNIRS were simultaneously recorded during the task. Neural activities and haemodynamic responses at prefrontal and parietal regions were analysed using time-frequency analysis and weighted phase-lag index (wPLI). We observed a significant enhancement in prefrontal and parietal ß suppression as WML increased. Furthermore, as WML increased, there was a notable enhancement in fronto-parietal connectivity (FPC), as evidenced by both EEG and fNIRS. Correlation analysis indicated that as WML increased, there was a potential for enhancement of neurovascular coupling (NVC) of FPC.

Food supply network disruption and mitigation: an integrated perspective of traceability technology and network structure.

The 2019 coronavirus disease (COVID-19) epidemic has caused serious disruptions in food supply networks. Based on the case of the remerging epidemic in China, this paper aims to investigate food supply network disruption and its mitigation from technical and structural perspectives. To solve the optimal policy choice problem that how to improve mitigation capability of food supply networks by using traceability technology and adjusting network structure, the occurrence mechanism of food supply network disruptions is revealed through a case study of the remerging COVID-19 outbreak in Beijing's Xinfadi market. Five typical traceability solutions are proposed to mitigate network disruptions and their technical attributes are analyzed to establish disruption mitigation models. The structure of food supply networks is also controlled to mitigate disruptions. The structural attributes of three fundamental networks are extracted to adjust the network connections pattern in disruption mitigation models. Next, simulation experiments involving the disruption mitigation models are carried out to explore the independent and joint effects of traceability technology and network structure on mitigation capability. The findings suggest that accuracy makes a more positive effect on the mitigation capability of food supply networks than timeliness due to the various technical compositions behind them; the difference between these effects determines the choice decision of supply networks on traceability solution types. Likewise, betweenness centralization makes a positive effect but degree centralization makes a negative effect on mitigation capability because intermediary firms and focal firms in food supply networks have different behavior characteristics; these effects are both regulated by supply network types and exhibit different sensitivities. As for the joint effect of technical and structural attributes on mitigation capability, the joint effect of accuracy and betweenness centralization is bigger than the independent effects but smaller than their sum; the joint effect of timeliness and betweenness centralization depends on networks type; while the positive effect of accuracy or timeliness on mitigation capability is greater than the negative effect of degree centralization; theses joint effects are caused by the complicated interactive effects between technical composition and behaviors of intermediary firms or focal firms. These findings contribute to disruption management and decision-making theories and practices.

Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk.

Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In this study, a non-specific optical sensor array was proposed for the identification of multiple pathogenic bacteria in contaminated milk samples. Fluorescence-labeled single-stranded DNA was efficiently quenched by two-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial species, enabling the sensor array to identify eight bacteria (pathogenic and spoilage) within a few hours. Four traditional machine learning models and two artificial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubation to 120 min increased the accuracy of the multiplayer perceptron to 98.4 %. This sensor array is a novel, low-cost, and high-accuracy approach for the identification of multiple bacteria, providing an alternative to plate counting and ELISA methods.

Cardiometabolic risk in first-episode schizophrenia (FES) patients with the earliest stages of both illness and antipsychotic treatment.

OBJECTIVE: It is well established that schizophrenia patients have high cardiovascular morbidity and mortality. However, the underlying risk factors in the earliest stages of both schizophrenia illness and antipsychotics treatment are less clear. This study aimed to characterize the metabolic features of those patients. METHODS: We performed a retrospective cohort study in a naturalistic setting, which included antipsychotic-naïve, first-episode schizophrenia (FES) inpatients with the baseline metabolic measurements and changes following a short term treatment with antipsychotic drugs. RESULTS: Although prevalence of hypertriglyceridemia, hypercholesterolemia, higher-LDL-C and hyperglycaemia in patients with FES were much lower than those of the general population (7.5% v.s. 14.9%, 9.2% v.s. 18.4%, 8.1% v.s. 14.9%, 8.6% v.s.18.3%, respectively), lower-HDL-C in patients with FES were much more prevalent than that of the general population (19.9% v.s. 6.4%). Despite significant metabolic risk profiles (as such lipid abnormalities and insulin resistance) increase, mean fasting glucose and glucosylated serum protein (GSP) were significantly decreased after the short term (median of 23days) antipsychotics exposure, compared to baseline. There is no significant difference of the metabolic profile change between monopharmacy and polypharmacy. CONCLUSION: These results indicated an early-onset nature of HDL-C abnormalities in drug-naïve FES patients. Lipids metabolism risk may develop early and quickly after antipsychotic exposure. Early monitoring is required for the purpose of early detection and hence prevention of the initial metabolic risk which may lead to diabetes mellitus and cardiovascular disease.

Early onset of cardiometabolic risk factor profiles in drug naïve adolescents and young adults with first-episode schizophrenia.

OBJECTIVE AND METHOD: We performed a case-control study, which included antipsychotic-naïve first-episode schizophrenia (FES) of adolescents and young adults and general population controls, to investigate the early-onset cardiometabolic risk factors in FES. RESULTS: FES had more frequent lower HDL-C when compared to controls. However, the distribution of BMI and the frequency of hypercholesterolemia, elevated LDL-C, hypertriglyceridemia in FES were not significantly different to controls. CONCLUSIONS: The results indicated that abnormal HDL-C might be an early-onset event in drug-naïve FES of adolescents and young adults who are unlikely to have other cardiometabolic risks.