Commutability evaluation of candidate reference materials and ERM-DA470k/IFCC for immunoglobulin M using two international approaches.

BACKGROUND: This study aimed to assess the commutability of frozen pooled human serum (PHS), high concentration of Immunoglobulin M (IgM) pure diluted materials (HPDM), commercialized pure materials (CPM), and dilutions of ERM-DA470k/IFCC in IgM detection using the CLSI and IFCC approaches, to support standardization or harmonization of IgM measurement. METHODS: Twenty-four serum samples, relevant reference materials (PHS, HPDM, CPM), and different ERM-DA470k/IFCC dilutions were analyzed in triplicate using six routine methods. The commutability of the relevant reference materials was carried out following CLSI EP30-A and IFCC bias analysis. RESULTS: According to the CLSI approach, low, medium, and high concentrations of PHS, HPDM, and CPM were commutable on 10, 13, 15, 13, and 8 of 15 assay combinations, respectively. Using the IFCC approach, low, medium, and high concentrations of PHS, HPDM, and CPM were commutable on 10, 11, 9, 15, and 10 of 15 assay combinations, respectively. The ERM-DA470k/IFCC dilutions with D-PBS and RPMI-1640 Medium were commutable on 13 of 15 assay combinations according to CLSI and were commutable on all 15 assay combinations using IFCC approach. CONCLUSIONS: High concentration of PHS were commutable on all six detection systems using the CLSI approach. Low and medium concentration of PHS showed unsatisfied commutability. HPDM, not CPM have good commutability, has the potential to become reference materials. ERM-DA470k/IFCC diluted with different medium showed different commutability.

EEG microstate temporal Dynamics Predict depressive symptoms in College Students.

Previous studies on resting-state electroencephalographic responses in patients with depressive disorders have identified electroencephalogram (EEG) parameters as potential biomarkers for the early detection and diagnosis of depressive disorders. However, these studies did not investigate the relationship between resting-state EEG microstates and the early detection of depressive symptoms in preclinical individuals. To explore the possible association between resting-state EEG microstate temporal dynamics and depressive symptoms among college students, EEG microstate analysis was performed on eyes-closed resting-state EEG data for approximately 5 min from 34 undergraduates with high intensity of depressive symptoms and 34 age- and sex-matched controls with low intensity of depressive symptoms. Five microstate classes (A-E) were identified to best explain the datasets of both groups. Compared to controls, the mean duration, occurrence, and coverage of microstate class B increased significantly, whereas the occurrence and coverage of microstate classes D and E decreased significantly in individuals with high intensity of depressive symptoms. Additionally, the presence of microstate class B was positively correlated with participants' Beck Depression Inventory-II (BDI-II) scores, and the presence of microstate classes D and E were negatively correlated with their BDI-II scores. Further, individuals with high intensity of depressive symptoms had higher transition probabilities of A→B, B→A, B→C, B→D, and C→B, with lower transition probabilities of A→D, A→E, D→A, D→E, E→A, E→C, and E→D than controls. These results highlight resting-state EEG microstate temporal dynamics as potential biomarkers for the early detection and timely treatment of depression in college students.

Simulation and Experiment Analysis of Temperature Field of Magnetic Suspension Support Based on FBG.

Temperature rise is an important factor limiting the development of magnetic suspension support technology. Traditional temperature sensors such as thermocouples are complicated and vulnerable to electromagnetic interference due to their point contact temperature measurement methods. In this paper, the equivalent model of magnetic suspension support is established, and the temperature field is simulated and analyzed by magnetic thermal coupling calculation in ANSYS software. Then, a quasi-distributed temperature measurement system is designed, and the FBG temperature sensor is introduced to measure the temperature of the magnetic suspension support system by "one-line and multi-point". By comparing the analysis experiments and simulations, the equivalent accuracy of the simulation model and the FBG temperature sensor can accurately measure the temperature of the magnetic suspension support.

Establishment of Multiple Myeloma Diagnostic Model Based on Logistic Regression in Clinical Laboratory.

BACKGROUND: Due to the insidious onset of multiple myeloma (MM), missed diagnosis and misdiagnosis have a serious impact on the health of MM patients. Simple, rapid, and valid laboratory screening is critical for MM clinical diagnosis. METHODS: We used routine laboratory tests to establish a simple, inexpensive, and non-invasive diagnostic model for MM based on logistic regression. In the retrospective analysis, a total of 273 newly diagnosed MM inpatients and 288 non-MM participants, from January 2016 to December 2018 in Beijing Chaoyang hospital, Capital Medical University, were divided into training set and validation set. Age, gender, and the related routine laboratory tests for MM, including albumin (ALB), globulin (GLB), lactate dehydrogenase (LDH), creatinine (Cr), calcium (Ca2+), hemoglobin (Hb) and platelet (PLT), were analyzed by multivariate logistic regression to develop a diagnostic model. RESULTS: A diagnostic model was calculated using the formula MM index=-((-18×gender-3×ALB-Hb)/10), based on the logistic regression. The MM index [22 (20 - 25)] of MM patients was significantly lower than that of non-MM [30 (29 - 31)] in the training set (p < 0.001). It showed an excellent diagnostic performance in diagnosing MM through a receiver operating characteristic (ROC) curve, and its corresponding sensitivity, specificity, and area under the curve (AUC) were 95.6%, 96.7%, and 0.982 (0.968, 0.997), respectively. At a diagnostic risk threshold of 28, the model identified MM with a sensitivity of 95.6% and a specificity of 98.1% by using independent validation data. There was a significant positive correlation (r = 0.845, p < 0.001) between the DS grading and the MM index among all the participants. CONCLUSIONS: The established diagnostic model of MM index can successfully identify newly diagnosed MM from healthy controls. The diagnostic model of MM index may also act as a predictor of the severity of MM without therapy.

Co-activation patterns during viewing of different video game genres.

Past research has revealed cognitive improvements resulting from engagement with both traditional action video games and newer action-like video games, such as action real-time strategy games (ARSG). However, the cortical dynamics elicited by different video gaming genres remain unclear. This study explored the temporal dynamics of cortical networks in response to different gaming genres. Functional magnetic resonance imaging (fMRI) data were obtained during eye-closed resting and passive viewing of gameplay videos of three genres: life simulation games (LSG), first-person shooter games (FPS), and ARSG. Data analysis used a seed-free Co-Activation Pattern (CAP) based on Regions of Interest (ROIs). When comparing the viewing of action-like video games (FPS and ARSG) to LSG viewing, significant dynamic distinctions were observed in both primary and higher-order networks. Within action-like video games, compared to FPS viewing, ARSG viewing elicited a more pronounced increase in the Fraction of Time and Counts of attentional control-related CAPs, along with an increased Transition Probability from sensorimotor-related CAPs to attentional control-related CAPs. Compared to ARSG viewing, FPS viewing elicited a significant increase in the Fraction of Time of sensorimotor-related CAPs, when gaming experience was considered as a covariate. Thus, different video gaming genres, including distinct action-like video gaming genres, elicited unique dynamic patterns in whole-brain CAPs, potentially influencing the development of various cognitive processes.

Behavioral state-dependent associations between EEG temporal correlations and depressive symptoms.

Previous studies have shown abnormal long-range temporal correlations in neuronal oscillations among individuals with Major Depressive Disorders, occurring during both resting states and transitions between resting and task states. However, the understanding of this effect in preclinical individuals with depression remains limited. This study investigated the association between temporal correlations of neuronal oscillations and depressive symptoms during resting and task states in preclinical individuals, specifically focusing on male action video gaming experts. Detrended fluctuation analysis (DFA), Lifetimes, and Waitingtimes were employed to explore temporal correlations across long-range and short-range scales. The results indicated widespread changes from the resting state to the task state across all frequency bands and temporal scales. Rest-task DFA changes in the alpha band exhibited a negative correlation with depressive scores at most electrodes. Significant positive correlations between DFA values and depressive scores were observed in the alpha band during the resting state but not in the task state. Similar patterns of results emerged concerning maladaptive negative emotion regulation strategies. Additionally, short-range temporal correlations in the alpha band echoed the DFA results. These findings underscore the state-dependent relationships between temporal correlations of neuronal oscillations and depressive symptoms, as well as maladaptive emotion regulation strategies, in preclinical individuals.

Defocused mode in depressed mood and its changes in time-frequency attention-related beta.

Depressed mood has been proposed to possibly possess a unique mode of defocused attention. However, this argument needs to be supported by experimental evidence based on attentional performance. The present study used a perceptual load paradigm, combining factors of perceptual load, distractor-target compatibility, and eccentricity, to investigate the degree of attentional distraction in depressed mood. In addition, the mode of attentional distraction associated with depressed mood was explored with the time-frequency features of electroencephalography (EEG). The behavioral results showed that the high depressed mood (HD) group had significantly higher attentional distraction than the low depressed mood (LD) group. EEG results showed that 1) the beta power (especially beta-2, 18-30 Hz) of the two groups differed in the medio-late part of the attentional distraction, with significantly lower power in the HD group than in the LD group; 2) the results of the correlation between beta-2 power and depression scores revealed a significant negative correlation. These results imply that beta-2 is a potential marker that may be sensitive to depressed mood during attentional processing, which was further supported by the classification results of the support vector machine (SVM) with 80.65% accuracy between the HD and LD groups.

A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts.

While disgust originates in the hard-wired mammalian distaste response, the conscious experience of disgust in humans strongly depends on subjective appraisal and may even extend to socio-moral contexts. Here, in a series of studies, we combined functional magnetic resonance imaging with machine-learning-based predictive modelling to establish a comprehensive neurobiological model of subjective disgust. The developed neurofunctional signature accurately predicted momentary self-reported subjective disgust across discovery (n = 78) and pre-registered validation (n = 30) cohorts and generalized across core disgust (n = 34 and n = 26), gustatory distaste (n = 30) and socio-moral (unfair offers; n = 43) contexts. Disgust experience was encoded in distributed cortical and subcortical systems, and exhibited distinct and shared neural representations with subjective fear or negative affect in interoceptive-emotional awareness and conscious appraisal systems, while the signatures most accurately predicted the respective target experience. We provide an accurate functional magnetic resonance imaging signature for disgust with a high potential to resolve ongoing evolutionary debates.

Routine blood biomarkers for the detection of multiple myeloma using machine learning.

INTRODUCTION: Primary laboratory tests performed in the diagnosis of multiple myeloma (MM) include bone marrow examination and free light chain assay; however, these may only be ordered after clinical suspicion of disease. In contrast, routine blood test results are readily available. METHODS: Machine learning algorithms (ML) combined with routine blood tests were used to detect MM. Feature selection was performed to achieve improved classification performance. The robustness of the classification models was assessed in an internal and external validation data set. To minimize the divergence, the training and validation data sets were combined and used to assess the performance of the ML algorithms. RESULTS: The AdaBoost-DecisionTable produced the best performance (accuracy =94.75%, sensitivity =87.70%, positive predictive value (PPV) =92.50%, F-measure =90.00%, and areas under the receiver operating characteristic curves (AUC) =97.50%) in the training data set using a 10-fold cross-validation. Performance in the validation data sets was affected by the divergence of the data sets, with accuracy greater than 85% and AUC greater than 90% in the validation data sets. The ML algorithm achieved a high accuracy of 92.61%, high AUC (96.80%), a sensitivity value of 85.20%, a PPV value of 88.50%, and an F-measure of 86.80% in a test set that was randomly selected from the combined data set. CONCLUSIONS: Combining ML and routine serum biomarkers hold a potential benefit in MM diagnosis.

Novel target sites for soybean yield enhancement by photosynthesis.

Photosynthesis plays an important role in plant growth and development. Increasing photosynthetic rate is a main objective of improving crop productivity. Chlorophyll fluorescence is an effective method for quickly evaluating photosynthesis. In this study, four representative chlorophyll fluorescence parameters, that is, maximum quantum efficiency of photosystem II, quantum efficiency of PSII, photochemical quenching, and non-photochemical quenching, of 219 diverse soybean accessions were measured across three environments. The underlying genetic architecture was analyzed by genome-wide association study. Forty-eight SNPs were detected to associate with the four traits and explained 10.43-20.41% of the phenotypic variation. Nine candidate genes in the stable QTLs were predicted. Great differences in the expression levels of the candidate genes existed between the high photosynthetic efficiency accessions and low photosynthetic efficiency accessions. In all, we uncover 17 QTLs associated with photosynthesis-related traits and nine genes that may participate in the regulation of photosynthesis, which can provide references for revealing the genetic mechanism of photosynthesis. These QTLs and candidate genes will provide new targets for crop yield improvement through increasing photosynthesis.

Information transmission in action video gaming experts: Inferences from the lateralized readiness potential.

Research showed that action real-time strategy gaming (ARSG) experience is related to cognitive and neural plasticity, including visual selective attention and working memory, executive control, and information processing. This study explored the relationship between ARSG experience and information transmission in the auditory channel. Using an auditory, two-choice, go/no-go task and lateralized readiness potential (LRP) as the index to partial information transmission, this study examined information transmission patterns in ARSG experts and amateurs. Results showed that experts had a higher accuracy rate than amateurs. More importantly, experts had a smaller stimulus-locked LRP component (250 - 450 ms) than amateurs on no-go trials, while the response-locked LRP component (0 - 300 ms) on go trials did not differ between groups. Thus, whereas amateurs used an asynchronous information transmission pattern, experts used a reduced asynchronous information transmission pattern or a synchronous pattern where most of processing occurred prior to response execution - an information transmission pattern that supports rapid, error-free performance. Thus, experts and amateurs may use different information transmission patterns in auditory processing. In addition, the information transmission pattern used by experts is typically observed only after long-term auditory training according to past research. This study supports the relationship between ARSG experience and the development of information processing patterns.

Action Video Gaming Experience Related to Altered Resting-State EEG Temporal and Spatial Complexity.

Action video gaming (AVG) places sustained cognitive load on various behavioral systems, thus offering new insights into learning-related neural plasticity. This study aims to determine whether AVG experience is associated with resting-state electroencephalogram (rs-EEG) temporal and spatial complexity, and if so, whether this effect is observable across AVG subgenres. Two AVG games - League of Legends (LOL) and Player Unknown's Battle Grounds (PUBG) that represent two major AVG subgenres - were examined. We compared rs-EEG microstate and omega complexity between LOL experts and non-experts (Experiment 1) and between PUBG experts and non-experts (Experiment 2). We found that the experts and non-experts had different rs-EEG activities in both experiments, thus revealing the adaptive effect of AVG experience on brain development. Furthermore, we also found certain subgenre-specific complexity changes, supporting the recent proposal that AVG should be categorized based on the gaming mechanics of a specific game rather than a generic genre designation.

P3a amplitude is related to conclusion specificity during category-based induction.

Category-based induction involves the generalization of a novel property (conclusion property) to a new category (conclusion category), based on the knowledge that a category exemplar (premise category) has the respective novel property. Previous studies have shown that conclusion specificity (i.e., specific [S] or generic categories [G]) influences category-based induction. However, the timing of brain activity underlying this effect is not well known, especially with controlling the similarities of premise and conclusion categories between S and G arguments. In this study, the event-related potential (ERP) responses to category-based induction between S and G arguments were compared under both congruent (+, premise and conclusion categories are related) and incongruent (-, premise and conclusion categories are unrelated) arguments; additionally, the similarities of premise and conclusion categories between S and G arguments were controlled. The results showed that replicating this effect, S+ arguments have increased "strong" response rates compared to G+ arguments, suggesting that category-based induction is contingent on factors beyond matched similarities. Moreover, S arguments have more liberal inductive decision thresholds than G arguments, which suggest that conclusion specificity affects the inductive decision reflected by inductive decision thresholds. Furthermore, G+ arguments elicit greater P3a amplitudes than S+ arguments, which suggest greater attention resources allocation to the review of decisions for G+ arguments than that for S+ arguments. Taken together, the conclusion specificity effect during semantic category-based induction can be revealed by "strong" response rates, inductive decision thresholds, and P3a component after controlling the premise-conclusion similarity, providing evidence that category-based induction rely on more than simple similarity judgment and conclusion specificity would affect category-based induction.

Action Real-Time Strategy Gaming Experience Related to Enhanced Capacity of Visual Working Memory.

Action real-time strategy gaming (ARSG)-a major genre of action video gaming (AVG)-has both action and strategy elements. ARSG requires attention, visual working memory (VWM), sensorimotor skills, team cooperation, and strategy-making abilities, thus offering promising insights into the learning-induced plasticity. However, it is yet unknown whether the ARSG experience is related to the development of VWM capacity. Using both behavioral and event-related potential (ERP) measurements, this study tested whether ARSG experts had larger VWM capacity than non-experts in a change detection task. The behavioral results showed that ARSG experts had higher accuracy and larger VWM capacity than non-experts. In addition, the ERP results revealed that the difference wave of the contralateral delay activity (CDA) component (size 4-size 2) elicited by experts was significantly larger than that of non-experts, suggesting that the VWM capacity was higher in experts than in non-experts. Thus, the findings suggested that prolonged ARSG experience is correlative with the enhancement of VWM.

Action Real-Time Strategy Gaming Experience Related to Increased Attentional Resources: An Attentional Blink Study.

Action real-time strategy gaming (ARSG) is a cognitively demanding task which requires attention, sensorimotor skills, team cooperation, and strategy-making abilities. A recent study found that ARSG experts had superior visual selective attention (VSA) for detecting the location of a moving object that could appear in one of 24 different peripheral locations (Qiu et al., 2018), suggesting that ARSG experience is related to improvements in the spatial component of VSA. However, the influence of ARSG experience on the temporal component of VSA-the detection of an item among a sequence of items presented consecutively and quickly at a single location-still remains understudied. Using behavioral and electrophysiological measures, this study examined whether ARSG experts had superior temporal VSA performance compared to non-experts in an attentional blink (AB) task, which is typically used to examine temporal VSA. The results showed that the experts outperformed the non-experts in their detection rates of targets. Furthermore, compared to the non-experts, the experts had faster information processing as indicated by earlier P3 peak latencies in an AB period, more attentional resources distributed to targets as indicated by stronger P3 amplitudes, and a more flexible deployment of attentional resources. These findings suggest that experts were less prone to the AB effect. Thus, long-term ARSG experience is related to improvements in temporal VSA. The current findings support the benefit of video gaming experience on the development of VSA.

FN400 and sustained negativity reveal a premise monotonicity effect during semantic category-based induction.

The premise monotonicity effect during category-based induction is a robust effect that occurs when generalization of a novel property shared by many cases is more likely than one shared by few cases. The timing of brain activity during this effect is unclear. Therefore, the event-related potentials (ERPs) underpinning this effect were measured by manipulating the premise sample size (single [S] vs. two [T]) in a semantic category-based induction task, with the conclusion categories either including the premise categories (congruent induction) or not (incongruent induction). The behavioral results replicated the premise monotonicity effect, and revealed that S arguments produced longer reaction times and more conservative response criteria than did T arguments. This suggests that the premise monotonicity effect was affected by both evidence accumulation speed and decision threshold. ERP results demonstrated that the premise monotonicity effect was reflected by two parameters during inductive decision: (1) S arguments elicited larger FN400 amplitudes than did T arguments under congruent induction, which was linked to reduced global similarity, decreased cognitive relevance, and attenuated conceptual fluency and (2) S arguments elicited larger sustained negativity (SN) in the 450-1050-ms time window than did T arguments, which is related to more inference-driven integration and interpretive processes. Our findings provide insight into the complex temporal course of the premise monotonicity effect during semantic category-based induction.

FN400 amplitudes reveal the differentiation of semantic inferences within natural vs. artificial domains.

Category-based inferences allow inductions about novel properties based on categorical memberships (e.g., knowing all trout have genes [premise] allows us to infer that all fish have genes [conclusion]). Natural (N) and artificial (A) domains are the most obvious and traditional distinctions in categorization. The distinct event-related potential (ERP) responses for N and A domains have not yet been examined during category-based inferences. In this study, the differences between ERP inference parameters within N and A domains were measured during inductive decision processing, while controlling the premise-conclusion similarity and premise typicality between those two domains. Twenty-two adults were asked to make a decision on whether a conclusion was definitely weak, possibly weak, possibly strong, or definitely strong, based on a premise. The behavioral results showed that semantic inferences within the N domain shared similar inductive strength, similar "correct" response rates, and similar reaction times with that within the A domain. However, the ERP results showed that semantic inferences elicited smaller frontal-distributed N400 (FN400) amplitudes within the N domain than within the A domain, which suggested that knowledge of the ontological domain of a category affects category-based inferences, and underlaid the increased categorical coherence and homogeneity in the N as compared to the A categories. Therefore, we have distinguished the cognitive course of semantic inferences between N and A domains.

Association analysis of selenoprotein S polymorphisms in Chinese Han with susceptibility to gastric cancer.

OBJECTIVE: selenoprotein S (SelS) gene polymorphism is closely related to a variety of malignant tumours. Here, we evaluate the association between SelS polymorphism and genetic susceptibility to gastric cancer. METHODS: A case-control study was conducted to investigate the role of two SelS single nucleotide polymorphisms (SNPs) on the susceptibility to gastric cancer. The genotypes and genotype frequencies of the SelS were determined in 260 gastric cancer patients and 278 age-matched healthy controls. Polymerase chain reaction restriction fragment length of polymorphism (PCR-RFLP) was taken to genotype rs28665122 (G-105A) and rs34713741 (G-254A) within the SelS gene. The differences in the genotypic distribution between gastric cancer patients and healthy controls were analyzed with the Chi-square test for trends. Logistic-regression analysis was used to estimate odds ratios (OR) and 95% confidence intervals (CI), adjusting for age and sex. RESULTS: For rs34713741 in SelS, the allele frequencies analysis indicated that the allele frequency of the T was higher in patients than in controls (P=0.001). There were significant differences of genotype frequencies and allele of rs34713741 polymorphism between gastric cancer group and control group (P<0.05). The relative risk of suffering from gastric cancer in T allele was 1.62 times of CC genotype in Hunan Han population (OR=1.62, 95% CI: 1.15~2.29). But there were no differences of genotype frequencies and allele of SelS rs28665122 polymorphism between gastric cancer group and control group. CONCLUSIONS: Allele T of SelS rs34713741 polymorphism is significantly associated with an increased risk of gastric cancer in Chinese population.