Association between triglyceride glucose index (TyG) and psychotic symptoms in patients with first-episode drug-naïve major depressive disorder

ObjectiveMajor depressive disorder (MDD) sufferers frequently have psychotic symptoms, yet the underlying triggers remain elusive. Prior research suggests a link between insulin resistance (IR) and increased occurrence of psychotic symptoms. Hence, this study sought to investigate the potential association between psychotic symptoms in Chinese patients experiencing their first-episode drug-naïve (FEDN) MDD and the triglyceride glucose (TyG) index, an alternative measure of insulin resistance (IR).MethodsBetween September 2016 and December 2018, 1,718 FEDN MDD patients with an average age of 34.9 ± 12.4 years were recruited for this cross-sectional study at the First Hospital of Shanxi Medical University in China. The study collected clinical and demographic data and included assessments of anxiety, depression, and psychotic symptoms using the 14-item Hamilton Anxiety Rating Scale (HAMA), the 17-item Hamilton Depression Rating Scale (HAMD-17), and the positive subscales of the Positive and Negative Syndrome Scale (PANSS), respectively. Measurements of metabolic parameters, fasting blood glucose (FBG), and thyroid hormones were also gathered. To assess the correlation between the TyG index and the likelihood of psychotic symptoms, the study used multivariable binary logistic regression analysis. Additionally, two-segmented linear regression models were employed to investigate possible threshold effects in case non-linearity relationships were identified.ResultsAmong the patients, 9.95% (171 out of 1,718) exhibited psychotic symptoms. Multivariable logistic regression analysis showed a positive correlation between the TyG index and the likelihood of psychotic symptoms (OR = 2.12, 95% CI: 1.21-3.74, P = 0.01) after adjusting for confounding variables. Moreover, smoothed plots revealed a nonlinear relationship with the TyG index, revealing an inflection point at 8.42. Interestingly, no significant link was observed to the left of the inflection point (OR = 0.50, 95% CI: 0.04-6.64, P = 0.60), whereas beyond this point, a positive correlation emerged between the TyG index and psychotic symptoms (OR = 2.42, 95% CI: 1.31-4.48, P = 0.01). Particularly, a considerable 142% rise in the probability of experiencing psychotic symptoms was found with each incremental elevation in the TyG index.ConclusionsUnderstanding the non-linear link between the TyG index and the risk of psychotic symptoms in Chinese patients with FEDN MDD highlights the potential for targeted therapeutic approaches. By acknowledging the threshold effect observed, there is an opportunity to mitigate risk factors associated with IR-related psychiatric comorbidities through tailored interventions. These preliminary results stress the need for further longitudinal research to solidify these insights and contribute to more effective therapeutic strategies

Nonsequential Speckle Reduction Method by Generating Uncorrelated Laser Subbeams with Equivalent Intensity Using a Reflective Spatial Light Modulator

Sequential speckle reduction methods demand the usage of fast modulators due to the short integration period of human eyes. Here, a nonsequential speckle reduction method by splitting one laser beam with short coherence length into uncorrelated laser subbeams(LSBs)isreported.Inordertorealizethemostefficientspecklereduction,withthe help of a polarization beam splitter, we have programmed a reflective spatial light modulator to make the LSBs intensities equivalent. Three uncorrelated LSBs with equivalent light intensity are designed to demonstrate this idea; the speckle contrast ratio is reduced to 0.55, which closes to the expected value of 0.58. This nonsequential speckle reduction method has no requirement of the modulators speed; thus, it has obvious merit comparing with the sequential speckle reduction methods

Exploring non-linear and spatially non-stationary relationships between commuting burden and built environment correlates

The heavy commuting burden group is growing rapidly. The imbalance in commuting hours may damage traffic equality. Exploring the elements associated with commuting burden is crucial for promoting transport equity. However, few studies considered non-linear and spatially non-stationary characteristicss when exploring their relationships. In this paper, the traditional gradient boosting decision tree (GBDT) model is improved by combining it with the geographically weighted regression model, to identify nonlinear correlation and spatial nonstationarity simultaneously. The results show that there is a nonlinear correlation between the commuter burden and all the potential explanatory variables selected in this paper. And the correlation between each potential explanatory variable and commuter burden is spatially heterogeneous. The densification of public transportation and jobs can shorten the commuting time, but the convenience is diminishing. In addition, a balanced job–housing ratio, the moderate mixing distribution of different land-use types, and the development of a new urban center at an appropriate location are also conducive to easing the commuting burden. We also identify the strong non-linear correlation regions between commuting burden and built environment factors, which can provide more reference for the planning to achieve traffic equity

Power spectrum entropy based detection and mitigation of low-rate DoS attacks

Low-Rate DoS (LDoS) attacks send periodical packet bursts to the bottleneck routers which can throttle the bandwidth of TCP flows. They are difficult to detect while severely degrading the Quality of Service (QoS) of TCP applications. By combining Power Spectrum Analysis with Information Entropy, we introduce two novel information metrics to detect the LDoS attacks: Fourier Power Spectrum Entropy (FPSE) and Wavelet Power Spectrum Entropy (WPSE). As the energy of LDoS attack signal is mostly concentrated in the low-frequency range, FPSE and WPSE of LDoS attacks both exhibit lower values compared to those of normal flows. Therefore, these two metrics can be applied here to detect LDoS attacks efficiently. By evaluating on NS-3 simulations and real network traces, the results validate the effectiveness of these two metrics to differentiate LDoS attacks from normal flows. They can detect the LDoS attacks efficiently with fewer false alarms compared to the other detection mechanisms. Based on these two metrics, we also propose a Power Spectrum Entropy-based Robust-RED (PRRED) queuing algorithm to mitigate LDoS attacks. The evaluation results in NS-3 demonstrate that the proposed algorithm is able to effectively preserve the TCP bandwidth while countering the different LDoS attacks

The Nonlinear Influence of Street Quality on Housing Prices Based on Random Forest Model: A Case Study of Guangzhou

Streets are important spaces for transportation and life. It is important to clarify the economic effects of street quality on the real estate industry to create high-quality street spaces and meet the environmental needs of residents. Based on factors influencing traditional housing prices, such as street quality, this study used the random forest model and an accumulated local effects plot to explore the nonlinear relationship between street quality and housing prices, with its threshold effect in Guangzhou. The results show that the random forest model's fitting accuracy is 0.145 times higher than that of traditional linear methods and that the random forest model effectively captures the nonlinear relationship and threshold effect between street quality characteristics and housing prices. Commercial location, global betweenness, and the years of community construction significantly impact housing prices. The total contribution of green vision rate, sky openness, and enclosure to housing prices reached 5.68%, and residents' preferences for a comfortable environment drew attention. When distance increased, the negative influence of the commercial and economic location on housing prices gradually decreased. When global betweenness is less than 1,715, it has an exponential positive correlation with housing prices. When local proximity exceeds 117, it has little impact on housing prices, revealing the alternative effects of transportation hubs at different levels. Positive externalities, such as convenient transportation and negative externalities, such as traffic congestion and environmental pollution, affect housing prices equally. Road construction should consider traffic efficiency and environmental livability. When the degree of road motorization is less than 23% and more than 26%, the construction ratio of protective isolation facilities is more than 4.27%, and the night light brightness is higher than 0.0037 W/(m2·sr·μm), they have a lesser impact on housing prices. The over-construction of safety facilities reduces the economic benefits of street quality. Moreover, to ensure fair street usage, the green vision rate of the living environment should not be less than 15%. The spatial distribution trends of sky openness and enclosures are opposite, but both negatively impact housing prices. In urban development and construction, the development intensity should be reasonably controlled to avoid psychological depression caused by space cramping. For future street-stock renewal, it is necessary to accurately understand the current situation of street quality and the needs of residents, specifically improving street quality. Avoiding street quality creates property premiums and strives to ensure the fairness of residents' access to and use of street spaces. It guides real estate developers to participate in street construction. This study compensates for the limitations of traditional characteristic price models and the lack of explanatory properties in traditional linear methods, thereby providing a scientific basis for improving residents' living environments and building a livable city

Laser Spatial Coherence Suppression With Refractive Optical Elements Toward the Improvement of Speckle Reduction by Light Pipes

We study laser speckle reduction by light pipes, and we propose a new method to improve its efficiency. Proof-of-concept refractive optical elements (ROEs) with a staircase-like structure are introduced before a holographic diffuser to split a laser beam into laser sub-beams. Optical paths of the laser sub-beams after transmitting through the ROEs are different, and these partially correlated (or uncorrelated) laser sub-beams are added in intensity basis because of the folded mirror reflections by the light pipe. Thus, laser spatial coherence is suppressed, which helps to reduce speckle. We demonstrate this method in a simplified laser projection system, where subjective speckle contrast is reduced from 0.33 to 0.24 before and after introducing a two-dimensional ROE, respectively. Comparing with other improved speckle reduction methods by light pipes, the proposed method is motionless and simpler

Land Use Change under Population Migration and Its Implications for Human–Land Relationship

With the rural-to-urban population migration under the new era of rapid urbanization, China has experienced dramatic rural land change, especially the change in cultivated land and rural residential land, resulting in the serious uncoordinated human–land relationships in rural areas. The efficient use of these two kinds of land resources becomes one of the paramount challenges for governments to achieve sustainable and balanced rural development. This challenge highlights the need for quantifying the formation mechanism of the relationship between cultivated land and rural residential land (RCR) and exploring the corresponding relation between human–land relationships with RCR to guide the high-efficiency rural land use structure and coordinated development of human–land relationships. This study aims to quantitatively characterize the matching modes of RCR and the underlying formation mechanism via a grid-based, integrated decoupling model and multiclass explainable boosting machine analysis method. The findings are as follows: (1) The variation in cultivated land and rural residential land is characterized by quantity match and spatial mismatch. The six matching modes of RCR are strong decoupling (SD) (33.36%), weak decoupling (9.86%), recessive decoupling (4.15%), expansive negative decoupling (15.05%), weak negative decoupling (4.92%), and strong negative decoupling (SND) (18.65%). (2) Average grain product per cultivated land and population variation have the highest relative importance and play the greatest role in determining the type of matching modes. A concomitant phenomenon is noted in the matching modes; that is, SD occurs with recessive decoupling and weak negative decoupling, and the weak decoupling and expansive negative decoupling occur with SND in the same conditions. (3) A significant corresponding relationship exists between the matching modes and human–land relationship, indicating that the six matching modes correspond to four different stages of the human–land relationship. The study could provide some decision-making guidance for sustainable rural development, so as to improve the differentiated land management and regional response strategies

Sensitively humidity-driven actuator and sensor derived from natural skin system

Precise control over the smart materials exhibiting reversible shape changes in response to environmental stimuli presents a considerable challenge. Here, with a self-assembly strategy by extracting natural materials from pigskin, a single layer bio-inspired, transparent, soft biological film (BF) with the primary characteristics of self-actuation and self-sensing is successfully developed. The self-assembly constructed BF can exchange water and reflect environmental humidity gradients rapidly to activate continuous rotary movement. Temperature which affects the thermal motion of water molecules will induce different orientation movement of the film, and on this basis, a humidity-driven energy transfer motor is developed. More characterizations highlight the behavior mechanism of BF through water exchanging by a hydrogen bonding interaction with the hydrophilic group of amino acids residues on the BF surface. Finally, a wearable, steady and ultrafast-response sensor to detect human breathing, especially for real-time obstructive sleep apnea (OSA) state, is fabricated. This study offers great potential in emerging applications including micro-sensors, switches, soft robots and power source technologies

Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection

Image outlier detection has been an important research issue for many computer vision tasks. However, most existing outlier detection methods fail in the high-dimensional image datasets. In order to address this problem, we propose a novel image outlier detection method by combining autoencoder with Adaboost (ADAE). By ensembling many weak autoencoders, our method can better capture the statistical correlations among the features of normal data than the single autoencoder. Therefore, the proposed ADAE is able to determine the outliers efficiently. In order to reduce the many parameters in ADAE, we introduce the Sparse Group Lasso (SGL) constraint into the learning objective of ADAE. We combine Adagrad with Proximal Gradient Descent to optimize this additional learning objective. We also propose the multi-objective evolutionary algorithm to determine the best penalty factors of SGL. By evaluating on several famous image datasets, the detection results testify to the outstanding outlier detection performance of ADAE. The evaluation results also show SGL can make the detection model more compact while maintaining the similar detection performance.Accepted versio