MultiWD: Multi-label wellness dimensions in social media posts.

BACKGROUND: Halbert L. Dunn's concept of wellness is a multi-dimensional aspect encompassing social and mental well-being. Neglecting these dimensions over time can have a negative impact on an individual's mental health. The manual efforts employed in in-person therapy sessions reveal that underlying factors of mental disturbance if triggered, may lead to severe mental health disorders. OBJECTIVE: In our research, we introduce a fine-grained approach focused on identifying indicators of wellness dimensions and mark their presence in self-narrated human-writings on Reddit social media platform. DESIGN AND METHOD: We present the MultiWD dataset, a curated collection comprising 3281 instances, as a specifically designed and annotated dataset that facilitates the identification of multiple wellness dimensions in Reddit posts. In our study, we introduce the task of identifying wellness dimensions and utilize state-of-the-art classifiers to solve this multi-label classification task. RESULTS: Our findings highlights the best and comparative performance of fine-tuned large language models with fine-tuned BERT model. As such, we set BERT as a baseline model to tag wellness dimensions in a user-penned text with F1 score of 76.69. CONCLUSION: Our findings underscore the need of trustworthy and domain-specific knowledge infusion to develop more comprehensive and contextually-aware AI models for tagging and extracting wellness dimensions.

In Situ Test and Numerical Analysis of the Subway-Induced Vibration Influence in Historical and Cultural Reserves.

Although the rapid expansion of urban rail transit offers convenience to citizens, the issue of subway vibration cannot be overlooked. This study investigates the spatial distribution characteristics of vibration in the Fayuan Temple historic and cultural reserve. It involves using a V001 magnetoelectric acceleration sensor capable of monitoring low amplitudes with a sensitivity of 0.298 V/(m/s2), a measuring range of up to 20 m/s2, and a frequency range span from 0.5 to 100 Hz for in situ testing, analyzing the law of vibration propagation in this area, evaluating the impact on buildings, and determining the vibration reduction scheme. The reserve is divided into three zones based on the vertical vibration level measured during the in situ test as follows: severely excessive, generally excessive, and non-excessive vibration. Furthermore, the research develops a dynamic coupling model of vehicle-track-tunnel-stratum-structure to verify the damping effect of the wire spring floating plate track and periodic pile row. It compares the characteristics of three vibration reduction schemes, namely, internal vibration reduction reconstruction, periodic pile row, and anti-vibration reinforcement or reconstruction of buildings, proposing a comprehensive solution. Considering the construction conditions, difficulty, cost, and other factors, a periodic pile row is recommended as the primary treatment measure. If necessary, anti-vibration reinforcement or reconstruction of buildings can serve as supplemental measures.

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey.

BACKGROUND: Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy. METHODS: An AI-assisted bioinformatics method that combines transcriptomic data and microRNA regulations were used to identify a key miRNA-mediated network module in GC progression. To reveal the module's function, we performed the gene expression analysis in 20 clinical samples by qRT-PCR, prognosis analysis by multi-variable Cox regression model, progression prediction by support vector machine, and in vitro studies to elaborate the roles in GC cells migration and invasion. RESULTS: A robust microRNA regulated network module was identified to characterize GC progression, which consisted of seven miR-200/183 family members, five mRNAs and two long non-coding RNAs H19 and CLLU1. Their expression patterns and expression correlation patterns were consistent in public dataset and our cohort. Our findings suggest a two-fold biological potential of the module: GC patients with high-risk score exhibited a poor prognosis (p-value < 0.05) and the model achieved AUCs of 0.90 to predict GC progression in our cohort. In vitro cellular analyses shown that the module could influence the invasion and migration of GC cells. CONCLUSIONS: Our strategy which combines AI-assisted bioinformatics method with experimental and clinical validation suggested that the miR-200/183 family-mediated network module as a "pluripotent module", which could be potential marker for GC progression.

Hepatic arterial infusion chemotherapy and sequential ablation treatment in large hepatocellular carcinoma.

AIM: To investigate the individualized survival benefit of hepatic arterial infusion chemotherapy (HAIC) and sequential ablation treatment in large hepatocellular carcinoma (HCC) patients. METHODS: Between February 2016 and December 2020, a total of 228 HCC patients (diameter > 5 cm) who underwent HAIC alone (HAIC group, n = 135) or HAIC and sequential ablation (HAIC-ablation group, n = 93) treatment were reviewed. We applied the inverse probability of treatment weighting (IPTW) to adjust for potential bias of two treatment groups. The overall survival (OS) and progression-free survival (PFS) were compared with Kaplan-Meier curves. The Cox regression model was used to identify independent prognostic factors. And a prediction nomogram based on these independent prognostic factors was built, aiming to make probabilistic survival predictions and estimate personalized ablation benefits. RESULTS: After a median follow-up of 17.9 months, HCC patients in the HAIC-ablation group have longer significantly OS and PFS than those in the HAIC alone group (median OS: 22.2 months vs. 14.5 months; median PFS: 8.5 months vs. 4.6 months; both, p < 0.001). The IPTW-adjusted analysis revealed similar findings (both, p < 0.001). Tumor size, tumor number, and treatment modality were identified as independent prognostic factors for OS. The nomogram based on these factors showed favorable discrimination and well calibration. CONCLUSIONS: HAIC and sequential ablation provided significant survival benefits in patients with large HCC. The nomogram could help predict individual survival probabilities and estimate personalized sequential ablation benefits.

Strong Self-Trapped Exciton Emissions in Two-Dimensional Na-In Halide Perovskites Triggered by Antimony Doping.

Soft lattice and strong exciton-phonon coupling have been demonstrated in layered double perovskites (LDPs) recently; therefore, LDPs represents a promising class of compounds as excellent self-trapped exciton (STE) emitters for applications in solid-state lighting. However, few LDPs with outstanding STE emissions have been discovered, and their optoelectronic properties are still unclear. Based on the three-dimensional (3D) Cs2 NaInCl6 , we synthesized two 2D derivatives (PEA)4 NaInCl8 :Sb (PEA=phenethylamine) and (PEA)2 CsNaInCl7 :Sb with monolayer and bilayer inorganic sheets by a combination of dimensional reduction and Sb-doping. Bright broadband emissions were obtained for the first time under ambient temperature and pressure, with photoluminescence quantum efficiency (PLQE) of 48.7 % (monolayer) and 29.3 % (bilayer), superior to current known LDPs. Spectroscopic characterizations and first-principles calculations of excited state indicate the broadband emissions originate from STEs trapped at the introduced [SbCl6 ]3- octahedron.

Tunable Dual-Emission in Monodispersed Sb3+ /Mn2+ Codoped Cs2 NaInCl6 Perovskite Nanocrystals through an Energy Transfer Process.

Double halide perovskites are a class of promising semiconductors applied in photocatalysis, photovoltaic devices, and emitters to replace lead halide perovskites, owing to their nontoxicity and chemical stability. However, most double perovskites always exhibit low photoluminescence quantum efficiency (PLQE) due to the indirect bandgap structure or parity-forbidden transition problem, limiting their further applications. Herein, the self-trapped excitons emission of Cs2 NaInCl6 by Sb-doping, showing a blue emission with high PLQE of 84%, is improved. Further, Sb/Mn codoped Cs2 NaInCl6 nanocrystals are successfully synthesized by the hot-injection method, showing a tunable dual-emission covering the white-light spectrum. The studies of PL properties and dynamics reveal that an energy transfer process can occur between the self-trapped excitons and dopants (Mn2+ ). The work provides a new perspective to design novel lead-free double perovskites for realizing a unique white-light emission.

Perfusion imaging with 320-slice spiral computed tomography and color-coded digital subtraction angiography for assessing acute skeletal muscle ischemia-reperfusion injury in a rabbit model.

BACKGROUND: In recent years, skeletal muscle-related ischemia-reperfusion injury (IRI) has become more common. IRI can lead to severe limb injuries, multiple organ failure, and even death in some cases. However, there is still a lack of rapid and sensitive detection methods for IRI in skeletal muscle. This study aims to explore the value of computed tomography perfusion imaging (CTPI) and color-coded digital subtraction angiography (DSA) in assessing acute IRI of skeletal muscle in a rabbit model. METHODS: Fifty New Zealand white rabbits were randomly assigned to the ischemia-reperfusion (IR) group (n = 40) or sham group (n = 10). After 3 h of surgically-induced hindlimb ischemia, the IR group underwent reperfusion and CTPI and color-coded DSA were taken to assess the skeletal muscle at 0, 6, 12, or 24 h post-reperfusion. The data from CTPI and DSA in the right and left hindlimbs, blood flow (AF-R/L), blood volume (BV-R/L), contrast clearance rate (C-R/L) and the maximum contrast enhancement values (peak-R/L) were obtained. Serum superoxide dismutase (SOD), creatine kinase (CK), lactic dehydrogenase (LDH) and malondialdehyde (MDA) were measured. The statistical correlation between the above parameters (CTPI, color-coded DSA, and biochemical markers) was analyzed. RESULTS: The mean value of AF-R/L, BV-R/L, C-R/L and peak-R/L decreased linearly from 1.07 ± 0.08 to 0.75 ± 0.11, 1.03 ± 0.06 to 0.85 ± 0.14, 0.93 ± 0.15 to 0.71 ± 0.18, and 1.07 ± 0.01 to 0.47 ± 0.04, respectively. The correlation coefficients between AF-R/L and SOD, CK, LDH and MDA were 0.57, - 0.44, - 0.60, and - 0.62, respectively (p < 0.001). The correlation coefficients between Peak-R/L and SOD, CK, LDH, MDA were 0.59, 0.68, 0.71 and 0.66, respectively (p < 0.001). The correlation coefficient between AF-R/L and Peak-R/L was 0.70 (p < 0.001). CONCLUSION: Both CTPI and color-coded DSA could dynamically assess skeletal muscle IRI in rabbits.

Stable and Highly Efficient Photocatalysis with Lead-Free Double-Perovskite of Cs2 AgBiBr6.

Composition engineering of halide perovskite allows the tunability of the band gap over a wide range so that photons can be effectively harvested, an aspect that is of critical importance for increasing the efficiency of photocatalysis under sunlight. However, the poor stability and the low photocatalytic activity of halide perovskites prevent use of these defect-tolerant materials in wide applications involving photocatalysis. Here, an alcohol-based photocatalytic system for dye degradation demonstrated high stability through the use of double perovskite of Cs2 AgBiBr6 . The reaction rate on Cs2 AgBiBr6 is comparable to that on CdS, a model inorganic semiconductor photocatalyst. The fact of fast reaction between free radicals and dye molecules indicates the unique catalytic properties of the Cs2 AgBiBr6 surface. Deposition of metal clusters onto Cs2 AgBiBr6 effectively enhances the photocatalytic activity. Although the stability (five consecutive photocatalytic cycles without obvious decrease of efficiency) requires further improvements, the results indicate the significant potential of Cs2 AgBiBr6 -based photocatalysis.

Reservoir Computing With Dynamic Reservoir using Cascaded DNA Memristors.

This article proposes molecular and DNA memristors where the state is defined by a single output variable. In past molecular and DNA memristors, the state of the memristor was defined based on two output variables. These memristors cannot be cascaded because their input and output sizes are different. We introduce a different definition of state for the molecular and DNA memristors. This change allows cascading of memristors. The proposed memristors are used to build reservoir computing (RC) models that can process temporal inputs. An RC system consists of two parts: reservoir and readout layer. The first part projects the information from the input space into a high-dimensional feature space. We also study the input-state characteristics of the cascaded memristors and show that the cascaded memristors retain the memristive behavior. The cascade connections in a reservoir can change dynamically; this allows the synthesis of a dynamic reservoir as opposed to a static one in the prior work. This reduces the number of memristors significantly compared to a static reservoir. The inputs to the readout layer correspond to one molecule per state instead of two; this significantly reduces the number of molecular and DSD reactions for the readout layer. A DNA RC system consisting of DNA memristors and a DNA readout layer is used to detect seizures from intra-cranial electroencephalogram (iEEG). We also demonstrate that a DNA RC system consisting of three cascaded DNA memristors and a DNA readout layer can be used to solve the time-series prediction task. The proposed approach can reduce the number of DNA strand displacement (DSD) reactions by three to five times compared to prior approaches.

Enhanced detection of acetamiprid via a gold nanoparticle-based colorimetric aptasensor integrated with a hybridization chain reaction.

This study presents a novel colorimetric aptasensor, which seamlessly integrates gold nanoparticles (AuNPs) with the amplification potential of the hybridization chain reaction (HCR) for enhanced detection of acetamiprid. The aptamer, hybridized with a partially complementary strand that is covalently linked to AuNPs, serves as the recognition element for acetamiprid. The free end sequence of the aptamer, distal from the AuNP surface, functions as the initiating strand for the HCR, triggering the amplification process. In the absence of acetamiprid, the HCR efficiently occurs, conferring robust salt tolerance to the AuNPs and maintaining their characteristic red coloration. However, in the presence of acetamiprid, the aptamer preferentially binds to its target, disrupting the double-stranded structure and leading to the dissociation of the aptamer from the AuNPs. This dissociation results in a decrease in the HCR product, subsequently diminishing the salt tolerance of AuNPs and triggering a colorimetric transition from red to gray. This integration enhances sensitivity to 3.14 nM. Additionally, carbon quantum dots (CQDs) transduce colorimetric signals to fluorescent ones, further boosting the sensitivity to 0.24 nM. The aptasensor exhibits excellent selectivity and robustness. Real-world testing on tomato, peach, and lettuce shows recoveries of 98.50% to 100.36% with low standard deviations, validating its utility for pesticide residue analysis and food safety. This study provides a powerful tool for rapid and accurate pesticide detection, crucial for food safety.

Fast and accurate tracking control of robotic manipulators subject to state constraints and input saturation by effectively integrating planning strategies.

This paper presents a two-loop control framework for robotic manipulator systems subject to state constraints and input saturation, which effectively integrates planning and control strategies. Namely, a stability controller is designed in the inner loop to address uncertainties and nonlinearities; an optimization-based generator is constructed in the outer loop to ensure that state and input constraints are obeyed while concurrently minimizing the convergence time. Furthermore, to dramatically the computational burden, the optimization-based generator in the outer loop is switched to a direct model-based generator when the tracking errors are sufficiently small. In this way, both a high tracking accuracy and fast dynamic response are obtained for constrained robotic manipulator systems with considerably lower computational burden. The superiority and effectiveness of the proposed structure are illustrated through comparative simulations and experiments.

Maize Leaf Disease Recognition Based on Improved Convolutional Neural Network ShuffleNetV2.

The occurrence of maize diseases is frequent but challenging to manage. Traditional identification methods have low accuracy and complex model structures with numerous parameters, making them difficult to implement on mobile devices. To address these challenges, this paper proposes a corn leaf disease recognition model SNMPF based on convolutional neural network ShuffleNetV2. In the down-sampling module of the ShuffleNet model, the max pooling layer replaces the deep convolutional layer to perform down-sampling. This improvement helps to extract key features from images, reduce the overfitting of the model, and improve the model's generalization ability. In addition, to enhance the model's ability to express features in complex backgrounds, the Sim AM attention mechanism was introduced. This mechanism enables the model to adaptively adjust focus and pay more attention to local discriminative features. The results on a maize disease image dataset demonstrate that the SNMPF model achieves a recognition accuracy of 98.40%, representing a 4.1 percentage point improvement over the original model, while its size is only 1.56 MB. Compared with existing convolutional neural network models such as EfficientNet, MobileViT, EfficientNetV2, RegNet, and DenseNet, this model offers higher accuracy and a more compact size. As a result, it can automatically detect and classify maize leaf diseases under natural field conditions, boasting high-precision recognition capabilities. Its accurate identification results provide scientific guidance for preventing corn leaf disease and promote the development of precision agriculture.

Effects of grazing exclusion on soil microbial diversity and its functionality in grasslands: a meta-analysis.

Grazing exclusion (GE) is considered an effective strategy for restoring the degradation of overgrazed grasslands on the global scale. Soil microbial diversity plays a crucial role in supporting multiple ecosystem functions (multifunctionality) in grassland ecosystems. However, the impact of grazing exclusion on soil microbial diversity remains uncertain. Here, we conducted a meta-analysis using a dataset comprising 246 paired observations from 46 peer-reviewed papers to estimate how GE affects microbial diversity and how these effects vary with climatic regions, grassland types, and GE duration ranging from 1 to 64 years. Meanwhile, we explored the relationship between microbial diversity and its functionality under grazing exclusion. Overall, grazing exclusion significantly increased microbial Shannon (1.9%) and microbial richness (4.9%) compared to grazing group. For microbial groups, GE significantly increased fungal richness (8.6%) and bacterial richness (5.3%), but decreased specific microbial richness (-11.9%). The responses of microbial Shannon to GE varied among climatic regions, grassland types, and GE duration. Specifically, GE increased microbial diversity in in arid, semi-arid, and dry sub-humid regions, but decreased it in humid regions. Moreover, GE significantly increased microbial Shannon in semidesert grasslands (5.9%) and alpine grasslands (3.0%), but not in temperate grasslands. Long-term (>20 year) GE had greater effects on microbial diversity (8.0% for Shannon and 6.7% for richness) compared to short-term (<10 year) GE (-0.8% and 2.4%). Furthermore, grazing exclusion significantly increased multifunctionality, and both microbial and plant Shannon positively correlated with multifunctionality. Overall, our findings emphasize the importance of considering climate, GE duration, and grassland type for biodiversity conservation and sustainable grassland ecosystem functions.

AEG-1 as a Novel Therapeutic Target in Colon Cancer: A Study from Silencing AEG-1 in BALB/c Mice to Large Data Analysis.

BACKGROUND: Astrocyte elevated gene-1 (AEG-1) is overexpressed in various malignancies. Exostosin-1 (EXT-1), a tumor suppressor, is an intermediate for malignant tumors. Understanding the mechanism behind the interaction between AEG-1 and EXT-1 may provide insights into colon cancer metastasis. METHODS: AOM/DSS was used to induce tumor in BALB/c mice. Using an in vivo-jetPEI transfection reagent, transient transfection of AEG-1 and EXT-1 siRNAs were achieved. Histological scoring, immunohistochemical staining, and gene expression studies were performed from excised tissues. Data from the Cancer Genomic Atlas and GEO databases were obtained to identify the expression status of AEG-1 and itsassociation with the survival. RESULTS: In BALB/c mice, the AOM+DSS treated mice developed necrotic, inflammatory and dysplastic changes in the colon with definite clinical symptoms such as loss of goblet cells, colon shortening, and collagen deposition. Administration of AEG-1 siRNA resulted in a substantial decrease in the disease activity index. Mice treated with EXT-1 siRNA showed diffusely reduced goblet cells. In vivo investigations revealed that PTCH-1 activity was influenced by upstream gene AEG-1, which in turn may affect EXT-1 activity. Data from The Cancer Genomic Atlas and GEO databases confirmed the upregulation of AEG-1 and downregulation of EXT-1 in cancer patients. CONCLUSIONS: This study revealed that AEG-1 silencing might alter EXT-1 expression indirectly through PTCH-1, influencing cell-ECM interactions, and decreasing dysplastic changes, proliferation and invasion.

Sex-specific effects of bisphenol-A on memory and synaptic structural modification in hippocampus of adult mice.

Humans are routinely exposed to low levels of bisphenol A (BPA), a synthetic xenoestrogen widely used in the production of polycarbonate plastics. The effects of long-term exposure to BPA on memory and modification of synaptic structure in hippocampus of adult mice were investigated in the present study. The adult mice were exposed to BPA (0.4, 4, and 40 mg/kg/day) or arachis oil for 12 weeks. In open field test, BPA at 0.4, 4, or 40 mg/kg/day increased the frequency of rearing and time in the central area of the males, while BPA at 0.4 mg/kg/day reduced the frequency of rearing in the females. Exposure to BPA (0.4 or 40 mg/kg/day) extended the average escape pathlength to the hidden platform in Morris water maze task and shortened the step-down latency 24 h after footshock of the males, but no changes were found in the females for these measures. Meanwhile, BPA induced a reduced numeric synaptic density and a negative effect on the structural parameters of synaptic interface, including an enlarged synaptic cleft and the reduced length of active zone and PSD thickness, in the hippocampus of the male mice. Western blot analyses further indicated that BPA down-regulated expressions of synaptic proteins (synapsin I and PSD-95) and synaptic NMDA receptor subunit NR1 and AMPA receptor subunit GluR1 in the hippocampus of the males. These results suggest that long-term exposure to low levels of BPA in adulthood sex-specifically impaired spatial and passive avoidance memory of mice. These effects may be associated with the higher susceptibility of the hippocampal synaptic plasticity processes, such as remodeling of spinal synapses and the expressions of synaptic proteins (e.g. synapsin I and PSD-95) and NMDA and AMPA receptors, to BPA in the adult male mice.

Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis.

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.

Navigating Sex-Specific Disease Dynamics in Incident Dementia.

Dementia is among the leading causes of cognitive and functional loss and disability in older adults. Past studies suggested sex differences in health conditions and progression of cognitive decline. Existing studies on the temporal trajectory of health conditions for patient characterization after dementia diagnosis are scarce and ambiguous. Thus, there's limited and unclear research on how health conditions change over time after a dementia diagnosis. To this end, we aim to analyze the shift in medical conditions and examine sex-specific changes in patterns of chronic health conditions after dementia diagnosis. We centered our analysis on a 15-year window around the point of dementia diagnosis, encompassing the 5 years leading up to the diagnosis and the 10 years following it. We introduce (i) MedMet, a network metric to quantify the contribution of each medical condition, and (ii) growth and decay function for temporal trajectory analysis of medical conditions. Our experiments demonstrate that certain health conditions are more prevalent among females than males. Thus, our findings underscore the pressing need to examine differences between men and women, which could be important for healthcare utilization after a dementia diagnosis.

Different types of milk consumption and the risk of dementia: Analysis from a large-scale cohort study.

BACKGROUND & AIMS: Previous studies have investigated whether milk consumption has a role in preventing the development of cognitive impairment, but the results were inconsistent. Importantly, most of them have disregarded the role of different types of milk. This study aimed to examine the associations between different types of milk consumption and the risk of dementia. METHODS: In this large-scale cohort study, participants without cognitive impairment at baseline were included from the UK Biobank. The type of milk mainly used was self-reported at baseline, including full-cream milk, skimmed-milk, soy milk, other milk, and no milk. The primary outcome was all-cause dementia. Secondary outcomes included Alzheimer's disease and vascular dementia. RESULTS: Of the 307,271 participants included in the study (mean age 56.3 [SD 8.1] years), 3789 (1.2%) incident all-cause dementia cases were observed over a median follow-up of 12.3 years. After adjustment for potential confounders, only soy milk consumers had a statistically significantly lower risk of all-cause dementia compared with no milk consumers (hazard ratio [HR], 0.69; 95% confidence interval [CI], 0.54 to 0.90). When compared with soy milk non-consumers consisting of full-cream milk, skimmed-milk, and other milk consumers, soy milk consumers still showed a lower risk of all-cause dementia (HR, 0.76; 95% CI, 0.63 to 0.92), and there was no significant interaction with genetic risk for dementia (P for interaction = 0.15). Soy milk consumers showed a lower risk of Alzheimer's disease (HR, 0.70; 95% CI, 0.51 to 0.94; P = 0.02), while the association was not significant for vascular dementia (HR, 0.72; 95% CI, 0.47 to 1.12; P = 0.14). CONCLUSIONS: The main consumption of soy milk was associated with a lower risk of dementia, particularly non-vascular dementia. Additional studies are needed to investigate how this association varies with the dose or frequency of the consumption of soy milk and to examine the generalizability of these findings in different populations.

Gestational and lactational exposure to bisphenol-A affects anxiety- and depression-like behaviors in mice.

Bisphenol-A (BPA), an environmental endocrine disruptor, has attracted attention because of its adverse effects on the brain and behavioral development. Previous evidence indicates that perinatal exposure to low levels of BPA affects anxiety-like and cognitive behaviors in adult rodents. The present study aims to investigate the changes of anxiety- and depression-like behaviors of perinatally exposed mice in adulthood following the gestational (gestation days 7 to 20) or lactational (postnatal days 1 to 14) exposure to BPA (0.4 or 4 mg/kg/d). The results indicated that both gestational and lactational exposures to BPA increased anxiety and depression-like behavior in mice of both sexes. The females with gestational exposure exhibited an increased anxiety-like state in the four models tested, including the open field, dark-light transition task, mirrored maze, and elevated plus maze tasks. Furthermore, the females with lactational exposure and the males with gestational exposure exhibited an anxiogenic-like behavior in two models, whereas the males with lactational exposure exhibited an anxiogenic-like behavior only in the elevated plus maze test. The results of the forced swim task showed that gestational exposure markedly increased the immobile time in both sexes, and the same effect was induced by lactational exposure only with 4 mg/kg/d BPA. Furthermore, western blot analyses showed that both gestational and lactational exposures inhibited the expression of the AMPA receptor subunit GluR1 in the hippocampus and amygdala in mice of both sexes, whereas the level of the NMDA receptor subunit NR1 was increased in the amygdala following gestational exposure but was reduced in the hippocampus of the females with lactational exposure. These results suggest that both gestational and lactational exposures to BPA increased anxiety- and depression-like behaviors of adult mice of both sexes. In addition gestational exposure exhibited a stronger effect on anxiety-like state in females. The altered levels of AMPA and NMDA receptors in the hippocampus and amygdala may be associated with BPA-induced behavioral changes.

DNA Memristors and Their Application to Reservoir Computing.

This paper introduces memristors realized by molecular and DNA reactions. Molecular memristors process one input molecule, generate two output molecules, and are realized using two molecular reactions with two different rate constants. The DNA memristors are realized using five DNA strand displacement (DSD) reactions with two effective rate constants. The hysteresis behavior is preserved in the proposed memristors, and this behavior can be altered by changing the ratios of the rate constants. The state of the memristor can be computed from the concentrations of the two output molecules using bipolar fractional coding. We describe how the proposed memristors can be used to learn the spatial and temporal properties of data via the reservoir computing (RC) model. An RC system can be divided into two parts: reservoir and readout layer. The first part transfers the information from the input space to a high-dimensional spatiotemporal feature space represented by the state of reservoirs. The connectivity structure of the reservoir will remain fixed through the dynamical evaluations. The readout layer effectively maps the projected features to the target output. A dynamical memristor array is used to implement an RC system that exploits the internal dynamical processes of the memristors. The readout layer implements a matrix-vector multiplication using molecular reactions, also based on bipolar fractional coding. All molecular reactions are mapped to DSD reactions. The RC system based on the DNA reservoir and the DNA readout layer is used to solve a handwritten digit recognition task and a second-order time series prediction task. The performance of the DNA RC system is comparable to that of an electronic memristor RC system for both tasks.