Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder.

BACKGROUND: There is a growing body of evidence suggesting that disturbance of the gut-brain axis may be one of the potential causes of major depressive disorder (MDD). However, the effects of antidepressants on the gut microbiota, and the role of gut microbiota in influencing antidepressant efficacy are still not fully understood. RESULTS: To address this knowledge gap, a multi-omics study was undertaken involving 110 MDD patients treated with escitalopram (ESC) for a period of 12 weeks. This study was conducted within a cohort and compared to a reference group of 166 healthy individuals. It was found that ESC ameliorated abnormal blood metabolism by upregulating MDD-depleted amino acids and downregulating MDD-enriched fatty acids. On the other hand, the use of ESC showed a relatively weak inhibitory effect on the gut microbiota, leading to a reduction in microbial richness and functions. Machine learning-based multi-omics integrative analysis revealed that gut microbiota contributed to the changes in plasma metabolites and was associated with several amino acids such as tryptophan and its gut microbiota-derived metabolite, indole-3-propionic acid (I3PA). Notably, a significant correlation was observed between the baseline microbial richness and clinical remission at week 12. Compared to non-remitters, individuals who achieved remission had a higher baseline microbial richness, a lower dysbiosis score, and a more complex and well-organized community structure and bacterial networks within their microbiota. These findings indicate a more resilient microbiota community in remitters. Furthermore, we also demonstrated that it was not the composition of the gut microbiota itself, but rather the presence of sporulation genes at baseline that could predict the likelihood of clinical remission following ESC treatment. The predictive model based on these genes revealed an area under the curve (AUC) performance metric of 0.71. CONCLUSION: This study provides valuable insights into the role of the gut microbiota in the mechanism of ESC treatment efficacy for patients with MDD. The findings represent a significant advancement in understanding the intricate relationship among antidepressants, gut microbiota, and the blood metabolome. Additionally, this study offers a microbiota-centered perspective that can potentially improve antidepressant efficacy in clinical practice. By shedding light on the interplay between these factors, this research contributes to our broader understanding of the complex mechanisms underlying the treatment of MDD and opens new avenues for optimizing therapeutic approaches. Video Abstract.

[Spherical amino-functionalized covalent organic frameworks: Synthesis and adsorption performance toward perfluorinated compounds].

Perfluorinated compounds (PFCs) are widely used in textiles, fire protection, metal electroplating, and semiconductor production owing to their hydrophobic and oil-repellent characteristics. However, they are also persistent organic pollutants. The uncontrolled discharge of PFCs into the environment has led to serious global pollution. PFCs pose severe reproductive, neural, immune, and other threats to human health by accumulating through the food chain. Thus, the development and application of high-performance extraction materials has become a research hotspot in efforts to achieve the accurate detection of trace PFCs in environmental waters. Most traditional PFC adsorbents present a number of disadvantages, such as low adsorption selectivity, slow diffusion, and poor reusability. Covalent organic frameworks (COFs) are crystalline polymers with ordered porous structures, large specific surface areas, and high chemical and thermal stability. These frameworks can easily be functionalized for the desired purpose. In this paper, spherical amino-functionalized COFs (denoted COF-NH2) were fabricated via a two-step method to effectively enrich/remove PFCs from water. First, vinyl covalent organic framework (Vinyl COF) was synthesized at room temperature using 1,4-diradical-2,5-divinylbenzene (Dva) and 1,3,5-tris(4-aminophenyl)benzene (Tab) as building blocks. Then, thioether-bridged aromatic amine-functionalized spherical COF-NH2 was synthesized through a thiol-alkenyl click reaction using 4-aminothiophenol as the functional monomer. COF-NH2 showed good dispersion in water owing to its abundant amino groups, forming multiple hydrogen bonds with the F atoms of PFCs. The synergistic hydrophobic interactions between the organic skeleton of the COF and alkyl carbon chains of the PFCs led to enhanced adsorption efficiency. The produced Vinyl COF and COF-NH2 were characterized by Fourier transform infrared spectroscopy (FT-IR), field-emission scanning electron microscopy (SEM), powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA), and Brunner-Emmet-Teller (BET) measurements. The results confirmed that spherical COF-NH2 materials with a homogeneous size distribution were successfully fabricated. The obtained COF-NH2 microspheres had a diameter of approximately 500 nm and exhibited high thermal stability as well as a large specific surface area and pore volume. The adsorption kinetics, isotherm adsorption models, pH effects, and regeneration properties of COF-NH2 were also investigated, and the results indicated that the adsorption of PFCs by COF-NH2 conformed to the pseudo-second-order kinetic and Langmuir isotherm adsorption models. The obtained COF-NH2 microspheres can be applied over a wide pH range, and the best adsorption effect was achieved in neutral and alkaline environments. After five cycles of regeneration and reuse, the COF-NH2 microspheres retained their good adsorption efficiency for PFCs. The adsorption mechanism was mainly attributed to the synergistic effect of hydrogen bonding and hydrophobic interactions between COF-NH2 and the PFCs. The extraction efficiencies of the microspheres toward five PFCs (perfluorobutyric acid, perfluorovaleric acid, perfluorohexanoic acid, perfluorooctanoic acid, and perfluorononanoic acid) in tap and Pearl River water samples were between 91.76% and 98.59%, with relative standard deviations (RSDs) (n=3) varying from 0.82% to 3.8%; these findings indicate that the obtained COF-NH2 is promising for the extraction of PFCs from complex water samples. Given their uniform size distribution, high thermal stability, good adsorption performance, and reusability, the novel spherical COF-NH2 materials developed in this study may be used as solid-phase extraction materials or filled into liquid chromatographic columns for the enrichment, separation, and detection of PFCs in complex samples.

"Two-in-one" dual-function luminescent MOF hydrogel for onsite ultra-sensitive detection and efficient enrichment of radioactive uranium in water.

In consideration of the severe hazards of radioactive uranium pollution and the growing demand of uranium resources, the novel sensor/adsorbent composite was creatively developed to integrate the dual functions for on-site detection of uranium contamination and efficient recovery of uranium resources. By hybridizing the luminescent 3D terbium (III) metal-organic framework (Tb-MOF) with sodium alginate (SA) gel using terbium (III) as cross-linker, the Tb-MOF/Tb-AG was fabricated with multi-luminescence centers and sufficient binding sites for uranium. Notably, the ultra-high sensitivity with detection limit as low as 1.2 ppt was achieved, which was 4 orders of magnitude lower than the uranium contamination standard in drinking water (USEPA) and even comparable to the sensitivity of the ICP-MS. Furthermore, the very wide quantification range (1.0 ×10-9-5.0 ×10-4 mol/L), remarkable adsorption capacity (549.0 mg/g) and outstanding anti-interference ability have been achieved without sophisticated sample preparation procedures. Applied in complex natural water samples from Uranium Tailings and the Pearl River, this method has shown good detection accuracy. The ultra high sensitivity and great adsorption capacity for uranium could be ascribed to the synergistic coordination, hydrogen bonding and ion exchange between uranium and Tb-MOF/Tb-AG. The mechanisms were explored by infrared spectroscopy, batch experiments, X-ray photoelectron studies and energy dispersive spectroscopic studies. In addition, the Tb-MOF/Tb-AG can be reused for uranium adsorption.

Novel biocompatible and sensitive visual sensor based on aggregation-induced emission for on-site detection of radioactive uranium in water and live cell imaging.

In consideration of the severe hazards of radioactive uranium pollution, the rapid assessment of uranium in field and in vivo are urgently needed. In this work a novel biocompatible and sensitive visual fluorescent sensor based on aggregation-induced emission (AIE) was designed for onsite detection of UO22+ in complex environmental samples, including wastewater from Uranium Plant, river water and living cell. The AIE-active sensor (named as TPA-SP) was prepared with a "bottom-up" strategy by introducing a trianiline group (TPA) with a single-bond rotatable helix structure into the salicylaldehyde Schiff-base molecule. The photophysical properties, cytotoxicity test, recognition mechanism and the analytical performance for the detection of UO22+ in actual water samples and cell imaging were systematically investigated. TPA-SP exhibited high sensitivity and selectivity toward UO22+ as well as outstanding anti-interference ability against large equivalent of different ions in a wide effective pH range. A good linear relationship in the UO22+ concentration range of 0.05-1 µM was obtained with a low limit of detection (LOD) of 39.4 nM (9.38 ppb) for uranium detection. The prepared visual sensor showed great potential for fast risk assessment of uranium pollution in environmental systems. In addition, our results also indicated that the TPA-SP exhibited very low cytotoxicity in cells and demonstrated great potential for uranium detection in vivo.

PassTCN-PPLL: A Password Guessing Model Based on Probability Label Learning and Temporal Convolutional Neural Network.

The frequent incidents of password leakage have increased people's attention and research on password security. Password guessing is an essential part of password cracking and password security research. The progression of deep learning technology provides a promising way to improve the efficiency of password guessing. However, the mainstream models proposed for password guessing, such as RNN (or other variants, such as LSTM, GRU), GAN and VAE still face some problems, such as the low efficiency and high repetition rate of the generated passwords. In this paper, we propose a password-guessing model based on the temporal convolutional neural network (PassTCN). To further improve the performance of the generated passwords, we propose a novel password probability label-learning method, which reconstructs labels based on the password probability distribution of the training set and deduplicates the training set when training. Experiments on the RockYou dataset showed that, when generating 108 passwords, the coverage rate of PassTCN with password probability label learning (PassTCN-PPLL) reached 12.6%, which is 87.2%, 72.6% and 42.9% higher than PassGAN (a password-guessing model based on GAN), VAEPass (a password-guessing model based on VAE) and FLA (a password-guessing model based on LSTM), respectively. The repetition rate of our model is 25.9%, which is 45.1%, 31.7% and 17.4% lower than that of PassGAN, VAEPass and FLA, respectively. The results confirm that our approach not only improves the coverage rate but also reduces the repetition rate.

A Cooperative Lightweight Translation Algorithm Combined with Sparse-ReLU.

In the field of natural language processing (NLP), machine translation algorithm based on Transformer is challenging to deploy on hardware due to a large number of parameters and low parametric sparsity of the network weights. Meanwhile, the accuracy of lightweight machine translation networks also needs to be improved. To solve this problem, we first design a new activation function, Sparse-ReLU, to improve the parametric sparsity of weights and feature maps, which facilitates hardware deployment. Secondly, we design a novel cooperative processing scheme with CNN and Transformer and use Sparse-ReLU to improve the accuracy of the translation algorithm. Experimental results show that our method, which combines Transformer and CNN with the Sparse-ReLU, achieves a 2.32% BLEU improvement in prediction accuracy and reduces the number of parameters of the model by 23%, and the sparsity of the inference model increases by more than 50%.

Past climate change and recent anthropogenic activities affect genetic structure and population demography of the greater long-tailed hamster in northern China.

The genetic diversity and the spatial structure of a species are likely consequences of both past and recent evolutionary processes, but relevant studies are still rare in East Asia where the Pleistocene climate has unique influences. In this study, we examined the impact of past climate change and recent anthropogenic activities on the genetic structure and population size of the greater long-tailed hamster (Tscherskia triton), an agricultural rodent pest species in northern China. DNA sequence data of 2 mitochondrial genes and genotypic data of 11 microsatellite DNA loci from 41 populations (545 individuals) were gathered. Phylogenetic and population genetic analyses, as well as species distribution modeling and coalescent simulations, were conducted to infer its historical and demographic patterns and processes. Two deeply diverged mitochondrial clades were recovered. A small one was restricted to the Shandong Peninsula while the main clade was further divided into 3 geographic clusters by their microsatellite DNA genotypes: Northwest, North-center and Northeast. Divergence dating indicated a Middle-to-Late Pleistocene divergence between the 2 clades. Demographic analysis indicated that all 3 and pooled populations showed consistent long-period expansions during last glacial period; but not during the Holocene, probably due to the impact of climate warming and human disturbances. Conflicting patterns between mtDNA and microsatellite markers imply an anthropogenic impact on North-center populations due to intensified agricultural cultivation in this region. Our study demonstrated that the impact of past glaciation on organisms in East Asia significantly differs from that of Europe and North America, and human activity is an important factor in determining the genetic diversity of a species, as well as its spatial structure.