Type IV pili trigger episymbiotic association of Saccharibacteria with its bacterial host.

Recent characterization of the obligate episymbiont Saccharibacteria (TM7) belonging to the candidate phyla radiation (CPR) has expanded the extent of microbial diversity. However, the episymbiotic lifestyle of TM7 is still underexploited due to the deficiency of cultivated representatives. Here, we describe gene-targeted TM7 cultivation guided by repurposing epicPCR (emulsion, paired isolation, and concatenation PCR) to capture in situ TM7‒host associations. Using this method, we obtained a novel Saccharibacteria isolate TM7i and its host Leucobacter aridicollis J1 from Cicadae Periostracum, the castoff shell of cicada. Genomic analyses and microscopic characterizations revealed that TM7i could bind to J1 through twitching-like motility mediated by type IV pili (T4P). We further showed that the inhibition of T4P extrusion suppressed the motility and host adherence of TM7i, resulting in its reduced growth. However, the inactivation of T4P had little effect on the growth of TM7i that had already adhered to J1, suggesting the essential role of T4P in host recognition by TM7i. By capturing CPR‒host association and elaborating the T4P-dependent episymbiotic association mechanism, our studies shed light on the distinct yet widespread lifestyle of CPR bacteria.

Enhancing Quality of Life in Rheumatoid Arthritis Patients: A Combined Approach of Methotrexate and Pain-specific Nursing Intervention.

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disorder affecting joint health and patients' overall well-being. While methotrexate (MTX) is a standard therapeutic intervention, enhancing its efficacy with pain-specific nursing remains underexplored. Objective: This study aims to assess the impact of combining MTX with pain-specific nursing on patients with RA, providing valuable insights for clinical practice and offering an improved therapeutic approach to alleviate pain and enhance the overall quality of life for affected individuals. Methods: We conducted a prospective cohort study, choosing a cohort of 86 RA patients admitted to our hospital from March 2021 to March 2023. After treatment, we compared the number of swollen and painful joints, duration of morning stiffness, and scores on the Visual Analogue Scale (VAS), Pittsburgh Sleep Quality Index (PSQI), and Self-rating Anxiety Scale/Self-rating Depression Scale (SAS/SDS) between the two groups. Nursing satisfaction was surveyed upon discharge, and patient quality of life was assessed using the 36-item Short Form Health Survey (SF-36). Results: The research group exhibited a notable decrease in the number of swollen and painful joints, significantly shorter morning stiffness duration, and marked reductions in VAS, PSQI, SAS, and SDS scores compared to the control group (P < .05). Additionally, nursing satisfaction and SF-36 scores were higher in the research group (P < .05). Conclusions: The combination of MTX and pain-specific nursing effectively alleviated pain and improved the quality of life and nursing satisfaction among RA patients.

Densely Connected Neural Networks for Nonlinear Regression.

Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimensions of the proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models, including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation with observations, which indicates that our model could advance environmental data science.

Tunable and Contamination-Free Injection with Microfluidics by Stepinjection.

Droplet microfluidics with picoinjection provides significant advantages to multistep reactions and screenings. The T-junction design for picoinjection is convenient in adding picoliter reagents into passing droplets to initiate reactions. However, conventional picoinjectors face difficulties in eliminating cross-contamination between droplets, preventing them from widespread use in sensitive biological and molecular assays. Here, we introduce stepinjection, which uses a T-junction with a stepped channel design to elevate the diffusional buffer zone into the main channel and consequently increases the pressure difference between droplets and the inlet of the injection channel. To demonstrate the stepinjector's ability to perform contamination-sensitive enzymatic assays, we inject casein fluorescein isothiocyanate (FITC-casein) into a mixture of savinase and savinase-free (labeled with a red fluorescent dye) droplets. We observe no cross-contamination using stepinjection but find a severe cross-talk using an optimal picoinjection design. We envision that the simple, tunable, and reliable stepinjector can be easily integrated in various droplet processing devices, and facilitate various biomedical and biochemical applications including multiplex digital PCR, single-cell sequencing, and enzymatic screening.

Transition Metal-Free De Novo Synthesis of Sulfonated Pyrazoles from Sulfonyl Hydrazides, 1,3-Diketones, and Sodium Sulfinates at Room Temperature.

A transition metal-free method for de novo construction of diverse sulfonated pyrazoles from readily available sulfonyl hydrazides, 1,3-diketones, and sodium sulfinates was established under mild conditions. Pyrazoles bearing two different sulfonyl groups were obtained in one step. The method features a diversity of substituents of the pyrazole products and a remarkably simple work-up.

Deep Residual Learning for Nonlinear Regression.

Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions. Convolutional layers and pooling layers are replaced by fully connected layers in the residual block. To evaluate the new regression model, we train and test neural networks with different depths and widths on simulated data, and we find the optimal parameters. We perform multiple numerical tests of the optimal regression model on multiple simulated data, and the results show that the new regression model behaves well on simulated data. Comparisons are also made between the optimal residual regression and other linear as well as nonlinear approximation techniques, such as lasso regression, decision tree, and support vector machine. The optimal residual regression model has better approximation capacity compared to the other models. Finally, the residual regression is applied into the prediction of a relative humidity series in the real world. Our study indicates that the residual regression model is stable and applicable in practice.

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition.

Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.

Dynamic Sessile-Droplet Habitats for Controllable Cultivation of Bacterial Biofilm.

Bacterial biofilms play essential roles in biogeochemical cycling, degradation of environmental pollutants, infection diseases, and maintenance of host health. The lack of quantitative methods for growing and characterizing biofilms remains a major challenge in understanding biofilm development. In this study, a dynamic sessile-droplet habitat is introduced, a simple method which cultivates biofilms on micropatterns with diameters of tens to hundreds of micrometers in a microfluidic channel. Nanoliter plugs are utilized, spaced by immiscible carrier oil to initiate and support the growth of an array of biofilms, anchored on and spatially confined to the micropatterns arranged on the bottom surface of the microchannel, while planktonic or dispersal cells are flushed away by shear force of aqueous plugs. The performance of the aforementioned method of cultivating biofilms is demonstrated by Pseudomonas aeruginosa PAO1 and its derived mutants, and quantitative antimicrobial susceptibility testing of PAO1 biofilms. This method could significantly eliminate corner effects, avoid microchannel clogging, and constrain the growth of biofilms for long-term observations. The controllable sessile droplet-based biofilm cultivation presented in this study should shed light on more quantitative and long-term studies of biofilms, and open new avenues for investigation of biofilm attachment, growth, expansion, and eradication.

Modafinil attenuates inflammation via inhibiting Akt/NF-κB pathway in apoE-deficient mouse model of atherosclerosis.

Modafinil, an FDA approved wakefulness drug prescribed to narcolepsy patients, has recently been shown to have anti-inflammatory effects and provides protection against neuroinflammation. It is unknown if modafinil can also protect against atherosclerosis, pathogenesis of which implicates inflammation. Using an apoE-deficient mouse model, we tried to elucidate the effects of modafinil treatment on the development of atherosclerosis. We tested serum levels of cytokines. We isolated mouse bone marrow-derived macrophages (BMDMs), detected effect of modafinil on the viability and proliferation of BMDMs, and on oxidized low-density lipoprotein-induced IL-6 and TNF-α, and supernatant level of IFN-γ as well as NF-κB activity in BMDMs. Modafinil inhibited the development of atherosclerosis in apoE-/- mice. Modafinil suppressed the secretion of pro-inflammatory cytokines IL-6, TNF and IFN-γ, and promoted secretion of anti-inflammatory cytokines IL-4 and IL-10. Modafinil inhibited viability and proliferation of macrophages by negatively regulating levels of pro-inflammatory cytokines, p-Akt, p-IKBα and NF-κB activity in macrophages. Modafinil mitigates inflammation in apoE-/- atherosclerosis mice via inhibiting NF-κB activity in macrophages, and could potentially serve as a therapeutic agent for atherosclerosis.

CD8(+) T activation attenuates CD4(+) T proliferation through dendritic cells modification.

Emerging evidence has suggested that CD8(+) T had modulatory function on CD4(+) T mediated autoimmune and inflammatory diseases. However, the underlying mechanisms remain unclear. In this study, we found that CD8(+) T activation inhibited OVA(323-339) antigen specific CD4(+) T cells proliferation in vitro and in vivo. Further investigation demonstrated that this immunosuppression largely depended on the soluble factor from activated CD8(+) T to modify the phenotype and functions of DCs. Moreover, not only the inhibitors for IDO or iNOS, but also IFN-γ neutralization markedly reversed this immunosuppression on OVA(323-339) antigen specific CD4(+) T cells proliferation. Interestingly, CD8(+) T cells absence aggravated the pathological damage in lung in OVA-induced asthma model, but alleviated by CD8(+) T transfer and activation. Thus, these findings suggested that activated CD8(+) T population exerted feedback regulation in DCs modification, and then attenuated CD4(+) T mediated immune response.

Benzoate metabolism intermediate benzoyl coenzyme A affects gentisate pathway regulation in Comamonas testosteroni.

A previous study showed that benzoate was catabolized via a coenzyme A (CoA)-dependent epoxide pathway in Azoarcus evansii (R. Niemetz, U. Altenschmidt, S. Brucker, and G. Fuchs, Eur. J. Biochem. 227:161-168, 1995), but gentisate 1,2-dioxygenase was induced. Similarly, we found that the Comamonas testosteroni strain CNB-1 degraded benzoate via a CoA-dependent epoxide pathway and that gentisate 1,2-dioxygenase (GenA) was also induced when benzoate or 3-hydroxybenzoate served as a carbon source for growth. Genes encoding the CoA-dependent epoxide (box genes) and gentisate (gen genes) pathways were identified. Genetic disruption revealed that the gen genes were not involved in benzoate and 3-hydroxybenzoate degradation. Hence, we investigated gen gene regulation in the CNB-1 strain. The PgenA promoter, a MarR-type regulator (GenR), and the GenR binding site were identified. We found that GenR took gentisate, 3-hydroxybenzoate, and benzoyl-CoA as effectors and that binding of GenR to its target DNA sequence was prohibited when these effectors were present. In vivo studies showed that the CNB-1 mutant that lost benzoyl-CoA synthesis was not able to activate PgenA promoter, while transcription of genA was upregulated in another CNB-1 mutant that lost the ability to degrade benzoyl-CoA. The finding that benzoyl-CoA (a metabolic intermediate of benzoate degradation) and 3-hydroxybenzoate function as GenR effectors explains why GenA was induced when CNB-1 grew on benzoate or 3-hydroxybenzoate. Regulation of gentisate pathways by MarR-, LysR-, and IclR-type regulators in diverse bacterial groups is discussed in detail.

Correlation Study between Multi-Scale Structure and In Vitro Digestibility of Starch Modified by Temperature Difference.

Physical techniques are widely applied in the food industry due to their positive impact on food quality and the environment. Temperature differences can effectively modify starch, but the resulting changes in starch structure and quality remain unclear. In this study, the corn starch was processed with high temperature, low temperature, and temperature difference (TD), including high temperature before low temperature (H-L) and low temperature before high temperature (L-H). The results showed that high temperature induced the umbilicus to concave inward shape and sharply decreased the amylose content, while low temperature increased the surface micropores and reduced the A-chain. TD reduced the fluorescence intensity and increased the clearness of the growth ring. TD elevated the relative crystallinity (RC), short-range order, A/B1 chains, hydrolysis parameters, and resistant starch (RS), and reduced amylose content, B2/B3 chains, and viscosity. Moreover, the corn starches treated by H-L had lower amylose content and higher RC, 1047/1022, A-chain, and RS than those treated by L-H. Overall, high temperature degraded the amylose and low temperature destroyed the amylopectin. During the TD, H-L can accelerate the starch molecular rearrangement more than the opposite temperature treatment order. These results will help produce novel starches for better food applications.

Structural basis of chemokine sequestration by CrmD, a poxvirus-encoded tumor necrosis factor receptor.

Pathogens have evolved sophisticated mechanisms to evade detection and destruction by the host immune system. Large DNA viruses encode homologues of chemokines and their receptors, as well as chemokine-binding proteins (CKBPs) to modulate the chemokine network in host response. The SECRET domain (smallpox virus-encoded chemokine receptor) represents a new family of viral CKBPs that binds a subset of chemokines from different classes to inhibit their activities, either independently or fused with viral tumor necrosis factor receptors (vTNFRs). Here we present the crystal structures of the SECRET domain of vTNFR CrmD encoded by ectromelia virus and its complex with chemokine CX3CL1. The SECRET domain adopts a ß-sandwich fold and utilizes its ß-sheet I surface to interact with CX3CL1, representing a new chemokine-binding manner of viral CKBPs. Structure-based mutagenesis and biochemical analysis identified important basic residues in the 40s loop of CX3CL1 for the interaction. Mutation of corresponding acidic residues in the SECRET domain also affected the binding for other chemokines, indicating that the SECRET domain binds different chemokines in a similar manner. We further showed that heparin inhibited the binding of CX3CL1 by the SECRET domain and the SECRET domain inhibited RAW264.7 cell migration induced by CX3CL1. These results together shed light on the structural basis for the SECRET domain to inhibit chemokine activities by interfering with both chemokine-GAG and chemokine-receptor interactions.

Assimilation of aromatic compounds by Comamonas testosteroni: characterization and spreadability of protocatechuate 4,5-cleavage pathway in bacteria.

Comamonas testosteroni strain CNB-1 was isolated from activated sludge and has been investigated for its ability to degrade 4-chloronitrobenzene. Results from this study showed that strain CNB-1 grew on phenol, gentisate, vanillate, 3-hydroxybenzoate (3HB), and 4-hydroxybenzoate (4HB) as carbon and energy sources. Proteomic data and enzyme activity assays suggested that vanillate, 3HB, and 4HB were degraded in strain CNB-1 via protocatechuate (PCA) 4,5-cleavage pathway. The genetics and biochemistry of the PCA 4,5-cleavage pathway were investigated. Results showed that the 4-oxalomesaconate (OMA) hydratase from C. testosteroni takes only enol-OMA as substrate. A previously functionally unknown gene pmdU encodes an OMA tautomerase and catalyzes conversion of OMAketo into OMAenol. The 4-carboxy-4-hydroxy-2-oxoadipate (CHA) aldolase is encoded by pmdF and catalyzes the last step of the PCA 4,5-cleavage pathway. We explored the 1,183 microbial genomes at GenBank for potential PCA 4,5-cleavage pathways, and 33 putative pmd clusters were found. Results suggest that PCA 4,5-cleavage pathways are mainly distributed in α- and ß-Proteobacteria.

Whole lifecycle observation of single-spore germinated Streptomyces using a nanogap-stabilized microfluidic chip.

Streptomyces is a model bacterium to study multicellular differentiation and the major reservoir for antibiotics discovery. However, the cellular-level lifecycle of Streptomyces has not been well studied due to its complexity and lack of research tools that can mimic their natural conditions. In this study, we developed a simple microfluidic chip for the cultivation and observation of the entire lifecycle of Streptomyces development from the single-cell perspective. The chip consists of channels for loading samples and supplying nutrients, microwell arrays for the seeding and growth of single spores, and air chambers beside the microwells that facilitate the development of aerial hyphae and spores. A unique feature of this chip is that each microwell is surrounded by a 1.5 µm nanogap connected to an air chamber, which provides a stabilized water-air interface. We used this chip to observe the lifecycle development of Streptomyces coelicolor and Streptomyces griseus germinated from single spores, which revealed differentiation of aerial hyphae with progeny spores at micron-scale water-air interfaces and air chambers. Finally, we demonstrated the applicability of this chip in phenotypic assays by showing that the microbial hormone A-Factor is involved in the regulatory pathways of aerial hyphae and spore formation. The microfluidic chip could become a robust tool for studying multicellular differentiation, single-spore heterogeneity, and secondary metabolism of single-spore germinated Streptomyces.

Fluorescence-activated droplet sorting of PET degrading microorganisms.

Enzymes that can decompose synthetic plastics such as polyethylene terephthalate (PET) are urgently needed. Still, a bottleneck remains due to a lack of techniques for detecting and sorting environmental microorganisms with vast diversity and abundance. Here, we developed a fluorescence-activated droplet sorting (FADS) pipeline for high-throughput screening of PET-degrading microorganisms or enzymes (PETases). The pipeline comprises three steps: generation and incubation of droplets encapsulating single cells, picoinjection of fluorescein dibenzoate (FDBz) as the fluorogenic probe, and screening of droplets to obtain PET-degrading cells. We characterized critical factors associated with this method, including specificity and sensitivity for discriminating PETase from other enzymes. We then optimized its performance and compatibility with environmental samples. The system was used to screen a wastewater sample from a PET textile mill. We successfully obtained PET-degrading species from nine different genera. Moreover, two putative PETases from isolates Kineococcus endophyticus Un-5 and Staphylococcus epidermidis Un-C2-8 were genetically derived, heterologously expressed, and preliminarily validated for PET-degrading activities. We speculate that the FADS pipeline can be widely adopted to discover new plastic-degrading microorganisms and enzymes in various environments and may be utilized in the directed evolution of degrading enzymes using synthetic biology.

Inhibition of B16 murine melanoma metastasis and enhancement of immunity by fever-range whole body hyperthermia.

PURPOSE: Whole body hyperthermia (WBH) has been regarded as a promising alternative therapy to cure late stage cancer with metastasis. As the final biological and therapeutic effects are dependent on the specific protocol, the potential of using a microwave-based WBH approach for metastasis inhibition is established and its typical results are discussed. MATERIALS AND METHODS: The effectiveness of a 30-min whole body hyperthermia (WH) on animals, raised to a rectal temperature of 40.2° ± 0.3°C for 30 min followed by 84 h observation by 2450 MHz microwave irradiation, were evaluated. In an experimental lung metastasis model by injection of B16-F10 melanoma, lungs were removed from sacrificed mice 16 days after tumour implantation, and the expression of heat shock protein, inter-cellular adhesion molecule 1 (ICAM-1), proliferating cell nuclear antigen (PCNA) and cyclin D(1) was examined. CD4(+), CD8(+) and NK cell subpopulation in peripheral blood were measured by flow cytometry before and after the last treatment. RESULTS: The best therapeutic effect was obtained when the mice were treated with WBH in combination with the initial chemotherapy with cis-diaminodichloroplatinum (CDDP) and dacarbazine (DTIC) (p < 0.05). The WBH alone has an advantage of reduced toxicity and lower cost. Heat shock protein (HSP) expression increased in the hyperthermia groups. Reduction of PCNA and cyclin D(1) was observed in the mice treated with WH alone or in combination with chemotherapy. In the hyperthermia groups, CD4(+)/CD8(+) decreased while the NK increased slightly. CONCLUSIONS: The whole body hyperthermia protocol described in this work inhibits B16 tumour metastasis by inhibiting cell proliferation, neovascularisation and stimulating favourable immune responses. It demonstrated that WBH treatment benefits therapy of metastasis cancers.

Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography.

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L 2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L 1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L 1/2 norm framework for feature extraction, and uses L 2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46-21.81%.

One cell at a time: droplet-based microbial cultivation, screening and sequencing.

Microbes thrive and, in turn, influence the earth's environment, but most are poorly understood because of our limited capacity to reveal their natural diversity and function. Developing novel tools and effective strategies are critical to ease this dilemma and will help to understand their roles in ecology and human health. Recently, droplet microfluidics is emerging as a promising technology for microbial studies with value in microbial cultivating, screening, and sequencing. This review aims to provide an overview of droplet microfluidics techniques for microbial research. First, some critical points or steps in the microfluidic system are introduced, such as droplet stabilization, manipulation, and detection. We then highlight the recent progress of droplet-based methods for microbiological applications, from high-throughput single-cell cultivation, screening to the targeted or whole-genome sequencing of single cells.

Sparse Logistic Regression With L 1/2 Penalty for Emotion Recognition in Electroencephalography Classification.

Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing, can effectively extract important features from the signal and has potential applications in EEG emotion recognition. Currently, the most popular regularization technique is Lasso (L 1) and Ridge Regression (L 2). In recent years, researchers have proposed many other regularization terms. In theory, L q -type regularization has a lower q value, which means that it can be used to find solutions with better sparsity. L 1/2 regularization is of L q type (0 < q < 1) and has been shown to have many attractive properties. In this work, we studied the L 1/2 penalty in sparse logistic regression for three-classification EEG emotion recognition, and used a coordinate descent algorithm and a univariate semi-threshold operator to implement L 1/2 penalty logistic regression. The experimental results on simulation and real data demonstrate that our proposed method is better than other existing regularization methods. Sparse logistic regression with L 1/2 penalty achieves higher classification accuracy than the conventional L 1, Ridge Regression, and Elastic Net regularization methods, using fewer but more informative EEG signals. This is very important for high-dimensional small-sample EEG data and can help researchers to reduce computational complexity and improve computational accuracy. Therefore, we propose that sparse logistic regression with the L 1/2 penalty is an effective technique for emotion recognition in practical classification problems.