Hot spring associated phototrophic microbial mats are purely microbial communities, in which phototrophic bacteria function as primary producers and thus shape the community. The microbial mats at Nakabusa hot springs in Japan harbor diverse photosynthetic bacteria, mainly Thermosynechococcus, Chloroflexus, and Roseiflexus, which use light of different wavelength for energy conversion. The aim of this study was to investigate the effect of the phototrophs on biodiversity and community composition in hot spring microbial mats. For this, we specifically activated the different phototrophs by irradiating the mats with different wavelengths in situ. We used 625, 730, and 890 nm wavelength LEDs alone or in combination and confirmed the hypothesized increase in relative abundance of different phototrophs by 16S rRNA gene sequencing. In addition to the increase of the targeted phototrophs, we studied the effect of the different treatments on chemotrophic members. The specific activation of Thermosynechococcus led to increased abundance of several other bacteria, whereas wavelengths specific to Chloroflexus and Roseiflexus induced a decrease in >50% of the community members as compared to the dark conditions. This suggests that the growth of Thermosynechococcus at the surface layer benefits many community members, whereas less benefit is obtained from an increase in filamentous anoxygenic phototrophs Chloroflexus and Roseiflexus. The increases in relative abundance of chemotrophs under different light conditions suggest a relationship between the two groups. Aerobic chemoheterotrophs such as Thermus sp. and Meiothermus sp. are thought to benefit from aerobic conditions and organic carbon in the form of photosynthates by Thermosynechococcus, while the oxidation of sulfide and production of elemental sulfur by filamentous anoxygenic phototrophs benefit the sulfur-disproportionating Caldimicrobium thiodismutans. In this study, we used an experimental approach under controlled environmental conditions for the analysis of natural microbial communities, which proved to be a powerful tool to study interspecies relationships in the microbiome.

Life care for disabled or semi-disabled elderly has become an increasingly common problem in society. In this paper, a novel wheelchair-stretcher assistive robot, which can meet the physiological needs of patients, is investigated and designed. The following tasks are conducted: (1) the mecanum wheel is adopted as the executive device of the walking mechanism, and its kinematics is analyzed in detail. (2) A five-link mechanism with single degree of freedom is proposed to realize the folding motion of the robot. Through the minimum conclusive area method, the optimal sizes of the armrests link and the side link are 507.9, and 332.5 mm, respectively. Based on the force analysis of the linkage mechanism, six torsion springs and RV (rotate vector) reduction motor are used as the driving device, which reduces the driving torque of the motor. (3) Based on the STM32 (STMicroelectronics 32-bits Microcontroller) chip, and combined with the theoretical analysis, the mechanical structure and the control system of the whole prototype are designed, and the feasibility of each module is verified by experimental research. The results confirm that the proposed robot has good performance, and that the control algorithm for the walking mechanism and the lifting mechanism are suitable.

The BCR constitutively transmits a “tonic” survival signal in the absence of exogenous antigen-binding. However, the strength of tonic BCR signal and its relationship with antigen-triggered survival signal are poorly understood. We found that primary B cells expressing high levels of BCR had elevated BCR tonic signal and increased survival compared with those expressing low levels of BCR. In addition, we found that crosslinking BCR with low doses of F(ab′)₂ α-IgM antibodies did not enhance, but rather decreased, B cell survival and that only when most of the BCR were occupied by F(ab′)₂ α-IgM antibodies was B cell survival enhanced. Based on these experimental results, we present a mathematical model integrating tonic and antigen-triggered BCR signals. Our model indicates that the signal generated from crosslinked BCR is 4.3 times as strong as the tonic signal generated from free BCR and that the threshold of B cell activation corresponds to the signal generated by crosslinking 61% of the surface BCR. This model also allows the prediction of the survival probability of a B cell based on its initial BCR level and the strength and duration of antigen stimulation, and fits with the mechanism of B cell tolerance.

(1) Background: The folate receptor (FR) is a target for cancer treatment and detection. Expression of the FR is restricted in normal cells but overexpressed in many types of tumors. Folate was conjugated with peptides for enhancing binding affinity to the FR. (2) Materials and Methods: For conjugation, folate was coupled with propargyl or dibenzocyclooctyne, and 4-azidophenylalanine was introduced in peptides for “click” reactions. We measured binding kinetics including the rate constants of association (k_a) and dissociation (k_d) of folate-peptide conjugates with purified FR by biolayer interferometry. After optimization of the conditions for the click reaction, we successfully conjugated folate with designed peptides. (3) Results: The binding affinity, indicated by the equilibrium dissociation constant (K_D), of folate toward the FR was enhanced by peptide conjugation. The enhanced FR binding affinity by peptide conjugation is a result of an increase in the number of interaction sites. (4) Conclusion: Such peptide-ligand conjugates will be important in the design of ligands with higher affinity. These high affinity ligands can be useful for targeted drug delivery system.

With the popularity of social networking services, people are increasingly inclined to share their opinions and feelings on social networks, leading to the rapid increase in multimodal posts on various platforms. Therefore, multimodal sentiment analysis has become a crucial research field for exploring users’ emotions. The complex and complementary interactions between images and text greatly heighten the difficulty of sentiment analysis. Previous works conducted rough fusion operations and ignored the study for fine fusion features for the sentiment task, which did not obtain sufficient interactive information for sentiment analysis. This paper proposes a global local fusion neural network (GLFN), which comprehensively considers global and local fusion features, aggregating these features to analyze user sentiment. The model first extracts overall fusion features by attention modules as modality-based global features. Then, coarse-to-fine fusion learning is applied to build local fusion features effectively. Specifically, the cross-modal module is used for rough fusion, and fine-grained fusion is applied to capture the interaction information between objects and words. Finally, we integrate all features to achieve a more reliable prediction. Extensive experimental results, comparisons, and visualization of public datasets demonstrate the effectiveness of the proposed model for multimodal sentiment classification.