Styrene-ethylene-butadiene-styrene copolymer/carbon nanotubes composite fiber based strain sensor with wide sensing range and high linearity for human motion detection

Flexible strain sensors have attracted extensive attention due to their potential applications in wearable electronics and health monitoring. However, it is still a challenge to obtain flexible strain sensors with both high stretchability and wide linear strain sensing range. In this study, styrene-ethylene-butadiene-styrene copolymer/carbon nanotubes (SEBS/CNTs) composite fiber which showed both electrical conductivity and high stretchability was fabricated through a scalable wet spinning method. The effect of CNTs content on the strain sensing behavior of the SEBS/CNTs fiber based strain sensor was investigated. The results showed that when the CNTs content reached 7 wt%, the SEBS/CNTs composite fiber was capable of sensing strains as high as 500.20% and showed a wide linear strain sensing range of 0-500.2% with a gauge factor (GF) of 38.57. Combining high stretchability, high linearity and reliable stability, the SEBS/CNTs composite fiber based strain sensor had the ability to monitor the activities of different human body parts including hand, wrist, elbow, shoulder and knee

A flexible dual-mode pressure sensor with ultra-high sensitivity based on BTO@MWCNTs core-shell nanofibers

Wearable flexible sensors have developed rapidly in recent years because of their improved capacity to detect human motion in wide-ranging situations. In order to meet the requirements of flexibility and low detection limits, a new pressure sensor was fabricated based on electrospun barium titanate/multi-wall carbon nanotubes (BTO@MWCNTs) core-shell nanofibers coated with styrene-ethylene-butene-styrene block copolymer (SEBS). The sensor material (BTO@MWCNTs/SEBS) had a SEBS to BTO/MWCNTs mass ratio of 20:1 and exhibited an excellent piezoelectricity over a wide range of workable pressures from 1 to 50 kPa, higher output current of 56.37 nA and a superior piezoresistivity over a broad working range of 20 to 110 kPa in compression. The sensor also exhibited good durability and repeatability under different pressures and under long-term cyclic loading. These properties make the composite ideal for applications requiring monitoring subtle pressure changes (exhalation, pulse rate) and finger movements. The pressure sensor developed based on BTO@MWCNTs core-shell nanofibers has demonstrated great potential to be assembled into intelligent wearable devices

Structural Analysis of Alkaline β-Mannanase from Alkaliphilic Bacillus sp. N16-5: Implications for Adaptation to Alkaline Conditions

Significant progress has been made in isolating novel alkaline β-mannanases, however, there is a paucity of information concerning the structural basis for alkaline tolerance displayed by these β-mannanases. We report the catalytic domain structure of an industrially important β-mannanase from the alkaliphilic Bacillus sp. N16-5 (BSP165 MAN) at a resolution of 1.6 Å. This enzyme, classified into subfamily 8 in glycosyl hydrolase family 5 (GH5), has a pH optimum of enzymatic activity at pH 9.5 and folds into a classic (β/α)8-barrel. In order to gain insight into molecular features for alkaline adaptation, we compared BSP165 MAN with previously reported GH5 β-mannanases. It was revealed that BSP165 MAN and other subfamily 8 β-mannanases have significantly increased hydrophobic and Arg residues content and decreased polar residues, comparing to β-mannanases of subfamily 7 or 10 in GH5 which display optimum activities at lower pH. Further, extensive structural comparisons show alkaline β-mannanases possess a set of distinctive features. Position and length of some helices, strands and loops of the TIM barrel structures are changed, which contributes, to a certain degree, to the distinctly different shaped (β/α)8-barrels, thus affecting the catalytic environment of these enzymes. The number of negatively charged residues is increased on the molecular surface, and fewer polar residues are exposed to the solvent. Two amino acid substitutions in the vicinity of the acid/base catalyst were proposed to be possibly responsible for the variation in pH optimum of these homologous enzymes in subfamily 8 of GH5, identified by sequence homology analysis and pKa calculations of the active site residues. Mutational analysis has proved that Gln91 and Glu226 are important for BSP165 MAN to function at high pH. These findings are proposed to be possible factors implicated in the alkaline adaptation of GH5 β-mannanases and will help to further understanding of alkaline adaptation mechanism

To Be or Not to Be? Strategic Analysis of Carbon Tax Guiding Manufacturers to Choose Low-Carbon Technology

This paper analyzes the environmental tax’s effect on manufacturers’ choice of low-carbon technology in competitive supply chains. The existing studies only consider a single oligopoly enterprise and ignore the competition between supply chains. Few papers study the manufacturer’s technology choice under the carbon tax policy in the competitive supply chains, especially investigating the factors influencing the technology choice, including the market volume, and technology carbon emission reduction efficiency because different industry sectors have their distinctive carbon emissions reduction efficiencies and facing the different market volume. The study adopts a game theoretical approach, including the three-level supply chain consisting of the regulator, the manufacturers, and the retailers. A high carbon tax does not always help firms choose low-carbon technology. However, the monotonous effect of the carbon tax on manufacturer technology selection is no longer valid if the market volume and the carbon-reducing efficiency are considered. When the market volume is large, the regulator can set a high carbon tax to induce the manufacturers to choose low-carbon technology. We identify cases where the manufacturers are caught in a prisoner’s dilemma. When the market volume is small, and the carbon-reducing efficiency is high, the competitive manufacturers adopt the common technology. However, if the regulator increases the carbon tax, the manufacturers acquire the differential technology strategic choice, which is the Pareto optimal. We also extend the base model to the imperfect substitutable Cournot model and the Bertrand model to check the robustness and find our main results still hold in these extensions

To Be or Not to Be? Strategic Analysis of Carbon Tax Guiding Manufacturers to Choose Low-Carbon Technology

This paper analyzes the environmental tax’s effect on manufacturers’ choice of low-carbon technology in competitive supply chains. The existing studies only consider a single oligopoly enterprise and ignore the competition between supply chains. Few papers study the manufacturer’s technology choice under the carbon tax policy in the competitive supply chains, especially investigating the factors influencing the technology choice, including the market volume, and technology carbon emission reduction efficiency because different industry sectors have their distinctive carbon emissions reduction efficiencies and facing the different market volume. The study adopts a game theoretical approach, including the three-level supply chain consisting of the regulator, the manufacturers, and the retailers. A high carbon tax does not always help firms choose low-carbon technology. However, the monotonous effect of the carbon tax on manufacturer technology selection is no longer valid if the market volume and the carbon-reducing efficiency are considered. When the market volume is large, the regulator can set a high carbon tax to induce the manufacturers to choose low-carbon technology. We identify cases where the manufacturers are caught in a prisoner’s dilemma. When the market volume is small, and the carbon-reducing efficiency is high, the competitive manufacturers adopt the common technology. However, if the regulator increases the carbon tax, the manufacturers acquire the differential technology strategic choice, which is the Pareto optimal. We also extend the base model to the imperfect substitutable Cournot model and the Bertrand model to check the robustness and find our main results still hold in these extensions

Pyrrolidine Dithiocarbamate (PDTC) Attenuates Cancer Cachexia by Affecting Muscle Atrophy and Fat Lipolysis

Cancer cachexia is a kind of whole body metabolic disorder syndrome accompanied with severe wasting of muscle and adipose tissue. NF-κB signaling plays an important role during skeletal muscle atrophy and fat lipolysis. As an inhibitor of NF-κB signaling, Pyrrolidine dithiocarbamate (PDTC) was reported to relieve cancer cachexia; however, its mechanism remains largely unknown. In our study, we showed that PDTC attenuated cancer cachexia symptom in C26 tumor bearing mice models in vivo without influencing tumor volume. What’s more, PDTC inhibited muscle atrophy and lipolysis in cells models in vitro induced by TNFα and C26 tumor medium. PDTC suppressed atrophy of myotubes differentiated from C2C12 by reducing MyoD and upregulating MuRF1, and preserving the expression of perilipin as well as blocking the activation of HSL in 3T3-L1 mature adipocytes. Meaningfully, we observed that PDTC also inhibited p38 MAPK signaling besides the NF-κB signaling in cancer cachexia in vitro models. In addition, PDTC also influenced the protein synthesis of skeletal muscle by activating AKT signaling and regulated fat energy metabolism by inhibiting AMPK signaling. Therefore, PDTC primarily influenced different pathways in different tissues. The study not only established a simple and reliable screening drugs model of cancer cachexia in vitro but also provided new theoretical basis for future treatment of cancer cachexia

The Anti-Breast Cancer Activity of Dihydroartemisinin-5-methylisatin Hybrids Tethered via Different Carbon Spacers

Sixteen dihydroartemisinin-5-methylisatin hybrids 6a–c and 7a–m tethered via different carbon spacers were assessed for their antiproliferative activity against MCF-7, MDA-MB-231, MCF-7/ADR and MDA-MB-231/ADR breast cancer cell lines as well as cytotoxicity towards MCF-10A cells to investigate the influence of the length of carbon spacers on the activity. The preliminary results illustrated that the length of the carbon spacer was the main parameter which affected the activity, and hybrids tethered via the two-carbon linker showed the highest activity. Amongst the synthesized hybrids, the representative hybrid 7a (IC50: 15.3–20.1 µM) not only demonstrated profound activity against both drug-sensitive and drug-resistant breast cancer cell lines, but also possessed excellent safety and selectivity profile. Collectivity, hybrid 7a was a promising candidate for the treatment of both drug-sensitive and drug-resistant breast cancers and worthy of further preclinical evaluations

PRRGNVis: Multi-Level Visual Analysis of Comparison for Predicted Results of Recurrent Geometric Network

The structure of a protein determines its function, and the advancement of machine learning has led to the rapid development of protein structure prediction. Protein structure comparison is crucial for inferring the evolutionary relationship of proteins, drug discovery, and protein design. In this paper, we propose a multi-level visual analysis method to improve the protein structure comparison between predicted and actual structures. Our method takes the predicted results of the Recurrent Geometric Network (RGN) as the main research object and is mainly designed following three levels of protein structure visualization on RGN. Firstly, at the prediction accuracy level of the RGN, we use the Global Distance Test—Total Score (GDT_TS) as the evaluation standard, then compare it with distance-based root mean square deviation (dRMSD) and Template Modeling Score (TM-Score) to analyze the prediction characteristics of the RGN. Secondly, the distance deviation, torsion angle, and other attributes are used to analyze the difference between the predicted structure and the actual structure at the structural similarity level. Next, at the structural stability level, the Ramachandran Plot and PictorialBar combine to be improved to detect the quality of the predicted structure and analyze whether the amino acid residues conform to the theoretical configuration. Finally, we interactively analyze the characteristics of the RGN with the above visualization effects and give reasons and reasonable suggestions. By case studies, we demonstrate that our method is effective and can also be used to analyze other predictive network results

High Levels of KAP1 Expression Are Associated with Aggressive Clinical Features in Ovarian Cancer

KAP1 is an universal corepressor for Kruppel-associated box zinc finger proteins in both normal and tumor cells. In this study, the biological function and clinical significance of KAP1 expression in ovarian cancer were investigated. Immunohistological staining of KAP1 was evaluated in 111 patients with ovarian epithelial cancer, 15 with ovarian borderline tumor, and 20 normal ovarian tissue. The correlations of KAP1 expression with clinicopathological features were studied. Kaplan-Meier analysis and Cox proportional hazard modeling were used to assess overall survival to analyze the effect of KAP1 expression on the prognosis of ovarian cancer. The positive rates of KAP1 were significantly higher in ovarian epithelial cancer (55.7%) and borderline tumor (20.0%) than in normal ovarian tissue (5.0%) (all p < 0.01). KAP1 expression correlated significantly with clinical stage (χ2 = 14.57, p < 0.0001), pathological grade (χ2 = 6.06, p = 0.048) and metastases (χ2 =10.38, p = 0.001). Patients with high KAP 1 levels showed poor survival (p < 0.0001). Multivariate analysis showed that KAP1 high expression was an independent predictor for ovarian cancer patients (hazard ratio = 0.463; 95% confidence interval = 0.230–0.9318, p = 0.031). Functionally, depletion of KAP1 by siRNA inhibited ovarian cancer cell proliferation, cell migration. KAP1 expression correlated with aggressive clinical features in ovarian cancer. High KAP1 expression was a prognostic factor of ovarian cancer