Explore the vast range of titles available.

This paper addresses the single‐machine group scheduling with effects of learning and resource allocations. By effects of learning and resource allocations, we mean that the group setup times and job processing times are functions of their resource allocations and positions. For the convex resource allocation, we give a bicriteria analysis where the first cost is the makespan, and the second cost is the total resource consumption (i.e., resource utilization). We show that three versions of both of these costs are polynomially solvable.

Flexible pressure sensors usually require functional materials with both mechanical compliance and appropriate electrical performance. Most sensors based on materials with limited compressibility can hardly balance between high sensitivity and broad pressure range. Here, we prepare a heterophasic ionogel with shape and stiffness memory for adaptive pressure sensors. By combining the microstructure alignment for stiffness changing and shape memory micro-inclusions for stiffness fixing, the heterophasic ionogels reveal tunable compressibility. This controllable pressure-deformation property of the ionogels results in the pressure sensors’ programmable pressure-resistance behavior with tunable pressure ranges, varied detection limits, and good resolution at high pressure. Broad pressure ranges to 220 and 380 kPa, and tunable detection limit from 120 to 330 and 950 Pa are realized by the stiffness memory ionogel sensors. Adaptive detection is also brought out to monitor tiny pressure changes at low stiffness and distinguish different human motions at high stiffness. Using shape and stiffness memory materials in pressure sensors is a general design to achieve programmable performance for more complex application scenarios. Flexible pressure sensors require functional materials accounting for mechanical compliance and electrical performance simultaneously but sensor materials often suffer from limited compressibility which decreases sensitivity over a large pressure range. Here, the authors demonstrate a heterophase ionogel with shape and stiffness memory for adaptive pressure sensing

Energy-dissipation elastomers relying on their viscoelastic behavior of chain segments in the glass transition region can effectively suppress vibrations and noises in various fields, yet the operating frequency of those elastomers is difficult to control precisely and its range is narrow. Here, we report a synergistic strategy for constructing polymer-fluid-gels that provide controllable ultrahigh energy dissipation over a broad frequency range, which is difficult by traditional means. This is realized by precisely tailoring the relaxation of confined polymer fluids in the elastic networks. The symbiosis of this combination involves: elastic networks forming an elastic matrix that displays reversible deformation and polymer fluids reptating back and forth to dissipate mechanical energy. Using prototypical poly (n-butyl acrylate) elastomers, we demonstrate that the polymer-fluid-gels exhibit a controllable ultrahigh energy-dissipation property (loss factor larger than 0.5) with a broad frequency range (10 −2 ~ 10 8  Hz). Energy absorption of the polymer-fluid-gels is over 200 times higher than that of commercial damping materials under the same dynamic stress. Moreover, their modulus is quasi-stable in the operating frequency range. In most cases the frequency range of a damping material is adapted to a specific application. Huang et al. design a gel filled with a polymeric fluid that bypasses this problem and offers an unusually broad window over which vibrational energy is effectively dissipated.

Background Metabolites disrupted by abnormal state of human body are deemed as the effect of diseases. In comparison with the cause of diseases like genes, these markers are easier to be captured for the prevention and diagnosis of metabolic diseases. Currently, a large number of metabolic markers of diseases need to be explored, which drive us to do this work. Methods The existing metabolite-disease associations were extracted from Human Metabolome Database (HMDB) using a text mining tool NCBO annotator as priori knowledge. Next we calculated the similarity of a pair-wise metabolites based on the similarity of disease sets of them. Then, all the similarities of metabolite pairs were utilized for constructing a weighted metabolite association network (WMAN). Subsequently, the network was utilized for predicting novel metabolic markers of diseases using random walk. Results Totally, 604 metabolites and 228 diseases were extracted from HMDB. From 604 metabolites, 453 metabolites are selected to construct the WMAN, where each metabolite is deemed as a node, and the similarity of two metabolites as the weight of the edge linking them. The performance of the network is validated using the leave one out method. As a result, the high area under the receiver operating characteristic curve (AUC) (0.7048) is achieved. The further case studies for identifying novel metabolites of diabetes mellitus were validated in the recent studies. Conclusion In this paper, we presented a novel method for prioritizing metabolite-disease pairs. The superior performance validates its reliability for exploring novel metabolic markers of diseases.

Background Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs. Results Firstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method ‘DRACP’ and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets. Conclusion We developed a novel method named ‘DRACP’ and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.

Functionalized hydrogels play an important part in chemistry, biology, and material science due to their unique microstructures. Characterization of these microstructures is the fundamental issue to improve the optical, mechanical, and biochemical performance of functionalized hydrogels. With the rapid development of fluorescence microscopy, a growing number of researchers have attempted to utilize this easily operated, noninvasive, and high-contrast technique to visualize the fine microstructure of hydrogels. Integration of a confocal system into fluorescence microscopy allows the sectioning and reconstruction of 3D hydrogel networks. The live recording function offers in situ and real-time images of dynamic behaviors within hydrogels. The development of super-resolution fluorescence microscopy has significantly promoted imaging quality from the submicron scale to the nanoscale. Based on these spectacular achievements, we reviewed the recent advances in fluorescence microscopic visualization of internal morphologies, mechanical properties, and dynamic structural changes. The scope of this review is to provide inspiration for researchers in chemistry, material science, and biology to study and fabricate functionalized hydrogels with the assistance of fluorescence microscopic visualization. This review highlights the recent advances in fluorescence microscopic visualization of synthetic hydrogels, bio-macromolecular hydrogels, organohydrogels, and supramolecular hydrogels. Topics related to the structural changes of hydrogels, hydrogel mechanics, and super-resolution imaging of hydrogels based on fluorescence microscopy are introduced. The design concepts, imaging mechanisms, and potential applications of the novel fluorescence visualization strategies are discussed in detail. Finally, our opinions on the major challenges of current research, possible solutions, and future directions are shared.

Background Alzheimer’s disease (AD) imposes a heavy burden on society and every family. Therefore, diagnosing AD in advance and discovering new drug targets are crucial, while these could be achieved by identifying AD-related proteins. The time-consuming and money-costing biological experiment makes researchers turn to develop more advanced algorithms to identify AD-related proteins. Results Firstly, we proposed a hypothesis “similar diseases share similar related proteins”. Therefore, five similarity calculation methods are introduced to find out others diseases which are similar to AD. Then, these diseases’ related proteins could be obtained by public data set. Finally, these proteins are features of each disease and could be used to map their similarity to AD. We developed a novel method ‘LRRGD’ which combines Logistic Regression (LR) and Gradient Descent (GD) and borrows the idea of Random Forest (RF). LR is introduced to regress features to similarities. Borrowing the idea of RF, hundreds of LR models have been built by randomly selecting 40 features (proteins) each time. Here, GD is introduced to find out the optimal result. To avoid the drawback of local optimal solution, a good initial value is selected by some known AD-related proteins. Finally, 376 proteins are found to be related to AD. Conclusion Three hundred eight of three hundred seventy-six proteins are the novel proteins. Three case studies are done to prove our method’s effectiveness. These 308 proteins could give researchers a basis to do biological experiments to help treatment and diagnostic AD.

Polymer nanocomposites with tuning functions are exciting candidates for various applications, and most current research has focused on static mechanical reinforcement. Actually, under service conditions of complex dynamic interference, stable dynamic mechanical properties with high energy dissipation become more critical. However, nanocomposites often exhibit a trade-off between static and dynamic mechanics, because of their contradictory underlying physics between chain crosslinking and chain relaxation. Here, we report a general strategy for constructing ultra-stable dynamic mechanical complex fluid nanocomposites with high energy dissipation by infusing complex fluids into the nanoconfined space. The key is to tailor full-scale polymer dynamics across an exceptionally broad timescale by single-chain confinement. These materials exhibit stable storage modulus (10 0  ~ 10 2   MPa) with high energy dissipation (loss factor > 0.4) over a broad frequency range (10 −1  ~ 10 7   Hz)/temperature range (−35 ~ 85°C). In the loss factor > 0.4 region, their dynamic mechanical stability (rate of modulus change versus temperature (k)) is 10 times higher than that of conventional polymer nanocomposites. Under service conditions of complex dynamic interference, stable dynamic mechanical properties with high energy dissipation becomes more critical but nanocomposites often exhibit a trade-off between static and dynamic mechanics. Here, the authors report a general strategy for constructing ultra-stable dynamic mechanical complex fluid nanocomposites with high energy dissipation by infusing complex fluids into the nanoconfined space.

Engineering surface wettability is of great importance in academic research and practical applications. The exploration of hydrogel‐based natural surfaces with superior properties has revealed new design principles of surface superwettability. Gels are composed of a cross‐linked polymer network that traps numerous solvents through weak interactions. The natural fluidity of the trapped solvents confers the liquid‐like property to gel surfaces, making them significantly different from solid surfaces. Bioinspired gel surfaces have shown promising applications in diverse fields. This work aims to summarize the fundamental understanding and emerging applications of bioinspired gel surfaces with superwettability and special adhesion. First, several typical hydrogel‐based natural surfaces with superwettability and special adhesion are briefly introduced, followed by highlighting the unique properties and design principles of gel‐based surfaces. Then, the superwettability and emerging applications of bioinspired gel surfaces, including liquid/liquid separation, antiadhesion of organisms and solids, and fabrication of thin polymer films, are presented in detail. Finally, an outlook on the future development of these novel gel surfaces is also provided. Recent progress and emerging applications of bioinspired gel surfaces with superwettability and special adhesion are summarized. Gel surfaces possess liquid‐like property, showing superwettability including superlyophobicity, slippery, and superspreading. By utilizing these unique properties, bioinspired gel surfaces exhibit exceptional performances in diverse applications, such as liquid/liquid separation, anti‐adhesion, anti‐friction, and synthesis of functional thin films.

Objectives The study of sex differences in hyperuricemia can provide not only a theoretical basis for this clinical phenomenon but also new therapeutic targets for urate-lowering therapy. In the current study, we aimed to confirm that estradiol can promote intestinal ATP binding cassette subfamily G member 2 (ABCG2) expression to increase urate excretion through the PI3K/Akt pathway. Methods The estradiol levels of hyperuricemia/gout patients and healthy controls were compared, and a hyperuricemia mouse model was used to observe the urate-lowering effect of estradiol and the changes in ABCG2 expression in the kidney and intestine. In vivo and in vitro intestinal urate transport models were established to verify the urate transport function regulated by estradiol. The molecular pathway by which estradiol regulates ABCG2 expression in intestinal cells was explored. Results The estradiol level of hyperuricemia/gout patients was significantly lower than that of healthy controls. Administering estradiol benzoate (EB) to both male hyperuricemic mice and female mice after removing the ovaries confirmed the urate-lowering effect of estradiol, and hyperuricemia and estradiol upregulated the expression of intestinal ABCG2. Estradiol has been confirmed to promote urate transport by upregulating ABCG2 expression in intestinal urate excretion models in vivo and in vitro. Estradiol regulates the expression of intestinal ABCG2 through the PI3K/Akt pathway. Conclusion Our study revealed that estradiol regulates intestinal ABCG2 through the PI3K/Akt pathway to promote urate excretion, thereby reducing serum urate levels.