Learning models for semantic classification of insufficient plantar pressure images

Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields

Analysis-of-marginal-Tail-Means (ATM): a robust method for discrete black-box optimization

We present a new method, called Analysis-of-marginal-Tail-Means (ATM), for effective robust optimization of discrete black-box problems. ATM has important applications to many real-world engineering problems (e.g., manufacturing optimization, product design, molecular engineering), where the objective to optimize is black-box and expensive, and the design space is inherently discrete. One weakness of existing methods is that they are not robust: these methods perform well under certain assumptions, but yield poor results when such assumptions (which are difficult to verify in black-box problems) are violated. ATM addresses this via the use of marginal tail means for optimization, which combines both rank-based and model-based methods. The trade-off between rank- and model-based optimization is tuned by first identifying important main effects and interactions, then finding a good compromise which best exploits additive structure. By adaptively tuning this trade-off from data, ATM provides improved robust optimization over existing methods, particularly in problems with (i) a large number of factors, (ii) unordered factors, or (iii) experimental noise. We demonstrate the effectiveness of ATM in simulations and in two real-world engineering problems: the first on robust parameter design of a circular piston, and the second on product family design of a thermistor network

Activation of phospholipase C beta4 by heterotrimeric GTP-binding proteins

Transient transfection assays were used to determine how the activity of phospholipase C beta 4, which is preferentially expressed in retina, was regulated. An expression vector carrying the full-length cDNA corresponding to phospholipase C beta 4 was constructed and co- transfected into COS-7 cells together with cDNA encoding the alpha subunits of the Gq class and various beta and gamma subunits corresponding to the heterotrimeric GTP-binding proteins. We found that all the alpha subunits of the Gq class, including G alpha q, G alpha 11, G alpha 14, G alpha 15, and G alpha 16 could activate PLC beta 4 and that none of the G beta gamma subunits that we tested including G beta 1 gamma 1, G beta 1 gamma 2, G beta 1 gamma 3, or G beta 2 gamma 2 activated phospholipase C beta 4. In control experiments, cotransfection with cDNA encoding the alpha subunit of transducin or Gi2 gave no activation of PLC beta 4. These results indicate that phospholipase C beta 4 is activated by G alpha subunits that are members of the Gq class, and, like the phospholipase C beta 1 isoform, it is refractory to activation in the transfection assay by many of the combinations of beta and gamma subunits found in the heterotrimeric G- proteins

Activation of phospholipase C beta 2 by the alpha and beta gamma subunits of trimeric GTP-binding protein

Cotransfection assays were used to show that the members of the GTP-binding protein Gq class of alpha subunits could activate phospholipase C (PLC) beta 2. Similar experiments also demonstrated that G beta 1 gamma 1, G beta 1 gamma 5, and G beta 2 gamma 5 could activate the beta 2 isoform of PLC but not the beta 1 isoform, while G beta 2 gamma 1 did not activate PLC beta 2. To determine which portions of PLC beta 2 are required for activation by G beta gamma or G alpha, a number of PLC beta 2 deletion mutants and chimeras composed of various portions of PLC beta 1 and PLC beta 2 were prepared. We identified the N-terminal segment of PLC beta 2 with amino acid sequence extending to the end of the Y box as the region required for activation by G beta gamma and the C-terminal region as the segment containing amino acid sequences required for activation by G alpha. Furthermore, we found that coexpression of G alpha 16 and G beta 1 gamma 1 but not G beta 1 gamma 5 in COS-7 cells was able to synergistically activate recombinant PLC beta 2. We suggest that G alpha 16 may act together with free G beta 1 gamma 1 to activate PLC beta 2, while G alpha 16 may form heterotrimeric complexes with G beta 1 gamma 5 and be stabilized in an inactive form. We conclude that the regions of PLC beta 2 required for activation by G beta gamma and G alpha are physically separate and that the nature of the G beta subunit may play a role in determining the relative specificity of the G beta gamma complex for effector activation while the nature of the G gamma subunit isoform may be important for determining the affinity of the G beta gamma complex for specific G alpha proteins

Pertussis Toxin-sensitive Activation of Phospholipase C by the C5a and fMet-Leu-Phe Receptors

Signal transduction pathways that mediate C5a and fMet-Leu-Phe (fMLP)-induced pertussis toxin (PTx)-sensitive activation of phospholipase C (PLC) have been investigated using a cotransfection assay system in COS-7 cells. The abilities of the receptors for C5a and fMLP to activate PLC beta 2 and PLC beta 3 through the Gbeta gamma subunits of endogenous Gi proteins in COS-7 cells were tested because both PLC beta 2 and PLC beta 3 were shown to be activated by the beta gamma subunits of G proteins in in vitro reconstitution assays. Neither of the receptors can activate endogenous PLC beta 3 or recombinant PLC beta 3 in transfected COS-7 cells. However, both receptors can clearly activate PLC beta 2 in a PTx-sensitive manner, suggesting that the receptors may interact with endogenous PTx-sensitive G proteins and activate PLC beta 2 probably through the Gbeta gamma subunits. These findings were further corroborated by the results that PLC beta 3 could only be slightly activated by Gbeta 1gamma 1 or Gbeta 1gamma 5 in the cotransfection assay, whereas the Gbeta gamma subunits strongly activated PLC beta 2 under the same conditions. PLC beta 3 can be activated by Galpha q, Galpha 11, and Galpha 16 in the cotransfection assay. In addition, the Ggamma 2 and Ggamma 3 mutants with substitution of the C-terminal Cys residue by a Ser residue, which can inhibit wild type Gbeta gamma -mediated activation of PLC beta 2, were able to inhibit C5a or fMLP-mediated activation of PLC beta 2. These Ggamma mutants, however, showed little effect on m1-muscarinic receptor-mediated PLC activation, which is mediated by the Gq class of G proteins. These results all confirm that the Gbeta gamma subunits are involved in PLC beta 2 activation by the two chemoattractant receptors and suggest that in COS-7 cells activation of PLC beta 3 by Gbeta gamma may not be the primary pathway for the receptors

Anomeric O-Functionalization of Carbohydrates for Chemical Conjugation to Vaccine Constructs.

Carbohydrates mediate a wide range of biological interactions, and understanding these processes benefits the development of new therapeutics. Isolating sufficient quantities of glycoconjugates from biological samples remains a significant challenge. With advances in chemical and enzymatic carbohydrate synthesis, the availability of complex carbohydrates is increasing and developing methods for stereoselective conjugation these polar head groups to proteins and lipids is critically important for pharmaceutical applications. The aim of this review is to provide an overview of commonly employed strategies for installing a functionalized linker at the anomeric position as well as examples of further transformations that have successfully led to glycoconjugation to vaccine constructs for biological evaluation as carbohydrate-based therapeutics

Specific Involvement of G Proteins in Regulation of Serum Response Factor-mediated Gene Transcription by Different Receptors

Regulation of serum response factor (SRF)-mediated gene transcription by G protein subunits and G protein-coupled receptors was investigated in transfected NIH3T3 cells and in a cell line that was derived from mice lacking G_(αq) and G_(α11). We found that the constitutively active forms of the α subunits of the G_q and G_(12) class of G proteins, including Gα_q, Gα_(11), Gα_(14), Gα_(16), Gα_(12), and Gα_(13), can activate SRF in NIH3T3 cells. We also found that the type 1 muscarinic receptor (m1R) and α_1-adrenergic receptor (AR)-mediated SRF activation is exclusively dependent on Gα_(q/11), while the receptors for thrombin, lysophosphatidic acid (LPA), thromboxane A2, and endothelin can activate SRF in the absence of Gα_(q/11). Moreover, RGS12 but not RGS2, RGS4, or Axin was able to inhibit Gα_(12) and Gα_(13)-mediated SRF activation. And RGS12, but not other RGS proteins, blocked thrombin- and LPA-mediated SRF activation in the Gα_(q/11)-deficient cells. Therefore, the thrombin, LPA, thromboxane A2, and endothelin receptors may be able to couple to Gα_(12/13). On the contrary, receptors including β_2- and α_2-ARs, m2R, the dopamine receptors type 1 and 2, angiotensin receptors types 1 and 2, and interleukin-8 receptor could not activate SRF in the presence or absence of Gα_(q/11), suggesting that these receptors cannot couple to endogenous G proteins of the G_(12) or G_q classes

A ripple reduction method for a two stages battery charger with multi-winding transformer using notch filter

This paper presents a two-stage battery charger consisting of a bridgeless Totem-pole power factor correction (TP-PFC) circuit and a full bridge converter with a multi-winding transformer. By using this transformer the cell equalizing operation can be achieved with no additional circuitry. In addition, a double-line frequency ripple reduction method is proposed to address the low frequency current ripples issues existing in both primary and secondary winding of the transformer which is caused by the voltage ripples across the intermediate DC link bus. Control and analysis of the converter at different operation modes is illustrated in detail and simulation results validate the effectiveness of the proposed converter and control algorithm

The Role of Components of Data Flow Diagram in Software Size

Managing and estimating a good software is not an easy task. Because software estimation activities are concerned not only with time and effort scheduling, but also with specifying work activities, skill levels and scheduling of necessary resources. With duration, effort and other factors overlooked, poor reliability and functionality of software may occur. Furthermore, inaccurate estimation will lead to high pressure for the working team, and poor quality of final development. Hence, designing a right software metric is an imp ortant task. Due to these reasons, a software size model is being developed using a sample consisting of 122 student projects. This model takes several advantages: (1) there tends to be fewer counting problems than other software metrics, because this model is based upon simple counts; (2) the predicted software projects were calibrated to specific local environments rather than being based upon industry weights; (3) basic size components can be identified easily at the early stage of the development life cycle; (4) the model provides clues to project designers in planning and scheduling the development of new information systems