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Bladder cancer is a leading human malignancy worldwide. Signal transducer and activator of transcription (STAT) 3 is an oncogenic transcription factor commonly hyperactivated in most human cancers, including bladder cancer. Notably, preclinical evidence has validated STAT3 blockade as a promising therapeutic strategy for bladder cancer. Hispolon Methyl Ether (HME) is a structural analog of hispolon, an anticancer component of the medicinal mushroom Phellinus linteus. Thus far, HME’s anticancer activity and mechanisms remain largely unknown. We herein report HME was cytotoxic, more potent than cisplatin, and proapoptotic to various human bladder transitional carcinoma cell lines. Of note, HME blocked STAT3 activation, evidenced by HME-elicited reduction in tyrosine 705-phosphorylated STAT3 levels constitutively expressed or induced by interleukin-6. Significantly, HME-induced cytotoxicity was abrogated in cells expressing a dominant-active STAT3 mutant (STAT3-C), confirming STAT3 blockage as a pivotal mechanism of HME’s cytotoxic action. We further revealed that survivin was downregulated by HME, while its levels were rescued in STAT3-C-expressing cells. Moreover, survivin overexpression abolished HME-induced cytotoxicity, illustrating survivin as a central downstream mediator of STAT3 targeted by HME. Lastly, HME was shown to lower tyrosine 416-phosphorylated SRC levels, suggesting that HME inhibits STAT3 by repressing the activation of SRC, a STAT3 upstream kinase. In conclusion, we present the first evidence of HME’s anti-bladder cancer effect, likely proceeding by evoking apoptosis through suppression of the antiapoptotic SRC/STAT3/survivin signaling axis.

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The falling ball tests were carried out on the Φ125 mm diameter Carbidic Austempered Ductile Iron (CADI) grinding balls with Mn content of 2.0wt.%, 2.3wt.%, 2.6wt.% and 3.0wt.%, respectively, and the kind of grinding ball with the most serious spalling was selected for stress simulation. The stress distribution between four grinding balls in the elbow of the falling ball test device when they collided with each other was simulated based on ANSYS. Results of the falling ball tests show that the spalling and mass loss of a CADI grinding ball increases with the increase of Mn content, and the CADI grinding ball with 3.0wt.% Mn shows the most obvious spalling and mass loss. The simulation results of stress distribution of a CADI grinding ball show that the maximum impact stress does not appear in the center of the contact area, but in the annular area (inner diameter of ~20 mm and outer diameter of ~50 mm) in the section with a depth of 5-15 mm below the grinding ball surface. The actual spalling of the ball is consistent with the simulation results, and the oxide-based impurities in CADI grinding ball accelerate the spalling of the grinding ball in service.

Ultrasound is acoustic waves that can penetrate deeply into tissue in a focused manner with limited adverse effects on cells. As such, ultrasound has been widely used for clinical diagnosis for several decades. Ultrasound induces bioeffects in tissues, providing potential value in therapeutic applications. However, the intrinsic millimeter scale of the ultrasound focal zone represents a challenge with respect to minimizing the illuminated regions to perturb target cells in a precise manner. To control a specific cell population or even single cells, sonogenetic tools that combine ultrasound and genetic methods have been recently developed. With these approaches, several ultrasound-responsive proteins are heterologously introduced into target cells, which enhances the cells' ability to respond to ultrasound stimulation. With optimization of the ultrasound parameters, these tools can specifically manipulate activities in genetically defined cells but not in unmodified cells present in the ultrasound-illuminated regions. These approaches provide new strategies for noninvasive modulation of target cells in various therapeutic applications.

Ultrasound is acoustic waves that can penetrate deeply into tissue in a focused manner with limited adverse effects on cells. As such, ultrasound has been widely used for clinical diagnosis for several decades. Ultrasound induces bioeffects in tissues, providing potential value in therapeutic applications. However, the intrinsic millimeter scale of the ultrasound focal zone represents a challenge with respect to minimizing the illuminated regions to perturb target cells in a precise manner. To control a specific cell population or even single cells, sonogenetic tools that combine ultrasound and genetic methods have been recently developed. With these approaches, several ultrasound-responsive proteins are heterologously introduced into target cells, which enhances the cells' ability to respond to ultrasound stimulation. With optimization of the ultrasound parameters, these tools can specifically manipulate activities in genetically defined cells but not in unmodified cells present in the ultrasound-illuminated regions. These approaches provide new strategies for noninvasive modulation of target cells in various therapeutic applications.

The effects of austempering temperature on microstructure and surface residual stress of carbidic austempered ductile iron (CADI) grinding balls were systematically investigated in this work. The microstructures were oberserved by optical metallography and analyized by X-ray diffraction. The surface residual stress measured by the cutting method is mainly composed of thermal stress and phase transformation stress. The thermal stress in grinding balls was determined by ANSYS simulation technique, and the surface phase transformation stress was obtained by subtracting the simulated surface thermal stress from the measured surface residual stress. Results show that all microstructures consist of ausferrite, white-bright zones (mixture of martensite and austenite), nodular graphite, and carbides. The distribution of ausferrite shows uniform. With the increase of austempering temperature, the volume fraction and carbon content of austenite increase, whereas the amount of white-bright zone decreases. In addition, the surface residual stress increases with the increase of austempering temperature. Only the tension exists at the austempering temperature of 200 °C, and the pressure exists at the austempering temperature of 220–260 °C. The thermal stress changes from the tension on the inside with the radius of 0–35 mm to the pressure on the outside with the radius of 35–62.5 mm, and the stress balance state presents at the radius of 35 mm. It is also found that the transformation stress is related to the content of carbon-rich austenite, and will reduce by 5.03 MPa accompanied with 1vol.% increase of the austenite. The thermal compressive stress and the transformation tensile stress on the surface both decrease with the increase of the austempering temperature.

Virtual metrology (VM) has been widely studied in the semiconductor industry with the purpose of decreasing the cycle time and reducing the expensive metrology measurements. Ideally, a VM model should not only be able to provide accurate predictions but also present an interpretable and rational structure to accommodate fundamental restrictions and relationships that are known to be present in the process. The last aspects have been missing in the VM models proposed hitherto. Therefore, in this article, we propose a novel framework by combining in a single VM model the capability to learn from data with the ability to incorporate the domain knowledge on the process. Thus, the new methodology can use the best of both information sources: data and the subject-matter expert (SME) knowledge. The framework consists of two phases. In the first phase, a Gaussian Bayesian network (GBN) is used to extract the implicit relationships between the metrology and production/process variables. In the second phase, the target response variable is defined, the predictors are selected through the associated Markov blanket, and finally, an empirical model is estimated to accurately predict the response. The proposed framework was tested and its effectiveness was confirmed through a real industrial data from a chemical–mechanical polishing (CMP) process in semiconductor fabrication. The physical meaning of the model obtained was also scrutinized by an SME. Note to Practitioners— Unlike the conventional virtual metrology (VM) techniques that model the x–y relationship based only on process data, the proposed method aims at consolidating the process knowledge from experts into an extensive relational structure. Not only the x–y model is inferred but also the relationships among the predictors are revealed. The structure is illustrated in a form of a connected graph so that the correlation between parameters can be expressed explicitly, which is also compatible with the physical laws. With the clear visualization of the correlation structure of variables, the practitioners are able to utilize the result in different applications. The learned structure can be further integrated into the plant advanced process control system, including VM, process monitoring and diagnosis, and run-to-run (R2R) control.

For decades, Run-to-Run (R2R) controllers have been widely implemented in semiconductor manufacturing. They operate over key process parameters on the basis of the metrological measurements acquired from the process and their deviations from the target setpoints. Conventionally, R2R controllers have been implemented independently of the actual equipment condition, which is obviously affecting the process stability and performance. Therefore, both equipment signals and process states shall be considered to make the R2R controllers more robust to the equipment condition drifts. In this paper, we propose a novel physics-informed framework to integrate the real-time equipment condition, based on the Fault Detection and Classification (FDC) data, into the R2R controllers. By utilizing Dynamic Bayesian Networks (DBN), the implicit relationship structure between metrology measurements, FDC indicators, and R2R regulators can be learned and reviewed explicitly. The structure shall be further reviewed to valid with the existing relationships and expert knowledge. Infeasible causalities on the structure will be constrained via setting up the blacklist at the structure learning stage. The proposed framework consists of the offline modeling stage, which incorporates the process, equipment variables, and the expert knowledge in the structure learning, and the online control stage, which constructs the Structured R2R controller (SRC) based on the relationship structure. As a result, the model is consistent by design with empirically known relationships and fundamental physical laws. The proposed SRC not only optimizes the operation with respect to the target control values but also considers the equipment and process states simultaneously. The effectiveness of SRC and the derivative control strategy are validated through a real dataset of a Chemical-Mechanical Polishing (CMP) process, and two simulated studies.

Run-to-Run (R2R) control has become a common process regulating approach in the semiconductor industry. Conventionally, key process parameters are regulated with respect to the measured metrology data. However, wafer quality can be affected by complex factors related to equipment condition. The steady progress of information technologies enable us to collect and handle larger amounts of data, open new perspectives to explore the interactions between FDC (Fault Detection and Classification) data and product quality, i.e., wafer metrology, which should be considered simultaneously for developing improved R2R controllers. In this paper, the equipment condition is explicitly modeled and integrated into the core of a R2R controller, in order to accommodate this critical aspect of the system in deriving the control law, in order to reduce process variability in more effective way. Therefore, a new R2R control framework is proposed, which is called: Controller with Real-time Equipment Condition (CREC). The effectiveness of the new control strategy is demonstrated and validated using the case study of a Chemical-Mechanical Polishing (CMP) process, in collaboration with our industrial partner.

A prediction model was developed to map road traffic noise in an area with significant motorcycle traffic in Taichung City, Taiwan. This model was modified from the Nordic prediction method by adding three types of traffic flow rates, including heavy vehicles, light vehicles, and motorcycles, as well as local traffic speeds and road characteristics to the calculating equations. The parameters that were input into the equations include traffic flow, vehicle speed, distance from the center of the road, height of the road surface, position and height of the barriers, thickness of the barriers, location of the receiver relative to the surrounding road surface or barriers, reflecting vertical surfaces, type of ground, and height of the buildings. The model was validated by comparing the measured noise levels at 42 sampling sites close to main roads with the predicted values. A significant correlation was found between the predicted and measured noise levels (Pearson correlation coefficient=0.75, p

Background: A few studies have investigated the interaction between exposure to road traffic noise, air pollutants, and cardiovascular disease (CVD), but their results were inconsistent. This cross-sectional study investigated whether road traffic noise, particulate matter with dynamic diameter less than 10 &mu, m (PM10) and nitrogen dioxides (NO2) exposure were independently associated with the risk of CVD. Methods: We recruited 663 volunteers who had been living near main roads for more than three years in 2008. Information concerning the subjects&rsquo, home addresses was combined with noise measurements at 42 locations and annual average of air pollutants from 2 monitoring stations to estimate individual exposure. Multivariate logistic regression was used to calculate the odds ratio (OR) for diagnosed CVD, adjusting for potential confounders and co-exposure. Results: Only per 5-dBA increase in road traffic noise was significantly associated with elevated risk of CVD (adjusted OR = 2.23, 95% confidence interval (CI) = 1.26&ndash, 3.93) in the single-exposure models. Such association was aggravated (adjusted OR = 2.96, 95% CI = 1.41&ndash, 6.23) after adjustment for total traffic and PM10 or NO2 in the two-exposure models. Conclusions: Road traffic noise exposure may be associated with the increasing prevalence of CVD. No synergistic association was observed between co-exposure to noise and air pollutants and the risk of CVD.

It has been suggested that fruit and vegetable consumption are associated with good bone health. Onion, in particular, has been verified in its efficacy in bone resorption activity. In this study, we further investigated the effects of an onion-containing diet on ovariectomy-induced bone loss using methods of serum marker assay, histomorphometric analysis and biomechanical tests. Sixty-four female Wistar rats (14-week-old) with sham operations or ovariectomy were assigned to 6 groups: CON, sham-operated control group; OVX, ovariectomized group; ALN, ovariectomized rats treated with alendronate (1 mg/kg/day, p.o.); and 3% ON, 7% ON and 14% ON, ovariectomized rats fed with diets containing 3%, 7% and 14% (wt/wt) onion powder, respectively. Animals were sacrificed after a six-week treatment course. In the serum marker assay, alendronate and all three onion-enriched diets significantly decreased serum calcium level (p

Exposure to road traffic noise, fine particulate matter (aerodynamic diameter ≤2.5 μm; PM2.5) and nitrogen oxides (NOx) has been associated with transient changes in blood pressure, but whether an interaction exists remains unclear. This panel study investigated whether noise, PM2.5 and NOx exposure were independently associated with changes in 24-h ambulatory blood pressure. We recruited 33 males and 33 females aged 18-32 years as study subjects. Personal noise exposure and ambulatory blood pressure were monitored simultaneously in 2007. During the data collection periods, 24-h data on PM2.5 and NOx from five air-quality monitors within 6 km of participants' home addresses were used to estimate their individual exposures. Linear mixed-effects regression models were used to estimate single and combined effects on ambulatory blood pressure. Exposure to both noise and PM2.5 was significantly associated with increased systolic blood pressure (SBP) and diastolic blood pressure (DBP) over 24h; NOx exposure was only significantly related to elevated DBP. Twenty-four-hour ambulatory blood pressure increased with the current noise exposure of 5 A-weighted decibels (dBA) (SBP 1.44 [95% confidence interval: 1.16, 1.71] mmHg and DBP 1.40 [1.18, 1.61] mmHg) and PM2.5 exposure of 10-µg/m(3) (SBP 0.81 [0.19, 1.43] mmHg and DBP 0.63 [0.17, 1.10] mmHg), as well as the current NOx exposure of 10-ppb (DBP 0.54 [0.12, 0.97] mmHg) after simultaneous adjustment. These findings suggest that exposure to noise and air pollutants may independently increase ambulatory blood pressure and the risk of cardiovascular diseases.

A portable motion replication system for posture guidance of upper extremity is presented in this paper. The system can capture user’s postures, compare the postures with corresponding ones in database, and give the user proper visual and verbal feedback for posture modifying in real time. The experiment of user test shows that the subjects under both visual and verbal feedback have better performance.

The paper describes a Yoga training system that is built based on motion replication technique (MoRep), including hardware, virtual scenario and feedback design. The motion replication technique proposed here can determine the similarity between Yoga master and student's postures and then provide feedback on the incorrect body posture of the student through multimodal channels. The key innovations of this project are also discussed.

The paper presents results from a virtual reality (VR)-based system for upper limb rehabilitation. The system incorporates a range of interchangeable direction sensing devices (the Optical Linear Encoder (OLE) and the inertial measurement unit (IMU)) that can be adjusted to a large range of different arm and hand sizes, and interactive practice applications designed for motivating and seamlessly driving users to perform the functional and non-functional motor recovery tasks. We describe the kinematic models of both arm and hand, the technical details of two motion track components (the arm suit and the SmartGlove) and the design of the interactive scenarios. The system thus promises to be a valuable complement to conventional therapeutic programs offered in rehabilitation clinics.

With the advancement of information technology and motion capture (MoCap) technology, a new concept—Motion Replication (MoRep)—hereby the learning of spatial body motions from a virtual teacher becomes possible. Posture comparison is the first step towards a complete MoRep system. This paper propose a simple and accurate method for evaluating the similarity between human postures from motion capture data.