Figure shows the power consumptions of different electronic devices

Figure 2 shows the power consumptions of different electronic devices ranging from medical devices to consumer applications (power consumption from around one microwatt up to several watts). This figure also presents the power output of an pyrilamine maleate harvester (a shoe-powered generator) and some human-operated generators for comparison purposes. Even supposing that human microgenerators do not have a power output high enough for some electronic devices (such as laptops), they can produce a lower power output, for low-power electronic applications, including some medical devices. Thus, it is clear that human energy harvesting is suitable for biomedical applications and other low-power devices (Chen & Fan, 2015; Peng, Tang, Yang, & Heo, 2014).
This paper is organized as follows. Section 2 presents a background of the harvesting energy and its purposes. In Section 3, the mechanism of energy harvesting from body motions and its relations are described. The design process of the microgenerator and its relations are presented in Section 4. In Section 5, the implementation process of the microgenerator and several experimental results related to the microgenerator are explained. Finally, in the last section, a conclusion of the design process and the experimental tests are presented.

Energy harvesting background
Energy harvesting is a research subject that is gaining relevance for powering electronic devices because of an almost infinite life-time potential (Levron, Shmilovitz, & Martinez-Salamero, 2011; Ylli et al., 2015). Energy harvesting from motion, temperature changes, and solar light has proven to be a confident alternative to batteries for commercial applications, such as flash lights, hand-cranking radios, thermal-powered wrist watches, and solar-powered calculators. Energy harvesting also addresses the possibility of using body motion for powering portable, implantable, or wearable systems, such as biomedical devices. Considering another point of view, the increasing use of small low-power wireless and electronics technologies for new medical monitoring devices, such as health-monitoring via body sensor networks (Hao & Foster, 2008; Jovanov, Milenkovic, Otto, & De Groen, 2005; Varshney, 2007), will challenge current technologies because of limited size and lifetime of batteries.
One of the first surveys on energy generation from the body motions was done by (Wahbah, Alhawari, Mohammad, Saleh, & Ismail, 2014). The explanation included the analysis of available power from body heat (0.2–0.3W on the neck, 0.6–1W on the head, and 3–5W on the entire body surface), blood pressure (∼1W), breathing (∼1W for breathing, ∼0.8W from chest movement), and other activities, such as typing (0.007–0.02W), arm lifting (∼60W), bicep curls (∼20W), and hiking (∼70W). Even though those locations and numbers show an expected power limit, they can harvest only a small amount of those levels.
Energy harvesting can be performed through several methods for energy production. Power generation commonly involves the use of electrostatic transduction, piezoelectric generation, or electromagnetic induction (Hwang, Hyoung, Park, & Kim, 2013; Khaligh, Zeng, & Zheng, 2010; Matiko, Grabham, Beeby, & Tudor, 2014). Other practical techniques include the use of photovoltaic cells, thermal gradients (Schmidt & Scott, 2011), or a combination of the above-mentioned methods. In the following sections, a brief description is given for each of them.
The method of electromagnetic power generation is based on the induced voltage in a winding when a magnet vibrates relative to it (Berdy, Valentino, & Peroulis, 2015). This is produced by the altering magnetic flux as described by the Faraday\’s law of induction (Eq. (1)):where ∅ is the magnetic flux in webers, and E is the magnitude of the electromotive force (EMF) in volts. This change is either due to having a fixed winding and a moving magnet, or the opposite, a fixed magnet and a moving winding. For a winding, the EMF depends on the strength of the magnetic flux, the number of winding turns, and the rate of change of the magnetic flux. A typical structure might be a magnet attached to a spring or a cantilever beam that swings with respect to a winding, or a free sliding magnet within a helical winding that is around the magnet.

pyrilamine maleate This study showed that BC

This study showed that BC fortification at the modelled vitamin D amounts would potentially increase the vitamin D status of individuals whose status is inadequate. Since usual BC consumption would be much less than the modelled worst-case scenario, pyrilamine maleate are unlikely to be at risk of exceeding safe vitamin D intakes.
: N/A.

Vitamin B deficiency has potentially serious lifelong consequences. The aim of this study is to explore the associations between markers of vitamin B status and other biochemical, dietary and physical measures.
Three data sets composed of young omnivore women ( = 65; age 24.5 ± 4.4 y; mean ± SD), randomly selected young women ( = 305; age 22.5 ± 3.9 y) and elderly women ( = 44; age 80.5 ± 7.6 y) were examined. Associations between vitamin B biomarkers and other selected biomarkers of nutritional status (i.e. serum folate, erythrocyte folate), lifestyle factors such as; dietary intake, smoking, alcohol intake, oral contraceptive pill (OCP) use (in the non-elderly groups), and other factors such as BMI and age were examined using mixed effects regression, accounting for study clusters.
Serum pyrilamine maleate vitamin B concentration (pmol/L) was related positively to both serum folate (nmol/L; = 0.018 95%CI: 0.009-0.026, < 0.001) and erythrocyte folate (nmol/L; = 0.456 95%CI: 0.164-0.747, < 0.01). Younger women who used the OCP had serum vitamin B concentrations that were 72.3 (SE = 12.4) pmol/L lower than non-users ( < 0.001). Vitamin B was marginally associated with the intakes of both protein ( < 0.05) and alcohol ( < 0.001). Serum vitamin B concentration was not related to age, smoking status or iron status. The association between vitamin B and folate status may be indicative of a higher diet quality, but requires further investigation. With vitamin B deficiency being linked with increased risk of neural tube defect, the use of the OCP may pose an increased risk in women of reproductive age. : N/A.
Breastfeeding mothers tend to modify their habitual diet and avoid certain foods. The reasons for this are not fully understood. The aim of this survey was to explore the motives and common dietary practices among breastfeeding mothers and if nutritional intake is compromised.
An on-line exploratory survey, invited women who had breastfed to participate.
Of the 1,293 respondents, 98% completed the survey and 77% mothers modified their usual diet. The most common reasons were ‘baby was unsettled’ (31%), ‘baby had lots of wind/gas’ (24%), ‘baby had reflux’ (17%) and ‘baby had colic’ (11%). The most common modifications were avoidance of alcohol (79%), coffee (44%), cow’s milk (24%), milk chocolate (22%), chilli (22%) and cabbage and onion (each 20%). Information was sourced from the internet (44%), maternal and child health nurses (40%), the Australian Breastfeeding Association (34%). Sourcing information from paediatricians was less common (10%) and 89% of respondents had never seen a dietitian. Thirty-three percent removed dairy, but did not replace it with other calcium-rich foods nor did they take a calcium supplement. Thirty-two percent that modified their diet did not take a suggested breastfeeding multi-vitamin for when the nutrition guidelines are difficult to meet.
Dietary modification among breastfeeding mothers is common practice. The most reported reason was due to an ‘unsettled baby’. The majority of information is sourced from the internet and not from experts in nutrition suggesting this group may be at risk of nutritional inadequacies.
: N/A.

Diet may be associated with depressive symptoms. The objective of this study was to determine the association between diet and depressive symptoms in first-time mothers.
Cross-sectional, baseline data (3 months postpartum) were obtained from the Melbourne InFANT (Infant Feeding, Activity and Nutrition Trial) Extend Program. Participants included first-time mothers aged 19-45 years from Victoria, Australia ( = 457). Diet over the past year was assessed via a validated, self-administered 137-item food frequency questionnaire. Adherence to the 2013 Australian Dietary Guidelines was assessed using a previously developed dietary guideline index (DGI) as a measure of diet quality. Depressive symptoms were determined using the Centre for Epidemiologic Studies Depression Scale (CES-D 10). Relationships between fruit and vegetable intake (serves per day), frequency of fish intake and diet quality and depressive symptoms were investigated using linear regression adjusted for covariates (age, socioeconomic position, smoking status, physical activity, television viewing time, sleep quality, and BMI).

Furthermore we report that a high number of

Furthermore, we report that a high number of patients (49/113) had at least a GS 7 that is considered as significant disease. In addition, it must be pointed out that 7/112 patients had a high-risk PCa according to the EAU and D׳Amico classifications, thereby associated per se with an increased risk of metastatic spread (
However, one can argue that the definition of “significant PCa” has been discussed in the recent pyrilamine maleate years as some authors define “significant PCa” only from GS 4+3 [16]. If this were the case, in our population only 16 or 12 patients would meet this grade or stage criteria. Moreover, this argument has to be considered when interpreting and comparing different studies on this topic.
In pyrilamine maleate to the work of Pepe et al. [10] where no patient had a biochemical recurrence, in our study 15 patients had a PSA recurrence and 6 patients even died of PCa. Winkler et al. [7] and Kurahashi et al. [13] found more than 20% of patients with biochemical recurrence. Another Asian study reported a biochemical recurrence rate of 8.8% and also Dell׳Atti [17] reported 9% biochemical recurrence rate in a small patient cohort [11].
As expected, most patients with biochemical recurrence had a high GS or adverse pathology (≥pT3a) or had both in the RC specimen. However, 2/15 patients in our patient collective had a GS 5 in the pathology specimens, indicating that also in low-risk PCa PSA measurement should be included in the oncological follow-up analyses. These data are contradictious to Bivalacqua et al. [18] who analyzed the effect of PSA measurement after RC in patients who had a benign prostate pathology. Thereby, the authors of this retrospective study concluded that PSA controls can be avoided in those cases in the routine follow-up. In contrast to these data, we strongly support the opinion that all men with PCa in the RC specimens should undergo regular PSA measurement in the postoperative follow-up visits.
In our patient collective, we found that 40% of patients with a biochemical recurrence after RC died of PCa 9 months to 4 years after surgery. This finding is in strong contrast to Pan et al. [11], Gakis et al. [5], or Winkler et al. [7] who analyzed the mortality rates in patients undergoing RC and found no single PCa-related cause of death.



Urothelial carcinoma of the bladder (UCB) is the most common urothelial malignancies which accounts for 3.3% of newly diagnosed cancer cases and 2.1% of cancer deaths in the world [1]. Overall, 70% of bladder tumors present as noninvasive urothelial carcinoma, and the remainder present as muscle-invasive disease [2]. So, an accurate biomarker or prognosis factor is essential for efficient management of UCB.
TMEM67, also named Meckelin, is a transmembrane protein encoded by TMEM67/MKS3, localizing to the primary cilium and the plasma membrane [3–5]. Genotype-phenotype analyses indicated that recessive mutations at the TMEM67 locus were associated with dysfunction of primary cilia [4–6]. Primary cilia are ubiquitously present in cell surface organelles with essential functions in cellular proliferation, differentiation and development. Emerging evidences suggest that in many cancers [7–10], primary cilia are markedly decreased or absent. To date, however, abnormalities in TMEM67 and their influence to UCB have not been declared.

Material and methods


UCB is genetically heterogeneous [11], with frequent alterations in genes regulating chromatin state, cell cycle, and receptor kinase signaling [12–15] and it is a cancer with high recurrent rate and the 5-year overall survival is still unsatisfactory for advanced disease in spite of the introduction of adjuvant chemotherapy [16]. Current patient prognosis is mainly based on TNM. The high recurrence rate requires close surveillance with cystoscopy that is an invasive test, so an accurate biomarker or prognosis factor is essential for efficient management of UCB.

Calculation of hillshade Hillshade is calculated

2.2.1. Calculation of hillshade
Hillshade is calculated as:
where ki is the hillshade within i km; ni is the grid number where the difference between sea level elevation and the height of the center point is greater than R within a square area subjected to the center point and i km of apothem; Ni is the total grid number in the square area of i km of apothem, excluding the center. In our study, i and R equal 2.5 km and 200 m, respectively. The raster data of hillshade were generated using the DEM.
2.2.2. Processing methods of meteorological data
The Mann–Kendall trend test (Modarres and Silva, 2007) was used to detect possible trends in air temperature and precipitation series at each station during the pyrilamine maleate 1961–2007. Using the Kriging interpolation method, the trend magnitudes were then spatially interpolated to attain a spatial resolution matching that of DEM data. Interpolation results for the glacier distribution region were obtained through GIS clipping. Similarly, the simulated data of air temperature and precipitation changes in the 2030s and 2050s were also obtained for the glacier distribution region.
2.2.3. Assignment of glacier type
Glaciers in China are classified into three types, maritime, sub-continental, and extreme continental (Shi and Liu, 2000). Maritime glaciers are mainly distributed in the Hengduan Mountains, in the southeastern part of the Tibetan Plateau (including the eastern Himalayas and the mid-east section of the Nyainqentanglha Mountains). Sub-continental glaciers are mainly located in the mountain ranges of Altai, Tianshan, mid-to-east of Qilian, eastern Kunlun and Tanggula, western Nyainqentanglha, the northern slope of the mid-to-western Himalayas, and Karakorum in China. Extreme continental glaciers are mainly located in the western part of the Tibetan Plateau (including the western Kunlun Mountains, the Qiangtang Plateau, eastern Pamir, western sections of Tanggula, Gangdise and Qilian Mountains). Previous research indicates that the three types of glaciers in China have different response patterns to global warming. For example, maritime glaciers are very sensitive to climate change, extreme continental glaciers are insensitive, and sub-continental glaciers have responses between those of maritime and extreme continental (Shi and Liu, 2000). Thus, glacier type is selected as an index of glacier vulnerability in the following text (in Section 2.3.2).
Glacier type, however, is not a quantitative value, and chemotrophs cannot be used directly in glacial vulnerability assessment. Based on the results reported in Shi and Liu (2000), a sociological method was employed in this study. This firstly involved designing a questionnaire, and then in 2010 conducting a questionnaire survey for completion by experts in the field of cryospheric science. A total of 61 questionnaires were sent to experts, and 48 responses were received—the ratio of callback of valid questionnaire is 98%. Using weighted average statistical calculations, results showed the value of extreme continental glaciers to be 2.6, sub-continental glaciers 3.5, and maritime glaciers 4.2, and therefore a value of 2.6 is assigned all extreme continental-type glaciers, 3.5 all subcontinental-type glaciers, and 4.2 all maritime-type glaciers.

Multiple linear regression analysis using all variables as

Multiple linear regression analysis using all variables as factors (Model 1) demonstrated that weight, BMI, and tidal volume were independently associated with the bilateral excursion of the diaphragms (all P < 0.05) after adjusting for other clinical variables, including age, gender, smoking history, height, VC, %VC, FEV1, FEV1%, and %FEV1. There were no significant associations between the excursion of the diaphragms and variables including age, gender, smoking history, height, VC, %VC, FEV1, FEV1%, and %FEV1 (Table 4). Additionally, a multiple linear regression model using age, gender, BMI, tidal volume, VC, FEV1, and smoking history as factors (Model 2) was also fit as a sensitivity analysis, taking into account the correlation among variables (eg, BMI, height, and weight; VC and %VC; FEV1, FEV1%, and %FEV1). Model 2 (Supplementary Data S1) gave results consistent with Model 1 (Table 4): higher BMI and higher tidal volume were independently associated with the increased bilateral excursion of the diaphragms (all P < 0.05). The adjusted R2 in Model 1 was numerically higher than that in Model 2 (right, 0.19 vs. 0.16, respectively; left, 0.16 vs. 0.13, respectively).


Our study determined the average excursion of the diaphragms during tidal breathing in a standing position in a health screening center cohort using dynamic chest radiography (“dynamic X-ray phrenicography”). These findings are important because they provide reference values of diaphragmatic motion during tidal breathing useful for the diagnosis of diseases related to respiratory kinetics. Our study also suggests that dynamic X-ray phrenicography is a useful method for the quantitative evaluation of diaphragmatic motion with a radiation dose comparable to conventional posteroanterior chest radiography (22).

Our study demonstrated that the average excursions of the bilateral pyrilamine maleate during tidal breathing (right: 11.0 mm, 95% CI 10.4 to 11.6 mm; left: 14.9 mm, 95% CI 14.2 to 15.5 mm) were numerically less than those during forced breathing in previous studies using other modalities 2; 7 ;  8. Using fluoroscopy, Alexander reported that the average right excursion was 27.5 mm and the average left excursion was 31.5 mm during forced breathing in the standing position in 127 patients (2). Using ultrasound, Harris et al. reported that the average right diaphragm excursion was 48 mm during forced breathing in the supine position in 53 healthy adults (7). Using MR fluoroscopy, Gierada et al. reported that the average right excursion was 44 mm and the average left excursion was 42 mm during forced breathing in the supine position in 10 healthy volunteers (8). The difference in diaphragmatic excursion during tidal breathing versus forced breathing is unsurprising.

Our pyrilamine maleate study showed that the excursion and peak motion speed of the left diaphragm are significantly greater and faster than those of the right. With regard to the excursion, the results of our study are consistent with those of previous reports using fluoroscopy in a standing position 2 ;  3. However, in the previous studies evaluating diaphragmatic motion in the supine position, the asymmetric diaphragmatic motion was not mentioned 7 ;  8. The asymmetric excursion of the bilateral diaphragm may be more apparent in the standing position, but may not be detectable or may disappear in the supine position. Although we cannot explain the reason for the asymmetry in diaphragmatic motion, we speculate that the presence of the liver may limit the excursion of the right diaphragm. Regarding the motion speed, to the best of our knowledge this study is the first to evaluate it. The faster motion speed of the left diaphragm compared to that of the right diaphragm would be related to the greater excursion of the left diaphragm.