The combined (non)impact of self-declared sustainability claims and business performance ratings on customer intentions

Purpose: To investigate constancy, within a treatment session, of the time lag relationship between implanted markers in abdominal tumors and an external motion surrogate. Methods: Six gastroesophageal junction and three pancreatic cancer patients (IRB‐approved protocol) received a respiration‐correlated CT (RCCT), and two cone‐beam CTs (CBCT), one before and one after the treatment. Time between initial and final scan varied from about 1 to 3 h. Each patient had at least one implanted fiducial marker near the tumor. In all scans, abdominal displacement (Varian RPM) was recorded as the external motion signal. In‐house software tracked fiducials, representing internal signal, in CBCT projection images and amplitude‐sorted RCCT. For each set of projection images, time lag between internal and external signals was found by maximizing the correlation coefficient in each breathing cycle and averaging over all cycles. For the RCCT, these quantities were calculated for the 1 –2 cycles during which the fiducial was imaged. Results: Mean ± standard deviation in time lag, over all scans and patients, was 0.10 ± 0.07 s (range 0.01 – 0.36 s). External signal lagged the internal in 20/27 scans. Change in time lag between pre‐and post‐RT CBCT was 0.06 ± 0.07s (range 0.01 – 0.22 s), corresponding to 3.1 ± 3.7% (range 0.6 – 10.8%) of the gate width (range 1.6 – 3.1 s). Including the RCCT scan, change in time lag increased to 6.5 ± 5.4% (range 0.2 – 16.1%) of the gate width, and in three patients, change in time lag exceeded 10% of the gate width. Conclusion: Time lag between internal and external signal is small compared to the treatment gate width in all patients. Change in time lag within a treatment session, inferred from pre to post‐RT measurements is also small, suggesting that a single measurement at the session start is adequate.NIH/NCI award R01 CA126993 and a research grant from Varian.

Self-absorption describes a pathological tendency towards the internal mental world (internalization) that often conflicts with the accurate monitoring of the external world. In performance monitoring, an augmented electrophysiological response evoked by internal signals in patients with anxiety or depressive disorder seems to reflect this tendency. Specifically, the error-related negativity (Ne/ERN), an index of error processing based on internal signals, is larger in patients compared to controls. In the present experiment, we investigated whether the preferential processing of internal signals in patients is linked to diminished and inflexible external signal processing. To this end, the electrophysiological response evoked by external signals was analysed in patients with panic disorder and healthy controls. Participants performed a choice-response task, where informative or uninformative feedback followed each response, and a passive viewing task. As a replication of previous studies, patients presented an augmented Ne/ERN, indexing enhanced processing of internal signals related to errors. Furthermore, the vertex positive potential (VPP) evoked by visual stimuli was larger in patients than in controls, suggesting enhanced attention to external signals. Moreover, patients and controls showed similar sensitivity to the feedback information content, indicating a normal flexibility in the allocation of monitoring resources to external signals depending on how informative these signals are for performance monitoring. These results suggest that the tendency towards internal signals in patients with panic disorder does not hinder the flexible processing of external signals. On the contrary, external signals seem to attract enhanced processing in patients compared to controls.

Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data-driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are based on external sensors, such as accelerometers and microphones, which require installation of extra devices. Some recent studies have proved the feasibility of online chatter detection using internal signals such as drive motor current. This study aims to investigate the effectiveness of different internal signals extracted from CNC system for chatter detection and compare them with external acceleration signals. The external and internal signals are first compared with time–frequency analysis using Discrete Fourier Transform and Ensemble Empirical Mode Decomposition approaches. Two chatter detection methods are then presented based on manually and automatically extracted features respectively. The first method uses two nonlinear dimensionless indicators, C0 complexity and Power Spectral Entropy, of filtered signals. The second approach uses autoencoder for automatic feature extraction and Support Vector Machine as classifier for chatter identification. A series of milling experiments are conducted and chatters are intentionally created by changing the milling process parameters. Multiple internal signals are collected using software provided by the machine manufacturer. Results show that several internal CNC signals, such as the nominal current signal and the actual torque signal, can achieve comparable performance to external signals for chatter detection.

The present study applies classical techniques of time series analysis to the separation of the external and internal signals from the geomagnetic field. We first differentiate the data with respect to time in order to get rid of the large secular variation over the long time span considered (up to 100 years). Next we filter the resulting data to eliminate short‐period components of the magnetic signals. We then proceed to identify the external signal by comparing the long‐term variations of the processed components of the geomagnetic field, as measured at different European observatories, with the long‐term variations of geomagnetic indices devised to characterize the fields from external sources. Finally, we remove the identified external signal from the geomagnetic field series and get a satisfactory estimate of the internal signal. This procedure is facilitated by the different behaviors of the two signals over the considered time span. The external signal is inherently of recurrent nature linked to solar‐terrestrial interaction; it oscillates around zero with a maximum amplitude of about 5 nT/yr. The internal signal, on the other hand, displays the characteristics of a secular trend, combining sustained monotonous behavior over periods of several decades with sudden slope variations and reversals; the total range of this internal secular signal is of the order of 50 nT/yr, far larger than any external contribution. Using the 1883–1983 magnetic series at Chambon‐la‐Forêt, the 1890–1983 series at Niemegk, and shorter series at the United Kingdom observatories of Eskdalemuir and Hartland, we have been able to get a coherent overall picture of the secular variation as measured in Europe. For instance, the first‐order time derivative of the Y (east) component essentially displays an increase in two steps from 1900 to 1925, a monotonous decrease from 1925 to 1969 with a regular steplike substructure, and a rapid increase since 1969, followed by a marked reversal of slope in 1979. These results emphasize the internal origin of the 1969 jerk and single out, in Europe, a 1979 event of opposite sign, these two features being quite reminiscent of the behavior of the secular variation during the first quarter of the century.

The purpose of this study is to evaluate the correlations between external markers and internal targets for radiation therapy of lung cancer patients. Using an infrared camera system coupled with a clinical simulator, the simultaneous motions of multiple external markers and an internal target were obtained. The correlation between external and internal signals was analyzed using a cross-covariance function. A linear regression model was employed to generate a composite signal from multiple external markers in order to predict the internal target motion. The external and internal signals, and their correlations, demonstrated a wide range of variation with respect to marker location, motion dimension, and breathing pattern. The performance of the composite signal indicates that when more external signals were taken into account, the mean correlation between the composite signal and internal signal was improved. This implies that a combination of multiple external signals might be an improved way to predict internal target motion. Also, since the characteristics of respiratory signals can vary significantly, certain methods of preprocessing and external signal combination are necessary.

Pregnane X receptor (PXR), an orphan member of the nuclear receptor superfamily, is a major xeno-sensing transcription factor. In response to xenobiotic exposure, PXR regulates genes involved in the metabolism and transport of xenobiotics to protect the body from their harmful effects. Recent progress has revealed that PXR responds not only to such external signals but also to internal signals to help the body adapt to changes in the internal environment, including dysregulation of the immune system. PXR responds to external and internal signals by up- or down-regulating certain metabolic pathways and cellular signals through gene regulation. PXR is a potential therapeutic target for inflammatory as well as metabolic diseases, although its activation may also have unfavorable effects on human health. This review will discuss the recent progress in the understanding of the physiological and pathophysiological roles of PXR and their implications in human diseases and drug therapy by elucidating the molecular mechanisms underlying PXR-mediated gene regulation.

Purpose: We investigated using kV fluoroscopy for adapting the respiratory gating window prior to each radiation treatment fraction. The gate adaptation was based on marker‐less tracking of the target and correlating the internal motion trace with the external breathing signal. Method and Materials: A phantom study was performed with the on‐board kV imager of the Novalis Tx machine. A dynamic chest phantom with a water‐filled sphere placed inside a movable lung density insert was utilized. Breathing traces from patients that underwent lung SBRT were used to drive the target and external surrogate motion. The Varian RPM gating system was employed to obtain an external breathing signal, while acquiring kV fluoroscopy images. The moving target was tracked and an internal signal was generated. One kV acquisition was used for adapting the gating window based on the correlation of the internal and external signal and another one for verifying the gated position of the target. Gating at end‐exhale is the most commonly used technique in the clinic, while gating at mid‐ventilation makes it more likely that the tolerance window for residual motion includes the tumor during the entire breathing cycle. The two gating windows were investigated and the results were compared. Results: No significant difference in the gated target position errors was observed between the two gating windows. The target was in the expected position about 90% of the time for each one. The maximum deviation outside the tolerance window for residual motion was 1.9mm for the end‐exhale and 2.2mm for the mid‐motion gating windows. Conclusion: The adapt and verify procedure examined in this study, if applied in real‐time, has the potential to improve the accuracy of lung cancer radiotherapy by facilitating the reduction of safety margins used for moving tumors.Conflict of Interest: Varian Medical Systems, Inc.

The role of external and internal signals in E-commerce

To investigate constancy, within a treatment session, of the time lag relationship between implanted markers in abdominal tumors and an external motion surrogate. Six gastroesophageal junction and three pancreatic cancer patients (IRB-approved protocol) received two cone-beam CTs (CBCT), one before and one after treatment. Time between scans was less than 30 min. Each patient had at least one implanted fiducial marker near the tumor. In all scans, abdominal displacement (Varian RPM) was recorded as the external motion signal. Purpose-built software tracked fiducials, representing internal signal, in CBCT projection images. Time lag between superior-inferior (SI) internal and anterior-posterior external signals was found by maximizing the correlation coefficient in each breathing cycle and averaging over all cycles. Time-lag-induced discrepancy between internal SI position and that predicted from the external signal (external prediction error) was also calculated. Mean ± standard deviation time lag, over all scans and patients, was 0.10 ± 0.07 s (range 0.01-0.36 s). External signal lagged the internal in 17/18 scans. Change in time lag between pre- and post-treatment CBCT was 0.06 ± 0.07 s (range 0.01-0.22 s), corresponding to 3.1% ± 3.7% (range 0.6%-10.8%) of gate width (range 1.6-3.1 s). In only one patient, change in time lag exceeded 10% of the gate width. External prediction error over all scans of all patients varied from 0.1 ± 0.1 to 1.6 ± 0.4 mm. Time lag between internal motion along SI and external signals is small compared to the treatment gate width of abdominal patients examined in this study. Change in time lag within a treatment session, inferred from pre- to post-treatment measurements is also small, suggesting that a single measurement of time lag at the session start is adequate. These findings require confirmation in a larger number of patients.

The influence of circadian rhythms on carbohydrate metabolism in health and in diabetes mellitus

Multitype chatter detection via multichannelinternal and external signals in robotic milling

Purpose:To develop a novel algorithm to generate internal respiratory signals for sorting of four‐dimensional (4D) computed tomography (CT) images.Methods:The proposed algorithm extracted multiple time resolved features as potential respiratory signals. These features were taken from the 4D CT images and its Fourier transformed space. Several low‐frequency locations in the Fourier space and selected anatomical features from the images were used as potential respiratory signals. A clustering algorithm was then used to search for the group of appropriate potential respiratory signals. The chosen signals were then normalized and averaged to form the final internal respiratory signal. Performance of the algorithm was tested in 50 4D CT data sets and results were compared with external signals from the real‐time position management (RPM) system.Results:In almost all cases, the proposed algorithm generated internal respiratory signals that visibly matched the external respiratory signals from the RPM system. On average, the end inspiration times calculated by the proposed algorithm were within 0.1 s of those given by the RPM system. Less than 3% of the calculated end inspiration times were more than one time frame away from those given by the RPM system. In 3 out of the 50 cases, the proposed algorithm generated internal respiratory signals that were significantly smoother than the RPM signals. In these cases, images sorted using the internal respiratory signals showed fewer artifacts in locations corresponding to the discrepancy in the internal and external respiratory signals.Conclusion:We developed a robust algorithm that generates internal respiratory signals from 4D CT images. In some cases, it even showed the potential to outperform the RPM system. The proposed algorithm is completely automatic and generally takes less than 2 min to process. It can be easily implemented into the clinic and can potentially replace the use of external surrogates.

Purpose: To estimate the delay time and relationship between external signals and internal organ motion for respiratory gated radiotherapyMethods and Materials: In 5 patients, we measured the external respiratory sensor signals, which included respiratory volume, respiratory temperature, and abdominal displacement with three sensors (spirometry, belt‐transducer, and thermistor), and internal organ motion with the fluoroscopy. To evaluate the relationship of the internal organ and external sensor signals, a linear least‐square fit was performed with two signals, and the correlation coefficient (R values) was determined. In order to test the presence of a time‐varying phase relationship, a unique cross‐correlation of the respiratory motion signals and internal organ motion data were performed. Cross‐correlation function (CCF) analysis allows for the identification and estimation of a phase or time delay in two related signals. Results: The correlation coefficient of respiratory signal showed that the internal organ motion to abdominal displacement by piezo respiratory belt‐transducer exhibited high correlation of 0.94 (range 0.98–0.85) with a standard deviation of about 0.06, whereas the respiratory volume and temperature to organ motion was a poor correlation (average 0.70, 0.71). The result of respiratory volume and temperature shows the influence of the phase shift, regarding time delay of 0.2 – 0.4 s. Two sensor signals considered the time delay correction generally correlated well with internal organ motion. Conclusion: This correlation in this study can be used to predict internal organ motion, based on the external sensor signals. If the time delay of external sensor signals was corrected carefully, the use of the respiratory sensor would improve the accuracy for respiratory gated radiotherapy. Thus, it is expected that the respiratory sensors will come into wider use.

Electroenterogram is the myoelectric signal of the smooth muscle of the small intestine. This biosignal traduces; bowel motility. However, an invasive method is necessary for placing electrodes on small bowel serosa. The aim of this paper is to relate surface recording spectral parameters of electroenterogram to intestinal motility Indexes from internal electoenterogram recordings. Bipolar electrodes where placed at different points along the small intestine serosa of two Beagle dogs in order to acquire internal myoelectric signals. Likewise two monopolar contact electrodes were situated on abdominal surface for external recording. Internal and surface signals were amplified and acquired simultaneously in fast state. Internal signals were parameterised in order to obtain intestinal motility indexes. In the same way surface recording was quantified to calculate several spectral parameters. Correlation coefficient functions are calculated and considered as results. Every spectral surface parameter reaches a high correlation with intestinal motility index of each internal recording point. Energy above 2 Hz from the external signal provides highest correlation (around 0.7). Small bowel contractile activity progression referred from abdominal surface recording point is detected. Best correlation is achieved when adjusting time lags. The energy over 2 Hz of surface recorded signal can be used to represent intestinal motility.

The Peripersonal Space in a social world