The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of 1.82±0.75 bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves.Quantification with satisfactory specificity and sensitivity of free 3-Nitro-l-tyrosine (3-NT), 3-Chloro-l-tyrosine (3-CT), and 3-Bromo-l-tyrosine (3-BT) in biological samples as potential inflammation, oxidative stress, and cancer biomarkers is analytically challenging. We aimed at developing a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based method for their simultaneous analysis without an extract purification step by solid-phase extraction. Validation of the developed method yielded the following limits of detection (LOD) and quantification (LOQ) for 3-NT, 3-BT, and 3-CT 0.030, 0.026, 0.030 ng/mL (LODs) and 0.100, 0.096, 0.098 ng/mL (LOQs). Coefficients of variation for all metabolites and tested concentrations were less then 10% and accuracy was within 95-105%. Method applicability was tested on colorectal cancer patients during the perioperative period. All metabolites were significantly higher in cancer patients than healthy controls. The 3-NT was significantly lower in advanced cancer and 3-BT showed a similar tendency. Dynamics of 3-BT in the early postoperative period were affected by type of surgery and presence of surgical site infections. In conclusion, a sensitive and specific LC-MS/MS method for simultaneous quantification of free 3-NT, 3-BT, and 3-CT in human plasma has been developed.
  As adolescent suicide rates continue to rise, innovation in risk identification is warranted. Machine learning can identify suicidal individuals based on their language samples. This feasibility pilot was conducted to explore this technology's use in adolescent therapy sessions and assess machine learning model performance.
 
  Natural language processing machine learning models to identify level of suicide risk using a smartphone app were tested in outpatient therapy sessions. Data collection included language samples, depression and suicidality standardized scale scores, and therapist impression of the client's mental state. Previously developed models were used to predict suicidal risk.
 
  267 interviews were collected from 60 students in eight schools by ten therapists, with 29 students indicating suicide or self-harm risk. During external validation, models were trained on suicidal speech samples collected from two separate studies. We found that support vector machines (AUC 0.75; 95% CI 0.69-0.81) and logistic regression (AUC 0.76; 95% CI 0.70-0.82) lead to good discriminative ability, with an extreme gradient boosting model performing the best (AUC 0.78; 95% CI 0.72-0.84).
 
  Voice collection technology and associated procedures can be integrated into mental health therapists' workflow. Collected language samples could be classified with good discrimination using machine learning methods.
 Voice collection technology and associated procedures can be integrated into mental health therapists' workflow. Collected language samples could be classified with good discrimination using machine learning methods.The study explores the effect of elevated temperatures on the bond strength between prestressing reinforcement and ultra-high performance concrete (UHPC). Laboratory investigations reveal that the changes in bond strength correspond well with the changes in compressive strength of UHPC and their correlation can be mathematically described. Exposition of specimens to temperatures up to 200 °C does not reduce bond strength as a negative effect of increasing temperature is outweighed by the positive effect of thermal increase on the reactivity of silica fume in UHPC mixture. Above 200 °C, bond strength significantly reduces; for instance, a decrease by about 70% is observed at 800 °C. The decreases in compressive and bond strengths for temperatures above 400 °C are related to the changes of phase composition of UHPC matrix (as revealed by X-ray powder diffraction) and the changes in microstructure including the increase of porosity (verified by mercury intrusion porosimetry and observation of confocal microscopy) and development cracks detected by scanning electron microscopy. Future research should investigate the effect of relaxation of prestressing reinforcement with increasing temperature on bond strength reduction by numerical modelling.Herein, a passive low-profile moisture sensor design based on radio frequency identification (RFID) technology is proposed. The sensor consists of an LC resonant loop, and the sensing mechanism is based on the fringing electric field generated by the capacitor in the circuit. A standard planar inductor and a two-layer interdigital capacitor (IDC) with a significantly higher fringing capacitance compared to that of a conventional parallel plate capacitor (PPC) are used, resulting in improved frequency offset and sensitivity of the sensor. Furthermore, a sensor tag was designed to operate at an 8.2 MHz electronic article surveillance (EAS) frequency range and the corresponding simulation results were experimentally verified. The IDC- and PPC-based capacitor designs were comprehensively compared. The proposed IDC sensor exhibits enhanced sensitivity of 10% in terms of frequency offset that is maintained over time, increased detection distance of 5%, and more than 20% increase in the quality factor compared to sensors based on PPC. The sensor's performance as a urine detector was experimentally qualified.  https://www.selleckchem.com/products/npd4928.html  Additionally, it was shown experimentally that the proposed sensor shows a faster response to moisture. Both simulation and experimental data are presented and elucidated herein.This paper describes the design and construction of a remotely controlled mobile interference device designed primarily for interference (jamming) and immunity testing of wireless sensors operating in the 2.4 GHz band (Wi-Fi). The main idea was to build a remotely controlled test device to test the immunity of wireless sensors operating in the 2.4 GHz band directly in field conditions. The remotely controlled mobile interference device is equipped with a special interference apparatus, using a special magnetron tube as a source of interference. Magnetron was selected due to its high performance, allowing interference with wireless sensors over long distances. As magnetron is powered by high voltage (3 kVDC) and is being used in a remotely controlled device, it was important to solve the issue of its power supply using an accumulator. The remotely controlled device was further equipped with the option of detecting and analysing signals in the frequency band of 1 GHz to 18 GHz, adding an extra operational mode that can be used in civil (commercial), industrial, and military applications.