Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.

Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring.

The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.

Hydrogen Production from Barley Straw and Miscanthus by the Hyperthermophilic Bacterium, Cadicellulosirupter bescii.

This work aimed to evaluate the feasibility of biohydrogen production from Barley Straw and Miscanthus. The primary obstacle in plant biomass decomposition is the recalcitrance of the biomass itself. Plant cell walls consist of cellulose, hemicellulose, and lignin, which make the plant robust to decomposition. However, the hyperthermophilic bacterium, Caldicellulosiruptor bescii, can efficiently utilize lignocellulosic feedstocks (Barley Straw and Miscanthus) for energy production, and C. bescii can now be metabolically engineered or isolated to produce more hydrogen and other biochemicals. In the present study, two strains, C. bescii JWCB001 (wild-type) and JWCB018 (ΔpyrFA Δldh ΔcbeI), were tested for their ability to increase hydrogen production from Barley Straw and Miscanthus. The JWCB018 resulted in a redirection of carbon and electron (carried by NADH) flow from lactate production to acetate and hydrogen production. JWCB018 produced ~54% and 63% more acetate and hydrogen from Barley Straw, respectively than its wild-type counterpart, JWCB001. Also, 25% more hydrogen from Miscanthus was obtained by the JWCB018 strain with 33% more acetate relative to JWCB001. It was supported that the engineered C. bescii, such as the JWCB018, can be a parental strain to get more hydrogen and other biochemicals from various biomass.

Structural features of a minimal intact methyltransferase of a type I restriction-modification system.

Type I restriction-modification enzymes are oligomeric proteins composed of methylation (M), DNA sequence-recognition (S), and restriction (R) subunits. The different bipartite DNA sequences of 2-4 consecutive bases are recognized by two discerned target recognition domains (TRDs) located at the two-helix bundle of the two conserved regions (CRs). Two M-subunits and a single S-subunit form an oligomeric protein that functions as a methyltransferase (M2S1 MTase). Here, we present the crystal structure of the intact MTase from Vibrio vulnificus YJ016 in complex with the DNA-mimicking Ocr protein and the S-adenosyl-L-homocysteine (SAH). This MTase includes the M-domain with a helix tail (M-tail helix) and the S1/2-domain of a TRD and a CR α-helix. The Ocr binds to the cleft of the TRD surface and SAH is located in the pocket within the M-domain. The solution- and negative-staining electron microscopy-based reconstructed (M1S1/2)2 structure reveals a symmetric (S1/2)2 assembly using two CR-helices and two M-tail helices as a pivot, which is plausible for recognizing two DNA regions of same sequence. The conformational flexibility of the minimal M1S1/2 MTase dimer indicates a particular state resembling the structure of M2S1 MTases.

Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning.

Although plastic scintillation detectors possess poor spectroscopic characteristics, they are extensively used in various fields for radiation measurement. Several methods have been proposed to facilitate their application of plastic scintillation detectors for spectroscopic measurement. However, most of these detectors can only be used for identifying radioisotopes. In this study, we present a multitask model for pseudo-gamma spectroscopy based on a plastic scintillation detector. A deep- learning model is implemented using multitask learning and trained through supervised learning. Eight gamma-ray sources are used for dataset generation. Spectra are simulated using a Monte Carlo N-Particle code (MCNP 6.2) and measured using a polyvinyl toluene detector for dataset generation based on gamma-ray source information. The spectra of single and multiple gamma-ray sources are generated using the random sampling technique and employed as the training dataset for the proposed model. The hyperparameters of the model are tuned using the Bayesian optimization method with the generated dataset. To improve the performance of the deep learning model, a deep learning module with weighted multi-head self-attention is proposed and used in the pseudo-gamma spectroscopy model. The performance of this model is verified using the measured plastic gamma spectra. Furthermore, a performance indicator, namely the minimum required count for single isotopes, is defined using the mean absolute percentage error with a criterion of 1% as the metric to verify the pseudo-gamma spectroscopy performance. The obtained results confirm that the proposed model successfully unfolds the full-energy peaks and predicts the relative radioactivity, even in spectra with statistical uncertainties.

Radiological analysis for radioactivity depth distribution in activated concrete using gamma-ray spectrometry.

In this research, in-situ measurement methods to analyze the radioactivity depth distribution were developed by measuring 152Eu emitted from activated concrete used the Peak to Compton (PTC) method using a high-purity germanium (HPGe) detector. The experimental results of various radioactivity depth distributions agree within 3% relative error with the results of an MCNP simulation. The correlation between the values obtained with the PTC method and the radioactivity depth distribution was derived. In order to use the PTC method in the field, the impact of the material composition, surface area, and density change of the measurement target was evaluated by an MCNP simulation. The developed method was applied to activated concrete in cyclotron facilities and the results were compared with the sample analysis, revealing a relative error of less than 20%. The results of this research will be useful in quickly and accurately evaluating the radioactivity depth distribution of activated concrete. Further study should be carried out in order to enable analysis of various forms of radioactivity depth distributions of activated structures in nuclear facilities.

Empirical Determination of Efficient Sensing Frequencies for Magnetometer-Based Continuous Human Contact Monitoring.

The high linear correlation between the smartphone magnetometer readings in close proximity can be exploited for physical human contact detection, which could be useful for such applications as infectious disease contact tracing or social behavior monitoring. Alternative approaches using other capabilities in smartphones have aspects that do not fit well with the human contact detection. Using Wi-Fi or cellular fingerprints have larger localization errors than close human contact distances. Bluetooth beacons could reveal the identity of the transmitter, threatening the privacy of the user. Also, using sensors such as GPS does not work for indoor contacts. However, the magnetometer correlation check works best in human contact distances that matter in infectious disease transmissions or social interactions. The omni-present geomagnetism makes it work both indoors and outdoors, and the measured magnetometer values do not easily reveal the identity and the location of the smartphone. One issue with the magnetometer-based contact detection, however, is the energy consumption. Since the contacts can take place anytime, the magnetometer sensing and recording should be running continuously. Therefore, how we address the energy requirement for the extended and continuous operation can decide the viability of the whole idea. However, then, we note that almost all existing magnetometer-based applications such as indoor location and navigation have used high sensing frequencies, ranging from 10 Hz to 200 Hz. At these frequencies, we measure that the time to complete battery drain in a typical smartphone is shortened by three to twelve hours. The heavy toll raises the question as to whether the magnetometer-based contact detection can avoid such high sensing rates while not losing the contact detection accuracy. In order to answer the question, we conduct a measurement-based study using independently produced magnetometer traces from three different countries. Specifically, we gradually remove high frequency components in the traces, while observing the correlation changes. As a result, we find that the human coexistence detection indeed tends to be no less, if not more, effective at the sampling frequency of 1 Hz or even less. This is because unlike the other applications that require centimeter-level precision, the human contacts detected anywhere within a couple of meters are valid for our purpose. With the typical smartphone battery capacity and at the 1 Hz sensing, the battery consumption is well below an hour, which is smaller by more than two hours compared with 10 Hz sampling and by almost eleven hours compared with 200 Hz sampling. With other tasks running simultaneously on smartphones, the energy saving aspect will only become more critical. Therefore, we conclude that sensing the ambient magnetic field at 1 Hz is sufficient for the human contact monitoring purpose. We expect that this finding will have a significant practicability implication in the smartphone magnetometer-based contact monitoring applications in general.

Feeling depleted and powerless: the construal-level mechanism.

Individuals exercise self-control daily to achieve desired goals; at the same time, people engage in social interaction daily and influence (feel powerful) or are influenced (feel powerless) by others. Does controlling the self have an unforeseen consequence for people's perception of their capacity to control others? Five studies-one correlational and four experimental-demonstrate that ego depletion from prior self-control determines one's personal sense of power; low-level, concrete mental construals account for this relationship. Our results showed that people with higher trait self-control reported a greater sense of power (Study 1). People who had depleted their self-control-related regulatory resources (vs. those who had not) experienced a lower sense of power (Study 2). The relationship between ego depletion and low sense of power was mediated by construal level (Study 3) and observed only when low-level, concrete construals were present, but not under high-level, abstract construals (Studies 4 and 5).