Browse Articles
Discover research articles across all indexed journals
Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment
Abstract Posttraumatic stress disorder (PTSD) is a complex mental health condition triggered by exposure to traumatic events that leads to physical health problems and socioeconomic impairments. Although the complex symptomatology of PTSD makes diagnosis difficult, early identification and intervention are crucial to mitigate the long-term effects of PTSD and provide appropriate treatment. In this study, we explored the potential for physiological habituation to stressful events to predict PTSD status. We used passive physiological data collected from 21 active-duty United States military personnel and veterans in an immersive virtual environment with high-stress combat-related conditions involving trigger events such as explosions or flashbangs. In our work, we proposed a quantitative measure of habituation to stressful events that can be quantitatively estimated through physiological data such as heart rate, galvanic skin response and eye blinking. Using a Gaussian process classifier, we prove that habituation to stressful events is a predictor of PTSD status, measured via the PTSD Checklist Military version (PCL-M). Our algorithm achieved an accuracy of 80.95% across our cohort. These findings suggest that passively collected physiological data may provide a noninvasive and objective method to identify individuals with PTSD. These physiological markers could improve both the detection and treatment of PTSD.
Structure-guided optimization of small molecules targeting Yck2 as a strategy to combat Candida albicans
Principle of Tibetan medicine fluoritum processing by vinegar based on the change of microstructure and chemical composition
Electronic structure of mononuclear and radical-bridged dinuclear cobalt(II) single-molecule magnets
Abstract Metal-organic compounds that feature magnetic bistability have been proposed as bits for magnetic storage, but progress has been slow. Four-coordinate cobalt(II) complexes feature high inversion barriers of the magnetic moment, but they lack magnetic bistability. Developing radical-bridged polynuclear systems is a promising strategy to encounter this; however detailed investigations of such species are scarce. We report an air-stable radical-bridged dinuclear cobalt(II) complex, studied by a combination of magnetometry and spectroscopy. Fits of the data give D = −113 cm −1 for the zero-field splitting (ZFS) and J = 390 cm −1 for the metal–radical exchange. Ab initio investigations reveal first-order spin–orbit coupling of the quasi-degenerate $${{{{\rm{d}}}}}_{{x}^{2}-{y}^{2}}$$ d x 2 − y 2 and d x y orbitals to be at the heart of the large ZFS. The corresponding transitions are spectroscopically observed, as are transitions related to the exchange coupling. Finally, signatures of spin-phonon coupling are observed and theoretically analyzed. Furthermore, we demonstrate that the spectral features are not predominantly spin excitations, but largely vibrational in character.
Thermal shock resistance of additive manufactured Inconel 718 by concentrated solar energy
Abstract Concentrated Solar Power (CSP) is a powerful tool for simulating the extreme high-temperature conditions that metallic materials encounter. Using a vertical parabolic solar furnace, it was possible to perform heating and cooling cycles between 250 and 950 °C in approximately 250 s per cycle. This capability is particularly relevant for the development of solar receivers used in solar thermal plants. Additive Manufacturing (AM) offers the potential to create new compositions and geometries that can enhance the efficiency of these solar receivers. In this study, Ni-base superalloys—identified as suitable materials for high-temperature solar receivers—were produced using AM and tested in two conditions: as-built and after thermal treatment. These were compared with a forged reference alloy. The results revealed the formation of a protective oxide layer on the surface in all cases. However, the oxide layer on the samples fabricated by additive manufacturing appeared to be more compact and adherent compared to that formed on the reference alloy.
Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis
A visual SLAM loop closure detection method based on lightweight siamese capsule network
Processing of pain and itch information by modality-specific neurons within the anterior cingulate cortex in mice
Energy efficiency of 100 fJ/bit for bit rates beyond 50 Gb/s for 940 nm single mode VCSELs
We demonstrate major advancements in the energy efficiency of single mode, oxide-confined, vertical-cavity surface-emitting lasers at 940 nm wavelength, being promising for optical interconnects. We were able to show error-free operation (Bit Error Rate of 10exp -12) at 50 Gb/s and at 60 Gb/s, for Non-Return-to-Zero modulation across 100 m of multimode optical fiber with an energy efficiency of 98/168 fJ/bit, respectively. These are presently the most energy-efficient directly modulated light sources available for data transmission.
Author Correction: An abundant ginger compound furanodienone alleviates gut inflammation via the xenobiotic nuclear receptor PXR in mice
A full-stack memristor-based computation-in-memory system with software-hardware co-development
Systems serology analysis shows IgG1 and IgG3 memory responses six years after one dose of quadrivalent HPV vaccine
Global spatiotemporal optimization of photovoltaic and wind power to achieve the Paris Agreement targets
Author Correction: Multi-ancestry meta-analysis of genome-wide association studies discovers 67 new loci associated with chronic back pain
Interplay between CTCF-binding and CTCF-lacking regulatory elements in generating an architectural stripe at the Igh locus
Proximity proteomics reveals a mechanism of fatty acid transfer at lipid droplet-mitochondria- endoplasmic reticulum contact sites
Asymmetrical estimator for training encapsulated deep photonic neural networks
Abstract Photonic neural networks (PNNs) are fast in-propagation and high bandwidth paradigms that aim to popularize reproducible NN acceleration with higher efficiency and lower cost. However, the training of PNN is known to be challenging, where the device-to-device and system-to-system variations create imperfect knowledge of the PNN. Despite backpropagation (BP)-based training algorithms being the industry standard for their robustness, generality, and fast gradient convergence for digital training, existing PNN-BP methods rely heavily on accurate intermediate state extraction or extensive computational resources for deep PNNs (DPNNs). The truncated photonic signal propagation and the computation overhead bottleneck DPNN’s operation efficiency and increase system construction cost. Here, we introduce the asymmetrical training (AsyT) method, tailored for encapsulated DPNNs, where the signal is preserved in the analogue photonic domain for the entire structure. AsyT offers a lightweight solution for DPNNs with minimum readouts, fast and energy-efficient operation, and minimum system footprint. AsyT’s ease of operation, error tolerance, and generality aim to promote PNN acceleration in a widened operational scenario despite the fabrication variations and imperfect controls. We demonstrated AsyT for encapsulated DPNN with integrated photonic chips, repeatably enhancing the performance from in-silico BP for different network structures and datasets.
Interplay between multipolar order and multipole-induced superconductivity in PrTi2Al20
Cellular interactions within the immune microenvironment underpins resistance to cell cycle inhibition in breast cancers
Abstract Immune evasion by cancer cells involves reshaping the tumor microenvironment (TME) via communication with non-malignant cells. However, resistance-promoting interactions during treatment remain lesser known. Here we examine the composition, communication, and phenotypes of tumor-associated cells in serial biopsies from stage II and III high-risk estrogen receptor positive (ER+ ) breast cancers of patients receiving endocrine therapy (letrozole) as single agent or in combination with ribociclib, a CDK4/6-targeting cell cycle inhibitor. Single-cell RNA sequencing analyses on longitudinally collected samples show that in tumors overcoming the growth suppressive effects of ribociclib, first cancer cells upregulate cytokines and growth factors that stimulate immune-suppressive myeloid differentiation, resulting in reduced myeloid cell- CD8 + T-cell crosstalk via IL-15/18 signaling. Subsequently, tumors growing during treatment show diminished T-cell activation and recruitment. In vitro, ribociclib does not only inhibit cancer cell growth but also T cell proliferation and activation upon co-culturing. Exogenous IL-15 improves CDK4/6 inhibitor efficacy by augmenting T-cell proliferation and cancer cell killing by T cells. In summary, response to ribociclib in stage II and III high-risk ER + breast cancer depends on the composition, activation phenotypes and communication network of immune cells.