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Item Thickness-Dependent Terahertz Permittivity of Epitaxially Grown PbTe Thin Films(MDPI, 2023) Kawahala, Nicolas M.; Matos, Daniel A.; Rappl, Paulo H. O.; Abramof, Eduardo; Baydin, Andrey; Kono, Junichiro; Hernandez, Felix G. G.; Smalley-Curl InstituteThe exceptional thermoelectric properties of PbTe are believed to be associated with the incipient ferroelectricity of this material, which is caused by strong electron–phonon coupling that connects phononic and electronic dynamics. Here, we have used terahertz time-domain spectroscopy measurements to generate complex permittivity spectra for a set of epitaxially grown PbTe thin films with thicknesses between 100 nm and 500 nm at temperatures from 10 K to 300 K. Using a Drude–Lorentz model, we retrieved the physical parameters of both the phononic and electronic contributions to the THz permittivity. We observed a strong decrease, or softening, of the transverse optical phonon mode frequency with decreasing temperature, determining a thickness-independent negative ferroelectric-transition critical temperature, while we found a thickness-dependent anharmonic phonon decay lifetime. The electronic contribution to the permittivity was larger in thinner films, and both the carrier density and mobility increased with decreasing temperature in all films. Finally, we detected a thickness-dependent longitudinal optical phonon mode frequency, indicating the presence of plasmon–phonon coupling.Item Joint embedding of biological networks for cross-species functional alignment(Oxford University Press, 2023) Li, Lechuan; Dannenfelser, Ruth; Zhu, Yu; Hejduk, Nathaniel; Segarra, Santiago; Yao, VickyModel organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein–protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA’s embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.https://github.com/ylaboratory/ETNAItem Supervised convex clustering(Wiley, 2023) Wang, Minjie; Yao, Tianyi; Allen, Genevera I.Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming up with meaningful interpretations of the estimated clusters has often been challenging precisely due to their unsupervised nature. Meanwhile, in many real-world scenarios, there are some noisy supervising auxiliary variables, for instance, subjective diagnostic opinions, that are related to the observed heterogeneity of the unlabeled data. By leveraging information from both supervising auxiliary variables and unlabeled data, we seek to uncover more scientifically interpretable group structures that may be hidden by completely unsupervised analyses. In this work, we propose and develop a new statistical pattern discovery method named supervised convex clustering (SCC) that borrows strength from both information sources and guides towards finding more interpretable patterns via a joint convex fusion penalty. We develop several extensions of SCC to integrate different types of supervising auxiliary variables, to adjust for additional covariates, and to find biclusters. We demonstrate the practical advantages of SCC through simulations and a case study on Alzheimer's disease genomics. Specifically, we discover new candidate genes as well as new subtypes of Alzheimer's disease that can potentially lead to better understanding of the underlying genetic mechanisms responsible for the observed heterogeneity of cognitive decline in older adults.Item Real-time, deep-learning aided lensless microscope(Optica Publishing Group, 2023) Wu, Jimin; Boominathan, Vivek; Veeraraghavan, Ashok; Robinson, Jacob T.Traditional miniaturized fluorescence microscopes are critical tools for modern biology. Invariably, they struggle to simultaneously image with a high spatial resolution and a large field of view (FOV). Lensless microscopes offer a solution to this limitation. However, real-time visualization of samples is not possible with lensless imaging, as image reconstruction can take minutes to complete. This poses a challenge for usability, as real-time visualization is a crucial feature that assists users in identifying and locating the imaging target. The issue is particularly pronounced in lensless microscopes that operate at close imaging distances. Imaging at close distances requires shift-varying deconvolution to account for the variation of the point spread function (PSF) across the FOV. Here, we present a lensless microscope that achieves real-time image reconstruction by eliminating the use of an iterative reconstruction algorithm. The neural network-based reconstruction method we show here, achieves more than 10000 times increase in reconstruction speed compared to iterative reconstruction. The increased reconstruction speed allows us to visualize the results of our lensless microscope at more than 25 frames per second (fps), while achieving better than 7 µm resolution over a FOV of 10 mm2. This ability to reconstruct and visualize samples in real-time empowers a more user-friendly interaction with lensless microscopes. The users are able to use these microscopes much like they currently do with conventional microscopes.Item Baseline leptin predicts response to metformin in adolescents with type 1 diabetes and increased body mass index(Wiley, 2023) Ismail, Heba M.; Barua, Souptik; Wang, Johnny; Sabharwal, Ashutosh; Libman, Ingrid; Bacha, Fida; Nadeau, Kristen J.; Tosur, Mustafa; Redondo, Maria J.Item An automated respiratory data pipeline for waveform characteristic analysis(Wiley, 2023) Lusk, Savannah; Ward, Christopher S.; Chang, Andersen; Twitchell-Heyne, Avery; Fattig, Shaun; Allen, Genevera; Jankowsky, Joanna L.; Ray, Russell S.Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic and degenerative diseases converging on autonomic control failure. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. We present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds and defining experimental variables. Breathe Easy was validated against manual selection by experts, which represents the current standard in the field. We demonstrate Breathe Easy's utility by examining a 2-year longitudinal study of an Alzheimer's disease mouse model to assess contributions of forebrain pathology in disordered breathing. Whole body plethysmography has become an important experimental outcome measure for a variety of diseases with primary and secondary respiratory indications. Respiratory dysfunction, while not an initial symptom in many of these disorders, often drives disability or death in patient outcomes. Breathe Easy provides an open-source respiratory analysis tool for all respiratory datasets and represents a necessary improvement upon current analytical methods in the field. Key points Respiratory dysfunction is a common endpoint for disability and mortality in many disorders throughout life. Whole body plethysmography in rodents represents a high face-value method for measuring respiratory outcomes in rodent models of these diseases and disorders. Analysis of key respiratory variables remains hindered by manual annotation and analysis that leads to low throughput results that often exclude a majority of the recorded data. Here we present a software suite, Breathe Easy, that automates the process of data selection from raw recordings derived from plethysmography experiments and the analysis of these data into operative outcomes and publication-worthy graphs with statistics. We validate Breathe Easy with a terabyte-scale Alzheimer's dataset that examines the effects of forebrain pathology on respiratory function over 2 years of degeneration.Item Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder(Hindawi, 2023) Moukaddam, Nidal; Lamichhane, Bishal; Salas, Ramiro; Goodman, Wayne; Sabharwal, AshutoshIntroduction. Suicide is one of the leading causes of death across different age groups. The persistence of suicidal ideation and the progression of suicidal ideations to action could be related to impulsivity, the tendency to act on urges with low temporal latency, and little forethought. Quantifying impulsivity could thus help suicidality estimation and risk assessments in ideation-to-action suicidality frameworks. Methods. To model suicidality with impulsivity quantification, we obtained questionnaires, behavioral tests, heart rate variability (HRV), and resting state functional magnetic resonance imaging measurements from 34 participants with mood disorders. The participants were categorized into three suicidality groups based on their Mini-International Neuropsychiatric Interview: none, low, and moderate to severe. Results. Questionnaire and HRV-based impulsivity measures were significantly different between the suicidality groups with higher subscales of impulsivity associated with higher suicidality. A multimodal system to characterize impulsivity objectively resulted in a classification accuracy of 96.77% in the three-class suicidality group prediction task. Conclusions. This study elucidates the relative sensitivity of various impulsivity measures in differentiating participants with suicidality and demonstrates suicidality prediction with high accuracy using a multimodal objective impulsivity characterization in participants with mood disorders.Item Clinical Characterization of Data-Driven Diabetes Clusters of Pediatric Type 2 Diabetes(Hindawi, 2023) Abbasi, Mahsan; Tosur, Mustafa; Astudillo, Marcela; Refaey, Ahmad; Sabharwal, Ashutosh; Redondo, Maria J.Background. Pediatric Type 2 diabetes (T2D) is highly heterogeneous. Previous reports on adult-onset diabetes demonstrated the existence of diabetes clusters. Therefore, we set out to identify unique diabetes subgroups with distinct characteristics among youth with T2D using commonly available demographic, clinical, and biochemical data. Methods. We performed data-driven cluster analysis (K-prototypes clustering) to characterize diabetes subtypes in pediatrics using a dataset with 722 children and adolescents with autoantibody-negative T2D. The six variables included in our analysis were sex, race/ethnicity, age, BMI Z-score and hemoglobin A1c at the time of diagnosis, and non-HDL cholesterol within first year of diagnosis. Results. We identified five distinct clusters of pediatric T2D, with different features, treatment regimens and risk of diabetes complications: Cluster 1 was characterized by higher A1c; Cluster 2, by higher non-HDL; Cluster 3, by lower age at diagnosis and lower A1c; Cluster 4, by lower BMI and higher A1c; and Cluster 5, by lower A1c and higher age. Youth in Cluster 1 had the highest rate of diabetic ketoacidosis (DKA) () and were most prescribed metformin (). Those in Cluster 2 were most prone to polycystic ovarian syndrome (). Younger individuals with lowest family history of diabetes were least frequently diagnosed with diabetic ketoacidosis () and microalbuminuria (). Low-BMI individuals with higher A1c had the lowest prevalence of acanthosis nigricans () and hypertension (). Conclusions. Utilizing clinical measures gathered at the time of diabetes diagnosis can be used to identify subgroups of pediatric T2D with prognostic value. Consequently, this advancement contributes to the progression and wider implementation of precision medicine in diabetes management.Item Temporal changes in bio-behavioral and glycemic outcomes following a produce prescription program among predominantly Hispanic/Latino adults with or at risk of type 2 diabetes(Elsevier, 2023) Sato Imuro, Sandra Emi; Sabharwal, Ashutosh; Conneely, Casey; Glantz, Namino; Bevier, Wendy; Barua, Souptik; Pai, Amruta; Larez, Arianna; Kerr, DavidIn the United States (U.S.), consumption of fresh vegetables and fruits is below recommended levels. Enhancing access to nutritious food through food prescriptions has been recognized as a promising approach to combat diet-related illnesses. However, the effectiveness of this strategy at a large scale remains untested, particularly in marginalized communities where food insecurity rates and the prevalence of health conditions such as type 2 diabetes (T2D) are higher compared to the background population. This study evaluated the impact of a produce prescription program for predominantly Hispanic/Latino adults living with or at risk of T2D. A total of 303 participants enrolled in a 3-month observational cohort received 21 medically prescribed portions/week of fresh produce. A subgroup of 189 participants used continuous glucose monitoring (CGM) to assess the relationship between CGM profile changes and HbA1c level changes. For 247 participants completing the study (76% female, 84% Hispanic/Latino, 32% with T2D, age 56·6 ± 11·9 years), there was a reduction in weight (−1·1 [-1·6 to −0·6] lbs., p < 0.001), waist circumference (−0·4 [-1·0 to 0·6] cm, p = 0·007) and systolic blood pressure (SBP) for participants with baseline SBP >120 mmHg (−4·2 [-6·8 to −1·8] mmHg, p = 0·001). For participants with an HbA1c ≥ 7·0% at baseline, HbA1c fell significantly (−0·5 [-0·9 to −0·1] %, p = 0·01). There were also improvements in food security (p < 0·0001), self-reported ratings of sleep, mood, pain (all p < 0·001), and measures of depression (p < 0·0001), anxiety (p = 0·045), and stress (p = 0·002) (DASS-21). There was significant correlation (r = 0·8, p = 0·001) between HbA1c change and the change in average glucose for participants with worsening HbA1c, but not for participants with an improvement in HbA1c. In conclusion, medical prescription of fresh produce is associated with significant improvements in cardio-metabolic and psycho-social risk factors for Hispanic/Latino adults with or at risk of T2D.Item Decoding Depression Severity From Intracranial Neural Activity(Elsevier, 2023) Xiao, Jiayang; Provenza, Nicole R.; Asfouri, Joseph; Myers, John; Mathura, Raissa K.; Metzger, Brian; Adkinson, Joshua A.; Allawala, Anusha B.; Pirtle, Victoria; Oswalt, Denise; Shofty, Ben; Robinson, Meghan E.; Mathew, Sanjay J.; Goodman, Wayne K.; Pouratian, Nader; Schrater, Paul R.; Patel, Ankit B.; Tolias, Andreas S.; Bijanki, Kelly R.; Pitkow, Xaq; Sheth, Sameer A.Background Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis. Methods We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings. Results Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression. Conclusions The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.Item Widefield imaging of rapid pan-cortical voltage dynamics with an indicator evolved for one-photon microscopy(Springer Nature, 2023) Lu, Xiaoyu; Wang, Yunmiao; Liu, Zhuohe; Gou, Yueyang; Jaeger, Dieter; St-Pierre, FrançoisWidefield imaging with genetically encoded voltage indicators (GEVIs) is a promising approach for understanding the role of large cortical networks in the neural coding of behavior. However, the limited performance of current GEVIs restricts their deployment for single-trial imaging of rapid neuronal voltage dynamics. Here, we developed a high-throughput platform to screen for GEVIs that combine fast kinetics with high brightness, sensitivity, and photostability under widefield one-photon illumination. Rounds of directed evolution produced JEDI-1P, a green-emitting fluorescent indicator with enhanced performance across all metrics. Next, we optimized a neonatal intracerebroventricular delivery method to achieve cost-effective and wide-spread JEDI-1P expression in mice. We also developed an approach to correct optical measurements from hemodynamic and motion artifacts effectively. Finally, we achieved stable brain-wide voltage imaging and successfully tracked gamma-frequency whisker and visual stimulations in awake mice in single trials, opening the door to investigating the role of high-frequency signals in brain computations.Item Topology stabilized fluctuations in a magnetic nodal semimetal(Springer Nature, 2023) Drucker, Nathan C.; Nguyen, Thanh; Han, Fei; Siriviboon, Phum; Luo, Xi; Andrejevic, Nina; Zhu, Ziming; Bednik, Grigory; Nguyen, Quynh T.; Chen, Zhantao; Nguyen, Linh K.; Liu, Tongtong; Williams, Travis J.; Stone, Matthew B.; Kolesnikov, Alexander I.; Chi, Songxue; Fernandez-Baca, Jaime; Nelson, Christie S.; Alatas, Ahmet; Hogan, Tom; Puretzky, Alexander A.; Huang, Shengxi; Yu, Yue; Li, MingdaThe interplay between magnetism and electronic band topology enriches topological phases and has promising applications. However, the role of topology in magnetic fluctuations has been elusive. Here, we report evidence for topology stabilized magnetism above the magnetic transition temperature in magnetic Weyl semimetal candidate CeAlGe. Electrical transport, thermal transport, resonant elastic X-ray scattering, and dilatometry consistently indicate the presence of locally correlated magnetism within a narrow temperature window well above the thermodynamic magnetic transition temperature. The wavevector of this short-range order is consistent with the nesting condition of topological Weyl nodes, suggesting that it arises from the interaction between magnetic fluctuations and the emergent Weyl fermions. Effective field theory shows that this topology stabilized order is wavevector dependent and can be stabilized when the interband Weyl fermion scattering is dominant. Our work highlights the role of electronic band topology in stabilizing magnetic order even in the classically disordered regime.Item Visible and short-wave infrared fiber-based snapshot imaging spectrometer with a custom high-throughput relay system(Optica Publishing Group, 2023) Lu, Jiawei; Zheng, Desheng; Stoian, Razvan-Ionut; Flynn, Christopher; Alexander, David; Tkaczyk, Tomasz S.This paper presents the design and fabrication of a fiber-based snapshot imaging spectrometer working in both visible (490 nm-732 nm) and short-wave infrared (1090 nm - 1310 nm) ranges. To maximize the light collection efficiency, a custom relay system with 0.25 NA and 20 mm field of view (FOV) was designed and integrated. The bench setup showed that the custom relay system could fully resolve 10 µm fiber cores over the entire FOV among visible and short-wave infrared ranges. The numerical aperture (NA) match provided a 2.07X fold throughout improvement in the visible range and about 10X fold in the SWIR range compared to the previous generations, enabling imaging with a fast frame rate and under low illumination conditions. The presented imaging spectrometer generated spectral datacubes with 35000 spatial samplings and 23 spectral channels. Spectral urban imaging results obtained by the spectrometer in both visible and SWIR ranges are presented. Finally, we collected spectral images of apple bruising to show potential applications in the food quality industry.Item Observation of colossal terahertz magnetoresistance and magnetocapacitance in a perovskite manganite(Optica Publishing Group, 2023) Tay, Fuyang; Chaudhary, Swati; He, Jiaming; Peraca, Nicolas Marquez; Baydin, Andrey; Fiete, Gregory A.; Zhou, Jianshi; Kono, Junichiro; Smalley-Curl InstituteTerahertz (THz) magnetoresistance effects have been extensively investigated and have shown promising results for applications in magnetic modulations of the amplitude of THz waves. However, THz magnetocapacitance in dielectric systems, which is essential for phase modulations of THz radiation, remains largely unexplored. Here, we study the THz response of a bulk single crystal of L a 0.875 S r 0.125 M n O 3 at around its Curie temperature, observing significant magnetic-field-induced changes in the THz resistance and capacitance extracted from the optical conductivity. We discuss possible mechanisms for the observed coexistence of colossal THz magnetoresistance and magnetocapacitance in a perovskite manganite that is not multiferroic. This work enhances our understanding of colossal magnetoresistance in a complex system with THz spectroscopy and demonstrates potential use of perovskite manganites in THz technology.Item Creating a near-perfect circularly polarized terahertz beam through the nonreciprocity of a magnetoplasma(Optica Publishing Group, 2023) Ju, Xuewei; Hu, Zhiqiang; Zhu, Guofeng; Huang, Feng; Chen, Yanqing; Guo, Cuixia; Belyanin, Alexey; Kono, Junichiro; Wang, XiangfengCompared to other parts of the electromagnetic spectrum, the terahertz frequency range lacks efficient polarization manipulation techniques, which is impeding the proliferation of terahertz technology. In this work, we demonstrate a tunable and broadband linear-to-circular polarization converter based on an InSb plate containing a free-carrier magnetoplasma. In a wide spectral region (∼ 0.45 THz), the magnetoplasma selectively absorbs one circularly polarized mode due to electron cyclotron resonance and also reflects it at the edges of the absorption band. Both effects are nonreciprocal and contribute to form a near-zero transmission band with a high isolation of –36 dB, resulting in the output of a near-perfect circularly polarized terahertz wave for an incident linearly polarized beam. The near-zero transmission band is tunable with magnetic field to cover a wide frequency range from 0.3 to 4.8 THz.Item Slow-light-enhanced on-chip 1D and 2D photonic crystal waveguide gas sensing in near-IR with an ultrahigh interaction factor(Optica Publishing Group, 2023) Peng, Zihang; Huang, Yijun; Zheng, Kaiyuan; Zheng, Chuantao; Pi, Mingquan; Zhao, Huan; Ji, Jialin; Min, Yuting; Liang, Lei; Song, Fang; Zhang, Yu; Wang, Yiding; Tittel, Frank K.Nanophotonic waveguides hold great promise to achieve chip-scale gas sensors. However, their performance is limited by a short light path and small light–analyte overlap. To address this challenge, silicon-based, slow-light-enhanced gas-sensing techniques offer a promising approach. In this study, we experimentally investigated the slow light characteristics and gas-sensing performance of 1D and 2D photonic crystal waveguides (PCWs) in the near-IR (NIR) region. The proposed 2D PCW exhibited a high group index of up to 114, albeit with a high propagation loss. The limit of detection (LoD) for acetylene (C2H2) was 277 parts per million (ppm) for a 1 mm waveguide length and an averaging time of 0.4 s. The 1D PCW shows greater application potential compared to the 2D PCW waveguide, with an interaction factor reaching up to 288%, a comparably low propagation loss of 10 dB/cm, and an LoD of 706 ppm at 0.4 s. The measured group indices of the 2D and 1D waveguides are 104 and 16, respectively, which agree well with the simulation results.Item Calorie Compensation Patterns Observed in App-Based Food Diaries(MDPI, 2023) Pai, Amruta; Sabharwal, AshutoshSelf-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal’s energy content based on the energy content of the previous meal(s). Preload test studies measure a single instance of compensation in a controlled setting. The measurement of calorie compensation in free-living conditions has largely remained unexplored. This paper proposes a methodology that leverages extensive app-based observational food diary data to measure an individual’s calorie compensation profile in free-living conditions. Instead of a single compensation index followed in preload–test studies, we present the compensation profile as a distribution of days a user exhibits under-compensation, overcompensation, non-compensation, and precise compensation. We applied our methodology to the public food diary data of 1622 MyFitnessPal users. We empirically established that four weeks of food diaries were sufficient to characterize a user’s compensation profile accurately. We observed that meal compensation was more likely than day compensation. Dinner compensation had a higher likelihood than lunch compensation. Precise compensation was the least likely. Users were more likely to overcompensate for missing calories than for additional calories. The consequences of poor compensatory behavior were reflected in their adherence to their daily calorie goal. Our methodology could be applied to food diaries to discover behavioral phenotypes of poor compensatory behavior toward forming an early behavioral marker for weight gain.Item Fabrication of waveguide directional couplers using 2-photon lithography(Optica Publishing Group, 2023) Flynn, Christopher; Cao, Haimu; Applegate, Brian E.; Tkaczyk, Tomasz S.Advances in 2-photon lithography have enabled in-lab production of sub-micron resolution and millimeter scale 3D optical components. The potential complex geometries are well suited to rapid prototyping and production of waveguide structures, interconnects, and waveguide directional couplers, furthering future development and miniaturization of waveguide-based imaging technologies. System alignment is inherent to the 2-photon process, obviating the need for manual assembly and allowing precise micron scale waveguide geometries not possible in traditional fused fiber coupler fabrication. Here we present the use of 2-photon lithography for direct printing of multi-mode waveguide couplers with air cladding and single mode waveguide couplers with uncured liquid photoresin cladding. Experimental results show reproducible coupling which can be modified by selected design parameters.Item Nanoporous Titanium Oxynitride Nanotube Metamaterials with Deep Subwavelength Heat Dissipation for Perfect Solar Absorption(American Chemical Society, 2023) Afshar, Morteza; Schirato, Andrea; Mascaretti, Luca; Hejazi, S. M. Hossein; Shahrezaei, Mahdi; Della Valle, Giuseppe; Fornasiero, Paolo; Kment, Štěpán; Alabastri, Alessandro; Naldoni, AlbertoWe report a quasi-unitary broadband absorption over the ultraviolet–visible–near-infrared range in spaced high aspect ratio, nanoporous titanium oxynitride nanotubes, an ideal platform for several photothermal applications. We explain such an efficient light–heat conversion in terms of localized field distribution and heat dissipation within the nanopores, whose sparsity can be controlled during fabrication. The extremely large heat dissipation could not be explained in terms of effective medium theories, which are typically used to describe small geometrical features associated with relatively large optical structures. A fabrication-process-inspired numerical model was developed to describe a realistic space-dependent electric permittivity distribution within the nanotubes. The resulting abrupt optical discontinuities favor electromagnetic dissipation in the deep sub-wavelength domains generated and can explain the large broadband absorption measured in samples with different porosities. The potential application of porous titanium oxynitride nanotubes as solar absorbers was explored by photothermal experiments under moderately concentrated white light (1–12 Suns). These findings suggest potential interest in realizing solar-thermal devices based on such simple and scalable metamaterials.Item Effect of an Internet–Delivered Cognitive Behavioral Therapy–Based Sleep Improvement App for Shift Workers at High Risk of Sleep Disorder: Single-Arm, Nonrandomized Trial(JMIR Publications, 2023) Ito-Masui, Asami; Sakamoto, Ryota; Matsuo, Eri; Kawamoto, Eiji; Motomura, Eishi; Tanii, Hisashi; Yu, Han; Sano, Akane; Imai, Hiroshi; Shimaoka, MotomuBackground: Shift workers are at high risk of developing sleep disorders such as shift worker sleep disorder or chronic insomnia. Cognitive behavioral therapy (CBT) is the first-line treatment for insomnia, and emerging evidence shows that internet-based CBT is highly effective with additional features such as continuous tracking and personalization. However, there are limited studies on internet-based CBT for shift workers with sleep disorders. Objective: This study aimed to evaluate the impact of a 4-week, physician-assisted, internet-delivered CBT program incorporating machine learning–based well-being prediction on the sleep duration of shift workers at high risk of sleep disorders. We evaluated these outcomes using an internet-delivered CBT app and fitness trackers in the intensive care unit. Methods: A convenience sample of 61 shift workers (mean age 32.9, SD 8.3 years) from the intensive care unit or emergency department participated in the study. Eligible participants were on a 3-shift schedule and had a Pittsburgh Sleep Quality Index score ≥5. The study comprised a 1-week baseline period, followed by a 4-week intervention period. Before the study, the participants completed questionnaires regarding the subjective evaluation of sleep, burnout syndrome, and mental health. Participants were asked to wear a commercial fitness tracker to track their daily activities, heart rate, and sleep for 5 weeks. The internet-delivered CBT program included well-being prediction, activity and sleep chart, and sleep advice. A job-based multitask and multilabel convolutional neural network–based model was used for well-being prediction. Participant-specific sleep advice was provided by sleep physicians based on daily surveys and fitness tracker data. The primary end point of this study was sleep duration. For continuous measurements (sleep duration, steps, etc), the mean baseline and week-4 intervention data were compared. The 2-tailed paired t test or Wilcoxon signed rank test was performed depending on the distribution of the data. Results: In the fourth week of intervention, the mean daily sleep duration for 7 days (6.06, SD 1.30 hours) showed a statistically significant increase compared with the baseline (5.54, SD 1.36 hours; P=.02). Subjective sleep quality, as measured by the Pittsburgh Sleep Quality Index, also showed statistically significant improvement from baseline (9.10) to after the intervention (7.84; P=.001). However, no significant improvement was found in the subjective well-being scores (all P>.05). Feature importance analysis for all 45 variables in the prediction model showed that sleep duration had the highest importance. Conclusions: The physician-assisted internet-delivered CBT program targeting shift workers with a high risk of sleep disorders showed a statistically significant increase in sleep duration as measured by wearable sensors along with subjective sleep quality. This study shows that sleep improvement programs using an app and wearable sensors are feasible and may play an important role in preventing shift work–related sleep disorders.