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Heterostructures of atomically thin van der Waals bonded monolayers have opened a unique platform to engineer Coulomb correlations, shaping excitonic1–3, Mott insulating4 or superconducting phases5,6. In transition metal dichalcogenide heterostructures7, electrons and holes residing in different monolayers can bind into spatially indirect excitons1,3,8–11 with a strong potential for optoelectronics11,12, valleytronics1,3,13, Bose condensation14, superfluidity14,15 and moiré-induced nanodot lattices16. Yet these ideas require a microscopic understanding of the formation, dissociation and thermalization dynamics of correlations including ultrafast phase transitions. Here we introduce a direct ultrafast access to Coulomb correlations between monolayers, where phase-locked mid-infrared pulses allow us to measure the binding energy of interlayer excitons in WSe2/WS2 hetero-bilayers by revealing a novel 1s–2p resonance, explained by a fully quantum mechanical model. Furthermore, we trace, with subcycle time resolution, the transformation of an exciton gas photogenerated in the WSe2 layer directly into interlayer excitons. Depending on the stacking angle, intra- and interlayer species coexist on picosecond scales and the 1s–2p resonance becomes renormalized. Our work provides a direct measurement of the binding energy of interlayer excitons and opens the possibility to trace and control correlations in novel artificial materials.Femtosecond pump–probe measurements of Coulomb correlations in WS2/WSe2 heterostructures reveal the interlayer exciton binding energy, determined from the 1s–2p resonance, as well as the dynamics of the conversion of intra- to interlayer excitons.

Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and as a part of our Precision education used to analyze and predict students' performance and provide timely interventions based on student learning profiles. This study applied learning analytics and educational big data approaches for the early prediction of students' final academic performance in a blended Calculus course. Real data with 21 variables were collected from the proposed course, consisting of video-viewing behaviors, out-of-class practice behaviors, homework and quiz scores, and after-school tutoring. This study applied principal component regression to predict students' final academic performance. The experimental results show that students' final academic performance could be predicted when only one-third of the semester had elapsed. In addition, we identified seven critical factors that affect students' academic performance, consisting of four online factors and three traditional factors. The results showed that the blended data set combining online and traditional critical factors had the highest predictive performance.

The field of dark matter detection is a highly visible and highly competitive one. In this paper, we propose recommendations for presenting dark matter direct detection results particularly suited for weak-scale dark matter searches, although we believe the spirit of the recommendations can apply more broadly to searches for other dark matter candidates, such as very light dark matter or axions. To translate experimental data into a final published result, direct detection collaborations must make a series of choices in their analysis, ranging from how to model astrophysical parameters to how to make statistical inferences based on observed data. While many collaborations follow a standard set of recommendations in some areas, for example the expected flux of dark matter particles (to a large degree based on a paper from Lewin and Smith in 1995), in other areas, particularly in statistical inference, they have taken different approaches, often from result to result by the same collaboration. We set out a number of recommendations on how to apply the now commonly used Profile Likelihood Ratio method to direct detection data. In addition, updated recommendations for the Standard Halo Model astrophysical parameters and relevant neutrino fluxes are provided. The authors of this note include members of the DAMIC, DarkSide, DARWIN, DEAP, LZ, NEWS-G, PandaX, PICO, SBC, SENSEI, SuperCDMS, and XENON collaborations, and these collaborations provided input to the recommendations laid out here. Wide-spread adoption of these recommendations will make it easier to compare and combine future dark matter results.

Clustering of earthquake magnitudes is still actively debated, compared to well-established spatial and temporal clustering. Magnitude clustering is not currently implemented in earthquake forecasting but would be important if larger magnitude events are more likely to be followed by similar sized events. Here we show statistically significant magnitude clustering present in many different field and laboratory catalogs at a wide range of spatial scales (mm to 1000 km). It is universal in field catalogs across fault types and tectonic/induced settings, while laboratory results are unaffected by loading protocol or rock types and show temporal stability. The absence of clustering can be imposed by a global tensile stress, although clustering still occurs when isolating to triggered event pairs or spatial patches where shear stress dominates. Magnitude clustering is most prominent at short time and distance scales and modeling indicates >20% repeating magnitudes in some cases, implying it can help to narrow physical mechanisms for seismogenesis. Clustering of earthquake magnitudes is actively debated. Here, the authors show statistically significant magnitude clustering present in many different field and laboratory catalogs at a wide range of spatial scales (mm to 1000 km).

Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems. MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an Orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets. High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using Orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained. The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.

There is a lack of mental health and substance use providers for youth in BC, particularly in rural and remote areas. To address these gaps, Canada's first child psychiatry access program, BC Children's Hospital Compass Program, was developed in 2018 to support providers across the province in providing evidence-based mental health and substance use care to youth under 25. This article describes the program's first five years and provides an overview of its creation, utilization, and clinical uses. Quantitative data collected by the Compass Program from September 2018 through September 2023 were analyzed. Participation and utilization of the service by providers in the province were analyzed and descriptive statistics, including means with standard deviations for quantitative variables have been used to describe demographic and other medical factors related to participants. A total of 2336 new providers have been enrolled since Compass' inception. Number of clinical calls into Compass remained steady over the five-year period with an average of 1085 individual providers served per year. Service use is highest in Vancouver Coastal Region (27.3%), followed by Northern Health (21.4%), Interior (15.7%), Vancouver Island (14.5%), and Fraser (13.4%), and Yukon (0.3%). General practitioners make up over a third of all encounters (34.6%), followed closely by pediatrician encounters making up 27.5% of total encounters from 2018-2023. These two provider types comprise over 60% of all encounters over the 5-year timespan. Encounters with other provider types were less common, with the third most common encounter being Child and Youth Mental Health (CYMH) clinicians, totalling 8.6% of total encounters. 37.6% of encounters were for male patients and 42.9% for female patients with 6.8% reporting \"Other\" genders and 12.7% declining to answer. Medication concerns are the most common reason for accessing Compass, regardless of gender. Therapy questions, resource coordination issues, and diagnostic clarification followed in frequency, comprising a similar amount of consults. Compass consultations have the potential to benefit three groups of people: the specific patient being consulted on, the provider requesting the consultation, as well as the provider's colleagues who might benefit from peer consultation. Capacity building is important given Compass receives calls from rural and remote areas where there are no psychiatrists or child psychiatrists where general practitioners and clinicians regularly work with patients along the entire spectrum of mental health and substance use disorders.

The genomic revolution has identified therapeutic targets for a plethora of diseases, creating a need to develop robust technologies for combination drug therapy. In the present work, we describe a self-assembled polymeric nanoparticle (NP) platform to target and control precisely the codelivery of drugs with varying physicochemical properties to cancer cells. As proof of concept, we code-livered cisplatin and docetaxel (Dtxl) to prostate cancer cells with synergistic cytotoxicity. A polylactide (PLA) derivative with pendant hydroxyl groups was prepared and conjugated to a platinum(IV) [Pt(IV)] prodrug, c,t,c-[Pt(NH₃)₂ (O₂CCH₂CH₂COOH)(OH)Cl₂] [PLA-Pt(IV)]. A blend of PLA-Pt(IV) functionalized polymer and carboxyl-terminated poly(d,l-lactic-co-glycolic acid)-block-poly(ethylene glycol) copolymer in the presence or absence of Dtxl, was converted, in microfluidic channels, to NPs with a diameter of ∼100 nm. This process resulted in excellent encapsulation efficiency (EE) and high loading of both hydrophilic platinum prodrug and hydrophobic Dtxl with reproducible EEs and loadings. The surface of the NPs was derivatized with the A10 aptamer, which binds to the prostate-specific membrane antigen (PSMA) on prostate cancer cells. These NPs undergo controlled release of both drugs over a period of 48–72 h. Targeted NPs were internalized by the PSMA-expressing LNCaP cells via endocytosis, and formation of cisplatin 1,2-d(GpG) intrastrand cross-links on nuclear DNA was verified. In vitro toxicities demonstrated superiority of the targeted dual-drug combination NPs over NPs with single drug or nontargeted NPs. This work reveals the potential of a single, programmable nanoparticle to blend and deliver a combination of drugs for cancer treatment.

A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S , are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R 2 of 0.664 ± 0.032 and 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

Free-electron lasers have been successfully operated with ultrahigh brightness and excellent transverse coherence at X-ray wavelengths 1 , 2 , 3 , 4 . One of the next goals for further improvements is full coherence 5 , 6 . An obvious approach is to seed the free-electron laser interaction using a conventional source that has good temporal coherence 7 , 8 , 9 , 10 , 11 , 12 . Here, we show the first lasing of a free-electron laser with an echo-enabled harmonic generation scheme 11 , which shows great promise for producing coherent lasing at short wavelengths, even in the X-ray regime. The experiment was conducted at a test facility 13 , 14 that combines a 135.4 MeV electron accelerator with an amplifier consisting of a series of undulator magnets. Lasing was achieved at the third harmonic of the seed with a gain of ∼100,000 over spontaneous radiation. The measurements show typical exponential growth and excellent spectral characteristics, as well as good intensity stability. Lasing in X-ray free-electron lasers is typically achieved by self-amplification of spontaneous emission, which is known to have non-ideal temporal coherence and suffer from beam fluctuations. Here researchers report lasing based on echo-enabled harmonic generation at the Shanghai Deep Ultraviolet free-electron laser facility.

Frailty is a pervasive condition among older people worldwide. Despite the association between higher protein intake and lower frailty risk has been well documented, older individuals encounter barriers to enhancing their protein consumption due to reduced appetite and impaired digestive capacity. This study aims to delve into the potential correlation between dietary protein diversity, protein patterns, and the risk of frailty among older Chinese individuals. Prospective cohort study. Community-based. 2,216 participants aged 65 and above and not frail at the baseline were recruited from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) dataset spanning from 2014 to 2018. Dietary protein diversity was evaluated utilizing a protein diversity score (PDS), calculated based on the results of a food frequency questionnaire. Dietary protein patterns were identified by employing principal component analysis (PCA). Frailty was ascertained using a 40-item frailty index (FI) where FI > 0.21 indicated frailty. Logistic analysis was employed to investigate the association between dietary variables and frailty. 541 participants were identified as frail after a 4-year follow-up. After adjusting for confounders, each 1-unit increase in PDS was linked to a 10% decrease in frailty risk. Compared to individuals with PDS ≤ 1, those with PDS scores of 2–3, 4–5, and 6 had lower risks of frailty, with OR (95% CI) of 0.78 (0.58–1.06), 0.58 (0.38–0.87), 0.42 (0.20–0.81), respectively (P trend = 0.038). Individuals who consistently maintained high PDS demonstrated a lower frailty risk in contrast to those who maintained low PDS (OR = 0.60, 95% CI, 0.41–0.87). Additionally, the “meat-fish” pattern exhibited a protective association with frailty, with OR Q4 versus Q1 (95% CI) of 0.54 (0.40–0.74), P trend < 0.001. Maintaining a variety of dietary protein sources and following a “meat-fish” protein pattern might decrease the likelihood of frailty among the older Chinese population.