Preparation of immunomagnetic composite nanostructures with bifunctional four-arm PEG derivatives as linkers for the ultrafast enrichment of zearalenone and its metabolites.

It is challenging to prepare sample pretreatment materials with simple use, strong selectivity and satisfactory enrichment performance. In this study, the antibody (3D4) that can specifically recognize zearalenone (ZEN) and its metabolites was immobilized on the surface of gold-coated magnetic Fe3O4 nanoparticles (GMN) by streptavidin (SA)-biotin interaction using GMN as the substrate and our designed four-arm PEG derivative (HS-4ARMPEG10K-(CM)3) as the linker. The immunomagnetic nanoparticles (GMN-4ARMPEG10K-SA-3D4) prepared by this strategy can achieve rapid enrichment (only 5 min) of analytes directly in the matrix, and higher enrichment capacity compared with the previous immunomagnetic particles. The sensitive and accurate analysis of ZEN and its metabolites can be achieved coupled with HPLC-MS/MS. The LODs and LOQs were 0.02-0.05 µg/kg and 0.05-0.10 µg/kg, respectively. The recoveries were 84.13%-112.67%, and the RSDs were 1.09%-9.39%. The method can provide a powerful tool for highly sensitive and rapid monitoring of mycotoxins in complex matrices due to its' strong selectivity and resistance to matrix interference.

Brain tumor segmentation based on the U-NET+⁣+ network with efficientnet encoder.

BACKGROUND: Brain tumor is a highly destructive, aggressive, and fatal disease. The presence of brain tumors can disrupt the brain's ability to control body movements, consciousness, sensations, thoughts, speech, and memory. Brain tumors are often accompanied by symptoms like epilepsy, headaches, and sensory loss, leading to varying degrees of cognitive impairment in affected patients. OBJECTIVE: The study goal is to develop an effective method to detect and segment brain tumor with high accurancy. METHODS: This paper proposes a novel U-Net+⁣+ network using EfficientNet as the encoder to segment brain tumors based on MRI images. We adjust the original U-Net+⁣+ model by removing the dense skip connections between sub-networks to simplify computational complexity and improve model efficiency, while the connections of feature maps at the same resolution level are retained to bridge the semantic gap. RESULTS: The proposed segmentation model is trained and tested on Kaggle's LGG brain tumor dataset, which obtains a satisfying performance with a Dice coefficient of 0.9180. CONCLUSION: This paper conducts research on brain tumor segmentation, using the U-Net+⁣+ network with EfficientNet as an encoder to segment brain tumors based on MRI images. We adjust the original U-Net+⁣+ model to simplify calculations and maintains rich semantic spatial features at the same time. Multiple loss functions are compared in this study and their effectiveness are discussed. The experimental results shows the model achieves a high segmention result with Dice coefficient of 0.9180.

Facet-Dependent Interfacial Charge Transfer between T-Phase VS2 Nanoflakes and Rutile TiO2 Single Crystals.

The hybridizations of two-dimensional (2D) metallic materials with semiconducting transition metal oxides (TMOs) register attractive heterojunctions, which can find various applications in photostimulated circumstances. In this work, we developed an ambient-pressure chemical vapor deposition method to directly grow T-VS2 on atomically smooth rutile TiO2 single crystals with different terminations and thus successfully constructed a heterojunction model of VS2/TiO2 with a well-defined clean interface. Detailed measurements with Kelvin probe force microscopy revealed the facet-dependent charge transfer occurring at the VS2/TiO2 interfaces, seeing variations not only in the amount and direction of the transferred electrons but also in the photoinduced surface potential changes and the dynamics of photogenerated charge carriers under ultraviolet irradiation. Interestingly, ultrathin T-VS2 was found with considerable magnetism at room temperature, disregarding the charge exchange with the TiO2 substrates. These results may bring deep insights into the photoinspired functionalities of the hybridized system combining metallic transition metal dichalcogenides and TMO materials.

Long COVID Characteristics and Experience: A Descriptive Study from the Yale LISTEN Research Cohort.

OBJECTIVE: To describe the experience of people with long COVID symptomatology and characterize the psychological, social, and financial challenges they experience. BACKGROUND: The experience of people with long COVID needs further amplification, especially with a comprehensive focus on symptomatology, treatments, and impact on daily life and finances. METHODS: We collected data from individuals aged 18 and older reporting long COVID as participants in the Yale Listen to Immune, Symptom and Treatment Experiences Now (LISTEN) Study. The sample population included 441 participants surveyed between May 2022 and July 2023. We evaluated their demographic characteristics, socioeconomic and psychological status, index infection period, health status, quality of life, symptoms, treatments, pre-pandemic comorbidities, and new-onset conditions. RESULTS: Overall, the median age of the participants with long COVID was 46 years (IQR: 38 to 57 years); 74% were women, 86% were Non-Hispanic White, and 93% were from the United States. Participants reported low health status measured by the Euro-QoL visual analogue scale, with a median score of 49 (IQR: 32 to 61). Participants documented a diverse range of symptoms, with all 96 possible symptom choices being reported. Additionally, participants had tried many treatments (median number of treatments: 19, IQR: 12 to 28). They were also experiencing psychological distress, social isolation, and financial stress. CONCLUSIONS: Despite having tried numerous treatments, participants with long COVID continued to experience an array of health and financial challenges-findings that underscore the failure of the healthcare system to address the medical needs of people with long COVID. These insights highlight the need for crucial medical, mental health, financial, and community support services, as well as further scientific investigation, to address the complex impact of long COVID.

Can a "Pandemic Life Adaptation" Digital Technology Curriculum Reduce the Digital Disadvantage of Older Adults During COVID-19? An Intervention Study from Shanghai.

Older adults are at a digital disadvantage because of social stereotypes and a lack of social support; however, smartphones have become a necessary technology to cope with crises and daily life in China, especially during the pandemic. This study aimed to help marginalized older adults take on new tasks by developing digital technology education that used a framework of social cognitive theory in social work. The study followed a quasi-experimental design in which 153 elderly people were recruited from three community service centers; 90 of the participants received 6-weekly intervention. Intent-to-treat analysis, effect size calculations, and sensitivity analysis were conducted. The findings show that digital education significantly enhanced two domains of digital life adaptation abilities: general digital life adaptation abilities [g = .50, 95% CI (.70, 2.69)] and pandemic digital life adaptation abilities [g = .89, 95% CI (.96, 2.07)]. The intervention also improved three domains of digital self-efficacy: sharing and communication [g = .55, 95% CI (.04, .48)], verification [g = .34, 95% CI (.01, .59)], and influencing others [g = .53, 95% CI (.13, .77)]. The study showed that the new intervention approach reduced the harm to vulnerable older adults in the digital wave, especially during the pandemic.

Strongly interacting Rydberg atoms in synthetic dimensions with a magnetic flux.

Synthetic dimensions, wherein dynamics occurs in a set of internal states, have found great success in recent years in exploring topological effects in cold atoms and photonics. However, the phenomena thus far explored have largely been restricted to the non-interacting or weakly interacting regimes. Here, we extend the synthetic dimensions playbook to strongly interacting systems of Rydberg atoms prepared in optical tweezer arrays. We use precise control over driving microwave fields to introduce a tunable U(1) flux in a four-site lattice of coupled Rydberg levels. We find highly coherent dynamics, in good agreement with theory. Single atoms show oscillatory dynamics controllable by the gauge field. Small arrays of interacting atoms exhibit behavior suggestive of the emergence of ergodic and arrested dynamics in the regimes of intermediate and strong interactions, respectively. These demonstrations pave the way for future explorations of strongly interacting dynamics and many-body phases in Rydberg synthetic lattices.

A Wash-Free Spheres-on-Sphere Strategy for On-Site and Multiplexed Biosensing.

Respiratory infections and food contaminants pose severe challenges to global health and the economy. A rapid on-site platform for the simultaneous detection of multiple pathogens is crucial for accurate diagnosis, appropriate treatment, and a reduced healthcare burden. Herein, we present a spheres-on-sphere (SOS) platform for multiplexed detection using a portable Coulter counter, which employs millimeter- and micron-sized spheres coupled with antibodies as multitarget probes. The assay allows for quantitative detection of multiple analytes within 20 min by simple mixing, enabling on-site detection. The platform shows high accuracy in identifying three respiratory viruses (SARS-CoV-2, influenza A virus, and parainfluenza virus) from throat swab samples, with LOD of 50.7, 32.4, and 49.1 pg/mL. It also demonstrates excellent performance in quantifying three mycotoxins (aflatoxin B1, deoxynivalenol, and ochratoxin A) from food samples. The SOS platform offers a rapid on-site approach with high sensitivity and specificity for applications in resource-limited settings.

Use of Electronic Health Records to Characterize Patients with Uncontrolled Hypertension in Two Large Health System Networks.

Background: Improving hypertension control is a public health priority. However, consistent identification of uncontrolled hypertension using computable definitions in electronic health records (EHR) across health systems remains uncertain. Methods: In this retrospective cohort study, we applied two computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥ 18 years) with hypertension receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single blood pressure (BP) measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit). Results: Overall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p < 0.001; 49.7% versus 41.2% in OneFlorida; p < 0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p = 0.04; 42.2% versus 34.8% in OneFlorida; p < 0.001). Patients with controlled and uncontrolled hypertension had similar rates of short-term (at 3 and 6 months) and long-term (at 12 and 24 months) clinical outcomes. The two computable definitions generated consistent results. Conclusions: Our findings illustrate the potential of leveraging EHR data, employing computable definitions, to conduct effective digital population surveillance in the realm of hypertension management.

GenoBaits®WheatplusEE: a targeted capture sequencing panel for quick and accurate identification of wheat-Thinopyrum derivatives.

KEY MESSAGE: A total of 90,000 capture probes derived from wheat and Thinopyrum elongatum were integrated into one chip, which served as an economical genotype for explorating Thinopyrumspecies and their derivatives. Thinopyrum species play a crucial role as a source of new genetic variations for enhancing wheat traits, including resistance to both abiotic and biotic factors. Accurate identification of exogenous chromosome(s) or chromosome segments or genes is essential following the introduction of alien genetic material into wheat, but this task remains challenging. This study aimed to develop a high-resolution wheat-Thinopyrum elongatum array, named GenoBaits®WheatplusEE, to trace alien genetic information by genotyping using a target sequencing system. This GenoBaits®WheatplusEE array included 90,000 capture probes derived from two species and integrated into one chip, with 10,000 and 80,000 originating from wheat and Th. elongatum, respectively. The capture probes were strategically positioned in genes and evenly distributed across the genome, facilitating the development of a roadmap for identifying each alien gene. The array was applied to the high-throughput identification of the alien chromosomes or segments in Thinopyrum and distantly related species and their derivatives. Our results demonstrated that the GenoBaits®WheatplusEE array could be used for direct identification of the breakpoint of alien segments, determine copy number of alien chromosomes, and reveal variations in wheat chromosomes by a single round of target sequencing of the sample. Additionally, we could efficiently and cost-effectively genotype, supporting the exploration of subgenome composition, phylogenetic relationships, and polymorphisms in essential genes (e.g., Fhb7 gene) among Thinopyrum species and their derivatives. We hope that GenoBaits®WheatplusEE will become a widely adopted tool for exporting wild germplasm for wheat improvement in the future.

An electrochemical biosensor based on phage-encoded protein RBP 41 for rapid and sensitive detection of Salmonella.

Salmonellosis caused by Salmonella contaminated food poses a serious threat to human health. The rapid and accurate detection of Salmonella is critical for preventing foodborne illness outbreaks. In this study, an electrochemical biosensor was developed using a newly identified biorecognition element, RBP 41, which is capable of specifically recognizing and binding to Salmonella. The biosensor was constructed through a layer-by-layer assembly of graphene oxide (GO), gold nanoparticles (GNPs), and RBP 41 on a glassy carbon electrode (GCE), with the GNPs amplifying the detection signal. The established biosensor was able to detect Salmonella in concentrations ranging from 3 to 106 CFU/mL within approximately 30 min by using differential pulse voltammetry (DPV) signal, and the estimated detection limit was to be 0.2984 Log10 CFU/mL. The biosensor demonstrated excellent specificity and was effective in detecting Salmonella in food matrices, such as skim milk and lettuce. Overall, this study highlights the potential of phage tail receptor binding proteins in biosensing and the proposed biosensor as a promising alternative for rapid and sensitive Salmonella detection in various samples.

Non-smooth dynamics of a SIR model with nonlinear state-dependent impulsive control.

The classic SIR model is often used to evaluate the effectiveness of controlling infectious diseases. Moreover, when adopting strategies such as isolation and vaccination based on changes in the size of susceptible populations and other states, it is necessary to develop a non-smooth SIR infectious disease model. To do this, we first add a non-linear term to the classical SIR model to describe the impact of limited medical resources or treatment capacity on infectious disease transmission, and then involve the state-dependent impulsive feedback control, which is determined by the convex combinations of the size of the susceptible population and its growth rates, into the model. Further, the analytical methods have been developed to address the existence of non-trivial periodic solutions, the existence and stability of a disease-free periodic solution (DFPS) and its bifurcation. Based on the properties of the established Poincaré map, we conclude that DFPS exists, which is stable under certain conditions. In particular, we show that the non-trivial order-1 periodic solutions may exist and a non-trivial order-$ k $ ($ k\geq 1 $) periodic solution in some special cases may not exist. Moreover, the transcritical bifurcations around the DFPS with respect to the parameters $ p $ and $ AT $ have been investigated by employing the bifurcation theorems of discrete maps.

Post-Vaccination Syndrome: A Descriptive Analysis of Reported Symptoms and Patient Experiences After Covid-19 Immunization.

Introduction: A chronic post-vaccination syndrome (PVS) after covid-19 vaccination has been reported but has yet to be well characterized. Methods: We included 241 individuals aged 18 and older who self-reported PVS after covid-19 vaccination and who joined the online Yale Listen to Immune, Symptom and Treatment Experiences Now (LISTEN) Study from May 2022 to July 2023. We summarized their demographics, health status, symptoms, treatments tried, and overall experience. Results: The median age of participants was 46 years (interquartile range [IQR]: 38 to 56), with 192 (80%) identifying as female, 209 (87%) as non-Hispanic White, and 211 (88%) from the United States. Among these participants with PVS, 127 (55%) had received the BNT162b2 [Pfizer-BioNTech] vaccine, and 86 (37%) received the mRNA-1273 [Moderna] vaccine. The median time from the day of index vaccination to symptom onset was three days (IQR: 1 day to 8 days). The time from vaccination to symptom survey completion was 595 days (IQR: 417 to 661 days). The median Euro-QoL visual analogue scale score was 50 (IQR: 39 to 70). The five most common symptoms were exercise intolerance (71%), excessive fatigue (69%), numbness (63%), brain fog (63%), and neuropathy (63%). In the week before survey completion, participants reported feeling unease (93%), fearfulness (82%), and overwhelmed by worries (81%), as well as feelings of helplessness (80%), anxiety (76%), depression (76%), hopelessness (72%), and worthlessness (49%) at least once. Participants reported a median of 20 (IQR: 13 to 30) interventions to treat their condition. Conclusions: In this study, individuals who reported PVS after covid-19 vaccination had low health status, high symptom burden, and high psychosocial stress despite trying many treatments. There is a need for continued investigation to understand and treat this condition.

OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection.

Compared to typical multi-sensor systems, monocular 3D object detection has attracted much attention due to its simple configuration. However, there is still a significant gap between LiDAR-based and monocular-based methods. In this paper, we find that the ill-posed nature of monocular imagery can lead to depth ambiguity. Specifically, objects with different depths can appear with the same bounding boxes and similar visual features in the 2D image. Unfortunately, the network cannot accurately distinguish different depths from such non-discriminative visual features, resulting in unstable depth training. To facilitate depth learning, we propose a simple yet effective plug-and-play module, One Bounding Box Multiple Objects (OBMO). Concretely, we add a set of suitable pseudo labels by shifting the 3D bounding box along the viewing frustum. To constrain the pseudo-3D labels to be reasonable, we carefully design two label scoring strategies to represent their quality. In contrast to the original hard depth labels, such soft pseudo labels with quality scores allow the network to learn a reasonable depth range, boosting training stability and thus improving final performance. Extensive experiments on KITTI and Waymo benchmarks show that our method significantly improves state-of-the-art monocular 3D detectors by a significant margin (The improvements under the moderate setting on KITTI validation set are 1.82 ~ 10.91% mAP in BEV and 1.18 ~ 9.36% mAP in 3D). Codes have been released at https://github.com/mrsempress/OBMO.

Carbon intensity constraint, economic growth pressure and China's low-carbon development.

Within the context of promotion tournaments among local governments, the management of economic growth goals plays a crucial role in China's economic development. Despite China's rise as the second-largest economy globally, it has also emerged as the largest emitter of carbon emissions. Since the implementation of the 12th Five-Year Plan in 2011, the Chinese central government has made carbon intensity targets mandatory indicators for national economic development. This has prompted local governments to pursue low-carbon growth and adjust their economic growth targets (EGT) to comply with carbon intensity constraints. In this study, a sample of 282 prefecture-level cities in China is used to empirically examine the impact of carbon intensity constraints on total factor carbon emission efficiency (TCE) using the intensity difference-in-differences (DID) framework. The study also emphasizes the role of the transmission channel for economic growth pressure (EGP). The findings of the study reveal several key results. Firstly, the implementation of carbon intensity constraints leads to an average increase of 8.24% in total factor carbon emission efficiency (TCE), which is supported by robustness tests, parallel trend analysis, and placebo tests. Secondly, these constraints result in an average decrease of 0.1828 in local governments' economic growth targets (EGT) and a reduction of 0.1269 in economic growth pressure (EGP). Thirdly, cities with a higher proportion of secondary industry experience a more significant mitigation effect, although the promotion of provincial EGT hinders this effect. Fourthly, synergistic policies can effectively promote low-carbon development, and government expenditure on technology and marketization can facilitate a positive relationship between carbon intensity constraints and TCE. Lastly, the effects of carbon intensity constraints vary across the east, middle, and west regions, suggesting the presence of heterogeneity. The article proposes a shift in the assessment of lower governments by superior governments, from growth assessment to low-carbon growth assessment.

Internal tremors and vibrations in long COVID: a cross-sectional study.

Importance: Internal tremors and vibrations symptoms have been described as part of neurologic disorders but not fully described as a part of long COVID. Objective: To compare demographics, socioeconomic characteristics, pre-pandemic comorbidities, new-onset conditions, and long COVID symptoms between people with internal tremors and vibrations as part of their long COVID symptoms and people with long COVID but without these symptoms. Design: A cross-sectional study, Listen to Immune, Symptom and Treatment Experiences Now (LISTEN), of adults with and without long COVID and post-vaccination syndrome, defined by self-report. Setting: Hugo Health Kindred, a decentralized digital research platform hosting a network of English-speaking adults interested in contributing to COVID-related research. No geographic limitation applied. Participants: The study population included 423 participants who enrolled in LISTEN between May 2022 and June 2023, completed the initial and the conditions and symptoms surveys, reported long COVID, and did not report post-vaccination syndrome. Exposure: Long COVID symptoms of internal tremors and vibrations. Main outcomes and Measures: Demographics, pre-pandemic comorbidities, and current conditions, other symptoms, and quality of life at the time of surveys. Results: Of the 423 participants (median age, 46 years [IQR, 38-56]), 74% were female, 87% were Non-Hispanic White, 92% lived in the United States, 46% were infected before the Delta wave, and 158 (37%) reported "internal tremors, or buzzing/vibration" as a long COVID symptom. Before long COVID, the groups had similar comorbidities. Participants with internal tremors were different from others in having worse health as measured by the Euro-QoL visual analogue scale (median: 40 points [IQR, 30-60] vs. 50 points [IQR, 35-62], P = 0.007), having financial difficulties caused by the pandemic (very much financial difficulties, 22% [95% CI, 16-30] vs. 11% [7.3-15], P < 0.001), often feeling socially isolated (43% [95% CI, 35-52] vs. 37% [31-43], P = 0.039), and having higher rates of self-reported new-onset mast cell disorders (11% [95% CI, 7.1-18] vs. 2.6% [1.2-5.6], Bonferroni-adjusted P = 0.008) and neurologic conditions (including but not limited to seizures, dementia, multiple sclerosis, Parkinson's disease, neuropathy, etc.; 22% [95% CI, 16-29] vs. 8.3% [5.4-12], Bonferroni-adjusted P = 0.004). Conclusions and Relevance: Among people with long COVID, those with internal tremors and vibrations have several other associated symptoms and worse health status, despite having similar pre-pandemic comorbidities, suggesting it may reflect a severe phenotype of long COVID. KEY POINTS: Question: Do people with long COVID symptoms of internal tremors and vibrations differ from others with long COVID but without these symptoms?Findings: In this cross-sectional study that included 423 adults with long COVID, 158 (37%) reported having "internal tremors, or buzzing/vibration," had worse quality of life, more financial difficulties, and higher rates of new-onset mast cell disorders and neurologic conditions, compared with others with long COVID but without internal tremors and vibrations.Meaning: Internal tremors and vibrations may reflect a severe phenotype of long COVID.

Phage-Based Biosensing for Rapid and Specific Detection of Staphylococcus aureus.

Staphylococcus aureus (S. aureus) is a major foodborne pathogen. Rapid and specific detection is crucial for controlling staphylococcal food poisoning. This study reported a Staphylococcus phage named LSA2302 showing great potential for applications in the rapid detection of S. aureus. Its biological characteristics were identified, including growth properties and stability under different pH and temperature conditions. The genomic analysis revealed that the phage has no genes associated with pathogenicity or drug resistance. Then, the phage-functionalized magnetic beads (pMB), serving as a biological recognition element, were integrated with ATP bioluminescence assays to establish a biosensing method for S. aureus detection. The pMB enrichment brought high specificity and a tenfold increase in analytical sensitivity during detection. The whole detection process could be completed within 30 min, with a broad linear range of 1 × 104 to 1 × 108 CFU/mL and a limit of detection (LOD) of 2.43 × 103 CFU/mL. After a 2 h pre-cultivation, this method is capable of detecting bacteria as low as 1 CFU/mL. The recoveries of S. aureus in spiked skim milk and chicken samples were 81.07% to 99.17% and 86.98% to 104.62%, respectively. Our results indicated that phage-based biosensing can contribute to the detection of target pathogens in foods.

Use of Electronic Health Records to Characterize Patients with Uncontrolled Hypertension in Two Large Health System Networks.

Background: Improving hypertension control is a public health priority. However, uncertainty remains regarding the optimal way to identify patients with uncontrolled hypertension using electronic health records (EHR) data. Methods: In this retrospective cohort study, we applied computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥18 years) with hypertension receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single blood pressure (BP) measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit). Results: Overall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p<0.001; 49.7% versus 41.2% in OneFlorida; p<0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p=0.04; 42.2% versus 34.8% in OneFlorida; p<0.001). Patients with controlled and uncontrolled hypertension had similar rates of short-term (at 3 and 6 months) and long-term (at 12 and 24 months) clinical outcomes. The two computable definitions generated consistent results. Conclusions: Computable definitions can be successfully applied to health system EHR data to conduct population surveillance for hypertension and identify patients with uncontrolled hypertension who may benefit from additional treatment.

Nano- and micro-plastic transport in soil and groundwater environments: Sources, behaviors, theories, and models.

With the increasing use of plastics, nano- and micro-plastic (NMP) pollution has become a hot topic in the scientific community. Ubiquitous NMPs, as emerging contaminants, are becoming a global issue owing to their persistence and potential toxicity. Compared with studies of marine and freshwater environments, investigations into the sources, transport properties, and fate of NMPs in soil and groundwater environments remain at a primary stage. Hence, the promotion of such research is critically important. Here, we integrate existing information and recent advancements to compile a comprehensive evaluation of the sources and transport properties of NMPs in soil and groundwater environments. We first provide a systematic description of the various sources and transport behaviors of NMPs. We then discuss the theories (e.g., clean-bed filtration and Derjaguin-Landau-Verwey-Overbeek theories) and models (e.g., single-site and dual-site kinetic retention and transport models) of NMP transport through saturated porous media. Finally, we outline the potential limitations of current research and suggest directions for future research. Overall, this review intends to assimilate and outline current knowledge and provide a useful reference frame to determine the sources and transport properties of NMPs in soil and groundwater environments.

A Novel Algorithmic Structure of EEG Channel Attention Combined With Swin Transformer for Motor Patterns Classification.

With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight often culminates in erroneous data classification, presenting a significant drawback of these conventional methodologies. In our study, we propose a novel algorithmic structure of EEG channel-attention combined with Swin Transformer for motor pattern recognition in BCI rehabilitation. Effectively, the self-attention module from transformer architecture could captures temporal-spectral-spatial features hidden in EEG data. The experimental results verify that our proposed methods outperformed other state-of-art approaches with the average accuracy of 87.67%. It is implied that our method can extract high-level and latent connections among temporal-spectral features in contrast to traditional deep learning methods. This paper demonstrates that channel-attention combined with Swin Transformer methods has great potential for implementing high-performance motor pattern-based BCI systems.