Miracle-dz/UniFAR-FTUs huggingface dataset:
CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery

Extracting roads from satellite imagery is a promising approach to update the dynamic changes of road networks efficiently and timely. However, it is challenging due to the occlusions caused by other objects and the complex traffic environment, the pixel-based methods often generate fragmented roads and fail to predict topological correctness. In this paper, motivated by the road shapes and connections in the graph network, we propose a connectivity attention network (CoANet) to jointly learn the segmentation and pair-wise dependencies. Since the strip convolution is more aligned with the shape of roads, which are long-span, narrow, and distributed continuously. We develop a strip convolution module (SCM) that leverages four strip convolutions to capture long-range context information from different directions and avoid interference from irrelevant regions. Besides, considering the occlusions in road regions caused by buildings and trees, a connectivity attention module (CoA) is proposed to explore the relationship between neighboring pixels. The CoA module incorporates the graphical information and enables the connectivity of roads are better preserved. Extensive experiments on the popular benchmarks (SpaceNet and DeepGlobe datasets) demonstrate that our proposed CoANet establishes new state-of-the-art results. The source code will be made publicly available at: https://mmcheng.net/coanet/.
LC/MS-MS Analyses and in vitro anticancer activity of Tourneuxia variifolia extracts

Abstract Several Saharan plants, despite their abundance of natural compounds, have received little attention. In this study, the chemical composition of polar extracts of Tourneuxia variifolia Coss. (Asteraceae), an endemic species to Algerian Sahara, was investigated and their anticancer activity was evaluated in vitro. The phytoconstituents of both ethyl acetate (EtOAc) and n-butanol (n-BuOH) extracts were screened using LC/MS-MS technique. The anticancer activity of the above extracts was measured against human cervical adenocarcinoma (HeLa) cell line. The LC/MS-MS analyses results revealed that twenty-seven phytochemicals in EtOAc extract and twenty-three in n-BuOH extract were identified and quantified from which isoquercetin and astragalin were the most present. Moreover; the EtOAc extract was found to have a strong anticancer activity (IC50: 46.797 ± 0.060 µg/mL). These findings identified T. variifolia as a potential plant exhibiting anticancer properties. Graphical Abstract
[Easily misdiagnosed pulmonary benign metastasizing leiomyoma with rich-mucus degeneration in intraoperative pathological section: report of a case].

肺良性转移性平滑肌瘤（PBML）是一种与子宫平滑肌瘤相关的罕见疾病。 当其伴广泛黏液变性时，术中快速易误诊为肺内富含黏液的其他肿瘤。 本文报道1例53岁女性的PBML，患者有子宫平滑肌瘤切除史（16年前）。 本次术中快速切片镜下瘤细胞呈簇状散在分布于广泛黏液变性的间质中，细胞温和，无核分裂象，形态与肺原发或转移的富含细胞外黏液的低级别黏液腺癌类似。 术后常规HE、免疫组织化学及荧光原位杂交结果支持PBML。
Influences of student race/ethnicity and gender on autism special education classification considerations

Although the prevalence of autism continues to rise, identification disparities across race/ethnicity and gender persist in schools. The under- and overidentification of specific populations receiving special education services under the classification of autism contribute to misappropriation or lack of intervention services for students. Practicing school psychologists (N = 229) reviewed one of eight vignettes depicting a student displaying possible autism symptoms that varied only by student race/ethnicity and gender. Afterward, they rated the likelihood that they would consider the classification of autism and their confidence in that likelihood rating. School psychologists were more likely to consider an autism classification for Asian female students than Latinx female students. Confidence in likelihood ratings was higher for Asian students than Black students. School psychologists endorsed specific attention to student problem behaviors with minimal notice of possible contributing cultural and linguistic factors. The results highlight that potential implicit biases and lack of cultural consideration play a role in the identification of autism in schools, which may contribute to the nationally documented race/ethnicity and gender identification disparities. Lay Abstract Autism prevalence has continued to rise in recent years. However, females and children from Black, Latinx, and Asian backgrounds are often misidentified or identified less often than White males. These identification disparities make it difficult for children to receive appropriate special education and school intervention services. In this study, school psychologists read a vignette featuring a student with possible autism symptoms. The vignette varied by student race/ethnicity and gender. Afterward, participants rated the likelihood that they would classify the student with autism and their confidence in this rating. Student race/ethnicity and gender influenced both classification likelihood and confidence. These results suggest that school psychologists are influenced by implicit bias and do not fully consider cultural factors in school autism evaluations. This may contribute to identification disparities.
When non-salient information becomes salient in conversational memory: Collaboration shapes the effects of emotion and self-production

People’s memory of what was said and who said what during dialogue plays a central role in mutual comprehension and subsequent adaptation. This article outlines that well-established effects in conversational memory such as the self-production and the emotional effects actually depend on the nature of the interaction. We specifically focus on the impact of the collaborative nature of the interaction, comparing participants’ conversational memory in non-collaborative and collaborative interactive settings involving interactions between two people (i.e., dialogue). The findings reveal that the amplitude of these conversational memory effects depends on the collaborative vs. non-collaborative nature of the interaction. The effects are attenuated when people have the opportunity to collaborate because information that remained non-salient in the non-collaborative condition (neutral and partner-produced words) became salient in the collaborative condition to a level similar to otherwise salient information (emotional and self-produced words). We highlight the importance of these findings in the study of dialogue and conversational memory.
Diagnostic Impact of Clinical Manifestations of Group A Streptococcal Pharyngitis

This study aims to identify the clinical characteristics for diagnosing streptococcal pharyngitis. The correlation between eighteen clinical manifestations and rapid antigen detection test results was analyzed. Among 205 patients, five clinical manifestations, pharyngeal hemorrhage (odds ratio [OR] = 11.85), palatal hemorrhage (OR = 9.32), tonsillar swelling (OR = 4.37), rash (OR = 3.02), and enlarged cervical nodes (OR = 1.91), were significantly correlated with group A Streptococcus (GAS) pharyngitis. Traditional indicators such as fever, pharyngeal redness, acute onset, headache, rhinorrhea, cough, tonsillar exudate, and cervical tenderness were not statistically related to GAS pharyngitis. Therefore, physicians should be cautious in using these traditional indicators.
Small Vessel Disease, a Marker of Brain Health: What the Radiologist Needs to Know

SUMMARY: Small vessel disease, a disorder of cerebral microvessels, is an expanding epidemic and a common cause of stroke and dementia. Despite being almost ubiquitous in brain imaging, the clinicoradiologic association of small vessel disease is weak, and the underlying pathogenesis is poorly understood. The STandards for ReportIng Vascular changes on nEuroimaging (STRIVE) criteria have standardized the nomenclature. These include white matter hyperintensities of presumed vascular origin, recent small subcortical infarcts, lacunes of presumed vascular origin, prominent perivascular spaces, cerebral microbleeds, superficial siderosis, cortical microinfarcts, and brain atrophy. Recently, the rigid categories among cognitive impairment, vascular dementia, stroke, and small vessel disease have become outdated, with a greater emphasis on brain health. Conventional and advanced small vessel disease imaging markers allow a comprehensive assessment of global brain heath. In this review, we discuss the pathophysiology of small vessel disease neuroimaging nomenclature by means of the STRIVE criteria, clinical implications, the role of advanced imaging, and future directions.
Transfer Learning Improves Accelerometer-Based Child Activity Recognition via Subject-Independent Adult-Domain Adaption

Wearable activity recognition can collate the type, intensity, and duration of each child’s physical activity profile, which is important for exploring underlying adolescent health mechanisms. Traditional machine-learning-based approaches require large labeled data sets; however, child activity data sets are typically small and insufficient. Thus, we proposed a transfer learning approach that adapts adult-domain data to train a high-fidelity, subject-independent model for child activity recognition. Twenty children and twenty adults wore an accelerometer wristband while performing walking, running, sitting, and rope skipping activities. Activity classification accuracy was determined via the traditional machine learning approach without transfer learning and with the proposed subject-independent transfer learning approach. Results showed that transfer learning increased classification accuracy to 91.4% as compared to 80.6% without transfer learning. These results suggest that subject-independent transfer learning can improve accuracy and potentially reduce the size of the required child data sets to enable physical activity monitoring systems to be adopted more widely, quickly, and economically for children and provide deeper insights into injury prevention and health promotion strategies.
Tritium secondary kinetic isotope effect on phenylalanine ammonia-lyase-catalyzed reaction.

The mechanism by which phenylalanine ammonia-lyase (PAL, EC 4.3.1.5) catalyzes the reversible elimination of ammonia from phenylalanine yielding (E)-cinnamic acid has gained much attention in the recent years. Dehydroalanine is essential for the catalysis. It was assumed that this prostetic group acts as the electrophile, leading to a covalently bonded enzyme-intermediate complex with quarternary nitrogen of phenylalanine. Recently, an alternative mechanism has been suggested in which the enzyme-intermediate complex is formed in a Friedel-Crafts reaction between dehydroalanine and orthocarbon of the aromatic ring. Using semiempirical calculations we have shown that these two alternative mechanisms can be distinguished on the basis of the hydrogen secondary kinetic isotope effect when tritium label is placed in the orthopositions. Our calculations indicated also that the kinetic isotope effect measured using ring-labeled d(5)-phenylalanine could not be used to differentiate these alternative mechanisms. Measured secondary tritium kinetic isotope effect shows strong dependence on the reaction progress, starting at the inverse value of k(H)/k(T) = 0.85 for 5% conversion and reaching the normal value of about 1.15 as the conversion increases to 20%. This dependence has been interpreted in terms of a complex mechanism with initial formation of the Friedel-Crafts type intermediate.
A WhatsApp-Based Intervention to Improve Maternal Social Support and Maternal–Child Health in Southern Brazil: The Text-Message Intervention to Enhance Social Support (TIES) Feasibility Study

Participatory learning and action cycles with women’s groups have been recommended by the WHO to promote maternal and newborn health, but few studies have tested its feasibility and acceptability in mobile health (mHealth) interventions among mothers of toddlers. This was a mixed-method feasibility assessment of an 8-week WhatsApp-based maternal support group for mothers of toddlers (12–18 months of age) enrolled in a birth cohort study in Southern Brazil. Daily messages and weekly activities were sent by moderators to promote maternal–child outcomes: child nutrition, child sleep, nurturing care, and maternal psychosocial well-being (assessed pre- and post-intervention via self-reported questionnaire). The implementation and engagement of the mothers in the program were assessed by message extraction. Acceptability was evaluated through in-depth interviews (n = 5) and open-ended surveys (n = 10). 1481 messages were exchanged in 3 WhatsApp groups (n = 30 mothers). Mothers were most active on weekdays (68.6% of messages sent on Tuesdays and 72.6% on Thursdays), afternoons (2:00–4:00pm), and evenings (9:00–11:00 pm). Engagement was higher at weeks 1–4. Mothers enjoyed and considered topics relevant. Group interaction was perceived as low, which influenced their participation. The prevalence of depression symptoms decreased from pre- to post-intervention (9% to 5%; P = .04). A moderated mobile-based support group for mothers of toddlers was feasible. mHealth services to promote maternal support are a promising strategy to improve maternal–child outcomes, but engagement and use of the service remains a challenge. Program managers should work with community members to identify ways to support engagement and participation throughout the intervention.
EFFICACY AND SAFETY OF AFLIBERCEPT FOR THE TREATMENT OF IDIOPATHIC CHOROIDAL NEOVASCULARIZATION IN YOUNG PATIENTS

This prospective noncomparative open-label Phase II trial evaluates patients with idiopathic choroidal neovascularization treated with aflibercept. We found a clinically significant functional and anatomical treatment effect for aflibercept. Purpose: To evaluate the mean change in visual acuity at 52 weeks in patients with idiopathic choroidal neovascularization treated with aflibercept. Methods: We conducted a prospective noncomparative open-label Phase-II trial. The dosage regimen evaluated in this study was structured into two periods: (1) from inclusion to 20 weeks: a treat-and-extend period composed of three mandatory intravitreal injections, and complementary intravitreal injections performed if needed; (2) from 21 weeks to 52 weeks: a pro re nata period composed of intravitreal injections performed only if needed. Results: A total of 19 patients were included, and 16 completed the 52-week study. At baseline, the mean best corrected visual acuity was 66.56 (±20.72) letters (≈20/50 Snellen equivalent), and the mean central retinal thickness was 376.74 µm (±93.77). At 52 weeks, the mean change in the best-corrected visual acuity was +19.50 (±19.36) letters [95% confidence interval = +9.18 to +29.82]. None of the patients included lost ≥15 letters at 24 weeks or 52 weeks. The mean change in central retinal thickness was −96.78 µm (±104.29) at 24 weeks and −86.22 µm (±112.27) at 52 weeks. The mean number of intravitreal injections was 5.4 (±3.0) at 52-weeks. No ocular serious adverse events related to the treatment were reported. Conclusion: The present analysis shows clinically significant functional and anatomical treatment effect of aflibercept in case of idiopathic choroidal neovascularization. The treat-and-extend regimen proposed after the first injection seems adequate to treat most neovessels.
Highly Robust Vehicle Lateral Localization Using Multilevel Robust Network

Vision-based vehicle lateral localization has been extensively studied in the literature. However, it faces great challenges when dealing with occlusion situations where the road is frequently occluded by moving/static objects. To address the occlusion problem, we propose a highly robust lateral localization framework called multilevel robust network (MLRN) in this article. MLRN utilizes three deep neural networks (DNNs) to reduce the impact of occluding objects on localization performance from the object, feature, and decision levels, respectively, which shows strong robustness to varying degrees of road occlusion. At the object level, an attention-guided network (AGNet) is designed to achieve accurate road detection by paying more attention to the interested road area. Then, at the feature level, a lateral-connection fully convolutional denoising autoencoder (LC-FCDAE) is proposed to learn robust location features from the road area. Finally, at the decision level, a long short-term memory (LSTM) network is used to enhance the prediction accuracy of lateral position by establishing the temporal correlations of positioning decisions. Experimental results validate the effectiveness of the proposed framework in improving the reliability and accuracy of vehicle lateral localization.
Seizures, Status Epilepticus, and Continuous EEG in the Intensive Care Unit

ABSTRACT PURPOSE OF REVIEW This article discusses the evolving definitions of seizures and status epilepticus in the critical care environment and the role of critical care EEG in both diagnosing seizure activity and serving as a predictive biomarker of clinical trajectory. RECENT FINDINGS Initial screening EEG has been validated as a tool to predict which patients are at risk of future seizures. However, accepted definitions of seizures and nonconvulsive status epilepticus encourage a treatment trial when the diagnosis on EEG is indeterminate because of periodic or rhythmic patterns or uncertain clinical correlation. Similarly, recent data have demonstrated the diagnostic utility of intracranial EEG in increasing the yield of seizure detection. EEG has additionally been validated as a diagnostic biomarker of covert consciousness, a predictive biomarker of cerebral ischemia and impending neurologic deterioration, and a prognostic biomarker of coma recovery and status epilepticus resolution. A recent randomized trial concluded that patients allocated to continuous EEG had no difference in mortality than those undergoing intermittent EEG but could not demonstrate whether this lack of difference was because of studying heterogeneous conditions, examining a monitoring tool rather than a therapeutic approach, or examining an outcome measure (mortality) perhaps more strongly associated with early withdrawal of life-sustaining therapy than to a sustained response to pharmacotherapy. SUMMARY Seizures and status epilepticus are events of synchronous hypermetabolic activity that are either discrete and intermittent or, alternatively, continuous. Seizures and status epilepticus represent the far end of a continuum of ictal-interictal patterns that include lateralized rhythmic delta activity and periodic discharges, which not only predict future seizures but may be further classified as status epilepticus on the basis of intracranial EEG monitoring or a diagnostic trial of antiseizure medication therapy. In particularly challenging cases, neuroimaging or multimodality neuromonitoring may be a useful adjunct documenting metabolic crisis. Specialized uses of EEG as a prognostic biomarker have emerged in traumatic brain injury for predicting language function and covert consciousness, cardiac arrest for predicting coma recovery, and subarachnoid hemorrhage for predicting neurologic deterioration due to delayed cerebral ischemia.
Event-Triggered Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays

This paper addresses the synchronization of multiple fractional-order recurrent neural networks (RNNs) with time-varying delays under event-triggered communications. Based on the assumption of the existence of strong connectivity or a spanning tree in the communication digraph, two sets of sufficient conditions are derived for achieving event-triggered synchronization. Moreover, an additional condition is derived to preclude Zeno behaviors. As a generalization of existing results, the criteria herein are also applicable to the event-triggered synchronization of multiple integer-order RNNs with or without delays. Two numerical examples are elaborated to illustrate the new results.
Pharmacokinetic Drug-Drug Interactions with Drugs Approved by the US Food and Drug Administration in 2020: Mechanistic Understanding and Clinical Recommendations

Drug-drug interaction (DDI) data for small molecular drugs approved by the US Food and Drug Administration in 2020 (N = 40) were analyzed using the University of Washington Drug Interaction Database. The mechanism(s) and clinical relevance of these interactions were characterized based on information available in the new drug application reviews. About 180 positive clinical studies defined as mean area under the curve ratios (AUCRs) ≥1.25 for inhibition DDIs or pharmacogenetic studies and ≤0.8 for induction DDIs were then fully analyzed. Oncology was the most represented therapeutic area, including 30% of 2020 approvals. As victim drugs, inhibition and induction of CYP3A explained most of all observed clinical interactions. Three sensitive substrates were identified: avapritinib (CYP3A), lonafarnib (CYP3A), and relugolix (P-glycoprotein), with AUCRs of 7.00, 5.07, and 6.25 when coadministered with itraconazole, ketoconazole, and erythromycin, respectively. As precipitants, three drugs were considered strong inhibitors of enzymes (AUCR ≥ 5): cedazuridine for cytidine deaminase and lonafarnib and tucatinib for CYP3A. No drug showed strong inhibition of transporters. No strong inducer of enzymes or transporters was identified. As expected, all DDIs with AUCRs ≥5 or ≤0.2 and almost all those with AUCRs of 2–5 and 0.2–0.5 triggered dosing recommendations in the drug label. Overall, all 2020 drugs found to be either sensitive substrates or strong inhibitors of enzymes or transporters were oncology treatments, underscoring the need for effective DDI management strategies in patients with cancer often receiving polytherapy. SIGNIFICANCE STATEMENT This minireview provides a thorough and specific overview of the most significant pharmacokinetic-based DDI data observed (or expected) with small molecular drugs approved by the US Food and Drug Administration in 2020. It will help to better understand mitigation strategies to manage the DDI risks in the clinic.
Inverse Reinforcement Learning for Adversarial Apprentice Games

This article proposes new inverse reinforcement learning (RL) algorithms to solve our defined Adversarial Apprentice Games for nonlinear learner and expert systems. The games are solved by extracting the unknown cost function of an expert by a learner using demonstrated expert’s behaviors. We first develop a model-based inverse RL algorithm that consists of two learning stages: an optimal control learning and a second learning based on inverse optimal control. This algorithm also clarifies the relationships between inverse RL and inverse optimal control. Then, we propose a new model-free integral inverse RL algorithm to reconstruct the unknown expert cost function. The model-free algorithm only needs online demonstration of the expert and learner’s trajectory data without knowing system dynamics of either the learner or the expert. These two algorithms are further implemented using neural networks (NNs). In Adversarial Apprentice Games, the learner and the expert are allowed to suffer from different adversarial attacks in the learning process. A two-player zero-sum game is formulated for each of these two agents and is solved as a subproblem for the learner in inverse RL. Furthermore, it is shown that the cost functions that the learner learns to mimic the expert’s behavior are stabilizing and not unique. Finally, simulations and comparisons show the effectiveness and the superiority of the proposed algorithms.