Philips (China)

= .002); scans obtained in stage 3 (9-13 days) showed consolidation (19 of 21 scans [91%]) and a peak in the total CT score (mean, 7 ± 4); and scans obtained in stage 4 (≥14 days) showed gradual resolution of consolidation (15 of 20 scans [75%]) and a decrease in the total CT score (mean, 6 ± 4) without crazy-paving pattern. Conclusion In patients recovering from coronavirus disease 2019 (without severe respiratory distress during the disease course), lung abnormalities on chest CT scans showed greatest severity approximately 10 days after initial onset of symptoms. © RSNA, 2020.

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Sparsity has been widely exploited for exact reconstruction of a signal from a small number of random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful signal reconstruction techniques in various compressed sensing (CS) studies. In this paper, we propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into CS of both photographic and MRI images. We also propose the use of a nonconvex log det ( X) as a smooth surrogate function for the rank instead of the convex nuclear norm and justify the benefit of such a strategy using extensive experiments. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using the alternative direction multiplier method technique. Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery.

Pregnancy and childbirth did not aggravate the course of symptoms or CT features of COVID-19 pneumonia. All the cases of COVID-19 pneumonia in the pregnant women in our study were the mild type. All the women in this study-some of whom did not receive antiviral drugs-achieved good recovery from COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Light signaling and phytohormones both influence plant growth, development, and stress responses; however, cross talk between these two signaling pathways in response to cold remains underexplored. Here, we report that far-red light (FR) and red light (R) perceived by phytochrome A (phyA) and phyB positively and negatively regulated cold tolerance, respectively, in tomato (Solanum lycopersicum), which were associated with the regulation of levels of phytohormones such as abscisic acid (ABA) and jasmonic acid (JA) and transcript levels of ABA- and JA-related genes and the C-REPEAT BINDING FACTOR (CBF) stress signaling pathway genes. A reduction in the R/FR ratio did not alter cold tolerance, ABA and JA accumulation, and transcript levels of ABA- and JA-related genes and the CBF pathway genes in phyA mutant plants; however, those were significantly increased in wild-type and phyB plants with the reduction in the R/FR ratio. Even though low R/FR treatments did not confer cold tolerance in ABA-deficient (notabilis [not]) and JA-deficient (prosystemin-mediated responses2 [spr2]) mutants, it up-regulated ABA accumulation and signaling in the spr2 mutant, with no effect on JA levels and signaling in the not mutant. Foliar application of ABA and JA further confirmed that JA functioned downstream of ABA to activate the CBF pathway in light quality-mediated cold tolerance. It is concluded that phyA and phyB function antagonistically to regulate cold tolerance that essentially involves FR light-induced activation of phyA to induce ABA signaling and, subsequently, JA signaling, leading to an activation of the CBF pathway and a cold response in tomato plants.

Abstract Working from home has become standard for employees with a university degree. The most common scheme, which has been adopted by around 100 million employees in Europe and North America, is a hybrid schedule, in which individuals spend a mix of days at home and at work each week 1,2 . However, the effects of hybrid working on employees and firms have been debated, and some executives argue that it damages productivity, innovation and career development 3–5 . Here we ran a six-month randomized control trial investigating the effects of hybrid working from home on 1,612 employees in a Chinese technology company in 2021–2022. We found that hybrid working improved job satisfaction and reduced quit rates by one-third. The reduction in quit rates was significant for non-managers, female employees and those with long commutes. Null equivalence tests showed that hybrid working did not affect performance grades over the next two years of reviews. We found no evidence for a difference in promotions over the next two years overall, or for any major employee subgroup. Finally, null equivalence tests showed that hybrid working had no effect on the lines of code written by computer-engineer employees. We also found that the 395 managers in the experiment revised their surveyed views about the effect of hybrid working on productivity, from a perceived negative effect (−2.6% on average) before the experiment to a perceived positive one (+1.0%) after the experiment. These results indicate that a hybrid schedule with two days a week working from home does not damage performance.

The impedance change that occurs when the user holds a mobile phone antenna is a well-known problem. This paper analyses the contributions to this change for a dual-band PIFA. In particular, the contribution of the slot is shown and a method of reducing it is proposed and analyzed

This paper presents the findings of a design, modeling, and fabrication study of ZnO and PbZr/sub 0.3/Ti/sub 0.7/O/sub 3/ thin-film bulk acoustic resonators and filters. Measurements of the high-frequency responses of ZnO resonators having different area are used to develop an acoustic model that accurately represents resonator impedance data. The models are also used to interpret S-parameter measurements on thin-film PbZr/sub 0.3/Ti/sub 0.7/O/sub 3/-based resonators and a value for the effective coupling coefficient deduced. ZnO and PbZr/sub 0.3/Ti/sub 0.7/O/sub 3/ ladder filters were designed based on measured impedance data from single resonators. Ladder filters based on PbZr/sub 0.3/Ti/sub 0.7/O/sub 3/ have been fabricated for the first time. It is shown that the high coupling coefficient in PbZr/sub 0.3/Ti/sub 0.7/O/sub 3/ leads to bandwidths in the range 100/spl sim/120 MHz at a center frequency of 1.6 GHz, larger than the bandwidths of ZnO-based filters.

Amide proton transfer (APT) imaging is capable of detecting mobile cellular proteins and peptides in tumor and monitoring pH effects in stroke, through the saturation transfer between irradiated amide protons and water protons. In this work, four healthy subjects, eight brain tumor patients (four with high-grade glioma, one with lung cancer metastasis, and three with meningioma), and four stroke patients (average 4.3 ± 2.5 days after the onset of the stroke) were scanned at 3 T, using different radiofrequency saturation powers. The APT effect was quantified using the magnetization transfer ratio (MTR) asymmetry at 3.5 ppm with respect to the water resonance. At a saturation power of 2 μT, the measured APT-MRI signal of the normal brain tissue was almost zero, due to the contamination of the negative conventional magnetization transfer ratio asymmetry. This irradiation power caused an optimal hyperintense APT-MRI signal in the tumor and an optimal hypointense signal in the stroke, compared to the normal brain tissue. The results suggest that the saturation power of 2 μT is ideal for APT imaging of these two pathologies at 3 T with the existing clinical hardware.

BACKGROUND: A cluster of patients with coronavirus disease 2019 (COVID-19) pneumonia were discharged from hospitals in Wuhan, China. We aimed to determine the cumulative percentage of complete radiological resolution at each time point, to explore the relevant affecting factors, and to describe the chest CT findings at different time points after hospital discharge. METHODS: Patients with COVID-19 pneumonia confirmed by RT-PCR who were discharged consecutively from the hospital between 5 February 2020 and 10 March 2020 and who underwent serial chest CT scans on schedule were enrolled. The radiological characteristics of all patients were collected and analysed. The total CT score was the sum of non-GGO involvement determined at discharge. Afterwards, all patients underwent chest CT scans during the 1st, 2nd, and 3rd weeks after discharge. Imaging features and distributions were analysed across different time points. RESULTS: A total of 149 patients who completed all CT scans were evaluated; there were 67 (45.0%) men and 82 (55.0%) women, with a median age of 43 years old (IQR 36-56). The cumulative percentage of complete radiological resolution was 8.1% (12 patients), 41.6% (62), 50.3% (75), and 53.0% (79) at discharge and during the 1st, 2nd, and 3rd weeks after discharge, respectively. Patients ≤44 years old showed a significantly higher cumulative percentage of complete radiological resolution than patients > 44 years old at the 3-week follow-up. The predominant patterns of abnormalities observed at discharge were ground-glass opacity (GGO) (125 [83.9%]), fibrous stripe (81 [54.4%]), and thickening of the adjacent pleura (33 [22.1%]). The positive count of GGO, fibrous stripe and thickening of the adjacent pleura gradually decreased, while GGO and fibrous stripe showed obvious resolution during the first week and the third week after discharge, respectively. "Tinted" sign and bronchovascular bundle distortion as two special features were discovered during the evolution. CONCLUSION: Lung lesions in COVID-19 pneumonia patients can be absorbed completely during short-term follow-up with no sequelae. Two weeks after discharge might be the optimal time point for early radiological estimation.

Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

BACKGROUND: Exposure to excessive levels of manganese (Mn) is known to induce psychiatric and motor disorders, including parkinsonian symptoms. Therefore, finding a reliable means for early detection of Mn neurotoxicity is desirable. OBJECTIVES: Our goal was to determine whether in vivo brain levels of γ-aminobutyric acid (GABA), N-acetylaspartate (NAA), and other brain metabolites in male smelters were altered as a consequence of Mn exposure. METHODS: We used T1-weighted magnetic resonance imaging (MRI) to visualize Mn deposition in the brain. Magnetic resonance spectroscopy (MRS) was used to quantify concentrations of NAA, glutamate, and other brain metabolites in globus pallidus, putamen, thalamus, and frontal cortex from a well-established cohort of 10 male Mn-exposed smelters and 10 male age-matched control subjects. We used the MEGA-PRESS MRS sequence to determine GABA levels in a region encompassing the thalamus and adjacent parts of the basal ganglia [GABA-VOI (volume of interest)]. RESULTS: Seven of 10 exposed subjects showed clear T1-hyperintense signals in the globus pallidus indicating Mn accumulation. We found a significant increase (82%; p = 0.014) in the ratio of GABA to total creatine (GABA/tCr) in the GABA-VOI of Mn-exposed subjects, as well as a distinct decrease (9%; p = 0.04) of NAA/tCr in frontal cortex that strongly correlated with cumulative Mn exposure (R = -0.93; p < 0.001). CONCLUSIONS: We demonstrated elevated GABA levels in the thalamus and adjacent basal ganglia and decreased NAA levels in the frontal cortex, indicating neuronal dysfunction in a brain area not primarily targeted by Mn. Therefore, the noninvasive in vivo MRS measurement of GABA and NAA may prove to be a powerful tool for detecting presymptomatic effects of Mn neurotoxicity.

Many applications using wireless sensor networks (WSNs) aim at providing friendly and intelligent services based on the recognition of human's activities. Although the research result on wearable computing has been fruitful, our experience indicates that a user-free sensor deployment is more natural and acceptable to users. In our system, activities were recognized through matching the movement patterns of the objects, to which tri-axial accelerometers had been attached. Several representative features, including accelerations and their fusion, were calculated and three classifiers were tested on these features. Compared with decision tree (DT) C4.5 and multiple-layer perception (MLP), support vector machine (SVM) performs relatively well across different tests. Additionally, feature selection are discussed for better system performance for WSNs

Many modalities of magnetic resonance imaging (MRI) have been confirmed to be of great diagnostic value in glioma grading. Contrast enhanced T1-weighted imaging allows the recognition of blood-brain barrier breakdown. Perfusion weighted imaging and MR spectroscopic imaging enable the quantitative measurement of perfusion parameters and metabolic alterations respectively. These modalities can potentially improve the grading process in glioma if combined properly. In this study, Bayesian Network, which is a powerful and flexible method for probabilistic analysis under uncertainty, is used to combine features extracted from contrast enhanced T1-weighted imaging, perfusion weighted imaging and MR spectroscopic imaging. The networks were constructed using K2 algorithm along with manual determination and distribution parameters learned using maximum likelihood estimation. The grading performance was evaluated in a leave-one-out analysis, achieving an overall grading accuracy of 92.86% and an area under the curve of 0.9577 in the receiver operating characteristic analysis given all available features observed in the total 56 patients. Results and discussions show that Bayesian Network is promising in combining features from multiple modalities of MRI for improved grading performance.

See also the editorial by Lee and Wi et al in this issue.

BACKGROUND: Plants attenuate their responses to a variety of bacterial and fungal pathogens, leading to higher incidences of pathogen infection at night. However, little is known about the molecular mechanism responsible for the light-induced defence response; transcriptome data would likely facilitate the elucidation of this mechanism. RESULTS: In this study, we observed diurnal changes in tomato resistance to Pseudomonas syringae pv. tomato DC3000 (Pto DC3000), with the greatest susceptibility before midnight. Nightly light treatment, particularly red light treatment, significantly enhanced the resistance; this effect was correlated with increased salicylic acid (SA) accumulation and defence-related gene transcription. RNA-seq analysis revealed that red light induced a set of circadian rhythm-related genes involved in the phytochrome and SA-regulated resistance response. The biosynthesis and signalling pathways of multiple plant hormones (auxin, SA, jasmonate, and ethylene) were co-ordinately regulated following Pto DC3000 infection and red light, and the SA pathway was most significantly affected by red light and Pto DC3000 infection. This result indicates that SA-mediated signalling pathways are involved in red light-induced resistance to pathogens. Importantly, silencing of nonexpressor of pathogensis-related genes 1 (NPR1) partially compromised red light-induced resistance against Pto DC3000. Furthermore, sets of genes involved in redox homeostasis (respiratory burst oxidase homologue, RBOH; glutathione S-transferases, GSTs; glycosyltransferase, GTs), calcium (calmodulin, CAM; calmodulin-binding protein, CBP), and defence (polyphenol oxidase, PPO; nudix hydrolase1, NUDX1) as well as transcription factors (WRKY18, WRKY53, WRKY60, WRKY70) and cellulose synthase were differentially induced at the transcriptional level by red light in response to pathogen challenge. CONCLUSIONS: Taken together, our results suggest that there is a diurnal change in susceptibility to Pto DC3000 with greatest susceptibility in the evening. The red light induced-resistance to Pto DC3000 at night is associated with enhancement of the SA pathway, cellulose synthase, and reduced redox homeostasis.

OBJECTIVE. This study aims to assess correlations of the time from symptom onset to diagnosis and treatment with the time to disease resolution and CT scores as based on findings from sequential chest CT examinations.

PURPOSE: To determine whether there are in vivo differences of γ-aminobutyric acid (GABA) levels in frontal and parietal regions of Alzheimer's disease (AD) patients, compared with healthy controls using magnetic resonance spectroscopy ((1) H-MRS). MATERIALS AND METHODS: Fifteen AD patients and fifteen age- and gender-matched healthy controls underwent (1) H-MRS of the frontal and parietal lobes using the "MEGA-Point Resolved Spectroscopy Sequence" (MEGA-PRESS) technique, and cognitive levels of subjects were evaluated using Mini-Mental State Examination (MMSE) tests. MRS data were processed using the Gannet program. Because the signal detected by MEGA-PRESS includes contributions from GABA, macromolecules and homocarnosine, it is labeled as "GABA+" rather than GABA. Differences of GABA+/Cr ratios between AD patients and controls were tested using covariance analysis, adjusting for gray matter fraction. The relationship between GABA+/Cr and MMSE scores was also analyzed. RESULTS: Significant lower GABA+/Cr ratios were found in the parietal region of AD patients compared with controls (P = 0.041). In AD patients, no significant correlations between GABA+/Cr and MMSE scores were found in either the frontal (r = -0.164; P = 0.558) or parietal regions (r = 0.025; P = 0.929). CONCLUSION: Decreased GABA+/Cr levels were present in the parietal region of patients with AD in vivo, suggesting that abnormalities of the GABAergic system may be present in the pathogenesis of AD.

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and to compare algorithm performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and subjective expert assessment for malignancy. Materials and Methods This retrospective study included consecutive women with ovarian tumors undergoing gray scale and color Doppler US from January 2019 to November 2019. Histopathologic analysis was the reference standard. The data set was divided into training (70%), validation (10%), and test (20%) sets. Algorithms modified from residual network (ResNet) with two fusion strategies (feature fusion [hereafter, DLfeature] or decision fusion [hereafter, DLdecision]) were developed. DL prediction of malignancy was compared with O-RADS risk categorization and expert assessment by area under the receiver operating characteristic curve (AUC) analysis in the test set. Results A total of 422 women (mean age, 46.4 years ± 14.8 [SD]) with 304 benign and 118 malignant tumors were included; there were 337 women in the training and validation data set and 85 women in the test data set. DLfeature had an AUC of 0.93 (95% CI: 0.85, 0.97) for classifying malignant from benign ovarian tumors, comparable with O-RADS (AUC, 0.92; 95% CI: 0.85, 0.97; P = .88) and expert assessment (AUC, 0.97; 95% CI: 0.91, 0.99; P = .07), and similar to DLdecision (AUC, 0.90; 95% CI: 0.82, 0.96; P = .29). DLdecision, DLfeature, O-RADS, and expert assessment achieved sensitivities of 92%, 92%, 92%, and 96%, respectively, and specificities of 80%, 85%, 89%, and 87%, respectively, for malignancy. Conclusion Deep learning algorithms developed by using multimodal US images may distinguish malignant from benign ovarian tumors with diagnostic performance comparable to expert subjective and Ovarian-Adnexal Reporting and Data System assessment. © RSNA, 2022 Online supplemental material is available for this article.