Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention V-Net: A Residual U-Net with Attention Gate Block for Lung Organs At Risk Segmentation

In this paper, we try to incorporate residual connection and Attention Gate block into medical image segmentation network. At first, we construct a 2D residual U-Net (a 2D V-Net) to incorporate residual connection for medical image segmentation. In order to incorporate Attention Gate block into the V-Net, we build up the Attention Residual Block which adds a shortcut into Attention Gate Block. The Attention Residual Block will be more adaptive than raw Attention Gate Block. We also insert the Attention Residul Block into the skip connection between the encoder and the decoder of 2D V-Net and create a new network called Attention V-Net. Then we train and evaluate the networks on the 16th CSTRO conference Lung OAR segmentation competition dataset. What's more, we find out when the mirrored OARs are segmented, the networks may mix up them together. Therefore, we use a postprocessing method to correct the result. Finally, we compare the model with the state-of-the-arts to show the superiority of the proposed network.
Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy

Voluntary movements, like point-to-point or oscillatory human arm movements, are generated by the interaction of several structures. High-level neuronal circuits in the brain are responsible for planning and initiating a movement. Spinal circuits incorporate proprioceptive feedback to compensate for deviations from the desired movement. Muscle biochemistry and contraction dynamics generate movement driving forces and provide an immediate physical response to external forces, like a low-level decentralized controller. A simple central neuronal command like “initiate a movement” then recruits all these biological structures and processes leading to complex behavior, e.g., generate a stable oscillatory movement in resonance with an external spring-mass system. It has been discussed that the spinal feedback circuits, the biochemical processes, and the biomechanical muscle dynamics contribute to the movement generation, and, thus, take over some parts of the movement generation and stabilization which would otherwise have to be performed by the high-level controller. This contribution is termed morphological computation and can be quantified with information entropy-based approaches. However, it is unknown whether morphological computation actually differs between these different hierarchical levels of the control system. To investigate this, we simulated point-to-point and oscillatory human arm movements with a neuro-musculoskeletal model. We then quantify morphological computation on the different hierarchy levels. The results show that morphological computation is highest for the most central (highest) level of the modeled control hierarchy, where the movement initiation and timing are encoded. Furthermore, they show that the lowest neuronal control layer, the muscle stimulation input, exploits the morphological computation of the biochemical and biophysical muscle characteristics to generate smooth dynamic movements. This study provides evidence that the system's design in the mechanical as well as in the neurological structure can take over important contributions to control, which would otherwise need to be performed by the higher control levels.
Global Warming Impacts Micro-Phytoplankton at a Long-Term Pacific Ocean Coastal Station

Understanding impacts of global warming on phytoplankton–the foundation of marine ecosystems–is critical to predicting changes in future biodiversity, ocean productivity, and ultimately fisheries production. Using phytoplankton community abundance and environmental data that span ∼90 years (1931–2019) from a long-term Pacific Ocean coastal station off Sydney, Australia, we examined the response of the phytoplankton community to long-term ocean warming using the Community Temperature Index (CTI), an index of the preferred temperature of a community. With warming of ∼1.8°C at the site since 1931, we found a significant increase in the CTI from 1931–1932 to 2009–2019, suggesting that the relative proportion of warm-water to cold-water species has increased. The CTI also showed a clear seasonal cycle, with highest values at the end of austral summer (February/March) and lowest at the end of winter (August/September), a pattern well supported by other studies at this location. The shift in CTI was a consequence of the decline in the relative abundance of the cool-affinity (optimal temperature = 18.7°C), chain-forming diatom Asterionellopsis glacialis (40% in 1931–1932 to 13% in 2009 onward), and a substantial increase in the warm-affinity (21.5°C), also chain-forming diatom Leptocylindrus danicus (20% in 1931–1932 to 57% in 2009 onward). L. danicus reproduces rapidly, forms resting spores under nutrient depletion, and displays a wide thermal range. Species such as L. danicus may provide a glimpse of the functional traits necessary to be a “winner” under climate change.
Local Sleep Slow-Wave Activity Colocalizes With the Ictal Symptomatogenic Zone in a Patient With Reflex Epilepsy: A High-Density EEG Study

Background: Slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep reflects synaptic potentiation during preceding wakefulness. Epileptic activity may induce increases in state-dependent SWA in human brains, therefore, localization of SWA may prove useful in the presurgical workup of epileptic patients. We analyzed high-density electroencephalography (HDEEG) data across vigilance states from a reflex epilepsy patient with a clearly localizable ictal symptomatogenic zone to provide a proof-of-concept for the testability of this hypothesis. Methods: Overnight HDEEG recordings were obtained in the patient during REM sleep, NREM sleep, wakefulness, and during a right facial motor seizure then compared to 10 controls. After preprocessing, SWA (i.e., delta power; 1–4 Hz) was calculated at each channel. Scalp level and source reconstruction analyses were computed. We assessed for statistical differences in maximum SWA between the patient and controls within REM sleep, NREM sleep, wakefulness, and seizure. Then, we completed an identical statistical comparison after first subtracting intrasubject REM sleep SWA from that of NREM sleep, wakefulness, and seizure SWA. Results: The topographical analysis revealed greater left hemispheric SWA in the patient vs. controls in all vigilance states except REM sleep (which showed a right hemispheric maximum). Source space analysis revealed increased SWA in the left inferior frontal cortex during NREM sleep and wakefulness. Ictal data displayed poor source-space localization. Comparing each state to REM sleep enhanced localization accuracy; the most clearly localizing results were observed when subtracting REM sleep from wakefulness. Conclusion: State-dependent SWA during NREM sleep and wakefulness may help to identify aspects of the potential epileptogenic zone. Future work in larger cohorts may assess the clinical value of sleep SWA to help presurgical planning.
A benchmark for clothes variation in person re‐identification

Person re‐identification (re‐ID) has drawn attention significantly in the computer vision society due to its application and research significance. It aims to retrieve a person of interest across different camera views. However, there are still several factors that hinder the applications of person re‐ID. In fact, most common data sets either assume that pedestrians do not change their clothing across different camera views or are taken under constrained environments. Those constraints simplify the person re‐ID task and contribute to early development of person re‐ID, yet a person has a great possibility to change clothes in real life. To facilitate the research toward conquering those issues, this paper mainly introduces a new benchmark data set for person re‐identification. To the best of our knowledge, this data set is currently the most diverse for person re‐identification. It contains 107 persons with 9,738 images, captured in 15 indoor/outdoor scenes from September 2019 to December 2019, varying according to viewpoints, lighting, resolutions, human pose, seasons, backgrounds, and clothes especially. We hope that this benchmark data set will encourage further research on person re‐identification with clothes variation. Moreover, we also perform extensive analyses on this data set using several state‐of‐the‐art methods. Our dataset is available at https://github.com/nkicsl/NKUP-dataset.
Lugol’s Iodine-Enhanced Micro-CT: A Potential 3-D Imaging Method for Detecting Tongue Squamous Cell Carcinoma Specimens in Surgery

Background A positive surgical margin (PSM) following oral cancer resection results in local recurrence and poor prognosis. Mono-block tumor specimens, especially from the tumor base, are difficult to evaluate. This inaccurate sampling ultimately leads to a false pathological diagnosis. Lugol’s iodine (I2-IK)-enhanced micro-CT is an emerging method to image tumor specimens. This study explores the feasibility of I2-IK-enhanced micro-CT to evaluate the surgical margin for tongue squamous cell carcinoma (TSCC) specimens and to further seek optimal staining parameters. Methods Rabbit tongue tissues and human TSCC samples were imaged via I2-IK-enhanced micro-CT. The optimal I2-IK concentration and staining time were determined before clinical application using tissue shrinkage, micro-CT image quality, and effect on pathological diagnosis as assessment criteria. Next, 6 TSCC specimens were used to verify the process feasibility of surgical margin imaging with the optimal parameters. Finally, the possible reason by which I2-IK could enhance micro-CT imaging was validated in vitro. Results I2-IK staining influenced specimen shrinkage, micro-CT image quality, and pathological image quality in a concentration- and time-dependent manner. After comprehensively considering these indicators, 3% I2-IK staining for 48 and 12 h were found to be optimal for rabbit tongue tissues and TSCC samples, respectively. This method could provide a detailed 3-D structure of TSCC samples compared with H&E sections. Moreover, tumor and normal tissues could be differentiated by their glycogen content, which has high affinity with I2-IK. Conclusions I2-IK-enhanced micro-CT could, thus, indicate the tumor margin and assist pathological sampling in patients with TSCC postoperation.
Molecular Identification and Management of Rhizoctonia Fragariae the Pathogen of Black Root Rot of Strawberry Plant

Strawberry plants are susceptible to a large number of pests and diseases and this can affect the quality and yield value of the fruit. Black root rot is an important disease of strawberry caused by a complex of fungi including Rhizoctonia. The most recognizable species of Rhizoctonia are R. solani and R. fragariae which are multinucleate and binucleate species, respectively. This work is aimed to isolate, identify and control the strawberry root rot caused by R. fragariae. Infected strawberry samples were collected from Erbil, Slemani, Duhok and Garmiyan Provinces. The identification of isolated fungi was achieved by using traditional methods along with molecular methods using polymerase chain reaction (PCR). In the later method, specific primers were designed and used to identify Rhizoctonia species. Several disease management options, including biological by using two species of Trichoderma, and chemical methods using Pristine fungicide, were also investigated. Sampling of strawberry plants revealed that the disease is prevalent in Kurdistan region and the isolated fungi, R. solani, Rhizoctonia sp. , and R. fragariae, were pathogens of the disease causing crown and root rot of strawberry. PCR amplification was confirmed the identification of the species of Rhizoctonia. The results of control methods revealed that the most effective treatments were achieved using the fungicide followed by the use of the combination of T. harzianum and T. viride.
Improving Biomedical Ontology Matching Using Domain-specific Word Embeddings

Biomedical ontology is an effective carrier of biomedical knowledge. In real-world applications, many biomedical ontologies describe knowledge in the same field. In order to make full use of existing knowledge, it is necessary to carry out knowledge fusion to obtain a unified knowledge structure. For this reason, it becomes particularly important to find mapping entities that refer to the same object in different ontologies. At present, a large number of automatic matching systems engineers features and match entities by the name of the entity, the ontology structure and external resources. These methods have achieved encouraging results, but they ignore the semantic information of the entity labels. On the other hand, the representation learning method has already shined in many areas of natural language processing. The word vector obtained by the word embedding method contains semantic information of words. However, a separate representation learning method cannot fully capture the structural information of the ontology. In this paper, we propose a method which combines the representation learning method as a component with traditional feature engineering methods to improve the performance of the matching systems. We tested our method on real-world datasets. Experimental results show that our method can improve the recall and F1-measure of the existing matching systems.
Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks

Nonlinear interactions in the dendritic tree play a key role in neural computation. Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks, such as the Neural Engineering Framework, tend to assume a linear superposition of postsynaptic currents. In this letter, we present a series of extensions to the Neural Engineering Framework that facilitate the construction of networks incorporating Dale's principle and nonlinear conductance-based synapses. We apply these extensions to a two-compartment LIF neuron that can be seen as a simple model of passive dendritic computation. We show that it is possible to incorporate neuron models with input-dependent nonlinearities into the Neural Engineering Framework without compromising high-level function and that nonlinear postsynaptic currents can be systematically exploited to compute a wide variety of multivariate, band-limited functions, including the Euclidean norm, controlled shunting, and nonnegative multiplication. By avoiding an additional source of spike noise, the function approximation accuracy of a single layer of two-compartment LIF neurons is on a par with or even surpasses that of two-layer spiking neural networks up to a certain target function bandwidth.
Gender Differences in Graphic Design Q&As

Question and answer (Q&A) sites can capture a range of user perspectives on using complex, feature-rich software. Little is known, however, on who is contributing to the sites. We look at contribution diversity from the perspective of gender in a domain with near gender parity: graphic design. Through content analysis of 330 answers from two popular Q&A sites and semi-structured interviews with 24 graphic designers, we examine who is contributing, what content, how the community shows appreciation towards their answers, and perceived motivations and barriers to participation. We find that despite gender balance in the field, women contribute far less frequently than men. We also see gender differences in contribution styles and user appreciation. Our interviews shed further light on how Q&A community cultures might be impacting men and women differently and how design choices made by the sites? developers might be exacerbating these differences. We suggest implications for design for improving gender inclusivity.
Analyzing Social Relations in Startup Ecosystems

A startup ecosystem is a creative environment aiming to promote entrepreneurship and innovation. Startups interact with other entities, building a complex social network. Those connections arise from the interdependence and co-evolution ties between different actors, who share resources to achieve common goals. A smart startup ecosystem demands an understanding of the interests and capabilities of each participant. It could allow identifying convergences and possible partnerships that can help in the new business growth. The Coral platform is a social network analysis based approach to assist in evaluating existing relationships in communities. This paper applies the Coral platform to support incubators’ activities in the local community improvements. Our case study investigated the members’ social and technological aspects in a small entrepreneurial group according to the essential indicators defined by managers: experts, reputation, and professional profile.
Changes of evening exposure to electronic devices during the COVID-19 lockdown affect the time course of sleep disturbances

Study Objectives: During the COVID-19 lockdown, there was a worldwide increase in electronic devices' daily usage. The exposure to backlit screens before falling asleep leads to negative consequences on sleep health through its influence on the circadian system. We investigated the relationship between the changes in evening screen exposure and the time course of sleep disturbances during the home confinement period due to COVID-19. Methods: 2123 Italians were longitudinally tested during the third and the seventh week of lockdown. The web-based survey evaluated sleep quality and insomnia symptoms through the Pittsburgh Sleep Quality Index and the Insomnia Severity Index. During the second assessment, respondents reported the changes in the backlit screen exposure in the two hours before falling asleep. Results: Participants who increased electronic device usage showed decreased sleep quality, exacerbated insomnia symptoms, reduced sleep duration, higher sleep onset latency, and delayed bedtime and rising time. In this subgroup, the prevalence of poor sleepers and clinical insomniacs increased. Conversely, respondents reporting decreased screen exposure exhibited improved sleep quality and insomnia symptoms. In this subgroup, the prevalence of poor sleepers and clinical insomniacs decreased. Respondents preserving their screen time habits did not show any change in the sleep parameters. Conclusions: Our investigation demonstrated a strong relationship between the modifications of the evening electronic device usage and the time course of sleep disturbances during the lockdown period. Interventions to raise public awareness about the risks of excessive exposure to backlit screens are necessary to prevent sleep disturbances and foster well-being during the home confinement due to COVID-19.
USP22 Protects Against Myocardial Ischemia–Reperfusion Injury via the SIRT1-p53/SLC7A11–Dependent Inhibition of Ferroptosis–Induced Cardiomyocyte Death

Myocardial ischemia–reperfusion (MI/R) injury is characterized by iron deposition and reactive oxygen species production, which can induce ferroptosis. Ferroptosis has also been proposed to promote cardiomyocyte death. The current study sought to define the mechanism governing cardiomyocyte death in MI/R injury. An animal model of MI/R was established by ligation and perfusion of the left anterior descending coronary artery, and a cellular model of IR was constructed in cardiomyocytes. ChIP assay was then conducted to determine the interaction among USP22, SIRT1, p53, and SLC7A11. Loss- and gain-of-function assays were also conducted to determine the in vivo and in vitro roles of USP22, SIRT1, and SLC7A11. The infarct size and pathological changes of myocardial tissue were observed using TCC and hematoxylin–eosin staining, and the levels of cardiac function– and myocardial injury–related factors of rats were determined. Cardiomyocyte viability and apoptosis were evaluated in vitro, followed by detection of ferroptosis-related indicators (glutathione (GSH), reactive oxygen species, lipid peroxidation, and iron accumulation). USP22, SIRT1, and SLC7A11 expressions were found to be down-regulated, whereas p53 was highly expressed during MI/R injury. USP22, SIRT1, or SLC7A11 overexpression reduced the infarct size and ameliorated pathological conditions, cardiac function, as evidenced by reduced maximum pressure, ejection fraction, maximum pressure rate, and myocardial injury characterized by lower creatine phosphokinase and lactate dehydrogenase levels in vivo. Moreover, USP22, SIRT1, or SLC7A11 elevation contributed to enhanced cardiomyocyte viability and attenuated ferroptosis-induced cell death in vitro, accompanied by increased GSH levels, as well as decreased reactive oxygen species production, lipid peroxidation, and iron accumulation. Together, these results demonstrate that USP22 overexpression could inhibit ferroptosis-induced cardiomyocyte death to protect against MI/R injury via the SIRT1/p53/SLC7A11 association.
Neuronal Activity at Synapse Resolution: Reporters and Effectors for Synaptic Neuroscience

The development of methods for the activity-dependent tagging of neurons enabled a new way to tackle the problem of engram identification at the cellular level, giving rise to groundbreaking findings in the field of memory studies. However, the resolution of activity-dependent tagging remains limited to the whole-cell level. Notably, events taking place at the synapse level play a critical role in the establishment of new memories, and strong experimental evidence shows that learning and synaptic plasticity are tightly linked. Here, we provide a comprehensive review of the currently available techniques that enable to identify and track the neuronal activity with synaptic spatial resolution. We also present recent technologies that allow to selectively interfere with specific subsets of synapses. Lastly, we discuss how these technologies can be applied to the study of learning and memory.
Identification of Theaflavin-3,3’-Digallate as a Novel Zika Virus Protease Inhibitor

Mounting evidence indicates that Zika virus (ZIKV) is closely related to neurological disorders such as microcephaly and Guillain-Barré syndrome. There are currently no effective vaccines and FDA-approved inhibitors against ZIKV infection. The flaviviral heterodimeric serine protease NS2B-NS3 plays an essential role in ZIKV maturation and replication, thus becoming a promising target in anti-ZIKV therapy. Herein, we developed a fluorescence-based screening assay to search for inhibitors targeting the ZIKV NS2B-NS3 protease (ZIKVpro), and identified theaflavin-3,3’-digallate (ZP10), a natural active compound derived from black tea, as a potent ZIKV protease inhibitor in vitro (IC50 = 2.3 μM). ZP10 exhibited dose-dependent inhibitory effect on ZIKV replication (EC50 = 7.65 μM). Western blot analysis suggested that ZP10 inhibited the cleavage processing of viral polyprotein precursor in cells either infected with ZIKV or expressing minimal self-cleaving proteinase NS2B-3 protease, resulting in inhibition of virus growth. Moreover, ZP10 was showed to directly bind to ZIKVpro, and a docking model further revealed that ZP10 interacted with several critical residues at the proteolytic cavity of the ZIKVpro. This study highlights that ZP10 has anti-ZIKV potency through ZIKVpro inhibition, which indicates its potential application in anti-ZIKV therapy.