Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Empathy on the Attentional Processing of Painful and Emotional Stimuli

Background Empathy is shown to affect the attentional processing of painful stimuli and emotional stimuli. However, whether the attentional effects on emotional stimuli depend on emotional valence and the nature of the relationship between the attentional effects on different stimuli are still unknown. Methods In the present study, 25 high-empathy (HE) participants and 25 low-empathy (LE) participants were recruited to perform dot-probe tasks on painful stimuli and emotional stimuli. Results The results showed that HE individuals had weak attentional disengagement to painful pictures. More importantly, regarding emotional pictures, HE individuals showed attentional avoidance to negative emotion pictures, while LE individuals showed attentional bias to positive emotion pictures. Correlation analysis showed that the attentional bias score and attentional disengagement score were only associated with each other within the same category of stimuli (painful, positive or negative stimuli). Conclusion These results revealed that HE individuals mainly showed attentional avoidance to negative stimuli, while LE individuals mainly showed attentional bias to positive stimuli.
Application of biological and fisheries attributes to assess the vulnerability and resilience of tropical marine fish species

Taking advantage of published data on life-history traits and short-term information on fishery parameters from 3132 records for 644 fish stocks along the coast of India, we calculated resilience (R) and vulnerability (V). Further, we developed an Index of Resilience and Vulnerability (IRV) for 133 species of tropical finfishes, crustaceans, and molluscs. Using 7 resilience and 6 vulnerability attributes, two-dimensional scatter plots of the resilience and vulnerability scores were generated and the Euclidean distance and angle from the origin to each point were calculated to determine IRV and the effect of fishing on fish species. By ranking the species, the top 10 highly resilient, highly vulnerable, and high-risk species (low IRV) were identified. While small-sized species with fast growth rate and low trophic level were among the highly resilient species, large predatory species such as sharks and barracudas were among the highly vulnerable and high-risk species. More than 100 of the 133 species were resilient-yet-vulnerable, and most crustaceans showed high resilience. Differences in IRV scores among species within the same family were discernible, indicating the differences in the biological characteristics and response to fishing. Sensitivity analysis indicated that an abridged IRV with 6 attributes works similar to 13 attributes and can be used in data-deficient situations. Comparison of R and V of IRV with other assessments showed different results because of divergences in the objectives, number and types of attributes, and thresholds used. These assessments do not convey the same information and therefore great care must be taken for reproducing these frameworks to other fisheries. The results of IRV analysis can be useful for stock assessments and in developing effective management measures in combination with other complementary information.
Electroconvulsive Therapy for Traumatic Brain Injury and Schizoaffective Disorder

Traumatic brain injury (TBI) is a source of disability and mortality with high rates of psychiatric disorders. Patients with comorbid TBI and psychiatric disorders may be safely treated with electroconvulsive therapy (ECT). In this case report, we present a 34-year-old man with the diagnosis of major neurocognitive disorder due to traumatic brain injury and schizoaffective disorder, bipolar type. He received an index course of 19 ECT treatments with clinical improvements in his psychosis, mood, and cognition. This case may support the utilization of ECT for patients with comorbid TBI and schizoaffective disorder.
Analysis of Causes of Hospitalization Among Children with Sickle Cell Disease in a Group of Private Hospitals in Jeddah, Saudi Arabia

Purpose Sickle cell anemia (SCA) is a chronic hematologic condition that requires frequent hospitalization representing a significant economic burden on the health services. The aim of this study was to explore the causes and underlying factors of hospitalization among children with SCA, as well as the factors affecting their length of hospital stay. Patients and Methods This retrospective study included children and adolescents less than 16 years old who were admitted in a group of private hospitals in Jeddah, Saudi Arabia, during the period from January 2018 to December 2019. Results A total of 94 patients were included in this study, 59.6% were males, with a mean age 7.29 ± 3.82 years. The majority of the patients (91.5%) had sickle cell disease. The most common cause of hospital admission was vaso-occlusive crises (VOC) (64.9%) followed by infection (24.5%), acute chest syndrome (ACS) (18.1%), and acute hemolytic crisis (12.8%). We found no significant difference between gender and different causes of admissions (p > 0.05). While in relation to age group, limb pain and back pain were found to be significantly more frequent among children ≥ 7 years old (p = 0.03,0.04), while infections were significantly more frequent among children < 7 years old (p = 0.003). We analyzed the length of hospital stay and different factors, and we found that the mean length of hospital stay was significantly higher among children who were admitted with infections (p = 0.01) and ACS (p < 0.001) and among children who are non-compliant on hydroxyurea (p = 0.04). Conclusion The most common cause of hospitalization among children with SCD in Jeddah, Saudi Arabia, was VOC followed by infection, ACS and acute anemia. The length of hospital stay was more prolonged among children with infection and ACS, as well as children who were non-compliant to hydroxyurea.
Successful delivery of subretinal aflibercept (new surgical technique) for the treatment of submacular hemorrhage in idiopathic polypoidal choroidal vasculopathy

Abstract Submacular hemorrhage (SMH) is often a result of trauma, wet age-related macular degeneration or IPCV and frequently leads to blindness secondary to extreme toxicity of hemoglobin products on photoreceptors. We describe a new technique of subretinal aflibercept injection during pars plana vitrectomy for the treatment of SMH in idiopathic polypoidal choroidal vasculopathy (IPCV). A 55-old male presented with sudden loss of vision (HM) secondary to massive subretinal hemorrhage associated with IPCV. Subretinal injection of aflibercept with a 25 g/42 g cannula coupled to the viscous fluid control unit of a standard vitrectomy system was performed during parsplana vitrectomy. Controlled injection of aflibercept intra-operatively has resulted in a resolution of SMH (confirmed with OCT and ICG). Visual acuity improved from HM to 20/20. This combined approach delivered anti-VEGF agent to target tissue in controlled fashion with the assistance of VFC system (similar to gene therapy) and restored full vision with resolution of SMH.
A Comprehensive Understanding of Choroidal Metastasis from Lung Cancer

Abstract The incidence of lung cancer with intraocular metastasis is low, of which choroidal metastasis is the most painful metastatic lesion. The clinical symptoms resulting from choroidal metastasis from lung cancer easily detected although they are rarely identified prior to the diagnosis of the primary malignancy. The quality of life of patients is inevitably impaired. Some lung cancer patients complain of ocular symptoms as the first manifestation of lung cancer. Early diagnosis and treatment can significantly overcome or delay the visual impairment and improve prognosis. The main therapeutic modalities include systemic and local treatments, while observation is also a treatment option. Currently, the feasibility and effectiveness of various treatment options are controversial worldwide. Herein, we summarize the underlying mechanisms, epidemiology, clinical features, auxiliary examinations, diagnosis, and recent treatment options for intraocular metastases.
Association Between NR4A2 Gene Polymorphism and Depressive Symptoms and Antidepressant Effect

Objective Although the pathogenesis of major depressive disorder (MDD) is still unclear, studies have shown that the dopaminergic system of depressed patients is defective, and that NR4A2 is an important transcription factor affecting the development and maintenance of dopaminergic neurons. As such, NR4A2 levels affected by NR4A2 single nucleotide polymorphisms (SNPs) may be associated with MDD. This study examined whether NR4A2 SNPs are associated with depressive symptoms and antidepressant efficacy. Methods A total of 441 patients with first-episode depression were enrolled in this study. We analysed three SNPs of NR4A2, using the 17-item Hamilton Depression Rating Scale (HAM-D) and its four factors to obtain scores at baseline and at the end of 6 weeks. UNPHASED software was employed for quantitative character analysis, and SPSS software was adopted for antidepressant efficacy analysis. Results Patients with rs12803-A exhibited higher scores of retardation symptoms. Patients with the rs834834-C allele and rs834834-CC genotype had higher retardation symptom scores. Patients with rs3769340 exhibited greater antidepressant efficacy. Conclusion NR4A2 gene polymorphisms are associated with retardation symptoms, somatic symptoms (gastro-intestinal), anxiety-based somatic symptoms, insight, and weight loss in patients with MDD. Additionally, rs3769340 may be a predictor of antidepressant efficacy in patients with major depressive disorder.
Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network

Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to evaluate a complete sample; additionally, it possesses the disadvantage of inconsistent reliability as the accuracy of the reading may differ depending on the performer’s skill. This study aims at detecting hyphae more quickly, conveniently, and consistently through deep learning using images obtained from microscopy used in real-world practice. An object detection convolutional neural network, YOLO v4, was trained on microscopy images with magnifications of 100×, 40×, and (100+40)×. The study was conducted at the Department of Dermatology at Veterans Health Service Medical Center, Seoul, Korea between January 1, 2019 and December 31, 2019, using 3,707 images (1,255 images for training, 1,645 images for testing). The average precision was used to evaluate the accuracy of object detection. Precision recall curve analysis was performed for the hyphal location determination, and receiver operating characteristic curve analysis was performed on the image classification. The F1 score, sensitivity, and specificity values were used as measures of the overall performance. The sensitivity and specificity were, respectively, 95.2% and 100% in the 100× data model, and 99% and 86.6% in the 40× data model; the sensitivity and specificity in the combined (100+40)× data model were 93.2% and 89%, respectively. The performance of our model had high sensitivity and specificity, indicating that hyphae can be detected with reliable accuracy. Thus, our deep learning-based autodetection model can detect hyphae in microscopic images obtained from real-world practice. We aim to develop an automatic hyphae detection system that can be utilized in real-world practice through continuous research.
Antibiotic resistance in uropathogens across northern Australia 2007–20 and impact on treatment guidelines

Abstract Background Urinary tract infections are common and are increasingly resistant to antibiotic therapy. Northern Australia is a sparsely populated region with limited access to healthcare, a relatively high burden of disease, a substantial regional and remote population, and high rates of antibiotic resistance in skin pathogens. Objectives To explore trends in antibiotic resistance for common uropathogens Escherichia coli and Klebsiella pneumoniae in northern Australia, and how these relate to current treatment guidelines in the community and hospital settings. Methods We used data from an antibiotic resistance surveillance system. We calculated the monthly and yearly percentage of isolates that were resistant in each antibiotic class, by bacterium. We analysed resistance proportions geographically and temporally, stratifying by healthcare setting. Using simple linear regression, we investigated longitudinal trends in monthly resistance proportions and correlation between community and hospital isolates. Results Our analysis included 177 223 urinary isolates from four pathology providers between 2007 and 2020. Resistance to most studied antibiotics remained <20% (for E. coli and K. pneumoniae, respectively, in 2019: amoxicillin/clavulanate 16%, 5%; cefazolin 17%, 8%; nitrofurantoin 1%, 31%; trimethoprim 36%, 17%; gentamicin 7%, 2%; extended-spectrum cephalosporins 8%, 5%), but many are increasing by 1%–3% (absolute) per year. Patterns of resistance were similar between isolates from community and hospital patients. Conclusions Antibiotic resistance in uropathogens is increasing in northern Australia, but treatment guidelines generally remain appropriate for empirical therapy of patients with suspected infection (except trimethoprim in some settings). Our findings demonstrate the importance of local surveillance data (HOTspots) to inform clinical decision making and guidelines.
EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments

In this article, we present an efficient planning system for automated vehicles in highly interactive environments (EPSILON). EPSILON is an efficient interaction-aware planning system for automated driving, and is extensively validated in both simulation and real-world dense city traffic. It follows a hierarchical structure with an interactive behavior planning layer and an optimization-based motion planning layer. The behavior planning is formulated from a partially observable Markov decision process (POMDP), but is much more efficient than naively applying a POMDP to the decision-making problem. The key to efficiency is guided branching in both the action space and observation space, which decomposes the original problem into a limited number of closed-loop policy evaluations. Moreover, we introduce a new driver model with a safety mechanism to overcome the risk induced by the potential imperfectness of prior knowledge. For motion planning, we employ a spatio-temporal semantic corridor (SSC) to model the constraints posed by complex driving environments in a unified way. Based on the SSC, a safe and smooth trajectory is optimized, complying with the decision provided by the behavior planner. We validate our planning system in both simulations and real-world dense traffic, and the experimental results show that our EPSILON achieves human-like driving behaviors in highly interactive traffic flow smoothly and safely without being overconservative compared to the existing planning methods.
TWO STREAMS AND TWO RESOLUTION SPECTROGRAMS MODEL FOR END-TO-END AUTOMATIC SPEECH RECOGNITION

The Transformer has shown tremendous progress in Automatic Speech Recognition (ASR), outperforming recurrent neural network-based approaches. Transformer architecture is good at parallelizing data to accelerate as well as capturing content-based global interaction. However, most studies with Transformer have been utilized only shallow features extracted from the backbone without taking advantage of the deep feature that possesses invariant property. In this paper, we propose a novel framework with the Two Streams and Two Resolution spectrograms Model (TSTRM) that consists of different resolution spectrograms for different stream aiming to capture both shallow and deep features. The feature extraction module consists of a deep network for low-resolution spectrogram and a shallow network for high-resolution spectrogram. The backbone obtains not only detailed acoustic information for speech-text alignment but also utterance-level representation that contains speaker information. Both features are fused with our proposed fusion method and then input into the Transformer encoder-decoder. The proposed framework shows the state-of-the-art results on the HKUST Mandarin telephone and Librispeech corpora. To the best of our knowledge, this is the ﬁrst investigation of incorporating deep features to the backbone and use both low and high resolutions spectrogram to focus on global and local information. Code is available at https://github.com/happyjin/TSTRM
Active Observer Visual Problem-Solving Methods are Dynamically Hypothesized, Deployed and Tested

The STAR architecture was designed to test the value of the full Selective Tuning model of visual attention for complex real-world visuospatial tasks and behaviors. However, knowledge of how humans solve such tasks in 3D as active observers is lean. We thus devised a novel experimental setup and examined such behavior. We discovered that humans exhibit a variety of problem-solving strategies whose breadth and complexity are surprising and not easily handled by current methodologies. It is apparent that solution methods are dynamically composed by hypothesizing sequences of actions, testing them, and if they fail, trying different ones. The importance of active observation is striking as is the lack of any learning effect. These results inform our Cognitive Program representation of STAR extending its relevance to real-world tasks.
FOX-NAS: Fast, On-device and Explainable Neural Architecture Search

Neural architecture search can discover neural networks with good performance, and One-Shot approaches are prevalent. One-Shot approaches typically require a supernet with weight sharing and predictors that predict the performance of architecture. However, the previous methods take much time to generate performance predictors thus are inefficient. To this end, we propose FOX-NAS that consists of fast and explainable predictors based on simulated annealing and multivariate regression. Our method is quantization-friendly and can be efficiently deployed to the edge. The experiments on different hardware show that FOX-NAS models outperform some other popular neural network architectures. For example, FOX-NAS matches MobileNetV2 and EfficientNet-Lite0 accuracy with 240% and 40% less latency on the edge CPU. Search code and pre-trained models are released at https://github.com/great8nctu/FOX-NAS.1
Characterization of manganese doped Y-TZP for biomedical applications

The densification behaviour and mechanical properties of manganese oxide doped yttria tetragonal zirconia polycrystals (MnO2-doped Y-TZP) were studied. Green samples containing varying amounts of MnO2 (0.05 to 1 wt%) were prepared using the wet colloidal technique. A pressure less sintering technique was used to sinter the specimens at temperatures varying between 1150°C to 1600°C. Sintered bodies were characterized to determine the bulk density, tetragonal content, Young’s Modulus, hardness and fracture toughness. The results showed that the sintered bulk density and Young’s Modulus was enhanced by the addition of up to 0.1 wt% MnO2. On the other hand, fracture toughness of the material was further improved and more prominent for dopant addition of up to 0.3 wt% but for the 0.5 wt% addition of MnO2, an initial decrease in trend was observed, which after a sintering temperature of 1350°C, a drastic increase in properties was observed. As for the hardness of the material, addition of all wt% of dopant exhibited an improvement up to a sintering temperature of 1400°C. In addition to that, hydrothermal ageing induced phase transformation of Y-TZP sintered at 1300°C for samples containing 0.5 and 1.0 wt% of MnO2 was suppressed.
Physical Drivers of Emission-line Diversity of SDSS Seyfert 2s and LINERs after Removal of Contributions from Star Formation

Ionization sources other than H ii regions give rise to the right-hand branch in the standard ([N ii]) BPT diagram, populated by Seyfert 2s and LINERs. However, because the majority of Seyfert/LINER hosts are star-forming (SF), H ii regions contaminate the observed lines to some extent, making it unclear if the position along the branch is merely due to various degrees of mixing between pure Seyferts/LINERs and SF, or whether it reflects the intrinsic diversity of Seyfert/LINER ionizing sources. In this study, we empirically remove SF contributions in ∼100,000 Seyferts/LINERs from SDSS using the doppelganger method. We find that mixing is not the principal cause of the extended morphology of the observed branch. Rather, Seyferts/LINERs intrinsically have a wide range of line ratios. Variations in ionization parameter and metallicity can account for much of the diversity of Seyfert/LINER line ratios, but the hardness of the ionization field also varies significantly. Furthermore, our k-means classification on seven decontaminated emission lines reveals that LINERs are made up of two populations, which we call soft and hard LINERs. The Seyfert 2s differ from both types of LINERs primarily by higher ionization parameter, whereas the two LINER types mainly differ from each other (and from star-forming regions) in the hardness of the radiation field. We confirm that the [N ii] BPT diagram more efficiently identifies LINERs than [S ii] and [O i] diagnostics, because in the latter many LINERs, especially soft ones, occupy the same location as pure starformers, even after the SF has been removed from LINER emission.
On Topological Bihyperbolic Modules

In this paper we introduce topological modules over the ring of bihyperbolic numbers. We discuss bihyperbolic convexity, bihyperbolic-valued seminorms and bihyperbolic-valued Minkowski functionals in topological bihyperbolic modules. Finally we introduce locally bihyperbolic convex modules. AMS Subject Classification (2010) : 30G35, 46A03, 46A19, 52A07.
Measurement of a wavelength sized optical vortex knot

In the past decade, optical vortex knots with axial dimensions on the order of $10^2-10^5$ wavelengths ($\lambda$) have been implemented in the laboratory. However, many potential applications require a drastic reduction on the the size of these knots to the order of $\lambda$. In this work, we show the first measurement of an optical vortex knot (trefoil) with an axial length of $1.99\, \lambda$ contained within a volume ($x\times y\times z$) of $1.90\times 2.19\times 1.99 \, \lambda^3$. Prior to focusing, the laser light is linearly polarized ($x$, horizontal) and we observe the knot in the dominant $x$ polarization component of the tightly focused field. We also observe the transition of the trefoil knot to a couple of vortex loops via reconnection of the vortex lines as we change the spatial scale of the angular spectrum representation of the field.