164 research outputs found
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample
sizes, along with significant intra-class variation and inter-class similarity.
While prior work has addressed intra-class variation using localization and
segmentation techniques, inter-class similarity may also affect feature
learning and reduce classification performance. In this work, we address this
problem using a novel optimization procedure for the end-to-end neural network
training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces
overfitting by intentionally {introducing confusion} in the activations. With
PC regularization, we obtain state-of-the-art performance on six of the most
widely-used FGVC datasets and demonstrate improved localization ability. {PC}
is easy to implement, does not need excessive hyperparameter tuning during
training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201
Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer
Novel drug-target interactions via link prediction and network embedding
BACKGROUND: As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. RESULTS: We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. CONCLUSIONS: The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04650-w
Enhanced Metallic Properties of SrRuO\u3csub\u3e3\u3c/sub\u3e Thin Films via Kinetically Controlled Pulsed Laser Epitaxy
Metal electrodes are a universal element of all electronic devices. Conducting SrRuO3 (SRO) epitaxial thin films have been extensively used as electrodes in complex-oxide heterostructures due to good lattice mismatches with perovskite substrates. However, when compared to SRO single crystals, SRO thin films have shown reduced conductivity and Curie temperatures (TC), which can lead to higher Joule heating and energy loss in the devices. Here, we report that high-quality SRO thin films can be synthesized by controlling the plume dynamics and growth rate of pulsed laser epitaxy (PLE) with real-time optical spectroscopic monitoring. The SRO thin films grown under the kinetically controlled conditions, down to ca. 16 nm in thickness, exhibit both enhanced conductivity and TC as compared to bulk values, due to their improved stoichiometry and a strain-mediated increase of the bandwidth of Ru 4d electrons. This result provides a direction for enhancing the physical properties of PLE-grown thin films and paves a way to improved device applications
Enhanced Metallic Properties of SrRuO\u3csub\u3e3\u3c/sub\u3e Thin Films via Kinetically Controlled Pulsed Laser Epitaxy
Metal electrodes are a universal element of all electronic devices. Conducting SrRuO3 (SRO) epitaxial thin films have been extensively used as electrodes in complex-oxide heterostructures due to good lattice mismatches with perovskite substrates. However, when compared to SRO single crystals, SRO thin films have shown reduced conductivity and Curie temperatures (TC), which can lead to higher Joule heating and energy loss in the devices. Here, we report that high-quality SRO thin films can be synthesized by controlling the plume dynamics and growth rate of pulsed laser epitaxy (PLE) with real-time optical spectroscopic monitoring. The SRO thin films grown under the kinetically controlled conditions, down to ca. 16 nm in thickness, exhibit both enhanced conductivity and TC as compared to bulk values, due to their improved stoichiometry and a strain-mediated increase of the bandwidth of Ru 4d electrons. This result provides a direction for enhancing the physical properties of PLE-grown thin films and paves a way to improved device applications
Online teacher training in a context for forced immobility: the Case of Gaza, Palestine
This article discusses an action research study that involved the design and delivery of an online training course for teachers of Arabic to speakers of other languages in the Gaza Strip (Palestine). Grounded in Freirean pedagogy, the course aimed to respond to the employment needs of university graduates by creating opportunities for online language teaching. The action research study explored the dynamics at play within the online educational environment, to evidence elements that challenged and/or facilitated effective collaboration between trainers and trainees. This article retraces and discusses the processes through which the course moved from didacticism to engaged critical pedagogy
Validation of OMPS Suomi NPP and OMPS NOAA‐20 Formaldehyde Total Columns With NDACC FTIR Observations
We validate formaldehyde (HCHO) vertical column densities (VCDs) from Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) instruments onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite for 2012–2020 and National Oceanic and Atmospheric Administration-20 (NOAA-20) satellite for 2018–2020, hereafter referred to as OMPS-NPP and OMPS-N20, with ground-based Fourier-Transform Infrared (FTIR) observations of the Network for the Detection of Atmospheric Composition Change (NDACC). OMPS-NPP/N20 HCHO products reproduce seasonal variability at 24 FTIR sites. Monthly variability of OMPS-NPP/N20 has a very good agreement with FTIR, showing correlation coefficients of 0.83 and 0.88, respectively. OMPS-NPP (N20) biases averaged over all sites are −0.9 (4) ± 3 (6)%. However, at clean sites (with VCDs 4.0 × 10 molecules cm, negative biases of −15% ± 4% appear for OMPS-NPP, but OMPS-N20 shows smaller bias of 0.5% ± 6% due to its smaller ground pixel footprints. Therefore, smaller satellite footprint sizes are important in distinguishing small-scale plumes. In addition, we discuss a bias correction and provide lower limit for the monthly uncertainty of OMPS-NPP/N20 HCHO products. The total uncertainty for OMPS-NPP (N20) at clean sites is 0.7 (0.8) × 10 molecules cm, corresponding to a relative uncertainty of 32 (30)%. In the case of HCHO VCDs > 4.0 × 10 molecules cm, however, the relative uncertainty in HCHO VCDs for OMPS-NPP (N20) decreases to 31 (18)%
Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.
BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700
巻頭言 : 所長新任挨拶
Scope: Existing research indicates that anti-inflammatory and antioxidant properties of berberine play major roles in coping with oxidative stress in neurodegenerative diseases, but it is not known if this isoquinoline alkaloid affects inflammatory cytokines such as interleukin 10 in focal cerebral ischemia. Methods and results: Male Wistar rats (10 weeks old) were treated with 40 mg/kg concentration of berberine 1 h after focal cerebral ischemia and the anti-inflammatory properties of berberine were evaluated by immunohistochemical analysis, water content measure and behavioral tests. Evaluation of infarct volume was performed by TTC staining. Immunohistochemistry and behavioral assessment indicated recovery in treatment group compared to only ischemia group. The infarct volume decreased in treatment group compared to ischemia group. Berberine administration significantly decreased brain edema and contributed to the restoration of motor function. Moreover, berberine potently contributed to neuroprotection in motor area through downregulation of pro-inflammatory cytokines and upregulation of anti-inflammatory cytokines. Conclusions: These findings confirm the validity of berberine as a potent anti-inflammatory agent in treatment of ischemic stroke. © 2017 The Author
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