Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cogniti...
Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide-spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub-tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develo
How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconst...
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We report on the confirmation and follow-up characterization of two long-period transiting substellar companions on low-eccentricity orbits around TIC 4672985 and TOI-2529, whose transit events were detected by the TE...
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Bioinformatics, an interdisciplinary field that combines biology, mathematics, computer science, medicine, and health science, to integrate, analyze, and interpret biological data, is now becoming increasingly data-in...
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Bioinformatics, an interdisciplinary field that combines biology, mathematics, computer science, medicine, and health science, to integrate, analyze, and interpret biological data, is now becoming increasingly data-intensive. To dig out the treasure from big data powered by high-throughput sequencing technologies, it is highly dependent on Bioinformatics Commons that involves a variety of fundamental resources, includ-
We experimentally demonstrate the ultra-high range resolution of a photonics-based microwave radar using a high repetition rate actively mode-locked laser(AMLL). The transmitted signal and sampling clock in the rada...
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We experimentally demonstrate the ultra-high range resolution of a photonics-based microwave radar using a high repetition rate actively mode-locked laser(AMLL). The transmitted signal and sampling clock in the radar originate from the same AMLL to achieve a large instantaneous bandwidth. A Ka band linearly frequency modulated signal with a bandwidth up to 8 GHz is successfully generated and processed with the electro-optical upconversion and direct photonic sampling. The minor lobe suppression(MLS) algorithm is adopted to enhance the dynamic range at a cost of the range resolution. Two-target discrimination with the MLS algorithm proves the range resolution reaches 2.8 cm. The AMLL-based microwave-photonics radar shows promising applications in high-resolution imaging radars having the features of high-frequency band and large bandwidth.
Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly impor...
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Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.
A prominent feature of earthquakes is their empirical laws including memory (clustering) in time and space. Several earthquake forecasting models, like the EpidemicType Aftershock Sequence (ETAS) model1,2, were develo...
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In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is provided. For a monotone and continuo...
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