Miracle-dz/UniFAR-FTUs huggingface dataset:
Alterations of Calcium Channels in a Mouse Model of Huntington’s Disease and Neuroprotection by Blockage of CaV1 Channels

Huntington’s disease (HD) is a neurodegenerative autosomal dominant disorder, characterized by symptoms of involuntary movement of the body, loss of cognitive function, psychiatric disorder, leading inevitably to death. It has been previously described that higher levels of brain expression of Cav1 channels are involved in major neurodegenerative disorders, such as Alzheimer’s disease and Parkinson’s disease. Our results demonstrate that a bacterial artificial chromosome (BAC)-mediated transgenic mouse model (BACHD mice) at the age of 3 and 12 months exhibits significantly increased Cav1.2 protein levels in the cortex, as compared with wild-type littermates. Importantly, electrophysiological analyses confirm a significant increase in L-type Ca2+ currents and total Ca2+ current density in cortical neurons from BACHD mice. By using an in vitro assay to measure neuronal cell death, we were able to observe neuronal protection against glutamate toxicity after treatment with Cav1 blockers, in wild-type and, more importantly, in BACHD neurons. According to our data, Cav1 blockers may offer an interesting strategy for the treatment of HD. Altogether, our results show that mutant huntingtin (mHtt) expression may cause a dysregulation of Cav1.2 channels and we hypothesize that this contributes to neurodegeneration during HD.
Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method

Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A “borrow” concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.
Impact of early rasburicase on incidence of clinical tumor lysis syndrome in lymphoma

Abstract Early administration of rasburicase to enhance uric acid (UA) elimination has been adopted without robust evidence in support of its impact on clinical outcomes in tumor lysis syndrome (TLS), specifically, the prevention of acute kidney injury (AKI). This was a retrospective cohort study of adult lymphoma patients at intermediate or high risk for TLS. Excluded patients had AKI or were on dialysis at hospital admission. The incidence of new AKI in the setting of TLS was described along with predictors of its development, including early rasburicase use. In 383 included patients, the incidence of new-onset AKI during hospitalization was 6%. Predictors included age, history of renal or cardiovascular disease, and UA >8 mg/dL. Rasburicase use did not significantly impact the risk of developing AKI (HR 2.3; p = .11). The UA level at the time of administration did not modify the effect of rasburicase on prevention of AKI (p = .36 for the interaction term).
The Risk of Autoimmune Disorders in Treated Celiac Disease Patients in Olmsted County, Minnesota.

BACKGROUND Patients with autoimmune disorders (ADs) are at increased risk for celiac disease (CD), but data is conflicting on the risk of ADs in treated patients with CD. We aimed to assess the incidence of ADs in treated CD patients. METHODS Using the Rochester Epidemiology Project, we retrospectively searched for the medical records at Mayo Clinic and Olmsted Medical Center from January 1997 to December 2015 for CD patients who met accepted diagnostic criteria. For each CD patient, we identified 2 age and sex matched controls during the same study period. The incidence rate of AD diagnosis 5 years after index date was calculated using Kaplan-Meier analysis for the CD cases and controls and compared using the log-rank test. RESULTS We identified 249 treated patients with CD during the study period and 498 matched controls, with mean (SD) ages of 32 (22) years and 33 (22) years, respectively. One-third of patients (n = 85) and controls (n = 170) were male. Five years after the index date, 5.0% of CD patients and 1.3% of controls had a de novo AD diagnosis (P = 0.006). In the presence of a prior AD, the cumulative risk of a de novo or additional AD was significantly higher in the CD group vs controls (P < 0.001). Children had a significantly higher risk of AD development compared with adults (P = 0.010). CONCLUSION Treated CD patients are at higher risk for the development of ADs. The risk of a new AD is higher in children, especially when more than one AD diagnosis exists.
Characteristics of persons who die on their first suicide attempt: results from the National Violent Death Reporting System

Abstract Background Much of suicide research focuses on suicide attempt (SA) survivors. Given that more than half of the suicide decedent population dies on their first attempt, this means a significant proportion of the population that dies by suicide is overlooked in research. Little is known about persons who die by suicide on their first attempt–and characterizing this understudied population may improve efforts to identify more individuals at risk for suicide. Methods Data were derived from the National Violent Death Reporting System, from 2005 to 2013. Suicide cases were included if they were 18–89 years old, with a known circumstance leading to their death based on law enforcement and/or medical examiner reports. Decedents with and without a history of SA were compared on demographic, clinical, and suicide characteristics, and circumstances that contributed to their suicide. Results A total of 73 490 cases met criteria, and 57 920 (79%) died on their first SA. First attempt decedents were more likely to be male, married, African-American, and over 64. Demographic-adjusted models showed that first attempt decedents were more likely to use highly lethal methods, less likely to have a known mental health problem or to have disclosed their intent to others, and more likely to die in the context of physical health or criminal/legal problem. Conclusions First attempt suicide decedents are demographically different from decedents with a history of SA, are more likely to use lethal methods and are more likely to die in the context of specific stressful life circumstances.
Reduced exercise ventilatory efficiency in Cystic Fibrosis adults with normal to moderately impaired lung function.

RATIONALE Despite being a hallmark and an independent prognostic factor in several cardiopulmonary diseases, ventilatory efficiency - i.e. minute ventilation/carbon dioxide output relationship (V'E/V'CO2) has never been systematically explored in cystic fibrosis (CF). OBJECTIVE To provide a comprehensive frame of reference regarding measures of ventilatory efficiency in CF adults with normal to moderately impaired lung function and to confirm the hypothesis that V'E/V'CO2 is a sensitive marker of early lung disease. METHODS CF patients were divided into 3 groups according to their spirometry: normal (G1), mild impairment (G2) and moderate impairment (G3) in lung function. All participants underwent incremental cardiopulmonary exercise testing on a cycle-ergometer. Lowest V'E/V'CO2 ratio (nadir) and the slope and the intercept of the linear region of the V'E/V'CO2 relationship were contrasted in a two-center retrospective analysis involving 72 CF patients and 36 healthy controls (HC). RESULTS Compared to HC, CF patients had significantly higher V'E/V'CO2 nadir, slope and intercept (p<0.001, p<0.001 and p=0.049, respectively). Subgroup analysis revealed significant differences in nadir (p=0.001) and slope (p=0.012) values even between HC and G1. Dynamic hyperinflation related negatively with slope (p=0.045) and positively with intercept (p=0.001), whilst no impact on nadir was observed. CONCLUSIONS Ventilatory inefficiency is a clear feature of adults with CF, even among patients with normal spirometry. V'E/V'CO2 nadir seems to be the most reliable metric to describe ventilatory efficiency in CF adults. Further prospective studies are needed to clarify whether V'E/V'CO2 could represent an useful marker in the evaluation of early lung disease in CF.
Rhythmic similarity effects in non-native listening?

Listeners rely on native- language rhythm in segmenting speech; in different languages, stress-, syllable- or mora-based rhythm is exploited. This language-specificity affects listening to non- native speech, if native procedures are applied even though inefficient for the non-native language. However, speakers of two languages with similar rhythmic interpretation should segment their own and the other language similarly. This was observed to date only for related languages (English-Dutch; French-Spanish). We now report experiments in which Japanese listeners heard Telugu, a Dravidian language unrelated to Japanese, and Telugu listeners heard Japanese. In both cases detection of target sequences in speech was harder when target boundaries mismatched mora boundaries, exactly the pattern that Japanese listeners earlier exhibited with Japanese and other languages. These results suggest that Telugu and Japanese listeners use similar procedures in segmenting speech, and support the idea that languages fall into rhythmic classes, with aspects of phonological structure affecting listeners' speech segmentation.
Creating a criterion-based information agent through data mining for automated identification of scholarly research on the world wide web

This dissertation creates an information agent that correctly identifies Web pages containing scholarly research approximately 96% of the time. It does this by analyzing the Web page with a set of criteria, and then uses a classification tree to arrive at a decision. The criteria were gathered from the literature on selecting print and electronic materials for academic libraries. A Delphi study was done with an international panel of librarians to expand and refine the criteria until a list of 41 operationalizable criteria was agreed upon. A Perl program was then designed to analyze a Web page and determine a numerical value for each criterion. A large collection of Web pages was gathered comprising 5,000 pages that contain the full work of scholarly research and 5,000 random pages, representative of user searches, that do not contain scholarly research. Datasets were built by running the Perl program on these Web pages. The datasets were split into model building and testing sets. Data mining was then used to create different classification models. Four techniques were used: logistic regression, non-parametric discriminant analysis, classification trees, and neural networks. The models were created with the model datasets and then tested against the test dataset. Precision and recall were used to judge the effectiveness of each model. In addition, a set of pages that were difficult to classify because of their similarity to scholarly research was gathered and classified with the models. The classification tree created the most effective classification model, with a precision of 96% and a recall of 95.6%. However, logistic regression created a model that was able to correctly classify more of the problematic pages. This agent can be used to create a database of scholarly research published on the Web. In addition, the technique can be used to create a database of any type of structured electronic information.
Risk Knowledge and Awareness of Coronary Heart Disease , and Health Promotion Behaviors Among Adults in Oman

Background and Purpose: The purpose of this study was to assess the relationships between an individual’s characteristics and experiences (age, gender, level of education, income, and employment), knowledge of coronary heart disease (CHD) risk factors, perception of health status, awareness of CHD, and health promotion behaviors among adult Omanis. Methods: Using Pender’s health promotion model, a cross-sectional and correlational survey design was employed. A self-administered questionnaire was distributed to 180 participants in Muscat, the capital of Oman. Descriptive and multivariate linear regression analyses were employed. Results: Increasing age was associated with high levels of awareness about CHD. Participants who were employed and knowledgeable about CHD risk factors were more likely to have lower perceptions of health. The regression model showed that knowledge of CHD risk factors and awareness of CHD had positive associations with health Original Study
Road centreline extraction from high‐resolution imagery based on multiscale structural features and support vector machines

This paper investigates road centreline extraction from high‐resolution imagery. A novel road detection system is proposed based on multiscale structural features and support vector machines (SVMs). The salient aspects of the strategy are: (1) structural features are exploited because road objects are narrow and extensive, with large perimeters and small radii; (2) the object‐based approach is used to extract multiscale information so as to reduce the local spectral variation caused by vehicles, shadows, road markings, etc.; (3) the hybrid spectral–structural features are analysed using the SVM classifier; and (4) multiple object levels are integrated because a multiscale approach can exploit the rich spatial information and detect multiscale road objects. Experiments were conducted on two IKONOS multispectral datasets and the results validated the proposed method.
Approximation of simulation-derived visual comfort indicators in office spaces: a comparative study in machine learning

In performance-oriented architectural design, the use of advanced computational simulation tools may provide valuable insight during design. However, the use of such tools is often a bottleneck in the design process, given that computational requirements are usually high. This is a fact that mostly affects the early conceptual stage of design, where crucial decisions mainly occur, and available time is limited. In order to deal with this, decision-makers frequently resort to drawing conclusions from experience, and, as such, valuable insight that advanced computational methods have to offer is lost. This paper explores an alternative approach, which builds on machine-learning algorithms that inductively learn from simulation-derived data, yielding models that approximate to a good degree and are orders of magnitude faster. We focus on visual comfort of office spaces. This is a type of space that specifically requires visual comfort more than others. Three machine-learning methods are compared with respect to applicability in approximating daylight autonomy and daylight glare probability. The comparison focuses on accuracy and time cost of training and estimation. Results demonstrate that machine-learning-based approaches achieve a favourable trade-off between accuracy and computational cost, and provide a worthwhile alternative for performance evaluations during architectural conceptual design.
Tropical cyclone pattern recognition for intensity and forecasting analysis from satellite imagery

Systematic procedures for both analysis and forecasting of tropical cyclone intensity have been developed over the years. These procedures were designed to improve both the reliability and the consistency of intensity estimates made from satellite imagery. Although procedures have been used and tested under operational conditions at the centers responsible for tropical storm surveillance, some difficulties and complexity are still there. Cloud features are analysed to find out the T-number of the tropical cyclone patterns. This can be solved by reinforcement learning for adaptive tropical cyclone patterns segmentation and feature extraction. Tropical cyclone recognition is a multilevel process requiring a sequence of algorithms at low, intermediate, and high levels. Generally such systems are open loop with no feedback between levels and assuring their robustness is key challenge in computer vision and patterns recognition research. A robust closed-loop system based tropical cyclone forecast method based on reinforcement learning is introduced.
A CHALLENGE IN HIDING ENCRYPTED MESSAGE IN LSB AND LSB+1 BITPOSITIONS IN VARIOUS Cover Files

The present work basically shows us how one can hide information in encrypted form to any cover file such as . exe files, Microsoft office files, . dbf files, image files, audio files and video files. However, the size of the hidden message must be very small in comparison to cover file which is an executable file. So far no one has tried to hide information inside any executable file. To make the system fully secured we first encrypt the secret message using MSA algorithm (Nath et al.(1)) and then we hide the encrypted message inside the cover file. introduced a new method for hiding any encrypted secret message inside a cover file. For encrypting secret message we have used new algorithm proposed by Nath et al(1). For hiding secret message we have changed both LSB and LSB+1 bits of each byte of the cover file. A generalized method was proposed by Nath etal(2) where they embed the secret message without going for any encryption. The MSA(1) algorithm introduced a new randomization method for generating the randomized key matrix to encrypt plain text file and to decrypt cipher text file. The MSA (1) method also incorporates the multiple encryption and decryption process. To initiate the MSA algorithm the user has to enter a text_key, which can be of 16 characters long. This text_key is used to calculate the randomization number and the encryption number from the given text_key. The size of the encryption key matrix is 16x16 and the total number of matrices can be formed from 16 x 16 is 256! which is quite large and the MSA algorithm ensures that any of the pattern may be used for encryption as well as decryption process. To hide encrypted secret message in the cover file we have inserted the 8 bits of each character of encrypted message file in 4 consecutive bytes of the cover file such that only LSB and LSB+1 bits are changed depending on the bit pattern of the encrypted secret message. To make system further secured one has to enter a password before the actual steganography process starts. We propose that our new method could be most appropriate for hiding any file in any non-standard cover file such as executable file, compiler, MS-Office files, Data Base files such as . DBF, text editor such as notepad plus the standard cover files such as image, audio, video files etc. The size of the secret message be very small in comparison to the executable cover file. The present method may be implemented in mobile network, Bank data transactions in government sectors, in police department.
Digital Signal Processing: An Experimental Approach

"Digital Signal Processing" is a mathematically rigorous but accessible treatment of digital signal processing that intertwines basic theoretical techniques with hands-on laboratory instruction. Divided into three parts, the book covers various aspects of the digital signal processing (DSP) 'problem'. It begins with the analysis of discrete-time signals and explains sampling and the use of the discrete and fast Fourier transforms. The second part of the book - covering digital to analog and analog to digital conversion - provides a practical interlude in the mathematical content before Part III lays out a careful development of the Z-transform and the design and analysis of digital filters. MATLAB[registered] and Simulink[registered] are employed extensively to allow the reader to experience the beautiful mathematics underlying this important discipline, and to demonstrate the subject's engineering relevance. The practical microprocessor-oriented parts of Digital Signal Processing are introduced with special reference to the ADuC841, a lab manual for which can be downloaded from online. These labs can be easily transposed for other microprocessors. Problems are provided at the end of each chapter and an electronic solutions manual is available for tutors. Academic tutors of courses in DSP will find this book to be an invaluable aid in explaining the fundamental mathematics of digital signal processing and drawing out its significance for engineers. Electrical and computer engineers working in signals- and communications-related fields can use the book as a reference or for self-tuition and for deepening their understanding of the techniques they use. Graduate applied mathematics, electrical, electronic and computer engineering students interested in DSP will learn the underlying mathematics of a vital engineering discipline and how to apply it in practical laboratory situations.
A 50 ns video signal processor

A 50-ns CMOS DSP (digital signal processor) with enhanced parallel architecture suited for video signal processing is reported. It has significant performance advantages, especially for video codecs in ISDN (integrated services digital network) video communication, is based on a 24-b fixed-point architecture, and operates in a five-stage pipeline (instruction-fetch, instruction-decode, source-data-transfer, execution, and destination-data-transfer). It contains 538 k transistors and typically consumes 1.4 W at an instruction cycle rate of 50 ns. The DSP was fabricated in a 1.0- mu m double-metal CMOS technology. Computation speed for the several coding procedures is approximately 3 to 10 times faster than that of traditional DSPs. A 64-kb/s video codec can be implemented with four or five DSPs for full common-source-interface-formats (CSIF) mode and one or two DSPs for 1/4 CSIF mode. <<ETX>>