Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermoregulatory Responses to Heat Stress in Four Species of Birds Weighing Approximately 40 Grams

Birds must balance avenues of heat production and gain with those of heat loss in order to maintain stable body temperatures in different thermal environments. When birds are exposed to high ambient temperatures, the amount of heat lost via conduction, convection, and radiation is reduced (under some conditions, these may be avenues of heat gain). All birds which have been studied increase evaporative water loss during heat stress. Birds do not sweat, and evaporative cooling is effected by panting, by gular flutter (in some species), and by cutaneous evaporation. In addition, most birds exposed to heat stress become hyperthermic and may minimize metabolic heat production by reducing their activity. It has been difficult to make quantitative, comparative statements about avian thermoregulatory responses to heat stress because relatively few species have been examined and the available data are not always comparable. Many earlier "open-flow" determinations of oxygen consumption and evaporative water loss in birds were made using constant, relatively low air flow rates, resulting in high ambient humidities. Therefore, thermoregulatory capabilities have been underestimated in many species (see Lasiewski, Acosta, and Bernstein 1966a for a discussion of this problem). This study was undertaken to examine and compare thermoregulatory responses of four diverse species of birds to controlled conditions of humidity and temperature. We selected species of approximately the same weight (about 40 g) which occur in warm to hot habitats: African village weaverbird, Ploceus cucullatus (Passeriformes, Africa); Chinese painted quail, Excalfactoria chinensis (Galliformes, Australia and Asia); Inca dove, Scardafella inca (Columbiformes, southwestern United States to Central America); poorwill, Phalaenoptilus nuttallii (Caprimulgiformes, southern Canada to Mexico). Previous studies on some aspects of thermoregulatory physiology are available for all four species: African village weaverbird (Bernstein 1971b); poorwill (Bartholomew, Howell, and Cade 1957; Bartholomew, Hudson, and Howell 1962; Lasiewski and Bartholomew 1966; Austin and Bradley 1969; Lasiewski 1969; Ligon 1970; Lasiewski, Bernstein, and Ohmart 1971); Inca dove (MacMillen and Trost 1967a, 1967b); and Chinese painted quail (Lasiewski et al. 1966a, 1966b; Bernstein 1969, 1970, 1971a). However, since precise comparisons among the data in these diverse papers were not possible because of differences in tech1 We thank G. A. Bartholomew, M. H. Bernstein, N. E. Collias, R. J. Epting, F. Fox, R. D. Ohmart, and G. K. Snyder for their advice and assistance. This study was supported in part by NSF GB 8445 (RCL) and USPHS training grant 5166 (RSS). 2 Deceased August 2, 1971.
PLAC: Piecewise Linear Approximation Computation for All Nonlinear Unary Functions

This article presents a piecewise linear approximation computation (PLAC) method for all nonlinear unary functions, which is an enhanced universal and error-flattened piecewise linear (PWL) approximation approach. Compared with the previous methods, PLAC features two main parts, an optimized segmenter to seek the minimum number of segments under the predefined software maximum absolute error (MAE), raising the segmentation performance to the highest theoretical level for logarithm, and a novel quantizer to completely simulate the hardware behavior and determine the required bit width and <inline-formula> <tex-math notation="LaTeX">${\text {MAE}}_{c}$ </tex-math></inline-formula> (MAE in circuits) for hardware implementation. In addition, the hardware architecture is also improved by simplifying the indexing logic, leading to nonredundant hardware overhead. The ASIC implementation results reveal that the proposed PLAC can improve all metrics without any compromise. Compared with the state-of-the-art methods, when computing logarithmic function, PLAC reduces 2.80% area, 3.77% power consumption, and 1.83% <inline-formula> <tex-math notation="LaTeX">${\text {MAE}}_{c}$ </tex-math></inline-formula> with the same delay; when approximating hyperbolic tangent function, PLAC reduces 6.25% area, 4.31% power consumption, and 18.86% <inline-formula> <tex-math notation="LaTeX">${\text {MAE}}_{c}$ </tex-math></inline-formula> with the same delay; when evaluating sigmoid function, PLAC reduces 16.50% area, 4.78% power consumption with the same delay, and <inline-formula> <tex-math notation="LaTeX">${\text {MAE}}_{c}$ </tex-math></inline-formula>; and when calculating softsign function, PLAC reduces 17.28% area, 11.34% power consumption, 12.50% delay, and 33.28% <inline-formula> <tex-math notation="LaTeX">${\text {MAE}}_{c}$ </tex-math></inline-formula>.
MI3DNet: A Compact CNN for Motor Imagery EEG Classification with Visualizable Dense Layer Parameters

Electroencephalography (EEG) based Brain Computer Interface (BCI) attracts more and more attention. Motor Imagery (MI) is a popular one among all the EEG paradigms. Building a subject-independent MI EEG classification procedure is a main challenge in practical applications. Recently, Convolutional Neural Network (CNN) has been introduced and achieved state-of-the-art performance in related areas. To extract subject-independent features in MI EEG classification, we propose the MI3DNet, using a remapped signal cubic as the input. Experiments show that MI3DNet has a higher performance with fewer parameters and layers. We also give a method to plot the parameters of the dense layer, and explain its effect.
Gaussian Process-Based Spatiotemporal Modeling of Electrical Wave Propagation in Human Atrium*

Rhythm regularity of the heart depends on how electrical impulses spread through the cardiac conduction system. Any abnormal activities in the electrical impulses can lead to serious cardiac disorders or sudden death. It is important to understand the electrical activities of the human heart in both healthy and diseased conditions to determine the cause of cardiac disorders and explore the best therapeutic designs. Mathematical models calibrated with clinical and/or in-vitro data are popularly used to study cardiac function and investigate treatment effects. Most of the current human heart models are highly integrated and couple over a hundred equations across different organizational scales of ion channel, cell, and muscle. The model complex poses a significant computational challenge on cardiac simulation. This study developed a metamodel to replace the time-consuming simulation model. Specifically, Gaussian Process (GP) is used to reconstruct the spatiotemporal variations of the cell membrane potential in left atrium. Four different covariance functions were used to infer the potential distributions. The GP model provides an accurate estimation of the spatiotemporal propagation of electrical waves with a small set of data and shows great advantage in computations as compared to traditional models.
Efficient Deep Learning-based Wound-bed Segmentation For Mobile Applications

This paper proposes a deep learning image segmentation method for the purpose of segmenting wound-bed regions from the background. Our contributions include proposing a fast and efficient convolutional neural networks (CNN)-based segmentation network that has much smaller number of parameters than U-Net (only 18.1% that of U-Net, and hence the trained model has much smaller file size as well). In addition, the training time of our proposed segmentation network (for the base model) is only about 40.2% of that needed to train a U-Net. Furthermore, our proposed base model also achieved better performance compared to that of the U-Net in terms of both pixel accuracy and intersection-over-union segmentation evaluation metrics. We also showed that because of the small footprint of our efficient CNN-based segmentation model, it could be deployed to run in real-time on portable and mobile devices such as an iPad.
The effects of Shiga toxin 1 on cytokine and chemokine production and apoptosis in a human monocytic cell line

The Effects of Shiga Toxin 1 on Cytokine and Chemokine Production and Apoptosis in a Human Monocytic Cell Line. (August 2004) Lisa Margaret Harrison, B.S., University of California at Davis Chair of Advisory Committee: Dr. Vernon L. Tesh Severe bloody diarrhea and subsequent serious post-diarrheal illnesses, including the hemolytic uremic syndrome and central nervous system complications, may develop following infections with Shiga toxin (Stx)producing bacteria. The cytotoxic actions of Stxs destroy the microvasculature of organs, preventing function. A role for the cytokines tumor necrosis factoralpha (TNF-α) and interleukin-1 beta (IL-1β) in exacerbating disease may lie in their ability to up-regulate the Stx receptor, Gb3, on endothelial cell surfaces. A main source of proinflammatory cytokines is the macrophage, thus leading us to utilize the monocytic/macrophage-like cell line, THP-1, as a model for cytokine production in Stx pathogenesis. In addition to treating THP-1 cells with purified Stx1, cells were also treated with lipopolysaccharides (LPS), since bacterial LPS are known to be potent inducers of cytokines, and may be present during infection. Undifferentiated THP-1 cells are sensitive to Stx1 and do not produce TNF-α or
Embedding Fuzzy Rules with YARA Rules for Performance Optimisation of Malware Analysis

YARA rules utilises string or pattern matching to perform malware analysis and is one of the most effective methods in use today. However, its effectiveness is dependent on the quality and quantity of YARA rules employed in the analysis. This can be managed through the rule optimisation process, although, this may not necessarily guarantee effective utilisation of YARA rules and its generated findings during its execution phase, as the main focus of YARA rules is in determining whether to trigger a rule or not, for a suspect sample after examining its rule condition. YARA rule conditions are Boolean expressions, mostly focused on the binary outcome of the malware analysis, which may limit the optimised use of YARA rules and its findings despite generating significant information during the execution phase. Therefore, this paper proposes embedding fuzzy rules with YARA rules to optimise its performance during the execution phase. Fuzzy rules can manage imprecise and incomplete data and encompass a broad range of conditions, which may not be possible in Boolean logic. This embedding may be more advantageous when the YARA rules become more complex, resulting in multiple complex conditions, which may not be processed efficiently utilising Boolean expressions alone, thus compromising effective decision-making. This proposed embedded approach is applied on a collected malware corpus and is tested against the standard and enhanced YARA rules to demonstrate its success.
Brain alertness evaluation based on SVM-DS

Alertness (also called continuous attention) is a description of a person's ability to maintain attention over a period of time and make appropriate timely feedback to external stimuli. It includes three aspects: the degree of awakening, the concentration of attention and the ability to respond to emergencies. Many human-computer interaction positions, all require alertness maintaining a high level. The accurate assessment and estimation of alertness has become a hot topic in international research. Many researchers use electroencephalogram to evaluate drowsiness and wakefulness, finding that different levels of alertness correspond to different brain activities. This paper uses power spectral density and short-time Fourier transform to extract feature of the denoised brain signals, then proposes the method of Support Vector Machine-DS to evaluate brain alertness based on EEG.
Laterally Coupled 2D MoS2 Synaptic Transistor With Ion Gating

The dynamically active synaptic elements are the fundamental building blocks in neuromorphic systems towards artificial intelligence with computing and sensing capabilities. Here, a two-dimensional (2D) MoS2 synaptic transistor is fabricated by using poly(ethylene oxide) (PEO) and lithium perchlorate (LiClO4) as a laterally coupled ion-conducting electrolyte. Due to the strong electric double layer (EDL) effect, a low operating voltage of 1 V and a high current on/off ratio of 105 have been obtained. In addition, short-term and long-term plasticity of typical synaptic behaviors have been successfully simulated, such as excitatory postsynaptic current, paired pulse facilitation long-term potentiation, long-term depression, and dynamic filtering. These results can provide new opportunities and strategies in building hybrid and low-dimensional neuromorphic systems for future artificial intelligence applications.
An Effective Disease Risk Indicator Tool

Each mixture of deficient molecular families of a specific disease induces the disease at a different time frame in the future. Based on this, we propose a novel methodology for personalizing a person’s level of future susceptibility to a specific disease by inferring the mixture of his/her molecular families, whose combined deficiencies is likely to induce the disease. We implemented the methodology in a working system called DRIT, which consists of the following components: logic inferencer, information extractor, risk indicator, and interrelationship between molecular families modeler. The information extractor takes advantage of the exponential increase of biomedical literature to extract the common biomarkers that test positive among most patients with a specific disease. The logic inferencer transforms the hierarchical interrelationships between the molecular families of a disease into rule-based specifications. The interrelationship between molecular families modeler models the hierarchical interrelationships between the molecular families, whose biomarkers were extracted by the information extractor. It employs the specification rules and the inference rules for predicate logic to infer as many as possible probable deficient molecular families for a person based on his/her few molecular families, whose biomarkers tested positive by medical screening. The risk indicator outputs a risk indicator value that reflects a person’s level of future susceptibility to the disease. We evaluated DRIT by comparing it experimentally with a comparable method. Results revealed marked improvement.
Error Rate Analysis for Random Linear Streaming Codes in the Finite Memory Length Regime

Streaming codes encode a string of source packets and output a string of coded packets in real time, which eliminate the queueing delay of block coding and are thus especially suitable for delay-sensitive applications. This work studies random linear streaming codes (RLSCs) and i.i. d. packet erasure channels. While existing works focused on the asymptotic error-exponent analyses, this work characterizes the error rate in the finite memory length regime and the contributions include: (i) A new information-debt-based description of the error event; (ii) A matrix-based characterization of the error rate; (iii) A closed-form approximation of the error rate that is provably tight for large memory lengths; and (iv) A new Markov-chainbased analysis framework, which can be of independent research interest. Numerical results show that the approximation, i.e. (iii), closely matches the exact error rate even for small memory length (≈ 20). The results can be viewed as a sequential- coding counterpart of the finite length analysis of block coding [Polyanskiy et al. 10] under the specialized setting of RLSCs.
Classifying Tongue Images using Deep Transfer Learning

Traditional Chinese Medicine (TCM) believes that the tongue image is closely related to the health of the human organs and tongues’ visual features can provide valuable clues for disease diagnosis. Applying tongue image analysis technique for automatic disease diagnosis is an active research filed in the modernization of TCM. Although deep learning has advantages over traditional methods in automatic extraction of high-dimensional features, it needs large training samples, which limits its application in medical image analysis, especially in tongue image, because it is difficult to collect enough labeled images. In this paper, we make the first attempt to use deep transfer learning for tongue image analysis. First, we extract the tongue features through the pre-trained networks (ResNet and Inception_v3), and then rewrite the output layer of the original network with global average pooling and full-connected layer to output classification results. A dataset of 2245 tongue images we collected from specialized TCM medical institutions is used for classification performance evaluation. The experimental results demonstrate that the proposed method achieves the better classification accuracy than the existing deep learning methods which proves the effectiveness of the proposed deep transfer learning for tongue image classification.
Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks

Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3, and D4, respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results.
Formicidae (Hymenoptera) community in corpses at different altitudes in a semiarid wild environment in the southeast of the Iberian Peninsula

The Formicidae are considered crucial components of the entomosarcosaprophagous fauna because they can delay the decomposition process, cause tissue postmortem damage and produce bloodstain patterns that may confuse investigations. Moreover, some studies suggest that the Formicidae can act as environmental and seasonal indicators. However, studies on this group on vertebrate carcasses are scarce, especially in the Iberian Peninsula and the southwest of Europe. Thus, comparative studies at different altitudes in a protected wild mountain area could provide useful information on its composition in such environmental conditions, their role as environmental indicators and their forensic implications. For this reason, the Formicidae sarcosaprophagous community was studied at three different altitudes, between 400 and 1,500 m, in a wild mountainous area in the southeast of Spain using a modified Schoenly trap, with two pitfall traps inside, baited with 5 kg piglets (Sus scrofa L.). This work illustrates an approach to the community of the Formicidae, as a representative of the sarcosaprophagous community in an altitudinal gradient, showing a great variability in its composition. Furthermore, when comparing our results with other studies carried out in the Iberian Peninsula, we are able to suggest certain species with a potential utility as geographic and environmental indicators. Thus, Iberoformica subrufa, Lasius brunneus, Lasius cinereus and Camponotus sylvaticus are species of special interest as they appeared in either one of the sampled areas or in the same region.
Exposure of domestic swine to influenza A viruses in Ghana suggests unidirectional, reverse zoonotic transmission at the human–animal interface

Influenza A viruses (IAVs) have both zoonotic and anthroponotic potential and are of public and veterinary importance. Swine are intermediate hosts and ‘mixing vessels’ for generating reassortants, progenies of which may harbour pandemic propensity. Swine handlers are at the highest risk of becoming infected with IAVs from swine but there is little information on the ecology of IAVs at the human–animal interface in Africa. We analysed and characterized nasal and throat swabs from swine and farmers respectively, for IAVs using RT‐qPCR, from swine farms in the Ashanti region, Ghana. Sera were also analysed for IAVs antibodies and serotyped using ELISA and HI assays. IAV was detected in 1.4% (n = 17/1,200) and 2.0% (n = 2/99) of swine and farmers samples, respectively. Viral subtypes H3N2 and H1N1pdm09 were found in human samples. All virus‐positive swine samples were subtyped as H1N1pdm09 phylogenetically clustering closely with H1N1pdm09 that circulated among humans during the study period. Phenotypic markers that confer sensitivity to Oseltamivir were found. Serological prevalence of IAVs in swine and farmers by ELISA was 3.2% (n = 38/1,200) and 18.2% (n = 18/99), respectively. Human H1N1pdm09 and H3N2 antibodies were found in both swine and farmers sera. Indigenous swine influenza A viruses and/or antibodies were not detected in swine or farmers samples. Majority (98%, n = 147/150) of farmers reported of not wearing surgical mask and few (4%, n = 6) reported to wear gloves when working. Most (n = 74, 87.7%) farmers reported of working on the farm when experiencing influenza‐like illness. Poor husbandry and biosafety practices of farmers could facilitate virus transmission across the human–swine interface. Farmers should be educated on the importance of good farm practices to mitigate influenza transmission at the human–animal interface.
Clinical Translation of Nanomedicine and Biomaterials for Cancer Immunotherapy: Progress and Perspectives

Advances in immuno‐oncology have significantly improved cancer therapy outcomes. Unfortunately, potent responses to immunotherapy have only been achieved in subsets of patients. Nanomedicines and biomaterials have enormous potential for making immunotherapy work better in more patients, which has been suggested by a large number of preclinical studies. However, it remains unclear whether and how these materials will really benefit cancer patients treated by immunotherapy. Therefore, reviewing current clinical studies on nanomedicines and biomaterials in immuno‐oncology may provide valuable information for future translation research. In this work, the clinical landscape of cancer immunotherapy involving nanomedicines and biomaterials, mainly Abraxane, Doxil, messenger ribonucleic acid (mRNA) nanovaccines, and a biomaterial scaffold‐supported autologous vaccine, is reviewed. Clinical results demonstrate the high potential of nano/biomaterials in immunotherapy, achieved by rationalized utilization of their targeted delivery and reduced toxicities/side effects. Future clinical translation in this field will likely benefit from designing rational combinations of nanomedicines and biomaterials with the right immunotherapeutics applied in the right patients/tumors, and this should be achieved with the help of immune biomarker analysis. By rationally utilizing the unique features and advantages of nanomedicines and biomaterials, they will highly impact patient care in combination with immunotherapy.
The Phenotype Characteristics of a Silicate Bacterium

A strain of Bacillus had been isolated from a garden soil of Nanchang.The phenotype characferistics of the morphology, physiology and many biochemical tests had been idenfified. In comparsion of these phenotype characferistics with related Bacillus SPP,the isolated bacferium is a silicate bacterium.It is a strain of Bacillus mucilaginosus.