Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Oral Domoic Acid Exposure on Maternal Reproduction and Infant Birth Characteristics in a Preclinical Nonhuman Primate Model

Domoic Acid (DA) is a naturally-occurring excitotoxin, produced by marine algae, which can bioaccumulate in shellfish and finfish. The consumption of seafood contaminated with DA is associated with gastrointestinal illness that, in the case of high DA exposure, can evolve into a spectrum of responses ranging from agitation to hallucinations, memory loss, seizures and coma. Because algal blooms that produce DA are becoming more widespread and very little is known about the dangers of chronic, low-dose exposure, we initiated a preclinical study focused on the reproductive and developmental effects of DA in a nonhuman primate model. To this end, 32 adult female Macaca fascicularis monkeys were orally exposed to 0, 0.075 or 0.15 mg/kg/day DA on a daily basis, prior to and during pregnancy. Females were bred to non-exposed males and infants were evaluated at birth. Results from this study provided no evidence of changes in DA plasma concentrations with chronic exposure. DA exposure was not associated with reproductive toxicity or adverse changes in the physical characteristics of newborns. However, in an unanticipated finding, our clinical observations battery revealed the presence of subtle neurological effects in the form of intentional tremors in the exposed adult females. While females in both dose groups displayed increased tremoring, the effect was dose-dependent and observed at a higher frequency in females exposed to 0.15 mg/kg/day. These results demonstrate that chronic, low-level exposure to DA is associated with injury to the adult CNS and suggest that current regulatory guidelines designed to protect human health may not be adequate for high-frequency shellfish consumers. Highlights 1) Domoic acid acts as a tremoragen after chronic, low-dose oral exposure in adults. 2) Exposure across pregnancy does not result in maternal reproductive toxicity. 3) In-utero exposure does not adversely impact physical characteristics of exposed newborns. 4) Current regulatory guidelines may not adequately protect high-frequency shellfish consumers from DA-induced neurological injury.
Preventing surgical site infections: Facilitators and barriers to nurses' adherence to clinical practice guidelines-A qualitative study.

AIMS To identify the facilitators of and barriers to nurses' adherence to evidence-based wound care clinical practice guidelines (CPGs) in preventing surgical site infections (SSIs) in an Australian tertiary hospital. BACKGROUND Current research suggests that up to 50% of nurses are unaware of the evidence-based recommendations to prevent SSIs and that adherence to evidence-based CPGs is suboptimal. However, little is known regarding the facilitators and barriers to adherence to evidence-based CPGs. DESIGN A qualitative study incorporating ethnographic data collection techniques. METHODS Data collection included semi-structured individual interviews and focus groups (N = 20), and examination of existing hospital policy and procedure documents. Thematic analysis using inductive and deductive approaches was conducted. This manuscript adheres to the COnsolidated criteria for REporting Qualitative research (COREQ) guidelines. FINDINGS Data analysis revealed four themes: adhering to aseptic technique, knowledge and information seeking, documenting wound care and educating and involving patients in wound care. Facilitators and barriers within each theme were identified. Facilitators included participants' active information-seeking behaviour, a clear understanding of the importance of aseptic technique, and patient participation in wound care. Barriers included participants' knowledge and skills deficits regarding application of aseptic technique principles in practice, the availability of the hospital's wound care procedure document, suboptimal wound care documentation and the timing of patient education. CONCLUSIONS There is a need to develop interventions to improve nurses' adherence to recommended CPGs including following aseptic technique principles, hand hygiene, documentation and patient education. Hospital procedure documents that outline wound care need to reflect current recommended CPGs. RELEVANCE TO CLINICAL PRACTICE Adhering to evidence-based CPGs has been found to be effective in reducing and preventing SSIs. Our study provides an in-depth understanding of the barriers and facilitators to nurses' adherence to recommended CPGs. The findings may inform future practice improvements in wound care.
Anthropometric characteristics, physical activity and risk of hematological malignancies: A systematic review and meta‐analysis of cohort studies

Overweight/obesity, adult attained height and physical activity are possible risk factors for hematological malignancies. This meta‐analysis aims to evaluate the associations between these factors and hematological cancer risk in adults. Eligible cohort studies were sought in PubMed up to May 31, 2016; overall, 44 studies were included in the present analyses. Pooled relative risk estimates were calculated using random‐effects models; separate analyses were conducted for non‐Hodgkin lymphoma (NHL) and subtypes (diffuse large B‐cell lymphoma, DLBCL; follicular cell lymphoma; small lymphocytic lymphoma/chronic lymphocytic leukemia, SLL/CLL), Hodgkin lymphoma (HL), multiple myeloma (MM), leukemia and subtypes (acute lymphoblastic leukemia, acute myeloid leukemia, AML). Obesity was associated with increased risk of NHL, HL, MM, leukemia overall and AML in both sexes, as well as with higher DLBCL risk in women; the dose–response meta‐regression analysis confirmed these associations. Less pronounced effects were observed regarding overweight, as it was associated with increased MM risk in both sexes, NHL risk in males, DLBCL and overall leukemia risk in females. Taller men presented with significantly higher risk of NHL and taller women were affected by higher risk of NHL, DLBCL, FL, CLL/SLL, MM, leukemia and AML. On the other hand, physical activity and abdominal fatness were not associated with the risk of hematological malignancies. In conclusion, this meta‐analysis highlights the pivotal role of anthropometric measures in shaping the risk of hematological malignancies in adults. Additional, well‐designed studies stemming from all the continents are needed for the further substantiation and generalization of the results.
Acute Myeloid Leukemia Affects Mouse Sperm Parameters, Spontaneous Acrosome Reaction, and Fertility Capacity

Leukemia is one of the most common cancers in patients of reproductive age. It is well known that chemotherapy, used as anti-cancer therapy, adversely affects male fertility. Moreover, the negative effect of leukemia on sperm quality, even before chemotherapy treatment, has been reported. However, the mechanisms behind this disease’s effect on sperm quality remains unknown. In this study, we examine the direct effect of leukemia and chemotherapy alone and in combination on sperm parameters and male fertility. For this, we developed an acute myeloid leukemia (AML) mouse model (mice were treated with AML cells C1498 and developed leukemia); these mice then received cytarabine chemotherapy. Our findings reveal a significant reduction in sperm concentration and motility and a significant increase in abnormal morphology and spontaneous acrosome reaction of the sperm following AML and chemotherapy treatment, alone and in combination. We also found a reduction in male fertility and the number of delivered offspring. Our results support previous findings that AML impairs sperm parameters and show for the first time that AML increases spontaneous acrosome reaction and decreases male fertility capacity and number of offspring.
The human innate immune protein calprotectin induces iron starvation responses in Pseudomonas aeruginosa

Most microbial pathogens have a metabolic iron requirement, necessitating the acquisition of this nutrient in the host. In response to pathogen invasion, the human host limits iron availability. Although canonical examples of nutritional immunity are host strategies that limit pathogen access to Fe(III), little is known about how the host restricts access to another biologically relevant oxidation state of this metal, Fe(II). This redox species is prevalent at certain infection sites and is utilized by bacteria during chronic infection, suggesting that Fe(II) withholding by the host may be an effective but unrecognized form of nutritional immunity. Here, we report that human calprotectin (CP; S100A8/S100A9 or MRP8/MRP14 heterooligomer) inhibits iron uptake and induces an iron starvation response in Pseudomonas aeruginosa cells by sequestering Fe(II) at its unusual His6 site. Moreover, under aerobic conditions in which the Fe(III) oxidation state is favored, Fe(II) withholding by CP was enabled by (i) its ability to stabilize this redox state in solution and (ii) the production and secretion of redox-active, P. aeruginosa–produced phenazines, which reduce Fe(III) to Fe(II). Analyses of the interplay between P. aeruginosa secondary metabolites and CP indicated that Fe(II) withholding alters P. aeruginosa physiology and expression of virulence traits. Lastly, examination of the effect of CP on cell-associated metal levels in diverse human pathogens revealed that CP inhibits iron uptake by several bacterial species under aerobic conditions. This work implicates CP-mediated Fe(II) sequestration as a component of nutritional immunity in both aerobic and anaerobic milieus during P. aeruginosa infection.
Elucidating Cell Size Control Mechanisms with Stochastic Hybrid Systems

It is outstanding and intriguing how robustly bacteria can maintain its preferred size despite recurrent rounds of growth and divisions. We model cell size using the stochastic hybrid system framework (SHS), where a cell grows either linearly or exponentially in size over time and random division events are fired at discrete time intervals. We ask for growth and division rates that reproduce the uncorrelated behavior of the added size at division and the newborn cell size in experiments. We provide simple close-form expression of the distribution of key cell cycle events (like the distribution of the size right after division). Furthermore, we propose scenarios (in the form of division rates) in which alternative size control strategies arise, and compare them with those observed in species different from bacteria. Additionally we discuss how division rates might be useful in teasing out biomolecular mechanisms that may explain the cell size control strategies observed in nature.
Reducing Inter-Application Interferences in Integrated CPU-GPU Heterogeneous Architecture

Current heterogeneous CPU-GPU architectures integrate general purpose CPUs and highly thread-level parallelized GPUs (Graphic Processing Units) in the same die. The contention in shared resources between CPU and GPU, such as the last level cache (LLC), interconnection network and DRAM, may degrade both CPU and GPU performance. Our experimental results show that GPU applications tend to have much more power than CPU applications to compete for the shared resources in LLC and on-chip network, and therefore make CPU suffer from more performance loss. To reduce the GPU’s negative impact on CPU performance, we propose a simple yet effective method based on probability to control the LLC replacement policy for reducing the CPU’s inter-core conflict misses caused by GPU without significantly impacting GPU performance. In addition, we develop two strategies to combine the probability based method for the LLC and an existing technique called virtual channel partition (VCP) for the interconnection network to further improve the CPU performance. The first strategy statically uses an empirically pre-determined probability value associated with VCP, which can improve the CPU performance by 26% on average, but degrades GPU performance by 5%. The second strategy uses a sampling method to monitor the network congestion and dynamically adjust the probability value used, which can improve the CPU performance by 24%, and only have 1 or 2% performance overhead on GPU applications.
Development of Complexity Index and Predictions of Accident Risks for Mixed Autonomous Driving Levels

The proposed study aims to address the complexities resulting from the mixing of manually driven and autonomous vehicles. First, the complexity of the driving situation was defined to evaluate a new road environment containing both types of vehicles. Assuming V2X technology, driving situation complexity (DSC) was defined by the concept of perplexity and standard deviation and calculated by measuring the driver-vehicle behaviors in a simulated driving environment. Second, according to the definition of autonomous driving proposed by the Society of Automotive Engineers International (SAE), a driver model of an autonomous vehicle was implemented using a PreScan simulator. The proportions of defined autonomous vehicles were modified and the DSCs for different scenarios, such as confluence, accident, stop, and safety sections, were measured. Then, the significance of the complexity of the driving situation, as presented by each section, was verified. In addition, the implications of the complexity were clarified by analyzing the correlation between DSC and accident risk measure (ARM), which is used to evaluate accident situations by monitoring the occurrence of accidents. Finally, the complexities of driving situations were analyzed for various mixed driving situations, including Levels 0-5 autonomous driving vehicles. Future situations were predicted to involve a mixture of manually driven and autonomous vehicles.
Anonymous Readers Counting: A Wait-Free Multi-Word Atomic Register Algorithm for Scalable Data Sharing on Multi-Core Machines

In this article we present Anonymous Readers Counting (ARC), a multi-word atomic (1,N) register algorithm for multi-core machines. ARC exploits Read-Modify-Write (RMW) instructions to coordinate the writer and reader threads in a wait-free manner and enables large-scale data sharing by admitting up to $(2^{32}-2)$ concurrent readers on off-the-shelf 64-bit machines, as opposed to the most advanced RMW-based approach which is limited to 58 readers on the same kind of machines. Further, ARC avoids multiple copies of the register content when accessing it—this is a problem that affects classical register algorithms based on atomic read/write operations on single words. Thus it allows for higher scalability with respect to the register size. Moreover, ARC explicitly reduces the overall power consumption, via a proper limitation of RMW instructions in case of read operations re-accessing a still-valid snapshot of the register content, and by showing constant time for read operations and amortized constant time for write operations. Our proposal has therefore a strong focus on real-world off-the-shelf architectures, allowing us to capture properties which benefit both performance and power consumption. A proof of correctness of our register algorithm is also provided, together with experimental data for a comparison with literature proposals. Beyond assessing ARC on physical platforms, we carry out as well an experimentation on virtualized infrastructures, which shows the resilience of wait-free synchronization as provided by ARC with respect to CPU-steal times, proper of modern paradigms such as cloud computing. Finally, we discuss how to extend ARC for scenarios with multiple writers and multiple readers—the so called (M,N) register. This is achieved not by changing the operations (and their wait-free nature) executed along the critical path of the threads, rather only changing the ratio between the number of buffers keeping the register snapshots and the number of threads to coordinate, as well as the number of bits used for counting readers within a 64-bit mask accessed via RMW instructions—just depending on the target balance between the number of readers and the number of writers to be supported.
Comparison of Machine Learning Algorithms for Detection of Network Intrusions

Detecting, analyzing, and defending against network intrusions is an important topic in cyber security. Various detection systems have been designed using machine learning techniques that help detect malicious intentions of network users. We apply Recurrent Neural Networks (RNNs) and Broad Learning System (BLS) machine learning algorithms to classify known network intrusions. The developed models are trained and tested using the NSL-KDD dataset containing information about both intrusion and regular network connections. The algorithms are used to classify various types of intrusion classes and regular data and are compared based on accuracy and F-Score. Comparison results indicate that the BLS algorithm shows comparable performance with shorter training time.
Circular Shape Prior in Efficient Graph Based Image Segmentation to Segment Nucleus

A graph based segmentation approach is proposed in this study to segment nucleus from cytology images. This approach utilizes a novel method applying weighted circular shape prior adaptively in efficient graph based image segmentation. The proposed method was evaluated by segmenting nucleus from two public Pap smear image datasets: ISBI 2014 challenge dataset (945 images) and DTU/Herlev intermediate squamous cell dataset (70 images). Segmentation results of the proposed method outperformed the standard one in terms of Dice similarity coefficient, pixel-based precision and recall, Hausdorff distance, and Ht metric. Quantitative measures and visual results indicate that the proposed technique produces better nucleus boundaries.
Collaborative Information Bottleneck

This paper investigates a multi-terminal source coding problem under a logarithmic loss fidelity which does not necessarily lead to an additive distortion measure. The problem is motivated by an extension of the information bottleneck method to a multi-source scenario where several encoders have to build cooperatively rate-limited descriptions of their sources in order to maximize information with respect to other unobserved (hidden) sources. More precisely, we study fundamental information-theoretic limits of the so-called: 1) two-way collaborative information bottleneck (TW-CIB) and 2) the collaborative distributed information bottleneck (CDIB) problems. The TW-CIB problem consists of two distant encoders that separately observe marginal (dependent) components <inline-formula> <tex-math notation="LaTeX">$X_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$X_{2}$ </tex-math></inline-formula> and can cooperate through multiple exchanges of limited information with the aim of extracting information about hidden variables <inline-formula> <tex-math notation="LaTeX">$(Y_{1},Y_{2})$ </tex-math></inline-formula>, which can be arbitrarily dependent on <inline-formula> <tex-math notation="LaTeX">$(X_{1},X_{2})$ </tex-math></inline-formula>. On the other hand, in CDIB, there are two cooperating encoders which separately observe <inline-formula> <tex-math notation="LaTeX">$X_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$X_{2}$ </tex-math></inline-formula> and a third node which can listen to the exchanges between the two encoders in order to obtain information about a hidden variable <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>. The relevance (figure-of-merit) is measured in terms of a normalized (per-sample) multi-letter mutual information metric (log-loss fidelity), and an interesting tradeoff arises by constraining the complexity of descriptions, measured in terms of the rates needed for the exchanges between the encoders and decoders involved. Inner and outer bounds to the complexity-relevance region of these problems are derived from which optimality is characterized for several cases of interest. Our resulting theoretical complexity-relevance regions are finally evaluated for binary symmetric and Gaussian statistical models, showing theoretical tradeoffs between the complexity-constrained descriptions and their relevance with respect to the hidden variables.
Cost Evaluation for Hybrid Inclusions: A Lyapunov Approach

Cost evaluation problems for hybrid inclusions are studied. Sufficient conditions, in the form of Lyapunov-like inequalities, are provided to derive an upper bound on the cost associated with the solution to a hybrid inclusion with respect to a hybrid cost functional. Under additional sufficient conditions, we determine the cost exactly without computing solutions. Constructive results are proposed to solve cost evaluation problems in some relevant applications. Numerical examples are presented.
Patch PlaNet: Landmark Recognition with Patch Classification Using Convolutional Neural Networks

In this work we address the problem of landmark recognition. We extend PlaNet, a model based on deep neural networks that approaches the problem of landmark recognition as a classification problem and performs the recognition of places around the world. We propose an extension of the PlaNet technique in which we use a voting scheme to perform the classification, dividing the image into previously defined regions and inferring the landmark based on these regions. The prediction of the model depends not only on the information of the features learned by the deep convolutional neural network architecture during training, but also uses local information from each region in the image for which the classification is made. To validate our proposal, we performed the training of the original PlaNet model and our variation using a database built with images from Flickr, and evaluated the models in the Paris and Oxford Buildings datasets. It was possible to notice that the addition of image division and voting structure improves the accuracy result of the model by 5-11 percentage points on average, reducing the level of ambiguity found during the inference of the model.
Joint Spreading Factor and Coding Rate Assignment in LoRaWAN Networks

LoRaWAN is a fast developing Low Power Wide Area Network technology that is predicted to support a large segment of the billions of estimated Internet of Things devices. It promises ubiquitous connectivity keeping network structures and management simple. One open key challenge is how to effectively assign a spreading factor so as to support a large number of end devices by a single gateway within a LoRaWAN cell. This paper confirms that if more clever spreading factor allocation than the conventionally deployed Adaptive Data Rate is considered, network performance will improve significantly. The proposed heuristics are based on two key features; first the fact that each subset of end devices allocated a certain spreading factor represents a spreading factor tier, each signify a logical pure Aloha network with a different packet length. Second, the datum to control coding rate transmission parameter which in turns control coverage sensitivities and thus system load in each spreading factor tier. Two proposed algorithms, namely sensitivitySF and assignmentSF, attempt to maximize the throughput of each spreading factor tier and thus enhance overall network performance. Simulation results show that the proposed algorithms can considerably improve network performance when compared to the basic adaptive data rate strategy, and particularly the assignmentSF algorithm consistently guarantees a high success rate for any generated traffic load.