Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning facilitates the study of gelatinous zooplankton, Benthocodon pedunculata , abundance over six years in the Northeast Pacific abyssal plain.

1. ABSTRACT Understanding the ecological variability of deep-sea organisms is critical for the preservation and protection of deep-sea ecosystems. Many animals, including gelatinous zooplankton, are commonly found within the benthic boundary layer (BBL), an important region in the water column that is essential for pelagic-benthic coupling processes (e.g. carbon and nutrient cycling). BBL processes, and changes in animal communities, have been studied at Station M (~ 4,000 m deep in Northeast Pacific) since 1989. In this present study we ask: how does animal abundance change in the BBL in relation to time? With its distinct identification features and observed high densities at Station M, the gelatinous hydrozoan, Benthocodon pedunculata, makes an ideal study candidate. Specifically, we looked for seasonality in the abundance of these hydromedusae and whether some years experienced higher densities. To achieve this, we leveraged 30 years of visual data collection at Station M, which includes remotely operated vehicle (ROV) video transects, still images from time-lapse cameras and a seafloor-transiting benthic rover. To analyze this high volume of imagery, we applied machine learning (ML) by (1) training
The Effects of Renal Denervation beyond Anti-hypertension

Renal denervation (RDN) disrupts afferent and efferent sympathetic nerves surrounding the renal artery and reduces sympathetic activity. The effects of RDN on uncontrolled hypertension have been well studied in previous clinical trials, and RDN may also have clinical roles in some diseases which are associated with sympathetic nervous overactivity. Early pilot trials revealed RDN had positive effects on atrial fibrillation, heart failure, obstructive sleep apnea, and insulin resistance. This review focuses on the most recent evidence from large-scale randomized controlled trials, non-randomized clinical trials and meta-analyses. Moreover, several small clinical trials have revealed that RDN could also benefit patients with advanced chronic kidney disease, who were excluded from previous studies. In conclusion, the emerging data warrants further investigation into the effect of RDN beyond preventing hypertension.
Anomalous flashovers of silicone rubber insulators under the artificial rain test

The wet flashover voltage of medium voltage insulators made of a silicone rubber is 8% lower than the wet flashover voltage of a porcelain insulator with an identical profile. These surprising results, obtained in 2012, were confirmed again in 2019. The flashover development on the composite insulator is very short (less than 30 ms). On the other hand, on the porcelain insulator, the flashover develops longer (1–3 seconds). The Koppelmann equation was modified, and the Obenaus model to calculate the flashover voltage of insulators under the artificial rain was presented. Attention was paid to the importance of insulator diameters and the phenomenon of water cascades.
Some Estimates for $θ$-type Calderόn–Zygmund Operators and Linear Commutators on Certain Weighted Amalgam Spaces

In this paper, we first introduce some new kinds of weighted amalgam spaces. Then we discuss the strong type and weak type estimates for a class of Calderón–Zygmund type operators Tθ in these new weighted spaces. Furthermore, the strong type estimate and endpoint estimate of linear commutators [b, Tθ ] formed by b and Tθ are established. Also we study related problems about two-weight, weak type inequalities for Tθ and [b, Tθ ] in the weighted amalgam spaces and give some results.
Entrepreneurship and an aging population

Several studies confirm a positive relationship between age and entrepreneurship. This paper analyses if this statement is true also for Sweden. By focusing on those above the age of 50, the paper adds knowledge about how individuals close to their retirement acts and how they contribute to the overall society. A first approach is to analyse the tendency at the regional level where the relationship between different age cohorts and the rate of new firm formation is analysed. In the second stage, an individual perspective is taken where the probability to become self-employed is expected to increase as individuals become older. By decomposing the population in different age cohorts, it is possible to find differences in the probability of becoming self-employed. To increase and deepen the knowledge about the relationship between age and entrepreneurship this paper further adds to existing literature by separating regions into different categories along the urbanrural hierarchy. The results confirm the finding that entrepreneurship is a positive function of age. Individuals above 55 and 64 have a positive influence on entrepreneurship at both the regional as well as the individual level where the impact is stronger in locations that are more rural.
Multi-Task Weakly-Supervised Attention Network for Dementia Prediction with Structural MRI

—Accurate prediction of clinical scores (of neuropsychological tests) based on non-invasive structural magnetic resonance imaging (MRI) helps understand the pathological stage of dementia (e.g., Alzheimer’s disease) and forecast its progression. Existing machine/deep learning approaches typically pre-select dementia-sensitive brain locations for MRI feature extraction and model construction, potentially leading to undesired heterogeneity between different stages and degraded prediction performance. Besides, these methods usually rely on prior anatomical knowledge (e.g., brain atlas) and time-consuming nonlinear registration for the pre-selection of brain locations, thereby ignoring individual-speciﬁc structural changes during dementia progression because all subjects share the same pre-selected brain regions. In this paper, we propose a multi-task weakly-supervised attention network (MWAN) for the joint prediction of multiple clinical scores from baseline MRI scans. Three sequential components are included in MWAN: 1) a backbone fully convolutional network for extracting MRI features; 2) a weakly-supervised dementia attention block for automatically identifying subject-speciﬁc discriminative brain locations; and 3) an attention-aware multi-task regression block for jointly predicting multiple clinical scores. The proposed MWAN is an end-to-end and fully-trainable deep learning model in which dementia-aware holistic feature learning and multi-task regression model construction are integrated into a uniﬁed framework. Our MWAN method was evaluated on two public Alzheimer’s disease datasets for predicting clinical scores of MMSE, CDRSB, and ADAS-Cog. Quantitative experimental results demonstrate that our method produces superior prediction performance compared with state-of-the-art methods. Importantly, qualitative results indicate that the dementia-sensitive brain locations automatically identiﬁed by our MWAN method well retain individual speciﬁcities and are biologically meaningful.
A transcriptional biosensor to monitor single cancer cell therapeutic responses by bioluminescence microscopy

When several life-prolonging drugs are indicated for cancer treatment, predictive drug-response tumor biomarkers are essential to guide management. Most conventional biomarkers are based on bulk tissue analysis, which cannot address the complexity of single-cell heterogeneity responsible for drug resistance. Therefore, there is a need to develop alternative drug response predictive biomarker approaches that could directly interrogate single-cell and whole population cancer cell drug sensitivity. In this study, we report a novel method exploiting bioluminescence microscopy to detect single prostate cancer (PCa) cell response to androgen receptor (AR)-axis-targeted therapies (ARAT) and predict cell population sensitivity. Methods: We have generated a new adenovirus-delivered biosensor, PCA3-Cre-PSEBC-ITSTA, which combines an integrated two-step transcriptional amplification system (ITSTA) and the activities of the prostate cancer antigen 3 (PCA3) and modified prostate-specific antigen (PSEBC) gene promoters as a single output driving the firefly luciferase reporter gene. This system was tested on PCa cell lines and on primary PCa cells. Single cells, exposed or not to ARAT, were dynamically imaged by bioluminescence microscopy. A linear discriminant analysis (LDA)-based method was used to determine cell population sensitivities to ARAT. Results: We show that the PCA3-Cre-PSEBC-ITSTA biosensor is PCa-specific and can dynamically monitor single-cell AR transcriptional activity before and after ARAT by bioluminescence microscopy. After biosensor transduction and bioluminescence microscopy single-cell luminescence dynamic quantification, LDA analysis could discriminate the cell populations overall ARAT sensitivity despite heterogeneous single-cell responses. Indeed, the biosensor could detect a significant decrease in AR activity following exposure to conventional ARAT in hormone-naive primary PCa cells, while in castration-resistant PCa patients, treatment response correlated with the observed clinical ARAT resistance. Conclusion: The exploitation of bioluminescence microscopy and multi-promoter transcriptionally-regulated biosensors can aptly define the overall treatment response of patients by monitoring live single cell drug response from primary cancer tissue. This approach can be used to develop predictive biomarkers for drug response in order to help clinicians select the best drug combinations or sequences for each patient.
Response time reduction in make-to-order and assemble-to-order supply chain design

Make-to-order and assemble-to-order systems are successful business strategies in managing responsive supply chains, characterized by high product variety, highly variable customer demand and short product life cycles. These systems usually spell long customer response times due to congestion. Motivated by the strategic importance of response time reduction, this paper presents models for designing make-to-order and assemble-to-order supply chains under Poisson customer demand arrivals and general service time distributions. The make-to-order supply chain design model seeks to simultaneously determine the location and the capacity of distribution centers (DCs) and allocate stochastic customer demand to DCs by minimizing response time in addition to the fixed cost of opening DCs and equipping them with sufficient assembly capacity and the variable cost of serving customers. The problem is setup as a network of spatially distributed M/G/1 queues, modeled as a non-linear mixed-integer program, and linearized using a simple transformation and a piecewise linear approximation. An exact solution approach is presented that is based on the cutting plane method. Then, the problem of designing a two-echelon assemble-to-order supply chain comprising of plants and DCs serving a set of customers is considered. A Lagrangean heuristic is proposed that exploits the echelon structure of the problem and uses the solution methodology for the make-to-order problem. Computational results and managerial insights are provided. It is empirically shown that substantial reduction in response times can be achieved with minimal increase in total costs in the design of responsive supply chains. Furthermore, a supply chain configuration that considers congestion is proposed and its effect on the response time can be very different from the traditional configuration that ignores congestion.
A survey on Static and Dynamic Hadoop Schedulers

Hadoop is an open source framework which tracks a distributed computing approach. The choice of Hadoop is growing day by day. The capability to deal with enormously large data which is unstructured in nature is truly challenging. But Hadoop is able to tackle all these challenges. This paper describes Hadoop and its architecture in detail. This paper also creates an overview of schedulers used in Hadoop: Static scheduler and Dynamic scheduler. A comparative analysis of various algorithms of static and dynamic scheduling is also presented.
The seismic vulnerability assessment of Québec churches: considerations on territorial specificities

Masonry churches have demonstrated to be vulnerable to earthquakes. The cause of such vulnerability is due to their intrinsic structural characteristics. Many examples of Italian churches could be cited to illustrate this structural vulnerability, based on the long seismic history which characterizes this territory. A series of systematic studies have been performed since the 1976 Friuli earthquake, providing methods for vulnerability assessment, identification of macroelements and the relative kinematic mechanisms, and damage classification. In this context, the present paper focuses on some territorial aspects, such as materials and techniques of construction, common in Québec churches. They should be considered in the evaluation of the structural performance of the church building under seismic actions. The long Italian history of seismicity and damage experience provides a knowledge base useful also in another context, such as the Canadian one. In this paper, some of the most important Italian earthquakes (Friuli, 1976, Emilia-Lombardy, 2012, Central Italy, 2016) are recalled for specific aspects that were observed and that constitute an experience in the field. These aspects are useful for a preventive analysis of some churches on the Island of Montreal, in the Province of Québec, Canada. Starting from a previous seismic vulnerability study, a research is in progress aiming to identify and define regional macro-elements or mechanisms specific to those churches. An adaptation of the Italian methodology for assessing the seismic vulnerability of churches to a different context, with proper materials and techniques of construction, is proposed in this paper. This approach leads to a method for the seismic vulnerability assessment for churches, specifically related to the territory, keeping as reference the already consolidated procedure.
Biological Factors Associated with Infectious Diarrhea in Calves

Received: Revised: Accepted: Published online: January 14, 2021 August 22, 2021 August 27, 2021 November 22, 2021 Calf diarrhea is a major cause of economic loss in the cattle industry. Identifying biomarkers associated with the pathological conditions may provide beneficial guidelines for detecting disease progression and monitoring effectiveness of therapeutic agents in calves suffering from diarrhea. The objective of this research was to find out biological factors associated with calf diarrhea, based on clinical findings and the presence of enteric pathogens. Fecal and blood samples were obtained from 73 non-diarrheal and 42 diarrheal calves. The presence of enteric pathogens, hemato-chemical parameters, acute phase proteins, and proinflammatory cytokines were investigated in the samples. According to the presence of diarrhea and pathogens, the calves were classified into healthy (n=35), subclinical (non-diarrheal but pathogen-positive, n=36), infectious diarrheal (diarrheal and pathogen-positive, n=39), and unknown diarrheal groups (diarrheal but pathogen-negative, n=3). The presence of bovine coronavirus, bovine rotavirus group A, and Cryptosporidium spp. were significantly associated with the development of calf diarrhea (P<0.05). The infectious diarrheal group showed increases in monocyte percentages and blood urea nitrogen level and the decreases in glucose, potassium, and phosphorus levels (P<0.05). Moreover, inflammatory proteins such as haptoglobin, fibrinogen, interleukin-6, and tumor necrosis factoralpha were elevated in the infectious diarrheal group compared to the healthy group (P<0.05). The current study identified altered blood biological factors in calves with infectious diarrhea. The results suggested that these factors may be useful targets for monitoring animal health in calf diarrheal disease, especially infectious diarrhea.
Dependable Systems 2010 Virtualization Fault Tolerance Project Report

Server virtualization is a common trend in modern IT infras- tructures. Virtualization of the x86 processor architecture brought this development a big step further, because this hardware architecture is widely spread and cheap compared to other platforms which supported virtualization for a long time, such as IBM mainframes. While virtual- ization has a lot of advantages, it introduces a considerable drawback in terms of dependability. If multiple physical machines are consolidated into virtual machines running on one single physical host, they all will fail, if the physical host breaks down. Vendors of virtualization solutions have recognized this problem and therefore developed solutions to in- crease fault tolerance in virtualized environments. In this report we will give a short survey of existing virtualization solutions and their relia- bility features and then deeply analyze one solution, namely VMware vSphere. In the end we will also say a few words about Kemari, an open source research project concerned with virtual machine synchronization for Xen and recently also for KVM. It uses a dierent synchronization
Bayesian Optimisation with Formal Guarantees

Application domains of Bayesian optimization include optimizing black-box functions or very complex functions. The functions we are interested in describe complex real-world systems applied in industrial settings. Even though they do have explicit representations, standard optimization techniques fail to provide validated solutions and correctness guarantees for them. In this paper we present a combination of Bayesian optimisation and SMT-based constraint solving to achieve safe and stable solutions with optimality guarantees.
Stochastic-Optimization-Based Stochastic Control Problem

Our project is to apply optimization methods from machine learning to stochastic control problems, which are traditionally thought to be outside their specialty. A stochastic control problem involves a stochastic dynamical system that can be observed and controlled in limited ways. The control strategy depends on observations from the dynamical system and is to optimize some measure of performance. The optimization methods we apply are variants of stochastic gradient descent that have been developed for machine learning. One advantage of machine learning optimization algorithms is that the control parameters can be adjusted based on only a few stochastic simulations, which are far fewer than what would be needed to get an accurate performance estimate of the system.
Design of simultaneous sampling systems based on fractional delay Lagrange filters

Digital filtering is a common approach to achieve simultaneous sampling of several input signals acquired with a multiplexing delay. In this work, an error bound is obtained for Lagrange interpolation filters as a function of the oversampling ratio of the input signals, the fractional delay, and the filter's order. This bound can be used to ensure that the error is small enough to maintain a desired resolution (number of significant bits), thus leading to design equations for simultaneous sampling systems. For example, using these equations, we are able to find that an oversampling ratio of 71 is necessary to maintain a resolution of 12 bits with a first order Lagrange's filter, while a sixth-order filter is required when the oversampling ratio is only five. The theoretical results are validated through simulation, and the computational cost of the Lagrange's interpolator is compared with a polyphase filter.
Bitcoin Price Prediction using LSTM Autoencoder Regularized by False Nearest Neighbor Loss

: We implement deep learning for predicting bitcoin closing prices. Identifying two new determiners, we propose a novel LSTM Autoencoder using Mean Squared Error (MSE) loss which is regularized by False Nearest Neighbor (FNN) algorithm. The method results in reduced error rates when compared to traditional forecasting algorithms and is statistically validated. This research contributes by developing a robust algorithm that accurately determines the fluctuation directions in bitcoin prices and results in values closer to the actual prices.
Concussion History and Alzheimer’s Disease Risk Affect Cognitive-Motor Integra- tion in Distinct Ways

This study compared cognitive-motor integration between young individuals with a concussion history to participants with Alzheimer’s disease risk. Participants made finger movements on a touchscreen from a central to peripheral target. We observed performance impairments in both groups relative to neurologically healthy control groups, even when age was accounted for. Interestingly, the pattern of performance impairments was distinct between the two clinical groups. A discriminant analysis differentiated individuals based on task performance with a history of concussion from those at Alzheimer’s disease risk with 91% accuracy. This suggests that the brain networks responsible for cognitive-motor integration are differently affected by concussion and Alzheimer’s disease risk. More generally, cognitive-motor integration provides a sensitive means to monitor Alzheimer’s disease-related functional decline, and to detect long-term functional impairment that can distinguish mild traumatic brain injury from dementia risk.
SUB CHRONIC EFFECT OF ETHANOL EXTRACT OF NUTMEG (myristica fragrans Houtt) SEED IN RAT KIDNEY

Background. Nutmeg (Myristica fragrans) is original plant from Indonesia has sedative, hepatoprotective, and anti-seizure activities. Objective. The aim of this study was to determine the toxic effects of ethanol extract of nutmeg seed on the rat kidney. Methods. Male rats grouped into 4 groups and each group consists of 7 animals. Group I as the control group 0.5% CMC-Na. Group II, III, and IV were given ethanol extract of nutmeg dose of 50 mg / kg, 100 mg / kg, and 200 mg / KgBW respectively. Samples were administered for 31 days, on the 32th day was taking blood from the orbital sinus eye for BUN, creatinine assay and histopathology examination. Outcome measured. BUN, creatinine, and histopathological profile Results. The results showed all doses of ethanol extract of nutmeg seed causes an increase BUN levels. While on the creatinine levels between the control group and the treatment group showed no significant difference. Histopathological examination showed no change in renal morphology was given ethanol extract of nutmeg seeds. Conclusion. The conclusions this research that the ethanol extracts of nutmeg (Myristica fragrans Houtt) seed did not cause toxic effects on the kidney. Keyword : Biji Pala (Myristica fragrans Houtt.), BUN, Creatinine, Histopathological morphology Proceeding of International Safety Management of Central Cytotoxic Reconstitution May 25th 2013 165