Miracle-dz/UniFAR-FTUs huggingface dataset:
S100B binding to RAGE in microglia stimulates COX‐2 expression

Besides exerting regulatory roles within astrocytes, the Ca2+‐modulated protein of the EF‐hand type S100B is released into the brain extracellular space, thereby affecting astrocytes, neurons, and microglia. However, extracellular effects of S100B vary, depending on the concentration attained and the protein being trophic to neurons up to nanomolar concentrations and causing neuronal apoptosis at micromolar concentrations. Effects of S100B on neurons are transduced by receptor for advanced glycation end produts (RAGE). At high concentrations, S100B also up‐regulates inducible NO synthase in and stimulates NO release by microglia by synergizing with bacterial endotoxin and IFN‐γ, thereby participating in microglia activation. We show here that S100B up‐regulates cyclo‐oxygenase‐2 expression in microglia in a RAGE‐dependent manner in the absence of cofactors through independent stimulation of a Cdc42‐Rac1‐JNK pathway and a Ras‐Rac1‐NF‐κB pathway. Thus, S100B can be viewed as an astrocytic endokine, which might participate in the inflammatory response in the course of brain insults, once liberated into the brain extracellular space.
Cross Layered MAC Design for RF Energy Harvesting Sensor Network

The main research objective in wireless sensor networks (WSN) domain is to develop algorithms and protocols to ensure minimal energy consumption with maximum network lifetime. In this paper, we propose a novel design for energy harvesting sensor node and cross-layered MAC protocol using three adjacent layers (Physical, MAC and Network) to economize energy for WSN. The basic idea behind our protocol is to re-energize the neighboring nodes using the radio frequency (RF) energy transmitted by the active nodes. This can be achieved by designing new energy harvesting sensor node and redesigning the MAC protocol. The results show that the proposed cross layer CL_EHSN improves the life time of the WSN by 40%.
Tracking Control of Surface Ships With Disturbance and Uncertainties Rejection Capability

This paper investigates a difficult problem of tracking control manipulation for surface ships. External disturbances and system uncertainties are simultaneously addressed. The tracking law design is accomplished in a framework of backstepping control. First, a backstepping-based controller is presented to achieve trajectory tracking with desired tracking performance guaranteed. On the basis of this baseline feedback control law, an adaptive version of this controller is further synthesized to handle the uncertainties and disturbances. It is shown by the Lyapunov stability analysis that the closed-loop tracking system is guaranteed to be asymptotically stable. The desired trajectory is successfully followed with the tracking error asymptotically converging to zero. The effectiveness of the controller is validated through a numerical example.
Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery

Variational Autoencoders (VAEs) have been shown to provide efficient neural-network-based approximate Bayesian inference for observation models for which exact inference is intractable. Its extension, the so-called Structured VAE (SVAE) allows inference in the presence of both discrete and continuous latent variables. Inspired by this extension, we developed a VAE with Hidden Markov Models (HMMs) as latent models. We applied the resulting HMM-VAE to the task of acoustic unit discovery in a zero resource scenario. Starting from an initial model based on variational inference in an HMM with Gaussian Mixture Model (GMM) emission probabilities, the accuracy of the acoustic unit discovery could be significantly improved by the HMM-VAE. In doing so we were able to demonstrate for an unsupervised learning task what is well-known in the supervised learning case: Neural networks provide superior modeling power compared to GMMs.
A 0.78mW/cm2 autonomous thermoelectric energy-harvester for biomedical sensors

A thin-film array-based thermoelectric energy-harvester (TEH) is assembled in a 0.83cm2 footprint and attached to a power management unit integrated in 65nm CMOS. A single-inductor topology with analog maximum-power-point tracking leads to a 68% peak end-to-end efficiency and <;20ms tracking time. A 645μW regulated output power (effective 3.5K) is harvested in-vivo from a rat implanted with our TEH, to demonstrate true energy autonomy in real environment while achieving a 7.9x improvement in regulated power density. The autonomous operation is measured down to 65mV TEH input.
Choreographies for Automatic Recovery

We propose a choreographic model of reversible computations based on a conservative extension of global graphs and communicating finite-state machines. The main advantage of our approach is that does not require to instrument models in order to control reversibility but for a minor decoration of branches. We show that our models are conservative extensions of existing ones and that the reversible semantics guarantees causal consistency.
Brain-machine interfaces in rat motor cortex: neuronal operant conditioning to perform a sensory detection task

A chief concern in the pursuit of controlling a neuroprosthetic device using direct brain signals is the question of how many bits of information are achievable through a direct brain-machine interface (BMI) via implantable microelectrode devices. This experiment begins to address this issue with implementation of a simple, software based decoding algorithm that allows the brain to adapt to the rules imposed upon it, To test this algorithm, two chronic 16-channel Michigan silicon microelectrode arrays were implanted into the primary motor cortex of two rats to record simultaneous unit spike activity. The animals were trained to perform an auditory detection task by modulating the recorded cortical spike activity in a prescribed manner. Both non-adaptive and adaptive neural decoding algorithms were evaluated. With the implementation of a non-adaptive decoding algorithm, the rats' behavioral (cortical) responses plateaued at approximately 75% correct; however, with the implementation of an adaptive algorithm, the rats' behavioral responses relatively quickly increased to 91% correct. The neural recordings suggest that the brain is able to modulate detailed cortical responses in accordance with the prescribed operant conditioning rules.
A Protease Isolated from the Latex of Plumeria rubra Linn (Apocynaceae) 2: Anti-inflammatory and Wound- Healing Activities

Purpose: The aim of this study was to investigate the anti-inflammatory and wound-healing activities of the protease isolated from the latex of Plumeria rubra Linn. Methods: The protease was isolated from the latex of the plant by acetone precipitation method and given a trivial name, Plumerin-R. The anti-inflammatory activity of the protease was based on its effects on carrageenan-induced paw oedema in rats. Its wound healing effect was investigated using an excision wound rat model. Results: Four hours after treatment, the reduction in carrageenan-induced rat paw oedema by 20, 40 and 80 mg/kg body weight of plumerin-R was 21.6, 33.8 and 48.8 %, respectively, while oedema reduction by indomethacin (10 mg/kg) was 58.0 %. Plumerin-R showed significant (p < 0.05) wound closure and epithelialization time compared with control. Conclusion: This study demonstrates that the protease, Plumerin-R, has significant anti-inflammatory and wound-healing properties. Keywords: Protease, Plumerin-R, Anti-inflammatory, Excision wound, Healing.
Increased monolayer domain size and patterned growth of tungsten disulfide through controlling surface energy of substrates

We report a surface energy-controlled low-pressure chemical vapor deposition growth of WS2 monolayers on SiO2 using pre-growth oxygen plasma treatment of substrates, facilitating increased monolayer surface coverage and patterned growth without lithography. Oxygen plasma treatment of the substrate caused an increase in the average domain size of WS2 monolayers by 78% ± 2% while having a slight reduction in nucleation density, which translates to increased monolayer surface coverage. This substrate effect on growth was exploited to grow patterned WS2 monolayers by patterned plasma treatment on patterned substrates and by patterned source material with resolutions less than 10 µm. Contact angle-based surface energy measurements revealed a dramatic increase in polar surface energy. A growth model was proposed with lowered activation energies for growth and increased surface diffusion length consistent with the range of results observed. WS2 samples grown with and without oxygen plasma were similar high quality monolayers verified through transmission electron microscopy, selected area electron diffraction, atomic force microscopy, Raman, and photoluminescence measurements. This technique enables the production of large-grain size, patterned WS2 without a post-growth lithography process, thereby providing clean surfaces for device applications.
Predicting HDD failures from compound SMART attributes

Hard Disk Drives (HDD s) sometimes fail with no apparent reason; some SMART (Self-Monitoring, Analysis and Reporting Technology) attributes present strong correlations with drive failures[3], yet a drive may also fail without (supposedly) any previous indication. Most of the host systems today utilize alert-methods which are reactive by nature-a drive is indicated to fail when some SMART attribute exceeds its vendor defined threshold for valid operation[4]. This approach does not take the cross correlation between different attributes into account and the fact that thresholds vary across different vendors. Unlike more conventional studies that focused on reliability statistics such as the annualized failure rate (AFR) at the population level[5], [6], we use machine learning (ML) algorithms that attempt to predict the failure of individual drives. Former ML approaches applied to the drive prediction failure domain include methods for dealing with sequential data, such as sliding windows and hidden Markov models [1], or anomaly detection algorithms, adhering to the often-low proportion of failed drives in the population [4] and [2]. We present a mechanism that performs a sophisticated samples aggregation inside a distributed database, allowing for the efficient extraction of compound features representing the behavior of the drive during a continuous time window prior its failure. We use here an open source dataset from BACKBLAZE, comprising extensive SMART information collected daily from a large drive population at its data center Thus we can use not only the last cumulative SMART counts but also include new features extracted over different time windows of drives last operational days. These capture the dynamics of the drives attributes, for example their growth rate which is highly indicative of the drive s failure probability [7]. Our results suggest that the use of compound features reduce the amount of false positives, which is primary performance measure for algorithms in the drive reliability domain[3], by as much as 60%. In a consecutive evaluation scenario a final decision is made based on a collection of the most recent data samples. This way we are able to capture more soon-to-fail drives at low cost to the false-positive rate. On average, a drive is predicted to fail long enough in advance (30 days) to allow for the modification of business strategy in fields such as drive replacement logistics. This approach, when implemented in a production environment may have a direct effect on business savings related to drive logistics.
Royal hermeneutics: Observations on the commentaries from Ashurbanipal's libraries at Nineveh*

The British excavations at Nineveh, initiated in 1845 by Austen Henry Layard, produced about 30,000 clay tablets or fragments of tablets, most of them coming from the citadel mound of Kuyunjik. This textual material can be divided into two main groups: on the one hand, library tablets, consisting of literary, lexical and historical texts, rituals, medical compendia, Sumerian prayers and above all omen texts, and on the other, archival documents, such as letters, contracts and administrative notes. The great, and rather unique, potential of the texts from Kuyunjik lies in the fact that they reveal to us, more than any other repository of cuneiform tablets ever found, how culture, represented by the first group of texts, and politics, represented by the second, were related to each other in ancient Mesopotamia. While the archival documents from Kuyunjik date to the reigns of several Assyrian rulers from the eighth and seventh centuries, the library texts seem to belong mostly, though not exclusively, to the reign of a single Assyrian king, Ashurbanipal (669 to about 630), a man deeply interested in the scribal arts of ancient Mesopotamia. His enthusiasm for reading and writing, which he seems to have shared with his wife, Libbāli-šarrat, can be traced back to his youth. From an autobiographical sketch about his intellectual socialization, we know that Ashurbanipal had received the education of a future scholar. In a passage somewhat reminiscent of the twelve-year-old Jesus in the temple, Ashurbanipal described the scribal training of his early years as follows: I learnt the craft of Adapa the sage, the hidden mystery of the scribal art. I used to watch the signs of heaven and earth and to study them in the assembly of the scholars. Together with the able experts in oil-divination, I deliberated upon (the tablet) “If the liver is a mirror of heaven”. … I looked at cuneiform signs on stones from before the flood.
Penicillin-binding proteins of bdellovibrios

We examined the predacious gram-negative bacterium Bdellovibrio bacteriovorous 109J and free-living strains 109J-A1 and 109J-KA1 derived therefrom for penicillin-binding proteins (PBPs). We compared their PBPs with those of the host bacterium, Escherichia coli, and with those of a facultatively predacious bdellovibrio, B. stolpii UKi2, grown axenically. The multiple PBPs of the 109J strains and of UKi2 differed from each other and from those of E. coli, which suggests that screening for PBPs may be a convenient way to determine to what extent the bdellovibrios may represent a diverse group of organisms. A method for labeling furazlocillin and cefaperizone with iodine-125 is also described.
Agricultural green and blue water consumption and its influence on the global water system

This study quantifies, spatially explicitly and in a consistent modeling framework (Lund‐Potsdam‐Jena managed Land), the global consumption of both “blue” water (withdrawn for irrigation from rivers, lakes and aquifers) and “green” water (precipitation) by rainfed and irrigated agriculture and by nonagricultural terrestrial ecosystems. In addition, the individual effects of human‐induced land cover change and irrigation were quantified to assess the overall hydrological impact of global agriculture in the past century. The contributions to irrigation of nonrenewable (fossil groundwater) and nonlocal blue water (e.g., from diverted rivers) were derived from the difference between a simulation in which these resources were implicitly considered (IPOT) and a simulation in which they were neglected (ILIM). We found that global cropland consumed >7200 km3 year−1 of green water in 1971–2000, representing 92% (ILIM) and 85% (IPOT), respectively, of total crop water consumption. Even on irrigated cropland, 35% (ILIM) and 20% (IPOT) of water consumption consisted of green water. An additional 8155 km3 year−1 of green water was consumed on grazing land; a further ∼44,700 km3 year−1 sustained the ecosystems. Blue water consumption predominated only in intensively irrigated regions and was estimated at 636 km3 year−1 (ILIM) and 1364 km3 year−1 (IPOT) globally, suggesting that presently almost half of the irrigation water stemmed from nonrenewable and nonlocal sources. Land cover conversion reduced global evapotranspiration by 2.8% and increased discharge by 5.0% (1764 km3 year−1), whereas irrigation increased evapotranspiration by up to 1.9% and decreased discharge by 0.5% at least (IPOT, 1971–2000). The diverse water fluxes displayed considerable interannual and interdecadal variability due to climatic variations and the progressive increase of the global area under cultivation and irrigation.
Estimation of Integrated Transition Hazards and Stage Occupation Probabilities for Non‐Markov Systems Under Dependent Censoring

Summary. We propose nonparametric estimators of the stage occupation probabilities and transition hazards for a multistage system that is not necessarily Markovian, using data that are subject to dependent right censoring. We assume that the hazard of being censored at a given instant depends on a possibly time‐dependent covariate process as opposed to assuming a fixed censoring hazard (independent censoring). The estimator of the integrated transition hazard matrix has a Nelson‐Aalen form where each of the counting processes counting the number of transitions between states and the risk sets for leaving each stage have an IPCW (inverse probability of censoring weighted) form. We estimate these weights using Aalen's linear hazard model. Finally, the stage occupation probabilities are obtained from the estimated integrated transition hazard matrix via product integration. Consistency of these estimators under the general paradigm of non‐Markov models is established and asymptotic variance formulas are provided. Simulation results show satisfactory performance of these estimators. An analysis of data on graft‐versus‐host disease for bone marrow transplant patients is used as an illustration.
Risk factors for development of sexually abusive behaviour in sexually victimised adolescent boys: cross sectional study

Abstract Objective: To identify factors that may increase the risk of a sexually victimised adolescent boy developing sexually abusive behaviour. Design: Sexually victimised boys who had sexually abused other children were compared with sexually victimised boys who had not done so. Setting: Social services departments in south east England were invited to refer sexually abused and sexually abusing boys to a London postgraduate teaching hospital. Subjects: 25 adolescent boys aged between 11 years and 15 years and 11 months. Main outcome measures: Adjusted odds ratios estimated from unconditional logistic regression. Results: Unadjusted odds rations for witnessing (8.1) as well as experiencing (18.0) intrafamilial violence and discontinuity of care (7.2) discriminated boys who had sexually abused from others who were solely victims of sexual abuse. Only the adjusted odds ratios for witnessing intrafamilial violence (39.7)discriminated the two groups. Conclusions: The risk of adolescent boys who have been victims of sexual abuse engaging in sexually abusive behaviour towards other children is increased by life circumstances which may be unrelated directly to the original abusive experience, in particular exposure to a climate of intrafamilial violence. Our findings have implications for the management of boys found to have been sexually abused and raise important questions about the possibility of secondary prevention of subsequent abusive behaviour in those at greatest risk.
Margining within a CCP : a ML approach

Ce document technique (draft) pour presenter une amelioration du calcul de marge initial (initial margin). Le calcul d'initial margin est un processus important dans la gestion du risque d'une chambre de compensation (CCP). Le processus de marge permet a un ou plusieurs contreparties de traiter en evitant les differents risques de contrepartie de part et d'autre. L'initial margin est sense couvrir les pertes potentielles (exprime comme un quantile de la distribution de pertes donc un calcul de VaR).
Soft Tissue Deformation and Cutting Simulation for the Multimodal Surgery Training

Energid is developing a realistic surgery simulator that delivers high fidelity visual and haptic feedback based on the physics of deformable objects. Modeling the interaction of surgical tools with soft biological tissue in real time poses challenges because the precise physical models of organs are not readily available, and the simulation of the behavior of tissue has a high computational burden. In this paper we present a realistic surgery simulation technique which includes novel algorithms for simulating surgical palpation and cutting. We implement a meshfree numerical technique for realistic surgery palpation simulation. Simulation of surgical cutting is one of the most challenging tasks in the development of a surgery simulator. Changes in topology during simulation render precomputed data unusable. Moreover, the process is nonlinear and the underlying physics is complex. We propose a hybrid approach to the simulation of surgical cutting procedures by combining a node snapping technique with a physically based meshfree computational scheme
The Phenomenon of Project Overfunding on Online Crowdfunding Platforms – Analyzing the Drivers of Overfunding

On online crowdfunding platforms, three stakeholder groups, i.e., platform operators, project founders, and funders, encounter each other and influence funding outcomes by their actions and behaviors. Interestingly, among the successfully funded projects, some projects are heavily overfunded. By our research paper, we address this phenomenon of project overfunding. Especially in reward-based crowdfunding, massive overfunding can lead to severe problems for project founders when vast amounts of rewards have to be delivered. Some people even argue that the amount of money that leads to overfunding should better be pledged to good but undervalued projects that fail to reach their funding goal. However, it is also a powerful mean to generate publicity and to sell products. In order to help understanding this phenomenon, we analyze its drivers. Our analysis gives evidence for all three stakeholder groups contributing to overfunding. We provide arguments for all three stakeholder groups to have certain egoistic incentives for a further backing of already funded projects instead of prioritizing a more demand-oriented distribution of funding. Our findings extend the understanding of funding processes on crowdfunding platforms and are of high interest for practitioners in the field.
A generic framework for monitoring local freight traffic movements using computer vision-based techniques

We address the problem of using computer vision based techniques to collect high-level traffic parameters in dynamic, real-world environments. This is a challenging problem that has not been sufficiently studied. Specifically, with a focus on freight vehicles, we propose a comprehensive framework for monitoring freight traffic movements at any well-defined freight traffic generator, using a network of road-side video cameras at strategic locations. The framework employs state-of-the-art computer vision algorithms to detect and recognize license plates. Moreover, we propose a plate merging algorithm to tackle occlusion of freight vehicles. We also propose an algorithm to match the video camera datasets across different locations to derive the operational history for individual freight vehicles. We have implemented the framework and deployed it for monitoring a large retail mall over 4 days in Singapore. The proposed framework has been used to process terabytes of videos and compute freight traffic parameters, with automatic license plate recognition of 91% accuracy under dynamic, uncontrolled video capturing conditions. With a minimal amount of additional manual processing, 98% accuracy can be achieved.