Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Anthrax Outbreaks in Humans, Livestock, and Wildlife in the Same Locality, Kenya, 2014–2017

Abstract. Epidemiologic data indicate a global distribution of anthrax outbreaks associated with certain ecosystems that promote survival and viability of Bacillus anthracis spores. Here, we characterized three anthrax outbreaks involving humans, livestock, and wildlife that occurred in the same locality in Kenya between 2014 and 2017. Clinical and epidemiologic data on the outbreaks were collected using active case finding and review of human, livestock, and wildlife health records. Information on temporal and spatial distribution of prior outbreaks in the area was collected using participatory epidemiology. The 2014–2017 outbreaks in Nakuru West subcounty affected 15 of 71 people who had contact with infected cattle (attack rate = 21.1%), including seven with gastrointestinal, six with cutaneous, and two with oropharyngeal forms of the disease. Two (13.3%) gastrointestinal human anthrax cases died. No human cases were associated with infected wildlife. Of the 54 cattle owned in 11 households affected, 20 died (attack rate = 37%). The 2015 outbreak resulted in death of 10.5% of the affected herbivorous wildlife at Lake Nakuru National Park, including 745 of 4,500 African buffaloes (species-specific mortality rate = 17%) and three of 18 endangered white rhinos (species-specific mortality rate = 16%). The species mortality rate ranged from 1% to 5% for the other affected wildlife species. Participatory epidemiology identified prior outbreaks between 1973 and 2011 in the same area. The frequency and severity of outbreaks in this area suggests that it is an anthrax hotspot ideal for investigating risk factors associated with long-term survival of anthrax spores and outbreak occurrence.
LDMBL: An architecture for reducing code duplication in heavyweight binary instrumentations

Emergence of instrumentation frameworks has vastly contributed to the software engineering practices. As the instrumentation use cases become more complex, complexity of instrumenting programs also increases, leading to a higher risk of software defects, increased development time, and decreased maintainability. In security applications such as symbolic execution and taint analysis, which need to instrument a large number of instruction types, this complexity is prominent. This paper presents an architecture based on the Pin binary instrumentation framework to abstract the low‐level OS and hardware‐dependent implementation details, facilitate code reuse in heavyweight instrumentation use cases, and improve instrumenting program development time. Instructions of x86 and x86‐64 hardware architectures are formally categorized using the Z language based on the Pin framework API. This categorization is used to automate the instrumentation phase on the basis of a configuration list. Furthermore, instrumentation context data such as register data are modeled in an object‐oriented scheme. This makes it possible to focus the instrumenting program development time on writing the essential analysis logics while access to low‐level OS and hardware dependencies are streamlined. The proposed architecture is evaluated by instrumenting 135 instruction types in a concrete symbolic execution engine, resulting in a reduction of the instrumenting program size by 59.7%. Furthermore, performance overhead measure against the SPEC CINT2006 programs is limited to 8.7%.
Quantitative exploration into the diffusion process of creative ideas in economics: Nobel prize laureates

In this study, we aim to keep track of how creative ideas in economic sciences have been created and diffused into successive research. Toward that end, we investigate the intellectual landscapes of the Nobel Prize awardees’ research and the articles citing the literature. Our study is based on two sets of bibliographic records retrieved from the Web of Science. The “Creation” dataset, obtained through an author search, contains 1,745 original research and review articles of the laureates. The “Diffusion” dataset, consisting of 134,866 records, was collected by citation expansion. We identified bursting keywords and intellectual landscapes of the domain in creation and diffusion perspectives. We found that the dominant research themes that had influenced the laureates’ research from 1980 till 1999 were macroeconomic tests and economic analysis in time series. Then, the creative ideas have diverged into a variety of issues such as labor market, economic governance, market design, asset pricing, and economic decision making. We identified that these ideas have been diffused into successive research topics such as intertemporal general equilibrium model, reciprocity in economics, estimating profitable asset pricing, and socio‐ecological economics. Finally, it is argued that economic research around the laureates has become divergent over time.
riboWaltz: Optimization of ribosome P-site positioning in ribosome profiling data

Ribosome profiling is a powerful technique used to study translation at the genome-wide level, generating unique information concerning ribosome positions along RNAs. Optimal localization of ribosomes requires the proper identification of the ribosome P-site in each ribosome protected fragment, a crucial step to determine trinucleotide periodicity of translating ribosomes, and draw correct conclusions concerning where ribosomes are located. To determine the P-site within ribosome footprints at nucleotide resolution, the precise estimation of its offset with respect to the protected fragment is necessary. Here we present riboWaltz, an R package for calculation of optimal P-site offsets, diagnostic analysis and visual inspection of data. Compared to existing tools, riboWaltz shows improved accuracies for P-site estimation and neat ribosome positioning in multiple case studies. Availability and Implementation riboWaltz was implemented in R and is available at https://github.com/LabTranslationalArchitectomics/RiboWaltz Contact gabriella.viero@cnr.it or fabio.lauria@unitn.it
Synthesis of a Graphene Carbon Nanotube Hybrid Film by Joule Self-Heating CVD for Thermal Applications

Hybrid films based on vertically aligned carbon nanotubes (CNTs) on graphene or graphite sheets have been proposed for application as thermal interface materials and micro heat sinks. However, the fabrication of these materials are limited to small scale, expensive and complicated chemical vapor deposition (CVD) for CNT synthesis. We present a new method for direct growth of CNTs on one or both sides of a thin graphene film (GF) using joule self-heating of the graphene film to provide the necessary heat for the thermal breakdown of carbon feedstock in a CVD process. The resulting CNT forests show good density and alignment consistent with regular CVD synthesis processes on silicon surfaces. The resulting double sided GF/CNT hybrid film is directly applicable as a thermal pad. The CNT forest has a thermal conductivity of 30 W/mK, measured by pulsed photothermal reflectance, and the total thermal interface resistance between aluminum blocks was measured to be 60 Kmm^2/W using an ASTM D5470 compliant 1-D measurement setup. This method of directly synthesizing CNTs on graphene films is more energy efficient and capable of larger volume production compared to traditional CVD methods. It is also compatible with scaling up towards continuous roll-to-roll production for large scale commercial production, one of the major limitations preventing CVD-grown CNTs from commercial applications.
A Criterion to Evaluate Feature Vectors Based on ANOVA Statistical Analysis

The objective of this research is to evaluate feature vectors based on statistical analysis, focusing on application in brain-computer interface (BCI) domain. Common spatial pattern (CSP) is one of the most frequently used algorithms in BCI to extract features from electroencephalogram (EEG). However, since CSP would be a greedy algorithm by solving it through eigenvalue decomposition method, choosing features in a sequential way does not necessarily result in the minimal achievable classification error for higher than 2-dimensional feature vectors. To overcome this issue, Unbalanced Factorial ANOVA (UF-ANOVA) analysis based on linear regression has been used in order to evaluate features extracted from CSP algorithm. Finally, a criterion based on Mahalanobis distance and F distribution parameter resulted from ANOVA table is introduced to evaluate feature vectors. It is shown that proposed criterion is compatible with widely used criterions such as Fisher score (FS) and Mutual information (MI). Moreover, proposed analysis is not limited to one-dimensional feature vectors and can be applied to higher dimensions.
Performance Analysis of Protograph-based LDPC Codes with Spatial Diversity

In wireless communications, spatial diversity techniques, such as space-time block code (STBC) and single-input multiple-output (SIMO), are employed to strengthen the robustness of the transmitted signal against channel fading. This paper studies the performance of protograph-based low-density parity-check (LDPC) codes with receive antenna diversity. We first propose a modified version of the protograph extrinsic information transfer (PEXIT) algorithm and use it for deriving the threshold of the protograph codes in a single-input multiple-output (SIMO) system. We then calculate the decoding threshold and simulate the bit error rate (BER) of two protograph codes (accumulate-repeat-by-3-accumulate (AR3A) code and accumulate-repeat-by-4-jagged-accumulate (AR4JA) code), a regular (3, 6) LDPC code and two optimized irregular LDPC codes. The results reveal that the irregular codes achieve the best error performance in the low signal-to-noise-ratio (SNR) region and the AR3A code outperforms all other codes in the high-SNR region. Utilizing the theoretical analyses and the simulated results, we further discuss the effect of the diversity order on the performance of the protograph codes. Accordingly, the AR3A code stands out as a good candidate for wireless communication systems with multiple receive antennas.
Electrically Pumped Hybrid III-V/Si Photonic Crystal Surface Emitting Lasers with Buried Tunnel-Junction

We report here an electrically pumped hybrid photonic crystal surface emitting laser (PCSELs) on silicon. The laser cavity consists of a transferred InGaAsP multi-quantum well (MQW) heterostructure membrane, printed on a single layer silicon photonic crystal (Si-PC) cavity. A buried tunnel-junction (BTJ) has been employed in the MQW structure for efficiency charge injection. Single mode emitted spectrum was achieved at 1504 nm for a large area laser in the continuous-wave (c.w.) mode under room temperature operation.
Through Glass Via (TGV) Based Band Pass Filter for 5G Communications

Due to low loss electrical properties and the potential to alter the coefficient of thermal expansion, through glass via (TGV) is apt for advanced packaging and high frequency applications. In this paper we present a Half Mode Substrate Integrated Waveguide (HMSIW) based 5th order band pass filter operating in K-band. HMSIW filters are gaining attention due to the similar performance as Substrate Integrated Waveguide (SIW) filters but with more compact size, easy integration and without necessity of transitioning connectors from HMSIW to microstrip line. In addition, a Complementary Split Ring Resonator (CSRR) is integrated to HMSIW to further reduce the device size. The center frequency of the filter is designed using the CSRR structure on the top ground plane and the out of band attenuation is improved by adding spur lines to the structure. The filter design is implemented on glass as well as Rogers substrates using High Frequency Structural Simulator (HFSS) software. The RF performance of the fabricated filter matches well with that of the simulated one.
Performance Enhancement of Split Length Compensated Operational Amplifiers

In this work, a technique to improve the performance parameters of the split length compensated operational amplifiers is proposed. The proposed technique uses an assistant amplifier for performance enhancement of the system. The assistant amplifier is designed in such a way that it draws only a small amount of power (<2%). This work is simulated in 180 nm CMOS technology with 1.8 V supply using Cadence Virtuoso. It achieves 78 dB DC gain, 40.8 MHz unity gain bandwidth and a slew rate of 27.17 V/uS for a load capacitance of 15 pF.
A GPU Kernel Transactionization Scheme for Preemptive Priority Scheduling

Mission-critical embedded systems are becoming heavily dependent on graphics processing unit (GPU) computing. In general, such systems simultaneously run multiple tasks with different criticality and timeliness requirements. Consequently, many research efforts have been made in both hardware and software aspects to support the preemptive priority scheduling of GPU kernels. However, hardware-supported preemption leads to lengthy scheduling delays and complicated design, and most of the software approaches depend on the voluntary yield of GPU resources from restructured kernels. Exploiting the shared physical memory of CPUs and GPUs in heterogeneous system architecture (HSA), we propose an approach to transactionize GPU kernels at the operating system (OS) level. A transactionized GPU kernel can be aborted at any point during its execution and rolled back to its initial state for re-execution. By transactionizing GPU kernels, it is possible to forcibly evict low-priority kernels and immediately schedule high-priority kernels. The preempted low-priority kernel instances can be re-executed after a GPU becomes available. The proposed approach was implemented in a Samsung Exynos 5422 system on chip (SoC) with a Mali-T628 MP6 GPU for evaluation. Our evaluation using the Rodinia benchmark suite showed that the proposed approach limits the preemption delay to 18 μs in the 99.9th percentile with an average delay in execution time less than 10 % in most cases for high-priority tasks under a heavy load.
Accurate Calibration in Multi-Material 3D Bioprinting for Tissue Engineering

Most of the studies in three-dimensional (3D) bioprinting have been traditionally based on printing a single bioink. Addressing the complexity of organ and tissue engineering, however, will require combining multiple building and sacrificial biomaterials and several cells types in a single biofabrication session. This is a significant challenge, and, to tackle that, we must focus on the complex relationships between the printing parameters and the print resolution. In this paper, we study the influence of the main parameters driven multi-material 3D bioprinting and we present a method to calibrate these systems and control the print resolution accurately. Firstly, poloxamer hydrogels were extruded using a desktop 3D printer modified to incorporate four microextrusion-based bioprinting (MEBB) printheads. The printed hydrogels provided us the particular range of printing parameters (mainly printing pressure, deposition speed, and nozzle z-offset) to assure the correct calibration of the multi-material 3D bioprinter. Using the printheads, we demonstrated the excellent performance of the calibrated system extruding different fluorescent bioinks. Representative multi-material structures were printed in both poloxamer and cell-laden gelatin-alginate bioinks in a single session corroborating the capabilities of our system and the calibration method. Cell viability was not significantly affected by any of the changes proposed. We conclude that our proposal has enormous potential to help with advancing in the creation of complex 3D constructs and vascular networks for tissue engineering.
Symmetric/Asymmetric Hybrid Multilevel Inverters Integrating Switched-Capacitor Techniques

In conventional hybrid multilevel inverters (MLIs), the voltage balance controls of floating capacitors are usually realized by redundant switching states using high-frequency modulation. The switching frequency, as well as the switching loss, is hence increased, and the topologies are limited to low-frequency output occasions as a result. In switched-capacitor MLIs (SCMLIs), the capacitors are self-balanced, since they are charged to fixed voltage levels by the input source directly or indirectly. By integrating switched-capacitor techniques into conventional hybrid MLIs, a pair of symmetric/asymmetric hybrid MLIs is proposed in this paper with boost ability. Complicated balance controls are avoided for capacitor voltages, and thus the modulation gets simplified. Compared with SCMLIs, the backend H-bridges that withstand the accumulated voltage stress are removed. The peak inverse voltages of devices are decreased, and thus only low-voltage components are needed. Meanwhile, the component numbers are reduced greatly. All these advantages contribute to high efficiency and reduced costs. Consequently, the proposed hybrid MLIs are especially appropriate for synthesizing a staircase output directly from an input source of low voltage. Analyses and their operational principles are firstly presented for the proposed topologies. Comparisons are then carried out to illustrate their superiority. To evaluate the performance, simulation and experimental prototypes are implemented, whose results confirm the feasibility of the proposed hybrid MLIs.
Computer-assisted selection of donor sites for autologous grafts

A new method is proposed for a precise planning of autologous bone grafts in cranio- and maxillofacial surgery. In patients with defects of the facial skeleton, autologous bone transplants can be harvested from various donor sites in the body. The preselection of a donor site depends i.a. on the morphological fit of the available bone mass and the shape of the part that is to be transplanted. A thorough planning and simulation of the surgical intervention based on 3D CT studies leads to a geometrical description and the volumetric characterization of the bone part to be resected and transplanted. Both, an optimal fit and a minimal lesion of the donor site are guidelines in this process. We use surface similarity and voxel similarity measures in order to select the optimal donor region for an individually designed transplant.
Age, Sex, and Pathology Effects on Stability of Electroencephalographic Biometric Features Based on Measures of Interaction

Electroencephalographic (EEG) biometric features have attracted considerable interest, but they have also drawn major criticism as having low stability. Moreover, most published studies ignore the potential effect of individual factors interacting with stability on the performance of the examined system. We examined the effects of age, sex, and neurological conditions in 60 subjects: 1) using a single EEG recording; 2) when pooling two EEG sessions; and 3) when performing cross-session cross-validation in a biometric system based on multivariate autoregressive measures, in order to extend previous work on autoregressive coefficients. Feature-level fusion and wrapped feature subset-selection in multivariate autoregressive coefficients (MVAR), power spectral density, and 14 additional measures derived from the MVAR model resulted in a maximum area under the curve of receiver operating characteristics (AUC of ROC)/top equal error rate of 98.85/5.34 for a single EEG, 95.51/9.70 for EEG-pooling, and 85.34/22 for cross-session cross-validation. This best result was obtained by the transfer function polynomial, which outperformed the MVAR coefficients, based on the example data set used in this paper. Age, sex, and pathology significantly interacted with the stability of features ( $p<.001$ ). We suggest further investigation of frequency-dependent measures derived from the MVAR model. We emphasize the serious problem of ignoring stability in most previously published research and recommend accurate reporting of individual factors when studying EEG biometric features in multiple sessions for enrolment and authentication on separate days.
Reasoning With Conditional Ceteris Paribus Preference Statem

In many domains it is desirable to assess the preferences of users in a qualitative rather than quantitative way. Such representations of qualitative preference orderings form an importnat component of automated decision tools. We propose a graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a representation is ofetn compact and arguably natural. We describe several search algorithms for dominance testing based on this representation; these algorithms are quite effective, especially in specific network topologies, such as chain-and tree- structured networks, as well as polytrees.
Dexmedetomidine pretreatment inhibits cerebral ischemia/reperfusion‑induced neuroinflammation via activation of AMPK.

Focal ischemia/reperfusion (I/R) injury induced cerebral inflammation, aggravates brain damage. The aim of the present study was to investigate the protective mechanisms of dexmedetomidine (DEX) on I/R brain injury in rats. Sprague‑Dawley rats were divided to seven experimental groups (18 rats/group): Sham surgery; middle cerebral artery occlusion (MCAO) surgery (90 min); DEX10 [10 µg/kg intraperitoneal (i.p.) injection 30 min prior to MCAO]; DEX50 (50 µg/kg i.p. 30 min prior to MCAO); DEX100 (100 µg/kg i.p. 30 min prior to MCAO); DEX50+Yohimbine [YOH; 5 mg/kg 10 min prior to DEX (50 µg/kg i.p.) administration and MCAO] and YOH (5 mg/kg 40 min prior to MCAO). At 24 h post‑MCAO surgery, neurological deficit was examined by staining damaged brain tissues with 2,3,5‑triphenyltetrazolium chloride. Neuronal apoptosis in the cerebral cortex was histologically assessed by terminal deoxynucleotidyl‑transferase‑mediated dUTP nick end labeling staining, and the expression levels of phosphorylated (p)‑AMP‑activated protein kinase (AMPK; Thr172) was detected by western blotting. In addition, the expression levels of tumor necrosis factor (TNF)‑α and interleukin (IL)‑1β were assessed by ELISA. At days 1, 2 and 5 following I/R, motor functions were assessed by an observer blinded to the study. The brain infarct size, neurological deficit scores, number of apoptotic neurons, expression levels of pro‑inflammatory cytokines TNF‑α and IL‑1β were increased following MCAO, whereas the motor function scores were reduced. Pretreatment with DEX prior to MCAO can reverse the effects induced by I/R. Compared with rats in the Sham group, the expression levels of p‑AMPK were mildly increased in the MCAO group and highly increased in the three DEX‑treatment groups. Pretreatment with YOH reversed the above effects of DEX and produced a similar level of cerebral I/R injury. The results demonstrated that precondition with DEX exhibited anti‑inflammatory effects on brain ischemic injury mediated by AMPK signal pathway.
An evaluation of data stream clustering algorithms

Data stream clustering is a hot research area due to the abundance of data streams collected nowadays and the need for understanding and acting upon such sort of data. Unsupervised learning (clustering) comprises one of the most popular data mining tasks for gaining insights into the data. Clustering is a challenging task, while clustering over data streams involves additional challenges such as the single pass constraint over the raw data and the need for fast response. Moreover, dealing with an infinite and fast changing data stream implies that the clustering model extracted upon such sort of data is also subject to evolution over time. Several stream clustering surveys exist already in the literature; however, they focus on a theoretical presentation of the surveyed algorithms. On the contrary, in this paper, we survey the state‐of‐the‐art stream clustering algorithms and we evaluate their performance in different data sets and for different parameter settings.