Miracle-dz/UniFAR-FTUs huggingface dataset:
Activity of Drug Combinations against Mycobacterium abscessus Grown in Aerobic and Hypoxic Conditions

Infections caused by Mycobacterium abscessus (Mab), an environmental non-tuberculous mycobacterium, are difficult to eradicate from patients with pulmonary diseases such as cystic fibrosis and bronchiectasis even after years of antibiotic treatments. In these people, the low oxygen pressure in mucus and biofilm may restrict Mab growth from actively replicating aerobic (A) to non-replicating hypoxic (H) stages, which are known to be extremely drug-tolerant. After the exposure of Mab A and H cells to drugs, killing was monitored by measuring colony-forming units (CFU) and regrowth in liquid medium (MGIT 960) of 1-day-old A cells (A1) and 5-day-old H cells (H5). Mab killing was defined as a lack of regrowth of drug-exposed cells in MGIT tubes after >50 days of incubation. Out of 18 drugs tested, 14-day treatments with bedaquiline-amikacin (BDQ-AMK)-containing three-drug combinations were very active against A1 + H5 cells. However, drug-tolerant cells (persisters) were not killed, as shown by CFU curves with typical bimodal trends. Instead, 56-day treatments with the nitrocompounds containing combinations BDQ-AMK-rifabutin-clarithromycin-nimorazole and BDQ-AMK-rifabutin-clarithromycin-metronidazole-colistin killed all A1 + H5 Mab cells in 42 and 56 days, respectively, as shown by lack of regrowth in agar and MGIT medium. Overall, these data indicated that Mab persisters may be killed by appropriate drug combinations.
Mechanical and electronic design of the MORFEO (formerly known as MAORY) calibration unit selector

This paper describes the mechanical and electronic design of the Calibration Unit selector for MORFEO. MORFEO is a post-focal adaptive optics module for the ELT designed to help compensate for distortions caused by turbulence in the Earth’s atmosphere [1][2]. MORFEO is designed to support MICADO near-infrared camera in order to have stable and sharp images across a large field of view in the near infrared [3]. The Calibration Unit position selector is a motorized slider that allows to choose between three different positions: MORFEO calibration, MICADO calibration and observation[17]. In this paper the mechanical design and the corresponding FE analyses are shown and compared to the requirements. The design of the motorized function, in terms of components, driver and harness is also described. Finally, a safety and reliability analysis on the electromechanical subsystem is performed.
Association between Endodontic Infection, Its Treatment and Systemic Health: A Narrative Review

The ‘Focal Infection Era in Dentistry’ in the late 19th and early 20th century resulted in widespread implementation of tooth extraction and limited the progress of endodontics. The theory proposed that bacteria and toxins entrapped in dentinal tubules could disseminate systemically to remote body parts, resulting in many types of degenerative systemic diseases. This theory was eventually refuted due to anecdotal evidence. However, lately there has been increased interest in investigating whether endodontic disease could have an impact on general health. There are reviews that have previously been carried out on this subject, but as new data have emerged since then, this review aims to appraise the available literature investigating the dynamic associations between apical periodontitis, endodontic treatment, and systemic health. The available evidence regarding focal infection theory, bacteraemia and inflammatory markers was appraised. The review also collated the available research arguing the associations of apical periodontitis with cardiovascular diseases, diabetes mellitus, adverse pregnancy outcome and autoimmune disorders, along with the effect of statins and immunomodulators on apical periodontitis prevalence and endodontic treatment prognosis. There is emerging evidence that bacteraemia and low-grade systemic inflammation associated with apical periodontitis may negatively impact systemic health, e.g., development of cardiovascular diseases, adverse pregnancy outcomes, and diabetic metabolic dyscontrol. However, there is limited information supporting the effect of diabetes mellitus or autoimmune disorders on the prevalence and prognosis post endodontic treatment. Furthermore, convincing evidence supports that successful root canal treatment has a beneficial impact on systemic health by reducing the inflammatory burden, thereby dismissing the misconceptions of focal infection theory. Although compelling evidence regarding the association between apical periodontitis and systemic health is present, further high-quality research is required to support and establish the benefits of endodontic treatment on systemic health.
Detecting Touch and Grasp Gestures Using a Wrist-Worn Optical and Inertial Sensing Network

Freehand gesture-based interaction promises to enable rich interaction in applications such as augmented reality, virtual reality, human-robot interaction, and robotic prosthetic devices. However, current sensing approaches are limited to mid-air whole hand gestures and fail to identify small-scale tactile interactions with unsensorized environments. Detecting tactile interactions may unlock potential new applications in augmented reality and human-robot interaction in which unsensorized surfaces are used as touch input devices. This work presents a novel wrist-worn sensing device that combines near-infrared and inertial measurement unit sensing to enable high-accuracy detection of surface touch and grasp interactions. Two convolutional neural networks were used to map device inputs to detect touch events, and classify them by gesture type or direction. We evaluated the accuracy and temporal precision of our system for event detection and classification. Results from an in-lab user study of 12 participants showed an average of 97% touch detection accuracy and 98% grasp detection accuracy. In our study, we found that near-infrared and inertial sensing are complementary and can be used in tandem to effectively address both touch event detection and directionality classification.
Identification of Railway Track Using RFID Application

Indian railway is 4th rank railway chain in the world. Due to this there are so many problems like collision, accidents, delay etc. are related to trains. To avoid these problems we used RFID application for identification of railway track and automatic track changer. In this setup ,we used RFID tags which are allocated at railway track and RFID reader attached in railway engine. Because of this, train follows its path perfectly with help of RFID reader which reads tags continuously on railway track. If route is appropriate then train perform continuously, in case route is wrong then train stop automatically and ‘WRONG PATH” information shows on LCD display located in train engine. The next advantage of our model is automatic track changer. When train reach at some distance before track changer that time track change automatically according to perfect route due to wireless communication. Our model is useful for perfect track identification and perfect track change.
A recursive algorithm for quantification of brain perfusion SPECT images

The accurate quantification of emission computed tomography data (PET-SPECT) is limited by partial volume effects (PVE). The influence of these effects on quantification depends on the size of the structure in relation to the spatial resolution and may lead to an over- and/or underestimation of the true tracer concentration. This particularly holds for brain perfusion SPECT imaging, where white matter and grey matter are two entangled tissue structures with different tracer uptake. This study proposes a recursive algorithm, which uses anatomical side information, to estimate the true tracer activity within the white matter tissue compartment which is needed to correct for partial volume effects. The algorithm first estimates the activity within the white matter and grey matter tissue compartment globally, taking into account the whole SPECT-image. The fitted activity values are then compared with the reconstructed SPELT activity values. For over- and underestimated regions, the activities of the two tissue compartments are estimated again locally, where only the reconstructed activity values within these regions are considered. Next, these fitted activity values are again compared with the reconstructed SPECT activity values. If necessary these regions are again divided in an over- and/or underestimated region, for which the estimation is redone and so on. Simulation studies show that this algorithm is capable of addressing non-homogenous activity distribution within the grey matter tissue compartment for brain perfusion SPECT-images.
Development of a flat calibration unit for TAO/MIMIZUKU

MIMIZUKU is a mid-infrared instrument for the TAO 6.5-m telescope under construction in the Atacama Desert, Chile, and will be the world’s first mid-infrared monitoring observation station. We aim to achieve a photometric accuracy of 1%. For this purpose, highly accurate flat fielding with an accuracy of 0.1% is needed. Although flat fielding has been conducted using sky images and dark images conventionally, the correction has uncertainties of several percent. The reason is that the non-linearity of the detector is not considered. To improve this, it is necessary to create flat frames from data in the same count level as during observation. Highly accurate flat frames were derived by taking differential counts against the time variation of the atmospheric radiation. However, this method cannot be used under stable conditions suitable for observations. Therefore, we developed a flat calibration unit which irradiates the detector uniformly and vary the irradiation intensity with time to enable the improved flat fielding under any conditions. We designed the unit that irradiates the detector uniformly by placing a silicon lens and a blackbody source in front of the camera. The blackbody source is put at the pupil position of the optical system. We made some tests to create flat images with the unit. By improving flat fielding, we have successfully corrected for patterns originating from the detector, which appeared in the conventional one. We also clarified that the accuracy of the improved flat fielding was 0.29%, while the accuracy of the conventional one was 1.3%.
Enhanced convnet based Latent Finger Print Recognition

Latent finger print recognition plays an important role in forensic, criminal cases etc. The latent images will not be recognised easily since they are impartial images, which find difficult to match with the registered database. Due to noisy images, it is very difficult for recognition. Autoencoder plays an important role in pre-processing the latent image. ConvNetbased method is an efficient approach used for latent image recognition. For each minutiae extraction, ConvNet descriptor is performed. Both minutiae and texture matcher is considered for comparison. This technique is compared with existing methods which shows, that the proposed method provides a higher accuracy than the existing methods like CNN, skeleton approach nonlinear mapping and product quantization. The proposed method provides an accuracy of 76.4%, 80.4% and 86.4% for rank1,5 and 10 respectively.
Design of compact highly matched broadband balanced low‐noise amplifier at X‐band using CRLH transmission lines

This article presents broadband balanced low‐noise amplifier (LNA) operating at X‐band, which uses composite right/left‐handed (CRLH) transmission lines as input–output matching networks. Proposed LNA eliminates lumped DC‐blocking capacitors and is combined with a hybrid coupler to achieve better stability and matching. Presented CRLH matching technique enhances the matching quality, reduces the overall size and considerably improves the bandwidth and noise performance. The matrix analysis of the CRLH structure is provided to determine the exact dimension of microstrip lines for the highest stable gain and the lowest noise figure (NF). The LNA structure uses a HJFET technology as an active device, which is simulated using an ADS momentum and small‐signal scattering parameters provided by the manufacturer. This LNA offers an improved impedance matching and measured the 20 dB S11 band‐width of 16% (8.3–9.7GHz), which is in good agreement with simulation. Total area occupied by the fabricated LNA is only 1. 7λ0×0. 9λ0 @ 8.3GHz. The LNA also provides <1.8 dB NF based on the IEEE standard temperature (16.85 Celsius), and 1 dB gain flatness throughout the entire band along with a 1 dB compression point of −1 dBm and an output third point intercept point of 19.5 dBm. The proposed CRLH‐based LNA can be a good candidate for compact receivers and sensors.
A 120-Gb/s 100–145-GHz 16-QAM Dual-Band Dielectric Waveguide Interconnect With Package Integrated Diplexers in Intel 16

This letter presents a dual-band superheterodyne transceiver system in Intel 16 operating at 109 and 135 GHz center frequencies with a measured rejection >60 dB within the 11 GHz guard band. The transceiver is flip-chip assembled on a multilayer organic package, which integrates hairpin resonator diplexers and electromagnetic waveguide launchers. A connector assembled on the organic package feeds a 3–4-m long PTFE dielectric waveguide ensuring a stable mechanical connection. This letter demonstrates up to 120 Gb/s data rates with measured error vector magnitudes (EVMs) between −19.2 dB and −16.2 dB at 3–4-m distances with combined power consumption of 1116 mW. This is the first work in the literature reporting beyond 60 Gb/s over dielectric waveguide channels at link distances greater than 1 m. Combining frequency division multiplexing with a low tap count FFE equalizer and sharp on-die analog and RF filtering with a low loss on package diplexer enabled the >100 Gb/s at multimeter distances.
Memory-Aware Scheduling of Tasks Sharing Data on Multiple GPUs with Dynamic Runtime Systems

The use of accelerators such as GPUs has become mainstream to achieve high performance on modern computing systems. GPUs come with their own (limited) memory and are connected to the main memory of the machine through a bus (with limited bandwidth). When a computation is started on a GPU, the corresponding data needs to be transferred to the GPU before the computation starts. Such data movements may become a bottleneck for performance, especially when several GPUs have to share the communication bus. Task-based runtime schedulers have emerged as a convenient and efficient way to use such heterogeneous platforms. When processing an application, the scheduler has the knowledge of all tasks available for processing on a GPU, as well as their input data dependencies. Hence, it is able to choose which task to allocate to which GPU and to reorder tasks so as to minimize data movements. We focus on this problem of partitioning and ordering tasks that share some of their input data. We present a novel dynamic strategy based on data selection to efficiently allocate tasks to GPUs and a custom eviction policy, and compare them to existing strategies using either a well-known graph partitioner or standard scheduling techniques in runtime systems. We also improved an offline scheduler recently proposed for a single GPU, by adding load balancing and task stealing capabilities. All strategies have been implemented on top of the STARPU runtime, and we show that our dynamic strategy achieves better performance when scheduling tasks on multiple GPU s with limited memory.
The model of encryption algorithm based on non-positional polynomial notations and constructed on an SP-network

Abstract This paper addresses to structures and properties of the cryptographic information protection algorithm model based on NPNs and constructed on an SP-network. The main task of the research is to increase the cryptostrength of the algorithm. In the paper, the transformation resulting in the improvement of the cryptographic strength of the algorithm is described in detail. The proposed model is based on an SP-network. The reasons for using the SP-network in this model are the conversion properties used in these networks. In the encryption process, transformations based on S-boxes and P-boxes are used. It is known that these transformations can withstand cryptanalysis. In addition, in the proposed model, transformations that satisfy the requirements of the “avalanche effect” are used. As a result of this work, a computer program that implements an encryption algorithm model based on the SP-network has been developed.
Effect of small wearable device antenna location on its impedance, bandwidth potential and radiation efficiency

In this paper, a simulation study of the effect of antenna locations on antenna matching, bandwidth potential and radiation efficiency performance operating in 3GPP Long Term Evolution (LTE) Band 3 (1710-1880 MHz), Band 20 UL (832-862 MHz) and 2.4 GHz WLAN band (2.4-2.5 GHz), in free space and with phantom, is presented. An inverted F antenna (IFA) is utilized in this study and mounted on a small cellular wrist device. The results indicate that for each band, with efficient excitation of its fundamental mode, an antenna whose electrical field maxima located at short edge of the chassis always has higher bandwidth potential and its radiation efficiency will have less reduction when introducing phantom. Moreover, changes in matching characteristic of different antenna positions vary from each band, indicating the optimal antenna placement to obtain robust matching feature. Therefore, by comparing the variations on antenna behaviors, it provides useful insights into how to select the optimal antenna locations on the device with reduced a user effect for a specific operation band.
Prediction of Conversion from CIS to Clinically Definite Multiple Sclerosis Using Convolutional Neural Networks

Multiple sclerosis (MS) is a chronic neurological disease of the central nervous system (CNS). Early diagnosis of MS is highly desirable as treatments are more effective in preventing MS-related disability when given in the early stages of the disease. The main aim of this research is to predict the occurrence of a second MS-related clinical event, which indicates the conversion of clinically isolated syndrome (CIS) to clinically definite MS (CDMS). In this study, we apply a branch of artificial intelligence known as deep learning and develop a fully automated algorithm primed with convolutional neural network (CNN) that has the ability to learn from MRI scan features. The basic architecture of our algorithm is that of the VGG16 CNN model, but amended such that it can handle MRI DICOM images. A dataset comprised of scans acquired using two different scanners was used for the purposes of verification of the algorithm. A group of 49 patients had volumetric MRI scans taken at onset of the disease and then again one year later using one of the two scanners. In total, this yielded 7360 images which were then used for training, validation, and testing of the algorithm. Initially, these raw images were taken through 4 steps of preprocessing. In order to boost the efficiency of the process, we pretrained our algorithm using the publicly available ADNI dataset used to classify Alzheimer's disease. Finally, we used our preprocessed dataset to train and test the algorithm. Clinical evaluation conducted a year after the first time point revealed that 26 of the 49 patients had converted to CDMS, while the remaining 23 had not. Results of testing showed that our algorithm was able to predict the clinical results with an accuracy of 88.8% and with an area under the curve (AUC) of 91%. A highly accurate algorithm was developed using CNN approach to reliably predict conversion of patients with CIS to CDMS using MRI data from two different scanners.
Experimental Investigation on Threshold Voltage Instability for β-Ga2O3 MOSFET Under Electrical and Thermal Stress

A <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> MOSFET is fabricated and its threshold voltage <inline-formula> <tex-math notation="LaTeX">$({V}_{\mathrm{TH}})$ </tex-math></inline-formula> instability mechanisms are experimentally investigated under different gate-biased voltages and ambient temperatures. Under the condition of the positive bias stress (PBS) of <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{GS}} = 4$ </tex-math></inline-formula> V for 1000 s at room temperature, the <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{TH}}$ </tex-math></inline-formula> positive shift of 0.61 V is mainly caused by the electrons trapped by border traps in Al<sub>2</sub>O<sub>3</sub>. Combining the hysteresis with temperature-dependent performance analysis, the clockwise to anticlockwise hysteresis inversion at 125 °C is discovered in <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> MOSFET for the first time, which is probably caused by the activation of deep-level acceptor-type interface states. The deep-level acceptor-type interface states with a fitting activation energy of 114 meV are found, and they also affect the <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{TH}}$ </tex-math></inline-formula> instability at high temperature. With the stress of <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{GS}} =4$ </tex-math></inline-formula> V at 125 °C for 1000 s, <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{TH}}$ </tex-math></inline-formula> is increased by 0.9 V. Analyzing electrical parameters, such as <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{TH}}$ </tex-math></inline-formula>, subthreshold slope (<italic>SS</italic>), and hysteresis width, can distinguish the quantitative contributions of interface states and border traps to <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{TH}}$ </tex-math></inline-formula> instability under electrical stress and thermal stress (TS). This work reveals an important indication for investigating the <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{TH}}$ </tex-math></inline-formula> instability of <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> MOSFET in high-power applications.
Study on the Mechanism of Mesaconitine-Induced Hepatotoxicity in Rats Based on Metabonomics and Toxicology Network

Mesaconitine (MA), one of the main diterpenoid alkaloids in Aconitum, has a variety of pharmacological effects, such as analgesia, anti-inflammation and relaxation of rat aorta. However, MA is a highly toxic ingredient. At present, studies on its toxicity are mainly focused on the heart and central nervous system, and there are few reports on the hepatotoxic mechanism of MA. Therefore, we evaluated the effects of MA administration on liver. SD rats were randomly divided into a normal saline (NS) group, a low-dose MA group (0.8 mg/kg/day) and a high-dose MA group (1.2 mg/kg/day). After 6 days of administration, the toxicity of MA on the liver was observed. Metabolomic and network toxicology methods were combined to explore the effect of MA on the liver of SD rats and the mechanism of hepatotoxicity in this study. Through metabonomics study, the differential metabolites of MA, such as L-phenylalanine, retinyl ester, L-proline and 5-hydroxyindole acetaldehyde, were obtained, which involved amino acid metabolism, vitamin metabolism, glucose metabolism and lipid metabolism. Based on network toxicological analysis, MA can affect HIF-1 signal pathway, MAPK signal pathway, PI3K-Akt signal pathway and FoxO signal pathway by regulating ALB, AKT1, CASP3, IL2 and other targets. Western blot results showed that protein expression of HMOX1, IL2 and caspase-3 in liver significantly increased after MA administration (p < 0.05). Combined with the results of metabonomics and network toxicology, it is suggested that MA may induce hepatotoxicity by activating oxidative stress, initiating inflammatory reaction and inducing apoptosis.