Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacterial Profiles and Antimicrobial Susceptibility Pattern of Isolates from Inanimate Hospital Environments at Tikur Anbessa Specialized Teaching Hospital, Addis Ababa, Ethiopia

Microbial contamination of hospital environment plays an important role in the spread of health care-associated infections (HCAIs). This study was conducted to determine bacterial contamination, bacterial profiles and antimicrobial susceptibility pattern of bacterial isolates from environmental surfaces and medical equipment. A cross-sectional study was conducted at Tikur Anbessa Specialized Hospital (TASH) from June to September, 2018. A total of 164 inanimate surfaces located at intensive care units (ICUs) and operation theaters (OTs) were swabbed. All isolates were identified by using routine bacterial culture, Gram staining and a panel of biochemical tests. For each identified bacteria, antibiogram profiles were determined by the Kirby Bauer disk diffusion method according to the guidelines of the Clinical and Laboratory Standards Institute (CLSI). Out of the 164 swabbed samples, 141 (86%) were positive for bacterial growth. The predominant bacteria identified from OTs and ICUs were S. aureus (23% vs 11.5%), Acinetobacter spp (3.8% vs 17.5%) and Coagulase negative Staphylococcus (CONS) (12.6% vs 2.7%) respectively. Linens were the most contaminated materials among items studied at the hospital (14.8%). The proportions of resistance among Gram-positive bacteria (GPB) were high for penicillin (92.8%), cefoxitin (83.5%) and erythromycin (54.1%). However, the most effective antibiotics were clindamycin with only 10.4% and 16.5% resistance rates, respectively. The antimicrobial susceptibility profiles of Gram-negative bacteria (GNB) revealed that the most effective antibiotics were amikacin, ciprofloxacin, and gentamicin with resistance rate of 25%, 37.5%, and 46.3%, respectively. However, the highest resistance was recorded against ampicillin (97.5%), ceftazidime (91.3%), ceftriaxone (91.3%) and aztreonam (90%). The inanimate surfaces near immediate patient environment and commonly touched medical equipment within OTs and ICUs are reservoirs of potential pathogenic bacteria that could predispose critically ill patients to acquire HCAIs. The proportions of antimicrobial resistance profile of the isolates are much higher from studied clean inanimate environments.
Hedonic representations in primary somatosensory cortices; Pain but not pleasure?

In the somatosensory system, hedonic information is coded by mechanoreceptors at the point of contact. Pleasure and pain signals travel along peripheral nerve pathways distinct from those for discriminative touch. Yet it remains unknown whether the central nervous system represents tactile hedonic information in sensory cortices as another dimension of exteroceptive information, similar to discriminative touch signals, or if tactile hedonic information is instantiated in regions mediating internal interoceptive states. Employing representational similarity analysis with a new approach of pattern component modeling, we decomposed multivoxel patterns to demonstrate that signals of painful but not pleasurable touch are represented in primary somatosensory cortices. By contrast, all hedonic touch representations were identified in regions associated with affect and interoception. This suggests that touch should be divided into external-exteroceptive and internal-interoceptive dimensions, with hedonic touch represented as an internal state, even though evoked by external stimulation.
Development of antibody-dependent cell cytotoxicity function in HIV-1 antibodies

A prerequisite for the design of an HIV vaccine that elicits protective antibodies is understanding the developmental pathways that result in desirable antibody features. The development of antibodies that mediate antibody-dependent cellular cytotoxicity (ADCC) is particularly relevant because such antibodies have been associated with HIV protection in humans. We reconstructed the developmental pathways of six human HIV-specific ADCC antibodies using longitudinal antibody sequencing data. Most of the inferred naïve antibodies did not mediate detectable ADCC. Gain of antigen binding and ADCC function typically required mutations in complementarity determining regions of one or both chains. Enhancement of ADCC potency often required additional mutations in framework regions. Antigen binding affinity and ADCC activity were correlated, but affinity alone was not sufficient to predict ADCC potency. Thus, elicitation of human ADCC antibodies may require mutations that first enable high affinity antigen recognition, followed by mutations that optimize factors contributing to functional ADCC activity.
New Species of Oscillatoriaceae

Stratum tenue pannosum fragilissimum griseo-aerugineum vel decoloratum, filis primum tortilibus unitrichomatiferis inter alias myxophyceas crescentibus, aetate provecta pluritrichomatiferis atque copiose fasciculato-ramosis, ramis in fasc? culos breves decumbentes tortis; vaginis arctis tenuibus hyalinis demum luteolis, ambitu erosis, chlorozincico iodurato laete caerulescentibus; trichomatibus pallide aerugineis, 2 ? ad 3 ? crassis, cylindricis, ad genicula constrictis, ad apices longe et sensim attenuatis et acuminatis; articulis subquadratis vel diametro plus minusve longioribus vel brevioribus, protoplasmate non aut vix granuloso, dissepimentis pellucidis; cellula apicali longe et fere acutissime conica.
Unsupervised Training of Denoisers for Low-Dose CT Reconstruction Without Full-Dose Ground Truth

Recently, deep neural network (DNN) based methods for low-dose CT have been investigated to achieve excellent performance in both image quality and computational speed. However, almost all methods using DNNs for low-dose CT require clean ground truth data with full radiation dose to train the DNNs. In this work, we attempt to train DNNs for low-dose CT reconstructions with reduced tube current by investigating unsupervised training of DNNs for denoising sensor measurements or sinograms without full-dose ground truth images. In other words, our proposed methods allow training of DNNs with only noisy low-dose CT measurements. First, the Poisson Unbiased Risk Estimator (PURE) is investigated to train a DNN for denoising CT measurements, and a method is proposed for reconstructing the CT image using filtered back-projection (FBP) and the DNN trained with PURE. Then, the CT forward model-based Weighted Stein's Unbiased Risk Estimator (WSURE) is proposed to train a DNN for denoising CT sinograms and to subsequently reconstruct the CT image using FBP. Our proposed methods achieve excellent performance in both fast computation and reconstructed image quality, which is more comparable to the results of the DNNs trained with full-dose ground truth data than other state-of-the-art denoising methods such as the BM3D, Deep Image Prior, and Deep Decoder.
Harnessing Pairwise-Correlating Data Prefetching With Runahead Metadata

Recent research revisits pairwise-correlating data prefetching due to its extremely low overhead. Pairwise-correlating data prefetching, however, cannot accurately detect where data streams end. As a result, pairwise-correlating data prefetchers either expose low accuracy or they lose timeliness when they are performing multi-degree prefetching. In this letter, we propose a novel technique to detect where data streams end and hence, control the multi-degree prefetching in the context of pairwise-correlated prefetchers. The key idea is to have a separate metadata table that operates one step ahead of the main metadata table. This way, the runahead metadata table harnesses the degree of prefetching by allowing/disallowing the main metadata table to issue prefetch requests. We evaluate our proposal in the context of a four-core chip multiprocessor and show that it significantly reduces erroneous prefetches, providing up to 16.1 percent performance improvement on top of a state-of-the-art pairwise-correlating prefetcher.
Toxicity of Tebufenpyrad to Tetranychus urticae (Acari: Tetranychidae) and Amblyseius womersleyi (Acari: Phytoseiidae) Under Laboratory and Field Conditions

The toxicity of the pyrazole acaricide tebufenpyrad to the twospotted spider mite, Tetranychus urticae Koch, and the predatory mite Amblyseius womersleyi Schicha was evaluated in laboratory and field studies. In a laboratory study with the susceptible T. urticae, tebufenpyrad was highly effective against eggs, immatures (larvae and protonymphs), and adults at 25 ppm (100% mortality). Moderate levels of resistance to tebufenpyrad were observed in dicofol-resistant DR-20 (resistance ratio, 15-fold), fenpyroximate-resistant FR-20 (24-fold), and pyridaben-resistant PR-20 strains (33-fold), indicating a careful use of this acaricide on apple orchards. Additionally, tebufenpyrad had no repellent activity against either adults or immatures of the susceptible and resistant T. urticae. In 1995 and 1996 field studies of a population of T. urticae, efficacy of tebefenpyrad, expressed as a protective value at 15 or 20 d in each treated plot relative to an untreated plot, was similar to that of the commonly used azocyclotin and fenazaquin. In the laboratory and field studies with A. womersleyi, tebufenpyrad exhibited adverse effects, but this predatory mite species was more tolerant than T. urticae: 2.8, 82.2, and 46.6% mortalities against eggs, immatures, and adults at 25 ppm, respectively. No phytotoxicity was observed with this acaricide in 17-yr-old ‘Fuji’ and 11-yr-old ‘Tsugaru’ apple trees. Based on laboratory and field data, tebufenpyrad may be used to prevent damage by T. urticae and in alteration with the earlier types of acaricides in a resistance management program if a properly timed tebufenpyrad treatment is done.
Computation Migration and Resource Allocation in Heterogeneous Vehicular Networks: A Deep Reinforcement Learning Approach

With the development of 5G technology, the requirements for data communication and computation in emerging 5G-enabled vehicular networks are becoming increasingly stringent. Computation-intensive or delay-sensitive tasks generated by vehicles need to be processed in real time. Mobile edge computing (MEC) is an appropriate solution. Wireless users or vehicles can offload computation tasks to the MEC server due to it has strong computation ability and is closer to the wireless users or vehicles. However, the communication and computation resources of the single MEC are not sufficient for executing the continuously generated computation-intensive or delay-sensitive tasks. We consider migrating computation tasks to other MEC servers to reduce the computation and communication pressure on current MEC server. In this article, we construct an MEC-based computation offloading framework for vehicular networks, which considers time-varying channel states and stochastically arriving computation tasks. To minimize the total cost of the proposed MEC framework, which consists of the delay cost, energy computation cost, and bandwidth cost, we propose a deep reinforcement learning-based computation migration and resource allocation (RLCMRA) scheme that requires no prior knowledge. The RLCMRA algorithm can obtain the optimal offloading and migration policy by adaptive learning to maximize the average cumulative reward (minimize the total cost). Extensive numerical results show that the proposed RLCMRA algorithm can adaptively learn the optimal policy and outperform four other baseline algorithms.
Monitoring Users' Behavior: Anti-Immigration Speech Detection on Twitter

The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the users’ data can be weaponized in political campaigns, which many times trigger hate speech and anti-immigration views. Hate speech detection is a challenging task due to the different sources of hate that can have an impact on the language used, as well as the lack of relevant annotated data. To tackle this, we collected and manually annotated an immigration-related dataset of publicly available Tweets in UK, US, and Canadian English. In an empirical study, we explored anti-immigration speech detection utilizing various language features (word n-grams, character n-grams) and measured their impact on a number of trained classifiers. Our work demonstrates that using word n-grams results in higher precision, recall, and f-score as compared to character n-grams. Finally, we discuss the implications of these results for future work on hate-speech detection and social media data analysis in general.
An Automated Biometric Identification System Using CNN-Based Palm Vein Recognition

Recently, automated biometric identification system (ABIS) has wide applications involving automatic identification and data capture (AIDC), which includes automatic security checking, verifying personal identity to prevent information disclosure or identity fraud, and so on. With the advancement of biotechnology, identification systems based on biometrics have emerged in the market. These systems require high accuracy and ease of use. Palm vein identification is a type of biometric that identifies palm vein features. Compared with other features, palm vein recognition provides accurate results and has received considerable attention. We developed a novel high-performance and noncontact palm vein recognition system by using high-performance adaptive background filtering to obtain palm vein images of the region of interest. We then used a modified convolutional neural network to determine the best recognition model through training and testing. Finally, the developed system was implemented on the low-level embedded Raspberry Pi platform with cloud computing technology. The results showed that the system can achieve an accuracy of 96.54%.
Body shape changes of hatchery-reared triploid sturgeon (Acipenser baeri) × (Huso huso) during early development using geometric morphometric technique.

This research was conducted to study the body shape changes of a hatchery-reared triploid sturgeon (male Acipenser baeri × female Huso huso) using a landmark-based geometric morphometric method. For this purpose, larvae from hatching till 50-day post hatching (dph) were sampled, then their left sides photographed and nine landmarks-points digitized on 2D pictures to extract body shape data in the geometric morphometric teqnique. Relative Warp (RW) analysis revealed a sharp body shape change during early ontogeny on 15-dph. The results also showed that ontogenetic shape changes encompassed a pre-inflection shape changes, which included increasing the head depth and length and tail length and a post-inflection shape changes, with the elongation of the trunk. The results showed that the growths of the head and tail regions were positive allometeric and isometric before and after the inflexion point, respectively. The growth of the trunk region of the triploid larvae was negative and isometric allometric pattern at the first and second growth phases, respectively. The body shape change during the early development of triploid larvae can be associated with development of feeding apparatus, swimming, respiration and sense organs. Based on the results, the triploid larvae showed similar growth patterns with those of its parents, i.e. A. baeri and H. huso. Body shape change of the triploid larvae at the length of 15-dph, reflects full swimming ability and external feeding that are necessary for its survival.
Create your own data and energy integrated communication network: A brief tutorial and a prototype system

In order to satisfy the ever-increasing energy appetite of the massive battery-powered and batteryless communication devices, radio frequency (RF) signals have been relied upon for transferring wireless power to them. The joint coordination of wireless power transfer (WPT) and wireless information transfer (WIT) yields simultaneous wireless information and power transfer (SWIPT) as well as data and energy integrated communication network (DEIN). However, as a promising technique, few efforts are invested in the hardware implementation of DEIN. In order to make DEIN a reality, this paper focuses on hardware implementation of a DEIN. It firstly provides a brief tutorial on SWIPT, while summarising the latest hardware design of WPT transceiver and the existing commercial solutions. Then, a prototype design in DEIN with full protocol stack is elaborated, followed by its performance evaluation.
UAV communications with millimeter-wave beamforming: Potentials, scenarios, and challenges

Unmanned aerial vehicle (UAV) has been widely used in many fields and is arousing global attention. As the resolution of the equipped sensors in the UAV becomes higher and the tasks become more complicated, much higher data rate and longer communication range are required in the foreseeable future. As the millimeter-wave (mmWave) band can provide more abundant frequency resources than the microwave band, much higher achievable rate can be guaranteed to support UAV services such as video surveillance, hotspot coverage, and emergency communications, etc. The flexible mmWave beamforming can be used to overcome the high path loss caused by the long propagation distance. In this paper, we study three typical application scenarios for mmWave-UAV communications, namely communication terminal, access point, and backbone link. We present several key enabling techniques for UAV communications, including beam tracking, multi-beam forming, joint Tx/Rx beam alignment, and full-duplex relay techniques. We show the coupling relation between mmWave beamforming and UAV positioning for mmWave-UAV communications. Lastly, we summarize the challenges and research directions of mmWave-UAV communications in detail.
Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

Typically, the operational lifetime of underground concrete structures is several decades. At present, many such structures are approaching their original life expectancy. In this stage, the essential functionality of the structures may be considerably degraded, leading to various safety hazards such as collapse roof and tunnel flooding. In general, to overcome such problems, the maintenance of underground structures has been conducted through manual subjective inspections so far. However, recently, several objective inspection technologies have been actively developed by fusing artificial intelligence and imaging techniques recently. In particular, deep learning algorithms have been developed to detect concrete cracks, based on a large amount of data for supervised learning, including numerous labeled images. Such data acquisition requires considerable time and effort. To reduce these costs, in this study, multiscale and adversarial learning techniques were applied to realize crack detection. A total of 1,200 labeled data and 3,000 unlabeled data were used to implement and verify the proposed method. The multiscale segmentation neural network, discriminator neural network, and adversarial learning technique were used to realize accurate crack detection, enhance the learning performance, and ensure the efficiency of training data, respectively. The resulting algorithm had a pixel accuracy, mean intersection over union, frequency weighted intersection over union, and F1 score of 98.176%, 88.936%, 96.525%, and 88.789%, respectively. The proposed technique can be used to examine the conditions to ensure the safe maintenance of aging structures.
Stroke Screening Feature Selection for Arm Weakness Using a Mobile Application

This work studies the features of a proposed automated stroke self-screening application that utilizes the gyroscope and accelerometer devices in smartphones to determine the possible onset of a stroke by assessing arm muscle weakness. The application requires users to perform two arm movements to evaluate arm weakness and pronation: Curl-up and Raise-up. For the purpose of the study, 68 subjects, consisting of 36 stroke patients with symptoms of arm weakness and 32 healthy subjects, consented to participate. A total of 78 handcrafted features were proposed, 26 of which were extracted from Curl-up and Raise-up for each arm. Then, the differences between corresponding features for each arm were calculated. These features were then tested on 63 combinations of three classical feature selection methods, three feature sets (i.e., Curl-up-only features, Raise-up-only features, and both-exercises combined features) and seven well-known classification methods. The results from ten runs of 10-fold cross-validation showed that Curl-up-only features achieved an average sensitivity of 83.3%, significantly higher than those of the Raise-up-only features or both-exercises features. From all possible combinations, the random forest classification based on information gain feature selection from Curl-up-only features achieved the most efficient results for arm-weakness-stroke screening. It achieved an average sensitivity of 94.8%, an average specificity of 75.2%, an average accuracy of 84.1%, and an average area under the receiver operating characteristic curve of 85.0%. Our work proposes a novel accessible method to screen symptoms of arm weakness that may indicate the onset of a stroke using a single mobile device. In the future, we can combine this method with other methods of evaluating facial drooping and slurred speech to create a complete Face, Arm, Speech, Time (FAST) assessment application.
Parallel Receive Beamforming Improves the Performance of Focused Transmit-Based Single-Track Location Shear Wave Elastography

Single-track location shear wave elastography (STL-SWEI) is robust against speckle-induced noise in shear wave speed (SWS) estimates; however, it is not immune to other incoherent sources of noise (such as electronic noise) that increases the variance in SWS estimates. Although estimation averaging enabled by parallel receive beamforming adequately suppresses these noise sources, these beamforming techniques often rely on broad transmit beams (plane or diverging). While broad beam approaches, such as plane-wave imaging, are becoming ubiquitous in research ultrasound systems, clinical systems usually employ focused transmit beams due to compatibility with hardware beamforming and deeper penetration. Consequently, improving the noise robustness of focused transmit-based STL-SWEI may enable easier translation to clinical scenarios. In this article, we experimentally evaluated the performance of parallel beamforming for STL-SWEI using fixed or multiple transmit focus. By imaging tissue-mimicking phantoms, we found that parallel beamforming improved the focal zone elastographic signal-to-noise ratio (SNRe) by 40.9%. For a receive line spacing equivalent to transducer pitch, averaging estimates from three parallel lines produced peak SNRe at the focal zone (25 mm), while, at the shallower regions (< 20 mm), a larger number of parallel lines (>7) were needed. Increasing the beamforming line density by a factor of 8 increased the focal zone SNRe only by 13.2%. When SWS quantification was desirable at a fixed depth (such as within the push focal depth), using a deeper tracking focal zone enabled higher parallel line count and improved the peak SNRe by 33%. The multifocusing strategy produced a lower SNRe than the single-focus configurations. For a fixed tracking focal zone, a depth-dependent averaging based on the simulated transmit intensity adequately accounted for the transmit beamwidth. The results in this work demonstrated that STL-SWEI can be implemented using focused transmit beams with robust noise-suppression capability.