Miracle-dz/UniFAR-FTUs huggingface dataset:
SERPINA9 and SERPINB2: Novel Cartilage Lineage Differentiation Markers of Human Mesenchymal Stem Cells with Kartogenin

Objective Human mesenchymal stem cells (hMSCs) are a promising source for regenerative medicine, especially mesodermal lineages. Clinical applications require an understanding of the mechanisms for transcriptional control to maintain the desired cell type. The aim of this study was to identify novel markers for differentiation of hMSCs into bone or cartilage with the use of Kartogenin, by RNA analysis using microarray technology, and explore the role of RhoA-Rho associated protein kinase (ROCK) inhibition in these. Methods Commercial human bone marrow derived primary mesenchymal stem cells were purchased from ATCC. Cells were differentiated in vitro in 2-dimensional cultures using Kartogenin as the main cartilage inducer and bone morphogenetic protein 2 for bone differentiation; cells were cultured with and without ROCK inhibitor Y-27632. After 21 days of culture, whole RNA was extracted and analyzed via Affimetrix microarrays. The most significant hits were validated by quantitative polymerase chain reaction. Results We found a total of 1,757 genes that were either up- or downregulated on differentiation, when compared to P1 hMSC (control) at day 0 of differentiation. Two members of the Serpin superfamily, SERPINA9 and SERPINB2, were significantly upregulated in the cartilage groups, whereas they were unchanged in the bone groups with and without ROCK inhibition. Conclusions SERPINA9 and SERPINB2 are novel differentiation markers, and molecular regulator candidates for hMSC lineage commitment toward bone and cartilage, providing a new tool for regenerative medicine. Our study highlights the roles of these 2 genes, with significant upregulation of both in cell cultures stimulated with Kartogenin.
Standardized protocols and procedures can precisely and accurately quantify non-structural carbohydrates

Abstract Non-structural carbohydrates (NSCs), the stored products of photosynthesis, building blocks for growth and fuel for respiration, are central to plant metabolism, but their measurement is challenging. Differences in methods and procedures among laboratories can cause results to vary widely, limiting our ability to integrate and generalize patterns in plant carbon balance among studies. A recent assessment found that NSC concentrations measured for a common set of samples can vary by an order of magnitude, but sources for this variability were unclear. We measured a common set of nine plant material types, and two synthetic samples with known NSC concentrations, using a common protocol for sugar extraction and starch digestion, and three different sugar quantification methods (ion chromatography, enzyme, acid) in six laboratories. We also tested how sample handling, extraction solvent and centralizing parts of the procedure in one laboratory affected results. Non-structural carbohydrate concentrations measured for synthetic samples were within about 11.5% of known values for all three methods. However, differences among quantification methods were the largest source of variation in NSC measurements for natural plant samples because the three methods quantify different NSCs. The enzyme method quantified only glucose, fructose and sucrose, with ion chromatography we additionally quantified galactose, while the acid method quantified a large range of mono- and oligosaccharides. For some natural samples, sugars quantified with the acid method were two to five times higher than with other methods, demonstrating that trees allocate carbon to a range of sugar molecules. Sample handling had little effect on measurements, while ethanol sugar extraction improved accuracy over water extraction. Our results demonstrate that reasonable accuracy of NSC measurements can be achieved when different methods are used, as long as protocols are robust and standardized. Thus, we provide detailed protocols for the extraction, digestion and quantification of NSCs in plant samples, which should improve the comparability of NSC measurements among laboratories.
Haptic Distance Ratio Estimation: The Geometry of Space Within the Hands

In the current study of haptic distance perception, 20 younger (median age: 22 years) and 20 older adults (median age: 72 years) used active touch to estimate distance ratios(one length relative to another). Nine tactile stimuli were created from wooden dowels; each consisted of two perpendicular dowels. The stimulus distance ratios ranged from 1.0 to 5.0. Each participant used both hands (without vision) to actively explore (30 s) a single stimulus object on every trial. The task was to numerically estimate the distance ratio. Overall, the participants’ judgments were precise; the overall magnitude of the Pearson r correlation coefficient was 0.943 and did not differ for younger and older adults. While the participants’ judgments were precise, they were not completely accurate: The average slope (of the relationship between actual and judged distance ratios) for all participants was significantly greater than 1.0 (1.15). Surprisingly, differences in manual dexterity had no apparent effect on distance ratio estimates. Older adults apparently retain an excellent ability to perceive distances using their sense of touch. Our results also demonstrate that the geometry of haptic space (at the scale of the hand) is approximately Euclidean in nature (and certainly not merely topological, projective, or affine).
Diacritization of Maghrebi Arabic Sub-Dialects

Diacritization process attempt to restore the short vowels in Arabic written text; which typically are omitted. This process is essential for applications such as Text-to-Speech (TTS). While diacritization of Modern Standard Arabic (MSA) still holds the lion share, research on dialectal Arabic (DA) diacritization is very limited. In this paper, we present our contribution and results on the automatic diacritization of two sub-dialects of Maghrebi Arabic, namely Tunisian and Moroccan, using a character-level deep neural network architecture that stacks two bi-LSTM layers over a CRF output layer. The model achieves word error rate of 2.7% and 3.6% for Moroccan and Tunisian respectively and is capable of implicitly identifying the sub-dialect of the input.
The immune microenvironment in vulvar (pre)cancer: review of literature and implications for immunotherapy

ABSTRACT Introduction: Vulvar squamous cell carcinoma (VSCC) develops via two different pathways: TP53 mutations in a background of lichen sclerosus or a persistent infection with high-risk human papilloma virus (HPV). The latter group of tumor responds better to treatment than the non-virally induced VSCC. This may be explained by a difference in the tumor immune microenvironment (TME). Areas covered: This review summarizes literature on TME of VSCC and its precursors, and extrapolates this to foster the development of new therapeutic strategies. Expert opinion: Both types of VSCC and their precursors are infiltrated with variable numbers of M2 macrophages, regulatory T cells and CD8+ T cells, indicating that they express targetable tumor antigens. Type 1 T cell immunity in precursor lesions is associated with fewer recurrences and better clinical responses to immunotherapy. Escape of these lesions and progression toward VSCC is associated with the downregulation of HLA Class I, increased expression of co-inhibitory molecules, infiltration with immunosuppressive cells and the local production of immunosuppressive enzymes and cytokines. More in-depth studies of the VSCC TME are required to fully comprehend the impact of the immune system on VSCC, and subsequently to identify patients who will benefit from immunotherapeutic strategies.
Development and evaluation of MicroBuilder: a Model-Driven tool for the specification of REST Microservice Software Architectures

ABSTRACT In this paper we present MicroBuilder, the tool used for the specification of a software architecture that follows REST microservice design principles. MicroBuilder comprises MicroDSL and MicroGenerator modules. The MicroDSL module provides the MicroDSL domain-specific language used for the specification of REST microservice software architecture. In this paper we present MicroDSL meta-model, specified in Ecore, together with textual and graphical concrete syntaxes, specified using Xtext and Sirius frameworks respectively. The MicroGenerator module is used to generate executable program code based on MicroDSL specification. In this paper we also present results of the MicroBuilder tool evaluation, alongside the case study which illustrates a usage of MicroBuilder.
Hierarchical Sub-Pixel Anomaly Detection Framework for Hyperspectral Imagery

Anomaly detection is an important task in hyperspectral processing. Some previous works, based on statistical information, focus on Reed-Xiaoli (RX), as it is one of the most classical and commonly used methods. However, its performance tends to be affected when anomaly target size is smaller than spatial resolution. Those sub-pixel anomaly target spectra are usually much similar with background spectra, and may results in false alarm for traditional RX method. To address this issue, this paper proposes a hierarchical RX (H-RX) anomaly detection framework to enhance the performance. The proposed H-RX method consists of several different layers of original RX anomaly detector. In each layer, the RX’s output of each pixel is restrained by a nonlinear function and then imposed as a coefficient on its spectrum for the next iteration. Furthermore, we design a spatial regularization layer to enhance the sub-pixel anomaly detection performance. To better illustrate the hierarchical framework, we provide a theoretical explanation of the hierarchical background spectra restraint and regularization process. Extensive experiments on three hyperspectral images illustrate that the proposed anomaly detection algorithm outperforms the original RX algorithm and some other classical methods.
Single-Dose Pharmacokinetics, Excretion, and Metabolism of Zoliflodacin, a Novel Spiropyrimidinetrione Antibiotic, in Healthy Volunteers

Zoliflodacin is a novel spiropyrimidinetrione with activity against bacterial type II topoisomerases that inhibits DNA biosynthesis and results in accumulation of double-strand cleavages in bacteria. We report results from two phase 1 studies that investigated the safety, tolerability, and pharmacokinetics (PK) of zoliflodacin and absorption, distribution, metabolism, and excretion (ADME) after single doses in healthy volunteers. ABSTRACT Zoliflodacin is a novel spiropyrimidinetrione with activity against bacterial type II topoisomerases that inhibits DNA biosynthesis and results in accumulation of double-strand cleavages in bacteria. We report results from two phase 1 studies that investigated the safety, tolerability, and pharmacokinetics (PK) of zoliflodacin and absorption, distribution, metabolism, and excretion (ADME) after single doses in healthy volunteers. In the single ascending dose study, zoliflodacin was rapidly absorbed, with a time to maximum concentration of drug in serum (Tmax) between 1.5 and 2.3 h. Exposure increased dose proportionally up to 800 mg and less than dose proportionally between 800 and 4,000 mg. Urinary excretion of unchanged zoliflodacin was <5.0% of the total dose. In the fed state, absorption was delayed (Tmax, 4 h), accompanied by an increase in the area under the concentration-time curve (AUC) at 1,500- and 3,000-mg doses. In the ADME study (3,000 mg orally), the PK profile of zoliflodacin had exposure (AUC and maximum concentration of drug in serum [Cmax]) similar to that of the ascending dose study and a median Tmax of 2.5 h. A total of 97.8% of the administered radioactivity was recovered in excreta, with urine and fecal elimination accounting for approximately 18.2% and 79.6% of the dose, respectively. The major clearance pathway was via metabolism and elimination in feces with low urinary recovery of unchanged drug (approximately 2.5%) and metabolites accounting for 56% of the dose excreted in the feces. Zoliflodacin represented 72.3% and metabolite M3 accounted for 16.4% of total circulating radioactivity in human plasma. Along with the results from these studies and based upon safety, PK, and PK/pharmacodynamics targets, a dosage regimen was selected for evaluation in a phase 2 study in urogenital gonorrhea. (The studies discussed in this paper have been registered at ClinicalTrials.gov under identifiers NCT01929629 and NCT02298920.)
Deep Brain Stimulation for the Dystonias: Evidence, Knowledge Gaps, and Practical Considerations

Deep brain stimulation (DBS) of the globus pallidus internus (GPi‐DBS) is among the most effective treatment options for dystonias. Because the term “dystonia” is defined by a characteristic phenomenology of involuntary muscle contractions, which may present with a large clinical and pathogenetic heterogeneity, decision making for or against GPi‐DBS can be difficult in individual patients.
Few-Shot Text and Image Classification via Analogical Transfer Learning

Learning from very few samples is a challenge for machine learning tasks, such as text and image classification. Performance of such task can be enhanced via transfer of helpful knowledge from related domains, which is referred to as transfer learning. In previous transfer learning works, instance transfer learning algorithms mostly focus on selecting the source domain instances similar to the target domain instances for transfer. However, the selected instances usually do not directly contribute to the learning performance in the target domain. Hypothesis transfer learning algorithms focus on the model/parameter level transfer. They treat the source hypotheses as well-trained and transfer their knowledge in terms of parameters to learn the target hypothesis. Such algorithms directly optimize the target hypothesis by the observable performance improvements. However, they fail to consider the problem that instances that contribute to the source hypotheses may be harmful for the target hypothesis, as instance transfer learning analyzed. To relieve the aforementioned problems, we propose a novel transfer learning algorithm, which follows an analogical strategy. Particularly, the proposed algorithm first learns a revised source hypothesis with only instances contributing to the target hypothesis. Then, the proposed algorithm transfers both the revised source hypothesis and the target hypothesis (only trained with a few samples) to learn an analogical hypothesis. We denote our algorithm as Analogical Transfer Learning. Extensive experiments on one synthetic dataset and three real-world benchmark datasets demonstrate the superior performance of the proposed algorithm.
Playing for Depth

Estimating the relative depth of a scene is a significant step towards understanding the general structure of the depicted scenery, the relations of entities in the scene and their interactions. When faced with the task of estimating depth without the use of Stereo images, we are dependent on the availability of large-scale depth datasets and high-capacity models to capture the intrinsic nature of depth. Unfortunately, creating datasets of depth images is not a trivial task as the requirements for the camera mainly limits us to areas where we can provide the necessities for the camera to work. In this work, we present a new depth dataset captured from Video Games in an easy and reproducible way. The nature of open-world video games gives us the ability to capture high-quality depth maps in the wild without the constrictions of Stereo cameras. Experiments on this dataset shows that using such synthetic datasets increases the accuracy of Monocular Depth Estimation in the wild where other approaches usually fail to generalize.
Recent Algorithmic Advances in Population Protocols

Population protocols are a popular model of distributed computing, introduced by Angluin, Aspnes, Diamadi, Fischer, and Peralta [6] a little over a decade ago. In the meantime, the model has proved a useful abstraction for modeling various settings, from wireless sensor networks [35, 26], to gene regulatory networks [17], and chemical reaction networks [21]. In a nutshell, a population protocol consists of n agents with limited local state that interact randomly in pairs, according to an underlying communication graph, and cooperate to collectively compute global predicates. From a theoretical prospective, population protocols, with the restricted communication and computational power, are probably one of the simplest distributed model one can imagine. Perhaps surprisingly though, solutions to many classical distributed tasks are still possible. Moreover, these solutions often rely on interesting algorithmic ideas for design and interesting probabilistic techniques for analysis, while known lower bound results revolve around complex combinatorial arguments.
Exploration by Random Network Distillation

We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards. We find that the random network distillation (RND) bonus combined with this increased flexibility enables significant progress on several hard exploration Atari games. In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods. To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access to the underlying state of the game, and occasionally completes the first level.
Electric and Acoustic Stimulation in Cochlear Implant Recipients with Hearing Preservation

Abstract Hearing loss affects 30 million people in the United States, and a subset of these patients have normal low-frequency hearing and ski-sloped high-frequency hearing loss. For these patients, hearing aids alone may not provide adequate benefit. Cochlear implantation alone has been utilized to improve speech perception. The addition of high-frequency electric hearing to low-frequency acoustic hearing in these patients is beneficial. Technical improvements have allowed preservation of low-frequency hearing in cochlear implant recipients, allowing for electric and acoustic stimulation in the same ear with significant improvements in speech perception, sound localization, music appreciation, and quality of life.
May perioperative ultrasound‐guided pelvic floor muscle training promote early recovery of urinary continence after robot‐assisted radical prostatectomy?

The efficacy of perioperative pelvic floor muscle training (PFMT) for continence recovery after robot‐assisted radical prostatectomy (RARP) remains unclear. Visualization of the bladder neck and urethra using transperineal ultrasound (US) may promote self‐recognition of urethral closure during PFM contraction. This study aimed to examine whether transperineal US‐guided PFMT promotes early recovery of post‐RARP incontinence.
Everybody hands‐on to avoid ESKAPE: effect of sustained hand hygiene compliance on healthcare‐associated infections and multidrug resistance in a paediatric hospital

Purpose. Hand hygiene is the most important strategy for preventing healthcare‐associated infections (HCAIs); however, the impact of hand hygiene in middle‐income countries has been poorly described. In this work, we describe the impact of the programme ‘Let's Go for 100' on hand hygiene adherence, HCAIs rates and multidrug‐resistant (MDR) bacteria, including the molecular typing of methicillin‐resistant Staphylococcus aureus (MRSA) strains. Methodology. A multimodal, hospital‐wide hand hygiene programme was implemented from 2013. ‘Let's Go for 100' involved all healthcare workers and encompassed education, awareness, visual reminders, feedback and innovative strategies. Monthly hand hygiene monitoring and active HCAI surveillance were performed in every ward. Molecular typing of MRSA was analysed by pulsed‐field gel electrophoresis (PFGE). Results/Key findings. Hand hygiene adherence increased from 34.9 % during the baseline period to 80.6 % in the last 3 months of this study. The HCAI rate decreased from 7.54 to 6.46/1000 patient‐days (P=0.004). The central line‐associated bloodstream infection (CLABSIs) rate fell from 4.84 to 3.66/1000 central line‐days (P=0.05). Negative correlations between hand hygiene and HCAIs rates were identified. The attack rate of MDR‐ESKAPE group bloodstream infections decreased from 0.54 to 0.20/100 discharges (P=0.024). MRSA pulsotypes that were prevalent during the baseline period were no longer detected after the 5th quarter, although new strains were identified. Conclusions. A multimodal hand hygiene programme in a paediatric hospital in a middle‐income country was effective in improving adherence and reducing HCAIs, CLABSIs and MDR‐ESKAPE bloodstream infections. Sustaining hand hygiene adherence at a level of >60 % for one year limited MRSA clonal transmission.
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: http://www.highD-dataset.com
Prevalence of Internet Addiction and Associated Psychological Comorbidities among College Students in Bhutan

ABSTRACT Introduction Globally 3.5 billion people have access to internet and most of them are young adults and adolescents. In South Asia, Bhutan has the highest proportion of population with internet access at 37% in 2016. Many studies in Asia have reported significantly high level of internet addiction among college going students. In this light, this study was designed to estimate the prevalence of internet addiction and other co-morbidities. Methods This cross sectional study included 823 first year and final year students aged 18–24 from six colleges in Bhutan. A self-administered questionnaire consisting of three parts was utilized for data collection. The data was entered and validated in Epidata and analyzed using STATA/IC 14. Results The prevalence of moderate and severe internet addiction was 282 (34.3%) and 10 (1%) respectively. Positive correlations between internet addiction and psychological well-being (r= 0.331 95% CI: 0.269, 0.390), between Internet Addiction score and years of internet usage (r=0.104 95% CI: 0.036, 0.171), age and years of using internet (r= 0.8 95% CI: 0.012, 0.148) were observed. The commonest mode of internet use was martphone 714 (86.8%). The use of computer laboratory (aPR 0.80, 95%CI: 0.66, 0.96) and internet use for news and educational purposes (aPR 0.76, 95%CI: 0.64, 0.9) showed protective effects. Conclusions The prevalence of internet addiction is high among the college going students in Bhutan. This warrants timely interventions to address the problems of internet addiction.