Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanoscale photoacoustic tomography for label-free super-resolution imaging: simulation study

Abstract. Resolutions higher than the optical diffraction limit are often desired in the context of cellular imaging and the study of disease progression at the cellular level. However, three-dimensional super-resolution imaging without reliance on exogenous contrast agents has so far not been achieved. We present nanoscale photoacoustic tomography (nPAT), an imaging modality based on the photoacoustic effect. nPAT can achieve a dramatic improvement in the axial resolution of the photoacoustic imaging. We derive the theoretical resolution and sensitivity of nPAT and demonstrate that nPAT can achieve a maximum axial resolution of 9.2 nm. We also demonstrate that nPAT can theoretically detect smaller numbers of molecules (∼273) than conventional photoacoustic microscopy due to its ability to detect acoustic signals very close to the photoacoustic source. We simulate nPAT imaging of malaria-infected red blood cells (RBCs) using digital phantoms generated from real biological samples, showing nPAT imaging of the RBC at different stages of infection. These simulations show the potential of nPAT to nondestructively image RBCs at the nanometer resolutions for in vivo samples without the use of exogenous contrast agents. Simulations of nPAT-enabled functional imaging show that nPAT can yield insight into malarial metabolism and biocrystallization processes. We believe that the experimental realization of nPAT has important applications in biomedicine.
Multicenter Experience with Stenting for Symptomatic Carotid Web

Background: A carotid web (CaW) is a shelf-like lesion in the posterior aspect of the internal carotid bulb and represents an intimal variant of fibromuscular dysplasia. CaW has been associated with recurrent strokes and conventionally treated with surgical excision. We report a multicenter experience of stenting in patients with symptomatic CaWs. Methods: Retrospective review of consecutive patients admitted to 5 comprehensive stroke centers who were identified to have a symptomatic CaW and treated with carotid stenting. A symptomatic CaW was defined by the presence of a shelf-like/linear, smooth filling defect in the posterior aspect of the carotid bulb diagnosed by neck CT angiography (CTA) and confirmed with conventional angiography in patients with negative stroke workup. Results: Twenty-four patients with stented symptomatic CaW were identified (stroke in 83% and transient ischemic attack in 17%). Their median age was 47 years (IQR 41–61), 14 (58%) were female, and were 17 (71%) black. The degree of stenosis by NASCET was 0% (range 0–11). All patients were placed on dual antiplatelets and stented at a median of 9 days (IQR 4–35) after the last event. Closed-cell stents were used in 18 (75%) of the cases. No periprocedural events occurred with the exception of 2 cases of asymptomatic hypotension/bradycardia. Clinical follow-up after stent placement occurred for a median of 12 months (IQR 3–19) with no new cerebrovascular events noted. Functional independence at 90 days was achieved in 22 (91%) patients. Follow-up vascular imaging (ultrasound n = 18/CTA n = 5) was performed at a median of 10 months (IQR 3–18) and revealed no stenosis. Conclusions: Stenting for symptomatic CaW appears to be a safe and effective alternative to surgical resection. Further studies are warranted.
The Cockayne syndrome protein B is involved in the repair of 5-AZA-2′-deoxycytidine-induced DNA lesions

The Cockayne Syndrome Protein B (CSB) plays an essential role in Transcription-Coupled Nucleotide Excision Repair (TC-NER) by recruiting repair proteins once transcription is blocked with a DNA lesion. In fact, CSB-deficient cells are unable to recover from transcription-blocking DNA lesions. 5-Aza-2′-deoxycytidine (5-azadC) is a nucleoside analogue that covalently traps DNA methyltransferases (DNMTs) onto DNA. This anticancer drug has a double mechanism of action: it reverts aberrant hypermethylation in tumour-suppressor genes, and it induces DNA damage. We have recently reported that Homologous Recombination and XRCC1/PARP play an important role in the repair of 5-azadC-induced DNA damage. However, the mechanisms involved in the repair of the DNMT adducts induced by azadC remain poorly understood. In this paper, we show for the first time the importance of CSB in the repair of azadC-induced DNA lesions. We propose a model in which CSB initiates a signalling pathway to repair transcription blocks induced by incorporated 5-azadC. Indeed, CSB-deficient cells treated with 5-azadC show a delay in the repair of trapped DNMT1, increased levels of DNA damage and reduced survival.
Database query optimisation based on measures of regret

The query optimiser in a database management system (DBMS) is responsible for �nding a good order in which to execute the operators in a given query. However, in practice the query optimiser does not usually guarantee to �nd the best plan. This is often due to the non-availability of precise statistical data or inaccurate assumptions made by the optimiser. In this thesis we propose a robust approach to logical query optimisation that takes into account the unreliability in database statistics during the optimisation process. In particular, we study the ordering problem for selection operators and for join operators, where selectivities are modelled as intervals rather than exact values. As a measure of optimality, we use a concept from decision theory called minmax regret optimisation (MRO). When using interval selectivities, the decision problem for selection operator ordering turns out to be NP-hard. After investigating properties of the problem and identifying special cases which can be solved in polynomial time, we develop a novel heuristic for solving the general selection ordering problem in polynomial time. Experimental evaluation of the heuristic using synthetic data, the Star Schema Benchmark and real-world data sets shows that it outperforms other heuristics (which take an optimistic, pessimistic or midpoint approach) and also produces plans whose regret is on average very close to optimal. The general join ordering problem is known to be NP-hard, even for exact selectivities. So, for interval selectivities, we restrict our investigation to sets of join operators which form a chain and to plans that correspond to left-deep join trees. We investigate properties of the problem and use these, along with ideas from the selection ordering heuristic and other algorithms in the literature, to develop a polynomial-time heuristic tailored for the join ordering problem. Experimental evaluation of the heuristic shows that, once again, it performs better than the optimistic, pessimistic and midpoint heuristics. In addition, the results show that the heuristic produces plans whose regret is on average even closer to the optimal than for selection ordering.
A Multi-Objective ACO to Solve the Daily Carpool Problem

In﻿urban﻿areas,﻿the﻿cost﻿of﻿road﻿congestion﻿has﻿paid﻿great﻿attention﻿to﻿the﻿sociological,﻿ technological﻿ and﻿ environmental﻿ aspects,﻿ such﻿ as﻿ the﻿ optimal﻿ route﻿ and﻿ fuel﻿ consumption. ﻿This﻿step﻿is﻿towards﻿a﻿smarter﻿vehicle﻿mobility﻿where﻿the﻿travel﻿time﻿ will﻿be﻿planned﻿and﻿dynamically﻿adapted﻿to﻿changes﻿with﻿actual﻿status﻿of﻿the﻿traffic﻿ flow. ﻿In﻿this﻿article﻿a﻿multi-objective﻿ACO﻿algorithm﻿is﻿proposed﻿to﻿solve﻿the﻿daily﻿ carpooling﻿problem. ﻿In﻿particular,﻿a﻿set﻿of﻿decision﻿variables﻿are﻿proposed﻿in﻿order﻿to﻿ minimize﻿three﻿objective﻿functions﻿subject﻿to﻿a﻿set﻿of﻿constraints﻿on﻿these﻿objectives. KeywORDS Daily Carpool Problem, Heuristic Function, Multi-Objective ACO, Solution Space
A 12-Month Lifestyle Intervention Program Improves Body Composition and Reduces the Prevalence of Prediabetes in Obese Patients

Background: The present study investigated the effects of a 12-month interdisciplinary standardized lifestyle program addressing physical activity and changes in dietary and lifestyle behavior in 2,227 obese prediabetic participants. Methods: Measures of obesity (BMI, waist circumference), cardiopulmonary fitness, and metabolic parameters were determined before and after the intervention period. Results: From the 2,227 participants who were initially prediabetic, 839 participants (-37.7%) did no longer show the criteria of prediabetes after the intervention and had normal HbA1c levels. Conclusion: The clinical effects are substantial, and it is likely that the applied intense and multidisciplinary lifestyle interventions could reduce the risk of developing diabetes and the prevalence of a full-blown metabolic syndrome in obese and prediabetic patients.
A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge.
Make Some Noise: Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis

Profiled side-channel attacks based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing a new Convolutional Neural Network instance that is able to reach high performance for a number of considered datasets. Additionally, for a dataset protected with the random delay countermeasure, our neural network is able to break the implementation by using only 2 traces in the attack phase. We compare our neural network with the one designed for a particular dataset with masking countermeasure and we show how both are good designs but also how neither can be considered as a superior to the other one. Next, we address how the addition of artificial noise to the input signal can be actually beneficial to the performance of the neural network. Such noise addition is equivalent to the regularization term in the objective function. By using this technique, we are able to improve the number of measurement needed to reveal the secret key by orders of magnitude in certain scenarios for both neural networks. To strengthen our experimental results, we experiment with a number of datasets which differ in the levels of noise (and type of countermeasure) where we show the viability of our approaches.
A study of neighbour selection strategies for POI recommendation in LBSNs

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.
How quantitative is metabarcoding: A meta‐analytical approach

Metabarcoding has been used in a range of ecological applications such as taxonomic assignment, dietary analysis and the analysis of environmental DNA. However, after a decade of use in these applications there is little consensus on the extent to which proportions of reads generated corresponds to the original proportions of species in a community. To quantify our current understanding, we conducted a structured review and meta‐analysis. The analysis suggests that a weak quantitative relationship may exist between the biomass and sequences produced (slope = 0.52 ± 0.34, p < 0.01), albeit with a large degree of uncertainty. None of the tested moderators, sequencing platform type, the number of species used in a trial or the source of DNA, were able to explain the variance. Our current understanding of the factors affecting the quantitative performance of metabarcoding is still limited: additional research is required before metabarcoding can be confidently utilized for quantitative applications. Until then, we advocate the inclusion of mock communities when metabarcoding as this facilitates direct assessment of the quantitative ability of any given study.
A novel approach to suicide prevention – Educating when it matters

Background: Looking at the burden of suicide, there is a dire need for medical schools to incorporate suicide prevention training. Effective communication helps in early detection and management of suicidal behavior. Medical students can act as a GATEKEEPER if they receive adequate training. Methodology: This was an educational intervention study done at tertiary care teaching hospital to assess the knowledge and attitude of medical students toward depression and suicide and to impart and assess communication skills for suicide prevention in one of the four batches of students in 4th semester. Pretest was conducted to assess knowledge and attitude toward depression and suicide, followed by training using interactive lectures, demonstration of interview, and hands-on training with patients and role-plays. The posttest and objective structured clinical examination (OSCE) were administered for skill assessment. Feedback was taken regarding this intervention. Results: The mean marks of the pre- and post-test were 8.96 (8.3–9.6) and 14.58 (13.8–15.3), respectively, out of 25. The difference was statistically significant (t = 13.24, P ≤ 0.0001) which suggests improvement in knowledge. We found mixed responses in attitude statements showing limited change. Mean obtained marks on OSCE examination out of 66 was 42.7. Among various components of OSCE, students scored high on rapport building. The most useful components of trainings were role-play, OSCE, and interaction with patients as per their feedback. Conclusion: The intervention was found effective in increasing knowledge, changing attitude, and enhancing communication skills of medical students toward suicide prevention. Training of communication skills for suicide prevention in depressed person should be given to every medical student as suggested by feedback.
Physical Modeling of Bitcell Stability in Subthreshold SRAMs for Leakage–Area Optimization under PVT Variations

Subthreshold SRAM design is crucial for addressing the memory bottleneck in energy constrained applications. While statistical optimization can be applied based on Monte-Carlo (MC) simulation, exploration of bitcell design space is time consuming. This paper presents a framework for model-based design and optimization of subthreshold SRAM bitcells under random PVT variations. By incorporating key design and process features, a physical model of bitcell static noise margin (SNM) has been derived analytically. It captures intra-die SNM variations by the combination of a folded-normal distribution and a non-central chi-squared distribution. Validations with MC simulation show its accuracy of modeling SNM distributions down to 25mV beyond 6-sigma for typical bitcells in 28nm. Model-based tuning of subthreshold SRAM bitcells is investigated for design tradeoff between leakage, area and stability. When targeting a specific SNM constraint, we show that an optimal standby voltage exists which offers minimum bitcell leakage power – any deviation above or below increases the power consumption. When targeting a specific standby voltage, our design flow identifies bitcell instances of 12× less leakage power or 3× reductions in area as compared to the minimum-length design.
Motion analysis and classification of salsa dance using music-related motion features

Learning couple dance such as Salsa is a challenge for modern human as it requires to assimilate and understand correctly all the required parameters. In this paper, we propose a set of music-related motion features (MMF) allowing to describe, analyse and classify salsa dancer couple in their respective learning state (beginner, intermediate and expert). These dance qualities have been proposed from a systematic review of papers cross linked with interviews from teacher and professionals in the field of social dance. We investigated how to extract these MMF from musical data and 3D movements of dancers in order to propose a new algorithm to compute them. For the presented study, a motion capture database (SALSA) has been recorded of 26 different couples with varying skill levels dancing on 10 different tempos (260 clips). Each recorded clips contains a basic steps sequence and an extended improvisation sequence during two minutes in total at 120 frame per second. We finally use our proposed algorithm to analyse and classify these 26 couples in three learning levels, which validates some proposed music-related motion features and give insights on others.
An estimate of the effect of waiting time in the Danish asylum system on post-resettlement employment among refugees: Separating the pure delay effect from the effects of the conditions under which refugees are waiting

We provide an estimate of the effect of refugees’ length of waiting time in the Danish asylum system on their subsequent employment using administrative data. In contrast to previous studies, we take into account that refugees’ labor market integration is delayed since their labor market access is restricted during the asylum-seeking phase. We find that an additional year of waiting time decreases subsequent employment by 3.2 percentage points on average. This effect is mostly driven by the delay in the labor market engagement among refugees. Waiting time may have an effect on subsequent employment that is additional to the delay effect, and this could be either positive or negative depending on the nature of the conditions under which asylum seekers live while waiting for their cases to be processed. We find that this additional effect is positive and statistically significant until observable individual characteristics are included, at which point it becomes small in magnitude and no longer significant.
Robust parfda Statistical Machine Translation Results

We build parallel feature decay algorithms (parfda) Moses statistical machine translation (SMT) models for language pairs in the translation task. parfda obtains results close to the top constrained phrase-based SMT with an average of 2.252 BLEU points difference on WMT 2017 datasets using significantly less computation for building SMT systems than that would be spent using all available corpora. We obtain BLEU upper bounds based on target coverage to identify which systems used additional data. We use PRO for tuning to decrease fluctuations in the results and postprocess translation outputs to decrease translation errors due to the casing of words. F1 scores on the key phrases of the English to Turkish testsuite that we prepared reveal that parfda achieves 2nd best results. Truecasing translations before scoring obtained the best results overall.
Progressive Disclosure: Designing for Effective Transparency

As we increasingly delegate important decisions to intelligent systems, it is essential that users understand how algorithmic decisions are made. Prior work has often taken a technocentric approach to transparency. In contrast, we explore empirical user-centric methods to better understand user reactions to transparent systems. We assess user reactions to global and incremental feedback in two studies. In Study 1, users anticipated that the more transparent incremental system would perform better, but retracted this evaluation after experience with the system. Qualitative data suggest this may arise because incremental feedback is distracting and undermines simple heuristics users form about system operation. Study 2 explored these effects in depth, suggesting that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems.
A Systematic Mapping Study on Software Comments Analysis

Context: Mining software repositories has been used as an important tool to support software engineering research. Recent studies indicate that code comments are one of the most explored objects of analysis in the area. Objective: This work investigates how analysis of comments has been used to support software engineering activities by identifying its purposes, focuses, techniques, tools, evaluation methods, and the research type performed in the area. Method: We performed a systematic mapping study of the literature that considered papers from 1990 to 2016. Results: We analyzed 36 primary studies. The collected data pointed out that comment analysis has been used mainly for understanding and identifying the quality of software artifacts. The Dictionary/Vocabulary and Natural Language Processing are among the most used techniques, and most of them are performed in a semiautomatic way. We also organized a set of tools that have been used for mining software comments. Most of the primary studies are a solution proposal paper. Regarding evaluation methods, we found that experiments and case studies are the most considered. Conclusion: The results of this mapping study can help to identify points that still require further investigation in comment analysis research. Keywords-Code comment analysis; mining software repository; systematic mapping study.