Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact COVID-19 Infection in Childhood Asthma. Review Study

Between December 2019 and first months of 2020 a novel coronavirus called nCoV2019 and SARS-CoV-2 rapidly spreads from city of Wuhan (China) to every country around the world caused Coronavirus Disease 2019 (COVID-19). Children can be affected by COVID-19 and present mild and generally less severe respiratory symptoms than adults. The percentage of pediatric cases is certainly underestimated. Admission of children to Intensive Care Unit (ICU) is rare and prognosis is favorable except very few cases. We aimed to perform a review study analysis about COVID-19 and childhood asthma. An analysis of electronic databases was performed using PubMed, Cochrane library and Embase and searched up to June 2020. The literature review was conducted independently by two reviewers and analyzed 43 published articles. The available information’s were limited, with few sources suggesting an underrepresentation and increased risk for severe COVID complications especially in patients with asthma associated with disease severity. The published data of COVID severity infection due to asthma in children are till now extremely rare. COVID-19 doesn’t have a significant impact on childhood asthma and there is no indication to change treatment strategies for asthmatic children. Furthermore, asthmatic children recommended continuing their therapies without making any dose adjustments.
Using an Intersectionality Framework to Examine Disparities in the Steps to Kidney Transplantation in Patients with End Stage Renal Disease

Intersectionality is a theory examining how individuals’ different identities intersect to create a new lived experience, often with compounded oppressions. This theory has recently been utilized in health research to help understand and find effective ways of addressing health disparities (Bowleg, 2012). However, while studies have theorized broadly about intersectionality theory in health research, it has seldom been used to specifically address health disparities in patients with End Stage Renal Disease (ESRD). This study conducted a systematic literature review of past research that has examined barriers to kidney transplantation among ESRD patients with intersectional identities, particularly concerning race and gender. The findings of this literature review found themes of race and gender interactions, lack of explicit use of the intersectionality framework, a focus on Black and White racial groups as well as cisgender individuals, and external validity limitations. In light of these themes, more large intersectional research studies concerning ESRD disparities involving diverse participants is recommended so that the literature base for intersectional identities can grow and lead to more targeted, efficient, and effective intervention programs.
RMA: Rapid Motor Adaptation for Legged Robots

Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA consists of two components: a base policy and an adaptation module. The combination of these components enables the robot to adapt to novel situations in fractions of a second. RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined foot trajectory generators and is deployed on the A1 robot without any fine-tuning. We train RMA on a varied terrain generator using bioenergetics-inspired rewards and deploy it on a variety of difficult terrains including rocky, slippery, deformable surfaces in environments with grass, long vegetation, concrete, pebbles, stairs, sand, etc. RMA shows state-of-the-art performance across diverse real-world as well as simulation experiments. Video results at https://ashish-kmr.github. io/rma-legged-robots/
Freedom for Proofs! Representation Independence is More than Parametricity

Representation independence allows programmers to give diﬀerent implementations for an abstract interface. Reynolds’ characterization of representation independence for System F uses parametricity, and free theorems derived from parametricity give an elegant formulation of how types limit the behavior of functions. This survey paper overviews the recent eﬀorts to eﬀectively bring representation independence re-sults to dependent type theories. The surveyed works demonstrate that extensional notions of equality, through means of extensional equality, univalence, and cubical type theory, can be fruitful for bringing expressive representation independence to dependently-typed programming.
Reduction in Chronic Low Back Pain Using Intervertebral Differential Dynamics Therapy (IDDT) and Routine Physiotherapy: A Retrospective Pre-Post Study

Background: The plethora of treatments for Low Back Pain (LBP) has increased in recent times. Opioids, spinal injection, bed rest, skin traction and surgery have remained the common forms of treatment. However, there is less emphasis on pharmacological and surgical treatments in national clinical practice guidelines. Non-surgical Spinal Decompression (NSD) is a modern, though investigational non-surgical treatment technique for LBP. The aim of this report was to analyse the outcome of LBP using NSD technique delivered by an Intervertebral Differential Dynamics Therapy (IDDT) device amidst other conservative treatments. Method: We conducted a retrospective pre-post study of 141 one hundred adult patients who visited a private physiotherapy clinic over a three and quarter-year period. Patients were treated for an average number of 10 sessions over a 2-month period using NSD therapy (IDDT), in addition to routine physiotherapy management for LBP intensity assessed using numerical pain rating scale. To analyse the obtained data, descriptive statistics and paired t-test were used, significance level was set at α = 0.05. Results: One hundred and forty-one patients (81 males, 60 females) were analysed. The mean age and weight of the patients were 54.73 ± 13.82 years and 192.39 ± 36.10 lbs (87.27 ± 16.37 kg) respectively. The mean starting and ending pain intensity scores were 5.03 ± 1.86 and 4.13 ± 1.82 respectively on an 11-point Numerical Pain Rating Scale (NPRS). There was a statistically significant decrease in pain intensity (t = 12.301, p < 0.001). Conclusion: Statistically significant improvement in LBP could be achieved using NSD and other traditional conservative management. Long-term follow up post NSD is needful.
Market Reaction on Reverse Stock Split Announcement: Empirical Evidence in Indonesian Stock Market

This research focuses on reverse stock split announcements. We are trying to examine stock returns behavior on days prior and following the reverse split announcements. The sample of this study is reverse stock split events on an Indonesian stock market within the year of 2002-2018. An earlier abnormal stock price movement before the announcement shows a sign of insider trading existences, and a delayed abnormal stock price movement following the announcement shows a slow respond of market reaction to particular new information. We are using cumulative abnormal return (CAR) and cumulative market-adjusted return (CMAR) to identify the abnormal stock price movement. The results show that there are positive abnormal returns before the announcement, and then it declines further into negative abnormal returns until post the announcement. However, when we segregate the sample into four price fractions, we find positive abnormal returns patterns only appear on two-five thousand rupiahs price fraction. Meanwhile, the other price fraction categories show declining patterns of negative abnormal returns. Overall, we temporarily suggest that there are illegal insider trading activities in the Indonesian Stock Market. The immediate market reactions show that the market is quite efficient, and its responses regarding the reverse stock split event follow the prediction of the trading range hypothesis.
Storage Temperature and Atmosphere Alleviates Chilling Injury of Marula (Sclerocarya birrea subspecies caffra) Fruit

Low temperature storage is the most effective technology for keeping quality and extending the postharvest life of fresh horticultural produce. However, horticultural produce of tropical and subtropical in origin such as marula fruit are susceptible to chilling injury (CI) when stored at temperatures below their critical minimum temperatures. Therefore, low temperature storage alone is not ideal for produce of tropical and subtropical in origin. The aim of this research was to elucidate the influence of modified atmosphere packaging (MAP) on CI of marula fruits. Storage temperature below 12oC significantly (P < 0.05) increased CI incidence and severity, and proline content of marula fruit. Marula fruit in MAP had significantly (P < 0.05) lower electrolyte leakage than fruit stored in Air. The results further showed that marula fruit stored at 12 ̊C in MAP had significantly longer shelf-life of 21 days than fruits in Air stored at various temperatures which had a shorter shelf life. It was concluded that marula fruits be stored in MAP at 12°C plus 90-95% RH to alleviate CI incidence and severity and maintain fruit quality and extend shelf-life.
Does Local Coherence Lead to Targeted Regressions and Illusions of Grammaticality?

Abstract Local coherence effects arise when the human sentence processor is temporarily misled by a locally grammatical but globally ungrammatical analysis (The coach smiled at the player tossed a frisbee by the opposing team). It has been suggested that such effects occur either because sentence processing occurs in a bottom-up, self-organized manner rather than under constant grammatical supervision, or because local coherence can disrupt processing due to readers maintaining uncertainty about previous input. We report the results of an eye-tracking study in which subjects read German grammatical and ungrammatical sentences that either contained a locally coherent substring or not and gave binary grammaticality judgments. In our data, local coherence affected on-line processing immediately at the point of the manipulation. There was, however, no indication that local coherence led to illusions of grammaticality (a prediction of self-organization), and only weak, inconclusive support for local coherence leading to targeted regressions to critical context words (a prediction of the uncertain-input approach). We discuss implications for self-organized and noisy-channel models of local coherence.
Extracting Information from Different Expectations

Long-term expectations are believed to play a crucial role in driving future inﬂation and guiding monetary policy responses. However, expectations are not directly observed and the available measures often present a wide range of values. To understand what drives these differences, we examine the evolution of survey and market-based consumer price inﬂation expectations in the United States between 2003-2019. We show that inﬂation forecasts can be improved by incorporating the differential between survey and market-based measures. Next, we decompose and extract the differentials in rigidity and information between these measures. While both information and rigidities play a role, the information differential is more important. Using machine learning methods, we ﬁnd that up to half of the information differential is explained by real-time changes in measures of liquidity. This also explains some past forecast improvements and helps predict the divergence in long-term inﬂation expectations in 2020.
Privacy-Preserving Approximate k-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns

Abstract We study the problem of privacy-preserving approximate kNN search in an outsourced environment — the client sends the encrypted data to an untrusted server and later can perform secure approximate kNN search and updates. We design a security model and propose a generic construction based on locality-sensitive hashing, symmetric encryption, and an oblivious map. The construction provides very strong security guarantees, not only hiding the information about the data, but also the access, query, and volume patterns. We implement, evaluate efficiency, and compare the performance of two concrete schemes based on an oblivious AVL tree and an oblivious BSkiplist.
Counter-Intuitive Characteristics of Rational Decision-Making Using Biased Inputs in Information Networks

We consider an information network comprised of nodes that are: rational-information-consumers (RICs) and/or biased-information-providers (BIPs). Making the reasonable abstraction that any external event is reported as an answer to a logical statement, we model each node’s information-sharing behavior as a binary channel. For various reasons, malicious or otherwise, BIPs might share incorrect reports of the event regardless of their private beliefs. In doing so, a BIP might favor one of the two outcomes, exhibiting intentional or unintentional bias (e.g. human cognitive biases). Inspired by the limitations of humans and low-memory devices in information networks, we previously investigated a graph-blind rational-information-consumer interested in identifying the ground truth. We concluded that to minimize its error probability, graph-blind RIC follows a counter-intuitive but tractable rule. In this work, we build on this foundational knowledge: “graph-blind RICs prefer the combination of information-providers that are all fully-biased against the a-priori likely input, over all other combinations. ” Upon studying RICs with partial knowledge of the network graph, we find that they act similar to graph-blind RICs when their BIPs “listen to” sufficiently many information-providers of their own. Furthermore, if a common node is informing/influencing all <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> BIPs of a partially-aware RIC, that RIC anticipates its discovery of the “influential node” to diminish the average error probability by a factor that increases exponentially with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. However, from the partially-aware RIC’s perspective, choosing <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> fully-, similarly-biased BIPs outweighs the discovery of influential nodes among its BIPs’ sources. These insights might inform the design of consumer-centric information networks.
Efficacy of tocilizumab in critically ill COVID-19 patients: a retrospective cohort

Background & Objective: Covid-19 is a complex disease with unpredictable blended pattern, and it resembles the cytokine release syndrome (CRS), marked with vasculopathy, coagulopathy, ARDS, multi-organ failure, and aggressive rise of inflammation markers in the serum. Tocilizumab (TCZ) has been in use as an off-label drug due to its antagonist activity on IL-6 receptor. This study compared the efficacy of standard of care (SOC), and SOC along with TCZ, in critically ill COVID-19 patients. Methodology: In this retrospective cohort, we included 74 critically ill COVID-19 patients, aged between 18 to 90 y. Those who received only SOC were placed in the SOC group, while patients who received TCZ in addition to SOC, were placed in the TCZ + SOC group. The SOC included low molecular weight heparin (LMWH) 60 mg S/C OD, dexamethasone 6 mg IV OD, remdesivir (antiviral) 200 mg IV stat then 100 mg IV OD for 5 days and when needed 10 days, antibiotics for secondary infection e.g., Azithromycin 500 mg IV OD, in the presence of High flow Oxygen (HFO) or CPAP. X-rays chest, serum levels of lactate dehydrogenase (LDH), D-Dimers, ferritin, pro-BNP, C-reactive protein (CRP), total leukocyte count (TLC), renal and liver markers, serum electrolytes, sugar levels, and ABGs were obtained from the data, at the time of admission in ICU and on the 7th day of ICU stay. Results: 57% patients received only SOC while 43% received TCZ in addition to SOC. Improvement in hypoxia and radiological findings on day-7 was more in SOC' group than TCZ+ SOC' group (52% vs. 34% and 52% vs. 31% respectively), but it was not statistically significant. Overall improvement in inflammation markers on day-7 was similar in both groups with p = 0.925. Survival was 45% vs. 37.5% in SOC' vs. TCZ+ SOC' group respectively (p = 0.504). Conclusion: Tocilizumab does not alter the overall survival of critically ill COVD-19 patients. Key words: COVID-19; Tocilizumab; Standard of care; Efficacy; Survival Abbreviations: TCZ – Tocilizumab; HFO - High flow Oxygen; LDH - lactate dehydrogenase; CRS - cytokine release syndrome; CRP - C-reactive protein; SOC - standard of care; LMWH - low molecular weight heparin; COVID-19 - Coronavirus Disease 2019; SARS-CoV-2 - Severe Acute Respiratory Syndrome Coronavirus 2 Citation: Sadaf S, Bashir B, Haider SS, Mustafa G, Naqvi SAA. Efficacy of tocilizumab in critically ill COVID-19 patients: a retrospective cohort. Anaesth. pain intensive care 2021;25(3):__. DOI: doi.org/10.35975/apic.v25i3. 1520 Received: January 8, 2021; Reviewed: March 11, 2021; Accepted: March 11, 2021
Predicting Upper-Extremity Function Recovery From Kinematics in Stroke Patients Following Goal-Oriented Computer-Based Training

Background: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, predicting impairment and recovery are enormous challenges in neurorehabilitation. Body function and structure, as well as activities, are assessed using clinical scales. For functional deficits of the upper extremities these include the Fugl-Meyer Assessment for the Upper Extremity (FM-UE), the Chedoke Arm and Hand Activity Inventory (CAHAI) and Barthel Index (BI), administered by clinicians. Although these scales are generally accepted for the evaluation of the motor and functional impairment of the upper-limbs, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. For these reasons, alternative methods need to be developed for efficient and objective assessment. Computer-based motion capture and classification tools have the potential to collect and process kinematic data to estimate impairment, function and recovery while overcoming these limitations.Methods: We present a method for estimating clinical scores from movement parameters that are entirely extracted from kinematic data recorded during unsupervised rehabilitation sessions performed with the Rehabilitation Gaming System (RGS). RGS is a rehabilitation technology that uses image-based motion capture, goal-oriented individualised training, virtual reality content delivery, and restricts compensatory trunk movements through feedback. The main protocol considered in this study asks patients to use their upper limbs to intercept spheres that are presented in a 3 dimensional virtual reality display. RGS maps the planar physical arm movements onto matching movements by an avatar presented in a first-person perspective. In this analysis, we performed a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS.Results: Our multivariate regression model reaches an accuracy of R2 : 0.38, with an error (σ : 12.8), in predicting FM-UE scores. We analyse our model by assessing reliability (r = 0:89 for test-retest), sensitivity to clinical improvements (95% true positive rate) and generalisation to other tasks that involve planar reaching movements (R2 : 0.39). The model achieves comparable accuracy also for the CAHAI (R2 : 0.40) and BI scales (R2 : 0.35). Conclusions: Our results highlight the clinically relevant predictive power of kinematic data collected during unsupervised goal-oriented motor training combined with automated inference techniques and provide new insight into factors underlying recovery and its biomarkers.
Posterior Promoted GAN with Distribution Discriminator for Unsupervised Image Synthesis

Sufficient real information in generator is a critical point for the generation ability of GAN. However, GAN and its variants suffer from lack of this point, resulting in brittle training processes. In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P2GAN), which promotes generator with the real information in the posterior distribution produced by discriminator. In our frame-work, different from other variants of GAN, the discriminator maps images to a multivariate Gaussian distribution and extracts real information. The generator employs the real information by AdaIN and a latent code regularizer. Besides, reparameterization trick and pretraining is applied to guarantee a stable training process in practice. The convergence of P2GAN is theoretically proved. Experimental results on typical high-dimensional multi-modal datasets demonstrate that P2GAN has achieved comparable results with the state-of-the-art variants of GAN on unsupervised image synthesis.
Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents' trajectories with the planning of the robot's trajectory. However, it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular, we show that preference distributions (probability density functions representing agents' intentions) can capture higher order statistics of agent behaviors, such as willingness to cooperate. Thus, coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space, and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis, we develop a sampling-based motion planning framework called DistNav that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments, and benchmark against a wide variety of model based and machine learning based approaches. The safety and efficiency statistics of our approach outperform all other models. Finally, we find that DistNav is competitive with human safety and efficiency performance.