Periodontal results of different therapeutic approaches (open vs. closed technique) and timing evaluation ( 2 year) of palatal impacted canines: a systematic review

Background This review evaluates, as a primary outcome, which surgical technique (open vs. closed) and which type of material used for the auxiliaries (elastic vs. metallic) were preferable in terms of periodontal results during the treatment of palatal-impacted canines. The timing of the evaluation of the results was also assessed as a secondary outcome. Methods An electronic search of the literature up to March 2021 was performed on Pubmed, MEDLINE (via Pubmed), EMBASE (via Ovid), Cochrane Reviews and Cochrane Register of Controlled Trials (RCTs) (CENTRAL). The risk of bias evaluation was performed using version 2 of the Cochrane risk of bias tool (RoB 2) for RCTs and the ACROBAT NRSI tool of Cochrane for non-RCTs. Results 11 articles met the inclusion criteria. Only one RCT was assessed as having a low risk of bias and all the non-RCTs were assessed as having a serious risk of bias. This review revealed better periodontal results for the closed technique and metallic auxiliaries. In addition, it revealed that the timing of the evaluation of the results affects the periodontal results with better results obtained 2 years after the end of treatment. Conclusion In the treatment of a palatal-impacted canine, the closed technique and metallic auxiliaries should be preferred in terms of better periodontal results. The timing of the evaluation of the results affects the periodontal results.

An Objective Search for Unrecognized Bias in Validated COVID-19 Prediction Models

The growing recognition of algorithmic bias has spurred discussions about fairness in artificial intelligence (AI) / machine learning (ML) algorithms. The increasing translation of predictive models into clinical practice brings an increased risk of direct harm from algorithmic bias; however, bias remains incompletely measured in many medical AI applications. Using data from over 56 thousand Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in four AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. We discuss that while a model can be biased against certain protected groups (i.e., perform worse) in certain tasks, it can be at the same time biased towards another protected group (i.e., perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. If the goal is to make a change in a positive way, the underlying roots of bias need to be fully explored in medical AI. Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

Security analysis of Subterranean 2.0

Subterranean 2.0 is a cipher suite that can be used for hashing, authenticated encryption, MAC computation, etc. It was designed by Daemen, Massolino, Mehrdad, and Rotella, and has been selected as a candidate in the second round of NIST’s lightweight cryptography standardization process. Subterranean 2.0 is a duplex-based construction and utilizes a single-round permutation in the duplex. It is the simplicity of the round function that makes it an attractive target of cryptanalysis. In this paper, we examine the single-round permutation in various phases of Subterranean 2.0 and specify three related attack scenarios that deserve further investigation: keystream biases in the keyed squeezing phase, state collisions in the keyed absorbing phase, and one-round differential analysis in the nonce-misuse setting. To facilitate cryptanalysis in the first two scenarios, we novelly propose a set of size-reduced toy versions of Subterranean 2.0: Subterranean-m. Then we make an observation for the first time on the resemblance between the non-linear layer in the round function of Subterranean 2.0 and SIMON’s round function. Inspired by the existing work on SIMON, we propose explicit formulas for computing the exact correlation of linear trails of Subterranean 2.0 and other ciphers utilizing similar non-linear operations. We then construct our models for searching trails to be used in the keystream bias evaluation and state collision attacks. Our results show that most instances of Subterranean-m are secure in the first two attack scenarios but there exist instances that are not. Further, we find a flaw in the designers’ reasoning of Subterranean 2.0’s linear bias but support the designers’ claim that there is no linear bias measurable from at most $$2^{96}$$ 2 96 data blocks. Due to the time-consuming search, the security of Subterranean 2.0 against the state collision attack in keyed modes still remains an open question. Finally, we observe that one-round differentials allow to recover state bits in the nonce-misuse setting. By proposing nested one-round differentials, we obtain a sufficient number of state bits, leading to a practical state recovery with only 20 repetitions of the nonce and 88 blocks of data. It is noted that our work does not threaten the security of Subterranean 2.0.

Evaluations of science are robustly biased by identity concerns

People are known to evaluate science based on whether it (dis)affirms their collective identities. We examined whether personal identity concerns also bias evaluation processes by manipulating the degree to which summaries of ostensible scientific research about an unfamiliar topic manipulating whether summaries were or inconsistent with how participants thought about themselves. In three preregistered experiments ( N = 644) conducted across two continents, participants were more likely to believe the science when its conclusions aligned with prior understanding of their self, effects that were mediated through positive emotional reactions. Two of the experiments also tested a de-biasing intervention: prior to evaluating science, participants received a brief tutorial on the ecological fallacy (of which, self-related biases represent a special case). The tutorial did not mitigate identity-biased evaluations. This experimental evidence raises questions about whether it is possible to engage global citizens more fully in science consumption while not further triggering identity-based biasing processes.

The use of non-functional clonotypes as a natural spike-in for multiplex PCR bias correction in immune receptor repertoire profiling

High-throughput sequencing of immune receptor repertoires is a valuable tool for receiving insights in adaptive immunity studies. Several powerful methods for TCR/BCR repertoire reconstruction and analysis have been developed in the past decade. However, detection and correction of the discrepancy between real and experimentally observed lymphocyte clone frequencies are still challenging. Here we formulated a quantitative measure of V- and J-genes frequency bias driven by multiplex PCR during library preparation called Over Amplification Rate (OAR). Based on OAR concept, we developed an original software for multiplex PCR-specific bias evaluation and correction named iROAR: Immune Repertoire Over Amplification Removal (https://github.com/smiranast/iROAR). The iROAR algorithm was successfully tested on previously published TCR repertoires obtained using both 5’ RACE (Rapid Amplification of cDNA Ends)-based and multiplex PCR-based approaches. The developed tool can be used to increase the accuracy and consistency of repertoires reconstructed by different methods making them more applicable for comparative analysis.

Bias evaluation and reduction in 3D OP-OSEM reconstruction in dynamic equilibrium PET studies with 11C-labeled for binding potential analysis

Iterative image reconstruction is widely used in positron emission tomography. However, it is known to contribute to quantitation bias and is particularly pronounced during dynamic studies with 11C-labeled radiotracers where count rates become low towards the end of the acquisition. As the strength of the quantitation bias depends on the counts in the reconstructed frame, it can differ from frame to frame of the acquisition. This is especially relevant in the case of neuro-receptor studies with simultaneous PET/MR when a bolus-infusion protocol is applied to allow the comparison of pre- and post-task effects. Here, count dependent changes in quantitation bias may interfere with task changes. We evaluated the impact of different framing schemes on quantitation bias and its propagation into binding potential (BP) using a phantom decay study with 11C and 3D OP-OSEM. Further, we propose a framing scheme that keeps the true counts per frame constant over the acquisition time as constant framing schemes and conventional increasing framing schemes are unlikely to achieve stable bias values during the acquisition time range. For a constant framing scheme with 5 minutes frames, the BP bias was 7.13±2.01% (10.8% to 3.8%) compared to 5.63±2.85% (7.8% to 4.0%) for conventional increasing framing schemes. Using the proposed constant true counts framing scheme, a stabilization of the BP bias was achieved at 2.56±3.92% (3.5% to 1.7%). The change in BP bias was further studied by evaluating the linear slope during the acquisition time interval. The lowest slope values were observed in the constant true counts framing scheme. The constant true counts framing scheme was effective for BP bias stabilization at relevant activity and time ranges. The mean BP bias under these conditions was 2.56±3.92%, which represents the lower limit for the detection of changes in BP during equilibrium and is especially important in the case of cognitive tasks where the expected changes are low.