Semiclassical folded string in AdS4 X CP3

We consider type IIA superstring theory on the background AdS4 x CP3, and the classical solution describing a folded string spinning in AdS4 with angular momentum in CP3. In the 't Hooft limit, it is the gravity dual of twist operators in the ABJM superconformal theory. We quantize the classical solution by algebraic curve methods and determine the first semiclassical correction to the energy. We provide an integral representation for this quantity valid for all values of the charges. We analyze its properties in the special regimes associated with a short or long string providing various accurate analytical expansions. Finally, we investigate various properties of the so-called slope, the leading term of the energy for short strings, collecting information that could be useful in attempts to generalize the exact results recently proposed for the folded string in AdS5 x S5.Comment: 23 pages, 1 pdf figure, JHEP style, typo correcte

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized

Effectiveness of Platform-Based Robot-Assisted Rehabilitation for Musculoskeletal or Neurologic Injuries: A Systematic Review

During the last ten years the use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. This systematic review aims to evaluate the effectiveness of platform-based robotic rehabilitation for individuals with musculoskeletal or neurological injuries. Thirty-eight studies met the inclusion criteria and evaluated the efficacy of platform-based rehabilitation robots. Our findings showed that rehabilitation with platform-based robots produced some encouraging results. Among the platform-based robots studied, the VR-based Rutgers Ankle and the Hunova were found to be the most effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson’s disease) and various musculoskeletal ankle injuries. Our results were drawn mainly from studies with low-level evidence, and we think that our conclusions should be taken with caution to some extent and that further studies are needed to better evaluate the effectiveness of platform-based robotic rehabilitation devices

Healthcare Students and Medical Residents as Second Victims: A Cross-Sectional Study

Background: The term second victim (SV) describes healthcare professionals who remain traumatized after being involved in a patient safety incident (PSI). They can experience various emotional, psychological, and physical symptoms. The phenomenon is quite common; it has been estimated that half of hospital workers will be an SV at least once in their career. Because recent literature has reported high prevalence (>30%) among nursing students, we studied the phenomenon among the whole population of healthcare students. Methods: We conducted a cross-sectional study with an online questionnaire among nursing students, medical students, and resident physicians at the teaching hospital of the University of the Piemonte Orientale located in Novara, Italy. The study included 387 individuals: 128 nursing students, 174 medical students, and 85 residents. Results: We observed an overall PSI prevalence rate of 25.58% (lowest in medical students, 14.37%; highest in residents, 43.53%). Of these, 62.63% experienced symptoms typical of an SV. The most common temporary symptom was the feeling of working badly (51.52%), whereas the most common lasting symptom was hypervigilance (51.52%). Notably, none of the resident physicians involved in a PSI spoke to the patient or the patient's relatives. Conclusion: Our findings highlighted the risk incurred by healthcare students of becoming an SV, with a possible significant impact on their future professional and personal lives. Therefore, we suggest that academic institutions should play a more proactive role in providing support to those involved in a PSI

Learning the optimal Tikhonov regularizer for inverse problems

Publisher Copyright: © 2021 Neural information processing systems foundation. All rights reserved.In this work, we consider the linear inverse problem y = Ax+ε, where A: X → Y is a known linear operator between the separable Hilbert spaces X and Y, x is a random variable in X and ε is a zero-mean random process in Y . This setting covers several inverse problems in imaging including denoising, deblurring and X-ray tomography. Within the classical framework of regularization, we focus on the case where the regularization functional is not given a priori, but learned from data. Our first result is a characterization of the optimal generalized Tikhonov regularizer, with respect to the mean squared error. We find that it is completely independent of the forward operator A and depends only on the mean and covariance of x. Then, we consider the problem of learning the regularizer from a finite training set in two different frameworks: one supervised, based on samples of both x and y, and one unsupervised, based only on samples of x. In both cases we prove generalization bounds, under some weak assumptions on the distribution of x and ε, including the case of sub-Gaussian variables. Our bounds hold in infinite-dimensional spaces, thereby showing that finer and finer discretizations do not make this learning problem harder. The results are validated through numerical simulations.Peer reviewe

Wrapping corrections, reciprocity and BFKL beyond the sl(2) subsector in N=4 SYM

We consider N=4 SYM and a class of spin N, length-3, twist operators beyond the well studied sl(2) subsector. They can be identified at one-loop with three gluon operators. At strong coupling, they are associated with spinning strings with two spins in AdS5. We exploit the Y-system to compute the leading weak-coupling four loop wrapping correction to their anomalous dimension. The result is written in closed form as a function of the spin N. We combine the wrapping correction with the known four-loop asymptotic Bethe Ansatz contribution and analyze special limits in the spin N. In particular, at large N, we prove that a generalized Gribov-Lipatov reciprocity holds. At negative unphysical spin, we present a simple BFKL-like equation predicting the rightmost leading poles.Comment: 18 page

Cauliflower Mosaic Virus TAV, a Plant Virus Protein That Functions like Ribonuclease H1 and is Cytotoxic to Glioma Cells

Recent comparisons between plant and animal viruses reveal many common principles that underlie how all viruses express their genetic material, amplify their genomes, and link virion assembly with replication. Cauliflower mosaic virus (CaMV) is not infectious for human beings. Here, we show that CaMV transactivator/viroplasmin protein (TAV) shares sequence similarity with and behaves like the human ribonuclease H1 (RNase H1) in reducing DNA/RNA hybrids detected with S9.6 antibody in HEK293T cells. We showed that TAV is clearly expressed in the cytosol and in the nuclei of transiently transfected human cells, similar to its distribution in plants. TAV also showed remarkable cytotoxic effects in U251 human glioma cells in vitro. *ese characteristics pave the way for future analysis on the use of the plant virus protein TAV, as an alternative to human RNAse H1 during gene therapy in human cells