Phase transitions in disordered systems

Disorder can have a wide variety of consequences for the physics of phase transitions. Some transitions remain unchanged in the presence of disorder while others are completely destroyed. In this thesis we study the effects of disorder on several classical and quantum phase transitions in condensed matter systems. After a brief introduction, we study the ferromagnetic phase transition in a randomly layered Heisenberg magnet using large-scale Monte-Carlo simulations. Our results provide numerical evidence for the exotic infinite-randomness scenario. We study classical and quantum smeared phase transitions in substitutional alloys A₁₋ₓBₓ. Our results show that the disorder completely destroys the phase transition with a pronounced tail of the ordered phase developing for all compositions x \u3c 1. In addition, we find that short-ranged disorder correlations can have a dramatic effect on the transition. Moreover, we show an experimental realization of the composition-tuned ferromagnetic-to-paramagnetic quantum phase transition in Sr₁₋ₓCaₓRuO₃. We investigate the effects of disorder on first-order quantum phase transitions on the example of the N-color quantum Ashkin-Teller model. By means of a strong disorder renormalization group, we demonstrate that disorder rounds the first-order transition to a continuous one for both weak and strong coupling between the colors. Finally, we investigate the superfluid-insulator quantum phase transition of one-dimensional bosons with off-diagonal disorder by means of large-scale Monte-Carlo simulations. Beyond a critical disorder strength, we find nonuniversal, disorder-dependent critical behavior --Abstract, page iv

Rh alleles and phenotypes among Saudi women in Hail Region, Saudi Arabia

Background: The Rh system is considered as the most complex among the human blood group systems, with 61 antigens identified to date. This study aimed to provide preliminary data on the distribution of Rh alleles and phenotypes among Saudi women and compare them with other ethnic groups.Methods: This retrospective cross-sectional study was conducted among Saudi women who visited the Maternity and Children Hospital of Hail from November 2019 to March 2020. A fully automated blood bank analyzer was used in determining the Rh subgroups (D, C, c, E, e) and phenotypes. Inferential statistics and chi-square tests were used appropriately for comparisons.Results: The study included a total of 500 Saudi female patients. The most prevalent antigen found was the “e” antigen, while phenotype CcDee has shown to have the highest frequency. A significant difference exists in comparison with the other studies from various ethnic groups.Conclusions: The prevalence and distributions of Rh alleles and phenotypes among Saudi women were revealed in this study. The findings showed that Rh alleles and phenotypes are diverse across various races and regions globally.Keywords: Rh allele; Rh phenotype; Rh system; Saudi Arabi

Automatic alignment of surgical videos using kinematic data

Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.Comment: Accepted at AIME 201

Neuroinflammation in Traumatic Brain Injury

Neuroinflammation following traumatic brain injury (TBI) is an important cause of secondary brain injury that perpetuates the duration and scope of disease after initial impact. This chapter discusses the pathophysiology of acute and chronic neuroinflammation, providing insight into factors that influence the acute clinical course and later functional outcomes. Secondary injury due to neuroinflammation is described by mechanisms of action such as ischemia, neuroexcitotoxicity, oxidative stress, and glymphatic and lymphatic dysfunction. Neurodegenerative sequelae of inflammation, including chronic traumatic encephalopathy, which are important to understand for clinical practice, are detailed by disease type. Prominent research topics of TBI animal models and biomarkers of traumatic neuroinflammation are outlined to provide insight into the advances in TBI research. We then discuss current clinical treatments in TBI and their implications in preventing inflammation. To complete the chapter, recent research models, novel biomarkers, and future research directions aimed at mitigating TBI will be described and will highlight novel therapeutic targets. Understanding the pathophysiology and contributors of neuroinflammation after TBI will aid in future development of prophylaxis strategies, as well as more tailored management and treatment algorithms. This topic chapter is important to both clinicians and basic and translational scientists, with the goal of improving patient outcomes in this common disease

A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning

We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model

Expression plasticity of Phlebotomus papatasi salivary gland genes in distinct ecotopes through the sand fly season

<p>Abstract</p> <p>Background</p> <p>Sand fly saliva can drive the outcome of <it>Leishmania </it>infection in animal models, and salivary components have been postulated as vaccine candidates against leishmaniasis. In the sand fly <it>Phlebotomus papatasi</it>, natural sugar-sources modulate the activity of proteins involved in meal digestion, and possibly influence vectorial capacity. However, only a handful of studies have assessed the variability of salivary components in sand flies, focusing on the effects of environmental factors in natural habitats. In order to better understand such interactions, we compared the expression profiles of nine <it>P. papatasi </it>salivary gland genes of specimens inhabiting different ecological habitats in Egypt and Jordan and throughout the sand fly season in each habitat.</p> <p>Results</p> <p>The majority of investigated genes were up-regulated in specimens from Swaymeh late in the season, when the availability of sugar sources is reduced due to water deprivation. On the other hand, these genes were not up-regulated in specimens collected from Aswan, an irrigated area less susceptible to drought effects.</p> <p>Conclusion</p> <p>Expression plasticity of genes involved with vectorial capacity in disease vectors may play an important epidemiological role in the establishment of diseases in natural habitats.</p

Taxifolin prevents cisplatin nephrotoxicity by modulating Nrf2/HO-1 pathway and mitigating oxidative stress and inflammation in mice

Cisplatin (CIS) is an effective chemotherapeutic agent used in the treatment of several malignancies. The clinical use of CIS is associated with adverse effects, including acute kidney injury (AKI). Oxidative stress and inflammation are key events in the development of CIS-induced AKI. This study investigated the protective effect of taxifolin (TAX), a bioactive flavonoid with promising health-promoting properties, on CIS-induced nephrotoxicity in mice. TAX was orally given to mice for 10 days and a single dose of CIS was injected at day 7. Serum blood urea nitrogen (BUN) and creatinine were elevated, and multiple histopathological alterations were observed in the kidney of CIS-administered mice. CIS increased renal malondialdehyde (MDA), nitric oxide (NO), nuclear factor-kappaB (NF-κB) p65, tumor necrosis factor (TNF)-α, and interleukin (IL)-1β, and decreased cellular antioxidants in mice. TAX remarkably prevented kidney injury, ameliorated serum BUN and creatinine, and renal MDA, NO, NF-κB p65, and pro-inflammatory cytokines, and boosted antioxidant defenses in CIS-administered mice. TAX downregulated Bax and caspase-3, and upregulated Bcl-2. These effects were associated with upregulation of nuclear factor erythroid 2-related factor 2 (Nrf2) expression and heme oxygenase (HO)-1 activity in CIS-administered mice. In conclusion, TAX prevented CIS-induced AKI by mitigating tissue injury, oxidative stress, inflammation, and cell death. The protective efficacy of TAX was associated with the upregulation of Nrf2/HO-1 signaling

Predicting risk factors for thromboembolic complications in patients with sickle cell anaemia - lessons learned for prophylaxis

Objective: To assess the clinical and laboratory predictors of venous thromboembolism (VTE) in patients with sickle cell anaemia (SCA) and its relationship to morbidity and mortality. Methods: This retrospective case-control study analysed data from patients with SCA that experienced VTE compared with matched control patients with SCA but no VTE (2:1 ratio). Results: A total of 102 patients with SCA were enrolled (68 cases with VTE and 34 controls). Amongst the 68 cases (median age, 29.5 years), 26 (38.2%) presented with isolated pulmonary embolism (PE). A higher prevalence of splenectomy (73.5% versus 35.3%) was observed in the cases compared with the controls. A significantly higher prevalence of central venous catheter (CVC) insertion (42.6% versus 8.8%) was observed in the cases compared with the controls. High white blood cell counts, serum lactic dehydrogenase (LDH), bilirubin and C-reactive protein (CRP) and low haemoglobin (Hb) and HbF were significant risk factors for VTE. Forty-two cases (61.8%) developed acute chest syndrome, 10 (14.7%) had a stroke and seven (10.3%) died. Conclusions: VTE in patients with SCA has a high impact on morbidity and mortality. PE was the leading presentation of VTE, with CVC insertion, high LDH, bilirubin, CRP and white blood cell counts along with low Hb and HbF constituting other significant risk factors