Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation between physical markers and psychiatric health in a Portuguese systemic lupus erythematosus cohort: The role of suffering in chronic autoimmune disease

Background Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects a large number of people throughout the world. Anxiety, depression and fatigue are common symptoms of SLE that substantially contribute to decreased quality of life. This study investigates the interplay between physical and psychiatric manifestations of lupus. To this end, an SLE patient cohort was examined for correlations between clinical presentation, laboratory tests, and psychological indicators. Methods Seventy-two lupus patients were evaluated for psychological status using a battery of instruments, including assessments for fatigue (CFS & FSS), depression (HADS), anxiety (HADS), overall health (SF-36 & PSQI) and intimate relationship satisfaction (RAS & CSI). Scores from these assessments were correlated with lupus clinical profiles and laboratory test values. Results The prevalence of depression in the SLE patient cohort was 41.7%, as measured by the hospital depression and anxiety scale. The study identified that pain (p = 0.001), body mass index (p = 0.026), Chalder’s fatigue scale (p < 0.001), fatigue severity scale (p < 0.001), and anxiety (p = 0.001) are all positively correlated with depression in SLE patients. Total complement (CH50) (p = 0.032), and SF-36 physical and mental characteristic assessments are negatively correlated with depression. Longitudinal analysis indicated that the disease related complaint alopecia (p = 0.008) and relationship assessment scale scores (p = 0.004) may also be correlated to depression in SLE patients. Multivariant scrutiny of the clinical and psychosocial characteristics identified the fatigue severity scale (p = 0.026), SF-36 physical function (p = 0.040), physical role function (0.030), and mental health (p = 0.002) as the best indicators directly correlated with depression for the SLE cohort. Conclusion These results reveal the influence of physical manifestations of lupus including fatigue, pain, body mass index and anxiety, as well as decreased physical and mental function, on depression. Fatigue is the strongest factor correlated with depression in SLE patients in the cohort. Both physical and social/psychological aspects likely contribute to the depression and anxiety in lupus.
Bioengineering of novel carbon-based biosensors for real-time biomedical use

The aim of this thesis was to develop novel carbon-based biosensors and sensors for real-time metabolite and drug detection, to provide the next generation of medical devices which can give clinicians relevant chemical information in real-time at the patient bedside. An autocalibration system was developed using LabSmith programmable components to give precise fluid delivery and excellent temporal control of multiple liquid streams. This enables a 5-point calibration to be carried out using two solutions in 12 minutes. Systems using chitosan, poly(ethylene glycol)diglycidyl ether hydrogel, electrodeposition and selfassembly to immobilise enzymes on a combined needle electrode surface were studied and their performances were investigated using a microfluidic platform. The autocalibration system was combined with the graphene oxide-based biosensors in a microchip coupled with a microdialysis probe and was examined as a proof-of-concept clinical on-line analysis system. A reduced graphene oxide-based sensor was fabricated using a combined needle electrode for on-line neurotransmitter detection of dopamine. Its performance was compared with that of a platinum electrode. A microfluidic sensor based on a carbon nanotube-epoxy composite was fabricated to detect the presence of carboplatin (anti-cancer drug) in healthy tissue in real time during chemotherapy. Detection of carboplatin was carried out using differential pulse voltammetry firstly in a beaker, in which carbon nanotube-epoxy composite electrodes performed better than glassy carbon electrodes for oxidation of free purine bases and than DNA-modified carbon nanotube-epoxy composite sensors for detection of carboplatin. Carboplatin detection was then performed in a microfluidic platform. The methodology for on-line carboplatin detection was optimised in terms of the analysis time and for the repeated determination of carboplatin using the same electrode. Microdialysis and microfluidic techniques have been combined to give a proof-of-concept system real-time carboplatin detection using the carbon nanotube-epoxy composite sensors.
Failure of Eruption of First Permanent Molar Teeth: A Diagnostic Challenge

Ankylosis, mechanical and primary failure of eruption of molar teeth are rare and often difficult to distinguish between. All may have significant repercussions on the occlusion and successful management may involve invasive procedures. We present a case where an initial presentation characteristic of mechanical failure of eruption (ankylosis) of a first permanent molar tooth was excluded following a period of monitoring. Subsequent relief of crowding using a removable and then sectional fixed orthodontic appliance allowed spontaneous eruption of the tooth obviating the need for surgical intervention. This case highlights the utilisation of conservative treatment options until a diagnosis was confirmed to minimise the risk of iatrogenic damage and unnecessary treatment.
Early effects of empagliflozin on exercise tolerance in patients with heart failure: A pilot study

Sodium‐glucose linked transporter 2 inhibition recently emerged as a promising therapy for reducing the risk of heart failure (HF) in patients with type 2 diabetes mellitus (T2DM). However, there is a lack of data endorsing its role in symptomatic HF patients. We sought to evaluate the short‐term effects of empagliflozin on maximal exercise capacity in these patients.
Anodal transcranial direct current stimulation relieves the unilateral bias of a rat model of Parkinson's disease

The unilaterally lesioned rat model of Parkinson's disease which fails to orient to the food stimuli presented on the contralateral side of its preferential side of body could be induced by the injection of 6-hydroxydopamine (6-OHDA) into the medial forebrain bundle (MFB). We employed transcranial direct current stimulation (tDCS, current intensity: 80 μA, and 40 μA; anodal electrode area: 3.14 mm2; stimulation time: 30 minutes) over the M1 area to relieve the ipsilateral bias in the rat model. A corridor test was set to count the ipsilateral bias of the rats. In this experiment, 30 Sprague-Dawley rats (80 μA: n = 8, 40 μA: n = 8, sham: n = 7, healthy control: n = 7) were chosen for the corridor test and the tDCS session. The lesioned rats exhibited increased ipsilateral bias 4 weeks after the lesion surgery (P < 0.01), and the anodal tDCS with the active electrode on the lesioned side relieved the ipsilateral bias significantly (P < 0.01) immediately after the surgery and the improvement lasted for nearly 1 day. The rats in the group of 80 μA exhibited more significant changes than the 40 μA group after one day. After all the experiments, the histological process showed no neurotrauma led by the tDCS. In conclusion, the modulatory function of the cortical excitability of the tDCS may awaken the compensatory mechanisms and the response mechanisms which modulate the loss of the brain function. Further studies should be done to provide more evidence about the assumption.
"I don't have a photograph, but you can have my footprints.": Revealing the Demographics of Location Data

Location data are routinely available to a plethora of mobile apps and third party web services. The resulting datasets are increasingly available to advertisers for targeting and also requested by governmental agencies for law enforcement purposes. While the re-identification risk of such data has been widely reported, the discriminative power of mobility has received much less attention. In this study we fill this void with an open and reproducible method. We explore how the growing number of geotagged footprints left behind by social network users in photosharing services can give rise to inferring demographic information from mobility patterns. Chiefly among those, we provide the first detailed analysis of ethnic mobility patterns in two metropolitan areas. This analysis allows us to examine questions pertaining to spatial segregation and the extent to which ethnicity can be inferred using only location data. Our results reveal that even a few location records at a coarse grain can be sufficient for simple algorithms to draw an accurate inference. Our method generalizes to other features, such as gender, offering for the first time a general approach to evaluate discriminative risks associated with location-enabled personalization.
Multidimensional item response theory and the Force Concept Inventory

Research on the test structure of the Force Concept Inventory (FCI) has largely been performed with exploratory methods such as factor analysis and cluster analysis. Multi-Dimensional Item Response Theory (MIRT) provides an alternative to traditional Exploratory Factor Analysis which allows statistical testing to identify the optimal number of factors. Application of MIRT to a sample of $N=4,716$ FCI post-tests identified a 9-factor solution as optimal. Additional analysis showed that a substantial part of the identified factor structure resulted from the practice of using problem blocks and from pairs of similar questions. Applying MIRT to a reduced set of FCI items removing blocked items and repeated items produced a 6-factor solution; however, the factors had little relation the general structure of Newtonian mechanics. A theoretical model of the FCI was constructed from expert solutions and fit to the FCI by constraining the MIRT parameter matrix to the theoretical model. Variations on the theoretical model were then explored to identify an optimal model. The optimal model supported the differentiation of Newton's 1st and 2nd law; of one-dimensional and three-dimensional kinematics; and of the principle of the addition of forces from Newton's 2nd law. The model suggested by the authors of the FCI was also fit; the optimal MIRT model was statistically superior.
AD5933-based electrical bioimpedance spectrometer. Towards textile-enabled applications

Advances on System-On-Chip and Textile technology allows the development of Textile-enabled measurement instrumentation. Textile Electrodes (Textrodes) have been proven reliable for performing Electrical Bioimpedance Spectroscopy (EBIS) measurements, and the availability of a integrated circuit impedance spectrometer, the AD5933, has allowed the implementation of small size EBIS spectrometers. In this work an AD5933-based spectrometer has been implemented, and its performance on 2R1C circuits and for tetrapolar total right side EBIS measurements has been compared against the commercially available spectrometer SFB7. The study has been focused on the working upper frequency range and the estimation of the Cole parameters required for assessment of body fluid distribution: R0 and R∞. The results indicate that AD5933-based spectrometer implemented in this work can perform accurate impedance measurements well above the upper limits recommended in the datasheet. The AD5933-EBIS presents a good performance compared with the SFB7 on the 2R1C circuit and the total right side measurements, showing a smaller error in the resistance spectrum and small deviation error in the reactance when measuring over 270 kHz. The comparison on the Cole parameters estimation obtained with the SFB7 and the AD5933-based spectrometer exhibit a difference below 1% for the estimation of R0 and R∞. Consequently the overall measurement performance shown by the implemented AD5933-based spectrometer suggests its feasible use for EBIS measurements using dry Textrodes. This is of special relevance for the proliferation of EBI-based personalized health monitoring systems for patients that require to monitor the distribution of body fluids, like in dialysis.
Exciton-Polaron Spectral Structures in Two-Dimensional Hybrid Lead-Halide Perovskites

Owing to both electronic and dielectric confinement effects, two-dimensional organic-inorganic hybrid perovskites sustain strongly bound excitons at room temperature. Here, we demonstrate that there are non-negligible contributions to the excitonic correlations that are specific to the lattice structure and its polar fluctuations, both of which are controlled via the chemical nature of the organic counter-cation. We present a phenomenological, yet quantitative framework to simulate excitonic absorption lineshapes in single-layer organic-inorganic hybrid perovskites, based on the two-dimensional Wannier formalism. We include four distinct excitonic states separated by $35\pm5$\,meV, and additional vibronic progressions. Intriguingly, the associated Huang-Rhys factors and the relevant phonon energies show substantial variation with temperature and the nature of the organic cation. This points to the hybrid nature of the lineshape, with a form well described by a Wannier formalism, but with signatures of strong coupling to localized vibrations, and polaronic effects perceived through excitonic correlations. Our work highlights the complexity of excitonic properties in this class of nanostructured materials.
Cross-cultural validation of a Brazilian version of the adapted manual wheelchair circuit (AMWC-Brazil)

ABSTRACT Purpose: To translate, culturally adapt and validate the Adapted Manual Wheelchair Circuit (AMWC) into Brazilian-Portuguese. Design: Cross-sectional study. Methods: Sixty-six men (median age of 30.5 years [percentiles 25 and 75: 24.0; 38.3 years]) with traumatic spinal cord injury were consecutively enrolled and divided into two groups: tetraplegia (TP) and paraplegia (PP). The participants performed the AMWC-Brazil and were evaluated by the Spinal Cord Injury Measure version III (SCIM-III). Translation, translation synthesis, back-translation, committee review and construct validity were adopted for the cross-cultural adaptation. Construct validity was performed by testing whether the test scores were significantly correlated (Spearman’s correlation coefficient) to the subjects’ injury level, age, time since injury, body mass index (BMI) and SCIM-III scale. Results: All the AMWC-Brazil’s outcomes were significantly correlated with SCIM-III total score and subscales (P ≤ 0.01). However, when the analyses were stratified over injury level, only the TP showed a high correlation between the AMWC-Brazil’s outcomes and the SCIM-III. For construct validity, 4 of 5 hypotheses were confirmed. Only BMI was not a significant predictor of the AMWC-Brazil outcomes. Conclusion: The AMWC was successfully translated to the Brazilian-Portuguese (AMWC-Brazil) and presented high and satisfactory construct validity.
Experimental and Model-based Terahertz Imaging and Spectroscopy for Mice, Human, and Phantom Breast Cancer Tissues

The goal of this work is to investigate terahertz technology for assessing the surgical margins of breast tumors through electromagnetic modeling and terahertz experiments. The measurements were conducted using a pulsed terahertz system that provides time and frequency domain signals. Three types of breast tissues were investigated in this work. The first was formalinfixed, paraffin-embedded tissues from human infiltrating ductal and lobular carcinomas. The second was human tumors excised within 24-hours of lumpectomy or mastectomy surgeries. The third was xenograft and transgenic mice breast cancer tumors grown in a controlled laboratory environment to achieve more data for statistical analysis. Experimental pulsed terahertz imaging first used thin sections (10-30 μm thick) of fixed breast cancer tissue on slides. Electromagnetic inverse scattering models, in transmission and reflection modes, were developed to retrieve the tissue refractive index and absorption coefficient. Terahertz spectroscopy was utilized to experimentally collect data from breast tissues for these models. The results demonstrated that transmission mode is suitable for lossless materials while the reflection model is more suitable for biological materials where the skin depth of terahertz waves does not exceed 100 μm. The reflection model was implemented to estimate the polarization of the incident terahertz signal of the system, which was shown to be a hybridization of TE and TM modes. Terahertz imaging of three-dimensional human breast cancer blocks of tissue embedded in paraffin was achieved through the reflection model. The terahertz beam can be focused at depths inside the block to produce images in the x-y planes (z-scan). The time-of-flight analysis was applied to terahertz signals reflected at each depth demonstrating the margins of cancerous regions inside the block as validated with pathology images at each depth. In addition, phantom tissues that mimic freshly excised infiltrating ductal carcinoma human tumors were developed with and without embedded carbon nanometer-scale onion-like carbon particles. These particles exhibited a strong terahertz signal interaction with tissue demonstrating a potential to greatly improve the image contrast. The results presented in this work showed, in most cases, a significant differentiation in terahertz images between cancer and healthy tissue as validated with histopathology images.
System Design for Large Scale Machine Learning

Author(s): Venkataraman, Shivaram | Advisor(s): Franklin, Michael J; Stoica, Ion | Abstract: The last decade has seen two main trends in the large scale computing: on the one hand we have seen the growth of cloud computing where a number of big data applications are deployed on shared cluster of machines. On the other hand there is a deluge of machine learning algorithms used for applications ranging from image classification, machine translation to graph processing, and scientific analysis on large datasets. In light of these trends, a number of challenges arise in terms of how we program, deploy and achieve high performance for large scale machine learning applications.In this dissertation we study the execution properties of machine learning applications and based on these properties we present the design and implementation of systems that can address the above challenges. We first identify how choosing the appropriate hardware can affect the performance of applications and describe Ernest, an efficient performance prediction scheme that uses experiment design to minimize the cost and time taken for building performance models. We then design scheduling mechanisms that can improve performance using two approaches: first by improving data access time by accounting for locality using data-aware scheduling and then by using scalable scheduling techniques that can reduce coordination overheads.
Association between Ambient Temperatures and Mental Disorder Hospitalizations in a Subtropical City: A Time-Series Study of Hong Kong Special Administrative Region

Background: Mental disorders have been found to be positively associated with temperature in cool to cold climatic regions but the association in warmer regions is unclear. This study presented the short-term association between temperatures and mental disorder hospitalizations in a subtropical city with a mean annual temperature over 21 °C. Methods: Using Poisson-generalized additive models and distributed-lagged nonlinear models, daily mental disorder hospitalizations between 2002 and 2011 in Hong Kong were regressed on daily mean temperature, relative humidity, and air pollutants, adjusted for seasonal trend, long-term trend, day-of-week, and holiday. Analyses were stratified by disease class, gender and age-group. Results: 44,600 admissions were included in the analysis. Temperature was positively associated with overall mental-disorder hospitalizations (cumulative relative risk at 28 °C vs. 19.4 °C (interquartile range, lag 0–2 days) = 1.09 (95% confidence interval 1.03, 1.15)), with the strongest effect among the elderly (≥75 years old). Transient mental disorders due to conditions classified elsewhere and episodic mood disorders also showed strong positive associations with temperature. Conclusion: This study found a positive temperature–mental-disorder admissions association in a warm subtropical region and the association was most prominent among older people. With the dual effect of global warming and an aging population, targeted strategies should be designed to lower the disease burden.
Large-Margin Structured Prediction Extensions of Neural Networks for Automatic Speech Recognition

Author(s): Ravuri, Suman | Advisor(s): Morgan, Nelson H | Abstract: Neural networks, especially those with more than one hidden layer, have re-emerged in Automatic Speech Recognition (ASR) systems as replacements to emission models based on Gaussian Mixture Models (GMMs). While the use of these so-called Deep Neural Networks (DNNs) has enjoyed widespread success due to improvements in recognition results, the exact source of better recognition accuracy is not entirely understood. Using a bootstrap resampling framework that generates synthetic test set data satisfying conditional independence assumptions of the model while still using real observations, I show that DNNs used for both feature generation and hybrid acoustic modeling help compensate for incorrect conditional independence assumptions and help fix poor phone duration estimates of the hidden Markov Model (HMM). Despite these improvements, the large increase in word error rates for DNN-HMM systems on real data compared to synthetic data suggests that one can improve recognition performance by modifying the training criterion. Since neural networks are log-linear at the output layer, I propose using sequences of last hidden layers as input to a log-linear model, and training that model with large-margin criteria. These Structured Support Vector Machine (SVM) approaches allow us to more directly minimize errors relevant to automatic speech recognition, and provide some guarantees on test set error. First, I show how one can generate better features by combining a neural network with a hidden Markov Support Vector Machine (HMSVM). Then, I propose a hybrid DNN-Structured SVM acoustic model and an online training algorithm that iteratively updates alignments for faster convergence. Training of this model falls under a class of approaches known as sequence-discriminative training, which are used to train state-of-the-art systems. This DNN-latent Structured SVM model beats alternative methods to sequence-discriminative training by 1.0% absolute, while needing 33-66% fewer utterances to converge.Finally, I analyze the Structured SVM approach to sequence-discriminative training and compare it to standard methods. I show how the loss function for boosted Maximum Mutual Information is an upper bound of the hinge loss for the Structured SVM, and how such a relaxation precludes the use of aggressive boosting parameters needed for better results. Finally, I analyze four of the most popular sequence-discriminative training criteria – Maximum Mutual Information, boosted Maximum Mutual Information, Minimum Phone Error, and state-level Minimum Bayes Risk – and the latent Structured SVM using the bootstrap resampling framework, and compare how different sequence-discriminative training criteria compensate for data/model mismatch. Structured SVM models perform better for real rather than synthetic data, likely because the model makes fewer distributional assumptions about the underlying data.