Detecting the Origin of Text Segments Efficiently

In the origin detection problem an algorithm is given a set S of documents, ordered by creation time, and a query document D. It needs to output for every consecutive sequence of k alphanumeric terms in D the earliest document in S in which the sequence appeared (if such a document exists). Algorithms for the origin detection problem can, for example, be used to detect the "origin" of text segments in D and thus to detect novel content in D. They can also find the document from which the author of D has copied the most (or show that D is mostly original). We propose novel algorithm for this problem and evaluate them together with a large number of previously published algorithms. Our results show that (1) detecting the origin of text segments efficiently can be done with very high accuracy even when the space used is less than 1% of the size of the documents in S, (2) the precision degrades smoothly with the amount of available space, (3) various estimation techniques can be used to increase the performance of the algorithms

Nucleotide Oligomerization Domain-like receptor 4 (NLR4) Gene Expression and Interleukin 1-β (IL 1-β) Level in Urine Samples Before and After Intravesical BCG Therapy For Treatment of Bladder Cancer

Bladder cancer is the 7th most commonly diagnosed cancer in males worldwide and the 11th when both genders are considered. Seventy five per cent of bladder cancer cases are non-muscle invasive bladder cancer (NMIBC). Bacillus Calmette–Gu rin (BCG) immunotherapy remains the standard intravesical agent for NMIBC. The exact mechanism by which BCG prevents recurrence is unknown. The aim of this study was to evaluate NLR4 gene expression and IL-1β as possible prognostic indicators for NMIBC recurrence and BCG treatment failure, and to detect the difference in their levels among muscle invasive bladder cancer (MIBC) and NMIBC that may aid in primary differentiation between cases. This study was conducted in 30 patients who had NMIBC and 17 patients who had MIBC. Urine samples were obtained in sterile cups before operation. From NMIBC cases, four more samples were obtained as mentioned below. Evaluation of NLR4 gene expression was performed in pre-surgical sample for MIBC and in 4 samples for NMIBC: pre-surgical sample, sample collected 4 hours after the 3rd dose of BCG instillation, and samples collected during follow up (3 and 6 months post-surgically). There was statistical significant increase in NLRP4 expression levels in NMIBC (CT=0.87±1.48) compared to MIBC (CT=2.82±2.07). As far as we searched, no published results were found regarding comparative gene expression levels between NMIBC and MIBC cases. Gene expression in recurrent cases was higher in pre-surgical urine samples than in non-recurrent cases. The expression level further increased up to 21 fold than the pre-surgical level in the sample taken after injection of the 3rd dose of BCG. This level decreased distinctly to become 1-fold increase over pre-surgical level at the 3rd month follow up then to only 0.9-fold at the 6th month. In non- recurrent cases, gene expression level started pre-surgically in much lower levels than those encountered in recurrent cases. There were 11-fold increase in expression level after 3rd dose of BCG instillation and then decreased to be 5.6 folds higher in the sample taken at 3rd month follow up than in presurgical samples. Gene expression further decreased to become 4.1 fold higher in samples taken at 6 month follow up than the pre-surgical levels. IL-1β levels were estimated for NMIBC and MIBC cases in urine samples pre-surgically and during BCG therapy in case of NMIBC before and 4 hours after the 3rd dose and during 3rd month follow-up of those cases for searching its possible use of for primary differentiation between NMIBC and MIBC, and also as a prognostic factor for possible recurrence in case of NMIBC cases. The level of IL-1β was generally higher in pre-surgical samples (0.62±0.12 pg/ml) when compared to its level before the 3rd dose of BCG induction therapy (0.53±0.13 pg/ml). Its level was distinctly higher four hours after administration of the 3rd dose BCG (1.96±0.62 pg/ml) than both previous levels. Levels decreased bellow pre-surgical level at 3rd month follow up (0.57±0.099 pg/ml). The levels of IL-1β estimated in samples collected four hours after the 3rd dose BCG was higher in cases that showed recurrence later on than non-recurrent cases. The levels decreased in both cases and became higher in non-recurrent cases (0.64±0.05 pg/ml) than in cases already developed recurrence at the 3rd month diagnosed during follow-up (0.45±0.05 pg/ml). To conclude, on following NLRP4 gene expression and IL-1β levels during BCG administration among recurrent and non-recurrent cases of thirty NMIBC cases, there was a significant statistical difference in both levels for the samples collected after the third dose BCG, being higher in patients who showed subsequent recurrence at the 3rd and 6th month of follow-up. If these preliminary reported findings will be confirmed in upcoming larger cohort’s studies, it could be promising in prognosis of such cases, with the possibility of early manipulation of individualized treatment schedule, keeping patients most probably prone to encounter recurrence safe from possible side effects of BCG therapy. The assessment of NLRP4 expression and IL-1β levels could help predict failure of BCG therapy, playing an appreciable role in early deciding radical surgery. When comparing NLRP4 expression and IL-1β levels between MIBC and NMIBC cases, increased values were noted among non-invasive ones. This finding may serve as a possible diagnostic tool, which represents a challenging issue. Hence, cut-off values for gene expression and cytokine level are to be specified

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Automatic Speech Recognition Using Deep Neural Networks: New Possibilities

Recently, automatic speech recognition (ASR) systems that use deep neural networks (DNNs) for acoustic modeling have attracted huge research interest. This is due to the recent results that have significantly raised the state of the art performance of ASR systems. This dissertation proposes a number of new methods to improve the state of the art ASR performance by exploiting the power of DNNs. The first method exploits domain knowledge in designing a special neural network (NN) structure called a convolutional neural network (CNN). This dissertation proposes to use the CNN in a way that applies convolution and pooling operations along frequency to handle frequency variations that commonly happen due to speaker and pronunciation differences in speech signals. Moreover, a new CNN structure called limited weight sharing is proposed to better suit special spectral characteristics of speech signals. Our experimental results have shown that the use of a CNN leads to 6-9% relative reduction in error rate. The second proposed method deals with speaker variations in a more explicit way through using a new speaker code based adaptation. This method adapts the speech acoustic model to a new speaker by learning a suitable speaker representation based on a small amount of adaptation data from each target speaker. This method alleviates the need to modify any model parameters as is done with other commonly used adaptation methods for neural networks. This greatly reduces the number of parameters to estimate during adaptation; hence, it allows rapid speaker adaptation. The third proposed method aims to handle the temporal structure within speech segments by using a deep segmental neural network (DSNN). The DSNN model alleviates the need to use an HMM model as it directly models the posterior probability of the label sequence. Moreover, a segment-aware NN structure has been proposed. It is able to model the dependency among speech frames within each segment and performs better than the conventional frame based DNNs. Experimental results show that the proposed DSNN can significantly improve recognition performance as compared with the conventional frame based models

Audiological and otological outcome in Bi-island chondroperichondrial graft type I tympanoplasty

Background: The principal aims of a tympanoplasty operation are to create an intact tympanic membrane and to restore an optimal hearing improvement. Many surgeons have used cartilage for grafting due to its increased stability and resistance to negative pressure. Cartilage has been criticized because of concerns regarding hearing results. Objectives: The aim of this study is to present the experience of using cartilage for grafting central perforations in type I tympanoplasty procedure with some novel modifications and evaluate its take rate and audiologic results. Methods: This is a prospective study including 40 patients (45 ears) who underwent type I cartilage tympanoplasty. All patients are primary cases of chronic suppurative otitis media of tubotympanic type. The following parameters were evaluated at least after 3 months from surgery: graft take and change between the pre- and post-operative pure-tone average air-bone gap (PTA-ABG). Results: Thirty-nine patients included in the study underwent 45 cartilage tympanoplasty type I operations. The mean age of the patients was 24.9 ± 9.5 years (range, 15–51 years). The mean follow up period was 6.2 months (range, 3–9 months). All perforations were found to be closed with a 100% graft take rate. The overall mean pre-operative PTA-ABG was 26.0 ± 4.4 dB, whereas the postoperative PTA-ABG was 13.8 ± 5 dB (p < 0.0001) which is highly significant. The percent of reduction of PTA-ABG was about 46.6%. Conclusion: Bi-island chondroperichondrial type I tympanoplasty is an effective and reliable technique with a high success rate and minimal complications

APPLYING CONVOLUTIONAL NEURAL NETWORKS CONCEPTS TO HYBRID NN-HMM MODEL FOR SPEECH RECOGNITION

Convolutional Neural Networks (CNN) have showed success in achieving translation invariance for many image processing tasks. The success is largely attributed to the use of local filtering and maxpooling in the CNN architecture. In this paper, we propose to apply CNN to speech recognition within the framework of hybrid NN-HMM model. We propose to use local filtering and max-pooling in frequency domain to normalize speaker variance to achieve higher multi-speaker speech recognition performance. In our method, a pair of local filtering layer and max-pooling layer is added at the lowest end of neural network (NN) to normalize spectral variations of speech signals. In our experiments, the proposed CNN architecture is evaluated in a speaker independent speech recognition task using the standard TIMIT data sets. Experimental results show that the proposed CNN method can achieve over 10 % relative error reduction in the core TIMIT test sets when comparing with a regular NN using the same number of hidden layers and weights. Our results also show that the best result of the proposed CNN model is better than previously published results on the same TIMIT test sets that use a pre-trained deep NN model. Index Terms — acoustic modeling, neural networks, speech recognition, local filtering, max-poolin