Advancements in Treatment for Sensorineural Hearing Loss: Implications for Rehabilitation Professionals

Rehabilitation professionals often work with individuals with sensorineural hearing loss. Sometimes the hearing loss is due to ototoxic medications that are prescribed as treatments for other conditions. An understanding of the types of ototoxic medications at the root of the sensorineural hearing loss combined with an understanding of the advancements in treatments will help the rehabilitation professional better serve consumers who fit this description

Perbandingan Kinerja Neural Network dengan Metode Klasifikasi Tradisional dalam Mendiagnosis Penyakit Jantung: Sebuah Studi Komparatif

Dalam dunia medis, penyakit jantung menjadi salah satu penyebab kematian terbanyak. Oleh karena itu, perlu dikembangkan sistem yang dapat membantu dalam deteksi dan diagnosis penyakit jantung. Dalam penelitian ini, kami menggunakan proses neural network untuk membantu dalam deteksi penyakit jantung dengan menggunakan data training dan testing yang telah dikumpulkan. Data yang digunakan terdiri dari berbagai fitur klinis dan faktor risiko yang dikumpulkan dari pasien yang terkena penyakit jantung. Hasil dari penelitian lain untuk mendiagnosa penyakit jantung dengan metode klasifikasi tradisional menunjukkan akurasi: Logistic Regression 88.52%, K-Nearest Neighbors 78.69%, Random Forest Classifier 86.89%, dan Tuned K-Nearest Neighbors 85.25%. Sedangkan, model neural network yang dikembangkan dapat mengklasifikasikan pasien berdasarkan kondisi jantung mereka dengan akurasi mencapai 91%. Proses pelatihan model melibatkan penggunaan algoritma optimasi RMSprop, dengan cross-validation dan parameter tuning yang dilakukan untuk mencapai hasil terbaik. Model ini mampu memproses input dengan kecepatan tinggi dan menghasilkan hasil klasifikasi yang akurat. Neural network dapat membantu diagnosis awal penyakit jantung bagi tenaga medis. Namun, peningkatan akurasi dan keandalan membutuhkan penelitian lebih lanjut dengan data yang lebih besar dan fitur klinis yang beragam. Dengan optimalisasi model ini, diharapkan penanganan penyakit jantung menjadi lebih efektif dan efisien

A topical approach to retrievability bias estimation

Retrievability is an independent evaluation measure that offers insights to an aspect of retrieval systems that performance and efficiency measures do not. Retrievability is often used to calculate the retrievability bias, an indication of how accessible a system makes all the documents in a collection. Generally, computing the retrievability bias of a system requires a colossal number of queries to be issued for the system to gain an accurate estimate of the bias. However, it is often the case that the accuracy of the estimate is not of importance, but the relationship between the estimate of bias and performance when tuning a systems parameters. As such, reaching a stable estimation of bias for the system is more important than getting very accurate retrievability scores for individual documents. This work explores the idea of using topical subsets of the collection for query generation and bias estimation to form a local estimate of bias which correlates with the global estimate of retrievability bias. By using topical subsets, it would be possible to reduce the volume of queries required to reach an accurate estimate of retrievability bias, reducing the time and resources required to perform a retrievability analysis. Findings suggest that this is a viable approach to estimating retrievability bias and that the number of queries required can be reduced to less than a quarter of what was previously thought necessary

Lifetime Measurements in 120Xe

Lifetimes for the lowest three transitions in the nucleus $^{120}$Xe have been measured using the Recoil Distance Technique. Our data indicate that the lifetime for the $2_{1}^{+} \to 0_{1}^{+}$ transition is more than a factor of two lower than the previously adopted value and is in keeping with more recent measurements performed on this nucleus. The theoretical implications of this discrepancy and the possible reason for the erroneous earlier results are discussed. All measured lifetimes in $^{120}$Xe, as well as the systematics of the lifetimes of the 2$_{1}^{+}$ states in Xe isotopes, are compared with predictions of various models. The available data are best described by the Fermion Dynamic Symmetry Model (FDSM).Comment: 9 pages, RevTeX, 3 figures with Postscript file available on request at [email protected], [email protected]. Submitted to Phys. Rev.

Klasifikasi Teks Multilabel pada Artikel Berita Menggunakan Long Short-Term Memory dengan Word2Vec

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.Klasifikasi teks multilabel adalah tugas mengategorikan teks ke dalam satu atau lebih kategori. Seperti pembelajaran mesin lainnya, kinerja klasifikasi multilabel terbatas ketika ada data kecil berlabel dan mengarah pada kesulitan menangkap hubungan semantik. Dibutuhkan teknik klasifikasi teks multilabel yang dapat mengelompokkan empat label dari artikel berita untuk penelitian ini. Deep Learning adalah metode yang diusulkan untuk memecahkan masalah dalam klasifikasi teks multilabel. Beberapa contoh metode deep learning yang digunakan untuk pengklasifikasian teks antara lain Convolutional Neural Networks, Autoencoder, Deep Belief Networks, dan Recurrent Neural Networks (RNN). RNN merupakan salah satu arsitektur yang paling popular yang digunakan dalam Pemrosesan Bahasa Alami (PBA) karena struktur recurrent cocok untuk proses teks bervariabel panjang. Salah satu metode deep learning yang diusulkan pada penelitian ini adalah RNN dengan penerapan arsitektur Long Short-Term Memory (LSTM). Dalam penelitian ini untuk mendapatkan model yang optimal pada klasifikasi teks dilakukan percobaan trial dan error menggunakan LSTM dengan fitur word embedding Word2Vec 300 dimensi. Dengan tuning hyperparameter dan membuat perbandingan delapan model LSTM yang diusulkan dengan dataset skala besar, dan untuk menunjukkan bahwa LSTM dengan fitur Word2Vec dapat mencapai kinerja yang baik dalam klasifikasi teks. Hasil penelitian menunjukkan bahwa klasifikasi teks menggunakan LSTM dengan fitur Word2Vec memperoleh akurasi tertinggi pada model kelima dengan 95,38%, sedangkan rata-rata nilai presisi, recall, dan F1-score adalah 95%. Selain itu, LSTM dengan fitur Word2Vec mendapatkan hasil grafik yang dekat dengan good-fit untuk model ketujuh dan kedelapan.  &nbsp

Fibrates downregulate apolipoprotein C-III expression independent of induction of peroxisomal acyl coenzyme A oxidase. A potential mechanism for the hypolipidemic action of fibrates.

Epidemiological and transgenic animal studies have implicated apo C-III as a major determinant of plasma triglyceride metabolism. Since fibrates are very efficient in lowering triglycerides, it was investigated whether fibrates regulate apo C-III gene expression. Different fibrates lowered rat liver apo C-III mRNA levels up to 90% in a dose- and time-dependent manner, whereas intestinal apo C-III mRNA remained constant. This decrease in liver apo C-III mRNA was rapid (1 d) and reversible, since it was restored to control levels within 1 wk after cessation of treatment. In addition, fenofibrate treatment abolished the developmental rise of hepatic apo C-III mRNA observed during the suckling-weaning period. Administration of fibrates to rats induced liver and intestinal expression of the acyl CoA oxidase gene, the rate-limiting enzyme for peroxisomal beta-oxidation of fatty acids. In primary cultures of rat and human hepatocytes, fenofibric acid lowered apo C-III mRNA in a time- and dose-dependent manner. This reduction in apo C-III mRNA levels was accompanied by a decreased secretion of apo C-III in the culture medium of human hepatocytes. In rat hepatocytes fenofibric acid induced acyl CoA oxidase gene expression, whereas acyl CoA oxidase mRNA remained unchanged in human hepatocytes. Nuclear run-on and transient transfection experiments of a reporter construct driven by the human apo C-III gene promoter indicated that fibrates downregulate apo C-III gene expression at the transcriptional level. In conclusion, these studies demonstrate that fibrates decrease rat and human liver apo C-III gene expression. In humans the mechanisms appears to be independent of the induction of peroxisomal enzymes. This downregulation of liver apo C-III gene expression by fibrates may contribute to the hypotriglyceridemic action of these drugs

Restructuring Reward Mechanisms in Nicotine Addiction: A Pilot fMRI Study of Mindfulness-Oriented Recovery Enhancement for Cigarette Smokers

The primary goal of this pilot feasibility study was to examine the effects of Mindfulness-Oriented Recovery Enhancement (MORE), a behavioral treatment grounded in dual-process models derived from cognitive science, on frontostriatal reward processes among cigarette smokers. Healthy adult (N=13; mean (SD) age 49 ± 12.2) smokers provided informed consent to participate in a 10-week study testing MORE versus a comparison group (CG). All participants underwent two fMRI scans: pre-tx and after 8-weeks of MORE. Emotion regulation (ER), smoking cue reactivity (CR), and resting-state functional connectivity (rsFC) were assessed at each fMRI visit; smoking and mood were assessed throughout. As compared to the CG, MORE significantly reduced smoking (d=2.06) and increased positive affect (d=2.02). MORE participants evidenced decreased CR-BOLD response in ventral striatum (VS; d=1.57) and ventral prefrontal cortex (vPFC; d=1.7) and increased positive ER-BOLD in VS (dVS=2.13) and vPFC (dvmPFC=2.66). Importantly, ER was correlated with smoking reduction (r’s = .68 to .91) and increased positive affect (r’s = .52 to .61). These findings provide preliminary evidence that MORE may facilitate the restructuring of reward processes and play a role in treating the pathophysiology of nicotine addiction

Measurement of Conversion Coefficients in Normal and Triaxial Strongly Deformed Bands in \u3csup\u3e167\u3c/sup\u3eLu

Internal conversion coefficients have been measured for transitions in both normal deformed and triaxial strongly deformed bands in 167Lu using the Gammasphere and ICE Ball spectrometers. The results for all in-band transitions are consistent with E2 multipolarity. Upper limits are determined for the internal conversion coefficients for linking transitions between TSD Band 2 and TSD Band 1, the nw = 1 and nw = 0 wobbling bands, respectively

Molecular identification of CTX-M and blaOXY/K1 β-lactamase genes in Enterobacteriaceae by sequencing of universal M13-sequence tagged PCR-amplicons

<p>Abstract</p> <p>Background</p> <p>Plasmid encoded ^<it>bla</it>CTX-M enzymes represent an important sub-group of class A β-lactamases causing the ESBL phenotype which is increasingly found in <it>Enterobacteriaceae </it>including <it>Klebsiella </it>spp. Molecular typing of clinical ESBL-isolates has become more and more important for prevention of the dissemination of ESBL-producers among nosocomial environment.</p> <p>Methods</p> <p>Multiple displacement amplified DNA derived from 20 <it>K. pneumoniae </it>and 34 <it>K. oxytoca </it>clinical isolates with an ESBL-phenotype was used in a universal CTX-M PCR amplification assay. Identification and differentiation of ^<it>bla</it>CTX-M and ^<it>bla</it>OXY/K1 sequences was obtained by DNA sequencing of M13-sequence-tagged CTX-M PCR-amplicons using a M13-specific sequencing primer.</p> <p>Results</p> <p>Nine out of 20 <it>K. pneumoniae </it>clinical isolates had a ^<it>bla</it>CTX-M genotype. Interestingly, we found that the universal degenerated primers also amplified the chromosomally located K1-gene in all 34 <it>K. oxytoca </it>clinical isolates. Molecular identification and differentiation between ^<it>bla</it>CTX-M and ^<it>bla</it>OXY/K1-genes could only been achieved by sequencing of the PCR-amplicons. <it>In silico </it>analysis revealed that the universal degenerated CTX-M primer-pair used here might also amplify the chromosomally located ^<it>bla</it>OXY and K1-genes in <it>Klebsiella </it>spp. and K1-like genes in other <it>Enterobacteriaceae</it>.</p> <p>Conclusion</p> <p>The PCR-based molecular typing method described here enables a rapid and reliable molecular identification of ^<it>bla</it>CTX-M, and ^<it>bla</it>OXY/K1-genes. The principles used in this study could also be applied to any situation in which antimicrobial resistance genes would need to be sequenced.</p