Miracle-dz/UniFAR-FTUs huggingface dataset:
An update on the discovery and development of selective heat shock protein inhibitors as anti-cancer therapy

ABSTRACT Introduction: Over the years, not a single HSP inhibitor has progressed into the post-market phase of drug development despite the success recorded in various pre-clinical and clinical studies. The inability of existing drugs to specifically target oncogenic HSPs has majorly accounted for these setbacks. Recent combinatorial strategies that incorporated computer-aided drug design (CADD) techniques are geared towards the development of highly specific HSP inhibitors with increased activities and minimal toxicities. Areas covered: In this review, strategic therapeutic approaches that have recently aided the development of selective HSP inhibitors were highlighted. Also, the significant contributions of CADD techniques over the years were discussed in detail. This article further describes promising computational paradigms and their applications towards the discovery of highly specific inhibitors of oncogenic HSPs. Expert opinion: The recent shift towards highly selective and specific HSP inhibition has shown great promise as evidenced by the development of paralog/isoform-selective HSP drugs. It could be further augmented with computer-aided drug design strategies, which incorporate reliable methods that would greatly enhance the design and optimization of novel inhibitors with improved activities and minimal toxicities.
Articulatory Feature Classification Using Convolutional Neural Networks

The ultimate goal of our research is to improve an existing speech-based computational model of human speech recognition on the task of simulating the role of fine-grained phonetic information in human speech processing. As part of this work we are investigating articulatory feature classifiers that are able to create reliable and accurate transcriptions of the articulatory behaviour encoded in the acoustic speech signal. Articulatory feature (AF) modelling of speech has received a considerable amount of attention in automatic speech recognition research. Different approaches have been used to build AF classifiers, most notably multi-layer perceptrons. Recently, deep neural networks have been applied to the task of AF classification. This paper aims to improve AF classification by investigating two different approaches: 1) investigating the usefulness of a deep Convolutional neural network (CNN) for AF classification; 2) integrating the Mel filtering operation into the CNN architecture. The results showed a remarkable improvement in classification accuracy of the CNNs over state-of-the-art AF classification results for Dutch, most notably in the minority classes. Integrating the Mel filtering operation into the CNN architecture did not further improve classification performance.
Real-Time Underwater Fish Tracking Based on Adaptive Multi-Appearance Model

Tracking live fish in an open underwater environment to investigate their behavior is of great value for many applications, e.g. biological and robotic research. However, tracking fish in real world environment is a challenging task due to complex non-rigid deformation and abrupt movement of fish. In this paper, we explore and incorporate motion property of fish and propose a real-time fish tracking method based on novel adaptive multi-appearance models and tracking strategy, which can be adapted to various changes of the fish appearance caused by non-rigid deformation. Experimental results show the promising performance of the proposed method can outperform the previous method by 13.4% in accuracy on average and is robust to real-time underwater fish tracking.
Recognizing Fine Facial Micro-Expressions Using Two-Dimensional Landmark Feature

Emotion recognition based on facial expressions is very important for interaction between human and artificial intelligence (AI) system such as social robots. On the other hand, it is much harder to recognize subtle facial expressions or facial micro-expressions than facial expressions rich in emotional expression in a real environment. In this paper, we propose a two-dimensional (2D) landmark feature for effectively recognizing facial micro-expression. The proposed 2D landmark feature is obtained by converting existing coordinate-based landmark information into 2D image information, and has an advantage of having a unique feature according to emotions regardless of the intensity of facial expression. Thus, we can achieve effective emotion recognition by learning the proposed 2D landmark feature information on a convolutional neural network (CNN) and a long-term term memory (LSTM)-based network. Experimental results show that the proposed method provides more than 77% classification performance for fine facial expression images even when learning with general facial expression images of CK+ dataset.
Developmental processes regulate craniofacial variation in disease and evolution

Variation in development mediates phenotypic differences observed in evolution and disease. Although the mechanisms underlying phenotypic variation are still largely unknown, recent research suggests that variation in developmental processes may play a key role. Developmental processes mediate genotype–phenotype relationships and consequently play an important role regulating phenotypes. In this review, we provide an example of how shared and interacting developmental processes may explain convergence of phenotypes in spliceosomopathies and ribosomopathies. These data also suggest a shared pathway to disease treatment. We then discuss three major mechanisms that contribute to variation in developmental processes: genetic background (gene–gene interactions), gene–environment interactions, and developmental stochasticity. Finally, we comment on evolutionary alterations to developmental processes, and the evolution of disease buffering mechanisms.
Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification

This paper introduces a new method to extract speaker embed-dings from a deep neural network (DNN) for text-independent speaker veriﬁcation. Usually, speaker embeddings are extracted from a speaker-classiﬁcation DNN that averages the hidden vectors over the frames of a speaker; the hidden vectors produced from all the frames are assumed to be equally important. We relax this assumption and compute the speaker embedding as a weighted average of a speaker’s frame-level hidden vectors, and their weights are automatically determined by a self-attention mechanism. The effect of multiple attention heads are also investigated to capture different aspects of a speaker’s input speech. Finally, a PLDA classiﬁer is used to compare pairs of embeddings. The proposed self-attentive speaker embedding system is compared with a strong DNN embedding baseline on NIST SRE 2016. We ﬁnd that the self-attentive embeddings achieve superior performance. Moreover, the improvement produced by the self-attentive speaker embeddings is consistent with both short and long testing utterances.
Transcriptome-Derived Microsatellites Demonstrate Strong Genetic Differentiation in Pacific White Sharks

Recent advances in genome-scale sequencing technology have allowed the development of high resolution genetic markers for the study of nonmodel taxa. In particular, transcriptome sequencing has proven to be highly useful in generating genomic markers for use in population genetic studies, allowing for insight into species connectivity, as well as local adaptive processes as many transcriptome-derived markers are found within or associated with functional genes. Herein, we developed a set of 30 microsatellite markers from a heart transcriptome for the white shark (Carcharodon carcharias), a widely distributed and globally vulnerable marine predator. Using these markers as well as 10 published anonymous genomic microsatellite loci, we provide 1) the first nuclear genetic assessment of the cross-Pacific connectivity of white sharks, and 2) a comparison of the levels of inferred differentiation across microsatellite marker sets (i.e., transcriptome vs. anonymous) to assess their respective utility to elucidate the population genetic dynamics of white sharks. Significant (FST = 0.083, P = 0.05; G″ST = 0.200; P = 0.001) genetic differentiation was found between Southwestern Pacific (n = 19) and Northeastern Pacific (n = 20) white sharks, indicating restricted, cross Pacific gene flow in this species. Transcriptome-derived microsatellite marker sets identified much higher (up to 2×) levels of genetic differentiation than anonymous genomic markers, underscoring potential utility of transcriptome markers in identifying subtle population genetic differences within highly vagile, globally distributed marine species.Subject areas: Population structure and phylogeography; Conservation genetics and biodiversity.
A Hybrid Approach to Grapheme to Phoneme Conversion in Assamese

Assamese is one of the low resource Indian languages. This paper implements both rule-based and data-driven grapheme to phoneme (G2P) conversion systems for As-samese. The rule-based system is used as the baseline which yields a word error rate of 35.3%. The data-driven systems are implemented using state-of-the-art sequence learning techniques such as —i) Joint-Sequence Model (JSM), ii) Recurrent Neural Networks with LTSM cell (LSTM-RNN) and iii) bidirectional LSTM (BiLSTM). The BiLSTM yields the lowest WER i.e., 18.7%, which is an absolute 16.6% improvement on the baseline sys-tem. We additionally implement the rules of syllabifi-cation for Assamese. The surface output is generated in two forms namely i) phonemic sequence with syllable boundaries, and ii) only phonemic sequence. The output of BiLSTM is fed as an input to Hybrid system. The Hybrid system syllabifies the input phonemic sequences to apply the vowel harmony rules. It also applies the rules of schwa-deletion as well as some rules in which the consonants change their form in clusters. The accuracy of the Hybrid system is 17.3% which is an absolute 1.4% improvement over the BiLSTM based G2P.
Disentangled Representation Learning for Non-Parallel Text Style Transfer

This paper tackles the problem of disentangling the latent representations of style and content in language models. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for style prediction and bag-of-words prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space. This disentangled latent representation learning can be applied to style transfer on non-parallel corpora. We achieve high performance in terms of transfer accuracy, content preservation, and language fluency, in comparison to various previous approaches.
High-quality Voice Conversion Using Spectrogram-Based WaveNet Vocoder

Waveform generator is a key component in voice conversion. Recently, WaveNet waveform generator conditioned on the Mel-cepstrum (Mcep) has shown better quality over standard vocoder. In this paper, an enhanced WaveNet model based on spectrogram is proposed to further improve voice conversion performance. Here, Mel-frequency spectrogram is converted from source speaker to target speaker using an LSTM-RNN based frame-to-frame feature mapping. To evaluate the performance, the proposed approach is compared to an Mcep based LSTM-RNN voice conversion system. Both STRAIGHT vocoder and Mcep-based WaveNet vocoder are elected to produce the converted speech for Mcep conversion system. The fundamental frequency ( F 0 ) of the converted speech in different systems is analyzed. The naturalness, similarity and intel-ligibility are evaluated in subjective measures. Results show that the spectrogram based WaveNet waveform generator can achieve better voice conversion quality compared to traditional WaveNet approaches. The Mel-spectrogram based voice conversion can achieve signiﬁcant improvement in speaker similarity and inherent F 0 conversion.
Treatment of horizontal defect with and without platelet-rich fibrin matrix: A randomized comparative clinical study

Aim: The aim of the present study was to assess the clinical and radiographical parameters in horizontal bone defects in patients with chronic periodontitis. Materials and Methods: In this randomized, controlled clinical trial study, nine individuals with 94 sites having moderate to deep periodontitis were selected and distributed to Group A – open flap debridement (OFD), Group B – open flap debridement and intra marrow penetration (OFD + IMP) and Group C – Open flap debridement + Intramarrow penetration + platelet rich fibrin matrix (OFD + IMP + PRFM). Plaque index (PI) and gingival index (GI) were evaluated at baseline, 6 months, and 9 months after surgery. Probing pocket depth (PPD) and clinical attachment level (CAL) were recorded at baseline and 9 months after surgery. Radiographic assessment was carried out to measure the periodontal defect depth and defect fill percentage (DF%) at baseline and 9 months after the surgery using radiovisiography. Results: The statistical evaluation obtained after 9 months showed no significant difference between PI and GI at 9 months interval. Intergroup comparison of PPD and CAL has shown significant difference in Group C as compared to Group A and B with P < 0.05. The defect depth was statistically significant at 9 months in all the groups, and DF percentage (DF%) has shown statistically significant results in Group C as compared to A and B with P = of 0.001. Conclusion: The addition of PRFM to horizontal type of periodontal defects has shown promising results over a 9-month follow-up period.
Improved Sentence Modeling using Suffix Bidirectional LSTM

Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP. However, BiLSTMs are known to suffer from sequential bias - the contextual representation of a token is heavily influenced by tokens close to it in a sentence. We propose a general and effective improvement to the BiLSTM model which encodes each suffix and prefix of a sequence of tokens in both forward and reverse directions. We call our model Suffix Bidirectional LSTM or SuBiLSTM. This introduces an alternate bias that favors long range dependencies. We apply SuBiLSTMs to several tasks that require sentence modeling. We demonstrate that using SuBiLSTM instead of a BiLSTM in existing models leads to improvements in performance in learning general sentence representations, text classification, textual entailment and paraphrase detection. Using SuBiLSTM we achieve new state-of-the-art results for fine-grained sentiment classification and question classification.
Plasma levels of mid-regional pro-adrenomedullin in sepsis are associated with risk of death.

BACKGROUND Identifying Intensive Care Unit (ICU) patients with sepsis and predicting the risk of death are unmet clinical needs. METHODS Prospective observational single-center study of 120 consecutive ICU patients with suspected severe sepsis at Jerez Hospital. Epidemiological, clinical, laboratory data and MR-proADM, Procalcitonin (PCT) and C-reactive protein (CRP) levels were recorded at ICU admission and follow-up. RESULTS At ICU discharge, 104 patients were diagnosed with severe sepsis and 39 died. Plasma MR-proADM was highly indicative of sepsis: 4.05 nmol/L vs. of 0.309 nmol/L (P<0.001), with area under the ROC curve (AUC-ROC) was 0.947. At 48 hours following admission, the median MR-proADM levels in surviving sepsis patients fell to 1.65 nmol/L but remained higher in the non-survivors (2.475 nmol/L) (P=0.04). On day 5 the levels fell to 1.36 nmol/L in surviving sepsis patients vs. 3.42 nmol/L in the non-survivors (P<0.001). On day 5 the survivors showed greater MR-proADM clearance (62.7% vs. 21.2%). The AUC-ROC on day 5 was 0.825, PCT 0.725 and CRP 0.700. The AUC-ROC to MR-proADM clearance on day 5 was 0.734. In a multivariable model, MR-proADM levels at 48 hours and on day 5 and clearance on day 5 following admission were statistically significant predictive factors of mortality. CONCLUSIONS In clinical practice, in ICU patients admitted with SIRS and organ dysfunction, an MR-proADM cut-off point of 1.425 nmol/L helps to identify those with sepsis. An MR-proADM value above 5.626 nmol/L 48 hours after admission was associated with a high risk of death.
L2-ARCTIC: A Non-native English Speech Corpus

In this paper, we introduce L2-ARCTIC, a speech corpus of non-native English that is intended for research in voice conversion, accent conversion, and mispronunciation detection. This initial release includes recordings from ten non-native speakers of English whose first languages (L1s) are Hindi, Korean, Mandarin, Spanish, and Arabic, each L1 containing recordings from one male and one female speaker. Each speaker recorded approximately one hour of read speech from the Carnegie Mellon University ARCTIC prompts, from which we generated orthographic and forced-aligned phonetic transcriptions. In addition, we manually annotated 150 utterances per speaker to identify three types of mispronunciation errors: substitutions, deletions, and additions, making it a valuable resource not only for research in voice conversion and accent conversion but also in computer-assisted pronunciation training. The corpus is publicly accessible at https://psi.engr. tamu.edu/l2-arctic-corpus/.
Impact of Tumor Regression Grade as a Major Prognostic Factor in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Proposal for a Modified Staging System

There is ongoing debate regarding the significance of complete or near-complete response after neoadjuvant chemoradiotherapy (CRT) for rectal cancer. This study assessed the prognostic value of the Dworak tumor regression grade (TRG) following neoadjuvant CRT and surgery primarily in patients with pathological stage (ypStage) II and III rectal cancer. The records of 331 patients who underwent neoadjuvant CRT followed by total mesorectal excision between 2004 and 2015 were retrospectively reviewed. Patients were categorized as having a good response (GR, TRG 3/4, n = 122) or a poor response (PR, TRG 1/2, n = 209). At a median follow-up of 65 months, five-year disease-free survival (DFS) was higher in the GR group than in the PR group (91.3% vs. 66.6%, p < 0.001). Patients with a GR and ypStage II disease had a five-year DFS that was indistinguishable from that of patients with ypStage 0–I disease (92.3% vs. 90.7%, p = 0.885). Likewise, patients with a GR and ypStage III disease had a five-year DFS similar to those with ypStage II disease (76.0% vs. 75.9%, p = 0.789). A new modified staging system that incorporates grouped TRG (GR vs. PR) was developed. The prognostic performance of this modified stage and the ypStage was compared with the Harrell C statistic. C statistic of the modified stage was higher than that of the ypStage (0.784 vs. 0.757, p = 0.012). The results remained robust after multivariate Cox regression analyses. In conclusion, a GR to neoadjuvant CRT is an independent predictor of good DFS and overall survival and further stratifies patients so as to estimate the risk of recurrence and survival among patients with ypStage II and III rectal cancer.
Improving CTC-based Acoustic Model with Very Deep Residual Time-delay Neural Networks

Connectionist temporal classiﬁcation (CTC) has shown great potential in end-to-end (E2E) acoustic modeling. The current state-of-the-art architecture for a CTC-based E2E model is based on a deep bidirectional long short-term memory (BLSTM) network that provides frame-wise outputs estimated from both forward and backward directions (BLSTM-CTC). Since this architecture can lead to a serious time latency problem in decoding, it cannot be applied to real-time speech recognition tasks. Considering that the CTC label of one current frame can only be affected by a few neighboring frames, we argue that using BLSTM traversing on a whole utterance from both directions is not necessary. In this paper, we use a very deep residual time-delay (VResTD) network for CTC-based E2E acoustic modeling (VResTD-CTC). The VResTD network provides frame-wise outputs with local bidirectional information without needing to wait for the whole utterance. Speech recognition experiments on Corpus of Spontaneous Japanese were carried out to test our proposed VResTD-CTC and the state-of-the-art BLSTM-CTC model. Comparable performance was obtained while the proposed VResTD-CTC does not suffer from the decoding time latency problem.
Auxiliary Feature Based Adaptation of End-to-end ASR Systems

Acoustic model adaptation has been widely used to adapt models to speakers or environments. For example, appending auxiliary features representing speakers such as i-vectors to the input of a deep neural network (DNN) is an effective way to realize unsupervised adaptation of DNN-hybrid automatic speech recognition (ASR) systems. Recently, end-to-end (E2E) models have been proposed as an alternative to conventional DNN-hybrid ASR systems. E2E models map a speech signal to a sequence of characters or words using a single neural network, which greatly simpliﬁes the ASR pipeline. However, adaptation of E2E models has received little attention yet. In this paper, we investigate auxiliary feature based adaptation for encoder-decoder E2E models. We employ a recently proposed sequence summary network to compute auxiliary features instead of i-vectors, as it can be easily integrated into E2E models and keep the ASR pipeline simple. Indeed, the sequence sum-mary network allows the auxiliary feature extraction module to be a part of the computational graph of the E2E model. We demonstrate that the proposed adaptation scheme consistently improves recognition performance of three publicly available recognition tasks.
Predicting Categorical Emotions by Jointly Learning Primary and Secondary Emotions through Multitask Learning

Detection of human emotions is an essential part of affect-aware human-computer interaction (HCI). In daily conversations, the preferred way of describing affects is by using categorical emotion labels (e.g., sad, anger, surprise). In categorical emotion classiﬁcation, multiple descriptors (with different degrees of relevance) can be assigned to a sample. Perceptual evaluations have relied on primary and secondary emotions to capture the ambiguous nature of spontaneous recordings. Primary emotion is the most relevant category felt by the evaluator. Secondary emotions capture other emotional cues also conveyed in the stimulus. In most cases, the labels collected from the secondary emotions are discarded, since assigning a single class label to a sample is preferred from an application perspective. In this work, we take advantage of both types of annotations to improve the performance of emotion classiﬁcation. We collect the labels from all the annotations available for a sample and generate primary and secondary emotion labels. A classiﬁer is then trained using multitask learning with both primary and secondary emotions. We experimentally show that considering secondary emotion labels during the learning process leads to relative improvements of 7.9% in F1-score for an 8-class emotion classiﬁcation task.