Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein structure prediction constrained by solution X-ray scattering data and structural homology identification.

Here we perform a systematic exploration of the use of distance constraints derived from small angle X-ray scattering (SAXS) measurements to filter candidate protein structures for the purpose of protein structure prediction. This is an intrinsically more complex task than that of applying distance constraints derived from NMR data where the identity of the pair of amino acid residues subject to a given distance constraint is known. SAXS, on the other hand, yields a histogram of pair distances (pair distribution function), but the identities of the pairs contributing to a given bin of the histogram are not known. Our study is based on an extension of the Levitt-Hinds coarse grained approach to ab initio protein structure prediction to generate a candidate set of C(alpha) backbones. In spite of the lack of specific residue information inherent in the SAXS data, our study shows that the implementation of a SAXS filter is capable of effectively purifying the set of native structure candidates and thus provides a substantial improvement in the reliability of protein structure prediction. We test the quality of our predicted C(alpha) backbones by doing structural homology searches against the Dali domain library, and find that the results are very encouraging. In spite of the lack of local structural details and limited modeling accuracy at the C(alpha) backbone level, we find that useful information about fold classification can be extracted from this procedure. This approach thus provides a way to use a SAXS data based structure prediction algorithm to generate potential structural homologies in cases where lack of sequence homology prevents identification of candidate folds for a given protein. Thus our approach has the potential to help in determination of the biological function of a protein based on structural homology instead of sequence homology.
Embryonic and induced pluripotent stem cell differentiation as a tool in neurobiology

Cell lines with the ability to differentiate into all types of somatic and germ cells represent a pluripotent developmental stage that transiently exists in vivo in the epiblast cells of the pre‐implantation embryo. Given the lack of access to human neurons, together with the limited numbers and heterogeneity of neurons obtainable from rodent primary cultures, the directed differentiation of pluripotent cell lines into defined cells of the neural lineage has provided a novel versatile tool in neurobiology. Offering a potentially unlimited source of material, directed differentiation of pluripotent cell lines has been particularly well combined with high‐throughput transcriptomic and epigenetic analyses. Here, we first overview the potential of different pluripotent lines to give rise to different types of neurons. Then, we discuss the emerging use of neuronal differentiation systems as a tool for unravelling mechanisms that regulate neuronal development and specification, modelling complex neurological diseases and understanding neuronal dysfunction. Beyond providing original insights in many aspects of neuronal biology, these tools have greatly facilitated the development of novel therapeutic interventions for neurological disorders.
Textual Affect Sensing for Computational Advertising

In this paper, a technique for the generation of advertising headlines based on affect is described. The system takes as input the target concept to evoke and possibly a sentiment to communicate. A fundamental dimension of advertising relies on the affective connotation of words. We have used it as an important component of our algorithms for the creative variation of familiar expressions such as proverbs and famous movie titles. Recognition of affective connotation is based on a semantic similarity acquired in an unsupervised way from large corpora and it allows the system selecting words that are slanted towards a desired emotion, or producing strong ironic effects.
Time‐resolved Fourier transform infrared/attenuated total reflection spectroscopy for the measurement of molecular diffusion in polymers

Over the past 2 decades, the use of time-resolved Fourier transform infrared/attenuated total reflection (ATR) spectroscopy for the measurement of diffusion in polymers has grown. ATR is a powerful technique for the measurement of diffusion in polymers because it is an in situ technique that is relatively inexpensive, provides reliable short-time data, and provides a wealth of information at the molecular level. This article highlights the technique and its application to numerous studies, ranging from the diffusion of drugs in human skin to chemical warfare agents in barrier materials. In addition to these topics, recent studies with ATR to quantify and model molecular interactions during the diffusion process are reviewed. In the future, the ATR technique may have an impact on a variety of emerging fields in which diffusion in polymers plays an important role, such as fuel cells, membrane separation, sensors, and drug delivery.
Preparation of Rat Gingival Mitochondria with an Improved Isolation Method

In order to establish a method of obtaining rat gingival mitochondria (Mt), Mt fractions were prepared in various combinations of homogenizing time with collagenase concentration. Rat gingival tissues were excised, minced, treated with collagenase, homogenized, and subjected to differential centrifugation rates. Both the respiratory control ratio (RCR) and adenosine diphosphate/oxygen (ADP/O) ratio of the Mt fraction prepared in a combination of 40, 50, or 60 sec homogenization with collagenase in a concentration range of 0.115%–0.130% (w/v) were measured. The values for the RCR and ADP/O ratio of the Mt fraction obtained in an optimal condition was 1.80 ± 0.05 and 1.65 ± 0.03, respectively. These results suggest that Mt of fairly high quality can be obtained through this refined combination of the homogenizing time and collagenase concentration.
Overcoming the Response Plateau in Multiple Myeloma: A Novel Bortezomib-Based Strategy for Secondary Induction and High-Yield CD34+ Stem Cell Mobilization

Purpose: This phase II study evaluated bortezomib-based secondary induction and stem cell mobilization in 38 transplant-eligible patients with myeloma who had an incomplete and stalled response to, or had relapsed after, previous immunomodulatory drug-based induction. Experimental Design: Patients received up to six 21-day cycles of bortezomib plus dexamethasone, with added liposomal doxorubicin for patients not achieving partial response or better by cycle 2 or very good partial response or better (≥VGPR) by cycle 4 (DoVeD), followed by bortezomib, high-dose cyclophosphamide, and filgrastim mobilization. Gene expression/signaling pathway analyses were conducted in purified CD34+ cells after bortezomib-based mobilization and compared against patients who received only filgrastim ± cyclophosphamide. Plasma samples were similarly analyzed for quantification of associated protein markers. Results: The response rate to DoVeD relative to the pre-DoVeD baseline was 61%, including 39% ≥VGPR. Deeper responses were achieved in 10 of 27 patients who received bortezomib-based mobilization; postmobilization response rate was 96%, including 48% ≥VGPR, relative to the pre-DoVeD baseline. Median CD34+ cell yield was 23.2 × 106 cells/kg (median of 1 apheresis session). After a median follow-up of 46.6 months, median progression-free survival was 47.1 months from DoVeD initiation; 5-year overall survival rate was 76.4%. Grade ≥3 adverse events included thrombocytopenia (13%), hand–foot syndrome (11%), peripheral neuropathy (8%), and neutropenia (5%). Bortezomib-based mobilization was associated with modulated expression of genes involved in stem cell migration. Conclusion: Bortezomib-based secondary induction and mobilization could represent an alternative strategy for elimination of tumor burden in immunomodulatory drug-resistant patients that does not impact stem cell yield. Clin Cancer Res; 19(6); 1534–46. ©2013 AACR.
Visual Feature Space Analysis for Unsupervised Effectiveness Estimation and Feature Engineering

The feature vector approach is one of the most popular schemes for managing multimedia data. For many data types such as audio, images, or 3D models, an abundance of different feature vector extractors are available. The automatic (unsupervised) identification of the best suited feature extractor for a given multimedia database is a difficult and largely unsolved problem. We here address the problem of comparative unsupervised feature space analysis. We propose two interactive approaches for the visual analysis of certain feature space characteristics contributing to estimated discrimination power provided in the respective feature spaces. We apply the approaches on a database of 3D objects represented in different feature spaces, and we experimentally show the methods to be useful (a) for unsupervised comparative estimation of discrimination power and (b) for visually analyzing important properties of the components (dimensions) of the respective feature spaces. The results of the analysis are useful for feature selection and engineering
Structure Preserving Mesh Parameterization

The traditional parameterization methods focused on preserving the local geometry properties by minimizing the distortions of angle and stretch. In this paper, we present a mesh parameterization method to preserve some global geometry properties, such as symmetry structure, of the input mesh. Given an input triangular mesh, the symmetry regions can be automatically detected using shape analysis method or manually specified by the users. For each vertex in the symmetry regions, we can identify the symmetric point and represent it as a triangle index and the corresponding bary centric coordinates. We extend the harmonic map to parameterize the input mesh by adding new error metric to measure how the symmetry property of the point pairs are preserved. Since the new error metric is non-linear, we develop an iterative update method to solve the parameterization problem. At last, we show some structure preservation parameterization results, and compare them with the results of the traditional harmonic map.
MemSpy: analyzing memory system bottlenecks in programs

To cope with the increasing difference between processor and main memory speeds, modern computer systems use deep memory hierarchies. In the presence of such hierarchies, the performance attained by an application is largely determined by its memory reference behavior—if most references hit in the cache, the performance is significantly higher than if most references have to go to main memory. Frequently, it is possible for the programmer to restructure the data or code to achieve better memory reference behavior. Unfortunately, most existing performance debugging tools do not assist the programmer in this component of the overall performance tuning task. This paper describes MemSpy, a prototype tool that helps programmers identify and fix memory bottlenecks in both sequential and parallel programs. A key aspect of MemSpy is that it introduces the notion of data oriented, in addition to code oriented, performance tuning. Thus, for both source level code objects and data objects, MemSpy provides information such as cache miss rates, causes of cache misses, and in multiprocessors, information on cache invalidations and local versus remote memory misses. MemSpy also introduces a concise matrix presentation to allow programmers to view both code and data oriented statistics at the same time. This paper presents design and implementation issues for MemSpy, and gives a detailed case study using MemSpy to tune a parallel sparse matrix application. It shows how MemSpy helps pinpoint memory system bottlenecks, such as poor spatial locality and interference among data structures, and suggests paths for improvement.
Numerical and experimental investigation of board level reliability of TEFCBGA

As far as components with flip chip interconnects are concerned, one of the popular packaging solutions available in the market is Thermally Enhanced Flip Chip Ball Grid Array (TEFCBGA) packages, which target mid to high performance applications. Traditional as it may be, the development of a reliable TEFCBGA package is still a very challenging task. The demands for high electrical performance with greater signal trace density, while constrained by fine core via manufacturing process, have been a driving factor of the reduction in core thickness of High Density Build Up (HDBU) substrate as a flip chip carrier. The thin core substrate not only increases the package warpage, which will inevitably create additional downstream assembly problems during surface mount process, but degrades the significance of the role the substrate is playing as an effective buffer between the silicon die and the Printed Circuit Board (PCB). To make things worse, the presence of an extra large Cu/Low-k die on the HDBU substrate induces, in addition to potential serious coplanarity and board level reliability issues, high stresses in the die itself, which increase the risk of delamination at Inter-Layer Dielectric (ILD) interfaces or even die cracking. No stones concerning package reliability can be left unturned during development phase of a TEFCBGA package in order to ensure that the flip chip packaging process is robust with high yield and compliance to JEDEC specifications. The focus of this paper, however, is on the board level reliability aspect of an extra large TEFCBGA package with body size of 50×50 mmxmm under Accelerated Thermal Cycling (ATC) test. Finite Element Analysis (FEA) was used to evaluate its sensitivity to the change of design, material and process variables. Experiments were carried out to validate the predictions made by FEA simulation for similar packages. It was found that they were in good agreement and the model can be extended to study the effect of a wider range of variables on the board level reliability of a TEFCBGA package.
Efficient Analysis of DynAlloy Specifications

DynAlloy is an extension of Alloy to support the definition of actions and the specification of assertions regarding execution traces. In this article we show how we can extend the Alloy tool so that DynAlloy specifications can be automatically analyzed in an efficient way. We also demonstrate that DynAlloy's semantics allows for a sound technique that we call program atomization, which improves the analyzability of properties regarding execution traces by considering certain programs as atomic steps in a trace. We present the foundations, case studies, and empirical results indicating that the analysis of DynAlloy specifications can be performed efficiently.
SAP: Student Attrition Predictor

Increasing rates of student drop-outs with increase in popularity of Massive Open Online Courses (MOOCs) makes predicting student attrition an important problem to solve. Recently, we developed an algorithm based on artificial neural network for predicting student attrition in MOOCs using student sentiments. In this paper, we present a web-based tool based on our algorithm which can be used by educators to predict and reduce attrition during a course and by researchers to design and train their own system to predict student attrition.
A cluster-based multiple deep neural networks method for large vocabulary continuous speech recognition

Recently a pre-trained context-dependent hybrid deep neural network (DNN) and HMM method has achieved significant performance gain in many large-scale automatic speech recognition (ASR) tasks. However, the error back-propagation (BP) algorithm for training neural networks is sequential in nature and is hard to parallelize into multiple computing threads. Therefore, training a deep neural network is extremely time-consuming even with a modern GPU board. In this paper we have proposed a new acoustic modelling framework to use multiple DNNs instead of a single DNN to compute the posterior probabilities of tied HMM states. In our method, all tied states of context-dependent HMMs are first grouped into several disjoined clusters based on the training data associated with these HMM states. Then, several hierarchically structured DNNs are trained separately for these disjoined clusters of data using multiple GPUs. In decoding, the final posterior probability of each tied HMM state can be calculated based on output posteriors from multiple DNNs. We have evaluated the proposed method on a 64-hour Mandarin transcription task and 309-hour Switchboard Hub5 task. Experimental results have shown that the new method using clusterbased multiple DNNs can achieve over 5 times reduction in total training time with only negligible performance degradation (about 1-2% in average) when using 3 or 4 GPUs respectively.
Oleaginous yeasts from Antarctica: Screening and preliminary approach on lipid accumulation

The capability of 17 Rhodotorula spp. isolated from Antarctica to accumulate intracellular lipids in nitrogen‐limited medium was investigated. As results, 10 isolates were selected by Nile red staining, while 12 isolates were selected as oleaginous by analysis of total lipid content (20.4–73%, w/w of dry biomass). The higher lipid production and accumulation was exhibited for six strains belonging to three species of Rhodotorula (Rhodotorula glutinis, Rhodotorula glacialis, and Rhodotorula laryngis). This is the first report where R. laryngis have been identified within oleaginous specie. Lipid accumulation was evaluated comparatively in two nitrogen‐limited glucose‐based media (MI and MII). MI (low C/N ratio) was more suitable for biomass and lipid production while in MII (high C/N ratio) total lipid content was improved. R. glutinis R4, R. glacialis R15, and R. glutinis R48 showed high lipid concentrations (4.65–6.93 g L−1) and they were able to accumulate large amounts of lipids per gram of biomass (47–77%, w/w). A similar profile in fatty acids composition and content of neutral lipids to vegetable oils was observed, indicating that lipids produced by oleaginous Antarctic yeasts can be considered an alternative feedstock for biodiesel production. Antarctica represents an important source of oleaginous yeasts with adaptive capabilities to accumulate considerable amounts of lipids with biotechnological interest at 15 °C and 25 °C.
Health monitoring of bonded composite repair in bridge rehabilitation

Structural rehabilitation with carbon fiber reinforced composite materials has proven to be an excellent way to enhance/repair steel reinforced concrete structures and prolong their service lives. However, disbonds between composite repair patches and host structures continue to be a great concern of this technology. In this paper, a built-in piezoelectric sensor network based structural health monitoring system is introduced for monitoring the disbonds between composite repair patches and the host structures. This diagnostic system combines the sensor network, diagnostic hardware and data analysis software allowing for real-time monitoring of the integrity of the bonded repair. The effectiveness of detecting disbonds using the system has been demonstrated on a full scale bridge model in a laboratory setting. The bridge model was loaded incrementally to failure, and disbond monitoring was carried out during the loading intervals. Test results showed that the system could detect the disbonds before they have a noticeable effect on the global stiffness of the bridge model.
Myocardial fibrosis in transforming growth factor-beta(1) (TGF-beta(1)) transgenic mice is associated with inhibition of interstitial collagenase.

BACKGROUND TGF-beta(1) mediates effects on fibroblast proliferation and collagen synthesis in the myocardium. The extracellular matrix remodeling depends on the fibrillar collagen degrading matrix metalloproteinases (MMPs) and their inhibitors (TIMPs). The in vivo effects of TGF-beta(1) on the MMP/TIMP system in TGF-beta(1) overexpressing transgenic mice were studied. METHODS Male Alb/TGF-beta(1)(cys(223,225)ser) transgenic mice (TG) and nontransgenic controls (C; 8 weeks) were examined. Protein expression of collagen type I, -III, interstitial collagenase (Int Coll), MMP-2, -9, TIMP-1, -2, -4 and TGF-beta(1) as well as enzyme activity (MMP-2, -9) were measured (Western blots, zymographic assays). mRNA expression of the interstitial collagenase and MMP-9 was studied with the Light-Cycler based real-time PCR. RESULTS Overexpression of TGF-beta(1) resulted in a 10-fold increase in plasma and a seven-fold increase in myocardial TGF-beta(1) concentrations. Relative heart weights increased (mg g(-1): 7.8 +/- 0.4 vs. 4.8 +/- 0.6, n = 6; P < 0.01) in TG compared to C. Collagen type I and III increased in TG (1.9-fold and 1.7-fold) compared to controls. Interstitial collagenase protein activity (- 91%) and mRNA expression (-75%) in TG were reduced (P < 0.05-P < 0.001). Gelatinase (MMP-2, MMP-9) expression and activity were not significantly alterated. MMP-inhibitors were increased 2.5-fold (TIMP-1, -4) and 6-fold (TIMP-2) in TG. CONCLUSIONS TGF-beta(1) produces myocardial fibrosis in vivo. This effect is not only produced by a stimulation of matrix protein formation: a complex regulation of MMP and TIMP interaction, namely decrease of expression and activity of interstitial collagenase and an enhanced inhibition by increased levels of TIMPs, are involved. These mechanisms are optional targets for therapeutic interventions in myocardial diseases.
Development and Analysis of Speech Emotion Corpus Using Prosodic Features for Cross Linguistics

In daily life communication, spoken language not only carries linguistic information but also conveys nonlinguistic information such as the speaker's emotions, gender, social status and age, etc. This paper introduces the Emotion-Pak corpus, a multilingual emotional speech database consisting of emotional sentences elicited in provincial languages of Pakistan: Urdu, Sindhi, Balochi, Punjabi and Pashto for observing the speech emotions present in acoustic signals.The proposed database is recorded from people (naive speakers)having different linguistics, ages, gender, education level and cultural backgrounds from different regions of Pakistan and cross linguistics compared with the Berlin database of emotional speech (EMO-DB)(actor speakers)in order to study whether emotions are gender and language dependent or independent using prosodic features. The statistical analysis of Emotion-Pak corpus shows that emotions with short duration and strong intensity (Anger and Happiness) or longer duration and weak intensity (Sadness and Comfort) have similar acoustic features. The subjective listening tests used for evaluating the quality of speech emotions in proposed emotions corpus. The subjective listening test results found quite similar with the results obtained from prosodic analysis of Emotion-Pak speech corpus.
Microbial gene identification using interpolated Markov models.

This paper describes a new system, GLIMMER, for finding genes in microbial genomes. In a series of tests on Haemophilus influenzae , Helicobacter pylori and other complete microbial genomes, this system has proven to be very accurate at locating virtually all the genes in these sequences, outperforming previous methods. A conservative estimate based on experiments on H.pylori and H. influenzae is that the system finds >97% of all genes. GLIMMER uses interpolated Markov models (IMMs) as a framework for capturing dependencies between nearby nucleotides in a DNA sequence. An IMM-based method makes predictions based on a variable context; i.e., a variable-length oligomer in a DNA sequence. The context used by GLIMMER changes depending on the local composition of the sequence. As a result, GLIMMER is more flexible and more powerful than fixed-order Markov methods, which have previously been the primary content-based technique for finding genes in microbial DNA.