Miracle-dz/UniFAR-FTUs huggingface dataset:
Response to Comment on "Human Neuroblasts Migrate to the Olfactory Bulb via a Lateral Ventricular Extension"

In contrast to a previous study of Sanai et al., our study had the advantage of using serial sagittal sections of the human basal forebrain, combined with 5-bromo-2′-deoxyuridine labeling, rigorous magnetic resonance imaging, and polymerase chain reaction analysis. We believe these methods convincingly demonstrate the presence of a rostral migratory stream in the human brain that resembles that in other mammals.
A feature extraction method for automatic speech recognition based on the cochlear nucleus

Motivated by the human auditory system, a feature extraction method for automatic speech recognition (ASR) based on the differential processing strategy of the AVCN, PVCN and the DCN of the cochlear nucleus is proposed. The method utilizes a zero-crossing with peak amplitudes (ZCPA) auditory model as synchrony detector to discriminate the low frequency formants. It utilizes the mean rate information in the synapse processing to capture the very rapidly changing dynamic nature of speech. Additionally, a temporal companding method is utilized for spectral enhancement through two-tone suppression. We propose to separate synchrony detection from synaptic processing as observed in the parallel processing methodology in the cochlear nucleus. HMM recognition using isolated digits showed improved recognition rates in clean and in nonstationary noise conditions than the existing auditory model. Index Terms: Speech recognition, zero-crossings, auditory model, cochlear nucleus, hidden Markov model.
Assessing medical technologies.

A MERICAN SOCIETY believes in and strongly supports technology. AcIcording to a 1974 poll commissioned by the National Science Board,1 75% of the public believed that science and technology have changed life for the better. Technology certainly has changed medical practice. Since 1940 a broad array of medical interventions have entered practice, including antibiotics; antihypertensive drugs; oral diuretics; oral contraceptives; psychopharmaceuticals; corticosteroids; polio, measles, mumps, and rubella vaccines; cardiovascular and open-heart surgery; genetic screening and amniocentesis; automated clinical laboratories; renal dialysis; and cardiac pacemakers. Numerous other examples could be cited.2 The National Science Board poll also found that about half of the respondents thought that technology had caused some of our problems. A large poll reported by Etzioni and Nunn3 found that public confidence in medicine (expressed as a "great deal" of confidence) fell from 56% in 1966 to 37% in 1973. In the National Science Board poll a substantial minority of those polled (28%) favored increased societal control over science and technology.1 The Food and Drug Administration has found that 81% of those polled (in a survey made in 1972) are concerned about the safety of drugs.4
MDL regularizer: a new regularizer based on the MDL principle

This paper proposes a new regularization method based on the MDL (minimum description length) principle. An adequate precision weight vector is trained by approximately truncating the maximum likelihood weight vector. The main advantage of the proposed regularizer over existing ones is that it automatically determines a regularization factor without assuming any specific prior distribution with respect to the weight values. Our experiments using a regression problem showed that the MDL regularizer significantly improves the generalization error of a second-order learning algorithm and shows a comparable generalization performance to the best tuned weight-decay regularizer.
Relationship of C5L2 Receptor to Skeletal Muscle Substrate Utilization

Objective To investigate the role of Acylation Stimulating Protein (ASP) receptor C5L2 in skeletal muscle fatty acid accumulation and metabolism as well as insulin sensitivity in both mice and human models of diet-induced insulin resistance. Design and Methods Male wildtype (WT) and C5L2 knockout (KO) mice were fed a low (LFD) or a high (HFD) fat diet for 10 weeks. Intramyocellular lipid (IMCL) accumulation (by oil red O staining) and beta-oxidation HADH enzyme activity were determined in skeletal muscle. Mitochondria were isolated from hindleg muscles for high-resolution respirometry. Muscle C5L2 protein content was also determined in obese type 2 diabetics and age- and BMI matched men. Results IMCL levels were increased by six-fold in C5L2KO-HFD compared to WT-HFD mice (p<0.05) and plasma insulin levels were markedly increased in C5L2KO-HFD mice (twofold, p<0.05). Muscle HADH activity was elevated in C5L2KO-LFD mice (+75%, p<0.001 vs. WT-LFD) and C5L2KO-HFD displayed increased mitochondrial fatty acid oxidative capacity compared to WT-HFD mice (+23%, p<0.05). In human subjects, C5L2 protein content was reduced (−48%, p<0.01) in type 2 diabetic patients when compared to obese controls. Further, exercise training increased C5L2 (+45%, p = 0.0019) and ASP (+80%, p<0.001) in obese insulin-resistant men. Conclusion The results suggest that insulin sensitivity may be permissive for coupling of C5L2 levels to lipid storage and utilization.
Advances in the prediction of protein targeting signals

Enlarged sets of reference data and special machine learning approaches have improved the accuracy of the prediction of protein subcellular localization. Recent approaches report over 95% correct predictions with low fractions of false‐positives for secretory proteins. A clear trend is to develop specifically tailored organism‐ and organelle‐specific prediction tools rather than using one general method. Focus of the review is on machine learning systems, highlighting four concepts: the artificial neural feed‐forward network, the self‐organizing map (SOM), the Hidden‐Markov‐Model (HMM), and the support vector machine (SVM).
A Computational Study with Finite Element Method and Finite Difference Method for 2D Elliptic Partial Differential Equations

In this paper, we consider two methods, the Second order Central Difference Method (SCDM) and the Finite Element Method (FEM) with P1 triangular elements, for solving two dimensional general linear Elliptic Partial Differential Equations (PDE) with mixed derivatives along with Dirichlet and Neumann boundary conditions. These two methods have almost the same accuracy from theoretical aspect with regular boundaries, but generally Finite Element Method produces better approximations when the boundaries are irregular. In order to investigate which method produces better results from numerical aspect, we apply these methods into specific examples with regular boundaries with constant step-size for both of them. The results which obtained confirm, in most of the cases, the theoretical results.
Stability Analysis of Simple and Online User Association Policies for Millimeter Wave Networks

In a millimeter wave (mmWave) network, user association– the process of deciding as to which base station (BS) a given user should associate with– is a crucial process, which affects the throughput and delay performance seen by users in the network and the amount of load at each BS. In the existing research literature, the stability region of a user association policy, i.e., the set of user arrival rates for which the user association policy stabilizes the network, has not been analytically characterized for any user association policy for mmWave networks. In this paper, we study the user association problem in mmWave networks and compare the performances of four user association policies: Signal to Noise Ratio (SNR) based, Throughput based, Load based and Mixed. All these policies are simple, easy to implement, distributed, and online. We use a Continuous Time Markov Chain (CTMC) model and Lyapunov function techniques to analytically characterize the stability region of each of the above four user association policies; our analysis provides several insights into the performances of the policies. We also evaluate the performances of the above four user association policies in a large mmWave network, in which link qualities fluctuate with time and users are mobile, via detailed simulations. Our results show that the Throughput based policy outperforms the other three user association policies in terms of stability region as well as average throughput, average delay, fairness and energy efficiency performance.
APOE polymorphism and the progression of diabetic nephropathy in Japanese subjects with type 2 diabetes: results of a prospective observational follow-up study.

OBJECTIVE The aim of this study is to clarify the conflicting results of the epsilon2/epsilon3/epsilon4 APOE polymorphism as a risk factor on diabetic nephropathy by a cohort study. RESEARCH DESIGN AND METHODS A total of 429 Japanese subjects with type 2 diabetes and with normoalbuminuria (n = 299) or with microalbuminuria (n = 130) were enrolled in a prospective observational follow-up study during 1995-1998 and followed until 2001 (for at least 3 years). The endpoint was the occurrence of a renal event defined as the progression to a higher stage of diabetic nephropathy. RESULTS During the study (the mean follow-up period: 4.4 +/- 1.0 years), 31 of 429 subjects progressed: 21 from normoalbuminuria to microalbuminuria and 10 from microalbuminuria to overt proteinuria. The allele frequency of the APOE polymorphism was significantly different between the progressors and the nonprogressors. Eight of 42 epsilon2 carriers (19%) progressed, whereas 23 of 387 noncarriers (6%) progressed with a relative risk of 3.2 (95% CI 1.5-6.7). When subjects were stratified by renal status at baseline, each relative risk for the progression in the epsilon2 carriers was 2.7 (0.99-7.4) in those with normoalbuminuria and 4.2 (1.3-13.3) in those with microalbuminuria. Furthermore, when analyzed only in subjects with normoalbuminuria and short duration of diabetes (<15 years) at baseline, the risk in the epsilon2 carriers became higher to 3.2 (1.2-8.8). CONCLUSIONS Our follow-up study indicates that the epsilon2 allele of the APOE polymorphism is a prognostic risk factor for both the onset and the progression of diabetic nephropathy in Japanese type 2 diabetes.
Similar MPA Exposure on Modified Release and Regular Tacrolimus

Abstract: Concomitant immunosuppression may affect the mycophenolate mofetil exposure. Astellas developed a once-daily modified release formulation of tacrolimus (TacMR) with the potential to reduce the likelihood of nonadherence. It is unknown whether mycophenolic acid (MPA) area under the concentration–time curve (AUC) differs between the 2 tacrolimus (Tac) formulations. In a 2-by-2 crossover design, 20 stable renal transplant recipients on twice-daily Tac either continued their usual Tac therapy (n = 10, group 1) or switched to TacMR for a 12-week period (n = 10, group 2), after which the patients crossed over to the other formulation for another 12-week period. Pharmacokinetic profiles using limited sampling strategies were obtained before randomization (visit 1), and at 12 (visit 2) and 24 weeks (visit 3) at steady state. MPA AUC was calculated using the Pawinski formula. When analyzing visits on Tac, TacMR, and back on Tac combined, the MPA AUC for all 20 patients at baseline was 42.24 (16.98), 37.18 (13.75), and 40.09 (16.69) mg·h·L−1, respectively, which was not statistically significant using repeated measures (P = 0.1327, R2 = 0.1109). We conclude that MPA pharmacokinetic profiles are not altered when converting patients from Tac to TacMR.
Impacts of Fertilization Regimes on Arbuscular Mycorrhizal Fungal (AMF) Community Composition Were Correlated with Organic Matter Composition in Maize Rhizosphere Soil

The understanding of the response of arbuscular mycorrhizal fungi (AMF) community composition to fertilization is of great significance in sustainable agriculture. However, how fertilization influences AMF diversity and composition is not well-established yet. A field experiment located in northeast China in typical black soil (Chernozem) was conducted and high-throughput sequencing approach was used to investigate the effects of different fertilizations on the variation of AMF community in the rhizosphere soil of maize crop. The results showed that AMF diversity in the maize rhizosphere was significantly altered by different fertilization regimes. As revealed by redundancy analysis, the application of organic manure was the most important factor impacting AMF community composition between samples with and without organic manure, followed by N fertilizer and P fertilizer inputs. Moreover, the organic matter composition in the rhizosphere, determined by GC–MS, was significantly altered by the organic manure amendment. Many of the chemical components displayed significant relationships with the AMF community composition according to the Mantel test, among those, 2-ethylnaphthalene explained the highest percentage (54.2%) of the variation. The relative contents of 2-ethylnaphthalene and 2, 6, 10-trimethyltetradecane had a negative correlation with Glomus relative abundance, while the relative content of 3-methylbiphenyl displayed a positive correlation with Rhizophagus. The co-occurrence patterns in treatments with and without organic manure amendment were analyzed, and more hubs were detected in the network of soils with organic manure amendment. Additionally, three operational taxonomic units (OTUs) belonging to Glomerales were identified as hubs in all treatments, indicating these OTUs likely occupied broad ecological niches and were always active for mediating AMF species interaction in the maize rhizosphere. Taken together, impacts of fertilization regimes on AMF community composition were correlated with organic matter composition in maize rhizosphere soil and the application of manure could activate more AMF species to interact with other species in the maize rhizosphere. This knowledge can be valuable in regulating the symbiotic system of plants and AMF, maintaining the health and high yields of crops and providing a primary basis for rational fertilization.
A SAS/IML  Macro for Goodness-of-Fit Testing in Logistic Regression Models with Sparse Data

The logistic regression model has become the standard analyzing tool for binary responses in a variety of disciplines. Methods for assessing goodness-of-fit, however, are less developed and this is especially pronounced in calculating goodness-of-fit tests with sparse data, when the standard tests (deviance and Pearson test) behave unsatisfactorily. In our paper we show two solutions to the problem that are implemented in the LOGISTIC procedure in SAS  software, and introduce five additional testing procedures from the statistical literature. By means of a simulation study we show that these additional tests are valid instruments for assessing goodness-of-fit in logistic regression models, even with sparse data. Finally, we present the SAS/IML macro %GOFLOGIT which allows calculation of the introduced tests and illustrate the macro with an example from occupational epidemiology on hand eczema in hairdressers.
A microwave metamaterial absorber with wide bandwidth

A wideband metamaterial absorber has been presented for practical applications in this article. The unit cell of the proposed structure comprises metallic patch of single circular split-ring imprinted on top surface of a metal-backed single layer dielectric substrate. The geometrical dimensions of the structure are suitably optimized so that full width half maxima bandwidth of 11.46 GHz is realized along with wideband absorption of 10.90 GHz from 5.94 GHz to 16.84 GHz with more than 80% absorptivity. Four distinct absorption peaks are observed at 6.46 GHz, 10.24 GHz, 15.26 GHz and 16.66 GHz. The absorption phenomena at these four frequencies are analyzed and the roles of several geometrical parameters of the design are investigated. The proposed structure has been studied under oblique incidence, both for TE and TM polarizations where it shows wideband absorption characteristics upto 45° incident angles for both cases. The proposed structure is very thin (~ λ/8.3 with respect to the centre frequency of the absorption bandwidth) compared to the commercially available microwave absorbers.
An indoor localization mechanism using active RFID tag

The location-aware technology and its application are prerequisite to realize the ubiquitous computing. However, another special system for indoor localization is needed in the complicated indoor environment, where the Global Positioning System (GPS) is not available. RFID technology makes the building up of indoor localization system with low cost possible. LANDMARC is an indoor localization system using RFID technology, which introduces the concept of reference tag to reduce the number of deployed RFID readers and provides high possibility to be used in reality, and we can guarantee the accuracy of localization and thus lower the cost of importing system with it. In this paper, we analyze the existing LANDMARC system, solve the potential problems within it and propose a more efficient mechanism to enhance the accuracy of indoor localization
Diagnosing colorectal cancer and inflammatory bowel disease in primary care: The usefulness of tests for faecal haemoglobin, faecal calprotectin, anaemia and iron deficiency. A prospective study

Abstract Objective: Abdominal complaints are common reasons to consult primary care but they are seldom caused by colorectal cancer (CRC), high-risk adenomas (HRAs), or inflammatory bowel disease (IBD). Reliable diagnostic aids would be helpful in deciding which patients to refer for bowel imaging. Our aim was to assess the value of a faecal immunochemical test (FIT) and a faecal calprotectin (FC) test in detecting CRC, HRAs and IBD in primary care, and the value of combining these tests with anaemia and iron-deficiency tests. Materials and methods: This prospective study included 373 consecutive patients that received a FIT or a FC test ordered by a primary care physician. We collected samples for FITs, FC tests, full blood counts and iron-deficiency tests. Physicians were instructed to refer patients with a positive FIT or FC test (cut-off ≥100μg/g) for bowel imaging. The patients’ presenting symptoms were recorded. Patients were followed for 2 years. Results: The best test for detecting CRC and IBD was the combination of the FIT and haemoglobin concentration. This test had a sensitivity, specificity, positive predictive value and negative predictive value of 100%, 61.7%, 11.7% and 100%, respectively. The FIT detected a significantly larger proportion of CRC, HRAs and IBD than the FC test (0.92 versus 0.46, 95% confidence interval 0.22–0.67). Conclusion: A negative FIT combined with a normal haemoglobin concentration could rule out CRC and IBD with a high degree of safety. This could be useful in prioritising referrals for bowel imaging from primary care.
On robust Capon beamforming and diagonal loading

Whenever the knowledge of the array steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. Diagonal loading (including its extended versions) has been a popular approach to improve the robustness of the Capon beamformer. In this paper we show that a natural extension of the Capon beamformer to the case of uncertain steering vectors also belongs to the class of diagonal loading approaches but the amount of diagonal loading can be precisely calculated based on the uncertainty set of the steering vector. The proposed robust Capon beamformer can be efficiently computed at a comparable cost with that of the standard Capon beamformer. Its excellent performance is demonstrated via a number of numerical examples.
Accurate seed points classification using invariant moments & neural network

Segmentation is a key topic in computer vision and medical image processing. Furthermore, it is used in many medical applications and techniques such as registration. Currently, an accurate segmentation is still a challenging task. In this study, the segmentation process starts by selecting seed points within the region of interest. Manual seed points selection can be time consuming and requires an expert to complete the selection. In this paper, we propose a novel method for automatic classification of the seed points in liver Magnetic Resonance Imaging (MRI) belonging to the same patient each time the segmentation is performed. The proposed method uses Geometric Moment Invariants as a feature vector to identify the locations of seed points. Artificial Neural Network (ANN) model is trained using the feature vector of each of the seed points to classify which region of the liver a testing point belongs. We have demonstrated the effectiveness of our technique in classifying three seed points. These seed points represent the left hepatic vein, central hepatic vein, and right hepatic vein of the liver. The proposed method shows high accuracy in classifying the input seed points.
Finding Similar Movements in Positional Data Streams

In this paper, we study the problem of efficiently finding similar movements in positional data streams, given a query trajectory. Our approach is based on a translation-, rotation-, and scale-invariant representation of movements. Nearneighbours given a query trajectory are then efficiently computed using dynamic time warping and locality sensitive hashing. Empirically, we show the efficiency and accuracy of our approach on positional data streams recorded from a real soccer game.
Agents that remember can tell stories: integrating autobiographic memory into emotional agents

For the past few years many new applications are being developed featuring interactive environments populated with autonomous virtual agents capable of acting according to their goals, beliefs and even social relations. Such agents must be able to interact with each other, and more importantly with the user, thus involving the users in an engaging narrative experience. To achieve these, in this paper we describe the essential event structure in an autobiographic memory, event reconstructions in memory retrieval process and the influences of such past events in interpersonal relations.
Using MAC addresses as efficient routing labels in data centers

A number of new technologies such as cloud services and/or virtualization have changed data center networks in the last few years. The benefits of the techniques are clear but the downside is that more forwarding entries are needed in network switches to support these techniques. Unfortunately, the number of forwarding entries in switches have a hard limit. We give a formal problem definition for minimizing the number of forwarding entries and a proof that the problem is NP complete. We show that the destination MAC address can be used as a universal label in software-defined networks and the ARP caches of hosts can exploited as an ingress label table, reducing the size of the forwarding tables of network devices. We have the additional advantage of not requiring a special type of data center network or additional hardware capabilities. We demonstrate that our technique can solve the problem of FIB sizes by introducing a greedy scheme for all pairs ECMP with a minimal number of FIB entries.