Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Trend of Multiple Myeloma and Associated Secondary Primary Malignancies in Asian Patients: A Taiwan Population–Based Study

Studies involving second malignancies in patients with multiple myeloma are limited for the Asian population. Using data from population-based insurance claims, we assessed the risk of developing secondary malignancies after multiple myeloma, in particular hematologic malignancies. A retrospective cohort study was conducted in 3970 patients with newly diagnosed multiple myeloma from the registry of catastrophic illnesses between 1997 and 2009. A total of 15880 subjects without multiple myeloma were randomly selected as comparisons from the insured population, frequency-matched based on gender, age, and the date of diagnosis. The incidence of secondary malignancies was ascertained through cross-referencing with the National Cancer Registry System. The Cox proportional hazards model was used for analyses. The incidence of multiple myeloma in the insured population increased annually. The overall incidence of secondary malignancy was lower in the multiple myeloma cohort than in the comparison cohort (93.6 vs. 104.5 per 10,000 person-years, IRR = 0.90, 95% CI = 0.78–1.04). The incidence of hematologic malignancies was 11-fold greater for multiple myeloma patients (47.2 vs. 4.09 per 10,000 person-years) with an adjusted HR of 13.0 (95% CI = 7.79–21.6) compared with the comparison cohort. The relative risk of secondary malignancy was also strong for myeloid leukemia (21.2 vs. 1.36 per 10,000 person-years). Gender- and age-specific analysis for secondary hematologic malignancies showed that males and patients with multiple myeloma <60 years of age had a higher risk of secondary malignancy than females and patients with multiple myeloma >60 years of age. In conclusion, patients with multiple myeloma, especially younger patients, are at a high risk of hematologic malignancies.
How ageing changes the mnemonic bias of visual behaviour

ABSTRACT Ageing is associated with deficits in cognitive control, including attention and working memory processes. However, how ageing influences the interactions between these cognitive systems is not well understood. The present study compared the oculomotor behaviour and search performance of two different age groups in a well-established memory-guided visual selection paradigm. The results show that ageing can lead to impairments in the way memory representations bias the control of attention, which notably dissociate in the early stages of oculomotor orientation in search and the later process of attentional disengagement from memory distracters. The implications of these findings for theories of cognitive ageing are discussed.
Performance Analyses and Improvements for the IEEE 802.15.4 CSMA/CA Scheme with Heterogeneous Buffered Conditions

Studies of the IEEE 802.15. 4 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme have been received considerable attention recently, with most of these studies focusing on homogeneous or saturated traffic. Two novel transmission schemes—OSTS/BSTS (One Service a Time Scheme/Bulk Service a Time Scheme)—are proposed in this paper to improve the behaviors of time-critical buffered networks with heterogeneous unsaturated traffic. First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M/G/1/K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of prioritization. Then, throughput, packet delay and energy consumption of unsaturated, unacknowledged IEEE 802.15. 4 beacon-enabled networks are predicted based on the overall point of view which takes the dependent interactions of different types of nodes into account. Moreover, performance comparisons of these two schemes with other non-priority schemes are also proposed. Analysis and simulation results show that delay and fairness of our schemes are superior to those of other schemes, while throughput and energy efficiency are superior to others in more heterogeneous situations. Comprehensive simulations demonstrate that the analysis results of these models match well with the simulation results.
Toward Robust Hidden Volumes Using Write-Only Oblivious RAM

With sensitive data being increasingly stored on mobile devices and laptops, hard disk encryption is more important than ever. In particular, being able to plausibly deny that a hard disk contains certain information is a very useful and interesting research goal. However, it has been known for some time that existing ``hidden volume'' solutions, like TrueCrypt, fail in the face of an adversary who is able to observe the contents of a disk on multiple, separate occasions. In this work, we explore more robust constructions for hidden volumes and present HiVE, which is resistant to more powerful adversaries with multiple-snapshot capabilities. In pursuit of this, we propose the first security definitions for hidden volumes, and prove HiVE secure under these definitions. At the core of HiVE, we design a new write-only Oblivious RAM. We show that, when only hiding writes, it is possible to achieve ORAM with optimal O(1) communication complexity and only poly-logarithmic user memory. This is a significant improvement over existing work and an independently interesting result. We go on to show that our write-only ORAM is specially equipped to provide hidden volume functionality with low overhead and significantly increased security. Finally, we implement HiVE as a Linux kernel block device to show both its practicality and usefulness on existing platforms.
Deep slip rates along the Longmen Shan fault zone estimated from repeating microearthquakes

[1] We investigated deep slip rates along the Longmen Shan fault zone where the devastating 2008 M7.9 Wenchuan earthquake occurred using repeating earthquake data recorded by two digital seismic networks near the fault. Using cross-correlation analysis of waveform data, we identified a total of 231 doublets and 224 multiplets that have highly similar waveforms. Most of these sequences are aperiodic with recurrence intervals varying from a few minutes to hundreds of days. For each multiplet, we first constructed a reference seismogram at each station and used it to measure the delay time of the P and S waves of each event within the cluster. We used the delay times to determine the relative distances between each event and the cluster centroid. The relative distances were used in screening repeating events in each multiplet. We identified a total of 12 repeating earthquake clusters in the source region of the M7.9 Wenchuan earthquake. Most of the clusters are located at the edge of locked areas where large coseismic slips were observed during the 2008 Wenchuan earthquake, suggesting a close spatial relationship between microearthquakes and impending large devastating earthquakes. The measured in situ deep slip rates appeared to increase with depth and varied from 3.5 to 9.6 mm/yr at depth range of 4–18 km. They are approximately twice as large as those inferred from surface GPS and geological data. Our results here have significant implications to the understanding of faulting as well as risk assessment of seismic hazards.
Evaluation of features for automatic detection of cell nuclei in fluorescence microscopy images

The problem of detecting cell nuclei in fluorescence images may be faced by means of a segmentation step, to get the neighbourhood of candidate nuclei, followed by a binary classification step. Important for the latter step is the choice of the descriptors (features) to be extracted from the neighbourhood and used by the classifier. In the present paper, based on a large set of manually labelled samples, we evaluate several of such descriptors combined with some common type of support vector machines. We show that equipping the detection algorithm with the best combination of features/classifier leads to a performance comparable to human labelling by experts.
Variations in activin receptor, inhibin/activin subunit and follistatin mRNAs in human prostate tumour tissues

The possible role of activin in the regulation of malignant prostatic growth was studied using RNAase protection assays of activin receptors, inhibin/activin subunits and follistatin mRNAs in the human prostatic carcinoma cell lines LNCaP-FGC, -R and -LNO, in human prostatic carcinoma xenografts and in human prostatic tissue. Activin receptor types IA (ActRIA), IB (ActRIB), IIA (ActRIIA) and IIB (ActRIIB) mRNAs were generally expressed in prostate pithelial cells, with significantly lower levels of ActRIB mRNA in prostate tumour aterial when compared to non-malignant tissue (P< 0.05; Mann–Whitney U -test). Inhibin/activin βA- and βB-subunit mRNA expression was also found in prostate tissue. Androgen-independent xenografts expressed significantly lower amounts of βB-subunit mRNA when compared to androgen-dependent xenografts (P< 0.05). While βB-subunit mRNA was expressed by LNCaP-FGC and -LNO cells, virtually no expression was found in the androgen-independent LNCaP-R line. Inhibin α-subunit mRNA levels were low or undetectable in all samples investigated. Follistatin mRNA was undetectable in LNCaP-sublines, while low levels were found in prostatic tissues. In androgen-independent LNCaP-R cells, activin inhibited cell growth in a dose-dependent manner. These results suggest that prostate tumour progression is accompanied by a decrease of the inhibitory effect of locally produced activin by either a decrease in the expression of activin βB-subunit mRNA or by a decrease of ActRIB mRNA levels. © 2000 Cancer Research Campaign
The Clinical Effectiveness of Intralesional Injection of 2% Zinc Sulfate Solution in the Treatment of Common Warts

Objective. To investigate the clinical efficacy and safety of intralesional injection of 2% zinc sulfate solution in the treatment of common warts. Patients and Methods. One hundred and twenty patients (78 females and 42 males) aged 5–55 years with 225 common warts participated in this prospective monocentric randomized study. All lesions were treated with intralesional injection of 2% zinc sulfate. Results. From 225 warts injected, 135 warts (60%) cured from the first session, 51 warts (22.67%) cured from the second session, and 12 warts (5.33%) cured from the third session. There is no significant relation between improvement and patient's ages, duration, or number of warts (P > 0.05). All patients complained from pain during injection, and all treated lesions showed redness, tenderness, and swelling in the first 3 days after injection. Late complications were postinflammatory hyperpigmentation in 90 patients (75%), scaring in 9 patients (7.5%), and ulceration in 3 patients (2.5%). Recurrence occurred in 3 lesions (1.33%). Conclusion. The clinical data indicate that intralesional injection of 2% zinc sulfate is an effective maneuver in the treatment of common warts; however, its associated complications limit its use.
A Flow Cytometric and Computational Approaches to Carbapenems Affinity to the Different Types of Carbapenemases

The synergy of carbapenem combinations regarding Enterobacteriaceae producing different types of carbapenemases was study through different approaches: flow cytometry and computational analysis. Ten well characterized Enterobacteriaceae (KPC, verona integron-encoded metallo-β-lactamases –VIM and OXA-48-like enzymes) were selected for the study. The cells were incubated with a combination of ertapenem with imipenem, meropenem, or doripenem and killing kinetic curves performed with and without reinforcements of the drugs. A cephalosporin was also used in combination with ertapenem. A flow cytometric assay with DiBAC4-(3), a membrane potential dye, was developed in order to evaluate the cellular lesion after 2 h incubation. A chemical computational study was performed to understand the affinity of the different drugs to the different types of enzymes. Flow cytometric analysis and time-kill assays showed a synergic effect against KPC and OXA-48 producing-bacteria with all combinations; only ertapenem with imipenem was synergic against VIM producing-bacteria. A bactericidal effect was observed in OXA-48-like enzymes. Ceftazidime plus ertapenem was synergic against ESBL-negative KPC producing-bacteria. Ertapenem had the highest affinity for those enzymes according to chemical computational study. The synergic effect between ertapenem and others carbapenems against different carbapenemase-producing bacteria, representing a therapeutic choice, was described for the first time. Easier and faster laboratorial methods for carbapenemase characterization are urgently needed. The design of an ertapenem derivative with similar affinity to carbapenemases but exhibiting more stable bonds was demonstrated as highly desirable.
Modulation of gut microbiota dysbioses in type 2 diabetic patients by macrobiotic Ma-Pi 2 diet

Abstract The gut microbiota exerts a role in type 2 diabetes (T2D), and deviations from a mutualistic ecosystem layout are considered a key environmental factor contributing to the disease. Thus, the possibility of improving metabolic control in T2D by correcting gut microbiome dysbioses through diet has been evaluated. Here, we explore the potential of two different energy-restricted dietary approaches – the fibre-rich macrobiotic Ma-Pi 2 diet or a control diet recommended by Italian professional societies for T2D treatment – to correct gut microbiota dysbioses in T2D patients. In a previous 21-d open-label MADIAB trial, fifty-six overweight T2D patients were randomised to the Ma-Pi 2 or the control diet. For the present study, stools were collected before and after intervention from a subset of forty MADIAB participants, allowing us to characterise the gut microbiota by 16S rRNA sequencing and imputed metagenomics. To highlight microbiota dysbioses in T2D, the gut microbiota of thirteen normal-weight healthy controls were characterised. According to our findings, both diets were effective in modulating gut microbiome dysbioses in T2D, resulting in an increase of the ecosystem diversity and supporting the recovery of a balanced community of health-promoting SCFA producers, such as Faecalibacterium, Roseburia, Lachnospira, Bacteroides and Akkermansia. The Ma-Pi 2 diet, but not the control diet, was also effective in counteracting the increase of possible pro-inflammatory groups, such as Collinsella and Streptococcus, in the gut ecosystem, showing the potential to reverse pro-inflammatory dysbioses in T2D, and possibly explaining the greater efficacy in improving the metabolic control.
Explaining differences in adult second language learning: The role of language input characteristics and learners’ cognitive aptitudes

This dissertation examines how adult learning of novel language structures is affected by the characteristics of the language input that learners are exposed to and by learners’ cognitive aptitudes, such as analytical ability and working memory capacity. In a series of experiments, adult native speakers of Dutch received brief auditory exposure to a miniature language based on Fijian. Within the language learners were also exposed to an agreement pattern between determiners and nouns: masculine determiner lep preceded nouns ending in –uk, and feminine determiner ris preceded nouns ending in –is (e.g., lep oseuk, ris burogis). After the exposure, learners’ knowledge of the agreement pattern and their ability to quickly predict nouns based on determiners were measured using grammaticality judgment tasks, a production task, and a processing task with eye-tracking. There were substantial differences between learners, both in terms of their learning success and the awareness of the underlying language patterns. The reliability of the language input (i.e., whether the agreement pattern in the input was fully regular or featured exceptions) affected learning in different ways depending on the length of the language exposure and learners’ awareness of the agreement structure. Learners’ working memory capacity and analytical abilities were positively related to their learning outcomes and to the occurrence of pattern awareness among learners. Some learners developed quick predictive processing based on determiners, but this was always accompanied by learners’ awareness that determiners were useful for making choices during the test with eye-tracking.
The Road Not Taken

To investigate individual differences in creativity as measured with a complex problem-solving task, we developed a computational model of the remote associates test (RAT). For 50 years, the RAT has been used to measure creativity. Each RAT question presents three cue words that are linked by a fourth word, which is the correct answer. We hypothesized that individuals perform poorly on the RAT when they are biased to consider high-frequency candidate answers. To assess this hypothesis, we tested individuals with 48 RAT questions and required speeded responding to encourage guessing. Results supported our hypothesis. We generated a norm-based model of the RAT using a high-dimensional semantic space, and this model accurately identified correct answers. A frequency-biased model that included different levels of bias for high-frequency candidate answers explained variance for both correct and incorrect responses. Providing new insight into the nature of creativity, the model explains why some RAT questions are more difficult than others, and why some people perform better than others on the RAT.
Generalization in Response to Mate Recognition Signals

Females usually exhibit strong and unequivocal recognition of conspecific mating signals and reject those of other sympatric heterospecifics. However, most species are allopatric with one another, and the degree to which females recognize mating signals of allopatric species is more varied. Such mating signals are often rejected but are sometimes falsely recognized as conspecific. We studied the dynamics of mate recognition in female túngara frogs (Physalaemus pustulosus) in response to a series of calls that were intermediate between the conspecific and each of five allopatric‐heterospecific calls: two that elicited recognition from females in previous studies and three that did not. This study shows that females perceive variation in allopatric mating signals in a continuous manner with no evidence of perceptual category formation. The strength of recognition is predicted by how different the target stimulus is from the conspecific call within a series of calls. But the differences in recognition responses among call series are not predicted by the similarity of the call series to the conspecific call. The latter result suggests that the strength of recognition of allopatric signals might be influenced by processes of stimulus generalization and past evolutionary history.
Alzheimer Disease detection on structural MRI

Alzheimer disease (AD) is the most common form of dementia. Structural (MRI) measurements allow detecting and following the evolution of brain atrophy which is a marker of the disease process. Recently, several pattern recognition methods have been proposed to automatically discriminate between patients with and without Alzheimer disease . Support Vector Machines help to identify patterns in data that differentiate between several classes. Hippocampus is the principal region involved in AD. In this work, we use visual description of the hippocampus ROI to detect Alzheimer disease from structural MR images.
An empirical evaluation of deep architectures on problems with many factors of variation

Recently, several learning algorithms relying on models with deep architectures have been proposed. Though they have demonstrated impressive performance, to date, they have only been evaluated on relatively simple problems such as digit recognition in a controlled environment, for which many machine learning algorithms already report reasonable results. Here, we present a series of experiments which indicate that these models show promise in solving harder learning problems that exhibit many factors of variation. These models are compared with well-established algorithms such as Support Vector Machines and single hidden-layer feed-forward neural networks.
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

Recovering a low-rank tensor from incomplete information is a recurring problem in signal processing and machine learning. The most popular convex relaxation of this problem minimizes the sum of the nuclear norms (SNN) of the unfolding matrices of the tensor. We show that this approach can be substantially suboptimal: reliably recovering a K-way n×n×. ×n tensor of Tucker rank (r, r,., r) from Gaussian measurements requires Ω(rnK-1) observations. In contrast, a certain (intractable) nonconvex formulation needs only O(rK+nrK) observations. We introduce a simple, new convex relaxation, which partially bridges this gap. Our new formulation succeeds with O(r⌊K/2⌋n⌈K/2⌉) observations. The lower bound for the SNN model follows from our new result on recovering signals with multiple structures (e.g. sparse, low rank), which indicates the significant suboptimality of the common approach of minimizing the sum of individual sparsity inducing norms (e.g. l1, nuclear norm). Our new tractable formulation for low-rank tensor recovery shows how the sample complexity can be reduced by designing convex regularizers that exploit several structures jointly.
Evaluation of Heavy Metals Contamination of Soil and Vegetation in the Vicinity of a Cement Factory in the Volta Region, Ghana

Heavy metal mobilization in the biosphere by human activities has become an important process in the geochemical recycling of these metals. In this work, 34 soil samples and 29 Tephrosia elegans plants collected in the vicinity of the Diamond Cement Factory, Aflao, Ghana were analyzed for As, Co, Cr, Cu, Mn, Ni, Pb and Zn using energy dispersive XRF. The results of the metal analysis indicated that some metallic levels were in excess of natural background and critical limits for the soil and plants respectively. The results further revealed that the metal distribution were in a fluctuating manner considering various distances and directions from the cement facility. However, it was observed that the mean metal concentrations of the soil and plants decrease as distance from the cement facility increased for most metals. This indicated that the facility which is the only industrial source in the area is the major cause of the pollutants contamination in its vicinity. In an attempt to understand the pattern of metal contamination in the area, useful tools including enrichment factor, geoaccumulation index and pollution load index were employed to indicate the sources of soil contamination were anthropogenic in character. Recommendations for the need for other studies on environmental and human health around the cement and similar facilities to protect human life and the environment are suggested.
An augmented reality application with hand gestures for learning 3D geometry

Geometry is an interesting area of mathematics. It opens to many different approaches and closely relates to our everyday lives. However, when students recall their experiences of learning geometry, many of them regards it as not only unpleasant experiences but often also difficult experiences. The traditional materials and tools such as pens, papers, blackboards, textbooks and/or classical methodologies like drawing, narrative teaching stills cannot be regarded as a great support for students who learn geometry. In this paper, we propose a framework for learning geometry using a software tool based on augmented reality (AR) [1] and hand gestures recognition technologies [2]. These technologies are combined into a system that can address some current issues in geometry education and provide students with an easier way for studying geometry.