Miracle-dz/UniFAR-FTUs huggingface dataset:
CHLOROPLAST STRUCTURE OF THE CRYPTOPHYCEAE

Selective extraction and morphological evidence indicate that the phycobiliproteins in three Cryptophyceaen algae (Chroomonas, Rhodomonas, and Cryptomonas) are contained within intrathylakoidal spaces and are not on the stromal side of the lamellae as in the red and blue-green algae. Furthermore, no discrete phycobilisome-type aggregates have thus far been observed in the Cryptophyceae. Structurally, although not necessarily functionally, this is a radical difference. The width of the intrathylakoidal spaces can vary but is generally about 200–300 A. While the thylakoid membranes are usually closely aligned, grana-type fusions do not occur. In Chroomonas these membranes evidence an extensive periodic display with a spacing on the order of 140–160 A. This periodicity is restricted to the membranes and has not been observed in the electron-opaque intrathylakoidal matrix.
MULTISPECTRAL AND HYPERSPECTRAL REMOTE SENSING OF ALPINE SNOW PROPERTIES

▪ Abstract Models of processes in the alpine snow cover fundamentally depend on the spatial distribution of the surface energy balance over areas where topographic variability causes huge differences in the incoming solar radiation and in snow depth because of redistribution by wind. At a spatial scale commensurate with that of the terrain, we want to know which areas are covered by snow, and we want to estimate the snow's spectral albedo, along with other properties such as grain size, contaminants, temperature, liquid water content, and depth or water equivalent. From multispectral and hyperspectral remote sensing at wavelengths from 0.4–15 μm, the retrievable properties include snow-covered area, albedo, grain size, liquid water very near the surface, and temperature. Spectral mixture analysis allows the retrieval of the subpixel variability of snow-covered area, along with the snow's albedo. Remaining research challenges include the remote sensing of absorbing impurities; accounting for variability in.
Modulation of radiotherapy-and chemotherapy-induced normal tissue response as prophylaxis of their side effects

Background. Ionising radiation and cytostatic agents used in cancer therapy induce an immune response in normal tissues mediated by cytokines and adhesion molecules. Strategies modulating this response may downregulate cancer therapy side effects. The data published on the given topic have been reviewed. Conclusions. The strategies influencing the tissue immune response with the aim to reduce the side effects of chemotherapy and radiotherapy are conflicting. Some of them inhibit this response supposing that an exaggerated reaction may have a damaging effect (e.g. corticosteroids, nonsteroidal anti-inflammatory drugs (NSAID), lisofylline, anti-cytokine antibodies, anti-sense oligonucleotides, sialyl Lewis X analogues), others promote this reaction by inducing endogenous production of cytokines (AS101) or use recombinant forms of appropriate cytokines involved in this response in order to intensify the physiologic tissue response. In clinical practice, corticosteroids and NSAID are widely used to modulate this response, while other agents are still experimental.
Developmental Delays at Arrival, Early Intervention Enrollment, and Adopted Chinese Girls’ Academic Performance and Internalizing Problems in Adolescence

ABSTRACT The author investigated the extent of developmental delays in girls adopted from China, their subsequent early intervention (EI) enrollment, and how the delays and EI were related to their academic performance and internalizing problems in adolescence. The sample included 180 adolescent girls (M = 13.4 years, SD = 2.0 years) who were adopted at 3–23.5 months (M = 11.5 months, SD = 3.7 months). Data on the adopted Chinese girls’ delays at arrival and EI enrollment in physical therapy (PT) and speech–language therapy (SLT) were collected from the adoptive mothers at the Baseline; data on the adopted Chinese girls’ present academic performance and internalizing problems were collected from the adoptive mothers and adopted girls at Wave 4 six years later. Data analyses revealed that 55% of the adoptees had moderate-to-severe delays when first arrived at the adoptive homes. Motor delays significantly increased the odds for PT (odds ratio [OR] = 3.98, 95% CI [2.18, 7.82], p <.001) and SLT (OR = 2.36, 95% CI [1.50–3.72, p <.001). Social-cognitive delays also significantly increased the odds for PT (OR = 1.90, 95% CI [1.36, 2.63], p <.001) and SLT (OR = 1.63, 95% CI [1.22, 2.17], p <.001). Motor delays were negatively associated with academic performance but positively associated with internalizing problems. General linear modeling showed that the adoptees who had developmental delays at arrival and subsequently enrolled in EI scored significantly lower on academic performance than their peers who had delays but did not enroll in EI, as well their peers who had no delays and did not enroll in EI. Implications of these findings are discussed.
Smart scaffolds in bone tissue engineering: A systematic review of literature.

AIM To improve osteogenic differentiation and attachment of cells. METHODS An electronic search was conducted in PubMed from January 2004 to December 2013. Studies which performed smart modifications on conventional bone scaffold materials were included. Scaffolds with controlled release or encapsulation of bioactive molecules were not included. Experiments which did not investigate response of cells toward the scaffold (cell attachment, proliferation or osteoblastic differentiation) were excluded. RESULTS Among 1458 studies, 38 met the inclusion and exclusion criteria. The main scaffold varied extensively among the included studies. Smart modifications included addition of growth factors (group I-11 studies), extracellular matrix-like molecules (group II-13 studies) and nanoparticles (nano-HA) (group III-17 studies). In all groups, surface coating was the most commonly applied approach for smart modification of scaffolds. In group I, bone morphogenetic proteins were mainly used as growth factor stabilized on polycaprolactone (PCL). In group II, collagen 1 in combination with PCL, hydroxyapatite (HA) and tricalcium phosphate were the most frequent scaffolds used. In the third group, nano-HA with PCL and chitosan were used the most. As variable methods were used, a thorough and comprehensible compare between the results and approaches was unattainable. CONCLUSION Regarding the variability in methodology of these in vitro studies it was demonstrated that smart modification of scaffolds can improve tissue properties.
Predicting postoperative pulmonary complications in the general population

Purpose of review Postoperative pulmonary complications (PPCs) are common and lead to longer hospital stays and higher mortality. A wide range of patient, anaesthetic and surgical factors have been associated with risk for PPCs. This review discusses our present understanding of PPC risk factors that can be used to plan preoperative risk reduction strategies. The methodological and statistical basis for building risk scores is also described. Recent findings Studies in specific surgical populations or large patient samples have identified a range of predictors of PPC risk. Factors such as age, types of comorbidity and surgical characteristics have been found to be relevant in most of these studies. Recently, researchers have begun to develop risk scoring systems for a PPC composite outcome or for specific PPCs, especially pneumonia and respiratory failure. Preoperative arterial oxyhaemoglobin saturation is an objective measure that is easy to record and discriminates level of risk for impaired cardiorespiratory function. Preoperative anaemia and recent respiratory infection are factors that have lately been found to confer risk for PPCs. Summary PPC risk prediction scales based on large population studies are being developed. New studies to confirm the validity of these scales in different geographic areas will be needed before we can be sure of their generalizability.
Deconvolution of Chromatin Immunoprecipitation-Microarray (ChIP-chip) Analysis of MBF Occupancies Reveals the Temporal Recruitment of Rep2 at the MBF Target Genes

ABSTRACT MBF (or DSC1) is known to regulate transcription of a set of G1/S-phase genes encoding proteins involved in regulation of DNA replication. Previous studies have shown that MBF binds not only the promoter of G1/S-phase genes, but also the constitutive genes; however, it was unclear if the MBF bindings at the G1/S-phase and constitutive genes were mechanistically distinguishable. Here, we report a chromatin immunoprecipitation-microarray (ChIP-chip) analysis of MBF binding in the Schizosaccharomyces pombe genome using high-resolution genome tiling microarrays. ChIP-chip analysis indicates that the majority of the MBF occupancies are located at the intragenic regions. Deconvolution analysis using Rpb1 ChIP-chip results distinguishes the Cdc10 bindings at the Rpb1-poor loci (promoters) from those at the Rpb1-rich loci (intragenic sequences). Importantly, Res1 binding at the Rpb1-poor loci, but not at the Rpb1-rich loci, is dependent on the Cdc10 function, suggesting a distinct binding mechanism. Most Cdc10 promoter bindings at the Rpb1-poor loci are associated with the G1/S-phase genes. While Res1 or Res2 is found at both the Cdc10 promoter and intragenic binding sites, Rep2 appears to be absent at the Cdc10 promoter binding sites but present at the intragenic sites. Time course ChIP-chip analysis demonstrates that Rep2 is temporally accumulated at the coding region of the MBF target genes, resembling the RNAP-II occupancies. Taken together, our results show that deconvolution analysis of Cdc10 occupancies refines the functional subset of genomic binding sites. We propose that the MBF activator Rep2 plays a role in mediating the cell cycle-specific transcription through the recruitment of RNAP-II to the MBF-bound G1/S-phase genes.
Integrated channel plasticity contributes to alcohol tolerance in neurohypophysial terminals.

Short-term ethanol challenge results in the reduction of peptide hormone release from the rat neurohypophysis. However, rats that have been maintained on an ethanol-containing diet for 3 to 4 weeks exhibit tolerance to this effect. Mechanistic underpinnings of this tolerance were probed by examining four ion channel conductances critical for neurohormone release. The voltage-gated L-type calcium channel and the functionally linked calcium-activated BK channel represent a functional dyad. Although these channels show opposite drug responses in the naive terminal (i.e., the L-type Ca2+ channel is inhibited whereas the BK channel is potentiated), the effect of long-term alcohol exposure is to decrease sensitivity to the short-term administration of drug in both instances. In addition to the shift in sensitivity, current density increased for the L-type Ca2+ current and decreased for the BK current, consistent with a compensatory change. Sensitivity to alcohol was also altered for two other channel types studied. Inhibition of the voltage-gated transient Ca2+ current was lessened after long-term treatment. I(A,) which is not sensitive to the drug at clinically relevant concentrations in terminals from the naive rat, acquires sensitivity after long-term exposure, representing a potentially novel type of tolerance. However, neither the transient Ca2+ current nor I(A) shows a change in current density, demonstrating the selectivity of this aspect of tolerance. Overall, these results demonstrate that channel plasticity can explain at least a portion of the behavioral tolerance resulting from changes in sensitivity of peptide hormone release. Furthermore, they suggest that an understanding of tolerance requires the examination of dynamically coupled channel populations.
Increasing session-to-session transfer in a brain–computer interface with on-site background noise acquisition

Objective. A brain–computer interface (BCI) usually requires a time-consuming training phase during which data are collected and used to generate a classifier. Because brain signals vary dynamically over time (and even over sessions), this training phase may be necessary each time the BCI system is used, which is impractical. However, the variability in background noise, which is less dependent on a control signal, may dominate the dynamics of brain signals. Therefore, we hypothesized that an understanding of variations in background noise may allow existing data to be reused by incorporating the noise characteristics into the feature extraction framework; in this way, new session data are not required each time and this increases the feasibility of the BCI systems. Approach. In this work, we collected background noise during a single, brief on-site acquisition session (approximately 3 min) immediately before a new session, and we found that variations in background noise were predictable to some extent. Then we implemented this simple session-to-session transfer strategy with a regularized spatiotemporal filter (RSTF), and we tested it with a total of 20 cross-session datasets collected over multiple days from 12 subjects. We also proposed and tested a bias correction (BC) in the RSTF. Main results. We found that our proposed session-to-session strategies yielded a slightly less or comparable performance to the conventional paradigm (each session training phase is needed with an on-site training dataset). Furthermore, using an RSTF only and an RSTF with a BC outperformed existing approaches in session-to-session transfers. Significance. We inferred from our results that, with an on-site background noise suppression feature extractor and pre-existing training data, further training time may be unnecessary.
Efficient Processing of Spatio-Temporal-Textual Queries

Devices with built-in GPS (e.g. smartphones) are producing a huge amount of data objects with spatial, temporal and textual information. For example, a significant part of Twitter messages sent from smartphones has spatial location (latitude and longitude), temporal information (timestamp) and textual information (the message itself). Therefore, there is a growing interest for new approaches that are able to select the data objects that are spatially, temporally and textually relevant from huge datasets. In this paper, we specify the spatio-temporal-textual query that returns the relevant data objects considering these three criteria simultaneously, presenting new indexes and algorithms to process such query efficiently. The proposed approaches are evaluated taking real datasets, potentially providing more accurate results.
Origin and evolutionary dynamics of Hepatitis B virus (HBV) genotype E in Madagascar

Abstract Africa is one of the endemic regions of HBV infection. In particular, genotype E is highly endemic in most of sub-Saharan Africa such as West African countries where it represents more than 90% of total infections. Madagascar, which is classified as a high endemic area for HBV and where the most prevalent genotype is E, might play a relevant role in the dispersion of this genotype due to its crucial position in the Indian Ocean. The aim of this study was to investigate the origin, population dynamics, and circulation of HBV-E genotype in Madagascar through high-resolution phylogenetic and phylodynamic approaches. The phylogenetic tree indicated that Malagasy isolates were intermixed and closely related with sequences mostly from West African countries. The Bayesian tree highlighted three statistically supported clusters of Malagasy strains which dated back to the years 1981 (95% HPD: 1971–1992), 1986 (95% HPD: 1974–1996), and 1989 (95% HPD: 1974–2001). Population dynamics analysis showed an exponential increase in the number of HBV-E infections approximately from the year 1975 until 2000s. The migration analysis was also performed and a dynamic pattern of gene flow was identified. In conclusion, this study confirms previous observation of HBV-E circulation in Africa and expands these findings at Madagascar demonstrating its recent introduction, and highlighting the role of the African countries in the spread of HBV-E genotype. Further studies on molecular epidemiology of HBV genotype E are needed to clarify the evolutionary history of this genotype.
Effects of weekly thyroxine administration on serum thyroxine, 3,5,3'-triiodothyronine, thyrotropin, and the thyrotropin response to thyrotropin-releasing hormone.

Once weekly administration of T4 to normal subjects was used to suppress pituitary TSH secretion. In four euthyroid subjects, 1.5 mg T4/week were given for 4 weeks. Seven days after each dose of T4, serum T4 was not significantly different from the baseline value, although at two intervals, serum Ta was modestly but significantly increased over the basal value. Serum TSH was suppressed to less than 0.5 μU/;ml throughout the period of T4 administration. A bolus of 500 μg TRH 3 weeks after the last dose of T4 resulted in TSH release which was identical in quantity to that found before T4 administration. A TRH infusion of 1 μg/;min for 7 h on the next day resulted in an increase in TSH to 6 μU/ml, where it remained throughout the period of the infusion. Serum T3 concentrations increased both after the 500-μg TRH test and after the 7-h infusion. Mean serum T4 was 1.3-1.6 μg/dl higher during the sixth and seventh hours of infusion than at the start of the infusion, and serum T3 increased from 135 to 205 ng/dl.
Autonomous track and land a MAV using a modified tracking-learning-detection framework

In our previous work, we mounted two separate sets of Pan/Tilt Unit (PTU) integrated with visible light camera on both sides of the runway for landing a Micro Aerial Vehicle (MAV) automatically. In this study, we focus on improving the precision of MAV tracking during the landing procedure. We seek to remedy the tracking-learning-detection (TLD) framework by using adapted Random Ferns methods and modified binary code system. Then, by introducing Extend Kalman Filter (EKF) to our framework, we make the algorithm more suitable for fully autonomous landing. Finally, several real flights in outdoor experiments show that the modified TLD has a better performance compared with our previous methods. It indicates that our approach can meet the requirements of robustness and real-time capability.
Subsets of posets minimising the number of chains

A well-known theorem of Sperner describes the largest collections of subsets of an $n$-element set none of which contains another set from the collection. Generalising this result, Erd\H{o}s characterised the largest families of subsets of an $n$-element set that do not contain a chain of sets $A_1 \subset \dotsc \subset A_k$ of an arbitrary length $k$. The extremal families contain all subsets whose cardinalities belong to an interval of length $k-1$ centred at $n/2$. In a far-reaching extension of Sperner's theorem, Kleitman determined the smallest number of chains of length two that have to appear in a collection of a given number $a$ of subsets of an $n$-element set. For every $a$, this minimum is achieved by the collection comprising $a$ sets whose cardinalities are as close to $n/2+1/4$ as possible. We show that the same is true about chains of an arbitrary length $k$, for all $a$ and $n$, confirming the prediction Kleitman made fifty years ago. We also characterise all families of $a$ subsets with the smallest number of chains of length $k$ for all $a$ for which this smallest number is positive. Our argument is inspired by an elegant probabilistic lemma from a recent paper of Noel, Scott, and Sudakov, which in turn can be traced back to Lubell's proof of Sperner's theorem.
Urinary Exosomal microRNA-451-5p Is a Potential Early Biomarker of Diabetic Nephropathy in Rats

Non-invasive renal signatures can help in serial monitoring of diabetic patients. We tested whether urinary exosomal (UE) microRNA (miR) analysis could non-invasively predict renal pathology in diabetic rats during the course of diabetes. Diabetes mellitus (DM) was induced in male Wistar rats by a single intraperitoneal injection of streptozotocin (STZ, 50 mg/kg body weight). Non-diabetic control (CTRL) rats were injected with vehicle. Insulin (INS) treatment (5U/d, s.c.) was provided to 50% of the DM rats. Urine samples were collected at weeks 3, 6, and 9 following injections and UE prepared. An increase in miR-451-5p and miR-16, observed by pilot small RNA sequencing of UE RNA, was confirmed by quantitative real-time polymerase chain reaction (qPCR) and selected for further study. Subsets of rats were euthanized after 3, 6, and 9 weeks of diabetes for renal pathology analysis, including determination of the tubulointerstitial fibrotic index (TFI) and glomerulosclerotic index (GI) scores. qPCR showed a substantial rise in miR-451-5p in UE from DM rats during the course of diabetes, with a significant rise (median fold change >1000) between 3 and 6 weeks. Moreover, UE miR-451-5p at 6 weeks predicted urine albumin at 9 weeks (r = 0.76). A delayed but significant rise was also observed for miR-16. In contrast, mean urine albumin only increased 21% between 3 and 6 weeks (non-significant rise), and renal TFI and GI were unchanged till 9 weeks. Renal expression of miR-451-5p and miR-16 (at 10 weeks) did not correlate with urine levels, and moreover, was negatively associated with indices of renal pathology (r≥-0.70, p = 0.005 for TFI and r≥-0.6, p≤0.02 for GI). Overall, a relative elevation in renal miR-451-5p and miR-16 in diabetes appeared protective against diabetes-induced kidney fibrosis; while UE miR-451-5p may hold prognostic value as an early and sensitive non-invasive indicator of renal disease.
Granular computing in the frame of rough mereology. A case study: Classification of data into decision categories by means of granular reflections of data

This work extends the authors' contribution to the International Conference on Rough Sets and Current Trends in Computing (RSCTC 2008) held at the University of Akron in October 2008. It is dedicated to the topic of granular computing, formalized within the theory of rough mereology, as proposed by Polkowski; as an application of the idea of a granular reflection of data and of classifiers induced from it (Polkowski, 2005), we give an account of recent results in this area. A scheme for classifier construction based on factoring a classifier through a granular reflection of data; voting by granules of training objects; voting by granules of decision rules induced from the training set; voting by granules induced from a granular reflection of data, is presented here. In voting cases, voting is based on weights computed by means of rough inclusions induced from residual implications of continuous t‐norms. The results show a high effectiveness of this approach as witnessed by the reported tests with some well‐‐known data sets from University of California, at Irvine (UCI) repository whose results are compared against the standard rough set exhaustive classifier whose accuracy is indicated under the radius of “nil. ” © 2011 Wiley Periodicals, Inc.