Miracle-dz/UniFAR-FTUs huggingface dataset:
Autologous tumor cells/bacillus Calmette-Guérin/formalin-based novel breast cancer vaccine induces an immune antitumor response

Autologous cancer cell vaccines represent a multivalent patient-specific treatment. Studies have demonstrated that these immunotherapies should be combined with immunomodulators to improve results. We tested in breast cancer the antitumor effects of a 200 µg autologous tumor cells homogenate combined with 0.0625 mg of bacillus Calmette-Guérin (BCG), and 0.02% formalin. We used a 4T1 murine model of BALB/c receiving four weekly injections of either this vaccine or control treatments. The control treatments were either Phosphate Buffer Saline, BCG treated with formalin, or the tumor cells homogenate plus BCG alone. We found that mice treated with the vaccine had the lowest tumor growth rate and mitosis percentage. The vaccinated group also showed a marked increase in infiltration of antitumor cells (natural killer, CD8+ T and CD4+ Th1 cells), as well as a decrease of myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs). Additionally, we also observed a possible activation of the immune memory response as indicated by plasma cell tumor infiltration. Our results demonstrate that our proposed breast cancer vaccine induces a potent antitumor effect in 4T1 tumor-bearing mice. Its effectiveness, low cost and simple preparation method, makes it a promising treatment candidate for personalized breast cancer immunotherapy.
Family Characteristics and Economic Development

This paper links economic development to age-old family characteristics through the propensity to invest and then, increase human productivity. Three family characteristics are identified as supporting investment. Inequality among siblings favors investment in physical capital whereas a high status of women and strong parental authority favor investment in human capital. To test this theory, we rely on Todd’s classification of traditional family types observed around the world. A family score is built according to the presence of these three characteristics in the family type of each country. This family score as well as basic characteristics are significantly associated with higher economic outcomes. These relationships are robust to other factors already identified as playing a role, such as geography, ethnic fractionalization, genetic diversity, religion, quality of institutions and legal origin.
Generation and characterization of ErbB2-CAR-engineered cytokine-induced killer cells for the treatment of high-risk soft tissue sarcoma in children

Pediatric patients with recurrent, refractory or advanced soft tissue sarcoma (STS) who are simultaneously showing signs of cumulative treatment toxicity are in need of novel therapies. In this preclinical analysis, we identified ErbB2 as a targetable antigen on STS cells and used cytokine-induced killer (CIK) cells transduced with the lentiviral 2nd-generation chimeric antigen receptor (CAR) vector pS-5.28. z-IEW to target ErbB2-positive tumors. Solely CIK cell subsets with the CD3+ T cell phenotype showed up to 85% cell surface expression of the respective CAR. A comparison of wildtype (WT), mock-vector and ErbB2-CAR-CIK cells showed, that engineered cells exhibited diminished in vitro expansion, retained WT CIK cell phenotype with higher percentages of differentiated effector memory/effector cells. Activating natural killer (NK) cell receptor NKG2D-restricted target cell recognition and killing of WT and ErbB2-CAR-CIK cells was maintained against ErbB2-negative tumors, while ErbB2-CAR-CIK cells demonstrated significantly increased cytotoxicity against ErbB2-positive targets, including primary tumors. ErbB2-CAR- but not WT CIK cells proliferated, infiltrated and efficiently lysed tumor cell monolayers as well as 3D tumor spheroids. Here, we demonstrate a potential cell therapeutic approach using ErbB2-CAR-CIK cells for the recognition and elimination of tumor cells expressing ErbB2, which we identified as a targetable antigen on high-risk STS cells.
Urinary catheterization in labour with high-dose vs mobile epidural analgesia: a randomized controlled trial.

BACKGROUND Dense perineal block from epidural analgesia increases the risk of urinary catheterization in labour. Mobile epidurals using low-dose local anaesthetic in combination with opioid preserve maternal mobility and may reduce the risk of bladder dysfunction. We conducted a three-arm randomized controlled trial to compare high-dose epidural pain relief with two mobile epidural techniques. METHODS A total of 1054 primparous women were randomized to receive high-dose bupivacaine, epidural analgesia (Control), combined spinal epidural (CSE), or low-dose infusion (LDI). The requirement for urinary catheterization during labour and postpartum was recorded. Both end points were pre-specified secondary trial outcomes. Women were evaluated by postnatal interview, when their bladder function had returned to normal. RESULTS Relative to Control, more women who received mobile epidural techniques maintained the ability to void urine spontaneously at any time (Control 11%, CSE 31% and LDI 32%) and throughout labour (Control 3.7%, CSE 13% and LDI 14%), for both mobile techniques P<0.01. There was no difference in the requirement for catheterization after delivery. Women in the CSE group reported a more rapid return of normal voiding sensation, relative to high-dose Control (P=0.02). CONCLUSIONS Relative to conventional high-dose block, mobile epidural techniques encourage the retention of normal bladder function and reduce the risk of urinary catheterization in labour.
Maximum likelihood detection for quantized MIMO systems

In this work, maximum likelihood-detection (ML-detection) for MIMO systems operating on quantized data is considered. It turns out that the optimal decision rule is generally intractable. Therefore, we propose a suboptimal solution with lower complexity. Assuming a Rayleigh fading MIMO environment, an upper bound on the error probability is derived for the case where the number of transmit and receive antennas are equal. Furthermore, we introduce a new performance measure that relates the outage properties to the bit resolution in this context.
Knee strength maintained despite loss of lean body mass during weight loss in older obese adults with knee osteoarthritis.

BACKGROUND The effects of weight loss on muscle function in older adults have not been well studied. This study determined the effects of a 6-month weight-loss intervention on muscle strength and quality in older obese adults with knee osteoarthritis. METHODS Participants were randomized to a weight loss (WL) (n = 44, 70 +/- 6 years) or weight stable (WS) (n = 43, 69 +/- 6 years) group. The WL intervention consisted of weekly educational meetings, a meal replacement diet, and a three-session-per-week structured exercise program to achieve 10%-12% weight loss. The WS intervention included bimonthly group meetings and newsletters. Body composition and knee extensor strength were measured at baseline and after intervention. RESULTS The WL group decreased body weight, lean body mass, fat mass, and percent body fat (p <.001 for all). Concentric extension strength increased 25% in WL (p >.05), whereas eccentric extension decreased 6% in WS (p =.028). Concentric muscle quality (strength per kg body weight or lean body mass) increased in WL (p <.05), whereas eccentric muscle quality decreased in WS (p <.05). Changes in lean body mass and fat mass were inversely associated with changes in most muscle strength and quality measures (p <.05). Men and women did not differ in response to the intervention in knee strength outcomes. CONCLUSIONS Hypocaloric dieting in combination with exercise training had beneficial effects on muscle strength/quality, despite loss of lean body mass in this sample of older men and women. Greater fat loss was associated with greater gains in muscle strength and quality. More studies are needed regarding the mechanisms by which loss of fat mass increases muscle strength and quality.
Aligning learning design and learning analytics through instructor involvement: a MOOC case study

ABSTRACT This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning learning design (LD) and learning analytics (LA) during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past massive open online course, where the learner data and the instructors were accessible for posterior analysis and additional data collection. Through a close collaboration with the instructors, the details of the prediction task were identified, such as the target variable to predict and the practical constraints to consider. Two predictive models were built: LD-specific model (with features based on the LD and pedagogical intentions), and a generic model (with cumulative features, not informed by the LD). Although the LD-specific predictive model did not outperform the generic one, some LD-driven features were powerful. The quantity and the power of such features were associated with the degree to which the students acted as guided by the LD and pedagogical intentions. The leading instructor’s opinion about the importance of the learning activities in the LD was compared with the results of the feature importance analysis. This comparison helped identify the problems in the LD. The implications for improving the LD are discussed.
PoTion: Pose MoTion Representation for Action Recognition

Most state-of-the-art methods for action recognition rely on a two-stream architecture that processes appearance and motion independently. In this paper, we claim that considering them jointly offers rich information for action recognition. We introduce a novel representation that gracefully encodes the movement of some semantic keypoints. We use the human joints as these keypoints and term our Pose moTion representation PoTion. Specifically, we first run a state-of-the-art human pose estimator [4] and extract heatmaps for the human joints in each frame. We obtain our PoTion representation by temporally aggregating these probability maps. This is achieved by 'colorizing' each of them depending on the relative time of the frames in the video clip and summing them. This fixed-size representation for an entire video clip is suitable to classify actions using a shallow convolutional neural network. Our experimental evaluation shows that PoTion outperforms other state-of-the-art pose representations [6, 48]. Furthermore, it is complementary to standard appearance and motion streams. When combining PoTion with the recent two-stream I3D approach [5], we obtain state-of-the-art performance on the JHMDB, HMDB and UCF101 datasets.
Lessons Learned from Thirteen Years of International Real Estate Study Abroad Programs hosted by Universities in the United States, The Netherlands, and Germany

Using data about a diverse set of course offerings from Real Estate Management master classes at the Technical University Berlin, New York University, the George Washington University, and the University of Amsterdam, we discuss the lessons learned from international real estate abroad programs organized by these universities in 2000–2013. The findings come from the participating students' responses to different surveys and the personal experiences of the authors. The programs are different in duration, learning intensity and content, as well as in education targets. We also discuss the risks and opportunities of this educational approach and the possible ways to improve these programs.
Estimation of new primary production in the Benguela upwelling area, using ENVISAT satellite data and a model dependent on the phytoplankton community size structure

[1] The spatial and temporal variations of oceanic new primary production have a crucial importance for the study of biogeochemical fluxes in the ocean; however, they have been poorly documented. In this paper, we propose a revision of the “shift-up” new production model developed for upwelling areas by Dugdale et al. (1989), and later adapted by Kudela and Dugdale (1996), using ocean color and sea surface temperature satellite data as inputs. The major improvement of the model is that the estimation of nitrogen uptake rates takes into account the phytoplankton community size structure, estimated for each pixel using the method of Uitz et al. (2006). Appropriate physiological parameters are then used for each size class. This revised model has been applied to Medium-Resolution Imaging Spectrometer and Advanced Along Track Scanning Radiometer data acquired over the year 2003, using the Benguela upwelling area as a test site. The combination of the new production model with a total production model allows the seasonal variations of new and total productions (including the respective contributions of the three size classes) to be estimated and compared. Even though microphytoplankton are generally considered to be responsible for new production, our results show that the contribution of small cells should not be overlooked in the upwelling-affected areas.
Boosting up Scene Text Detectors with Guided CNN

Deep CNNs have achieved great success in text detection. Most of existing methods attempt to improve accuracy with sophisticated network design, while paying less attention on speed. In this paper, we propose a general framework for text detection called Guided CNN to achieve the two goals simultaneously. The proposed model consists of one guidance subnetwork, where a guidance mask is learned from the input image itself, and one primary text detector, where every convolution and non-linear operation are conducted only in the guidance mask. On the one hand, the guidance subnetwork filters out non-text regions coarsely, greatly reduces the computation complexity. On the other hand, the primary text detector focuses on distinguishing between text and hard non-text regions and regressing text bounding boxes, achieves a better detection accuracy. A training strategy, called background-aware block-wise random synthesis, is proposed to further boost up the performance. We demonstrate that the proposed Guided CNN is not only effective but also efficient with two state-of-the-art methods, CTPN and EAST, as backbones. On the challenging benchmark ICDAR 2013, it speeds up CTPN by 2.9 times on average, while improving the F-measure by 1.5%. On ICDAR 2015, it speeds up EAST by 2.0 times while improving the F-measure by 1.0%.
The galaxy-halo connection from a joint lensing, clustering and abundance analysis in the CFHTLenS/VIPERS field

We present new constraints on the relationship between galaxies and their host dark matter haloes, measured from the location of the peak of the stellar-to-halo mass ratio (SHMR), up to the most massive galaxy clusters at redshift z ∼ 0.8 and over a volume of nearly 0.1 Gpc3. We use a unique combination of deep observations in the CFHTLenS/VIPERS field from the near-UV to the near-IR, supplemented by ∼60 000 secure spectroscopic redshifts, analysing galaxy clustering, galaxy–galaxy lensing and the stellar mass function. We interpret our measurements within the halo occupation distribution (HOD) framework, separating the contributions from central and satellite galaxies. We find that the SHMR for the central galaxies peaks at Mh,peak=1.9+0.2−0.1×1012M⊙ Mh,peak=1.9−0.1+0.2×1012M⊙ with an amplitude of 0.025, which decreases to ∼0.001 for massive haloes ( Mh>1014M⊙ Mh>1014M⊙ ). Compared to central galaxies only, the total SHMR (including satellites) is boosted by a factor of 10 in the high-mass regime (cluster-size haloes), a result consistent with cluster analyses from the literature based on fully independent methods. After properly accounting for differences in modelling, we have compared our results with a large number of results from the literature up to z = 1: we find good general agreement, independently of the method used, within the typical stellar-mass systematic errors at low to intermediate mass ( M⋆<1011M⊙ M⋆<1011M⊙ ) and the statistical errors above. We have also compared our SHMR results to semi-analytic simulations and found that the SHMR is tilted compared to our measurements in such a way that they over- (under-) predict star formation efficiency in central (satellite) galaxies.
Effects of Particle Size on Cell Function and Morphology in Titanium and Nickel

The dependence of cytotoxicity on particles size in titanium (Ti) and nickel (Ni) was investigated by biochemical functional analysis and by microscopic observation of cellular morphology, in vitro using human neutrophils as probes and in vivo in animal implantation test. The biochemical analyses of cell survival rate, LDH, superoxide anion, cytokines of tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β) and observation by scanning electron microscopy (SEM) showed that Ti fine particles (2 µm) stimulate neutrophils and increases the quantity of released superoxide anions, whereas Ni particles deform or disrupt the cell membrane of neutrophils. The 2 µm Ti particles, smaller than neutrophils of about 5‐10µm, were phagocytized by cells in vivo and the results were similar in vitro, which lead to the remarkable release of TNF-α. These results showed that there is the size-dependent cytotoxic effect in Ti fine particles and the effect is the most pronounced when they are smaller than cells. On the other hand, Ni particles caused the disruption of neutrophils in vitro and necrosis of tissue in vivo mainly through ions produced by their dissolution.
Receptor architecture of human cingulate cortex: Evaluation of the four‐region neurobiological model

The structural and functional organization of the human cingulate cortex is an ongoing focus; however, human imaging studies continue to use the century‐old Brodmann concept of a two region cingulate cortex. Recently, a four‐region neurobiological model was proposed based on structural, circuitry, and functional imaging observations. It encompasses the anterior cingulate, midcingulate, posterior cingulate, and retrosplenial cortices (ACC, MCC, PCC, and RSC, respectively). For the first time, this study performs multireceptor autoradiography of 15 neurotransmitter receptor ligands and multivariate statistics on human whole brain postmortem samples covering the entire cingulate cortex. We evaluated the validity of Brodmann's duality concept and of the four‐region model using a hierarchical clustering analysis of receptor binding according to the degree of similarity of each area's receptor architecture. We could not find support for Brodmann's dual cingulate concept, because the anterior part of his area 24 has significantly higher AMPA, kainate, GABAB, benzodiazepine, and M3 but lower NMDA and GABAA binding site densities than the posterior part. The hierarchical clustering analysis distinguished ACC, MCC, PCC, and RSC as independent regions. The ACC has highest AMPA, kainate, α2, 5‐HT1A, and D1 but lowest GABAA densities. The MCC has lowest AMPA, kainate, α2, and D1 densities. Area 25 in ACC is similar in receptor‐architecture to MCC, particularly the NMDA, GABAA, GABAB, and M2 receptors. The PCC and RSC differ in the higher M1 and α1 but lower M3 densities of PCC. Thus, multireceptor autoradiography supports the four‐region neurobiological model of the cingulate cortex. Hum Brain Mapp, 2009. © 2008 Wiley‐Liss, Inc.
Index of Unfairness

Objective. Objective scientific knowledge for many authors more valuable than true subjective belief is determined by research on primary data but a renewed analysis of already recorded or published data is common too. Ever since, an appropriate experimental or study design is an important and often a seriously underappreciated aspect of the informativeness and the scientific value of any (medical) study. The significance of study design for the reliability of the conclusions drawn and the ability to generalize the results from the sample investigated for the whole population cannot be underestimated. In contrast to an inappropriate statistical evaluation of a medical study, it is difficult to correct errors in study design after the study has been completed. Various mathematical aspects of study design are discussed in this article. Methods. In assessing the significance of a fair study design of a medical study, important measures of publication bias are introduced. Methods of data or publication bias analysis in different types of studies are illustrated through examples with fictive data. Formal mathematical requirements of a fair study design which can and should be fulfilled carefully with regard to the planning or evaluation of medical research are developed. Results. Various especially mathematical aspects of a fair study design are discussed in this article in detail. Depending on the particular question being asked, mathematical methods are developed which allow us to recognize data which are self-contradictory and to exclude these data from systematic literature reviews and meta-analyses. As a result, different individual studies can be summed up and evaluated with a higher degree of certainty. Conclusions. This article is intended to give the reader guidance in evaluating the design of studies in medical research even ex post which should enable the reader to categorize medical studies better and to assess their scientific quality more accurately.
Glucocorticoids modulate gastrointestinal microbiome in a wild bird

It has recently been hypothesized that stress exposure (e.g. via glucocorticoid secretion) may dysregulate the bacterial gut microbiome, a crucial ‘organ' in animal health. However, whether stress exposure (e.g. via glucocorticoid secretion) affects the bacterial gut microbiome of natural populations is unknown. We have experimentally altered the basal glucocorticoid level (corticosterone implants) in a wild avian species, the yellow-legged gull Larus michahellis, to assess its effects on the gastrointestinal microbiota. Our results suggest underrepresentation of several microbial taxa in the corticosterone-implanted birds. Importantly, such reduction included potentially pathogenic avian bacteria (e.g. Mycoplasma and Microvirga) and also some commensal taxa that may be beneficial for birds (e.g. Firmicutes). Our findings clearly demonstrate a close link between microbiome communities and glucocorticoid levels in natural populations. Furthermore, they suggest a beneficial effect of stress in reducing the risk of infection that should be explored in future studies.
HLA-DR polymorphism in a Senegalese Mandenka population: DNA oligotyping and population genetics of DRB1 specificities.

HLA class II loci are useful markers in human population genetics, because they are extremely variable and because new molecular techniques allow large-scale analysis of DNA allele frequencies. Direct DNA typing by hybridization with sequence-specific oligonucleotide probes (HLA oligotyping) after enzymatic in vitro PCR amplification detects HLA allelic polymorphisms for all class II loci. A detailed HLA-DR oligotyping analysis of 191 individuals from a geographically, culturally, and genetically well-defined western African population, the Mandenkalu, reveals a high degree of polymorphism, with at least 24 alleles and a heterozygosity level of . 884 for the DRB1 locus. The allele DRB1*1304, defined by DNA sequencing of the DRB1 first-domain exon, is the most frequent allele (27.1%). It accounts for an unusually high DR13 frequency, which is nevertheless within the neutral frequency range. The next most frequent specificities are DR11, DR3, and DR8. Among DRB3-encoded alleles, DR52b (DRB3*02) represents as much as 80.7% of all DR52 haplotypes. A survey of HLA-DR specificities in populations from different continents shows a significant positive correlation between genetic and geographic differentiation patterns. A homozygosity test for selective neutrality of DR specificities is not significant for the Mandenka population but is rejected for 20 of 24 populations. Observed high heterozygosity levels in tested populations are compatible with an overdominant model with a small selective advantage for heterozygotes.