Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Characterisation and Drug Target Validation of a Mitotic Kinesin-13 in Trypanosoma brucei

Mitotic kinesins are essential for faithful chromosome segregation and cell proliferation. Therefore, in humans, kinesin motor proteins have been identified as anti-cancer drug targets and small molecule inhibitors are now tested in clinical studies. Phylogenetic analyses have assigned five of the approximately fifty kinesin motor proteins coded by Trypanosoma brucei genome to the Kinesin-13 family. Kinesins of this family have unusual biochemical properties because they do not transport cargo along microtubules but are able to depolymerise microtubules at their ends, therefore contributing to the regulation of microtubule length. In other eukaryotic genomes sequenced to date, only between one and three Kinesin-13s are present. We have used immunolocalisation, RNAi-mediated protein depletion, biochemical in vitro assays and a mouse model of infection to study the single mitotic Kinesin-13 in T. brucei. Subcellular localisation of all five T. brucei Kinesin-13s revealed distinct distributions, indicating that the expansion of this kinesin family in kinetoplastids is accompanied by functional diversification. Only a single kinesin (TbKif13-1) has a nuclear localisation. Using active, recombinant TbKif13-1 in in vitro assays we experimentally confirm the depolymerising properties of this kinesin. We analyse the biological function of TbKif13-1 by RNAi-mediated protein depletion and show its central role in regulating spindle assembly during mitosis. Absence of the protein leads to abnormally long and bent mitotic spindles, causing chromosome mis-segregation and cell death. RNAi-depletion in a mouse model of infection completely prevents infection with the parasite. Given its essential role in mitosis, proliferation and survival of the parasite and the availability of a simple in vitro activity assay, TbKif13-1 has been identified as an excellent potential drug target.
Both Doublecortin and Doublecortin-Like Kinase Play a Role in Cortical Interneuron Migration

Type I lissencephaly, a genetic disease characterized by disorganized cortical layers and gyral abnormalities, is associated with severe cognitive impairment and epilepsy. Two genes, LIS1 and doublecortin (DCX), have been shown to be responsible for a large proportion of cases of type I lissencephaly. Both genes encode microtubule-associated proteins that have been shown to be important for radial migration of cortical pyramidal neurons. To investigate whether DCX also plays a role in cortical interneuron migration, we inactivated DCX in the ganglionic eminence of rat embryonic day 17 brain slices using short hairpin RNA. We found that, when DCX expression was blocked, the migration of interneurons from the ganglionic eminence to the cerebral cortex was slowed but not absent, similar to what had previously been reported for radial neuronal migration. In addition, the processes of DCX-deficient migrating interneurons were more branched than their counterparts in control experiments. These effects were rescued by DCX overexpression, confirming the specificity to DCX inactivation. A similar delay in interneuron migration was observed when Doublecortin-like kinase (DCLK), a microtubule-associated protein related to DCX, was inactivated, although the morphology of the cells was not affected. The importance of these genes in interneuron migration was confirmed by our finding that the cortices of Dcx, Dclk, and Dcx/Dclk mutant mice contained a reduced number of such cells in the cortex and their distribution was different compared with wild-type controls. However, the defect was different for each group of mutant animals, suggesting that DCX and DCLK have distinct roles in cortical interneuron migration.
Adolescent mothers' postpartum contraceptive use: a qualitative study.

CONTEXT Effective contraceptive use among first-time adolescent mothers can reduce the risk of a rapid repeat pregnancy and associated negative maternal and child health outcomes. Many adolescent mothers begin using a highly effective method after delivery; however, their rates of contraceptive discontinuation are high. Little research has explored the factors that influence adolescents' postpartum contraceptive use. METHODS In-depth interviews were conducted with 21 black, white and Latina adolescent first-time mothers from rural and urban areas of North Carolina between November 2007 and February 2009. In addition, interviews were conducted with 18 key informants-professionals who work closely with adolescent mothers. Interviews explored adolescent mothers' health behaviors, including contraceptive use, before and after pregnancy. Content analysis was used to identify key themes and patterns. RESULTS Teenagers' use of contraceptives, particularly injectables, IUDs and implants, increased postpartum. Reasons for this improvement included improved clarity of intention to avoid pregnancy and improved contraceptive knowledge, support and access after delivery. However, this increased access often did not continue long after delivery, and levels of method switching were high. Among the barriers to postpartum contraceptive use that key informants cited were lack of information and parental support, as well as the loss of Medicaid and continuity of care. CONCLUSIONS Ongoing follow-up may help reduce adolescent mothers' risk of contraceptive discontinuation postpartum. Increasing use of long-acting methods also may help reduce their vulnerability to gaps in contraceptive use and discontinuation, which increase the risk of unintended pregnancy.
Drought risk in the Bolivian Altiplano associated with El Niño
Southern Oscillation using satellite imagery data

Abstract. Drought is a major natural hazard in the Bolivian Altiplano that causes large losses to farmers, especially during positive ENSO phases. However, empirical data for drought risk estimation purposes are scarce and spatially uneven distributed. Due to these limitations, similar to many other regions in the world, we tested the performance of satellite imagery data for providing precipitation and temperature data. The results show that droughts can be better predicted using a combination of satellite imagery and ground-based available data. Consequently, the satellite climate data were associated with the Normalized Difference Vegetation Index (NDVI) in order to evaluate the crop production variability. Moreover, NDVI was used to target specific drought hotspot regions. Furthermore, during positive ENSO phase (El Niño years), a significant decrease in crop yields can be expected and we indicate areas where losses will be most pronounced. The results can be used for emergency response operations and enable a pro-active approach to disaster risk management against droughts. This includes economic-related and risk reduction strategies such as insurance and irrigation.
Comparison of the pharmacokinetics and pharmacodynamics of dapagliflozin in patients with type 1 versus type 2 diabetes mellitus

To compare the pharmacokinetics and pharmacodynamics of dapagliflozin in patients with type 1 diabetes mellitus (T1DM) versus type 2 diabetes mellitus (T2DM) in order to explore the potential of dapagliflozin as add‐on therapy to insulin in patients with T1DM.
Fellows’ Forum in Dialysis. Depression: A Common but Underrecognized Condition Associated with End‐Stage Renal Disease

We describe a patient with end‐stage renal disease (ESRD) who developed depression over the period of dialysis initiation. Depression is an extremely common but underrecognized disorder in the dialysis population, which is one of the rationales for this case report. Here we present the epidemiology, mechanisms for diagnosis, associations with medical morbidity, and treatment modalities specifically for patients on dialysis.
Shedding Light on the Chemical Diversity of Ectopic Calcifications in Kidney Tissues: Diagnostic and Research Aspects

In most industrialized countries, different epidemiologic studies show that chronic renal failure is dramatically increasing. Such major public health problem is a consequence of acquired systemic diseases such as type II diabetes, which is now the first cause for end stage renal failure. Furthermore, lithogenic diseases may also induce intratubular crystallization, which may finally result in end-stage renal failure (ESRF). Up to now, such rare diseases are often misdiagnosed. In this study, based on twenty four biopsies, we show that SR µFTIR (Synchrotron Radiation-µFourier transform infrared) spectroscopy constitutes a significant opportunity to characterize such pathological µcalcifications giving not only their chemical composition but also their spatial distribution in the tissues. This experimental approach offers new opportunities to the clinicians to describe at the cell level the physico-chemical processes leading to the formation of the pathological calcifications which lead to ESRF.
Pseudofunctional testing

Recent research results have shown that the traditional structural testing for delay and signal integrity faults may result in overtesting due to the nontrivial number of such faults that are untestable in the functional mode although testable in the test mode. This paper presents a pseudofunctional-test methodology that attempts to minimize the overtesting problem of the scan-based circuits in automatic test pattern generation (ATPG) and built-in self-test (BIST) test generation approaches. The first pattern of a two-pattern test is still delivered by scan in the test mode but the pattern is generated in such a way that it does not violate the functional constraints extracted from the functional logic. The second pattern is then generated in a functional mode using the functional justification (also called broadside) test application scheme. The authors use a sequential boolean satisfiability solver to extract a set of functional constraints that consists of illegal states and internal signal correlation. The functional constraints are imposed upon an ATPG tool to generate pseudofunctional tests and/or implemented as a monitor in the BIST environment to allow only functional-like patterns generated from the random test pattern generator as tests. The experimental results for delay faults indicate that the percentage of functionally untestable delay faults is nontrivial for many circuits. This finding supports the hypothesis of the overtesting problem in delay testing. In addition, the results indicate the effectiveness of the proposed constraint extraction method and the proposed BIST scheme.
Quantifying the Evidence for the Risk of Metabolic Syndrome and Its Components following Androgen Deprivation Therapy for Prostate Cancer: A Meta-Analysis

Background No meta-analysis is yet available for the risk of metabolic syndrome (MetS) following androgen deprivation therapy (ADT) for men with prostate cancer. To summarize the evidence for the link between ADT and MetS or its components quantitatively with a meta-analysis including all studies published to date. Methods PubMed and Embase were searched using predefined inclusion criteria to perform meta-analyses on the association between metabolic syndrome, hyperglycemia, diabetes, hypertension, dyslipidemia or obesity and androgen deprivation therapy in patients with prostate cancer. Random effects methods were used to estimate pooled relative risks (RRs) and 95% confidence intervals (CI). Results A total of nine studies was included. There was a positive association between ADT and risk of MetS (RR: 1.75 (95% CI: 1.27–2.41)). Diabetes was the only MetS component present in more than 3 studies, and also showed an increased risk following ADT (RR: 1.36 (95% CI: 1.17–1.58)). Conclusion This is the first quantitative summary addressing the potential risk of MetS following ADT in men with PCa. The positive RRs indicate that there is a need to further elucidate how type and duration of ADT affect these increased risks of MetS and diabetes as the number of men with PCa treated with ADT is increasing.
Multi-objective evolutionary algorithm for variable selection in calibration problems: A case study for protein concentration prediction

This paper presents a multi-objective formulation for variable selection in calibration problems. The prediction of protein concentration on wheat is obtained by a linear regression model using variables obtained by a spectrophotometer device. This device measure hundreds of correlated variables related with physicochemical properties and that can be used to estimate the protein concentration. The problem is the selection of a subset informative and uncorrelated variables that help the minimization of prediction error. In this work we propose the use of two objectives in this problem: the prediction error and the number of variables in the model, both related to linear equations system stability. We proposed a multi-objective formulation using two multi-objective algorithms: the NSGA-II and the SPEA-II. Additionally we propose a final decision maker method to choice the final subset of variables from the Pareto front. For the case study is used wheat data obtained by NIR spectrometry where the objective is the determination of a variable subgroup with information about protein concentration. The results of traditional techniques of multivariate calibration as the Successive Projections Algorithm (SPA), Partial Least Square (PLS) and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 45%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares (PLS).
Depression and Self-Harm Risk Assessment in Online Forums

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.
Factors Influencing Spatial Variability in Nitrogen Processing in Nitrogen-Saturated Soils

Nitrogen (N) saturation is an environmental concern for forests in the eastern U.S. Although several watersheds of the Fernow Experimental Forest (FEF), West Virginia exhibit symptoms of N saturation, many watersheds display a high degree of spatial variability in soil N processing. This study examined the effects of temperature on net N mineralization and nitrification in N-saturated soils from FEF, and how these effects varied between high N-processing vs. low N-processing soils collected from two watersheds, WS3 (fertilized with [NH4]2SO4) and WS4 (untreated control). Samples of forest floor material (O1 horizon) and mineral soil (to a 5-cm depth) were taken from three subplots within each of four plots that represented the extremes of highest and lowest rates of net N mineralization and nitrification (hereafter, high N and low N, respectively) of untreated WS4 and N-treated WS3: control/low N, control/high N, N-treated/low N, N-treated/high N. Forest floor material was analyzed for carbon (C), lignin, and N. Subsamples of mineral soil were extracted immediately with 1 N KCl and analyzed for NH4+ and NO3 to determine preincubation levels. Extracts were also analyzed for Mg, Ca, Al, and pH. To test the hypothesis that the lack of net nitrification observed in field incubations on the untreated/low N plot was the result of absence of nitrifier populations, we characterized the bacterial community involved in N cycling by amplification of amoA genes. Remaining soil was incubated for 28 d at three temperatures (10, 20, and 30°C), followed by 1 NKCl extraction and analysis for NH4 and NO3. Net nitrification was essentially 100% of net N mineralization for all samples combined. Nitrification rates from lab incubations at all temperatures supported earlier observations based on field incubations. At 30°C, rates from N-treated/high N were three times those of N-treated/low N. Highest rates were found for untreated/high N (two times greater than those of N-treated/high N), whereas untreated/low N exhibited no net nitrification. However, soils exhibiting no net nitrification tested positive for presence of nitrifying bacteria, causing us to reject our initial hypothesis. We hypothesize that nitrifier populations in such soil are being inhibited by a combination of low Ca to Al ratios in mineral soil and allelopathic interactions with mycorrhizae of ericaceous species in the herbaceous layer.
Optimizing debt collections using constrained reinforcement learning

The problem of optimally managing the collections process by taxation authorities is one of prime importance, not only for the revenue it brings but also as a means to administer a fair taxing system. The analogous problem of debt collections management in the private sector, such as banks and credit card companies, is also increasingly gaining attention. With the recent successes in the applications of data analytics and optimization to various business areas, the question arises to what extent such collections processes can be improved by use of leading edge data modeling and optimization techniques. In this paper, we propose and develop a novel approach to this problem based on the framework of constrained Markov Decision Process (MDP), and report on our experience in an actual deployment of a tax collections optimization system at New York State Department of Taxation and Finance (NYS DTF).
Globalization and Insecurity: Reviewing Some Basic Issues

We argue that the costs of domestic and transnational insecurity are large and economically significant and that they may vary with the trade regime of a country. Then, in evaluating trade regimes, the gains from trade need to be weighed against the change in the security costs they induce. Within a simple model of trade, small countries that import a contested resource unambiguously gain from free trade. However, exporters of a contested resource incur additional security costs that are higher than the gains from trade compared to autarky, as long as the international price of the contested resource is not too high. We conclude with a discussion of how domestic and transnational governance could reduce insecurity.
Definitive erythropoiesis in the trunk of zebrafish embryos

In a recent paper in Development , Jin et al. ([Jin et al., 2009][1]) reported that, in zebrafish embryos, the definitive haematopoietic cells of the trunk and tail have different differentiation outputs; that is, whereas cells in the tail can give rise to erythrocytes, those remaining in the trunk
Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.
A Survey of Neuromorphic Computing and Neural Networks in Hardware

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.
Psychophysical investigations into the neural basis of synaesthesia

We studied two otherwise normal, synaesthetic subjects who 'saw' a specific colour every time they saw a specific number or letter. We conducted four experiments in order to show that this was a genuine perceptual experience rather than merely a memory association. (i) The synaesthetically induced colours could lead to perceptual grouping, even though the inducing numerals or letters did not. (ii) Synaesthetically induced colours were not experienced if the graphemes were presented peripherally. (iii) Roman numerals were ineffective: the actual number grapheme was required. (iv) If two graphemes were alternated the induced colours were also seen in alternation. However, colours were no longer experienced if the graphemes were alternated at more than 4 Hz. We propose that grapheme colour synaesthesia arises from 'cross–wiring' between the 'colour centre' (area V4 or V8) and the 'number area', both of which lie in the fusiform gyrus. We also suggest a similar explanation for the representation of metaphors in the brain: hence, the higher incidence of synaesthesia among artists and poets.