Cocoa: Driver of Deforestation in the Democratic Republic of the Congo?

The Democratic Republic of the Congo (DRC) accounts for the largest part of the Congo Basin forest: two-thirds (some 155 million hectares) are forested and 69 percent of which is dense humid forest. With a surge in world market commodity prices for cocoa in 2008 and a steady 2-5% annual increase in global demand, incentives to expand cocoa production are significant. This study determines the role of cocoa in the RDC in terms of current deforestation, identifies actors, and estimates future expansion. Although cocoa in occurs in eight regions in the DRC, authors focused on the four major growing regions in this study, using a rough estimation to get an “order of magnitude” of cocoa as a driver of deforestation. The authors found that cocoa expansion could lead to the loss of 176-395 square kilometres of forest in the next decade, strongest in the Mambasa region and in Equatorial Province surrounding Mbandaka, Bikoro and Lukolela. However, migration of internally displaced people, production of food crops, and illegal logging are likely more severe drivers of deforestation in the Mambasa region. Deforestation caused by cocoa growing in Equatorial Province will largely depend on rejuvenation of existing fields

Renal Normothermic Machine Perfusion:The Road Toward Clinical Implementation of a Promising Pretransplant Organ Assessment Tool

The increased utilization of high-risk renal grafts for transplantation requires optimization of pretransplant organ assessment strategies. Current decision-making methods to accept an organ for transplantation lack overall predictive power and always contain an element of subjectivity. Normothermic machine perfusion (NMP) creates near-physiological conditions, which might facilitate a more objective assessment of organ quality prior to transplantation. NMP is rapidly gaining popularity, with various transplant centers developing their own NMP protocols and renal viability criteria. However, to date, no validated sets of on-pump viability markers exist nor are there unified NMP protocols. This review provides a critical overview of the fundamentals of current renal NMP protocols and proposes a framework to approach further development of ex vivo organ evaluation. We also comment on the potential logistical implications of routine clinical use of NMP, which is a more complex procedure compared to static cold storage or even hypothermic machine perfusion. Supplemental Visual Abstract; http://links.lww.com/TP/C232

Good Random Matrices over Finite Fields

The random matrix uniformly distributed over the set of all m-by-n matrices over a finite field plays an important role in many branches of information theory. In this paper a generalization of this random matrix, called k-good random matrices, is studied. It is shown that a k-good random m-by-n matrix with a distribution of minimum support size is uniformly distributed over a maximum-rank-distance (MRD) code of minimum rank distance min{m,n}-k+1, and vice versa. Further examples of k-good random matrices are derived from homogeneous weights on matrix modules. Several applications of k-good random matrices are given, establishing links with some well-known combinatorial problems. Finally, the related combinatorial concept of a k-dense set of m-by-n matrices is studied, identifying such sets as blocking sets with respect to (m-k)-dimensional flats in a certain m-by-n matrix geometry and determining their minimum size in special cases.Comment: 25 pages, publishe

Tasquinimod suppresses tumor cell growth and bone resorption by targeting immunosuppressive myeloid cells and inhibiting c-MYC expression in multiple myeloma

Background: Immunotherapy emerged as a promising treatment option for multiple myeloma (MM) patients. However, therapeutic efficacy can be hampered by the presence of an immunosuppressive bone marrow microenvironment including myeloid cells. S100A9 was previously identified as a key regulator of myeloid cell accumulation and suppressive activity. Tasquinimod, a small molecule inhibitor of S100A9, is currently in a phase Ib/IIa clinical trial in MM patients (NCT04405167). We aimed to gain more insights into its mechanisms of action both on the myeloma cells and the immune microenvironment. Methods: We analyzed the effects of tasquinimod on MM cell viability, cell proliferation and downstream signaling pathways in vitro using RNA sequencing, real-time PCR, western blot analysis and multiparameter flow cytometry. Myeloid cells and T cells were cocultured at different ratios to assess tasquinimod-mediated immunomodulatory effects. The in vivo impact on immune cells (myeloid cell subsets, macrophages, dendritic cells), tumor load, survival and bone disease were elucidated using immunocompetent 5TMM models. Results: Tasquinimod treatment significantly decreased myeloma cell proliferation and colony formation in vitro, associated with an inhibition of c-MYC and increased p27 expression. Tasquinimod-mediated targeting of the myeloid cell population resulted in increased T cell proliferation and functionality in vitro. Notably, short-term tasquinimod therapy of 5TMM mice significantly increased the total CD11b+ cells and shifted this population toward a more immunostimulatory state, which resulted in less myeloid-mediated immunosuppression and increased T cell activation ex vivo. Tasquinimod significantly reduced the tumor load and increased the trabecular bone volume, which resulted in prolonged overall survival of MM-bearing mice in vivo. Conclusion: Our study provides novel insights in the dual therapeutic effects of the immunomodulator tasquinimod and fosters its evaluation in combination therapy trials for MM patients

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

Coronary fractional flow reserve measurements of a stenosed side branch: A computational study investigating the influence of the bifurcation angle

Background: Coronary hemodynamics and physiology specific for bifurcation lesions was not well understood. To investigate the influence of the bifurcation angle on the intracoronary hemodynamics of side branch (SB) lesions computational fluid dynamics simulations were performed. Methods: A parametric model representing a left anterior descending-first diagonal coronary bifurcation lesion was created according to the literature. Diameters obeyed fractal branching laws. Proximal and distal main branch (DMB) stenoses were both set at 60%. We varied the distal bifurcation angles (40°, 55°, and 70°), the flow splits to the DMB and SB (55%:45%, 65%:35%, and 75%:25%), and the SB stenoses (40, 60, and 80%), resulting in 27 simulations. Fractional flow reserve, defined as the ratio between the mean distal stenosis and mean aortic pressure during maximal hyperemia, was calculated for the DMB and SB (FFRSB) for all simulations. Results: The largest differences in FFRSB comparing the largest and smallest bifurcation angles were 0.02 (in cases with 40% SB stenosis, irrespective of the assumed flow split) and 0.05 (in cases with 60% SB stenosis, flow split 55%:45%). When the SB stenosis was 80%, the difference in FFRSB between the largest and smallest bifurcation angle was 0.33 (flow split 55%:45%). By describing the PSB-QSB relationship using a quadratic curve for cases with 80% SB stenosis, we found that the curve was steeper (i.e. higher flow resistance) when bifurcation angle increases (P=0.451*Q+0.010*Q 2 and P=0.687*Q+0.017*Q 2 for 40° and 70° bifurcation angle, respectively). Our analyses revealed complex hemodynamics in all cases with evident counter-rotating helical flow structures. Larger bifurcation angles resulted in more pronounced helical flow structures (i.e. higher helicity intensity), when 60 or 80% SB stenoses were present. A good correlation (R2=0.80) between the SB pressure drop and helicity intensity was also found. Conclusions: Our analyses showed that, in bifurcation lesions with 60% MB stenosis and 80% SB stenosis, SB pressure drop is higher for larger bifurcation angles suggesting higher flow resistance (i.e. curves describing the PSB-QSB relationship being steeper). When the SB stenosis is mild (40%) or moderate (60%), SB resistance is minimally influenced by the bifurcation angle, with differences not being clinically meaningful. Our findings also highlighted the complex interplay between anatomy, pressure drops, and blood flow helicity in bifurcations

Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards

BACKGROUND: Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards. METHODS: High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically. RESULTS: A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice. CONCLUSIONS: Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance

Quantitative real-time imaging of intracellular FRET biosensor dynamics using rapid multi-beam confocal FLIM

Fluorescence lifetime imaging (FLIM) is a quantitative, intensity-independent microscopical method for measurement of diverse biochemical and physical properties in cell biology. It is a highly effective method for measurements of Förster resonance energy transfer (FRET), and for quantification of protein-protein interactions in cells. Time-domain FLIM-FRET measurements of these dynamic interactions are particularly challenging, since the technique requires excellent photon statistics to derive experimental parameters from the complex decay kinetics often observed from fluorophores in living cells. Here we present a new time-domain multi-confocal FLIM instrument with an array of 64 visible beamlets to achieve parallelised excitation and detection with average excitation powers of ~ 1–2 μW per beamlet. We exemplify this instrument with up to 0.5 frames per second time-lapse FLIM measurements of cAMP levels using an Epac-based fluorescent biosensor in live HeLa cells with nanometer spatial and picosecond temporal resolution. We demonstrate the use of time-dependent phasor plots to determine parameterisation for multi-exponential decay fitting to monitor the fractional contribution of the activated conformation of the biosensor. Our parallelised confocal approach avoids having to compromise on speed, noise, accuracy in lifetime measurements and provides powerful means to quantify biochemical dynamics in living cells

a dynamic model of firms strategic location choice

This paper analyzes the optimal location choice of a firm in a dynamic Cournot framework, in which firms' absorptive capacities may depend on their knowledge stock. The firm decides whether to locate irreversibly in a cluster or in isolation. In the cluster the firm benefits from inward spillovers from its competitors, but also generates outward spillovers. If the firm chooses to locate in isolation no knowledge flows occur. All firms' production costs are determined by their knowledge stocks, which evolve over time due to own R&D investments and potentially inward spillovers. It is shown that, if absorptive capacity is constant, the incentive to locate in the cluster decreases with respect to the firm's knowledge stock. Conversely, if absorptive capacity depends positively on knowledge stock, the firm's incentive to join the cluster is larger the more knowledge it has. It is also shown that qualitative properties of the equilibrium paths of R&D investments and knowledge stocks differ substantially depending on whether absorptive capacities are constant or knowledge dependent