Intermediate Target Research Articles

A Glimpse into a Potential Influencing Factor of China’s Exchange Rate: Social Financing Scale

The indicator of social financing scale (SFS) has become a new intermediate target for China’s socioeconomic regulation since 2011, which has a better tracking performance on important economic indicators such as CPI and is also the new intermediate target of monetary policy used by China’s central bank to replace M2 at present. So, is there an impact of the social financing scale on another important economic indicator, the RMB exchange rate? This paper conducted an OLS regression analysis of the time series data of social financing scale and the nominal effective exchange rate (NEER) of RMB for a total of 61 months from October 2016 to October 2021 and find that SFS has a significant positive impact on the exchange rate of RMB. This result means that the expansion of SFS will lead to the appreciation of RMB. In addition, the paper fills the research gap on the combination of SFS and RMB exchange rate, and suggests that mechanisms such as monetary policy or domestic interest rates may be responsible for this correlation at the theoretical level.

Game theory and partner representation in joint action: toward a computational theory of joint agency

The sense of agency – the subjective feeling of being in control of our own actions – is one central aspect of the phenomenology of action. Computational models provided important contributions toward unveiling the mechanisms underlying the sense of agency in individual action. In particular, the sense of agency is believed to be related to the match between the actual and predicted consequences of our own actions (comparator model). In the study of joint action, models are even more necessary to understand the mechanisms underlying the development of coordination strategies and how the subjective experiences of control emerge during the interaction. In a joint action, we not only need to predict the consequences of our own actions; we also need to predict the actions and intentions of our partner, and to integrate these predictions to infer their joint consequences. Understanding our partner and developing mutually satisfactory coordination strategies are key components of joint action and in the development of the sense of joint agency. Here we discuss a computational architecture which addresses the sense of agency during intentional, real-time joint action. We first reformulate previous accounts of the sense of agency in probabilistic terms, as the combination of prior beliefs about the action goals and constraints, and the likelihood of the predicted movement outcomes. To look at the sense of joint agency, we extend classical computational motor control concepts - optimal estimation and optimal control. Regarding estimation, we argue that in joint action the players not only need to predict the consequences of their own actions, but also need to predict partner’s actions and intentions (a ‘partner model’) and to integrate these predictions to infer their joint consequences. As regards action selection, we use differential game theory – in which actions develop in continuous space and time - to formulate the problem of establishing a stable form of coordination and as a natural extension of optimal control to joint action. The resulting model posits two concurrent observer-controller loops, accounting for ‘joint’ and ‘self’ action control. The two observers quantify the likelihoods of being in control alone or jointly. Combined with prior beliefs, they provide weighing signals which are used to modulate the ‘joint’ and ‘self’ motor commands. We argue that these signals can be interpreted as the subjective sense of joint and self agency. We demonstrate the model predictions by simulating a sensorimotor interactive task where two players are mechanically coupled and are instructed to perform planar movements to reach a shared final target by crossing two differently located intermediate targets. In particular, we explore the relation between self and joint agency and the information available to each player about their partner. The proposed model provides a coherent picture of the inter-relation of prediction, control, and the sense of agency in a broader range of joint actions.

Microstructures, Mechanical Behavior, and Radiation Damage of (TiVCr)x-(TaW)1-x Binary System High-Entropy Alloy Films

An experimental method for preparing high-entropy thin films with gradient changes of alloying elements by magnetron sputtering co-deposition is proposed in this work to evaluate the effect of alloying element composition changes on the properties of non-equal molar ratio high-entropy alloys. The (TiVCr)x-(TaW)1-x binary system thin films were deposited by a magnetron sputtering system with two intermediate alloy targets. The surface morphology, element composition, roughness, and phase structure of the coatings were studied with scanning electron microscope (SEM), energy dispersive X-ray (EDX), X-ray diffractometer (XRD), atomic force microscope (AFM), and transmission electron microscope (TEM). The results show that at x = 0.51, the films had the best mechanical properties under the action of multiple strengthening mechanisms, and the hardness and modulus reached 27.61 GPa and 274.42 GPa, respectively. Due to higher hardness and special surface morphology, the films showed a lower average friction coefficient and had excellent wear resistance with wear rates of 0.34 and 5.01 × 10−9 mm3/(N·mm), respectively. On the other hand, it was found that forming a BCC polycrystalline structure and an amorphous-mixed structure can improve the radiation resistance of the material.

Impact of financial inclusion on the effectiveness of the interest rate channel of monetary policy transmission in West Africa

Monetary policy has been identified as an indispensable tool for macroeconomic stabilization. The transmission of monetary policy is conditioned on the effectiveness of the monetary policy intermediate targets of which interest rate, exchange rate depreciation and money growth are important. Many factors have been shown to affect monetary policy, of which, the role of financial inclusion is important. However, empirical studies on the relationship between financial inclusion and the interest rate of monetary policy transmission are few; as a result, this study examined the impact of financial inclusion on the effectiveness of the interest rate channel of monetary policy transmission in West Africa. In doing this, the study employed a panel dataset consisting of 15 West African countries for periods between 2005 and 2020. The data were from a secondary source, in particular, the World Bank’s World Development Indicator database. The analysis involves descriptive statistics and inferential analysis of the Generalized Method of Moments approach. The result of the generalized methods of the moment showed that financial inclusion improves the effectiveness of the interest rate channel of monetary policy transmission. Based on findings, this study recommends that policy authorities in West Africa should embark on a renewed commitment to ensuring access for all in West Africa.

Accuracy of Serum Leucine-Rich Alpha-2 Glycoprotein in Evaluating Endoscopic Disease Activity in Crohn's Disease.

Mucosal healing, confirmed by endoscopic evaluation, is the long-term goal of treatment for Crohn's disease (CD). Leucine-rich alpha-2 glycoprotein (LRG) is a new serum biomarker correlated with disease activity in inflammatory bowel disease. However, studies evaluating its relationship with CD, particularly in the context of small intestinal lesions, are scarce. The aim of this study was to investigate the accuracy of LRG in assessing endoscopic activity, especially remission, in patients with CD. Between July 2020 and March 2021, 72 patients with CD who underwent LRG testing and double-balloon endoscopy at the same time were included. Endoscopic activity was evaluated using the applied Simple Endoscopic Score for Crohn's disease, including small intestine lesions. The relationship of LRG with clinical symptoms and endoscopic activity was assessed, and its predictive accuracy was evaluated. Leucine-rich alpha-2 glycoprotein showed a significant positive correlation with endoscopic activity (r = 0.619, P < .001), even in patients with active lesions in the small intestine (r = 0.626, P < .001). Multivariate logistic regression revealed that LRG was the only factor associated with endoscopic remission. An LRG cutoff value of 8.9 μg/mL had a sensitivity of 93.3%; specificity of 83.3%; positive predictive value of 96.6%; negative predictive value of 71.4%; accuracy of 91.7%; and area under the curve of 0.904 for the prediction of endoscopic remission. Leucine-rich alpha-2 glycoprotein can be used in assessing endoscopic activity and is a reliable marker of endoscopic remission in CD patients. It can be an intermediate target in the treatment of CD.

Nanosensors in clinical development of CAR-T cell immunotherapy

Immunotherapy using CAR-T cells is a new technological paradigm for cancer treatment. To avoid severe side effects and tumor escape variants observed for conventional CAR-T cells approach, adaptor CAR technologies are under development, where intermediate target modules redirect immune cells against cancer. In this work, silicon nanowire field-effect transistors are used to develop target modules for an optimized CAR-T cell operation. Focusing on a library of seven variants of E5B9 peptide that is used as CAR targeting epitope, we performed multiplexed binding tests using nanosensor chips. These peptides had been immobilized onto the sensor to compare the transistor signals upon titration with anti-La 5B9 antibodies. The correlation of binding affinities and sensor sensitivities enabled a selection of candidates for the interaction between CAR and target modules. An extremely low detection limit was observed for the sensor, down to femtomolar concentration, outperforming the current assay of the same purpose. Finally, the CAR T-cells redirection capability of selected peptides in target modules was proven successful in an in-vitro cytotoxicity assay. Our results open the perspective for the nanosensors to go beyond the early diagnostics in clinical cancer research towards developing and monitoring immunotherapeutic treatment, where the quantitative analysis with the standard techniques is limited.

Quantitative Comparisons of Deep-learning-based and Atlas-based Auto- segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma.

Manual segment target volumes were time-consuming and inter-observer variability couldn't be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem. To evaluate the accuracy and stability of Atlas-based and deep-learning-based auto-segmentation of the intermediate risk clinical target volume, composed of CTV2 and CTVnd, for nasopharyngeal carcinoma quantitatively. A cascade-deep-residual neural network was constructed to automatically segment CTV2 and CTVnd by deep learning method. Meanwhile, a commercially available software was used to automatically segment the same regions by Atlas-based method. The datasets included contrast computed tomography scans from 102 patients. For each patient, the two regions were manually delineated by one experienced physician. The similarity between the two auto-segmentation methods was quantitatively evaluated by Dice similarity coefficient, the 95th Hausdorff distance, volume overlap error and relative volume difference, respectively. Statistical analyses were performed using the ranked Wilcoxon test. The average Dice similarity coefficient (±standard deviation) given by the deep-learning- based and Atlas-based auto-segmentation were 0.84 (±0.03) and 0.74 (±0.04) for CTV2, 0.79 (±0.02) and 0.68 (±0.03) for CTVnd, respectively. For the 95th Hausdorff distance, the corresponding values were 6.30±3.55 mm and 9.34±3.39 mm for CTV2, 7.09±2.27 mm and 14.33±3.98 mm for CTVnd. Besides, volume overlap error and relative volume difference could also predict the same situations. Statistical analyses showed significant difference between the two auto-segmentation methods (p<0.01). Compared with the Atlas-based segmentation approach, the deep-learning-based segmentation method performed better both in accuracy and stability for meaningful anatomical areas other than organs at risk.

Transcriptional Control of Axon Guidance at Midline Structures

The development of the nervous system is a time-ordered and multi-stepped process that includes neurogenesis and neuronal specification, axonal navigation, and circuits assembly. During axonal navigation, the growth cone, a dynamic structure located at the tip of the axon, senses environmental signals that guide axons towards their final targets. The expression of a specific repertoire of receptors on the cell surface of the growth cone together with the activation of a set of intracellular transducing molecules, outlines the response of each axon to specific guidance cues. This collection of axon guidance molecules is defined by the transcriptome of the cell which, in turn, depends on transcriptional and epigenetic regulators that modify the structure and DNA accessibility to determine what genes will be expressed to elicit specific axonal behaviors. Studies focused on understanding how axons navigate intermediate targets, such as the floor plate of vertebrates or the mammalian optic chiasm, have largely contributed to our knowledge of how neurons wire together during development. In fact, investigations on axon navigation at these midline structures led to the identification of many of the currently known families of proteins that act as guidance cues and their corresponding receptors. Although the transcription factors and the regulatory mechanisms that control the expression of these molecules are not well understood, important advances have been made in recent years in this regard. Here we provide an updated overview on the current knowledge about the transcriptional control of axon guidance and the selection of trajectories at midline structures.

Monetary policy in a world of radical uncertainty

Monetary policy in a world of radical uncertainty

Sewing spacetime with Lorentzian threads: complexity and the emergence of time in quantum gravity

Holographic entanglement entropy was recently recast in terms of Riemannian flows or ‘bit threads’. We consider the Lorentzian analog to reformulate the ‘complexity=volume’ conjecture using Lorentzian flows — timelike vector fields whose minimum flux through a boundary subregion is equal to the volume of the homologous maximal bulk Cauchy slice. By the nesting of Lorentzian flows, holographic complexity is shown to obey a number of properties. Particularly, the rate of complexity is bounded below by conditional complexity, describing a multi-step optimization with intermediate and final target states. We provide multiple explicit geometric realizations of Lorentzian flows in AdS backgrounds, including their time-dependence and behavior near the singularity in a black hole interior. Conceptually, discretized flows are interpreted as Lorentzian threads or ‘gatelines’. Upon selecting a reference state, complexity thence counts the minimum number of gatelines needed to prepare a target state described by a tensor network discretizing the maximal volume slice, matching its quantum information theoretic definition. We point out that suboptimal tensor networks are important to fully characterize the state, leading us to propose a refined notion of complexity as an ensemble average. The bulk symplectic potential provides a specific ‘canonical’ thread configuration characterizing perturbations around arbitrary CFT states. Consistency of this solution requires the bulk satisfy the linearized Einstein’s equations, which are shown to be equivalent to the holographic first law of complexity, thereby advocating for a principle of ‘spacetime complexity’. Lastly, we argue Lorentzian threads provide a notion of emergent time. This article is an expanded and detailed version of [1], including several new results.

Joint motion planning of industrial robot based on hybrid polynomial interpolation

To plan an industrial robot's point-to-point joint motion that has velocity constraints, the cubic polynomial interpolation planning method has angular acceleration discontinuity. The quintic polynomial interpolation planning method needs to set in advance the angular acceleration values of target points and easily causes angular velocity fluctuation. Because of the existence of quintic polynomials, hybrid polynomial planning 3-5-…-5-3 also easily causes large angular velocity fluctuation. To solve these problems, a hybrid polynomial interpolation planning method based on cubic and quartic polynomial interpolation is proposed. Cubic polynomial interpolation planning is applied to the first planning, and quartic polynomial interpolation planning is used for the rest of planning. The planning method can not only specify the angular velocities of intermediate target points but also ensure the continuity of angular acceleration. In addition, there is no need to set in advance the angular acceleration values of target points so as to avoid the angular velocity fluctuation caused by the unreasonable presetting of angular acceleration. Furthermore, in order to avoid excessive peaks of angular velocity during the joint motion planning, a calculation method for finding out reasonable motion time is put forward, under which the larger speed at two target points is taken as maximum, thus making the planned angular velocity fluctuation small. A case study is performed to verify the rationality and effectiveness of the planning method. Compared with the other two planning methods used for the case study, our planning method can effectively solve the problems of point-to-point joint motion planning with velocity constrains, therefore being beneficial to the working performance and service life of an industrial robot.

DOP83 Normalisation of biomarkers and improvement in clinical outcomes in patients with Crohn’s Disease treated with risankizumab in the phase 3 ADVANCE, MOTIVATE, and FORTIFY studies

Abstract Background The efficacy of risankizumab (RZB), an interleukin 23 p19 inhibitor, in patients with Crohn’s disease (CD) has been reported. Normalisation of high-sensitivity C-reactive protein (hs-CRP) and faecal calprotectin (FCP) are intermediate treatment targets in CD. Here, we evaluated changes in these objective inflammatory biomarkers and clinical outcomes with RZB treatment. Methods In 2 phase 3, randomised, double-blind studies (ADVANCE, NCT03105128; MOTIVATE, NCT03104413), patients with moderately to severely active CD received 12-week intravenous (IV) RZB induction therapy or placebo (PBO). Patients with clinical response to RZB IV induction were rerandomised in a 52-week maintenance study (FORTIFY, NCT03105102) to receive subcutaneous (SC) RZB or PBO (ie, RZB withdrawal). Induction analyses included patients who received either 600 mg RZB IV or PBO for 12 weeks. Maintenance analyses included patients who received 360 mg RZB SC every 8 weeks or withdrawal (PBO SC) for 52 weeks. Outcomes assessed were normalisation of hs-CRP and FCP concentrations at week 12 of induction and at week 52 of maintenance in patients with elevated biomarkers at baseline (hs-CRP &amp;gt; 5 mg/L and/or FCP &amp;gt; 250 μg/g), clinical biomarker response (defined as enhanced clinical response [≥ 60% average daily stool frequency (SF) decrease and/or ≥ 35% average daily abdominal pain score (APS) decrease] and ≥ 50% reduction in hs-CRP or FCP), and clinical biomarker remission (defined as clinical remission per CD Activity Index or SF/APS criteria and normal hs-CRP or FCP) during maintenance treatment. Nonresponder imputation was used for missing data. Results Greater proportions of patients receiving RZB vs PBO achieved normalisation of hs-CRP and FCP at the end of the 12-week induction period and the 52-week maintenance period (P &amp;lt; .0001 for all; Table). Among patients with clinical response to RZB IV induction and entered maintenance, rates of clinical biomarker response were maintained through week 52 in patients receiving 360 mg RZB SC and declined over time among patients in the withdrawal (PBO SC) arm (Figure A). Rates of clinical biomarker remission increased over time in patients receiving 360 mg RZB SC. At week 52, clinical remission and normalisation of hs-CRP or FCP was achieved by 41% of patients receiving RZB vs 28%–29% of patients in the withdrawal (PBO SC) arm (Figure B-C). The safety profile of RZB in CD was previously reported. Conclusion Normalisation of objective biomarkers of inflammation in CD was achieved with RZB induction and maintenance therapy. Improvements in both clinical outcomes and biomarker levels were sustained with continuous RZB maintenance therapy and decreased over time in patients discontinuing RZB.

P375 Faecal calprotectin variation after induction therapy with infliximab to predict clinical, endoscopic and ultrasonographic remission in Crohn’s disease patients

Abstract Background Faecal calprotectin (FCal) is considered an intermediate target for monitoring disease activity in IBD. However, it is unknown whether early FCal variations during induction therapy may be a significant predictor of treatment response. We aimed to investigate FCal variation after infliximab induction therapy and its association with clinical, endoscopic, and ultrasonographic remission by the end of one year of treatment. Methods Prospective multicentric cohort study including patients with active CD without previous intestinal surgeries, who were starting IFX therapy and were followed for, 54 weeks. FCal was measured at week, 0 and after induction therapy (week, 14) and relative FCal changes from baseline (%FCal0-14) were calculated. At week, 54, clinical remission (defined as Harvey-Bradshaw index (HBI) &amp;lt;5), endoscopic healing (defined as SES-CD score &amp;lt;3) and transmural healing (defined as bowel wall thickness (BWT) assessed by intestinal ultrasound (IUS) ≤3mm) were evaluated. Results We included, 33 patients (60.6% male; median age, 30 years old (IQR, 24–42)). Most patients were diagnosed between, 17–40 years old (75.8%) had ileocolonic disease (57.6%) and an inflammatory phenotype (57.6%). At week, 54, 90.9% of the patients were in clinical remission, 30.3% had endoscopic healing and, 39.4% had transmural healing. The median FCal value at week, 0 was, 765µg/g (IQR, 312–1313) and at week, 14 was, 124.5µg/g (IQR, 46–432.5). At week, 14, 20 patients (60.6%) had FCal&amp;lt;250µg/g. There was no correlation between FCal at week, 0 and the baseline total SES-CD (r=0.28, p=0.13) and BWT of the most affected segment (r=0.02, p=0.90). Patients who achieved clinical remission had a low variation in %FCal0-14 (87.4% vs, 80.1%, p=0.42), as opposed to patients who achieved endoscopic healing (58.5 vs, 91.3%, p&amp;lt;0.001) and transmural healing (68.6% vs, 84.4%, p=0.16) who had more significant changes in %FCal0-14, although only statistically significant for endoscopic healing. When stratifying %FCal0-14 changes in, 25% quartiles, FCal variation at the end of induction was only able to predict endoscopic healing (OR, 8.13 (95%CI, 1.01–65.46), p=0.049), being non-significant for clinical remission (OR, 0.51 (95%CI, 0.09–2.92), p=0.45) and transmural healing (OR, 1.61 (95%CI, 0.73–3.57), p=0.24). A normalization of FCal below, 250 µg/g was also a predictor of endoscopic healing (OR, 9.82 (95%CI, 1.06–90.59), p=0.04). The AUC for the prediction of endoscopic healing with %FCal0-14 changes was, 0.85 (Figure, 1) and a cut-off of variation of, 80.3% was calculated. Conclusion FCal variation at the end of induction seems to be a good predictor of endoscopic healing at week, 54 but does not seem to be able to predict clinical and ultrasonographic remission.

Conflicts with Momentum

Take the fort, then take the city. In a two-stage, two-party contest, victory in the initial stage can provide an advantage in the final stage. We examine such momentum in conflict scenarios and investigate how valuable it must be to avoid a Pyrrhic victory. Our main finding is that although the elasticity of effort—which we allow to vary between the two stages—does impact the contestants’ effort levels, it has no bearing on the endogenously determined value of momentum itself. Further, rent dissipation in the two-stage conflict is equal across party whether or not an individual obtains first-stage momentum. Thus, momentum helps a player solely by enhancing marginal ability for victory in the second-stage contest. It does not, however, change the player’s net calculus of second-stage contest spending. Such contestable advantage is also found to be more rent-dissipative than innate/uncontestable advantage. Therefore, Pyrrhic victories should be more common for contests with an intermediate stage or stages in which advantages can be earned, ceteris paribus. While intermediate targets appear as useful conflict benchmarks, they dissipate additional expected contest rents. This additional rent-dissipative toll exists even for backward-inductive equilibrium behavior in a complete information setting. Whereas the quagmire theory suggests parties can become involved in problematic conflicts due to incomplete information, the present paper finds that the setting of conflict—namely, contestable intermediate advantage—can alternatively generate rent-dissipative tolls. Similarly, contestable advantage can lead parties to optimally forego contest participation (i.e., if conflict parameters do not meet the participation constraint). This is in contrast to a one-stage simultaneous contest with second-stage parametric values of the present contest.

An inverse DEA model for intermediate and output target setting in serially linked general two-stage processes

Abstract In this paper, we formulate an inverse data envelopment analysis (DEA) model for a serially linked two-stage production process operating under constant returns to scale technology. The inverse DEA model determines a set of intermediate and output targets for an input augmented decision-making unit (DMU) to maintain its relative efficiency at a pre-specified level. We solve the inverse DEA model using the constraint method used in multi-objective optimization. The input augmented DMU with intermediate and output targets obtained in the inverse DEA model is a hypothetical DMU. Under our modelling framework, when such a hypothetical DMU established on an inefficient DMU is included in the observed DMU set, the frontier established with observed DMU set remains intact. This is important in practice as the intermediate and output targets of the hypothetical DMU would be feasible. When overall efficiency of the hypothetical DMU is decomposed, individual stages have the same efficiency level as that of the hypothetical DMU. This is important to DMU managers as sub-processes also maintain the desired overall efficiency level. We apply our inverse DEA model to a sample of Australian superannuation funds. We demonstrate that each unique Pareto optimal solution of the inverse DEA model obtained through the constraint method provides a specific set of intermediate and output targets and they may offer trade-off between intermediates and outputs. When fund managers anticipate expansion or growth in their funds, choice of targets allows comparison of different trade-off scenarios and makes informed decisions.

Dual-quality map based no reference image quality assessment using deformable convolution

Traditional feature-based no-reference image quality assessment (NR-IQA) technologies rely heavily on the correlation between the handcrafted features and distortions, and often fail to predict perceptual quality because of the diversity of image distortion types. End-to-end deep learning-based metrics are driven mainly by human-labeled data, which are prohibitively labor-expensive to collect. An NR-IQA method that imitates the hierarchical and inferential perceptual behavior of the human vision system (HVS) is proposed to address the challenges of distortion diversity and lack of labeled data. In the proposed method, first, a dual-quality map is generated for use as an intermediate target, which contains multiscale features that are highly related to image quality. To capture human eye-sensitive distortion information in distorted images, a dual attention mechanism is utilized in the quality map generation network. Then, a regression network is designed for mapping the intermediate target to a quality score. The deformable convolution which has adaptive receptive field is introduced to extract multiple scale features from the dual-quality map. Experiments on eight benchmark databases show the superiority of the proposed method over other state-of-the-art NR-IQA metrics.

Loss of Neuropilin2a/b or Sema3fa alters olfactory sensory axon dynamics and protoglomerular targeting

BackgroundOlfactory Sensory Neuron (OSN) axons project from the zebrafish olfactory epithelium to reproducible intermediate target locations in the olfactory bulb called protoglomeruli at early stages in development. Two classes of OSNs expressing either OMP or TRPC2 exclusively target distinct, complementary protoglomeruli. Using RNAseq, we identified axon guidance receptors nrp2a and nrp2b, and their ligand sema3fa, as potential guidance factors that are differentially expressed between these two classes of OSNs.MethodsTo investigate their role in OSN axon guidance, we assessed the protoglomerular targeting fidelity of OSNs labeled by OMP:RFP and TRPC2:Venus transgenes in nrp2a, nrp2b, or sema3fa mutants. We used double mutant and genetic interaction experiments to interrogate the relationship between the three genes. We used live time-lapse imaging to compare the dynamic behaviors of OSN growth cones during protoglomerular targeting in heterozygous and mutant larvae.ResultsThe fidelity of protoglomerular targeting of TRPC2-class OSNs is degraded in nrp2a, nrp2b, or sema3fa mutants, as axons misproject into OMP-specific protoglomeruli and other ectopic locations in the bulb. These misprojections are further enhanced in nrp2a;nrp2b double mutants suggesting that nrp2s work at least partially in parallel in the same guidance process. Results from genetic interaction experiments are consistent with sema3fa acting in the same biological pathway as both nrp2a and nrp2b. Live time-lapse imaging was used to examine the dynamic behavior of TRPC2-class growth cones in nrp2a mutants compared to heterozygous siblings. Some TRPC2-class growth cones ectopically enter the dorsal-medial region of the bulb in both groups, but in fully mutant embryos, they are less likely to correct the error through retraction. The same result was observed when TRPC2-class growth cone behavior was compared between sema3fa heterozygous and sema3fa mutant larvae.ConclusionsOur results suggest that nrp2a and nrp2b expressed in TRPC2-class OSNs help prevent their mixing with axon projections in OMP-specific protoglomeruli, and further, that sema3fa helps to exclude TRPC2-class axons by repulsion from the dorsal-medial bulb.

Monetary policy in times of uncertainties: evidence from Tunisia, Egypt and Morocco

ABSTRACT This study assesses the actual monetary policy stance in three countries in the MENA region, namely, Tunisia (between 2000 and 2017) and Egypt and Morocco (between 2007 and 2017), based on the Taylor rule framework. Especially, it explores the impact of transition periods and high uncertainties following the so-called Arab Spring on the central bank decision-making process. The results provide no strong evidence in favor of rule-based monetary strategies on the part of the central banks in these countries, which still largely rely on discretion when deciding their policy rates. Nevertheless, they show a remarkable presence of interest rate inertia in the policymaking of these monetary authorities. Moreover, our findings reveal a strong dependence of the policy rates in Tunisia and Egypt on the variations of exchange rates, especially during the agitated and inflationary periods of transition. However, some signs of an orientation toward rule-based policies seem to arise in case of Tunisia and Morocco in the transition period, with increased sensitivity of short-term interest rate to inflation gap in the former and to output gap in case of the latter country; yet the confirmation of this evolution requires more time. Furthermore, the estimation of a threshold model where the threshold variable is allowed to vary over time demonstrates the existence of certain opportunistic behavior on the part of the central banks of Tunisia and Egypt. The responsiveness of their monetary policies is more intense when the inflation rate exceeds a certain intermediate target.

Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks

Abstract Understanding the meteorological drivers of extreme impacts in social or environmental systems is important to better quantify current and project future climate risks. Impacts are typically an aggregated response to many different interacting drivers at various temporal scales, rendering such driver identification a challenging task. Machine learning–based approaches, such as deep neural networks, may be able to address this task but require large training datasets. Here, we explore the ability of Convolutional Neural Networks (CNNs) to predict years with extremely low gross primary production (GPP) from daily weather data in three different vegetation types. To circumvent data limitations in observations, we simulate 100,000 years of daily weather with a weather generator for three different geographical sites and subsequently simulate vegetation dynamics with a complex vegetation model. For each resulting vegetation distribution, we then train two different CNNs to classify daily weather data (temperature, precipitation, and radiation) into years with extremely low GPP and normal years. Overall, prediction accuracy is very good if the monthly or yearly GPP values are used as an intermediate training target (area under the precision-recall curve AUC $ \ge $ 0.9). The best prediction accuracy is found in tropical forests, with temperate grasslands and boreal forests leading to comparable results. Prediction accuracy is strongly reduced when binary classification is used directly. Furthermore, using daily GPP during training does not improve the predictive power. We conclude that CNNs are able to predict extreme impacts from complex meteorological drivers if sufficient data are available.

Lorentzian Threads as Gatelines and Holographic Complexity.

The continuous min flow-max cut principle is used to reformulate the "complexity=volume" conjecture using Lorentzian flows-divergenceless norm-bounded timelike vector fields whose minimum flux through a boundary subregion is equal to the volume of the homologous maximal bulk Cauchy slice. The nesting property is used to show the rate of complexity is bounded below by "conditional complexity," describing a multistep optimization with intermediate and final target states. Conceptually, discretized Lorentzian flows are interpreted in terms of threads or gatelines such that complexity is equal to the minimum number of gatelines used to prepare a conformal field theory (CFT) state by an optimal tensor network (TN) discretizing the state. We propose a refined measure of complexity, capturing the role of suboptimal TNs, as an ensemble average. The bulk symplectic potential provides a "canonical" thread configuration characterizing perturbations around arbitrary CFT states. Its consistency requires the bulk to obey linearized Einstein's equations, which are shown to be equivalent to the holographic first law of complexity, thereby advocating a notion of "spacetime complexity."