BMP signaling in the dorsal-ventral patterning system of the milkweed bug Oncopeltus fasciatus

BMP signaling plays an essential role in dorsal-ventral (DV) axis patterning throughout the animal kingdom. However, in insects Toll signaling also has a remarkable influence on DV patterning and, in higher branching lineages, fulfills some functions, which depend on BMP signaling in more basally branching lineages. Thus, the DV patterning system of the highly derived fruit fly Drosophila melanogaster is extremely dependent on Toll, which determines also the pattern of dorsal cell fates by specifying the polarity of BMP signaling. However, the wasp Nasonia vitripennis, which belongs to the most basally branching holometabolous lineage, the hymenopterans, uses Toll signaling only as mesoderm inductor. In this study for the first time the DV patterning system of a hemimetabolous insect, the milkweed bug Oncopeltus fasciatus was analyzed. O. fasciatus is a short germ insect, i.e. its posterior segments successively arise from a posterior growth zone, after the onset of gastrulation. In contrast, the anterior segments are synchronously established during the blastoderm stage. A different emergence of anterior and posterior segments might also be reflected in the DV patterning system. To understand the DV patterning system of O. fasciatus candidate genes were knocked down via parental RNA interference (pRNAi) and the resulting phenotypes were investigated for morphological as well as molecular deviations from wild type embryos. Nuclear staining techniques, in situ hybridization (ISH) and antibody staining were performed for this purpose. BMP signaling was found to be able to completely repress mesodermal fates and to be required to restrict it to the ventral side. Furthermore the repression of the BMP inhibitor short gastrulation (sog) seemed also to be mediated by BMP signaling. The lack of DV polarity upon depletion of the extracellular BMP inhibitors Sog and Twisted gastrulation provided further evidence for the high impact of BMP signaling on the O. fasciatus DV patterning system. The absence of DV asymmetry upon depletion of Toll was indicated to be due to a loss of later but not initial expression of sog. These results led to the proposal of a highly self-regulating BMP-dependent DV patterning system for O. fasciatus, which is only polarized by Toll signaling. In addition, differences between the early blastoderm and the later germ band DV pattern in knockdown embryos suggested that the transition of the two dimensional blastoderm DV patterning system into the three dimensional growth zone DV patterning system requires the establishment of two opposing signaling center located close to the posterior pole at the onset of gastrulation

Making smart recommendations for perishable and stockout products

Food waste and stockouts are widely recognized as an important global challenge. While inventory management aims to address these challenges, the tools available to inventory managers are often limited and the usefulness of their decisions is dependent on demand realizations, which are not within their control. Recommender systems (RS) can influence and direct customer demand, e.g., by sending personalized emails with promotions for different items. We propose a novel approach that combines the opportunities provided by RS with inventory management considerations. Under the assumption that there is a known set of customers to receive a promotion consisting of items, we use mixed-integer programming (MIP) to allocate recommended items across customers taking both individual preferences and the current state of inventory with uncertainties into account. Our approach can solve problems with both stochastic supply (inventory and perishability) and demand. We propose heuristics to improve scalability and compare their performance with the optimal solution using data from an online grocery retailer. The goal is to target the right set of customers who are likely to purchase an item, while simultaneously considering which items are prone to expire or be out-of-stock soon. We show that creating recommendation lists exclusively considering user preferences can be counterproductive to users due to possible excessive stockouts. Similarly, focusing only on the retailer can be counterproductive to retailer sales due to the number of expired products that can be considered lost income. We thus avoid the loss of customer goodwill due to stockouts and reduce waste by selling inventory before it expires

An aggregation-based approximate dynamic programming approach for the periodic review model with random yield

A manufacturer places orders periodically for products that are shipped from a supplier. During transit, orders get damaged with some probability, that is, the order is subject to random yield. The manufacturer has the option to track orders to receive information on damages and to potentially place additional orders. Without tracking, the manufacturer identifies potential damages after the order has arrived. With tracking, the manufacturer is informed about the damage when it occurs and can respond to this information. We model the problem as a dynamic program with stochastic demand, tracking cost, and random yield. For small problem sizes, we provide an adjusted value iteration algorithm that finds the optimal solution. For moderate problem sizes, we propose a novel aggregation-based approximate dynamic programming (ADP) algorithm and provide solutions for instances for which it is not possible to obtain optimal solutions. For large problem sizes, we develop a heuristic that takes tracking costs into account. In a computational study, we analyze the performance of our approaches. We observe that our ADP algorithm achieves savings of up to 16% compared to existing heuristics. Our heuristic outperforms existing ones by up to 8.1%. We show that dynamic tracking reduces costs compared to tracking always or never and identify savings of up to 3.2%

Models for sequential sorting facility staff allocation

Sequential sorting facilities are a key step in the courier, express, and parcel delivery industry. In these facilities, staff are assigned to work areas (WAs) to sequentially process different commodities as they move through the facility. When setting the staff levels at these WAs, the shift manager needs to balance different objectives, such as the overall number of staff, the cost of unsorted mail, and how frequently the shift levels change. However, existing literature on staffing these facilities (particularly in the field of mail delivery) focuses on longer timescales, assumes simpler operational constraints, and generally assumes deterministic mail volumes. In this thesis, we develop novel deterministic and stochastic models to staff these facilities for a mail sorting centre. We also propose a framework for general problem-based scenario reduction to use with the stochastic model. The deterministic model is a time-expanded network design model, using staff numbers to increase throughput capacities between WAs. To account for the uncertainty of commodity volumes, we also propose a novel stochastic model. This model is a stochastic programming model where the workplan is the first stage decision, the mail volumes are stochastic, and how the mail is routed over time is the second-stage decision. To solve the stochastic model (and other similar models) more efficiently, we propose a framework to generalise several problem-based scenario reduction methods. We show the applicability of the framework by performing numerical tests using different combinations of candidate solutions and scenario reduction techniques on three different test problems, including the stochastic mail centre staffing problem

Molecular evolutionary trends and feeding ecology diversification in the Hemiptera, anchored by the milkweed bug genome.

BACKGROUND: The Hemiptera (aphids, cicadas, and true bugs) are a key insect order, with high diversity for feeding ecology and excellent experimental tractability for molecular genetics. Building upon recent sequencing of hemipteran pests such as phloem-feeding aphids and blood-feeding bed bugs, we present the genome sequence and comparative analyses centered on the milkweed bug Oncopeltus fasciatus, a seed feeder of the family Lygaeidae. RESULTS: The 926-Mb Oncopeltus genome is well represented by the current assembly and official gene set. We use our genomic and RNA-seq data not only to characterize the protein-coding gene repertoire and perform isoform-specific RNAi, but also to elucidate patterns of molecular evolution and physiology. We find ongoing, lineage-specific expansion and diversification of repressive C2H2 zinc finger proteins. The discovery of intron gain and turnover specific to the Hemiptera also prompted the evaluation of lineage and genome size as predictors of gene structure evolution. Furthermore, we identify enzymatic gains and losses that correlate with feeding biology, particularly for reductions associated with derived, fluid nutrition feeding. CONCLUSIONS: With the milkweed bug, we now have a critical mass of sequenced species for a hemimetabolous insect order and close outgroup to the Holometabola, substantially improving the diversity of insect genomics. We thereby define commonalities among the Hemiptera and delve into how hemipteran genomes reflect distinct feeding ecologies. Given Oncopeltus's strength as an experimental model, these new sequence resources bolster the foundation for molecular research and highlight technical considerations for the analysis of medium-sized invertebrate genomes

Podcast "Beratung und Schule"

„Beratung und Schule“ ist eine Podcast-Reihe für angehende Lehrer*innen zu den Themen Beratung und Gesprächsführung mit Eltern und Schüler*innen. Hier bekommt Ihr einen Einblick in die Thematik und vertiefte Informationen zu einzelnen Themenbereichen. Der Podcast entsteht 2023 im Rahmen eines Projektes zur Förderung von Beratungskompetenzen von Lehramtsstudierenden, finanziert durch QS-Mittel der Verfassten Studierendenschaft der PH Freiburg. Redaktion: Lena Sachs Episode 1: Erfahrungen aus Forschung und Praxis Episode 2: Elternarbeit und Beratung in der Praxis – Gespräch mit einem Grundschulrektor Episode 3: Lerngespräche mit Schüler*innen Episode 4: Autismus im Schulkontext Episode 5: Kinderschutz und Gespräche im Gefährdungskotext Episode 6: Selbstverletzendes Verhalten im Schulkontext Episode 7: Beratungslehrkräfte an Schulen Episode 8: Schulpsychologische Beratungsstell

Retail Analytics:Integrated Forecasting and Inventory Management for Perishable Products in Retailing

This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products

The Price-Setting Newsvendor with Poisson Demand

The price-setting newsvendor (PSN) model has received considerable attention since it was first introduced by Whitin (1955). However, the existing publications that study this model consistently assume the existence of a continuous density function of demand. In this paper, we study the PSN model with Poisson demand — that is, a discrete demand distribution without density function. The Poisson PSN has an important property, it combines price-dependency of variance and coefficient of variation of the (standard) additive and multiplicative models: demand variance decreases and the coefficient of variation increases in the selling price. We develop an analytical solution approach that covers a broad class of demand models, including linear and logit demand, explain how to apply our approach to more general demand functions via piece-wise linear approximation, and develop analytical and numerical insights. We characterize the behavior of the optimal price and we analyze the performance gap of different price-setting heuristics. Among other insights, we observe some instances in which a significant share of profits would be lost if the discrete nature of demand were not modeled explicitly. To help companies overcome this risk, we present an easily applicable decision rule with which to determine when to use simple heuristics and when to solve the associated discrete optimization problem

Safety stock planning under causal demand forecasting

Mainstream inventory management approaches typically assume a given theoretical demand distribution and estimate the required parameters from historical data. A time series based framework uses a forecast (and a measure of forecast error) to parameterize the demand model. However, demand might depend on many other factors rather than just time and demand history. Inspired by a retail inventory management application where customer demand, among other factors, highly depends on sales prices, price changes, weather conditions, this paper presents two data-driven frameworks to set safety stock levels when demand depends on several exogenous variables. The first approach uses regression models to forecast demand and illustrates how estimation errors in this framework can be utilized to set required safety stocks. The second approach uses Linear Programming under different objectives and service level constraints to optimize a (linear) target inventory function of the exogenous variables. We illustrate the approaches using a case example and compare two methods with respect to their ability to achieve target service levels and the impact on inventory levels in a numerical study. We show that considerable improvements of the overly simplifying method of moments are possible and that the ordinary least squares approach yields a better performance than the LP-method, especially when the data sample for estimation is small and the objective is to satisfy a non-stockout probability constraint. However, if some of the standard assumptions of ordinary least squares regression are violated, the LP approach provides more robust inventory levels