Large Basket Research Articles

Material selection and weaving techniques for the oldest basketry in Japan found at the Higashimyou site, Saga Prefecture

Compared with North America or northern Africa, prehistoric basketry has rarely been studied in East Asia. Even in Japan where plant remains excavated from lowland sites have been studied extensively in the past thirty years, the study of basketry lagged behind that of other wooden artifacts, and materials of less than 1000 baskets of the Jomon period have been identified versus more than 21,300 wooden artifacts and 32,500 natural woods of the same period so far identified. The Higashimyou site in Saga prefecture of western Japan yielded over 700 baskets including basket fragments of the initial Jomon period from 8000 to 7300 cal BP and provided a good material to examine the material selection and basketry technique of the incipient basketry manufacture in Japan. The identification of basket materials revealed that large baskets were made mostly with splints of two arboreal species, Sapindus mukorossi and Ficus erecta, and that small ones were made mostly with stems of two vine species, Sinomenium acutum and Trachelospermum. The basketry techniques used at this site showed that the bodies of the baskets were made by various types of twill, weave, and twining, and that the bodies of large ones were usually made of two kinds of technique across belts in the middle of the bodies. The employment of various basketry techniques at this site showed that most of the basketry techniques used in later Jomon and following periods were already established in the initial Jomon period. Occurrences of remains of the material plants around the site, the archaeological record of these plants in Japan, and floristic studies of present Japan indicated that appropriate materials used for the baskets were not easily available around the site and that some kind of resources management for basketry materials must have existed around the settlement.

Forecast accuracy hardly improves with method complexity when completing cohort fertility

Forecasts of completed fertility predict how many children will be born on average by women over their entire reproductive lifetime. These forecasts are important in informing public policy and influencing additional research in the social sciences. However, nothing is known about how to choose a forecasting method from a large basket of variants. We identified 20 major methods, with 162 variants altogether. The approaches range from naive freezing of current age-specific fertility rates to methods that use statistically sophisticated techniques or are grounded in demographic theory. We assess each method by evaluating the overall accuracy and if provided, uncertainty estimates using fertility data of all available birth cohorts and countries of the Human Fertility Database, which covers 1,096 birth cohorts from 29 countries. Across multiple measures of forecast accuracy, we find only four methods that consistently outperform the naive freeze rates method, and only two methods produce uncertainty estimates that are not severely downward biased. Among the top four, there are two simple extrapolation methods and two Bayesian methods. The latter are demanding in terms of input data, statistical techniques, and computational power but do not consistently complete cohort fertility more accurately at all truncation ages than simple extrapolation. This broad picture is unchanged if we base the validation on 201 United Nations countries and six world regions, including Africa, Asia, Europe, Latin America and the Caribbean, northern America, and Oceania.

Measuring Relationships between Doctor Densities and Patient Visits: A Dog’s Breakfast of Small Area Health Geographies

A number of small area geographies are used in Australia to investigate primary care relevant outcomes/behaviours and to manage the supply of Primary Care Providers (PCP) that influence these outcomes. However, very little research exists on the choice of a small area geography suitable for these purposes. We evaluated a large basket of Australian small area geographies to determine which geography is optimal for investigating relationships between PCP supply and the use of PCP services. We used linked data to evaluate the relationship between PCP supply and the likelihood of a patient visiting a PCP, after adjusting for individual level covariates. PCP supply was measured at different geographies including Local Government Areas (LGAs), Primary Health Networks (PHNs), Statistical Areas-1/2/3 and Remoteness Areas. Overall, the strongest relationships between PCP density and PCP use were found when LGAs were used to measure PCP density. Large geographies such as PHNs also detected strong relationships while custom built geographies such as Primary Care Service Areas were not significantly better than the rest. Existing geographies such as LGAs may be suitable for investigating the effect of PCP supply at state or national scales.

Preferential inputs from cholecystokinin-positive neurons to the somatic compartment of parvalbumin-expressing neurons in the mouse primary somatosensory cortex

Parvalbumin-positive (PV+) neurons in the cerebral cortex, mostly corresponding to fast-spiking basket cells, have been implicated in higher-order brain functions and psychiatric disorders. We previously demonstrated that the somatic compartment of PV+ neurons received inhibitory inputs mainly from vasoactive intestinal polypeptide (VIP)+ neurons, whereas inhibitory inputs to the dendritic compartment were derived mostly from PV+ and somatostatin (SOM)+ neurons. However, a substantial number of the axosomatic inputs have remained unidentified. Here we show preferential innervation of the somatic compartment of PV+ neurons by cholecystokinin (CCK)+ neurons in the mouse primary somatosensory cortex. CCK+ neurons, a minor population of GABAergic neurons (3.2%), displayed no colocalization with PV or SOM immunoreactivity but partial overlap with VIP immunoreactivity (27.7%). Confocal laser scanning microscopy observation of CCK+ synaptic inputs to PV+ neurons revealed that CCK+ neurons preferred the somatic compartment to the dendritic compartment of PV+ neurons and provided approximately 33% of the axosomatic inhibitory inputs to PV+ neurons. Additionally, 20.9% and 12.1% of the axosomatic inputs were derived from CCK+/VIP+ and CCK+/VIP-negative (−) neurons, presumably double bouquet and large basket cells, respectively. Furthermore, the densities of the axosomatic inputs from CCK+ and/or VIP+ neurons to PV+ neurons were not significantly different among the cortical layers. The present findings suggest that, by preferentially innervating the cell bodies of PV+ neurons, both CCK+/VIP− basket and CCK+/VIP+ double bouquet cells might efficiently interfere with action potential generation of PV+ neurons, and that the two types of CCK+ neurons might have a large impact on cortical activity through PV+ neuron inhibition.

The Impact of Mobile-App Adoption on Grocery-Purchase Behavior

The authors investigate customer response to the addition of a mobile shopping channel to existing online and offline channels. They investigate the cannibalization by the new mobile channel of existing channels and evaluate changes in households’ purchasing behavior following adoption of the mobile channel. They find that introducing the mobile channel cannibalizes the online channel, but the impact on the offline channel is small. As households adopt the mobile channel, they increase weekly expenditure by 16.5%, primarily due to higher shopping frequency rather than larger basket size per trip. They also find considerable heterogeneity in the impact of mobile adoption. Online-only shoppers, hybrid-online, and hybrid-offline shoppers showed modest increases in their expenditure and little or no change in their channel-usage patterns. However, offline-only shoppers became multi-channel users, and they increased their weekly expenditure by 43.1%. They also find a stronger effect of the adoption for households who used the chain less often. Furthermore, they demonstrate mobile apps can be a competitive tool to increase market share.

Improving bedload transport determination by grain-size fraction using the Swiss plate geophone recordings at the Erlenbach stream

Direct bedload samples were taken with a large metal basket at the steep Erlenbach stream in Switzerland. These measurements were compared with the signal of the Swiss impact plate geophone system to derive information about bedload transport. The so-called amplitude histogram (AH) method was developed in an earlier study to estimate the bedload flux for different grain-size classes at the Erlenbach. A new analysis of a larger set of measurements was made here to improve the performance of the AH method. The approach relies on an identification of the transported grain sizes through their dependency on the signal amplitude. As a new element we introduce here the impulse rate, which is found to affect the number of impulses recorded for each amplitude class. As compared to the original method, the new version of the AH method shows a slightly improved performance for total calculated bedload mass, and results in a clearly better agreement between calculated and measured characteristic grain sizes.

Basket Composition and Choice Among Direct Channels: A Latent State Model of Shopping Costs

Shoppers of multi-channel retailers often place orders using different channels on different shopping occasions. The differential use of channels is related to both basket composition and channel characteristics, such as the ability of the channel to provide additional information that resolves uncertainty about the purchase. In this paper, we examine the impact of basket composition on the choice among direct channels. We develop a two-stage, shopping cost model with two, latent states. Given a shopping basket, the shopper first decides if she needs additional information about items in the basket. If she is uncertain about the items in the basket meeting her needs, she uses an information rich channel, such as the retailer's website or call center, and risk reduction costs become salient in addition to the other shopping costs. If she does not require additional information, she places her order by choosing among all available channels, and she may incur a welfare loss from making a purchase that does not optimally meet her needs. We operationalize welfare loss with Shannon information and various metrics based on purchase history.Our empirical setting is a data set from a catalog retailer that offers multiple direct channels. Our estimates show that basket composition impacts channel choice. Large baskets shift to the Internet channel, suggesting that the Internet channel has lower ordering costs. High-risk baskets shift to call centers and this suggests that the call center has lower risk reduction costs. Collectively these estimates provide evidence for the notion of channel specialization—some channels are better at addressing certain shopping costs compared to others. Our estimates also show that electronic self-service channels have high initial access costs and a significant learning curve compared to the call center suggesting that these channels might be better suited to heavy users. We use the estimated model to quantify the value of channels, to identify categories that need risk reduction, and to segment and target shoppers for Internet ordering based on basket size and the potential to accumulate experience.

Development of joint models for channel, store, and travel mode choice: Grocery shopping in London

The nature of shopping activity is changing in response to innovation in retailing and the growth in online channels. There is a growing interest from transport researchers, policy makers, marketing and retail businesses in understanding the implications of this change. However, existing tools and techniques developed for analysing behaviour in traditional retail environments do not adequately represent emerging complexities resulting from digital innovation. In this paper, we advance existing destination and mode choice models by incorporating online channels in a unified framework. This is a critical extension to existing transport literature on destination choice which largely ignores online activity. Specifically, we develop discrete choice models using elemental store (including both online and in-store) alternatives for joint choice of channel, store, and travel mode. We demonstrate the use of a widely-accepted consumer panel dataset with minor modifications, for the first time in transport research, together with API based data mining tools that offer great potential for enrichment.The analysis focuses on grocery shopping and uses consumer data collected from two selected boroughs in London; results from multinomial logit and nested logit estimations are reported. The extension presented here provides the tools to quantify the effects of increased online shopping on traditional store formats and travel patterns. Our results showed virtual alternatives currently offer an attractive substitute among early adopters for large basket shopping mostly for high income groups. This might suggest a significant reduction in shopping trips to hypermarkets often associated with large basket shopping potentially leading to store closures. Online deliveries mostly draw from driving trips and less so from walking and public transport trips. The present study also confirmed previous findings related to smaller stores and longer travel distances being associated with declining utilities, agglomeration and competition significantly influencing store choice.

Basket Composition and Choice Among Direct Channels: A Latent State Model of Shopping Costs

Shoppers of multi-channel retailers often place orders using different channels on different shopping occasions. The differential use of channels is related to both basket composition and channel characteristics, such as the ability of the channel to provide additional information that resolves uncertainty about the purchase. In this paper, we examine the impact of basket composition on the choice among direct channels. We develop a two-stage, shopping cost model with two, latent states. Given a shopping basket, the shopper first decides if she needs additional information about items in the basket. If she is uncertain about the items in the basket meeting her needs, she uses an information rich channel, such as the retailer’s website or call center, and risk reduction costs become salient in addition to the other shopping costs. If she does not require additional information, she places her order by choosing among all available channels, and she may incur a welfare loss from making a purchase that does not optimally met her needs. We operationalize welfare loss with Shannon information and various metrics based on purchase history. Our empirical setting is a data set from a catalog retailer that offers multiple direct channels. Our estimates show that basket composition impacts channel choice. Large baskets shift to the Internet channel, suggesting that the Internet channel has lower ordering costs. High-risk baskets shift to call centers and this suggests that the call center has lower risk reduction costs. Collectively these estimates provide evidence for the notion of channel specialization — some channels are better at addressing certain shopping costs compared to others. Our estimates also show that electronic self-service channels have high initial access costs and a significant learning curve compared to the call center suggesting that these channels might be better suited to heavy users. We use the estimated model to quantify the value of channels, to identify categories that need risk reduction, and to segment and target shoppers for Internet ordering based on basket size and the potential to accumulate experience.

Pharmaceutical Pricing Policy in Greece: Toward a Different Path.

Affordable, accessible, and innovation-promoting pharmaceutical care is essential to the operation of a sustainable health system. External reference pricing (ERP), a common pharmaceutical policy in Europe, suffers today from indigenous weaknesses that may cause market distortions and barriers to care, burdening mostly the weak economies, and hence, raising ethical and political worrying. A non-randomized experiment was conducted, in order to examine the influence of flexible and adaptable to health systems' affordability ERP structures. Outcomes were assessed by measuring deviations from Greek prices' level ex ante, as well as effects on pharmaceutical markets affiliated to the European ERP system. Pharmaceutical pricing models that fit prices to income and affordability are better in all aspects, as they produce fairer results, while resulting in low external costs for the European ERP network as a whole. Small sets of reference countries are preferred to large baskets, as they produce similar results, while presenting better qualities by increasing the flexibility of the reimbursement system and the transparency of the market.

From Neuron Biophysics to Orientation Selectivity in Electrically Coupled Networks of Neocortical L2/3 Large Basket Cells.

In the neocortex, inhibitory interneurons of the same subtype are electrically coupled with each other via dendritic gap junctions (GJs). The impact of multiple GJs on the biophysical properties of interneurons and thus on their input processing is unclear. The present experimentally based theoretical study examined GJs in L2/3 large basket cells (L2/3 LBCs) with 3 goals in mind: (1) To evaluate the errors due to GJs in estimating the cable properties of individual L2/3 LBCs and suggest ways to correct these errors when modeling these cells and the networks they form; (2) to bracket the GJ conductance value (0.05–0.25 nS) and membrane resistivity (10 000–40 000 Ω cm2) of L2/3 LBCs; these estimates are tightly constrained by in vitro input resistance (131 ± 18.5 MΩ) and the coupling coefficient (1–3.5%) of these cells; and (3) to explore the functional implications of GJs, and show that GJs: (i) dynamically modulate the effective time window for synaptic integration; (ii) improve the axon's capability to encode rapid changes in synaptic inputs; and (iii) reduce the orientation selectivity, linearity index, and phase difference of L2/3 LBCs. Our study provides new insights into the role of GJs and calls for caution when using in vitro measurements for modeling electrically coupled neuronal networks.

Successful Removal of a Large Common Bile Duct Stone by Using Direct Peroral Cholangioscopy and Laser Lithotripsy in a Patient with Severe Kyphosis

A 75-year-old woman with hypertension presented with acute suppurative cholangitis. Chest radiography revealed severe kyphosis. Abdominal computed tomography revealed a large stone impacted in the common bile duct (CBD). The patient underwent emergent endoscopic retrograde cholangiopancreatography, and cholangiography revealed a large stone (7×3 cm) in the CBD that could not be captured using a large basket. We could not use the percutaneous approach for stone fragmentation by using a cholangioscope because of severe degenerative kyphosis. Finally, we performed holmium laser lithotripsy under peroral cholangioscopy by using an ultraslim endoscope, and the large stone in the CBD was successfully fragmented and removed without complications.

NCI Prepares to Launch MATCH Trial

Abstract In July, the NCI will launch NCI-MATCH, a large basket trial that groups cancer patients into phase II drug trials based on genetic alterations in their tumors. The trial, and others like it, may eventually lead to cancer drug approvals based on a tumor's genomic profile rather than its tissue of origin.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric properties were most relevant for this classification. A set of 118 digitally reconstructed interneuronal morphologies classified into the common basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of the world's leading neuroscientists, quantified by five simple morphometric properties of the axon and four of the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. We then removed this class information for each type separately, and applied semi-supervised clustering to those cells (keeping the others' cluster membership fixed), to assess separation from other types and look for the formation of new groups (subtypes). We performed this same experiment unlabeling the cells of two types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixture of Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performed the described experiments on three different subsets of the data, formed according to how many experts agreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least 26 (47 neurons). Interneurons with more reliable type labels were classified more accurately. We classified HT cells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy, respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, and no subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette width and ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively, confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a single type also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometric properties were more relevant that dendritic ones, with the axonal polar histogram length in the [π, 2π) angle interval being particularly useful. The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heterogeneous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones for distinguishing among the CB, HT, LB, and MA interneuron types.

Large Bowel Obstruction Due to a Big Gallstone Successfully Treated with Endoscopic Mechanical Lithotripsy.

Colonic gallstone ileus in an uncommon mechanical bowel obstruction caused by intraluminal impaction of one or more gallstones. The surgical management of gallstone ileus is complex and is potentially of high risk. There have been reports of gallstone extractions using various endoscopic modalities to relieve the obstruction. In this report we present the technique employed to successfully perform a mechanical lithotripsy and extraction of a large gallstone embedded in a sigmoid colon affected by diverticular stenosis. We passed through the stenosis with a 11.3 mm videoscope with 3.7 mm channel. A large lithotripsy extraction basket was used to catch and break up the stone and fragments were removed using the same basket. The patient was discharged asymptomatic three days after the procedure. Using appropriate devices mechanical lithotripsy is a safe and effective method to treat colonic obstruction and avoid surgery in the setting of gallstone ileus even in case of big stones.

Bayesian network classifiers for categorizing cortical GABAergic interneurons.

An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1-F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts' assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell's label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1-F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52% accuracy, and single out the number of branches at 180 μm from the soma, the convex hull 2D area, and the axonal features F1-F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.

Optimal Pricing of Access and Secondary Goods with Repeat Purchases: Evidence from Online Grocery Shopping and Delivery Fees

In this paper we investigate optimal pricing strategies for an online grocery retailer who derives its profits from delivery fees and grocery sales. We base our theoretical framework upon the well-established work of Schmalensee (1981) in two-part pricing, while allowing for repeat purchase occasions as in Phillips and Battalio (1983). We derive testable implications that we take to data using a unique dataset detailing transaction information from an online grocery retailer in a Western European country. We find that the number of transactions and the average size of grocery baskets purchased are positively correlated. We also observe two customer groups in our data that differ in their willingness to pay. This observation together with robust evidence that price-sensitive customers buy larger baskets are consistent with an optimal pricing strategy that offers discounts for B2B customers and charges higher prices to households for grocery delivery. We conclude that firms may increase profits by implementing alternative and simpler pricing strategies that combine second and third degree price discrimination schemes.

Robust Risk Budgeting Algorithms in R

In this paper we reviewed some numerical algorithms, implemented in R language which solve the Risk Budgeting (RB) allocation problem. We demonstrated that the well known Sequential Quadratic Programming (SQP) whose objective function is not strictly convex, fails to converge for high dimensional baskets. On the contrary, the new explored algorithms tackle this issue by transforming the objective function into a strictly convex one. Amongst them, the ”Spinu Algorithm” proves to be the most robust and fastest algorithm, both for large equity and small multi-asset portfolios. Surprisingly, the promising Cyclical Coordinate Descent (CCD) algorithm proposed by Griveau et.al. (2013) which suits well for risk budgeting on a large basket of equity stocks, lacks robustness when performed on a multi-asset universe. Our results confirm that the ”Spinu Algorithm” is the most robust framework to implement risk-budgeting portfolios for any type of investment universe.

The Eurozone crisis and its contagion effects on the European stock markets

Purpose– This paper aims to examine the contagion effects of Greece, Ireland, Portugal, Spain and Italy (GIPSI) and US stock markets on seven Eurozone and six non-Eurozone stock markets.Design/methodology/approach– In this paper, a dynamic conditional correlation (DCC) model popularly known as DCC-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model given by Engle (2002) is applied to estimate the DCCs across sample markets.Findings– Analyzing the Eurozone crisis period, the empirical results suggest that among GIPSI stock markets, Spain, Italy, Portugal and Ireland appear to be most contagious for Eurozone and non-Eurozone markets. The study finds that France, Belgium, Austria and Germany in Eurozone and UK, Sweden and Denmark in non-Eurozone are strongly hit by the contagion shock.Practical implications– The findings of the study have significant implications for the concerned regulatory authorities, as it may provide an important direction for further policy research with regard to financial integration in the European Union (EU). From global investors’ perspective, the EU-based diversification strategies seem to be inefficient especially during Eurozone crisis.Originality/value– To the best of the authors’ knowledge, this is the first study that examines the issue of financial contagion of Eurozone crisis for a large basket of stock markets of European countries comprising seven Eurozone and six non-Eurozone markets for the period 2009-2012. The study uses the Markov regime switching model to identify crisis period and utilizes the DCC estimates of DCC-GARCH to examine the patterns of financial contagion. The finding of this study is quite interesting and is different in several ways than existing studies in the literature.

Autaptic Connections and Synaptic Depression Constrain and Promote Gamma Oscillations

Computational models of gamma oscillations have helped increase our understanding of the mechanisms that shape these 40–80 Hz cortical rhythms. Evidence suggests that interneurons known as basket cells are responsible for the generation of gamma oscillations. However, current models of gamma oscillations lack the dynamic short term synaptic plasticity seen at basket cell-basket cell synapses as well as the large autaptic synapses basket cells are known to express. Hence, I sought to extend the Wang-Buzsáki model of gamma oscillations to include these features. I found that autapses increased the synchrony of basket cell membrane potentials across the network during neocortical gamma oscillations as well as allowed the network to oscillate over a broader range of depolarizing drive. I also found that including realistic synaptic depression filtered the output of the network. Depression restricted the network to oscillate in the 60–80 Hz range rather than the 40–120 Hz range seen in the standard model. This work shows the importance of including accurate synapses in any future model of gamma oscillations.