Subtle Variances Research Articles

Lithium isotope systematics in an endorheic saline lacustrine system: Insights from Qinghai Lake, China

The study of lithium isotopic (δ7Li) signatures in sedimentary deposits has become a powerful tool to infer past silicate weathering regimes, thus informing our knowledge of the geological carbon cycle and paleoclimate evolution. Sediments from Qinghai Lake, the largest saltwater lake in China (4625 km2), offer an unparalleled archive for investigating the climatic history of the Qinghai-Tibet Plateau. However, prior to leveraging the δ7Li proxy in this context, it is imperative to unravel the mechanisms of lithium (Li) isotope fractionation and elemental cycling within the lake's aqueous and sedimentary systems. In this study, we collected and analyzed samples of Qinghai Lake's water, sediments, and recharge waters (rivers, groundwater, and rainfall) to investigate the processes controlling the δ7Li value recorded in Qinghai Lake sediments.Our data reveal subtle variances in Qinghai Lake water Li concentration ([Li]), ranging from 652 to 873 ng/g, suggesting interactions with iron oxides or suspended matter. The δ7Li signature, however, exhibits remarkable uniformity across the lake at 32.1‰ (±0.4‰). Near the estuary of the Buha River, there is a swift homogenization of [Li] and δ7Li, stabilizing within just 3 km of the inflow. Lake sediments exhibit δ7Li values ranging from 1.5‰ to 6.6‰, exceeding those of the upper continental crust (∼0‰ ± 4‰), yet approximately 30‰ lower than those in lake waters. This significant discrepancy between the δ7Li of lake water and sediments is likely due to the preferential incorporation of 6Li over 7Li during the neoformation of clay minerals.Lithium mass balance modeling for Qinghai Lake, incorporating inputs from river and groundwater (∼46.5 t/a with δ7Li ∼18.3‰) and outputs via clay mineral uptake (∼44 t/a with δ7Li ∼5.1‰), indicates that the lake's Li system is currently out of steady state. The model predicts a gradual rise in the lake's Li inventory, estimated to achieve steady state within 1.2 ka and the δ7Li value of lake water will increase until reaching ∼45‰ assuming constant climate conditions.

A comparative analysis of particulate bovine bone substitutes for oral regeneration: a narrative review.

Particulate bovine bone substitutes (BS) are commonly used in oral regeneration. However, more literature is needed focusing on comparative analyses among various particulate bovine BS. This study evaluates pre-clinical and clinical data of different particulate bovine BS in oral regeneration. A narrative review was conducted by screening the PubMed database Included in the review were pre-clinical and clinical studies until 2024 comparing a minimum of two distinct particulate bovine BS. In addition to examining general data concerning manufacturing and treatment processes, biological safety, physical and chemical characteristics, and graft resorption, particular emphasis was placed on assessing pre-clinical and clinical data related to ridge preservation, sinus floor elevation, peri-implant defects, and various forms of alveolar ridge augmentation utilizing particulate bovine BS. Various treatment temperatures ranging from 300 to 1,250°C and the employment of chemical cleaning steps were identified for the manufacturing process of particulate bovine BS deemed to possess biosecurity. A notable heterogeneity was observed in the physical and chemical characteristics of particulate bovine BS, with minimal or negligible graft resorption. Variations were evident in particle and pore sizes and the porosity of particulate bovine BS. Pre-clinical assessments noted a marginal inclination towards favorable outcomes for particulate bovine BS subjected to higher treatment temperatures. However, clinical data are insufficient. No distinctions were observed regarding ridge preservation, while slight advantages were noted for high-temperature treated particulate bovine BS in sinus floor elevation. Subtle variances in both pre-clinical and clinical outcomes were observed in across various particulate bovine BS. Due to inadequate data, numerous considerations related to diverse particulate bovine BS, including peri-implant defects, must be more conclusive. Additional clinical studies are imperative to address these knowledge gaps effectively.

The Effects of Brassinosteroids on Nitrogen Utilization in Rice

Nitrogen and brassinosteroids (BRs) play a vital role in modulating the growth, development, and yield of rice. However, the influences of BRs on nitrogen assimilation and metabolism in rice are not fully understood. In this study, we analyzed the impact of BRs on nitrogen utilization in rice using the indica variety ‘Zhongjiazao 17’ and the japonica variety ‘Nipponbare’ in hydroponic conditions. The results showed that BR treatment could efficiently elevate nitrate and ammonium nitrogen accumulation in both shoots and roots. Furthermore, some genes involved in the uptake of nitrate and ammonium in roots were stimulated by BRs, though we noted subtle variances between the two rice cultivars. Moreover, BRs augmented the activity of nitrate reductase (NR) and glutamine synthetase (GS) in roots, along with NR in shoots. Interestingly, BRs also spiked the total free amino acid content in both the shoots and roots. Gene expression analysis uncovered a robust induction by BRs of NR genes and GS-related genes in the roots of both ‘Nipponbare’ and ‘Zhongjiazao 17’. Collectively, our data suggest that BRs significantly enhance the accumulation of both nitrate and ammonium in rice and trigger a series of reactions related to nitrogen utilization.

A tunable star-shaped highly sensitive microwave sensor for solid and liquid sensing

This paper presents the development of an innovative, tunable star-shaped microwave sensor operating at two distinct frequency bands C and X band, optimized for high-sensitivity sensing of both solid and liquid mediums. The sensor's innovative star-shaped geometry augments the surface interaction with the target substances, significantly enhancing the sensor's response and sensitivity. Furthermore, tunability allows the sensor to adapt to different sensing environments, maximizing the detection potential at both frequencies. This research delves into the sensor's performance when using four different substrates for solid sensing containing FR-4, RO-3035, RO-5880, and RO-6202. Each substrate, characterized by unique electromagnetic properties, contributes differently to the sensor's sensitivity and resolution. The extensive experimental results highlight the sensor's proficiency in detecting and differentiating between these substrates, revealing the impact of material selection on sensor performance. The sensor's ability to distinguish between various liquids, including water, milk, oil, and energy drinks, is evaluated in the liquid sensing scenario. The sensor successfully detects the subtle variances in the electromagnetic properties of these liquids, suggesting its capability for high-resolution liquid sensing. The proposed MTM-based sensor resonates at 4.46 GHz and 11.48 GHz, respectively. Numerical software CST was used to analyze all the parameters. A 3 × 3 array was used in the prototype design, and it was used to measure the C-band resonance frequency. On the other hand, a compact unit cell was used to measure the X-band resonance frequency. The outcomes suggest a substantial potential for this sensor design in various industries and scientific fields, including food quality control, the oil industry, biomedical applications, and environmental monitoring. The unique combination of star-shaped design, tunable frequencies, and various substrates offers unprecedented flexibility and sensitivity in solid and liquid sensing on microwave frequency ranges.

The Multidimensional Analysis of the Translator Style in English Translations of The True Story of Ah Q

This research investigates the characteristics of the translator style in five English translations of The True Story of Ah Q, with particular attention paid to lexicons, syntax and overall text structure. The primary goal is to examine patterns between translation versions as well as identify subtle variances that exist among translation versions. By employing Multidimensional Analysis techniques, we delve deeper into the concept of Translator Style. Explore their choices and patterns across translation projects. Our findings illustrate elements that define a translator style at a level, such as syntactic structures and overall text composition in The True Story of Ah Q. This study emphasises the significance of translators and scholars of translation studies recognising how translator styles impact translated text. Further research should be undertaken to gain more insights into translator styles and their effects; furthermore, findings from such research could also be integrated into translation training programs to promote greater appreciation of translation as an art form.

Clinical Practice Gaps in Managing Patients with Myelofibrosis: Comparison of Healthcare Professional and Expert Treatment Choices Provided to an Online Decision Support Tool

Background: Myelofibrosis (MF) is a rare hematologic malignancy for which treatment has evolved considerably in recent years, with 3 JAK inhibitors now approved in the United States to manage this disease. With numerous therapeutics in phase III trials, the complexity of treatment selection is likely to increase in the coming years. To assist hematology/oncology healthcare professionals (HCPs), we developed an online decision support tool designed to provide case-specific treatment recommendations from 5 MF experts. Here, we report an analysis of cases entered into the tool by HCPs, comparing their planned treatment with expert recommendations to determine differences in clinical practice and areas of educational need. Methods: In February 2023, 5 MF experts provided evidence-based treatment recommendations for 45 distinct MF case scenarios that were defined by multiple key factors, including the presence of MF symptoms, splenomegaly, or anemia; platelet count; prognostic risk group; and previous JAK inhibitor therapy. To use the tool, HCPs entered their patients' characteristics and their intended treatment plan; recommendations from each of the 5 experts based on those same characteristics were then provided. In this analysis, the intended treatment plans of HCPs were compared with expert recommendations to determine areas of discordance and agreement. Results: In May/June 2023, 91 cases were entered into the tool (84% of cases by physicians); 72% of respondents reported treating 1-4 patients with MF per month (n = 32), and 61% sought recommendations for a specific patient in their practice (n = 28). A lack of concordance between HCP and expert treatment choices was observed for many case scenarios (Table). For example, for case patients with lower prognostic risk and MF signs/symptoms, experts most frequently recommended ruxolitinib (85%) in contrast to HCPs, who recommended ruxolitinib for 38% of these cases. Similarly, discordance was noted for several case scenarios of patients with higher-risk MF who were not suitable for transplant and required a treatment change following initial therapy with ruxolitinib. Of note, subtle variance was observed among experts for specific second-line case scenarios, including when to add on to current therapy vs switching to a new JAK inhibitor, which may reflect the burgeoning area of clinical practice in defining JAK inhibitor failure and determining subsequent second-line treatment paradigms with several JAK inhibitors now available. Of the 23 HCP cases seeking recommendations for patients with higher prognostic risk, 74% sought recommendations in the post-ruxolitinib setting. Overall, after reviewing expert recommendations, 81% of HCP respondents indicated that they would change their treatment approach or that the recommendations confirmed their treatment strategy (n = 32). Conclusions: Analysis of data from an online decision support tool indicates differences among experts and HCPs in contemporary management of patients with MF, and data suggest that a decision support tool can affect HCP treatment decisions in a rapidly evolving therapeutic landscape, potentially improving patient care. In-depth analyses with additional HCP cases will be presented.

Comparative analysis of the thermal insulation performance of a façade enclosure integrated by vegetation under simultaneous windy and rainy climatic conditions

The literature offers some studies on the capacity of the greenery apparatus to decrease wind speed and regulate temperatures with the combination of the moisture retained by the plants and the air passing through them, but there is little on the maintenance of performance under particular weather conditions. The aim of this contribution is to verify the effectiveness of a vegetal façade in particularly windy conditions combined with rainy and/or high-irradiation events. The subject of the study is the enclosure of the Technology Innovation Centre for Development (itdUPM), on the Polytechnic University of Madrid, where a green wall prototype has been installed. For the purposes of the analysis, the environmental variables are examined and the monitoring data received from sensors positioned at the walls and skin of the insulated envelope are compared with the green face and without, comparing the differences in surface temperatures. These analyses are further examined by considering the correlation with different weather conditions. Experimentation shows a maintenance of performance, retaining an insulating capacity in all seasons, in both wind and rain, with results more evident in daylight hours. This contribute want to analyse the subtle variance between the performance of south and west facades. The strongest effect came forward during the summer season because the wall is affected by continuous irradiation on the south that is, also increased by hot weather.

Differential Activity of Orthosteric Agonists and Allosteric Modulators at Metabotropic Glutamate Receptor 7.

Metabotropic glutamate receptor 7 (mGlu7) is a G protein coupled receptor that has demonstrated promise as a therapeutic target across a number of neurologic and psychiatric diseases. Compounds that modulate the activity of mGlu7, such as positive and negative allosteric modulators, may represent new therapeutic strategies to modulate receptor activity. The endogenous neurotransmitter associated with the mGlu receptor family, glutamate, exhibits low efficacy and potency in activating mGlu7, and surrogate agonists, such as the compound L-(+)-2-Amino-4-phosphonobutyric acid (L-AP4), are often used for receptor activation and compound profiling. To understand the implications of the use of such agonists in the development of positive allosteric modulators (PAMs), we performed a systematic evaluation of receptor activation using a system in which mutations can be made in either protomer of the mGlu7 dimer; we employed mutations that prevent interaction with the orthosteric site as well as the G-protein coupling site of the receptor. We then measured increases in calcium levels downstream of a promiscuous G protein to assess the effects of mutations in one of the two protomers in the presence of two different agonists and three positive allosteric modulators. Our results reveal that distinct PAMs, for example N-[3-Chloro-4-[(5-chloro-2-pyridinyl)oxy]phenyl]-2-pyridinecarboxamide (VU0422288) and 3-(2,3-Difluoro-4-methoxyphenyl)-2,5-dimethyl-7-(trifluoromethyl)pyrazolo[1,5-a]pyrimidine (VU6005649), do exhibit different maximal levels of potentiation with L-AP4 versus glutamate, but there appear to be common stable receptor conformations that are shared among all of the compounds examined here. SIGNIFICANCE STATEMENT: This manuscript describes the systematic evaluation of the mGlu7 agonists glutamate and L-(+)-2-Amino-4-phosphonobutyric acid (L-AP4) in the presence and absence of three distinct potentiators examining possible mechanistic differences. These findings demonstrate that mGlu7 potentiators display subtle variances in response to glutamate versus L-AP4.

Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies.

To determine whether a CT-based machine learning (ML) can differentiate benign renal tumors from renal cell carcinomas (RCCs) and improve radiologists' diagnostic performance, and evaluate the impact of variable CT imaging phases, slices, tumor sizes, and region of interest (ROI) segmentation strategies. Patients with pathologically proven RCCs and benign renal tumors from our institution between 2008 and 2020 were included as the training dataset for ML model development and internal validation (including 418 RCCs and 78 benign tumors), and patients from two independent institutions and a public database (TCIA) were included as the external dataset for individual testing (including 262 RCCs and 47 benign tumors). Features were extracted from three-phase CT images. CatBoost was used for feature selection and ML model establishment. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the ML model. The ML model based on 3D images performed better than that based on 2D images, with the highest AUC of 0.81 and accuracy (ACC) of 0.86. All three radiologists achieved better performance by referring to the classifier's decision, with accuracies increasing from 0.82 to 0.87, 0.82 to 0.88, and 0.76 to 0.87. The ML model achieved higher negative predictive values (NPV, 0.82-0.99), and the radiologists achieved higher positive predictive values (PPV, 0.91-0.95). A ML classifier based on whole-tumor three-phase CT images can be a useful and promising tool for differentiating RCCs from benign renal tumors. The ML model also perfectly complements radiologist interpretations. • A machine learning classifier based on CT images could be a reliable way to differentiate RCCs from benign renal tumors. • The machine learning model perfectly complemented the radiologists' interpretations. • Subtle variances in ROI delineation had little effect on the performance of the ML classifier.

Decision Choice under Pareto Optimal Criteria

According to the axiomatic foundations of social choice theory, not all decisions benefit everyone. Often, decisions that do not have any implied benefit for the decision maker are made in the (best) interests of others. When a decision is made concerning welfare of others, some individuals - including the decision maker, may be on the receiving end. For, it is impossible to make social decisions by taking into account individual preferences that satisfy all and everyone. This is because of a great variety in individual choices and preferences ubiquitous among different individuals. Tastes vary among different people—so does individual preferences, and that is natural. Conflict of interests arises due to subtle variances in individual preferences. In this paper, we discuss about the decision choice that seldom works for every conceivable set of individual preferences. Following Arrovian precepts, it is impossible to satisfy all, for there remains a great diversity in individual preferences that result in the problem of choice. Hence, in this research, we develop a taste-based theory of social choice that attempts to address the problem of choice by helping individuals choose the best and the most effective and optimal option among a given set of alternatives that’s assumed to be rational.

Spatial–temporal modeling for prediction of stylized human motion

Human motion prediction refers to forecasting human motion in the future given a past motion sequence, which has significant applications in human tracking, automatic motion generation, autonomous driving, human-robotics interaction, etc. Previous works usually used RNN-based methods, focusing on modeling the temporal dynamics of human motion, which have made great effort on content motions. However, it is unclear for their performance on stylized motion, which is with more expressive emotions and states of the human motion. Different styles within the same motion type have similar motion patterns but also subtle variances. This makes it difficult to be predicted. The main idea of this paper is to learn the spatial characteristic of stylized motion and combine it with the temporal dynamics to achieve accurate prediction. We adopt a transformer-based style encoder to learn the motion representation in the pose space and then maps it to the latent space modeled by the constant variance Gaussian mixture model; meanwhile, we use the hierarchical multi-scale RNN as a temporal encoder to capture the temporal dynamics of human motion; finally, we feed the spatial and temporal features into the prediction decoder to predict the next frame. Our experiments on the Human 3.6 M and Stylized MotionDatasets demonstrate that our model has comparable prediction performance with the state-of-the-art motion prediction works on Human 3.6 M and outperforms previous works on stylized human motion prediction.

Academic House Style and Acknowledgement Writing: the Case of Two Ghanaian Universities

This descriptive study sought to examine the structural features of Thesis Acknowledgements (TA) from two disciplines in two Ghanaian Universities. Forty TAs were sampled from the fields of Chemistry and English. Hyland’s (2004) generic move structure was modified in analyzing the sampled TAs. The data was also examined on the basis of the two institutions’ academic style guides for conformity. The findings indicate that the structural features of TAs from the two fields differ. It also revealed that out of the seven steps identified in the obligatory Elaborative Move, DoC writers generally use three steps, and DoE writers mostly use four steps. It is further revealed that while writers from the DoC mostly acknowledged contributors for their technical support, writers from DoE acknowledged their helpers for providing materials for the research. It was revealed that the data followed the institutional style guide. The paper argues that though the contextual difference in the TAs from both fields is minimal, subtle variances and parallels still exist between the two fields. Therefore, it is suggested that these variations in linguistic forms that account for the discursive styles of the TAs should be further examined. Finally, additional analysis by corpus analytic tools applied to large data from the research context could complement this study.

Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning

The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations. Making the two views distinctive is a core to guarantee that unsupervised methods can learn meaningful information. However, such frameworks are sometimes fragile on overfitting if the augmentations used for generating two views are not strong enough, causing the over-confident issue on the training data. This drawback hinders the model from learning subtle variance and fine-grained information. To address this, in this work we aim to involve the soft distance concept on label space in the contrastive-based unsupervised learning task and let the model be aware of the soft degree of similarity between positive or negative pairs through mixing the input data space, to further work collaboratively for the input and loss spaces. Despite its conceptual simplicity, we show empirically that with the solution -- Unsupervised image mixtures (Un-Mix), we can learn subtler, more robust and generalized representations from the transformed input and corresponding new label space. Extensive experiments are conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard ImageNet-1K with popular unsupervised methods SimCLR, BYOL, MoCo V1&V2, SwAV, etc. Our proposed image mixture and label assignment strategy can obtain consistent improvement by 1~3% following exactly the same hyperparameters and training procedures of the base methods. Code is publicly available at https://github.com/szq0214/Un-Mix.

A VR-based volumetric medical image segmentation and visualization system with natural human interaction

Volume rendering produces informative two-dimensional (2D) images from a 3-dimensional (3D) volume. It highlights the region of interest and facilitates a good comprehension of the entire data set. However, volume rendering faces a few challenges. First, a high-dimensional transfer function is usually required to differentiate the target from its neighboring objects with subtle variance. Unfortunately, designing such a transfer function is a strenuously trial-and-error process. Second, manipulating/visualizing a 3D volume with a traditional 2D input/output device suffers dimensional limitations. To address all the challenges, we design NUI-VR^2, a natural user interface-enabled volume rendering system in the virtual reality space. NUI-VR^2 marries volume rendering and interactive image segmentation. It transforms the original volume into a probability map with image segmentation. A simple linear transfer function will highlight the target well in the probability map. More importantly, we set the entire image segmentation and volume rendering pipeline in an immersive virtual reality environment with a natural user interface. NUI-VR^2 eliminates the dimensional limitations in manipulating and perceiving 3D volumes and dramatically improves the user experience.

Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification

Deep convolutional neural networks (CNNs) have shown a strong ability in mining discriminative object pose and parts information for image recognition. For fine-grained recognition, context-aware rich feature representation of object/scene plays a key role since it exhibits a significant variance in the same subcategory and subtle variance among different subcategories. Finding the subtle variance that fully characterizes the object/scene is not straightforward. To address this, we propose a novel context-aware attentional pooling (CAP) that effectively captures subtle changes via sub-pixel gradients, and learns to attend informative integral regions and their importance in discriminating different subcategories without requiring the bounding-box and/or distinguishable part annotations. We also introduce a novel feature encoding by considering the intrinsic consistency between the informativeness of the integral regions and their spatial structures to capture the semantic correlation among them. Our approach is simple yet extremely effective and can be easily applied on top of a standard classification backbone network. We evaluate our approach using six state-of-the-art (SotA) backbone networks and eight benchmark datasets. Our method significantly outperforms the SotA approaches on six datasets and is very competitive with the remaining two.

Structural ordering of the Plasmodium berghei circumsporozoite protein repeats by inhibitory antibody 3D11.

Plasmodium sporozoites express circumsporozoite protein (CSP) on their surface, an essential protein that contains central repeating motifs. Antibodies targeting this region can neutralize infection, and the partial efficacy of RTS,S/AS01 - the leading malaria vaccine against P. falciparum (Pf) - has been associated with the humoral response against the repeats. Although structural details of antibody recognition of PfCSP have recently emerged, the molecular basis of antibody-mediated inhibition of other Plasmodium species via CSP binding remains unclear. Here, we analyze the structure and molecular interactions of potent monoclonal antibody (mAb) 3D11 binding to P. berghei CSP (PbCSP) using molecular dynamics simulations, X-ray crystallography, and cryoEM. We reveal that mAb 3D11 can accommodate all subtle variances of the PbCSP repeating motifs, and, upon binding, induces structural ordering of PbCSP through homotypic interactions. Together, our findings uncover common mechanisms of antibody evolution in mammals against the CSP repeats of Plasmodium sporozoites.

Propulsive efficiency of alternative underwater flykick techniques for swimmers

Analysis of video and speed data is used to evaluate the efficiency of human underwater flykick. The authors show that by coupling Lighthill’s theory of fish locomotion with human musculoskeletal modelling, it is possible to evaluate the effectiveness of the mechanical and hydrodynamic propulsive components of human underwater flykick. This allows the effect of subtle variances in technique to be assessed by measurement of athlete motion alone. This is demonstrated in an experimental case study of an elite athlete performing two different techniques; one more knee-based or thunniform, and the second more undulatory or carangiform/anguilliform. In finding the mean kinematics of each technique, it is first shown that maintaining stroke-by-stroke consistency of technique leads to an increase in propulsive efficiency. It is further demonstrated that in changing technique, an athlete may swim at the same kick rate but have different propulsive efficiency. This demonstrates the need to determine the energy cost in order to evaluate differing techniques. For the sprint athlete in this case study, it was shown to be more effective to swim with a thunniform technique when at higher velocities and a more anguilliform at lower velocities.

Vowel, consonant, and tone variation exert asymmetrical effects on spoken word recognition: Evidence from 6-year-old monolingual and bilingual learners of Mandarin

Most children are raised in a bilingual environment. However, compared with monolingual language acquisition, relatively little is known about how bilingual children acquire native phonology. Moreover, much less is known about how children acquire knowledge of tone languages in comparison with consonant–vowel languages such as English. In this study, 6-year-old Mandarin monolingual and English–Mandarin bilingual learners were tested on their sensitivity to vowel, consonant, and lexical tone mispronunciations of known words in a preferential looking task (N = 48; Experiment 1) and in an explicit judgment task (N = 48; Experiment 2). Results demonstrated that in Experiment 1 both monolingual and bilingual participants were sensitive to vowel mispronunciations. Bilingual children were also sensitive to consonant and tone mispronunciations. When sensitivity to mispronunciations was compared with correct pronunciations, monolingual and bilingual participants were similarly sensitive to consonant mispronunciations and similarly insensitive to tones, but they demonstrated subtle variance in vowel sensitivity. In Experiment 2, both groups demonstrated reduced sensitivity to tone mispronunciations relative to vowel and consonant mispronunciations. Therefore, in both implicit and explicit tasks, participants demonstrated asymmetrical sensitivity to segments (vowels and consonants) and tones. Our findings are discussed in terms of influences of phonological variation and language background on children’s word knowledge.

Consumer Arbitration and Subtle Variances in its Effectiveness in Selected EU Member States (Czech Republic, Slovakia, Austria, Germany)

Thorough legal regulation of arbitration involving consumers is significant for their protection, which is provided in the EU by the restrictive model, embedded in Council Directive 93/13/EEC on Unfair Terms in Consumer Contracts. It is submitted that EU Member States’ legislative reaction to achieve compatibility of their national laws with Directive 93/13 EEC was not identical and led to a debate on the effectiveness of consumer arbitration in the EU. The following article is an attempt to inform this debate. It provides a compact analysis of consumer arbitration agreements and discusses relevant arbitration law and practice in the Czech Republic, Slovakia, Austria and Germany – four EU Member States and also neighbors with intertwined mutual relationships. The thesis of this article is that even the most consumer-friendly legislation does not secure the success of consumer arbitration in all arbitrated cases. Equally, strongly pro-consumer legislation does not always mean arbitration-friendly legislation when legislative assimilation of litigation with arbitration is not avoided – indeed, quite the opposite.

Hues of Red: The Facades of Leftist Insurgency and Crisis in India in Select Fiction

Literature on the leftist insurgency is a field where sociopolitical turmoil meets revolution. For the last fifty years, leftist insurgency entails a problematic premise in India. From the example of countries like the Soviet Union and China, India incorporated the idea of Communism in the 1970s. Communism in India found manifestation through two movements namely Naxalism and Maoism, with their basis in Marxist–Leninist ideology but with a subtle variance. This subtlety breaks open a crisis that has been understated often when the movements have been referred to as synonymous terms in media jargon and popular discourse. The policy of the “annihilation line” forms an ideological crisis in India; which is expansively reflected in select fiction from the country. Therefore, this article attempts at dissecting two pieces of fiction chosen from the whole repertoire of literature written on Naxalism and Maoism to elucidate this crisis. Neel Mukherjee's The Lives of Others (2014) is placed against Nilima Sinha's Red Blooms in the Forest (2013) in order to establish how parallel discourses from two different time periods of the movement facilitate a study of the trajectory of the Naxalite–Maoist insurgency.