bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
Authors: Takahashi Yuta , Murata Shingo , Idei Hayato , Tomita Hiroaki , Yamashita Yuichi 
Published: July 2021

AbstractThe mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown. Therefore, this study proposes a system-level explanation for understanding the facial emotion recognition process and its alteration in autism spectrum disorder (ASD) from the perspective of predictive processing theory. Predictive processing for facial emotion recognition was implemented as a hierarchical recurrent neural network (RNN). The RNNs were trained to predict the dynamic changes of facial expression movies for six basic emotions without explicit emotion labels as a developmental learning process, and were evaluated by the performance of recognizing unseen facial expressions for the test phase. In addition, the causal relationship between the network characteristics assumed in ASD and ASD-like cognition was investigated. After the developmental learning process, emotional clusters emerged in the natural course of self-organization in higher-level neurons, even though emotional labels were not explicitly instructed. In addition, the network successfully recognized unseen test facial sequences by adjusting higher-level activity through the process of minimizing precision-weighted prediction error. In contrast, the network simulating altered intrinsic neural excitability demonstrated reduced generalization capability and impaired emotional clustering in higher-level neurons. Consistent with previous findings from human behavioral studies, an excessive precision estimation of noisy details underlies this ASD-like cognition. These results support the idea that impaired facial emotion recognition in ASD can be explained by altered predictive processing, and provide possible insight for investigating the neurophysiological basis of affective contact.
The impact of human mobility data scales and processing on movement predictability
Authors: Smolak Kamil , Siła-Nowicka Katarzyna , Delvenne Jean-Charles , Wierzbiński Michał , Rohm Witold 
Published: July 2021

AbstractPredictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies.
Highly sensitive detection of dengue biomarker using streptavidin-conjugated quantum dots
Authors: Tran Linh , Park Sangkwon 
Published: July 2021

AbstractA highly sensitive immunosensor using streptavidin-conjugated quantum dots (QDs/SA) was developed to detect dengue biomarker of non-structural protein 1 (NS1) at very low concentration, so that it can probe dengue infection even in the early stage. The QDs/SA were first bound to biotinylated NS1 antibody (Ab) and the QDs/SA-Ab conjugates were then used to detect the NS1 antigen (Ag) in the Ag concentration range of 1 pM to 120 nM. The formation of QDs/SA-Ab and QDs/SA-Ab-Ag conjugates was confirmed by the measurements of field emission scanning electron microscopy (FF-SEM), field emission transmission electron microscopy (FE-TEM), dynamic light scattering (DLS), and zeta-potential. Fluorescence emission spectra of QDs/SA-Ab-Ag conjugates showed that the magnitude of fluorescence quenching was linearly proportional to the NS1 Ag concentration and it nicely followed the Stern–Volmer (SV) equation in phosphate buffer solution. However, in human plasma serum solution, the fluorescence quenching behavior was negatively deviated from the SV equation presumably due to interference by the serum component biomolecules, and it was well explained by the Lehrer equation. These results suggest that the current approach is promising because it is highly sensitive, fast, simple, and convenient, and thus it has a potential of application for point-of-care.
First modern human settlement recorded in the Iberian hinterland occurred during Heinrich Stadial 2 within harsh environmental conditions
Authors: Alcaraz-Castaño M. , Alcolea-González J. J. , de Andrés-Herrero M. , Castillo-Jiménez S. , Cuartero F. , Cuenca-Bescós G. , Kehl M. , López-Sáez J. A. , Luque L. , Pérez-Díaz S. , Piqué R. , Ruiz-Alonso M. , Weniger G.-C. , Yravedra J. 
Published: July 2021

AbstractAs the south-westernmost region of Europe, the Iberian Peninsula stands as a key area for understanding the process of modern human dispersal into Eurasia. However, the precise timing, ecological setting and cultural context of this process remains controversial concerning its spatiotemporal distribution within the different regions of the peninsula. While traditional models assumed that the whole Iberian hinterland was avoided by modern humans due to ecological factors until the retreat of the Last Glacial Maximum, recent research has demonstrated that hunter-gatherers entered the Iberian interior at least during Solutrean times. We provide a multi-proxy geoarchaeological, chronometric and paleoecological study on human–environment interactions based on the key site of Peña Capón (Guadalajara, Spain). Results show (1) that this site hosts the oldest modern human presence recorded to date in central Iberia, associated to pre-Solutrean cultural traditions around 26,000 years ago, and (2) that this presence occurred during Heinrich Stadial 2 within harsh environmental conditions. These findings demonstrate that this area of the Iberian hinterland was recurrently occupied regardless of climate and environmental variability, thus challenging the widely accepted hypothesis that ecological risk hampered the human settlement of the Iberian interior highlands since the first arrival of modern humans to Southwest Europe.
Proteomics reveals disturbances in the immune response and energy metabolism of monocytes from patients with septic shock
Authors: de Azambuja Rodrigues Pedro Mendes , Valente Richard Hemmi , Brunoro Giselle Villa Flor , Nakaya Helder Takashi Imoto , Araújo-Pereira Mariana , Bozza Patricia Torres , Bozza Fernando Augusto , Trugilho Monique Ramos de Oliveira 
Published: July 2021

AbstractSepsis results from a dyshomeostatic response to infection, which may lead to hyper or hypoimmune states. Monocytes are central regulators of the inflammatory response, but our understanding of their role in the genesis and resolution of sepsis is still limited. Here, we report a comprehensive exploration of monocyte molecular responses in a cohort of patients with septic shock via proteomic profiling. The acute stage of septic shock was associated with an impaired inflammatory phenotype, indicated by the down-regulation of MHC class II molecules and proinflammatory cytokine pathways. Simultaneously, there was an up-regulation of glycolysis enzymes and a decrease in proteins related to the citric acid cycle and oxidative phosphorylation. On the other hand, the restoration of immunocompetence was the hallmark of recovering patients, in which an upregulation of interferon signaling pathways was a notable feature. Our results provide insights into the immunopathology of sepsis and propose that, pending future studies, immunometabolism pathway components could serve as therapeutic targets in septic patients.
Beneficial insects are associated with botanically rich margins with trees on small farms
Authors: Arnold Sarah E. J. , Elisante Filemon , Mkenda Prisila A. , Tembo Yolice L. B. , Ndakidemi Patrick A. , Gurr Geoff M. , Darbyshire Iain A. , Belmain Steven R. , Stevenson Philip C. 
Published: July 2021

AbstractBeneficial insect communities on farms are influenced by site- and landscape-level factors, with pollinator and natural enemy populations often associated with semi-natural habitat remnants. They provide ecosystem services essential for all agroecosystems. For smallholders, natural pest regulation may be the only affordable and available option to manage pests. We evaluated the beneficial insect community on smallholder bean farms (Phaseolus vulgaris L.) and its relationship with the plant communities in field margins, including margin trees that are not associated with forest fragments. Using traps, botanical surveys and transect walks, we analysed the relationship between the floral diversity/composition of naturally regenerating field margins, and the beneficial insect abundance/diversity on smallholder farms, and the relationship with crop yield. More flower visits by potential pollinators and increased natural enemy abundance measures in fields with higher plant, and particularly tree, species richness, and these fields also saw improved crop yields. Many of the flower visitors to beans and potential natural enemy guilds also made use of non-crop plants, including pesticidal and medicinal plant species. Selective encouragement of plants delivering multiple benefits to farms can contribute to an ecological intensification approach. However, caution must be employed, as many plants in these systems are introduced species.
AC conductivity and correlation effects in nano-granular Pt/C
Authors: Hanefeld Marc , Gruszka Peter , Huth Michael 
Published: July 2021

AbstractNano-granular metals are materials that fall into the general class of granular electronic systems in which the interplay of electronic correlations, disorder and finite size effects can be studied. The charge transport in nano-granular metals is dominated by thermally-assisted, sequential and correlated tunneling over a temperature-dependent number of metallic grains. Here we study the frequency-dependent conductivity (AC conductivity) of nano-granular Platinum with Pt nano-grains embedded into amorphous carbon (C). We focus on the transport regime on the insulating side of the insulator metal transition reflected by a set of samples covering a range of tunnel-coupling strengths. In this transport regime polarization contributions to the AC conductivity are small and correlation effects in the transport of free charges are expected to be particularly pronounced. We find a universal behavior in the frequency dependence that can be traced back to the temperature-dependent zero-frequency conductivity (DC conductivity) of Pt/C within a simple lumped-circuit analysis. Our results are in contradistinction to previous work on nano-granular Pd/$$\hbox {ZrO}_2$$

ZrO
2

in the very weak coupling regime where polarization contributions to the AC conductivity dominated. We describe possible future applications of nano-granular metals in proximity impedance spectroscopy of dielectric materials.
Corneal inflammatory cell infiltration predicts disease activity in chronic inflammatory demyelinating polyneuropathy
Authors: Motte Jeremias , Grüter Thomas , Fisse Anna Lena , Bulut Yesim , Stykova Zornitsa , Greiner Tineke , Enax-Krumova Elena , Yoon Min-Suk , Gold Ralf , Tegenthoff Martin , Sturm Dietrich , Pitarokoili Kalliopi 
Published: July 2021

AbstractThe assessment of disease activity is fundamental in the management of chronic inflammatory demyelinating polyneuropathy (CIDP). Previous studies with small patient numbers found an increase of corneal immune cell infiltrates as a potential marker of inflammation in patients with CIDP. However, its clinical relevance remained unclear. The present study aimed to determine whether the amount of corneal inflammatory cells (CIC) measured by corneal confocal microscopy (CCM) detects disease activity in CIDP. CIC were measured in 142 CCM-investigations of 97 CIDP-patients. Data on clinical disease activity, disability (INCAT-ODSS) and need for therapy escalation at the timepoint of CCM, 3 and 6 months later were analyzed depending CIC-count. Pathological spontaneous activity during electromyography was examined as another possible biomarker for disease activity in comparison to CIC-count. An increased CIC-count at baseline was found in patients with clinical disease activity and disability progression in the following 3–6 months. An increase to more than 25 CIC/mm2had a sensitivity of 0.73 and a specificity of 0.71 to detect clinical disease activity and a sensitivity of 0.77 and a specificity of 0.64 to detect disability progression (increasing INCAT-ODSS) in the following 6 months. An increase to more than 50 CIC/mm2had a sensitivity of about 0.51 and a specificity of 0.91 to detect clinical disease activity and a sensitivity of 0.53 and a specificity of 0.80 to detect disability progression. CIC count is a non-invasive biomarker for the detection of disease activity in the following 6 months in CIDP.
Simple recycling of biowaste eggshells to various calcium phosphates for specific industries
Authors: Laohavisuti Nongnuch , Boonchom Banjong , Boonmee Wimonmat , Chaiseeda Kittichai , Seesanong Somkiat 
Published: July 2021

AbstractEgg consumption is very high throughout the world and with it comes enormous amount of waste eggshells. To reduce and utilize these wastes, eggshell wastes were simply transformed to low- or high-purity calcium carbonate grades by washing, crushing, and drying to use as raw materials for producing highly valuable calcium phosphate products. Low-purity calcium carbonate grade was used to prepare triple superphosphate for using in fertilizer industry, whereas high-purity calcium carbonate grade was used to produce dicalcium phosphate dihydrate, monocalcium phosphate monohydrate, and tricalcium phosphate for using in mineral feed and food additive industries. All calcium phosphate samples obtained by simple, rapid, cheap, and environmentally safe method using eggshells and phosphoric acid were identified and their structural phases and impurities were determined by XRF, XRD and FTIR techniques. Thermal behaviors of raw materials and the prepared calcium phosphates excepted tricalcium phosphate were investigated by TG/DTG techniques. The methodologies described here will be useful to manage eggshells by converting them to highly valuable products, which can solve eggshell wastes problem from industries and communities. This finding supports the viewpoint of zero waste operation to produce value-added products for obtaining sustainable development, which may be selected as an alternative way for material recycling and waste management in the future.
Benchmarking the Covid-19 pandemic across countries and states in the USA under heterogeneous testing
Authors: Asahi Kenzo , Undurraga Eduardo A. , Wagner Rodrigo 
Published: July 2021

AbstractScientists and policymakers need to compare the incidence of Covid-19 across territories or periods with various levels of testing. Benchmarking based on the increase in total cases or case fatality rates is one way of comparing the evolution of the pandemic across countries or territories and could inform policy decisions about strategies to control coronavirus transmission. However, comparing cases and fatality rates across regions is challenging due to heterogeneity in testing and health systems. We show two complementary ways of benchmarking across territories and in time. First, we used multivariate regressions to estimate the test-elasticity of Covid-19 case incidence. Cases grow less than proportionally with testing when assessing weekly changes or looking across states in the USA. They tend to be proportional or even more than proportional when comparing the month-to-month evolution of an average country in the pandemic. Our results were robust to various model specifications. Second, we decomposed the growth in cases into test growth and positive test ratio growth to intuitively visualize the components of case growth. We hope these results can help support evidence-based decisions by public officials and help the public discussion when comparing across territories and in time.
Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data
Authors: García-Cremades Santi , Morales-García Juan , Hernández-Sanjaime Rocío , Martínez-España Raquel , Bueno-Crespo Andrés , Hernández-Orallo Enrique , López-Espín José J. , Cecilia José M. 
Published: July 2021

AbstractWe are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create a decision support system for policy-makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14-day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14-day CI in scenarios with and without trend changes, reaching 0.93 $$R^2$$

R
2

, 4.16 RMSE and 1.08 MAE.
Integrative analysis reveals the prognostic value and functions of splicing factors implicated in hepatocellular carcinoma
Authors: Wang Yue , Yang Fan , Shang Jiaqi , He Haitao , Yang Qing 
Published: July 2021

AbstractSplicing factors (SFs) play critical roles in the pathogenesis of various cancers through regulating tumor-associated alternative splicing (AS) events. However, the clinical value and biological functions of SFs in hepatocellular carcinoma (HCC) remain obscure. In this study, we identified 40 dysregulated SFs in HCC and established a prognostic model composed of four SFs (DNAJC6, ZC3H13, IGF2BP3, DDX19B). The predictive efficiency and independence of the prognostic model were confirmed to be satisfactory. Gene Set Enrichment Analysis (GSEA) illustrated the risk score calculated by our prognostic model was significantly associated with multiple cancer-related pathways and metabolic processes. Furthermore, we constructed the SFs-AS events regulatory network and extracted 108 protein-coding genes from the network for following functional explorations. Protein–protein interaction (PPI) network delineated the potential interactions among these 108 protein-coding genes. GO and KEGG pathway analyses investigated ontology gene sets and canonical pathways enriched by these 108 protein-coding genes. Overlapping the results of GSEA and KEGG, seven pathways were identified to be potential pathways regulated by our prognostic model through triggering aberrant AS events in HCC. In conclusion, the present study established an effective prognostic model based on SFs for HCC patients. Functional explorations of SFs and SFs-associated AS events provided directions to explore biological functions and mechanisms of SFs in HCC tumorigenesis.
Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks
Authors: McAvoy Malia , Prieto Paola Calvachi , Kaczmarzyk Jakub R. , Fernández Iván Sánchez , McNulty Jack , Smith Timothy , Yu Kun-Hsing , Gormley William B. , Arnaout Omar 
Published: July 2021

AbstractA subset of primary central nervous system lymphomas (PCNSL) are difficult to distinguish from glioblastoma multiforme (GBM) on magnetic resonance imaging (MRI). We developed a convolutional neural network (CNN) to distinguish these tumors on contrast-enhanced T1-weighted images. Preoperative brain tumor MRIs were retrospectively collected among 320 patients with either GBM (n = 160) and PCNSL (n = 160) from two academic institutions. The individual images from these MRIs consisted of a training set (n = 1894 GBM and 1245 PCNSL), a validation set (n = 339 GBM; 202 PCNSL), and a testing set (99 GBM and 108 PCNSL). Three CNNs using the EfficientNetB4 architecture were evaluated. To increase the size of the training set and minimize overfitting, random flips and changes to color were performed on the training set. Our transfer learning approach (with image augmentation and 292 epochs) yielded an AUC of 0.94 (95% CI: 0.91–0.97) for GBM and an AUC of 0.95 (95% CI: 0.92–0.98) for PCNL. In the second case (not augmented and 137 epochs), the images were augmented prior to training. The area under the curve for GBM was 0.92 (95% CI: 0.88–0.96) for GBM and an AUC of 0.94 (95% CI: 0.91–0.97) for PCNSL. For the last case (augmented, Gaussian noise and 238 epochs) the AUC for GBM was 0.93 (95% CI: 0.89–0.96) and an AUC 0.93 (95% CI = 0.89–0.96) for PCNSL. Even with a relatively small dataset, our transfer learning approach demonstrated CNNs may provide accurate diagnostic information to assist radiologists in distinguishing PCNSL and GBM.
Survival outcomes after traumatic brain injury during national academic meeting days in Japan
Authors: Hosomi Sanae , Kitamura Tetsuhisa , Sobue Tomotaka , Ogura Hiroshi , Shimazu Takeshi 
Published: July 2021

AbstractSurgeons and medical staff attend academic meetings several times a year. However, there is insufficient evidence on the influence of the “meeting effect” on traumatic brain injury (TBI) treatments and outcomes. Using the Japan Trauma Data Bank, we analyzed the data of TBI patients admitted to the hospital from 2004 to 2018 during the national academic meeting days of the Japanese Association for Acute Medicine, the Japanese Society of Intensive Care Medicine, the Japanese Association for the surgery of trauma, the Japan Society of Neurotraumatology and the Japan Neurosurgical Society. The data of these patients were compared with those of TBI patients admitted 1 week before and after the meetings. The primary outcome was in-hospital death. We included 7320 patients in our analyses, with 5139 and 2181 patients admitted during the non-meeting and meeting days, respectively; their in-hospital mortality rates were 15.7% and 14.5%, respectively. No significant differences in in-hospital mortality were found (adjusted odds ratio, 0.93; 95% confidence interval, 0.78–1.11). In addition, there were no significant differences in in-hospital mortality during the meeting and non-meeting days by the type of national meeting. In Japan, it is acceptable for medical professionals involved in TBI treatments to attend national academic meetings without impacting the outcomes of TBI patients.