Miracle-dz/UniFAR-FTUs huggingface dataset:
Alcohol-induced place conditioning in moderate social drinkers.

AIMS To test whether non-dependent drinkers show place preference for a location paired with alcohol, and to test if the amount of time spent in the alcohol-paired location is related to self-reported subjective alcohol effects experienced in that environment. DESIGN Two groups of subjects completed six conditioning sessions: three with alcohol (0.8 g/kg) and three without alcohol. Individuals were assigned randomly to two groups, paired and unpaired, in a 2 : 1 ratio. The paired group (n = 78) received alcohol in one testing room and no-alcohol in another testing room (biased assignment). The unpaired group (n = 30) received alcohol and no-alcohol in each testing room. SETTING Human Behavioral Pharmacology Laboratory, University of Chicago, Chicago, IL, USA (single site). PARTICIPANTS Healthy male and female social drinkers (n = 108) aged 21-40 years participated in the study (consisting of 10 separate laboratory visits) between March 2012 and August 2014 (an average of 36 separate subject visits per month). MEASUREMENTS The primary outcome measure was the pre- to post-conditioning change in the percentage of time spent in the least preferred room (obtained during drug-free exploration tests conducted at separate visits before and after the six conditioning sessions were completed). Secondary measures included self-reported subjective mood and drug effects obtained during the conditioning sessions. FINDINGS The groups differed in the change in the percentage of time spent in the initially least preferred room, from pre- to post-conditioning; paired group = 11.0%, unpaired group = -1.4%, mean difference = 12.4%, 95% confidence interval = 1.9-23.0, P = 0.02. The change in the percentage of time spent in the least-preferred room was related to the self-reported sedative effects of alcohol during conditioning sessions among paired group participants only. CONCLUSIONS Non-dependent consumers of alcohol appear to develop a behavioral preference for locations paired with alcohol consumption, more so for those who experience sedative effects from alcohol in those locations.
Imaging in multiple sclerosis: A new spin on lesions

This article evaluates the most relevant state‐of‐the‐art magnetic resonance (MR) techniques that are clinically available to investigate multiple sclerosis (MS). The presence of hypo‐ and hyperintense lesions on T1‐ and T2‐weighted magnetic resonance imaging (MRI) sequences in white matter (WM) is a common finding that is occasionally a diagnostic challenge for the radiologist. The technical requirements and how they may help to understand, classify or follow‐up these pathologies are briefly summarized. The gold standard for MS diagnosis is pathological correlation. Yet due to limited availability of biopsy and autopsy material, there is a high demand for imaging as a diagnostic as well as prognostic indicator. With the progress in MRI during the last decade, MRI now plays a leading role in the diagnosis and follow‐up of MS. A number of correlative pathological and MR studies have helped to define pathological substrates of MS in focal lesions and normal appearing white matter (NAWM). Vascular spaces mimicking MS lesions have been minimized by the enhanced differentiation of WM and grey (GM) matter parenchyma. The aim of this article is to enhance the current understanding of histopathology and radiological characteristics of MS lesions in space and time.
Anti-Inflammatory Triterpene Glycosides from the Roots of Ilex dunniana Levl

A new triterpene glycoside ilexdunnoside A (1) and a new sulfated triterpene derivative ilexdunnoside B (2), together with five known analogues 3–7 were isolated from the roots of Ilex dunniana Levl. The structures were established by NMR spectroscopic analysis and acid hydrolysis. Results of an in vivo study of the biological activity showed that 75% ethanol and n-butanol extracts of the plant displayed anti-inflammatory activities against ear edema in mice, with inhibition rates of 23.5% and 37.5%, respectively, at a dose of 50 mg/kg. Furthermore, Compounds 1, 2 and 3 exhibited moderate indirect inhibitory effects on lipopolysaccharide-induced NO production in BV2 microglial cells in vitro, with IC50 values of 11.60, 12.30 and 9.70 μM, respectively.
Characterization and analysis of a de novo transcriptome from the pygmy grasshopper Tetrix japonica

The pygmy grasshopper Tetrix japonica is a common insect distributed throughout the world, and it has the potential for use in studies of body colour polymorphism, genomics and the biology of Tetrigoidea (Insecta: Orthoptera). However, limited biological information is available for this insect. Here, we conducted a de novo transcriptome study of adult and larval T. japonica to provide a better understanding of its gene expression and develop genomic resources for future work. We sequenced and explored the characteristics of the de novo transcriptome of T. japonica using Illumina HiSeq 2000 platform. A total of 107 608 206 paired‐end clean reads were assembled into 61 141 unigenes using the trinity software; the mean unigene size was 771 bp, and the N50 length was 1238 bp. A total of 29 225 unigenes were functionally annotated to the NCBI nonredundant protein sequences (Nr), NCBI nonredundant nucleotide sequences (Nt), a manually annotated and reviewed protein sequence database (Swiss‐Prot), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. A large number of putative genes that are potentially involved in pigment pathways, juvenile hormone (JH) metabolism and signalling pathways were identified in the T. japonica transcriptome. Additionally, 165 769 and 156 796 putative single nucleotide polymorphisms occurred in the adult and larvae transcriptomes, respectively, and a total of 3162 simple sequence repeats were detected in this assembly. This comprehensive transcriptomic data for T. japonica will provide a usable resource for gene predictions, signalling pathway investigations and molecular marker development for this species and other pygmy grasshoppers.
Robust Portfolio Optimization

We propose a robust portfolio optimization approach based on quantile statistics. The proposed method is robust to extreme events in asset returns, and accommodates large portfolios under limited historical data. Specifically, we show that the risk of the estimated portfolio converges to the oracle optimal risk with parametric rate under weakly dependent asset returns. The theory does not rely on higher order moment assumptions, thus allowing for heavy-tailed asset returns. Moreover, the rate of convergence quantifies that the size of the portfolio under management is allowed to scale exponentially with the sample size of the historical data. The empirical effectiveness of the proposed method is demonstrated under both synthetic and real stock data. Our work extends existing ones by achieving robustness in high dimensions, and by allowing serial dependence.
Semantic Discriminative Metric Learning for Image Similarity Measurement

Along with the arrival of multimedia time, multimedia data has replaced textual data to transfer information in various fields. As an important form of multimedia data, images have been widely utilized by many applications, such as face recognition and image classification. Therefore, how to accurately annotate each image from a large set of images is of vital importance but challenging. To perform these tasks well, it is crucial to extract suitable features to character the visual contents of images and learn an appropriate distance metric to measure similarities between all images. Unfortunately, existing feature operators, such as histogram of gradient, local binary pattern, and color histogram, care more about the visual character of images and lack the ability to distinguish semantic information. Similarities between those features cannot reflect the real category correlations due to the well-known semantic gap. In order to solve this problem, this paper proposes a regularized distance metric framework called semantic discriminative metric learning (SDML). SDML combines geometric mean with normalized divergences and separates images from different classes simultaneously. The learned distance metric can treat all images from different classes equally. And distinctions between similar classes with entirely different semantic contents are emphasized by SDML. This procedure ensures the consistency between dissimilarities and semantic distinctions and avoids inaccuracy similarities incurred by unbalanced locations of samples. Various experiments on benchmark image datasets show the excellent performance of the novel method.
Reduced Nonexercise Activity Attenuates Negative Energy Balance in Mice Engaged in Voluntary Exercise

Exercise alone is often ineffective for treating obesity despite the associated increase in metabolic requirements. Decreased nonexercise physical activity has been implicated in this resistance to weight loss, but the mechanisms responsible are unclear. We quantified the metabolic cost of nonexercise activity, or “off-wheel” activity (OWA), and voluntary wheel running (VWR) and examined whether changes in OWA during VWR altered energy balance in chow-fed C57BL/6J mice (n = 12). Energy expenditure (EE), energy intake, and behavior (VWR and OWA) were continuously monitored for 4 days with locked running wheels followed by 9 days with unlocked running wheels. Unlocking the running wheels increased EE as a function of VWR distance. The metabolic cost of exercise (kcal/m traveled) decreased with increasing VWR speed. Unlocking the wheel led to a negative energy balance but also decreased OWA, which was predicted to mitigate the expected change in energy balance by ∼45%. A novel behavioral circuit involved repeated bouts of VWR, and roaming was discovered and represented novel predictors of VWR behavior. The integrated analysis described here reveals that the weight loss effects of voluntary exercise can be countered by a reduction in nonexercise activity.
Development and cross-cultural testing of the International Depression Symptom Scale (IDSS): a measurement instrument designed to represent global presentations of depression

Background Self-report measurement instruments are commonly used to screen for mental health disorders in Low and Middle-Income Countries (LMIC). The Western origins of most depression instruments may constitute a bias when used globally. Western measures based on the DSM, do not fully capture the expression of depression globally. We developed a self-report scale design to address this limitation, the International Depression Symptom Scale-General version (IDSS-G), based on empirical evidence of the signs and symptoms of depression reported across cultures. This paper describes the rationale and process of its development and the results of an initial test among a non-Western population. Methods We evaluated internal consistency reliability, test–retest reliability and inter-rater reliability of the IDSS-G in a sample N = 147 male and female attendees of primary health clinics in Yangon, Myanmar. For criterion validity, IDSS-G scores were compared with diagnosis by local psychiatrists using the Structured Clinical Interview for DSM (SCID). Construct validity was evaluated by investigating associations between the IDSS-G and the Patient Health Questionnaire (PHQ), impaired function, and suicidal ideation. Results The IDSS-G showed high internal consistency reliability (α = 0.92), test–retest reliability (r = 0.87), and inter-rater reliability (ICC = 0.90). Strong correlations between the IDSS-G and PHQ-9, functioning, and suicidal ideation supported construct validity. Criterion validity was supported for use of the IDSS-G to identify people with a SCID diagnosed depressive disorder (major depression/dysthymia). The IDSS-G also demonstrated incremental validity by predicting functional impairment beyond that predicted by the PHQ-9. Results suggest that the IDSS-G accurately assesses depression in this population. Future testing in other populations will follow.
Accuracy and precision of manual segmentation of the maxillary sinus in MR images-a method study.

OBJECTIVE To assess the accuracy and precision of segmentation of the maxillary sinus in MR images to evaluate the potential usefulness of this modality in longitudinal studies of sinus development. METHODS A total of 15 healthy subjects who had been both craniofacial CT and MR scanned were included and the 30 maxillary sinus volumes were evaluated using segmentation. Two of the authors did segmentation of MRI and one of these authors did double segmentation. Agreement in results between CT and MRI as well as inter- and intraexaminer errors were evaluated by statistical and three-dimensional analysis. RESULTS The intraclass correlation coefficient for volume measurements for both method error, inter- and intraexaminer agreement were > 0.9 [maximal 95% confidence interval of 0.989-0.997, p < 0.001] and the limit of agreement for all parameters were < 5.1%. Segmentation errors were quantified in terms of overlap [Dice Coefficient (DICE) > 0.9 = excellent agreement] and border distance [95% percentile Hausdorff Distance (HD) < 2 mm = acceptable agreement]. The results were replicable and not influenced by systematic errors. CONCLUSION We found a high accuracy and precision of manual segmentation of the maxillary sinus in MR images. The largest mean errors were found close to the orbit and the teeth. Advances in knowledge: MRI can be used for 3D models of the paranasal sinuses with equally good results as CT and allows longitudinal follow-up of sinus development.
Reconfigurable Data Planes for Scalable Network Virtualization

Network virtualization presents a powerful approach to share physical network infrastructure among multiple virtual networks. Recent advances in network virtualization advocate the use of field-programmable gate arrays (FPGAs) as flexible high performance alternatives to conventional host virtualization techniques. However, the limited on-chip logic and memory resources in FPGAs severely restrict the scalability of the virtualization platform and necessitate the implementation of efficient forwarding structures in hardware. The research described in this manuscript explores the implementation of a scalable heterogeneous network virtualization platform that integrates virtual data planes implemented in FPGAs with software data planes created using host virtualization techniques. The system exploits data plane heterogeneity to cater to the dynamic service requirements of virtual networks by migrating networks between software and hardware data planes. We demonstrate data plane migration as an effective technique to limit the impact of traffic on unmodified data planes during FPGA reconfiguration. Our system implements forwarding tables in a shared fashion using inexpensive off-chip memories and supports both Internet Protocol (IP) and non-IP-based data planes. Experimental results show that FPGA-based data planes can offer two orders of magnitude better throughput than their software counterparts, and FPGA reconfiguration can facilitate data plane customization within 12 seconds. An integrated system that supports up to 15 virtual networks has been validated on the NetFPGA platform.
Model based Design of Networked Embedded Systems - A Modeling Approach using FlexRay as an Example

This paper presents a work in progress on a method to create system level models of networked systems in automotive applications. It introduces an example, that shows a strategy to create models, providing high flexibility in terms of interoperability, field of application, reusability and replaceability. The chosen modeling tool contains a multi-domain simulator and allows a mission and system level design. Beside the exposition of the basic architecture of the model there is a description of various model parts showing the variety of different levels of abstraction. The grade of reuseability of the developed building blocks is very high. Finally a perspective for future extensions towards a general modeling strategy for various networked applications in embedded systems is provided.
Bispecific antibody process development: Assembly and purification of knob and hole bispecific antibodies

Production of knob and hole dual light chain bispecific antibodies poses several unique challenges for development of a feasible industrial scale manufacturing process. We developed an efficient process for the assembly and purification of knob and hole dual light chain bispecific antibodies. Two distinct half‐antibodies targeting two different antigens were expressed separately in Escherichia coli cells and captured independently using Protein A chromatography. When combined, the knob and hole mutations in the CH3 domains promoted heterodimer formation. The hinge region disulfides were reduced and reoxidized to form the disulfide bridge between the two complementary half antibodies. Unreacted half antibodies, noncovalently linked homodimers, covalently linked homodimers, and noncovalently linked heterodimers are impurities closely related to the product of interest and are challenging to remove by standard processes. Characterization of the molecular properties of the half antibodies and high‐throughput screening predicted column chromatography performance and allowed for rapid development of downstream purification steps for removal of unique product‐related and process‐related impurities. © 2018 American Institute of Chemical Engineers Biotechnol. Prog. , 34:397–404, 2018
Altered Odor-Induced Brain Activity as an Early Manifestation of Cognitive Decline in Patients With Type 2 Diabetes

Type 2 diabetes is reported to be associated with olfactory dysfunction and cognitive decline. However, whether and how olfactory neural circuit abnormalities involve cognitive impairment in diabetes remains uncovered. This study thus aimed to investigate olfactory network alterations and the associations of odor-induced brain activity with cognitive and metabolic parameters in type 2 diabetes. Participants with normal cognition, including 51 patients with type 2 diabetes and 41 control subjects without diabetes, underwent detailed cognitive assessment, olfactory behavior tests, and odor-induced functional MRI measurements. Olfactory brain regions showing significantly different activation between the two groups were selected for functional connectivity analysis. Compared with the control subjects, patients with diabetes demonstrated significantly lower olfactory threshold score, decreased brain activation, and disrupted functional connectivity in the olfactory network. Positive associations of the disrupted functional connectivity with decreased neuropsychology test scores and reduced pancreatic function were observed in patients with diabetes. Notably, the association between pancreatic function and executive function was mediated by olfactory behavior and olfactory functional connectivity. Our results suggested the alteration of olfactory network is present before clinically measurable cognitive decrements in type 2 diabetes, bridging the gap between the central olfactory system and cognitive decline in diabetes.
Whole genome sequence resource of Indian Zaprionus indianus

This article documents the whole genome sequence information of the Indian Zaprionus indianus, a member of the fruit fly family Drosophilidae. The sequences were generated on an Illumina platform and reads and whole genome sequence submitted to NCBI to the SRA and BioProject databases, respectively. This is the first Indian Z. indianus whole genome (draft) submitted to the sequence repository with SRA reads. The details of methodology, assembly statistics and functional annotation are presented in this work.
Estimation based erasure-coding routing in delay tolerant networks

Wireless Delay Tolerant Networks (DTNs) are intermittently connected mobile wireless networks. Some well-known assumptions of traditional networks are no longer true in DTNs, which makes routing in DTNs a challenging problem. We observe that mobile nodes in realistic wireless DTNs may always have some mobility pattern information which can be used to estimate one node's ability to deliver a specific message. This estimation can greatly enhance the routing performance in DTNs. Furthermore, we adopt an alternative way to generate redundancy using erasure coding. With a fixed overhead, the erasure coding can generate a large number of message-blocks instead of a few replications, and therefore it allows the transmission of only a portion of message to a relay. This can greatly increase the routing diversity when combined with estimation-based approaches. We have conducted extensive simulations to evaluate the performance of our scheme. The results demonstrate that our scheme outperforms previously proposed schemes.
Infectious Diseases in Older Adults of Long‐Term Care Facilities: Update on Approach to Diagnosis and Management

The diagnosis, treatment, and prevention of infectious diseases in older adults in long‐term care facilities (LTCFs), particularly nursing facilities, remains a challenge for all health providers who care for this population. This review provides updated information on the currently most important challenges of infectious diseases in LTCFs. With the increasing prescribing of antibiotics in older adults, particularly in LTCFs, the topic of antibiotic stewardship is presented in this review. Following this discussion, salient points on clinical relevance, clinical presentation, diagnostic approach, therapy, and prevention are discussed for skin and soft tissue infections, infectious diarrhea (Clostridium difficile and norovirus infections), bacterial pneumonia, and urinary tract infection, as well as some of the newer approaches to preventive interventions in the LTCF setting.
Fabrication of Extracellular Matrix-derived Foams and Microcarriers as Tissue-specific Cell Culture and Delivery Platforms

Cell function is mediated by interactions with the extracellular matrix (ECM), which has complex tissue-specific composition and architecture. The focus of this article is on the methods for fabricating ECM-derived porous foams and microcarriers for use as biologically-relevant substrates in advanced 3D in vitro cell culture models or as pro-regenerative scaffolds and cell delivery systems for tissue engineering and regenerative medicine. Using decellularized tissues or purified insoluble collagen as a starting material, the techniques can be applied to synthesize a broad array of tissue-specific bioscaffolds with customizable geometries. The approach involves mechanical processing and mild enzymatic digestion to yield an ECM suspension that is used to fabricate the three-dimensional foams or microcarriers through controlled freezing and lyophilization procedures. These pure ECM-derived scaffolds are highly porous, yet stable without the need for chemical crosslinking agents or other additives that may negatively impact cell function. The scaffold properties can be tuned to some extent by varying factors such as the ECM suspension concentration, mechanical processing methods, or synthesis conditions. In general, the scaffolds are robust and easy to handle, and can be processed as tissues for most standard biological assays, providing a versatile and user-friendly 3D cell culture platform that mimics the native ECM composition. Overall, these straightforward methods for fabricating customized ECM-derived foams and microcarriers may be of interest to both biologists and biomedical engineers as tissue-specific cell-instructive platforms for in vitro and in vivo applications.