Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Departures from Linearity in Word Translation

We investigate the behavior of maps learned by machine translation methods. The maps translate words by projecting between word embedding spaces of different languages. We locally approximate these maps using linear maps, and find that they vary across the word embedding space. This demonstrates that the underlying maps are non-linear. Importantly, we show that the locally linear maps vary by an amount that is tightly correlated with the distance between the neighborhoods on which they are trained. Our results can be used to test non-linear methods, and to drive the design of more accurate maps for word translation.
Sleep Deprivation in Neurointensive Care Unit Patients From Serial Neurological Checks: How Much Is Too Much?

ABSTRACT Objective: Hourly neurological examinations are frequently performed in the neurointensive care unit (NICU) to quickly detect neurological deterioration. These examinations require the patient to be awakened hourly for days disrupting the sleep cycle and potentially causing neurological deterioration through sleep deprivation and the development of delirium. This pilot study’s aim was to describe the prevalence of neurologic deterioration and delirium in patients receiving hourly neuro checks. Design: A 6-month prospective observational cohort study was conducted within the NICU at Mayo Clinic Florida. Twenty subjects were enrolled between July 24, 2016, and January 30, 2017. Results: Neurological deterioration as defined as a decrease in Glasgow Coma Scale score of 2 or more, an increase in National Institute of Health Stroke Scale score by 4 or more, or a change in Confusion Assessment Method score for use in intensive care unit patients from negative to positive occurred in 19 of 20 patients (95%) for a total of 67 events, with most patients having multiple events. Seventy-five percent of the subjects experienced a decrease in Glasgow Coma Scale score of 2 or more at least once during the study period. The largest number of events occurred within the first 24 hours (39%). Surprisingly, 75% of the subjects also developed delirium. Forty percent of the subjects demonstrating neurological deterioration received computed tomography imaging to evaluate the change. Sixty-seven percent lacked deterioration evident on computed tomography imaging, and the deterioration was not attributed to medication effect or seizure by an experienced provider. Conclusions: Neurological deterioration is prevalent in the NICU population. Although hourly neurological examinations may be beneficial in the acute phase of neurological injury, prolonged use may be paradoxically harmful due to sleep deprivation.
Exploration of Novel Anti-Oxidant Protein Sestrin in Frailty Syndrome in Elderly

Frailty in elderly is very much familiar with a decline in the musculoskeletal system. Muscle degeneration in the lower organism was observed due to loss of anti-oxidant protein Sestrin. The aim of the study is to determine the level of Sestrin1 and Sestrin2 in the serum of frail and non-frail elderly to associate their impact in frailty syndrome. Subjects with age ≥ 65 years were enrolled from Geriatric Medicine OPD of All India Institute of Medical Sciences, New Delhi (N= 92). Among them, 51 subjects were identified as frail and rest 41 were regarded as non-frail according to “deficit accumulation model of Rockwood. ” The study was performed by surface plasmon resonance and validated by western blot. Sestrin1 and Sestrin2 were found to be significantly reduced in frail compare to non-frail elderly. Furthermore, even after the adjustment for age, gender and education, the level of Sestrin1 and Sestrin2 remain significantly lower across the groups. The Sestrin1 level was significantly lower in various categories like age, gender, BMI, education, ADL, number of co-morbidity along with other clinico-pathological features. ROC analysis also revealed the distinction of frail and non-frail in respect to serum Sestrin1 and Sestrin2. This study highlighted the new and promising role of serum Sestrin in frail and non-frail elderly. In future, it can be utilized as molecular marker to assess the potential diagnostic value for clinical purpose.
Heterozygous Mylk3 Knockout Mice Partially Recapitulate Human DCM With Heterozygous MYLK3 Mutations

Backgrounds: Recent studies identified heterozygous variants in MYLK3 gene that encodes cardiac myosin light chain kinase (cMLCK) are related to familial dilated cardiomyopathy (DCM) for the first time. Autosomal dominant traits suggest that pathogenesis of DCM could be related to heterozygous MYLK3 loss-of-function variants (haploinsufficiency). We previously generated and examined homozygous Mylk3 knockout mice that lead to heart failure. It had yet to be examined whether heterozygous Mylk3 knockout mice represent a DCM-like phenotype. Methods and Results: Heterozygous knockout (Mylk3wild/-) mice were examined regarding cardiac function, heart histology and expression of cMLCK protein and mRNA relative to age-matched wild-type controls (Mylk3wild/wild). At 4 months of age, cardiac contractility in heterozygous knockout mice was reduced with percent fractional shortening of 23.3 ± 1.2% compared to 30.1 ± 1.8% in control (Mylk3wild/- vs. Mylk3wild/wild, n = 9 each). In 4-month-old heterozygous knockout hearts, expression of cMLCK mRNA was expectedly reduced by almost half, however, protein expression was reduced by approximately 75% relative to the control wild-type (Mylk3wild/- vs. Mylk3wild/wild, n = 9 each). Isolated ventricular cardiomyocytes from heterozygous knockout mice were larger with increase of short-axis length relative to the cardiomyocytes from control mice. However, increase of heart failure markers as well as interstitial fibrosis were not evident in heterozygous knockout mice compared to controls. Conclusion: Heterozygous Mylk3 knockout mice show mild reduction of cardiac contractility by 4 months of age, and proteins reduced by approximately 75% relative to the control wild-type mice. These mice partly resemble human with the heterozygous MYLK3 mutation, but the reduction in cardiac contractility was milder.
Non-structural carbohydrate dynamics associated with drought-induced die-off in woody species of a shrubland community

Background and Aims The relationship between plant carbon economy and drought responses of co-occurring woody species can be assessed by comparing carbohydrate (C) dynamics following drought and rain periods, relating these dynamics to species' functional traits. We studied nine woody species coexisting in a continental Mediterranean shrubland that experienced severe drought effects followed by rain. Methods We measured total non-structural carbohydrates (NSC) and soluble sugars (SS) in roots and stems during drought and after an autumn rain pulse in plants exhibiting leaf loss and in undefoliated ones. We explored whether their dynamics were related to foliage recovery and functional traits (height [H], specific leaf area [SLA], wood density [WD]). Key Results During drought, NSC concentrations were overall lower in stems and roots of plants experiencing leaf loss, while SS decreases were smaller. Roots had higher NSC concentrations than stems. After the rain, NSC concentrations continued to decrease, while SS increased. Green foliage recovered after rain, particularly in plants previously experiencing higher leaf loss, independently of NSC concentrations during drought. Species with lower WD tended to have more SS during drought and lower SS increases after rain. In low-WD species, plants with severe leaf loss had lower NSC relative to undefoliated ones. No significant relationship was found between H or SLA and C content or dynamics. Conclusions Our community-level study reveals that, while responses were species-specific, C stocks overall diminished in plants affected by prolonged drought and did not increase after a pulse of seasonal rain. Dynamics were faster for SS than NSC. We found limited depletion of SS, consistent with their role in basal metabolic, transport and signalling functions. In a scenario of increased drought under climate change, NSC stocks in woody plants are expected to decrease differentially in coexisting species, with potential implications for their adaptive abilities and community dynamics.
Neural network cloud top pressure and height for MODIS

Abstract. Cloud top height retrieval from imager instruments is important for nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) dataset. Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 Level 2 height product and the cloud top temperature and height algorithm in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height. Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighboring pixels are very important. The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found to give the most useful information of the spread of the errors. For all descriptive statistics presented MAE, IQR, RMSE (root mean square error), SD, mode, median, bias and percentage of absolute errors above 0.25, 0.5, 1 and 2 km the neural network perform better than the reference algorithms both validated with CALIOP and CPR (CloudSat). The neural networks using the brightness temperatures at 11 and 12 µm show at least 32 % (or 623 m) lower MAE compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 430 m) reduction of MAE.
Reloading Pupils’ Batteries: Impact of Green Spaces on Cognition and Wellbeing

Cognitive functioning and academic performance of pupils depend on regular breaks from classroom work. However, it is unclear which settings during such breaks provide the best environment to restore cognitive performance and promote wellbeing of adolescent pupils. Therefore, we investigated the effects of staying in different urban green spaces during breaks. Sixty-four pupils (16–18 years old) participated in a cross-over experiment. They were placed into one of three settings (small park, larger park, forest) for one hour during a lunch break. Wellbeing was assessed four times (Nitsch scale), and a cognitive test (d2-R Test of Attention) was applied in the classrooms before and after the break. Wellbeing was almost always highest after the stay in the green spaces. However, a sustained effect was only found for the forest. Concentration performance values of the d2-R test were significantly higher after the pupils’ stay in green spaces for all sites. The highest increase of performance was found for the larger park type. In conclusion, this pilot study showed that study breaks in green spaces improved wellbeing and cognitive performance of adolescents. It also found that larger green spaces, either parks or forests, have stronger positive impacts on wellbeing and cognitive performance than small parks.
Global etiology of bacterial meningitis: A systematic review and meta-analysis

Bacterial meningitis is a global public health concern, with several responsible etiologic agents that vary by age group and geographical area. The aim of this systematic review and meta-analysis was to assess the etiology of bacterial meningitis in different age groups across global regions. PubMed and EMBASE were systematically searched for English language studies on bacterial meningitis, limited to articles published in the last five years. The methodological quality of the studies was assessed using a customized scoring system. Meta-analyses were conducted to determine the frequency (percentages) of seven bacterial types known to cause meningitis: Escherichia coli, Haemophilus influenzae, Neisseria meningitidis, Streptococcus pneumoniae, group B Streptococcus agalactiae, Staphylococcus aureus, and Listeria monocytogenes, with results being stratified by six geographical regions as determined by the World Health Organization, and seven age groups. Of the 3227 studies retrieved, 56 were eligible for the final analysis. In all age groups, S. pneumoniae and N. meningitidis were the predominant pathogens in all regions, accounting for 25.1–41.2% and 9.1–36.2% of bacterial meningitis cases, respectively. S. pneumoniae infection was the most common cause of bacterial meningitis in the ‘all children’ group, ranging from 22.5% (Europe) to 41.1% (Africa), and in all adults ranging from 9.6% (Western Pacific) to 75.2% (Africa). E. coli and S. pneumoniae were the most common pathogens that caused bacterial meningitis in neonates in Africa (17.7% and 20.4%, respectively). N. meningitidis was the most common in children aged ±1–5 years in Europe (47.0%). Due to paucity of data, meta-analyses could not be performed in all age groups for all regions. A clear difference in the weighted frequency of bacterial meningitis cases caused by the different etiological agents was observed between age groups and between geographic regions. These findings may facilitate bacterial meningitis prevention and treatment strategies.
Characterization of three Yarrowia lipolytica strains in respect to different cultivation temperatures and metabolite secretion

Despite the increasing relevance, ranging from academic research to industrial applications, only a limited number of nonconventional, oleaginous Yarrowia lipolytica strains are characterized in detail. Therefore, we analyzed three strains in regard to their metabolic and physiological properties and in respect to important characteristics of a production strains. A flow cytometry method was set up to evaluate their fitness in a rapid manner. By investigating different cultivation conditions and media compositions, similarities and differences between the distinct strain backgrounds could be derived. Especially sugar alcohol production, as well as a agglomeration of cells were found to be connected with growth at high temperatures. In addition, sugar alcohol production was independent of high substrate concentrations under these conditions. To investigate particular traits, including growth characteristics and metabolite concentrations, genomic analysis were performed. We found sequence variations for one third of the annotated proteins but no obvious link to all phenotypic features.
Prevalence and Determinants of Antibiotic Self-Medication among Adult Patients with Respiratory Tract Infections in the Mboppi Baptist Hospital, Douala, Cameroon: A Cross-Sectional Study

Antibiotic self-medication in patients with respiratory tract infections (RTI) is increasing globally and has been reported to be one of the prime contributors to antimicrobial resistance (AMR). Our study aims to provide data on the prevalence of antibiotic self-medication and identify the factors contributing to self-medication in adult patients with respiratory tract infection in an urban setting in Cameroon. This was cross-sectional study carried out at Mboppi Baptist Hospital, Douala, Cameroon. A validated structured questionnaire was administered to 308 consenting participants with diagnosed RTIs, to collect data on socio-demographic characteristics and history of antibiotic self-medication. Significance was set at a p-value < 0.05. The prevalence of antibiotic self-medication amongst individuals with RTIs was 41.9% (95% CI 36.5% to 47.5%). Patients with a history of pulmonary tuberculosis (TB) were significantly less likely to self-medicate with antibiotics (p-value = 0.043). The most common source of antibiotic self-medication was pharmacies (62%) and Cotrimoxazole and Amoxicillin were the most commonly used antibiotics (38.8% (50), 26.4% (34), respectively). Self-medication with antibiotics in adult patients with RTIs is common in Cameroon. Control of the use of antibiotics, organisation of medication stewardship programs, and education of the general population on the adverse consequences of antibiotic self-medication are required.
Antibody Responses to Influenza A/H1N1pdm09 Virus After Pandemic and Seasonal Influenza Vaccination in Healthcare Workers: A 5-Year Follow-up Study

Abstract Background The 2009 influenza pandemic was caused by the A/H1N1pdm09 virus, which was subsequently included in the seasonal vaccine, up to 2016/2017, as the A/H1N1 strain. This provided a unique opportunity to investigate the antibody response to H1N1pdm09 over time. Methods Healthcare workers (HCWs) were immunized with the AS03-adjuvanted H1N1pdm09 vaccine in 2009 (N = 250), and subsequently vaccinated with seasonal vaccines containing H1N1pdm09 for 4 seasons (repeated group), <4 seasons (occasional group), or no seasons (single group). Blood samples were collected pre and at 21 days and 3, 6, and 12 months after each vaccination, or annually (pre-season) from 2010 in the single group. The H1N1pdm09-specific antibodies were measured by the hemagglutination inhibition (HI) assay. Results Pandemic vaccination robustly induced HI antibodies that persisted above the 50% protective threshold (HI titers ≥ 40) over 12 months post-vaccination. Previous seasonal vaccination and the duration of adverse events after the pandemic vaccination influenced the decision to vaccinate in subsequent seasons. During 2010/2011–2013/2014, antibodies were boosted after each seasonal vaccination, although no significant difference was observed between the repeated and occasional groups. In the single group without seasonal vaccination, 32% of HCWs seroconverted (≥4-fold increase in HI titers) during the 4 subsequent years, most of whom had HI titers <40 prior to seroconversion. When excluding these seroconverted HCWs, HI titers gradually declined from 12 to 60 months post–pandemic vaccination. Conclusions Pandemic vaccination elicited durable antibodies, supporting the incorporation of adjuvant. Our findings support the current recommendation of annual influenza vaccination in HCWs. Clinical Trials Registration NCT01003288.
Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model

Neural machine translation (NMT) systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring inter-sentence information. This may make the translation of a sentence ambiguous or even inconsistent with the translations of neighboring sentences. In order to handle this issue, we propose an inter-sentence gate model that uses the same encoder to encode two adjacent sentences and controls the amount of information flowing from the preceding sentence to the translation of the current sentence with an inter-sentence gate. In this way, our proposed model can capture the connection between sentences and fuse recency from neighboring sentences into neural machine translation. On several NIST Chinese-English translation tasks, our experiments demonstrate that the proposed inter-sentence gate model achieves substantial improvements over the baseline.
Fully Convolutional Network-Based Multifocus Image Fusion

As the optical lenses for cameras always have limited depth of field, the captured images with the same scene are not all in focus. Multifocus image fusion is an efficient technology that can synthesize an all-in-focus image using several partially focused images. Previous methods have accomplished the fusion task in spatial or transform domains. However, fusion rules are always a problem in most methods. In this letter, from the aspect of focus region detection, we propose a novel multifocus image fusion method based on a fully convolutional network (FCN) learned from synthesized multifocus images. The primary novelty of this method is that the pixel-wise focus regions are detected through a learning FCN, and the entire image, not just the image patches, are exploited to train the FCN. First, we synthesize 4500 pairs of multifocus images by repeatedly using a gaussian filter for each image from PASCAL VOC 2012, to train the FCN. After that, a pair of source images is fed into the trained FCN, and two score maps indicating the focus property are generated. Next, an inversed score map is averaged with another score map to produce an aggregative score map, which take full advantage of focus probabilities in two score maps. We implement the fully connected conditional random field (CRF) on the aggregative score map to accomplish and refine a binary decision map for the fusion task. Finally, we exploit the weighted strategy based on the refined decision map to produce the fused image. To demonstrate the performance of the proposed method, we compare its fused results with several start-of-the-art methods not only on a gray data set but also on a color data set. Experimental results show that the proposed method can achieve superior fusion performance in both human visual quality and objective assessment.
Normalization of Duplicate Records from Multiple Sources

Data consolidation is a challenging issue in data integration. The usefulness of data increases when it is linked and fused with other data from numerous (Web) sources. The promise of Big Data hinges upon addressing several big data integration challenges, such as record linkage at scale, real-time data fusion, and integrating Deep Web. Although much work has been conducted on these problems, there is limited work on creating a uniform, standard record from a group of records corresponding to the same real-world entity. We refer to this task as record normalization. Such a record representation, coined normalized record, is important for both front-end and back-end applications. In this paper, we formalize the record normalization problem, present in-depth analysis of normalization granularity levels (e.g., record, field, and value-component) and of normalization forms (e.g., typical versus complete). We propose a comprehensive framework for computing the normalized record. The proposed framework includes a suit of record normalization methods, from naive ones, which use only the information gathered from records themselves, to complex strategies, which globally mine a group of duplicate records before selecting a value for an attribute of a normalized record. We conducted extensive empirical studies with all the proposed methods. We indicate the weaknesses and strengths of each of them and recommend the ones to be used in practice.