Power attenuation from restricting range of motion is minimized in subjects with fast RTD and following isometric training

Time-dependent measures consisting of rate of torque development (RTD), rate of velocity development (RVD), and rate of neuromuscular activation can be used to evaluate explosive muscular performance, which becomes critical when performing movements throughout limited ranges of motion (ROM). Using a HUMAC NORM dynamometer, seven males (27 ± 7 years) and six females (22 ± 3 years) underwent 8 weeks of maximal isometric dorsiflexion training 3 days/week. One leg was trained at 0° (short-muscle tendon unit (MTU) length) and the other at 40° of plantar flexion (long-MTU length). RTD and rate of neuromuscular activation were evaluated during 'fast' maximal isometric contractions. Power, RVD, and rate of neuromuscular activation were assessed during maximal isotonic contractions in four conditions (small (40° to 30° of plantar flexion) ROM at 10 and 50% MVC; large (40° to 0° of plantar flexion) ROM at 10 and 50% MVC) for both legs, pre- and post-training. Despite no change in rate of neuromuscular activation following training, peak power, RTD, and RVD increased at both MTU lengths (p < 0.05). Strong relationships (R2=0.73) were observed between RTD and peak power in the small ROM, indicating that fast time-dependent measures are critical for optimal performance when ROM is constrained. Meanwhile, strong relationships (R2=0.90) between RVD and power were observed at the 50% load, indicating that RVD is critical when limited by load and ROM is not confined. Maximal isometric dorsiflexion training can be used to improve time-dependent measures (RTD, RVD) to minimize power attenuation when ROM is restricted.

Exploring relationships between in-hospital mortality and hospital case volume using random forest: results of a cohort study based on a nationwide sample of German hospitals, 2016–2018

Background Relationships between in-hospital mortality and case volume were investigated for various patient groups in many empirical studies with mixed results. Typically, those studies relied on (semi-)parametric statistical models like logistic regression. Those models impose strong assumptions on the functional form of the relationship between outcome and case volume. The aim of this study was to determine associations between in-hospital mortality and hospital case volume using random forest as a flexible, nonparametric machine learning method. Methods We analyzed a sample of 753,895 hospital cases with stroke, myocardial infarction, ventilation > 24 h, COPD, pneumonia, and colorectal cancer undergoing colorectal resection treated in 233 German hospitals over the period 2016–2018. We derived partial dependence functions from random forest estimates capturing the relationship between the patient-specific probability of in-hospital death and hospital case volume for each of the six considered patient groups. Results Across all patient groups, the smallest hospital volumes were consistently related to the highest predicted probabilities of in-hospital death. We found strong relationships between in-hospital mortality and hospital case volume for hospitals treating a (very) small number of cases. Slightly higher case volumes were associated with substantially lower mortality. The estimated relationships between in-hospital mortality and case volume were nonlinear and nonmonotonic. Conclusion Our analysis revealed strong relationships between in-hospital mortality and hospital case volume in hospitals treating a small number of cases. The nonlinearity and nonmonotonicity of the estimated relationships indicate that studies applying conventional statistical approaches like logistic regression should consider these relationships adequately.

The Importance of Relationships in Education: Reflections of Current Educators

The purpose of this study was to seek to understand and describe current educators’ perceptions of building relationships in education. Thirty-five K-12 educators completed reflective writing responses as part of this phenomenological study. The thematic analysis constructed four themes: (a) “students are motivated to learn when strong relationships are formed,” (b) “if they don’t trust you and don’t think you care, learning decreases,” (c) “teachers had a great impact on me that I admire deeply-these teachers are the reason I went into education,” and (d) “they can always google the content.” The findings highlight the significant impact of relationships.

Shopping and Basket Analysis by Using an Improved Apriori Algorithm in WEKA

In the past years, e-commerce and online shopping grew fast. It became more helpful by letting people buy the desired product online. Also, to help their users to find the product of their desire easily and make the process simpler, the online shopping websites use some kinds of an algorithm to provide recommendation systems. Often, these systems use techniques like basket analyzing and association rules which is finding the relation between the products together or between users too, so apriori algorithm is one of the famous ones among the recommendation systems. Although it has some limitations while implementing which makes the algorithm less confident or even useless, Let us assume we have 100K records in the sold item list in a system in which about 10K refers to the customers buying only one or two items in their purchase. Therefore, this ten per cent will not affect finding the relation between the items, at the same time these records will make the system less efficient and take more time to analyze, in this paper, we try to show how we can improve the apriori algorithm efficiency and accuracy by some preprocessing on the dataset before applying apriori algorithm by eliminating the unnecessary records, this process helps to make the algorithm better because of reducing the number of transactions, hence finding strong relationships between items easier for the rest of the records.

Needs of Youth and Parents From Multi-Problem Families in the Search for Youth-Initiated Mentors

Youth-initiated mentoring is an innovative youth care approach in which youth recruit supportive adults from their social networks as a mentor for youth and a partner for parents and professionals. This qualitative interview study documents what youth ( n = 15) and parents ( n = 13) from multi-problem families look for in a mentor, what mentors ( n = 8) believe they have to offer, and whether what mentors believe to offer matches youth’s and parents’ needs. Youth and parents indicated that a strong connection and trust were most important, or even prerequisites, as youth who were unable to find mentors did not have strong relationships of trust. Youth and parents also voiced preferences for an understanding, sensitive mentor who offered youth perspective by providing support and advice and (according to some) setting rules. What mentors believed to offer matched youth’s and parents’ needs, suggesting that most youth successfully recruited suitable mentors.

Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt

Standard methods are limited for monitoring and managing water quality indicators (WQIs) in real-time and on a large scale. Consequently, there is an urgent need to use reliable, practical, swift, and cost-effective monitoring tools that can be easily deployed and assist decision makers in assessing key indicators relevant to surface water quality in a comprehensive manner. Surface water samples were collected and evaluated for water quality at 16 distinct sites across the Qaroun Lake in 2018 and 2019. Different WQIs, including total dissolved solids (TDS), transparency, total suspended solids (TSS), chlorophyll-a (Chl-a), and total phosphorus (TP), were tested for aquatic utilization. An integrated approach comprising WQIs, geospatial techniques, hyperspectral reflectance indices (SRIs) (commonly used SRIs, two-band and three-band SRIs (Spectral index calculated from water spectral reflectance of two or three wavelengths)), and partial least square regression (PLSR) models were used to assess the water quality of Qaroun Lake. According to the findings, the water quality attributes are polluted to varying degrees. The majority of commonly used SRIs presented moderately relationship with four WQIs (transparency, TSS, Chl-a, and TP) (R2 = 0.45 to 0.64), while the majority of newly two-band SRIs (NSRIs-2b) indicated moderate to strong relationships with WQIs (R2 = 0.51 to 0.74), and the majority of newly three band SRIs (NSRIs-3b) presented strong relationships with WQIs (R2 = 0.67 to 0.81). Broadly, the highest coefficients of determination were noticed with the NSRIs-3b followed by the NSRIs-2b and then the commonly used SRIs. For example, the NSRIs-3b (NDSI648,712,696) had stronger relationships with transparency, TSS, and Chl-a with R2 = 0.77, 0.66, and 0.81, respectively, than other SRIs. In addition, the NSRIs-3b (NDSI620,610,622) showed the highest R2 of 0.73 with TSS. The NSRIs-3b coupling with PLSR predicted the WQIs with satisfactory accuracy in the calibration (reach up R2 = 0.85) and validation (reach up R2 = 0.81) datasets. The overall findings of this research study showed that deriving an optimized NSRIs-3b from spectrum region and combining it with PLSR model could be a practical tool for managing water quality of the Qaroun Lake by accurately, timely, and non-destructively monitoring the WQIs.

Dominant role of adult neurogenesis-induced structural heterogeneities in driving plasticity heterogeneity in dentate gyrus granule cells

ABSTRACTNeurons and synapses manifest pronounced variability in the amount of plasticity induced by identical activity patterns. The mechanisms underlying such plasticity heterogeneity, implicated in context-specific resource allocation during encoding, have remained unexplored. Here, we employed a systematic, unbiased, and physiologically constrained search to identify the mechanisms behind plasticity heterogeneity in dentate gyrus granule cells. We found that each of intrinsic, synaptic, and structural heterogeneities independently yielded heterogeneous plasticity profiles obtained with two different induction protocols. However, prior predictions about strong relationships between neuronal intrinsic excitability and plasticity emerged only when adult-neurogenesis-induced structural heterogeneities were accounted for. Strikingly, despite the concomitant expression of heterogeneities in structural, synaptic, and intrinsic neuronal properties, similar plasticity profiles were attainable through synergistic interactions among these heterogeneities. Importantly, consequent to strong relationships with intrinsic excitability measurements, we found that synaptic plasticity in the physiological range was achieved in immature cells despite their electrophysiologically-observed weak synaptic strengths. Together, our analyses unveil the dominance of neurogenesis-induced structural heterogeneities in driving plasticity heterogeneity in granule cells. Broadly, these analyses emphasize that the mechanistic origins of and the implications for plasticity heterogeneities need quantitative characterization across brain regions, particularly focusing on context-specific encoding of learned behavior.

Temporal patterns in Ixodes ricinus microbial communities: an insight into tick-borne microbe interactions

Background Ticks transmit pathogens of medical and veterinary importance and are an increasing threat to human and animal health. Assessing disease risk and developing new control strategies requires identifying members of the tick-borne microbiota as well as their temporal dynamics and interactions. Methods Using high-throughput sequencing, we studied the Ixodes ricinus microbiota and its temporal dynamics. 371 nymphs were monthly collected during three consecutive years in a peri-urban forest. After a Poisson lognormal model was adjusted to our data set, a principal component analysis, sparse network reconstruction, and differential analysis allowed us to assess seasonal and monthly variability of I. ricinus microbiota and interactions within this community. Results Around 75% of the detected sequences belonged to five genera known to be maternally inherited bacteria in arthropods and to potentially circulate in ticks: Candidatus Midichloria, Rickettsia, Spiroplasma, Arsenophonus and Wolbachia. The structure of the I. ricinus microbiota varied over time with interannual recurrence and seemed to be mainly driven by OTUs commonly found in the environment. Total network analysis revealed a majority of positive partial correlations. We identified strong relationships between OTUs belonging to Wolbachia and Arsenophonus, evidence for the presence of the parasitoid wasp Ixodiphagus hookeri in ticks. Other associations were observed between the tick symbiont Candidatus Midichloria and pathogens belonging to Rickettsia. Finally, more specific network analyses were performed on TBP-infected samples and suggested that the presence of pathogens belonging to the genera Borrelia, Anaplasma and Rickettsia may disrupt microbial interactions in I. ricinus. Conclusions We identified the I. ricinus microbiota and documented marked shifts in tick microbiota dynamics over time. Statistically, we showed strong relationships between the presence of specific pathogens and the structure of the I. ricinus microbiota. We detected close links between some tick symbionts and the potential presence of either pathogenic Rickettsia or a parasitoid in ticks. These new findings pave the way for the development of new strategies for the control of ticks and tick-borne diseases.

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

Objective To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus. Methods We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0. Findings We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors—population between 20–34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality—as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive. Conclusion Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.

Risk, risk management between healthcare and economy.

The aim of the paper is to talk about the risk management system especially today in pandemic time. The authors would like to analyze the issue of risk management in an economic and healthcare context. taking into account that there are strong relationships between society and health such as the question of social responsibility and organization, social responsibility and social impact and social responsibility and competitiveness. The correlation between economy and health is highlighted in the healthcare sector, where the risk profile is in fact considered complex and extremely dynamic.