Optimal Point Research Articles

Evaluating Wilson sims tool: Quality improvement on fall risk in UAE psychiatric patients

BACKGROUND Falls among patients in psychiatric inpatient healthcare settings present a significant global challenge, despite ongoing efforts to reduce the risks. Falls negatively impact patient safety, post-treatment recovery, and medical costs. AIM This study aimed to evaluate the predictive validity of the Wilson Sims Fall Risk Assessment Tool (WSFRAT), investigate predictors of falls, and determine the optimal cut-off point for the WSFRAT. Additionally, we aimed to assess the predictive validity of clinical judgement in identifying individuals at high risk of falls. METHODS We conducted a psychometric study at a specialized psychiatry hospital in Dubai, United Arab Emirates, using data collected from hospital-wide quality projects between April 16, 2019, and March 31, 2021. Our sample comprised 492 patients. RESULTS Contrary to the recommended cut-off point of 7 in the literature, this study results indicate that the optimal cut-off point for the WSFRAT was 5+. This yielded an accuracy of 87%, a diagnostic odds ratio (DOR) of 0.728, a kappa value of 0.208, a sensitivity of 83%, and a specificity of 87%. Furthermore, the regression analyses identified significant predictors of fall risk, including age, gender, assistive device, WSFRAT, and evaluation duration. Notably, clinical judgement did not significantly predict fall risk (p=0.331). CONCLUSION In conclusion, the present research demonstrates that the WSFRAT is a reliable tool with high sensitivity and specificity for predicting falls in psychiatric inpatient settings. The findings emphasize the importance of employing evidence-based tools and a comprehensive assessment approach to prevent falls. Furthermore, our findings challenge the recommended cut-off point of 7 and highlight the need for further research to confirm the optimal cut-off point. Finally, this investigation revealed that clinical judgement alone is not an effective method for predicting falls in this population.

Role of Transcranial Ultrasound and Doppler Studies to Evaluate Intracranial Pathologies in Preterm and High-risk Term Neonates

Abstract Background: Transcranial grayscale neurosonography (NSG) and Doppler studies have major role in diagnosing neonate intracranial pathologies. The aim of the study is to evaluate the role of NSG and Doppler studies in correlation with clinical hypotonia and seizures in preterm neonates and high-risk term neonates. The prevalence of intracranial pathology is the second aim of this study. Methods: The present cross-sectional study was done in a tertiary care teaching hospital for 2 years. The study population of 120 cases comprised two groups: one group of 60 preterm neonates and the other of 60 high-risk term neonates with a history of well-defined episode of fetal distress. The NSG and Doppler findings (resistance index ≤0.62 is the optimum cutoff point for diagnosing perinatal asphyxia) are recorded. The sensitivity and specificity values for the NSG study alone, the Doppler study alone, and the combined NSG and Doppler studies are calculated. Results: The majority (46%) of preterm neonates had presented with germinal matrix hemorrhage, whereas a majority (46%) of high-risk term neonates had presented with periventricular and subcortical cysts. Comparison of the sensitivity of NSG versus Doppler versus combined NSG and Doppler in evaluating hypotonia and seizures in preterm (P = 0.0442) and high-risk term neonates (P = 0.0399) was significant. Conclusion: NSG combined with the Doppler study has significantly higher sensitivity than NSG alone in both groups. The specificity of the Doppler study is also high in both groups. Thus, it is strongly recommended to include Doppler during every NSG study to increase the detection rate.

Quest to find compromised spatial and temporal resolutions for integrating remote sensing data with an agro-ecosystem model for grasslands

This paper addressed one of the main challenges in assimilating remote sensing derived variables into process-based crop model simulations, which is the inconsistent spatial and temporal resolution between information obtained from remote sensing and the outputs of process-based agroecosystem model. We proposed an applied method to reduce the number of required simulations by identifying (i) optimal points in time where additional information from remote sensing has the largest positive influence on the model performance and (ii) options to cluster 10 m grid cells to larger cells without compromising their information content. The MONICA (Model for Nitrogen and Carbon) model was applied to simulate above and below ground biomass in two grassland sites located in southern and eastern parts of Germany. The model was calibrated using LAI values obtained from Sentinel-2 and the sensitivity of output variables to two key root parameters (Root Form factor and specific root length) was evaluated. Our results showed that one or two satellite images covering the critical time periods right after cutting events significantly improved the predictions of grass yields produced by a mechanistic agroecosystem models (by up to 30 %). A larger number of images at other grass growth stages would not further improve the predictive power of the model. We also found that the sensitivity to these critical time periods was independent of model parameters. The mixed-resolution scheme (between 10 and 50 m) achieved better results compared with the high-resolution standalone state-updating method, yet it reduced computational costs by more than 50 %. In conclusion, we proposed a methodology to reduce the number of required simulations for data assimilation by aggregating data from fine to coarse resolutions. Our method was promising for applying data assimilation over large areas and benefiting more from satellite information for real-time prediction of agricultural productivity.

Do admission glucose levels independently predict coagulopathy in multiple trauma patients? A retrospective cohort analysis.

Coagulopathy is prevalent in multiple trauma patients and worsens bleeding complications, leading to higher morbidity and mortality rates. Hyperglycemia upon admission predicts hemorrhagic shock and mortality in severely injured patients. This study aimed to assess admission glucose levels as an independent prognostic factor for coagulopathy in multiply injured patients. This retrospective cohort study observed multiple trauma patients treated at a level I trauma center between January 1, 2005, and December 31, 2020. Coagulopathy was defined as an international normalized ratio (INR) > 1.4 and/or activated thromboplastin time (APTT) > 40 s. Analysis of variance compared clinical and laboratory parameters of patients with and without coagulopathy. Receiver-operating-characteristic (ROC) and multivariate logistic regression analyses identified risk factors associated with coagulopathy. The study included 913 patients, of whom 188 (20%) had coagulopathy at admission. Coagulopathy patients had higher mortality than those without (26% vs. 5.0%, p < 0.001). Mean glucose level in coagulopathy patients was 10.09 mmol/L, significantly higher than 7.97 mmol/L in non-coagulopathy patients (p < 0.001). Admission glucose showed an area under the curve (AUC) of 0.64 (95% CI [0.59-0.69], p < 0.001) with an optimal cut-off point of 12.35 mmol/L. After adjusting for other factors, patients with high admission glucose had a 1.99-fold risk of developing coagulopathy (95% CI 1.07-3.60). Other laboratory parameters associated with coagulopathy included haemoglobin, bicarbonate (HCO3), and lactate levels. This study emphasizes the significance of admission blood glucose as an independent predictor of coagulopathy. Monitoring hyperglycemia can aid in identifying high-risk patients.

An efficient and robust gradient reinforcement learning: Deep comparative policy

Recently, actor-critic architectures such as deep deterministic policy gradient (DDPG) are able to understand higher-level concepts for searching rich reward, and generate complex actions in continuous action space, and widely used in practical applications. However, when action space is limited and has dynamic hard margins, training DDPG can be problematic and inefficiency. Since real-world actuators always have margins and interferences, after initialization, the actor network is likely to be stuck at a local optimal point on action space margin: actor gradient orients to the outside of action space but actuators stop at the margin. If the hard margins are complex, dynamic and unknown to the DDPG agent, it is unable to use penalty functions to recover from local optimum. If we enlarge the random process for local exploration, the training could be in potential risk of failure. Therefore, simply relying on gradient of critic network to train the actor network is not a robust method in real environment. To solve this problem, in this paper we modify DDPG to deep comparative policy (DCP). Rather than leveraging critic-to-actor gradient, the core training process of DCP is regulated by a T-fold compare among random proposed adjacent actions. The performance of DDPG, DCP and related algorithms are tested and compared in two experiments. Our results show that, DCP is effective, efficient and qualified to perform all tasks that DDPG can perform. More importantly, DCP is less likely to be influenced by the action space margins, DCP can provide more safety in avoiding training failure and local optimum, and gain more robustness in applications with dynamic hard margins in the action space. Another advantage is that, complex penalty for margin touching detection is not required, the reward function can always be brief and short.

A prediction-based model to optimize construction programs: Considering time, cost, energy consumption, and CO2 emissions trade-off

Efforts to combat climate change demand global action to promote a sustainable development worldwide. Given the construction industry's significant energy consumption, it is crucial to curtail energy consumption and CO2 emissions in this sector. This study aims to predict and trade-off four essential objectives: time, cost, energy consumption, and CO2 emissions. Initially, various factors affecting time and cost was identified using the CoCoSo method. Subsequently, 11 machine learning algorithms were employed to predict all four targets. Results revealed that support vector regression achieved an accuracy of 95.77% in project duration prediction, while the neural network algorithm demonstrated accuracy of 93.64%, 97.63%, and 97.99% in predicting cost, energy consumption, and CO2 emissions, respectively. Additionally, the research findings from six scenarios highlighted the non-linear relationship among project objectives. Deviating from the optimal point resulted in increases on both sides, with more significant impacts observed at values less than optimal duration. This research equips engineers with a valuable tool for accurately predicting project outcomes by adjusting input values, ensuring successful project execution and efficient resource allocation. Additionally, the framework's predictive abilities are beneficial for companies with energy and CO2 policies, influencing various sectors, regions, and policies tied to energy, emissions, costs, climate change, ESG, and UN SDGs. Its application extends to shaping regulations, strategies, and targets.

Determination of Serum Procalcitonin Level in Kawasaki Disease

Background: The most frequent cause of coronary artery aneurysm in children is Kawasaki disease (KD). Recently, limited studies on procalcitonin (PCT) were performed to find a biomarker for the diagnosis or prognosis of children with KD. Objectives: This study aimed to compare serum PCT levels between complete and incomplete KD and testify to the predictive validity of PCT for intravenous immunoglobulin (IVIG) resistance and predicting coronary artery lesions (CALs). Methods: This cross-sectional study was conducted at Namazi hospital in Shiraz, Iran, in 2019. All KD patients admitted to the hospital were included, with parental consent obtained. Kawasaki disease patients were categorized as complete KD (cKD) or incomplete KD (iKD). Two-dimensional echocardiography was performed, and peripheral venous blood was examined for PCT levels and other markers. All patients received IVIG and aspirin as standard treatment. The presence of coronary artery abnormalities (CAAs) was determined based on coronary artery size and morphology. We used Stata software version 14.0 for data analysis. Logistic regression models identified predictive factors for coronary complications. This study determined the optimal PCT cut-off point for predicted coronary complications using receiver operating characteristic (ROC) curve analysis. Approval was obtained from the Research Ethics Committees of Shiraz University of Medical Sciences. Results: Procalcitonin values in 38 children hospitalized with acute KD ranged from 0.2 ng/mL to 10 ng/mL, with a mean of 2.65 ng/m. There was no correlation of PCT with patients’ age or gender, incomplete KD, or IVIG resistance. The serum PCT concentration was higher in patients with CAL (P = 0.009). The best PCT cut-off value for CAL prediction was 2.5 ng/mL (corresponding sensitivity = 81.8% and specificity = 68.7%), considering CAL prevalence as the studied group. Conclusions: Supplementary research is needed to determine the sensitivity and specificity of PCT for the diagnosis and prognosis of KD. Procalcitonin might be of value in predicting which children are at increased risk for CALs to intensify therapy.

Concept Drift Adaptation by Exploiting Drift Type

Concept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the past need to be re-learned for the current data. Two design questions need to be addressed in designing a strategy to re-learn models: which type of concept drift has occurred, and how to utilize the drift type to improve re-learning performance. Existing drift detection methods are often good at determining when drift has occurred. However, few retrieve information about how the drift came to be present in the stream. Hence, determining the impact of the type of drift on adaptation is difficult. Filling this gap, we designed a framework based on a lazy strategy called Type-Driven Lazy Drift Adaptor (Type-LDA). Type-LDA first retrieves information about both how and when a drift has occurred, then it uses this information to re-learn the new model. To identify the type of drift, a drift type identifier is pre-trained on synthetic data of known drift types. Furthermore, a drift point locator locates the optimal point of drift via a sharing loss. Hence, Type-LDA can select the optimal point, according to the drift type, to re-learn the new model. Experiments validate Type-LDA on both synthetic data and real-world data, and the results show that accurately identifying drift type can improve adaptation accuracy.

Prolonged critical avalanche burial for nearly 23 h with severe hypothermia and severe frostbite with good recovery: a case report

BackgroundAccidental hypothermia with severe frostbite is a rare combination of injuries with a high risk for long-term sequelae. There are widely accepted recommendations for the management of avalanche victims and for frostbite treatment, but no recommendation exists for the treatment of frostbite in severe hypothermic patients, specifically for the management of hypothermic avalanche victims presenting with frostbite.Case presentationWe present a case of a previously healthy, 53-year-old male skier who was critically buried by an avalanche at 2300 m of altitude at an ambient temperature of − 8 °C for nearly 23 h. The victim was found with the right hand out of the snow and an air connection to outside. He was somnolent with Glasgow Coma Scale 11 (Eye 4, Verbal 2, Motor 5) and spontaneously breathing, in a severely hypothermic state with an initial core temperature of 23.1 °C and signs of cold injuries in all four extremities. After rescue and active external forced air rewarming in the intensive care unit, the clinical signs of first-degree frostbite on both feet and the left hand vanished, while third- to fourth-degree frostbite injuries became apparent on all fingers of the right hand. After reaching a core body temperature of approximately 36 °C, aggressive frostbite treatment was started with peripheral arterial catheter-directed thrombolysis with alteplase, intravenous iloprost, ibuprofen, dexamethasone and regional sympathicolysis with a right-sided continuous axillary block. After ten months, the patient had no tissue loss but needed neuropathic pain treatment with pregabalin.ConclusionThe combination of severe accidental hypothermia and severe frostbite is rare and challenging, as drug metabolism is unpredictable in a hypothermic patient and no recommendations for combined treatment exist. There is general agreement to give hypothermia treatment the priority and to begin frostbite treatment as early as possible after full rewarming of the patient. More evidence is needed to identify the optimal dosage and time point to initiate treatment of frostbite in severely hypothermic patients. This should be taken into consideration by future treatment recommendations.

Diagnostic performance of coronary computed tomography angiography stenosis score for coronary stenosis

BackgroundCoronary computed tomography angiography stenosis score (CCTA-SS) is a proposed diagnosis score that considers the plaque characteristics, myocardial function, and the diameter reduction rate of the lesions. This study aimed to evaluate the diagnostic performance of the CCTA-SS in seeking coronary artery disease (CAD).MethodsThe 228 patients with suspected CAD who underwent CCTA and invasive coronary angiography (ICA) procedures were under examination. The diagnostic performance was evaluated with the receiver operating curve (ROC) for CCTA-SS in detecting CAD (defined as a diameter reduction of ≥ 50%) and severe CAD (defined as a diameter reduction of ≥ 70%).ResultsThe area under ROC (AUC) of CCTA-SS was 0.909 (95% CI: 0.864–0.943), which was significantly higher than that of CCTA (AUC: 0.826; 95% CI: 0.771–0.873; P = 0.0352) in diagnosing of CAD with a threshold of 50%. The optimal cutoff point of CCTA-SS was 51% with a sensitivity of 90.66%, specificity of 95.65%, positive predictive value of 98.80%, negative predictive value of 72.13%, and accuracy of 91.67%, whereas the optimal cutoff point of CCTA was 55%, and the corresponding values were 87.36%, 93.48%, 98.15%, 65.15%, and 88.60%, respectively. With a threshold of 70%, the performance of CCTA-SS with an AUC of 0.927 (95% CI: 0.885–0.957) was significantly higher than that of CCTA with an AUC of 0.521 (95% CI: 0.454–0.587) (P < 0.0001).ConclusionsCCTA-SS significantly improved the diagnostic accuracy of coronary stenosis, including CAD and severe CAD, compared with CCTA.

Novel 2,5-Diketopiperazines with In Vitro Activities against Protozoan Parasites of Tropical Diseases.

Malaria, Chagas disease, and leishmaniasis are tropical diseases caused by protozoan parasites of the genera Plasmodium, Trypanosoma and Leishmania, respectively. These diseases constitute a major burden on public health in several regions worldwide, mainly affecting low-income populations in economically poor countries. Severe side effects of currently available drug treatments and the emergence of resistant parasites need to be addressed by the development of novel drug candidates. Natural 2,5-Diketopiperazines (2,5-DKPs) constitute N-heterocyclic secondary metabolites with a wide range of biological activities of medicinal interest. Its structural and physicochemical properties make the 2,5-DKP ring a versatile, peptide-like, and stable pharmacophore attractive for synthetic drug design. In the present work, twenty-three novel synthetic 2,5-DKPs, previously synthesized through the versatile Ugi multicomponent reaction, were assayed for their anti-protozoal activities against P. falciparum, T. cruzi, and L. infantum. Some of the 2,5-DKPs have shown promising activities against the target protozoans, with inhibitory concentrations (IC50) ranging from 5.4 to 9.5 µg/mL. The most active compounds also show low cytotoxicity (CC50), affording selectivity indices ≥ 15. Results allowed for observing a clear relationship between the substitution pattern at the aromatic rings of the 2,5-DKPs and their corresponding anti-Plasmodium activity. Finally, calculated drug-like properties of the compounds revealed points for further structure optimization of promising drug candidates.

The strategic prize game Le Her

AbstractLe Her is a strategic two-person prize game the classic version of which uses a deck of cards. Despite its simple rules, the analysis of the game is surprisingly complex. I describe a modern variant of Le Her that is amenable to elementary analysis, yet retains and highlights all central aspects of the classical game. This elementary analysis combines naturally central concepts of calculus (partial derivatives, multidimensional optimization, double integrals, fixed and saddle points) with major probabilistic concepts (random variable, event, independence) and basic notions in decision theory (Nash equilibrium). Due to its stimulating context as a strategic prize game Le Her offers an attractive illustration and an appealing application of learning goals and curricular contents which are otherwise often perceived as remote and abstract.

Predicting the Outcome of Patients with Severe COVID-19 with Simple Inflammatory Biomarkers: The Utility of Novel Combined Scores-Results from a European Tertiary/Referral Centre.

Background: The clinical significance of combinations of inflammatory biomarkers in severe COVID-19 infection is yet to be proved. Although several studies have evaluated the prognostic value of biomarkers in patients with COVID-19, there are limited data regarding the value of the combination scores that could take full advantage of the prognostic value of several biomarkers and that could account for the heterogeneity of patients with severe COVID-19. We investigated the prognostic value of combination scores of admission values of inflammatory biomarkers in adults with severe COVID-19. Methods: Adults admitted to the Department of Respiratory Medicine of the UHL with severe COVID-19 (April-September 2021, NCT05145751) were included. Demographics, medical history, laboratory tests and outcome (high-flow nasal cannula (HFNC), admission to Intensive Care Unit (ICU) or death) were recorded. The optimal cut-off points of on admission values of C-reactive protein (CRP), CRP to lymphocyte ratio (CLR), lymphocyte to neutrophil ratio (LNR) and derived variation of neutrophil to lymphocyte ratio (dv-NLR (neutrophil/white blood count-lymphocyte)) for the predetermined outcome were defined. Based on the cut-off of CRP, LNR, dv-NLR and CLR, which were found to be predictors for HFNC, 3 scores were defined: CRP and LNR (C-CRP #1), CRP and dv-NLR (C-CRP #2), CRP and CLR (C-CRP #3). Likewise, based on the cut-off of CRP and CLR, which were found to be predictors for death, the score of CRP and CLR (C-CRP #3*) was defined. The combination scores were then classified as: 2 points (both biomarkers elevated); 1 point (one biomarker elevated) and 0 points (normal values). None of the biomarkers was predictive for the ICU admission, so no further analysis was performed. Binomial logistic regression analysis was used to establish the predictive role for each biomarker. Results: One hundred and fifteen patients (60% males, mean age 57.7 years) were included. Thirty-seven (32.2%) patients required HFNC, nine (7.8%) died and eight (7%) were admitted to ICU, respectively. As far as HFNC is concerned, the cut-off point was 3.2 for CRP, 0.231 for LNR, 0.90 for dv-NLR and 0.004 for CLR. Two points of C-CRP #1 and 2 points of C-CRP #3 predicted HFNC with a probability as high as 0.625 (p = 0.005) and 0.561 (p < 0.001), respectively. Moreover, 1 point of C-CRP #2 and 2 points of C-CRP #2 predicted HFNC with a probability of 0.333 and 0.562, respectively. For death, the optimal cut-off point for CRP was 1.11 and for CLR 3.2*1033. Two points of C-CRP #3* with an accuracy of 0.922 predicted mortality (p = 0.0038) in severe COVID-19. Conclusions: The combination scores of CRP and inflammatory biomarkers, based on admission values, are promising predictors for respiratory support using HFNC and for mortality in patients suffering from severe COVID-19 infection.

Study on Linewidth and Phase Noise Characteristics of a Narrow Linewidth External Cavity Diode Laser.

In the field of inter-satellite laser communication, achieving high-quality communication and compensating for the Doppler frequency shift caused by relative motion necessitate lasers with narrow linewidths, low phase noise, and the ability to achieve mode-hop-free tuning within a specific range. To this end, this paper investigates a novel external cavity diode laser (ECDL) with a frequency-selective F-P etalon structure, leveraging the external cavity F-P etalon structure in conjunction with an auxiliary filter to achieve single longitudinal mode selection. The laser undergoes linewidth testing using a delayed self-heterodyne beating method, followed by the testing of its phase noise and frequency noise characteristics using a noise analyzer, yielding beat spectra and noise power spectral density profiles. Furthermore, the paper introduces an innovative bidirectional temperature-scanning laser method to achieve optimal laser-operating point selection and mode-hop-free tuning. The experimental results showcase that the single longitudinal mode spectral side-mode suppression ratio (SMSR) is around 70 dB, and the output power exceeds 10 mW. Enhancing the precision of the F-P etalon leads to a more pronounced suppression of low-frequency phase noise, reducing the Lorentzian linewidth from the initial 10 kHz level to a remarkable 5 kHz level. The bidirectional temperature-scanning laser method not only allows for the selection of the optimal operating point but also enables mode-hop-free tuning within 160 pm.

Morphometric variations and nonmetric anatomical traits or anomalies of the primary molar teeth, plus the molars' size thresholds for sex identification

IntroductionMorphological and morphometric features of the teeth are of interest to various clinical and academic dental and medical fields including prosthodontics, orthodontics, anatomy and anthropology, pathology, archeology, and forensic dentistry. These have been more or less researched in the case of the permanent dentition. However when it comes to the primary dentition, the literature is scarce and controversial. No study worldwide exists on the cutoff points (thresholds) for sex identification; no study exists on metric or nonmetric traits of deciduous teeth in Iranians. Hence, the aim of the study was to assess both the metric and nonmetric traits of primary molars, as well as their cut-off points for sex identification.MethodsIn this epidemiological cross-sectional study, pretreatment casts of 110 children (51 boys and 59 girls) aged 6 to 12 years were collected. Maxillary and mandibular first and second primary molars were evaluated regarding their metric traits (mesiodistal and buccolingual widths) and 9 nonmetric traits (Accessory cusp on the upper D, Accessory cusp on the lower D, Fifth cusp on the upper E, Carabelli’s cusp on the upper E, Protostylid on the lower E, Fifth cusp on the lower E, Sixth cusp on the lower E, Tuberculum intermedium [metaconulid] on the lower E, and Deflecting wrinkle on the lower E). ROC curves were used to identify cut-off points for sex determination as well as the usefulness of metric measurements for this purpose. Data were analyzed using independent-samples and paired-samples t-tests, McNemar, Fisher, and chi-square tests, plus Pearson and Spearman correlation coefficients (α = 0.05).ResultsAll the primary molars’ coronal dimensions (both mesiodistal and buccolingual) were extremely useful for sex identification (ROC curves, all P values ≤ 0.0000099). Especially, the mandibular primary molars (areas under ROC curves [AUCs] between 85.6 and 90.4%, P values ≤ 0.0000006) were more useful than the maxillary ones (AUCs between 80.4 and 83.1%, P values ≤ 0. 0000099). In the mandible, the first primary molar (maximum AUC = 90.4%) was better than the second molar (maximum AUC = 86.0%). The optimum thresholds for sex determination were reported. Sex dimorphism was significant in buccolingual and mesiodistal crown widths of all the primary molars (all P values ≤ 0.000132), but it was seen only in the case of 2 nonmetric traits: Deflecting wrinkle (P = 0.001) and Tuberculum intermedium (metaconulid, P = 0.029) on the lower Es, taking into account the unilateral and bilateral cases. The occurrence of nonmetric traits was symmetrical between the right and left sides (all P values ≥ 0.250). All mesiodistal and two buccolingual molar measurements were as well symmetrical (P > 0.1); however, two buccolingual measurements were asymmetrical: in the case of the maxillary E (P = 0.0002) and mandibular D (P = 0.019). There were three weak-to-moderate correlations between the nonmetric traits of the mandibular second molars (Spearman correlations between 22.7 and 37.5%, P values ≤ 0.045). Up to 6 concurrent nonmetric traits were observed in the sample, with 53.6% of the sample showing at least 2 concurrent nonmetric traits at the same time, without any sex dimorphism (P = 0.658).ConclusionSex dimorphism exists considerably in primary molars’ sizes, but it is not as prevalent in their nonmetric traits or abnormalities. Primary molars’ crown sizes are useful for sex identification; we calculated optimum cut-off points for this purpose, for the first time.

De-obstruction of bladder outlet in humans reverses organ remodelling by normalizing the expression of key transcription factors

BackgroundBenign prostatic hyperplasia in elderly males often causes bladder outlet obstruction termed benign prostatic obstruction (BPO). BPO induces lower urinary tract symptoms and quantifiable urodynamic alterations in bladder function. When conservative medical treatments are exhausted, surgical interventions like transurethral resection of the prostate (TURP) are employed for bladder outlet de-obstruction. Elucidating the molecular changes in the human bladder resulting from BPO and their reversal post-de-obstruction is pivotal for defining the “point of no return”, when the organ deterioration becomes irreversible. In this study we carried out a comprehensive molecular and urodynamic characterization of the bladders in men with BPO before TURP and 3 months after the relief of obstruction.MethodsWe report integrated transcriptome and proteome analysis of bladder samples from male patients with BPO before and 3 months after de-obstruction surgery (TURP). mRNA and protein profiles were correlated with urodynamic findings, specifically voiding detrusor pressure (PdetQmax) before TURP. We delineated the molecular classifiers of each group, pointing at the different pre-TURP bladder status.ResultsAge-matched patients with BPO without DO were divided into two groups based on the PdetQmax values recorded by UDI before de-obstruction: high and medium pressure (HP and MP) groups. Three months after de-obstruction surgery, the voiding parameters PdetQmax, Qmax and RV were significantly improved in both groups, without notable inter-group differences in the values after TURP. Patients with high PdetQmax showed less advanced remodeling and inflammatory changes than those with lower values. We detected significant dysregulation of gene expression, which was at least partially reversed by de-obstruction in both patients’ groups. Transcription factor SOX21 and its target thrombospondin 4 (THBS4) demonstrated normalization post-TURP.ConclusionsOur findings reveal substantial yet incomplete reversal of cell signalling pathways three months after TURP, consistent with improved urodynamic parameters. We propose a set of biomarker genes, indicative of BPO, and possibly contributing to the bladder changes. This study unveils the stages of progressive obstruction-induced bladder decompensation and offers insights into selecting an optimal intervention point to mitigate loss of contractility.

On the Optimal Point of the Weighted Simpson Index

In this short communication, following a brief introduction, we undertake a comprehensive analytical study of the weighted Simpson index. Our primary emphasis concerns the precise determination of the optimal point (minimizer) coordinates and of the minimum value of the index, a differentiable convex function, which is related to the harmonic mean concept. Furthermore, we address and solve the inversion problem and show the tight connection between both approaches. Last, we give some insights and final remarks on this subject.

Analytical Optimization of Hydraulic Fracturing

Hydraulic fracturing optimization is a critical aspect of improving the recovery of unconventional shale formation. This paper discusses the use of different types of proppants, rate optimization, and proppant amount optimization to improve hydraulic fracturing techniques. The paper begins with a discussion of proppant selection, which is a critical aspect of hydraulic fracturing. The authors highlight the importance of proppant endurance in holding the fracture opening and provide a range of proppants suitable for different confining pressures. Tables and charts are included to illustrate the permeability values of various proppants under different closure stress values. This section also emphasizes the significance of proppant shape in creating a more conductive path in the fracture. The next section of the paper discusses the methodology used in the study, including the Fracpro software simulation parameters. The authors then delve into the optimization of proppant specific gravity and the results of their experiments with five different types of proppants. The paper highlights the impact of proppant specific gravity on fracture width and dimensionless conductivity (FCD). The author also focusses on the optimization of pumping rate, which is an essential parameter of hydraulic fracturing operations. The paper includes simulation studies conducted to determine the effects of pumping rate on fracture parameters such as propped length and propped height. The authors highlight the relationship between rate and FCD and how it is affected by permeability values of the proppant. Finally, the paper discusses proppant amount optimization, which is a critical point of hydraulic fracturing optimization. The authors provide an overview of the results of the experiments conducted to determine the optimal amount of proppant required for different hydraulic fracturing operations. Overall, this paper provides valuable insights for researchers and engineers working to improve hydraulic fracturing techniques for tight shales formation. The authors use a combination of theory, experiments, and charts to provide a comprehensive overview of the various aspects of hydraulic fracturing optimization.

Managing the exploitation/exploration paradox: the role of digital transformation

ABSTRACT Ambidexterity, characterized by persistent tensions between exploration and exploitation, requires dynamic management. This study explores the impact of digital transformation (DT) on balancing these elements, revealing an inverted U-shaped relationship between exploration proportion in innovation ambidexterity and performance; as firms advance in DT, the optimal balance point between exploration and exploitation shifts towards exploitation.

OptMSP: A toolbox for designing optimal multi-stage (bio)processes

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking.Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.