How the “Folding Funnel” Depends on Size and Structure of Proteins?A View from the Scoring Function Perspective

It has been well accepted that the folding energy landscape may resemble a funnel according to the theory of protein folding. This theory of ＂folding funnel＂ has been extensively studied and thought to play an important role in guiding the sampling process of the protein folding and refinement in protein structure prediction. Here, we have investigated the relationship between the ＂funnel likeness＂ of protein folding and the size/structure of the proteins based on a set of non-homologous proteins we have recently evaluated using a statistical mechanicsbased scoring function ITScorePro. It was found that larger proteins that consist of more helix/sheet structures tend to have a higher score-Root Mean Square Deviation（RMSD） correlation（or a more funnel like energy landscape）.Another measurement in protein folding, Z-score, has also shown some correlation with the size of the proteins.As expected, proteins with a better ＂olding funnel likeness＂（or score-RMSD correlation） tend to have a betterpredicted conformation with a lower RMSD from their native structures. These findings can be extremely valuable for the development and improvement of sampling and scoring algorithms for protein structure prediction.

Nutech functional score: A novel scoring system to assess spinal cord injury patients

AIM To develop a new scoring system, nutech functional scores(NFS) for assessing the patients with spinal cord injury(SCI).METHODS The conventional scale, American Spinal Injury Association's(ASIA) impairment scale is a measure which precisely describes the severity of the SCI.However, it has various limitations which lead to incomplete assessment of SCI patients.We have developed a 63 point scoring system, i.e., NFS for patients suffering with SCI.A list of symptoms either common or rare that were found to be associated with SCI was recorded for each patient.On the basis of these lists, we have developed NFS.RESULTS These lists served as a base to prepare NFS, a 63 point positional(each symptom is sub-graded and get points based on position) and directional(moves in direction BAD → GOOD) scoring system.For non-progressive diseases, 1, 2, 3, 4, 5 denote worst, bad, moderate, good and best(normal), respectively.NFS for SCI has been divided into different groups based on the affected part of the body being assessed, i.e., motor assessment(shoulders, elbow, wrist, fingers-grasp, fingers-release, hip, knee, ankle and toe), sensory assessment, autonomic assessment, bed sore assessment and general assessment.As probability based studies required a range of(-1, 1) or at least the range of(0, 1) to be useful for real world analysis, the grades were converted to respective numeric values.CONCLUSION NFS can be considered as a unique tool to assess the improvement in patients with SCI as it overcomes the limitations of ASIA impairment scale.

Effect of non-pharmacological treatment on the full recovery of social functioning in patients with attention deficit hyperactivity disorder

BACKGROUND Long-term treatment of attention deficit/hyperactivity disorder(ADHD)is associated with adverse events,such as nausea and vomiting,dizziness,and sleep disturbances,and poor maintenance of late ADHD medication compromises treatment outcomes and prolongs the recovery of patients’social functioning.AIM To evaluate the effect of non-pharmacological treatment on the full recovery of social functioning in patients with ADHD.METHODS A total of 90 patients diagnosed with ADHD between May 2019 and August 2020 were included in the study and randomly assigned to either the pharmacological group(methylphenidate hydrochloride and tomoxetine hydrochloride)or the non-pharmacological group(parental training,behavior modification,sensory integration therapy,and sand tray therapy),with 45 cases in each group.Outcome measures included treatment compliance,Swanson,Nolan,and Pelham,Version IV(SNAP-IV)scores,Conners Parent Symptom Questionnaire(PSQ)scores,and Weiss Functional Impairment Rating Scale(WFIRS)scores.RESULTS The non-pharmacological interventions resulted in significantly higher compliance in patients(95.56%)compared with medication(71.11%)(P<0.05).However,no significant differences in SNAP-IV and PSQ scores,in addition to the learning/school,social activities,and adventure activities of the WFIRS scores were observed between the two groups(P>0.05).Patients with non-pharmacological interventions showed higher WFIRS scores for family,daily life skills,and self-concept than those in the pharmacological group(P<0.05).CONCLUSION Non-pharmacological interventions,in contrast to the potential risks of adverse events after longterm medication,improve patient treatment compliance,alleviate patients’behavioral symptoms of attention,impulsivity,and hyperactivity,and improve their cognitive ability,thereby improving family relationships and patient self-evaluation.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

The idea of linear Diophantine fuzzy set(LDFS)theory with its control parameters is a strong model for machine learning and optimization under uncertainty.The activity times in the critical path method(CPM)representation procedures approach are initially static,but in the Project Evaluation and Review Technique(PERT)approach,they are probabilistic.This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy(LDF)environment.The LDF expected task time,LDF variance,LDF critical path,and LDF total expected time for determining the project network are all computed using LDF numbers as the time of each activity in the project network.The primary premise of the LDF-PERT approach is to address ambiguities in project network activity timesmore simply than other approaches such as conventional PERT,Fuzzy PERT,and so on.The LDF-PERT is an efficient approach to analyzing symmetries in fuzzy control systems to seek an optimal decision.We also present a new approach for locating LDF-CPM in a project network with uncertain and erroneous activity timings.When the available resources and activity times are imprecise and unpredictable,this strategy can help decision-makers make better judgments in a project.A comparison analysis of the proposed technique with the existing techniques has also been discussed.The suggested techniques are demonstrated with two suitable numerical examples.

Multivariate Approach to Characterizing Soil Quality of Gabonese’s Ferralitic Soils

Assessing soil quality is essential for crop management and soil temporal changes. The present study aims to evaluate soil quality in the Ferralitic soils context countrywide. This assessment was done using multivariate soil quality indice (SQI) models, such as additive quality index (AQI), weighted quality indexes (WQIadd and WQIcom) and Nemoro quality index (NQI), applied to two approaches of indicator selection: total data set (TDS) and minimum data set (MDS). Physical and chemical soil indicators were extracted from the ORSTOM’s reports resulting from a sampling campaign in different provinces of Gabon. The TDS approach shows soil quality status according to eleven soil indicators extracted from the analysis of 1,059 samples from arable soil layer (0 - 30 cm depth). The results indicated that 87% of all provinces presented a very low soil quality (Q5) whatever the model. Among soil indicators, exchangeable K+ and Mg2+, bulk density and C/N ratio were retained in MDS, using principal component analysis (PCA). In the MDS approach, 50 to 63% of provinces had low soil quality grades with AQI, WQIadd and NQI, whereas the total was observed with WQIcom. Only 25% of provinces had medium soil quality grades with AQI and NQI models, while 12.5% (NQI) and 25% (AQI) presented high quality grades. Robust statistical analyses confirmed the accuracy and validation (0.80 r P ≤ 0.016) of AQI, WQIadd and NQI into the TDS and MDS approaches. The same sensitivity index value (1.53) was obtained with AQI and WQIadd. However, WQIadd was chosen as the best SQI model, according to its high linear regression value (R2 = 0.82) between TDS and MDS. This study has important implications in decision-making on monitoring, evaluation and sustainable management of Gabonese soils in a pedoclimatic context unfavorable to plant growth.

Floyd-Warshall Algorithm Based on Picture Fuzzy Information

The Floyd-Warshall algorithm is frequently used to determine the shortest path between any pair of nodes.It works well for crisp weights,but the problem arises when weights are vague and uncertain.Let us take an example of computer networks,where the chosen path might no longer be appropriate due to rapid changes in network conditions.The optimal path from among all possible courses is chosen in computer networks based on a variety of parameters.In this paper,we design a new variant of the Floyd-Warshall algorithm that identifies an All-Pair Shortest Path(APSP)in an uncertain situation of a network.In the proposed methodology,multiple criteria and theirmutual associationmay involve the selection of any suitable path between any two node points,and the values of these criteria may change due to an uncertain environment.We use trapezoidal picture fuzzy addition,score,and accuracy functions to find APSP.We compute the time complexity of this algorithm and contrast it with the traditional Floyd-Warshall algorithm and fuzzy Floyd-Warshall algorithm.

Computational prediction of RNA tertiary structures using machine learning methods

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.

Using junction trees for structural learning of Bayesian networks

The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint- based, and search-and-score techniques in a principled and ef- fective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.

RNAGCN:RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions.Since experimental approaches of determining their three-dimensional(3D)structures are costly and less efficient,it is greatly advantageous to develop computational methods to predict RNA 3D structures.For these methods,designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges.In this study,we designed and trained a deep learning model to tackle this problem.The model was based on a graph convolutional network(GCN)and named RNAGCN.The model provided a natural way of representing RNA structures,avoided complex algorithms to preserve atomic rotational equivalence,and was capable of extracting features automatically out of structural patterns.Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions.Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions.RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.

Protein–protein docking with interface residue restraints

The prediction of protein–protein complex structures is crucial for fundamental understanding of celluar processes and drug design. Despite significant progresses in the field, the accuracy of ab initio docking without using any experimental restraints remains relatively low. With the rapid advancement of structural biology, more and more information about binding can be derived from experimental data such as NMR experiments or chemical cross-linking. In addition, information about the residue contacts between proteins may also be derived from their sequences by using evolutionary analysis or deep learning. Here, we propose an efficient approach to incorporate interface residue restraints into protein–protein docking, which is named as HDOCKsite. Extensive evaluations on the protein–protein docking benchmark 4.0 showed that HDOCKsite significantly improved the docking performance and obtained a much higher success rate in binding mode predictions than original ab initio docking.

Effect of Xuesaitong Soft Capsule (血塞通软胶囊) on Hemorrheology and in Auxiliarily Treating Patients with Acute Cerebral Infarction

Objective: To observe the therapeutic effect of Xuesaitong soft capsule(血塞通软胶囊, XST)and its effect on platelet counts, coagulation factor 1 (CF1) as well as hemorrheologic indexes in treating patients with acute cerebral infarction (ACI). Methods: Two hundred and four patients with ACI were assigned into two groups, the control group (n=96) and the treated group (n=108). They were all treated with conventional Western medicines, including mannitol, troxerutin, citicoline, piracetam and aspirin, while to the treated group, XST was given additionally through oral intake, twice a day, 2 capsules each time for 8 successive weeks. The clinical efficacy was evaluated according to the nerve function deficits scoring and the changes of platelet count. CF1 and hemorrheological indexes were measured before and after treatment.Results: The total effective rate was 87.0% in the treated group, and 87.5% in the control group, respectively, showing insignificant difference between them. But the markedly effective rate in the treated group ( 66.7%) was significantly higher than that in the control group (27.1%, P<0.01). The count of platelet was not changed significantly in both groups after treatment, while CF1 in them evidently lowered at the end of the 4th and 8th weeks of treatment, but showed insignificant difference between the two groups. The hematocrit, whole blood viscosity and plasma viscosity in both groups were all improved significantly after treatment, but also showed insignificant difference in comparison of the two groups. Conclusion: XST has good efficacy in auxiliary treatment of patients with ACI, though its mechanism remains to be further explored.

Effects of the information–knowledge–attitude–practice nursing model combined with predictability intervention on patients with cerebrovascular disease

BACKGROUND Cerebrovascular disease(CVD)poses a serious threat to human health and safety.Thus,developing a reasonable exercise program plays an important role in the long-term recovery and prognosis for patients with CVD.Studies have shown that predictive nursing can improve the quality of care and that the information–knowledge–attitude–practice(IKAP)nursing model has a positive impact on patients who suffered a stroke.Few studies have combined these two nursing models to treat CVD.AIM To explore the effect of the IKAP nursing model combined with predictive nursing on the Fugl–Meyer motor function(FMA)score,Barthel index score,and disease knowledge mastery rate in patients with CVD.METHODS A total of 140 patients with CVD treated at our hospital between December 2019 and September 2021 were randomly divided into two groups,with 70 patients in each.The control group received routine nursing,while the observation group received the IKAP nursing model combined with predictive nursing.Both groups were observed for self-care ability,motor function,and disease knowledge mastery rate after one month of nursing.RESULTS There was no clear difference between the Barthel index and FMA scores of the two groups before nursing(P>0.05);however,their scores increased after nursing.This increase was more apparent in the observation group,and the difference was statistically significant(P<0.05).The rates of disease knowledge mastery,timely medication,appropriate exercise,and reasonable diet were significantly higher in the observation group than in the control group(P<0.05).The satisfaction rate in the observation group(97.14%)was significantly higher than that in the control group(81.43%;P<0.05).CONCLUSION The IKAP nursing model,combined with predictive nursing,is more effective than routine nursing in the care of patients with CVD,and it can significantly improve the Barthel index and FMA scores with better knowledge acquisition,as well as produce high satisfaction in patients.Moreover,they can be widely used in the clinical setting.

Multi-attribute group decision making method under 2-dimension uncertain linguistic variables

A method is proposed to deal with the uncertain multiple attribute group decision making problems,where 2-dimension uncertain linguistic variables(2DULVs)are used as the reliable way for the experts to express their fuzzy subjective evaluation information.Firstly,in order to measure the 2DULVs more accurately,a new method is proposed to compare two 2DULVs,called a score function,while a new function is defined to measure the distance between two 2DULVs.Secondly,two optimization models are established to determine the weight of experts and attributes based on the new distance formula and a weighted average operator is used to determine the comprehensive evaluation value of each alternative.Then,a score function is used to determine the ranking of the alternatives.Finally,the effectiveness of the proposed method is proved by an illustrated example.

Effect of cluster needling at scalp acupoints on differential protein expression in rat brain tissue after acute focal cerebral ischemia

Objective:To explore the function of cluster needling at scalp points therapy on regulating differential protein's expression at different time points in middle cerebral artery occlusion(MCAO)model rats.Methods:Fifty-four rats were divided into three groups randomly and 18 rats in each group.The groups respectively were the model group(group M,n=18),cluster needling at scalp points group(group C,n=18),false operation group(group F,n=18).Each group was then assigned in three subgroups,including 24-h,7-day,and 14-day subgroups.Six rats in each subgroup.Acupuncture at Baihui(GV20)and 2 points beside Baihui,which was 3 e4 mm away from the midline.Longa score was used to evaluated neurological effects.Proteomics methods were used to identify differentially expression proteins with a standard of fold change greater than 1.5 and P<.05 at different times.Results:1.Nerve function scoring:The nerve function scores at 7 and 14 days decreased in group C,which showed better neural function than group M(P<.05).2.Fold change in proteins:Group M showed932 differentially expressed proteins compared with group F,and among them,414 proteins showed significant changes in expression after acupuncture.The expression levels of Cdc42 and GFAP were increased,and Mag,Shank2,and MBP levels were decreased.In the Gene Ontology analysis,the cellular component consisted of the terms cytoplasm,cytoskeleton,lysosome,and plasma membrane.The main related biological processes were cellecell signaling,protein transport,aging,and cell adhesion.Many synaptic and metabolic pathways were found by KEGG analysis.Conclusion:Cluster needling at scalp acupoints can improve the nerve function score and improve dyskinesia in MCAO model rats.Cluster needling at scalp acupoints can regulate the expression of 414 proteins,including Cdc42,GFAP,Mag,Shank2,and MBP,which are related to cerebral ischemia.The differential proteins are major concentration in cytoplasm,cytoskeleton,lysosomes,and plasma membrane,participate in cellecell signaling,protein transport,aging,and cell adhesion,and act through multiple synaptic and metabolic pathways to exert their biological functions.

A New Method to Evaluate Linear Programming Problem in Bipolar Single-Valued Neutrosophic Environment

A bipolar single-valued neutrosophic set can deal with the hesitation relevant to the information of any decision making problem in real life scenarios,where bipolar fuzzy sets may fail to handle those hesitation problems.In this study,we first develop a new method for solving linear programming problems based on bipolar singlevalued neutrosophic sets.Further,we apply the score function to transform bipolar single-valued neutrosophic problems into crisp linear programming problems.Moreover,we apply the proposed technique to solve fully bipolar single-valued neutrosophic linear programming problems with non-negative triangular bipolar single-valued neutrosophic numbers(TBSvNNs)and non-negative trapezoidal bipolar single-valued neutrosophic numbers(TrBSvNNs).

Some Likelihood Based Properties in Large Samples: Utility and Risk Aversion, Second Order Prior Selection and Posterior Density Stability

The likelihood function plays a central role in statistical analysis in relation to information, from both frequentist and Bayesian perspectives. In large samples several new properties of the likelihood in relation to information are developed here. The Arrow-Pratt absolute risk aversion measure is shown to be related to the Cramer-Rao Information bound. The derivative of the log-likelihood function is seen to provide a measure of information related stability for the Bayesian posterior density. As well, information similar prior densities can be defined reflecting the central role of likelihood in the Bayes learning paradigm.

Preliminary Report on Phenom^®Femoral Component in Total Hip Replacement: The Correlation between Outcome Scores in a Cross-Seccional Study

Background: The main purpose of the present study was to assess the short term performance of a cementless femoral stem in total hip replacement. Methods: Cross-sectional observational study of a 48-patient cohort with Phenom? femoral stems implanted between June 1, 2014 and September 1, 2018, to determine clinical performance, stability, and radiographic osseointegration. Patients were followed-up from 13 to 76 months (mean: 44.5 months) and assessed using the Harris Hip Score-HHS, the Hip Disability and Osteoarthritis Outcome Score-HOOS and radiographs. Results: All stems were radiologically stable. Mean Harris Hip Score was 89.8 and the HOOS was 80.4. No statistical differences were observed among patients with different diagnoses. Conclusions: The short-term results revealed satisfactory clinical outcomes and radiological signs of implant stability in all cases. Using two functional scores was useful in detecting biases and a low to moderate agreement was found between the scores.

Synergistic application of molecular docking and machine learning for improved binding pose

Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying on machine-learning approaches may lead to invalid conformations.In this study,we propose a novel strategy that combines molecular docking and machine learning methods.Firstly,the protein-ligand binding poses are predicted using a deep learning model.Subsequently,position-restricted docking on predicted binding poses is performed using Uni-Dock,generating physically constrained and valid binding poses.Finally,the binding poses are re-scored and ranked using machine learning scoring functions.This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking.Evaluation experiments on multiple datasets demonstrate that,compared to using molecular docking or machine learning methods alone,our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.

NOTES ON “MULTICRITERIA FUZZY DECISION-MAKING METHOD BASED ON A NOVEL ACCURACY FUNCTION UNDER INTERVAL-VALUED INTUITIONISTIC FUZZY ENVIRONMENT”

This note points outs the inappropriateness of an accuracy function introduced by Ye [Ye, J. （2009）. Multicriteria fuzzy decision-making method based on a novel accuracy function under interval-valued intuitionistic fuzzy environment. Expert Systems with Applications, 36 （3）： 6899-6902] and its misleading use for comparing two interval-valued intuitionistic fuzzy numbers.