Incidentally, these studies are essential but could be costly and limited by their inability to estimate the detailed microstructural phenomenon.The musculoskeletal system is a complex multi-articular structure which provides a relative range of motion for functional activities. It seems similarities between the joint biomechanics of the upper and the lower extremities. However, the upper extremity is more specialized for daily activities with greater ranges of motion, and the lower extremity structures are well prepared for locomotion and its weight-bearing . The basIn the past few decades, different mathematical and computational methodologies of modeling [such as musculoskeletal and finite element (FE) modeling] have emerged as powerful and practical tools for non-invasive biomechanical investigations. These modeling approaches can explore the various aspects of the lower extremities, from the response of the health conditions in different movements, to evaluations of the effects of different pathologies and injuries, and to relevant treatment modalities and techniques. The current Research Topic presents a collection of cutting-edge studies which enhance our knowledge about biomechanical investigations of lower extremities and stimulate deliberations for the improvement of research techniques used for the development of relevant studies for injuries, diseases, diagnoses, and treatments.Li et al. have quantified the kinetic and kinematic features of pedestrian active and avoidance behaviors in dangerous impact scenarios and pedestrian-vehicle interaction in the pre-crash phase using immersive virtual reality technology. They confirmed that the pedestrian kinetics and kinematics at the instant of occurrence of a collision were significantly influenced by their active avoidance behavior. In addition, the physical conditions, sufficient time, and a safe distance are required for effective avoidance motion. This study provided comprehensive experimental data which can be utilized as a valuable reference for developing and validating the FE models and multi-body simulations for lower limbs. Herrera-Valenzuela et al. have used a novel mathematical methodology behind the Gait Deviation Index (GDI) proposed by Prasad et al. proposed a simulation-based generalized framework for modelling and evaluation of cable-driven mobile lower limb exoskeleton is proposed which can be used for rehabilitation procedures. Simulation of the conceptual configurations in this study indicated that the 4-cable configuration is an encouraging design option for a lower limb rehabilitation exoskeleton based on tracking performance, component forces exerted on the lower limb, and motor power requirements.Three studies focused on investigating the kinematics and kinetics of the whole lower extremity. Jiang et al. conducted a retrospective investigation for patients with femoral neck fractures (FNF) to comprehensively study the effect of vertical and oblique inclinations on fracture stability and reoperation risks using an innovative multivariant model. It was shown that fracture inclination in vertical and oblique planes is closely related to reoperation outcomes and evaluating the fracture stability using this methodology may be a beneficial complement to the classic Pauwels technique  on stress distribution of the hip joint, which could enhance the clinical diagnosis of the severity of hip dysplasia. This study showed that in the patients with lower ACEA, the area ratio of high stress increased. They concluded that for cases with borderline developmental dysplasia of the hip, both the ACEA and the area of facies lunata played critical roles in characterizing the severity of hip dysplasia. Vega et al. used a patient-specific neuro-musculoskeletal model and direct collocation optimal control to estimate the impact of ipsilateral psoas muscle strength on gait cycle following internal hemipelvectomy surgery with prosthesis reconstruction. The results showed that when post-surgery psoas strength was increased, stance width and stride length returned to pre-surgery values. Hence, this study suggests that retention and strengthening of the psoas muscle on the operated side may be vital for maximizing post-surgery post-surgery gait function. Alexander et al. developed personalized musculoskeletal models to study lower limb kinematics and kinetics during gait in pediatric and adolescent patients with increased, isolated femoral anteversion compared to typically developing controls. The contribution of this study was to identify the specific subgroups of patients who may have a higher risk of joint overloading as a consequence of both altered morphology and kinematics. In conclusion, it was shown that the altered femoral morphology does not always result in an increased risk of joint overloading. However, personalized kinematics should be considered for better judgment.During routine activities of daily living, the hip joint plays a crucial role in the mobility and stability of the lower extremities as the primary link between the upper and lower limbs. Hence, investigation of the hip biomechanics principles can be beneficial for the enhancement of the knowledge of the pathological mechanisms, diagnosis, and treatment techniques . Jiang eechnique . Chen etCen et al. developed multiscale FE models of the proximal femur for both the elderly and young population to evaluate the mechanical responses at the tissue and cell scales in the mid-stance of gait. Mechanical responses of cortical bone and osteocytes in different quadrants of the mid-femoral neck were studied in the mid-stance of gait. It was confirmed that the mass and bone mineral density of femoral neck cortical bone could be correlated to the mechanical response of osteocytes. This could be valuable to adopt novel treatment techniques based on the improvement of cortical bone quality by stimulating osteocytes. In addition, quadrantal differences in bone quality in the mid-femoral neck might be deliberated to improve fracture risk prediction in future investigations. Wang et al. used a combination of the experimental study, topological optimization analyses, and FE modeling to evaluate the performance of a novel strategy for a minimally invasive plate osteosynthesis of distal tibia fractures. They showed that this technique which is called bionic lightweight design plating. It was shown that the application of FE simulation analyses combined with a topology optimization algorithm could improve the capacity of soft tissue protection by reducing the volume of the implant. They concluded that a smaller plate volume results in decreasing the excessive mechanical stimulation of soft tissues caused by the implants. Three of these investigations were focused on tibial plateau fractures (TPF) which are among the most common fractures in knee trauma. Ren et al. developed a novel plate through an anterolateral approach for posterolateral TPF and assessed the biomechanical performance of six different groups by the biomechanical experimental testing and FE simulations. This study concluded that the proposed patented plate had a proper biomechanical advantage for posterolateral TPF including balanced stress distribution which can reduce the risk of fixation failure. Moufid et al. measured the mechanical effects of cement injection on adjacent bone structures for stabilizing the TPF using the combination of the in-vitro experimental studies and FE analyses. They concluded that cement can provide proper reduction and primary stabilization, permitting the patient to undergo rehabilitation with active and passive mobilization. However, it is suggested that no weight-bearing would be authorized while the cortical bone is not stabilized. Vendeuvre et al. performed in-vitro experiments to comprehensively compare different minimally invasive fixations in association with balloon reduction based on the tuberoplasty method. They evaluated the primary structural strength of the TPF fixation regarding the separation and depression components and tested the mechanical influence of injectable bone cement filling on three different types of percutaneous screws. It was determined that cement filling during tuberoplasty could be a promising approach in association with percutaneous osteosynthesis to provide a better surgical outcome.Five studies focused on the pathological mechanism and surgical techniques for the treatment of fractures in Femur and Tibia. Guo et al. provided a systematic review and performed a meta-analysis on the impact of anterior cruciate ligament (ACL) defects and ACL integrity on clinical outcomes. They determined that no differences were observed in the primary clinical outcomes such as postoperative revision, and Tegner activity score and Oxford Knee Score in ACL deficient and unicondylar knee arthroplasty intact patients. Chen et al. performed experiments and developed a validated musculoskeletal model to evaluate the ACL biomechanics and the variation between the knee joint variables in three landing test actions. This study showed minimal differences in knee valgus, knee valgus moment, and ACL forces between the aforementioned landing actions. Nonetheless, it was reflected that knee flexion angle, knee extension moments sagittal factors, and quadriceps and gastrocnemius forces are the main risk factors for ACL injury. Cheng et al. developed a validated 3D FE model of a healthy cadaveric knee to find the optimal femoral drilling angle for ACL reconstruction with the anteromedial (AM) portal technique. It was presented that the femoral tunnel drilling angle could directly affect both the strain and stress distribution on the femoral tunnel, tibial tunnel, and ACL graft. A femoral tunnel drilling angle of 45° coronal/45° sagittal represented the lowest peak stress, maximum strain on the femoral and tibial tunnel entrance, and the lowest peak stress on the ACL graft. Ren et al. adapted a gait perturbation process induced the mechanism of human falls and quantified the lower limb response to examine the compensatory strategies of patients with knee osteoarthritis (KOA). It was found that patients with KOA have a higher risk of falling in response to a backward slip perturbation compared with healthy older individuals. Patients with KOA have a better focus on the strength and activation of the muscles that play a critical role in hip extension during gait. Lin et al. used a custom-made knee extension force measurement system to evaluate the effect of cycling training with a central fatigue challenge resistance on individuals with Parkinson’s disease (PD). This study confirmed that this training methodology could be beneficial, efficient, and feasible for patients with early-stage PD in strengthening both central and peripheral components of knee extensor forces and can be considered for developing rehabilitation interventions for these patients.The knee, as the main lower limb motor joint, is comprising four bones and an extensive network of ligaments and muscles. Knee diseases, including musculoskeletal and neurological disorders, and the relevant treatment strategies may extremely affect the knee biomechanical response. Five studies were focused on knee biomechanics and the treatment modalities in this Research Topic. Jiang et al. developed a multi-rigid body modeling technique to analyze the kinetics and kinematics of the lower limb in the half-squat parachuting landing to evaluate the protective effects of an ankle brace. It was found that this ankle brace might provide more effective protection for the lower-extremity joints in the half-squat parachuting landing with a backpack than no backpack landing. Zeng et al. developed a 3D fracture line mapping methodology to define the distribution of fracture lines throughout the ankle joint to describe a novel classification for the fibular and tibial fracture lines. They have classified 228 patients with ankle fractures using this methodology and claimed that this technique can include fracture types that cannot be identified by both AO and Lauge-Hansen (LH) classification. Rebelo et al. utilized a validated lower limb FE model of underbody blast (UBB) to estimate the effect of stature on injury risk. This study showed that there is a higher risk of calcaneal injury associated with short statures than medium and tall statures. In addition, they found that shorter individuals are at a higher risk of leg injury compared to taller ones. A combat boot and a floor mat were revealed to have a variable attenuating efficiency depending on the UBB loading characteristics. Funaro et al. developed a personalized FE model which contained the sub-tendons structure and their relative sliding to study the impact of rehabilitation maneuvers and different twists on Achilles tendon (AT) strains. It was shown that the rehabilitation maneuvers ranking was independent of the AT twist. However, the findings in this study could provide a better guide for clinicians to prescribe rehabilitation exercises for different twists on AT strains. Lv et al. utilized a 3D FE model of foot with a calcaneal fracture to assess the biomechanical performance of double-point fixation (DPF) compared to the three-point fixation technique. They concluded that DPF fixation by volar distal radius plates revealed favorable and sufficient fixation stability with a lower risk of postoperative stress fracture, which may possibly assist surgeons in planning a new fixation modality. Li et al. used a foot multi-segmental modeling to explore the kinematics and GRFs in marathon-experienced recreational runners before, during, and after long-distance running. It was found that excessive foot motion after 5 km of running may hypothetically increase the risks of running-related injuries (IRR). Moreover, the forefoot space of footwear may affect the biomechanical response of foot in long-distance running. In addition, Matias et al. studies the biomechanical-related risk factors for RRI and stated the effects of the 8-week foot-core exercise training program. They observed that changes in foot-joint kinematics resulting from the foot-core training program could be in charge of the reduction in RRI incidence. Xiao et al. measured the foot muscle strength, passive ankle kinesthesia, and postural control to examine the effects of 4 weeks of foot core exercise and multi-session high-definition transcranial direct current stimulation (HD-tDCS). The achieved results showed that HD-tDCS might increase the metatarsophalangeal joint flexor strength and the passive kinesthesia thresholds of ankle inversion and eversion. Footwear is generally designed to provide protection, assist in motion control and stability, and treat musculoskeletal deformities and injuries  and running speed on the biomechanical response of lower limb biomechanics using the inverse kinematics algorithm. This investigation showed that shoes with −8 mm HTD possibly will have a better ability to store and return energy that was related to running performance. Wang et al. combined the FE simulations with musculoskeletal modeling to study the effect of heel height on the strain distribution of plantar fascia. The results from the present research could reveal the strain variation on the plantar fascia and facilitate understanding of potential causes of plantar fasciitis induced by using high-heel shoes. In addition, this study claimed that the findings found that heel elevation as a treatment recommendation for plantar fasciitis could be questionable. Peng et al. used a detailed flatfoot orthosis FE model combined with Taguchi methodology to calculate the effect of design combinations on the response of foot-ankle biomechanics. It was shown that the foot orthosis with higher arch support and medial inclination angle could be more effective in reducing the peak plantar pressure. Besides, adding the medial forefoot posting may modify the forefoot deformity and forefoot pressure. Zhang et al. and Su et al. used a high-speed dual fluoroscopic imaging system to investigate the alterations of the first metatarsophalangeal joint (MTPJ) and medial longitudinal arch’s (MLA) kinematics between shod and barefoot running. Zhang et al. determined that shoe-wearing could limit the extension and joint translation of the first MTPJ and increase the horizontal adduction angle. This conclusion revealed that shoemakers would increase the capacity of the forefoot movement to reduce the injury risk factors. Su et al. presented that shoe-wearing may limit the partial movements of the MLA which may affect the storage and release of elastic energy during running.Studying foot and ankle biomechanics is another major field in lower extremities research investigations which focuses on the function of the foot and ankle in the normative, pathologic, and clinically treated states . Jiang einjuries . HencefoCollectively, 31 peer-reviewed papers presented in this Research Topic tackle different challenges in the topic of biomechanical investigations of injuries, diseases, diagnoses, and treatments of lower extremities. The outputs of these studies could be generally utilized to either understand the causes of musculoskeletal system disorders, predict the risk factors, or enhance the effectiveness of certain treatment management strategies. Every study in this Research Topic reports its potential and existing limitations of the established technique and highlights the progress in its relevant research area. Hence, we believe that the current Research Topic could be fundamental to enhance understanding of lower limb biomechanics which may be used for clinical applications. However, various studies and clinical observations may need to be considered in the future to see these methodologies used routinely. As guest editors, and contributors, we hope this Research Topic can serve as state-of-the-art research investigations which may be beneficial for the design and development of new and more effective interventions for lower limb disorders.


Saturated fatty acids are simple linear chains of singly-bonded carbon atoms which are differentiated only by chain length. However, the simplicity of their chemical structure belies the complexity of their metabolic properties and their nutritional and health impacts. Linear saturated fatty acids are generally divided into the following groups: short-chain , medium-chain , and long-chain . MCFAs are naturally found in a limited number of sources, such as mammalian milk and seed oils, in particular coconut oil.The objective of this collection of research and review papers is to highlight the unique roles that various saturated fatty acids play. These articles show that, despite their structural simplicity and apparent similarities, various saturated fatty acids, in particular MCFAs, have different metabolic properties which impact on nutrition and health. This collection of research articles on saturated fatty acids covers topics ranging from nutrition of the pregnant mother and infant to diet, cancer, obesity, diabetes and other metabolic diseases.Yuan et al. showed how the digestion and absorption of different MCFAs in infants are influenced by the position of the fatty acid on the MCT. Human breastmilk, which is made up of about 15% MCFA, is an important source of infant nutrition and MCFAs play a role in the development of the immune system. The authors note how MCFAs modulate the infant's gut microbiota which can prevent obesity and enhance brain development. There is growing interest in the triglyceride structure of MCFAs for the development of healthier formula milk for infants. This knowledge can also be used in the formulation of nutritional products for geriatric care as well.Although the fatty acid composition of fats and oils are the usual focus of interest, the position of the fatty acid on the triglyceride can affect their digestion and absorption. Sun et al. studied gestational diabetes mellitus (GDM) in pregnant women analyzing fatty acids in the blood plasma, from myristic acid (C14:0) to lignoceric acid (C24:0). They found that high concentrations of palmitic acid (C16:0) coupled with low concentrations of very long chain saturated fatty acids (C20:0 to C24:0) were positively associated with GDM. This study provides more information on the observed positive correlation of palmitic acid and inverse correlation of very long chain saturated fatty acids on hepatic insulin resistance through their ceramide derivatives. This highlights the impact of the diet of pregnant women and the importance of studying lipid metabolism in greater depth.Nonaka et al. studied the effects in mice of MCFAs on glucose homeostasis and lipid metabolism through gut hormone GLP-1. Their mouse study showed that tridecanoate (the MCT of C10:0) improved glucose homeostasis and prevented obesity induced by a high fat (lard) diet. This highlights the beneficial properties of MCFAs and shows that saturated fatty acids of different chain lengths have different effects. This study showed that MCFAs are not only energy-rich compounds but are also able to influence metabolic processes. These results may contribute to the development of MCTs as functional foods for the prevention of metabolic disorders, such as obesity and type 2 diabetes.Watanabe and Tsujino contributed to the theme of the applications of MCTs in functional foods. They recalled that MCTs were used in the 1960's mainly as a source of energy. However, recent discoveries have shown their potential in treating many metabolic dysfunctions that afflict modern society. MCTs have been shown to promote protein anabolism and inhibit catabolism which can delay frailty in the elderly. Of particular interest today is the relatively large ingestion of MCTs to elevate ketone bodies which can be used to support nutrition in conditions such as cancer, Alzheimer's disease, and epilepsy. Recent research is providing more knowledge for the wider use of MCTs in food.The paper of All of the papers in this collection call for more studies on saturated fatty acids, in particular MCFAs, so that their full potential can be developed. The new knowledge on saturated fatty acids, which are the simplest biomolecules of life, are leading to a new paradigm regarding their health benefits. With the rise of obesity worldwide, scientifically-informed guidelines on the use of MCFAs is urgently needed.The author confirms being the sole contributor of this work and has approved it for publication.


Background: The present study evaluated the cortical activation during emotional information recognition. Methods: The study group included 16 patients with depression, and 16 healthy subjects were enrolled as a control group. Patients received eight weeks of antidepressant therapy. Functional MRI evaluated the cortical activation twice in the patient group and once in the control group. The fMRI task processed the emotional information with face demonstration from the PennCNP test battery. Results: During the processing of emotional information, patients showed activation in the middle and the inferior frontal gyri, the fusiform gyrus, and the occipital cortex. After treatment, patients showed a significant decrease in the frontal cortex activation for negative face demonstration and no frontal activation for positive emotion recognition. The left superior temporal gyrus activation zone appeared in patients after treatment and in the control group. Healthy subjects showed more intense frontal cortex activation when processing neutral emotions and less when showing happy and sad faces. Activation zones in the amygdala and the insula and deactivation zones in the posterior cingulate cortex were revealed in the controls. Conclusion: This study confirms the hypothesis that anomalies in the processing of emotional stimuli can be a sign of a depressive disorder. Impaired emotion recognition and regulation plays a crucial role in the pathogenesis of depressive disorders . The conNeuronal models of depression supposed that negative emotions are associated with frontal-limbic connectivity dysfunction , manifesThe recognition of emotional facial expressions is a standard paradigm for studying the features of processing emotional information . DistortBrain imaging techniques, such as functional magnetic resonance imaging (fMRI), can significantly contribute to understanding how facial expressions are processed in humans. However, the results obtained in individual studies remain contradictory . For exaThis study was an fMRI radiology sub-study of a larger multidisciplinary research project that included a clinical psychiatry section, an fMRI section (currently underway) and the following study (currently in progress) to evaluate the effect of transcranial stimulation on the identified cortical zones.The clinical section of this study showed that patients with depression are slower to identify positive emotions but have a similar time recognising negative emotions as patients without depression . This sun = 32; mean age of 37.3 ± 9.7 years) originated from the prospective, observational study of the Recognition of Facial Emotions Expressions in Patients with Depressive Disorders [Participants , the Sechenov University Local Ethics Committee (Ref: 01-22/2020), the Independent Ethics Committee for clinical trials from the National Medical Research Center of Cardiology (Ref: 262/2020). All participants provided informed consent according to the Declaration of Helsinki.Functional MRI scans and the severity of depressive symptoms evaluation using the MADRS were performed twice in the patients’ group at baseline and after eight weeks of antidepressant therapy and once in the controls.3, matrix 228 × 228, flip angle = 8°, slice thickness = 1.1 mm, 250 slices per scan) and functional scans  were performed [MRI data were collected using a 3.0 T MRI scanner Philips Achieva TX with a standard phased-array head coil. Anatomical scans  and a rest period (30 s). We used the standardised images of 8 faces expressing happiness, sadness, or no emotion from the Pennsylvania Computerized Neuropsychological Test Battery (PennCNP) that was recommended for use in neurocognitive studies. Examples of images can be found through a link . Each taAll participants were instructed and trained to perform the experimental tasks. Face images for the tasks and the commands for rest periods were visually presented using the image projection in the magnet device. The first 30 s rest period was discarded from analysis to ensure the T1 equilibrium.http//www.fil.ion.ucl.ac.uk/spm, accessed on 20 February 2023, the Functional Imaging Laboratory, University College, London) running in MATLAB r2014b . fMRI scans were pre-processed using a standard algorithm consisting of slice-time correction, realignment and re-slicing, co-registration of the fMRI data to the corresponding anatomical scans, unified segmentation, and normalisation of anatomical and functional data to MNI space, and spatial smoothing using an 8 mm full-width half-maximum Gaussian kernel [Functional MRI data were processed using Statistical Parametric Mapping version 12  ≥ 2.8 with p < 0.05 corrected to control the whole-brain family-wise error rate (FWE) [Patients’ activation differences between two evaluation points were assessed using two-tailed paired te (FWE) . The MADDetailed activation cluster and peak statistics for all study groups can be found in The left fusiform gyrus BA37, considered a face recognition area , was actp < 0.001). The right inferior frontal gyrus activation for the neutral faces increased, but the difference was not significant. No frontal cortex activation was revealed for the recognition of positive emotions  the activation of the frontal cortex in depressive patients when processing positive and negative emotions decreased after treatment, while healthy controls and patients after treatment showed more intense frontal activation on neutral faces; (b) the activation zones in the insula and amygdala and the deactivation zone in the cingulate gyrus, detected in healthy subjects, did not function in patients both before and after treatment.More than 25 studies have been published that have evaluated the activation of the cerebral cortex in response to the demonstration of emotional faces in patients with depression. Most of them are investigated in detail in the meta-analysis by Stuhrmann et al. . AlthougSeveral studies have reported that the prefrontal cortex is involved in processing emotional information ,25,26,27The mechanism of the critical role of the prefrontal cortex in depressive states is complex and based on three abnormalities most productively investigated in preclinical models: Anhedonia, negative processing biases, and despair-like behaviour (learned helplessness) . These dThe activation zones in the insula and amygdala and the deactivation zone in the cingulate gyrus, which have been demonstrated in previous studies to be involved during the processing of emotional information ,26,27, wThe deactivation zones in the limbic cortex in the control group were more intense when recognising neutral emotions but were also present on the demonstration of the happy and the sad faces. The study of M. Spies et al. showed that deactivation of the posterior cingulate cortex and the precuneus in patients with depression is a predictor of an early antidepressant response during the first two weeks . The patThe activation zone in the left superior temporal gyrus was of particular interest. Our study observed this area in the control group and the patients after treatment. Previous studies have revealed that the activation of this area may be a potential biomarker of the antidepressant-related early therapeutic effects underlying the influence of dopamine pathway genes in major depressive disorder .The activation of facial recognition zones in the fusiform gyrus has been described in previous studies ,27. AnotThe visual cortex activation in the occipital lobes in patients before treatment and the control group could be considered non-specific.A further unresolved issue concerned the heterogeneity of the presentation paradigms . This stSome limitations of this study should be noted. The sample size was small, however it was enough to detect significant cerebral cortex activation in the study groups and compare them at two-time points. The control group included healthy volunteers, and no placebo-controlled group was included. The fMRI study was part of an observational study , and it In the previous decade, several studies using the morphological features of anatomic MRI images (volumetric asymmetry indices) evinced specific gyri to discriminate between male and female patients . There iWhich additional evidence will be needed to consider these fMRI features as contemporary imaging biomarkers for depressive disorders remains a question to be answered. It is logical to propose assessing the correlations between the mentioned gyri with their volumetric parameters and comparing the “difference in correlations” between patients and healthy subjects in a future study. This study will give insight into the latent relations between volumes, asymmetry indices, and fMRI features.Readers might be interested to know the “diagnostic performance” of the fMRI activation zones discovered and the possible predictive ability of this biomarker to detect or predict depression in healthy subjects. This assessment will approach more of these fMRI biomarkers in day-to-day clinical practice.Another section of this study is now in progress, in which the results of the transcranial magnetic stimulation (TMS) of the identified cortical zones will be evaluated. A recent review and meta-analysis confirmed evidence of the efficacy of TMS in adolescents with major depressive disorder ,43.The overall results of this study may allow us to move from theoretical fMRI studies to a practical approach where the exact information about the cortical activity features in patients with depression will contribute to improving the effectiveness of the treatment of such patients.Antidepressant therapy reduced the frontal cortex activation in patients while processing positive and negative emotional information. It increased the frontal cortex activation when demonstrating neutral faces, which corresponded to the results obtained in healthy volunteers. The activation zone in the left superior temporal gyrus observed in patients after treatment also corresponded to the results obtained in the control group. The activation zones in the left insula left amygdala, and the deactivation zone in the cingulate gyrus, identified in the healthy group, were not reproduced in patients after treatment. This study confirmed the hypothesis that anomalies in the processing of emotional stimuli can be a sign of a depressive disorder. These data are consistent with previous neuroimaging studies of patients with depression. In this study, we aimed to find an approach to the issues that currently remain unresolved. These data can be considered another step in understanding the pathogenesis of depressive disorders.


Following the disruption of Russian natural gas flows to Europe, we investigate the impact of collaborative and selfish behavior of European countries to tackle energy scarcity and supply electricity, heat, and industrial gas to end users. We study how the operation of the European energy system will need to adapt to the disruption and identify optimal strategies to overcome the unavailability of Russian gas. Those strategies include diversifying gas imports, shifting energy generation to non-gas-based technologies, and reducing energy demands. Findings suggest that: (1) selfish behavior of Central European countries exacerbates the energy scarcity for many Southeastern European countries; (2) proactive collaborative energy savings, together with a mild winter, can fully relieve the stress of the gas shortage; (3) diversification of gas imports leads to bottlenecks in the gas network, especially in Southeastern Europe; and (4) electricity generation is mostly shifted to coal-based power plants, causing higher carbon emissions.  •Assessing the energy crisis in Europe after the disruption of Russian natural gas•Selfish behavior of European countries exacerbates the crisis for Eastern Europe•Then, significant involuntary electricity and heat demand reductions are necessary•Diversification of gas imports leads to bottlenecks in the European gas network Energy resources; Energy policy; Energy systems Europe is in the tight grip of the worst energy crisis in decades.Natural gas is primarily consumed to provide energy services, i.e., electricity, residential and commercial heat, and industrial services.,,,Assessing the current situation is especially challenging because of the multitude of possible levers to compensate the gas shortage. These include increasing non-Russian gas imports, shifting to non-gas-based electricity and heat generation, and reducing gas consumption through voluntary and involuntary demand reduction mechanisms. Various aspects of the current gas shortage in Europe have been investigated in the scientific literature, namely, the economic assessment of Russia’s market power,,,,,,However, in times of crisis, countries tend to put their self-interest before the common interest of the international community.a-priori assumptions about what demand reductions will materialize.,,,European countries have shown and continue to make concessions to mitigate the impact of the gas shortage on the electricity, heating, and industry sectors.,,The objective of all presented studies of the energy crisis is to close the gas balance, i.e., match supply and demand of gas under a Russian gas disruption. However, most works only investigate the gas sector in isolation, neglecting the electricity and heating sector.1.How much do the European countries need to voluntarily and involuntarily reduce the electricity, residential and commercial heat, and industrial gas demand under a Russian gas disruption if they act collaboratively or selfishly?2.How are the gas flows between countries redistributed because of the diversification of gas imports? Is the European gas flow network capable of accommodating the redistributed flows?3.What are strategies to substitute the missing Russian gas imports in Europe? To what extent can the coupling of the gas, electricity, and heating sectors support the reduction of gas consumption by shifting energy generation to non-gas-based technologies?4.To what extent do proactive energy savings and a mild or severe winter impact Europe’s ability to mitigate the gas shortage?5.How can the energy system prepare for the following winter while supplying the energy demands as much as possible during the year without Russian gas to fill the gas storages?Overall, we fill these gaps by investigating the risk of energy scarcity in Europe through the lens of selfish versus collaborative behavior of European countries. We do so by considering the entire gas supply chains, from increased imports of natural gas to shifts in the electricity and heat generation mix and necessary reductions of final energy demands. More specifically, we extend the existing studies by addressing the following questions.We tackle these questions through a European energy system model that optimizes the operation of the gas, electricity, and heating sectors in combination for 28 European countries to assess the impact of a complete disruption of the Russian gas supply. The objective of the optimizer is to minimize the total social welfare cost of the energy system, which is composed of the cost to operate the technologies to supply final energy demands and of the cost for endogenous demand reductions if the demands cannot be fully supplied. The exchange of energy is modeled as an electricity and gas transfer model between the countries. This sector-coupled model is described in We assess the different adaption strategies if the European countries act in a collaborative spirit or selfishly. In detail, our definition of selfish behavior is that countries will first attempt to supply their entire final energy demands before they share any excess gas and electricity with neighboring countries. To the best of our knowledge, the presented approach is the first centrally planned energy system optimization model which allows the investigation of selfish behavior of individual countries to tackle energy scarcity.Unlike other studies, we treat the demand reduction as an endogenous choice of the optimizer, thus we are able to indicate where and when to reduce the electricity, heat, and industrial gas demand to maximize social welfare. To assess the real-world consequences of reducing final energy demands, and thus the impact of the energy crisis, we differentiate between voluntary and involuntary demand reductions. To this end, we develop a modeling approach where the energy demand of each sector can be reduced on two levels: The first, voluntary reduction level leads to lower costs than the second, involuntary level. However, voluntary demand reductions are limited to a share of the demand. After reducing the entire voluntary share, if still necessary, the demand must be reduced on the second, involuntary level at higher costs. Finally, this study focuses on short-term techno-economic mitigation strategies and does not emphasize the macroeconomic feedback of the energy crisis.(i)Additional gas imports from other sources than Russia;(ii)shift of electricity and heat generation to non-gas-fired technologies;(iii)reduction of final energy demands.This study provides a cost-optimal pathway through the energy crisis by investigating all three mitigation strategies along the entire gas supply chain.We first analyze the demand reductions in the electricity, heating, and industrial sector as these reductions are the direct impact of the gas shortage and thus quantify how well the energy crisis can be overcome if all countries collaborate. Subsequently, we contrast collaborative with selfish behavior where each country first attempts to fully supply its domestic energy demands. In all presented results, the gas storage levels at the end of the year must match the initial levels for each country to ensure that the energy system is prepared for the winter of 2023.The electricity demand is the last to be reduced voluntarily and multiple technologies exist to replace gas-based electricity generation –(3) when Russian gas is available A: (1) NoWithout Russian gas, Eastern European countries and Germany can neither import gas via pipelines from outside Europe nor count on sufficient domestic production . Those cThe flows to the Southwest are reduced so that the corridors to France and to Italy carry less gas. The flow from Austria to Italy is almost entirely reduced, which is compensated for by additional flows from Switzerland to Italy. To this end, much more gas is transported eastward from Spain via France to Switzerland. The new gas corridor is formed from Germany to the East and Southeast, which leads to several cross-border capacities in Central Europe being fully utilized, i.e., from Germany to Austria, and from Slovakia and Romania to Hungary. The cross-border capacity between Spain and France becomes fully utilized because Spain is the country with the most abundant access to natural gas through pipelines from Northern Africa and liquefied natural gas (LNG) imports. Because this cross-border capacity is the only gas flow connection from Spain to Central Europe, Spain cannot increase gas exports to further support other countries.The flow pattern in the selfish scenario is similar to the one in the collaborative scenario, although the flows decrease Additional supply of gas from sources other than Russia. This includes additional pipeline and LNG imports;(ii)shift to non-gas-based electricity and heat generation, including the reduction of electrified heat;(iii)reduction of final energy demands.Three short-term compensation strategies can help mitigate the gas shortage.In both the collaborative and selfish scenarios, the missing Russian gas imports are replaced by increasing non-Russian gas imports , by increasing the share of non-gas-based electricity and heat generation (about 28%), and by reducing final energy demands . The collaborative and selfish scenarios are almost identical in the absolute amount of replaced gas through additional supply and shifted generation. However, in the selfish scenario, more electricity demand is reduced in Europe whereas the reductions in heat demand decrease .,Experimental procedure S7Non-Russian imports account for around 520 TWh, which is within the range reported by other authors.The IEAWithout Russian gas available, the electricity generation from natural gas in Europe is reduced by around 29% in the collaborative scenario and around 39% in the selfish scenario. Gas-based electricity generation is mainly substituted by increasing the utilization of hard coal (+37%) and lignite power plants (+19%). Electricity generated from nuclear power plants can be increased by around 2%, because very little available nuclear capacity is currently unused. Renewable technologies are always the first to dispatch because of their low marginal cost of production. Hence, they already exploit their entire available capacity and cannot further contribute to compensate for the gas shortage. Substantially higher electricity generation from renewable technologies can only be achieved through additional investments. In the case of selfish behavior, it also becomes necessary to reduce substantial amounts of electricity demand .The existing electricity generation capacities determine the amount of electricity that can be shifted away from natural gas in each country. With Russian gas available, Italy generates more than half of its electricity from natural gas and does not have large additional idle capacities of other technologies that are available to replace it. Thus, Italy can only reduce its gas-based electricity generation by 16% in the collaborative scenario by exploiting the available idle hard coal capacity, and net imports must be increased  increased non-Russian gas imports (42%), (2) a shift of electricity and heat generation to non-gas-based technologies (28%), and (3) demand reductions as a last resort (30%).Shifting from Russian gas to importing pipeline gas and LNG from other entry points changes the direction of gas flow in Europe. However, the European gas infrastructure is not designed for this shift, resulting in many vital cross-border capacities being operated at the limit. These bottlenecks can be relieved by additional investments in the gas infrastructure. LNG can help to mitigate the gas shortage even in the short term. Europe appears to have secured significant additional LNG imports and our results show that the gas infrastructure can in fact accept large additional quantities. However, if there is one lesson Europe should learn from the energy crisis, then that being dependent on a foreign country for energy supply is dangerous. Shifting the dependence from Russia to other countries might only postpone the discussion. We recommend harnessing the current movement to intensify the investment in domestic energy supply and in renewable energy technologies.Shifting the electricity generation mix to non-gas-based technologies, mainly hard coal and lignite, can reduce the utilization of natural gas turbines by around 30% and thus compensate for 15% of Russian gas imports. Many countries such as Germany and Poland have ample additional coal power capacities, even when taking into account that many units are currently non-operational and cannot be brought back in a reasonable time. We show that by shifting the electricity generation to mainly coal, the electricity demand of almost all countries can be fully supplied in the energy crisis under collaborative action. However, the increased consumption of hard coal and lignite increases the carbon emissions of the electricity sector by 5%, which thwarts Europe’s ability to achieve its climate targets. The total carbon emissions of the entire energy system remain constant with respect to when Russian gas is available, albeit significantly less final energy demand is supplied. Focusing exclusively on the short-term energy crisis while neglecting the long-term decarbonization strategy will probably make us miss Europe’s climate goals.This analysis focuses on the technical feasibility of mitigating the gas shortage and does not represent the actual socio-economic decision-making in times of crisis. This leads to several limitations that influence the adaption strategy and thus our presented recommendations.,First, we model the adaption strategy as a cost-minimization problem. Although the value of lost load in the industrial sector can be assessed monetarily to a certain degree,Second, the study does not attempt to depict and incorporate political decisions that were or are made during this crisis. As examples, these include Germany’s decision to phase out electricity generation from nuclear in April 2023,Finally, the current energy crisis impacts all participants in the energy system, from large industrial gas consumers to individual citizens. Predicting optimal mitigation strategies through a single techno-economic optimization model neglects the suboptimality and heterogeneity of real-world decision-making. The trade-offs (1) between the sectors of the energy system and (2) between countries are in reality not optimally solved.sansavig@ethz.ch).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Giovanni Sansavini . The countries are resolved at NUTS0 level (country level) with edges connecting the centroids. We solve a linear optimization problem with hourly resolved input data for an entire year, from November 1, 2022, until October 31, 2023.The objective of the optimization is to supply the exogenous and inelastic demands of electricity, heat, and industrial gas at the lowest cost for the entire energy system. The final energy demands are supplied by various conversion technologies in each sector . The eleOnly the two energy carriers gas and electricity can be transported and stored. Gas is transported via natural gas pipelines and stored in natural gas storages; electricity is transported via power lines and stored in pumped hydro power plants. We apply a periodicity constraint for the storage technologies to enforce that the storage levels at the beginning and at the end of the year match.We aggregate the input time series to 1400 representative time steps, using the time series aggregation package tsam 2.1.0.Demand reductions Below Table lists the order, cost The order, the demand reduction price, and the maximum share of reduced demand are based on educated, personal assessment since no prioritization is published by official sources. We follow the regulation by the EUThe optimization model is formulated as a centrally planned model, where the total operational cost is minimized for the entire energy system. Central planning inherently leads to collaborative behavior among countries. Specifically, it is more beneficial for the entire system to reduce demand voluntarily in one country to keep other countries from reducing demand involuntarily since this is significantly more expensive.Our definition of selfish behavior is that countries will first attempt to supply their entire final energy demands before they share any excess gas and electricity with neighboring countries. Hence, countries will not support other countries by voluntarily reducing their domestic energy demands and exporting gas and electricity. We model the selfish behavior of countries by implementing a binary decision between voluntary demand reductions and energy exports. In this model, transportable energy carriers M disjuncts In our model, the binary decision is implemented as a generalized disjunctive programming (GDP) formulation. GDP sets logical relationships between groups of constraints, called a disjunction, where The selfish decision-making is a disjunction of The second disjunct constrains the annual voluntary demand reduction The disjunction for all The disjunction is reformulated internally in Pyomo to binary big-M constraints.


Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research. Cerebral small vessel disease of the brain refers to blood vessels affecting the arteries or veins of the cerebellum. This type of disease causes dysfunction of the tiny circulation from the small vessels to the small vessels, which can cause neurological dysfunction and impaire brain function and cognitive impairment. The white matter is made up of nerve fibers and neuronal axons responsible for transmitting information between different brain regions . The whiThe current clinical tools for detecting white matter abnormalities include observing MRI images , using nIn order to help physicians diagnose small vessel diseases more accurately in clinical work, automatic 3D segmentation of cerebral white matter hyperintensities provides a scientific basis for clinical treatment of small vessel diseases by analyzing white matter morphology. In order to enhance the detection of cerebral white matter hyperintensities, this paper proposes an automatic 3D segmentation framework, which avoids incomplete segmentation and over-segmentation in the process of segmenting cerebral white matter. In order to more comprehensively understand the difference of cerebral white matter hyperintensities volume in three cognitive states, this paper explores the correlation between cerebral white matter hyperintensities volume and cognitive assessment coefficient.For brain white matter hyperintensities segmentation, we propose a deep multi-mapping encoder-decoder structure as an automatic 3D segmentation framework for brain white matter. A 3D residual mapping function is introduced in the overall segmentation framework. The residual function is widely used in image segmentation algorithms, which is known for its ability to solve the address information loss and ambiguity problem in image segmentation algorithms. The encoder-decoder structure is composed of multiple convolutional and pooling layers, which can result in missing image information due to layer-by-layer compression and downsampling, it leads to efficiency degradation and gradient explosion, due to the network's increased depth. To avoid this situation, this paper proposes an automatic 3D segmentation framework adapted to the white matter hyperintensities.In this paper, we propose a three-dimensional Multi-layer Cross-connected Residual Mapping Module (MCRCM) in the encoding stage. The MCRCM Module can enhance the feature depth and width in the encoder stage, by adding multiple cross-connected residual structures, features at different levels can be cross-connected and fused, it enhances the feature representation capability and feature discriminability of the brain white matter hyperintensities. The 3D Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is introduced in the decoding stage to upsample and reconstruct the features extracted by the 3D encoder, adding 3D spatial attention enhanced structures, which can adaptively adjust the feature weights and responses according to the spatial distribution of the features. The addition of a 3D spatial attention enhancement structure allows for adaptive adjustment of feature weights and responses according to the spatial distribution of the features, it enhances the supervisory hyperintensities through the 3D spatial attention mechanism, facilitating the convergence and generalization of the network.The contributions of this paper are as follows:The 3D automatic segmentation framework proposed in this paper achieves accurate segmentation of white matter hyperintensities, which helps to identify brain changes associated with cerebrovascular diseases and cognitive impairment.The proposed MCRCM model enhances the medical feature representation capability of the model by extracting feature semantic information at different levels, while the proposed SAWESM model improves the medical segmentation accuracy of the model by capturing image boundary information and detail information during reconstruction.Through correlation analysis and multiple comparisons of cognitive function coefficients, we found that the cognitive function coefficients in the three cognitive states showed strong significance, MMSE and MoCA showed negative correlation with brain white matter hyperintensities.post-hoc interpretability techniques is analyzed for 3D brain tumor segmentation models (An adaptive fully dense (AFD) neural network is proposed for CT image segmentation . A hybrin models . Wu propn models . An imprn models .A method based on 2D registration is proposed to gradually propagate labels between consecutive 2D slices and used 3D UNet to leverage volume information, to alleviate the burden of manual annotation . A novelDilated Transformer is proposed to enlarge the receptive field without involving patches . NeuroevAn advanced deep learning network is proposed for 3D medical image segmentation . 3D ASPPThe method is proposed to achieve competitive accuracy from “weakly annotated" images, where weak annotations are obtained by representing the 3D boundary of the object of interest . A reinf2, matrix = 128–128, slice thickness = 2 mm, number of slices = 66. The visualization of the hyperintensities in the white matter of the brain is shown in MRI was performed with a 3T MRI scanner  using a 64-channel head coil. T2-fluid attenuated inversion recovery (FLAIR) images were acquired with the following parameters: TE = 150 ms, TR = 9,075 ms, TI = 2,250 ms, FOV = 256–256 mmThe three-dimensional residual structure with multi-level cross-connected proposed in the encoder stage can deepen the extraction of network ability for three-dimensional features by introducing the residual structure, which can effectively alleviate the problems such as gradient disappearance because of the network depth problem and adjust the weights of the previous neural network layers by learning the residual mapping function, the network can better adjust the input and output relationships of three-dimensional features. It is proposed that adding multi-level cross-connected to the three-dimensional residual structure can improve the nonlinear learning ability of the network and enhance the information exchange between different layers. The multi-level three-dimensional residual cross-connected structure incorporates multiple residual blocks and cross-connected structures in the encoder stage to achieve three-dimensional feature extraction and information exchange between the multi-level structures of the three-dimensional network. As is shown in The encoder of the proposed model consists of multiple convolutional blocks, each of which followed by batch normalization and ELU activation. The cross-level residual connections are introduced by adding shortcut connections between the different levels of feature maps in the encoder. The cross-level residual connections enable information flow across different levels of feature maps, which helps to capture both low-level and high-level features for more accurate segmentation. This is achieved by adding the output of the previous block to the input of the current block, which allows the model to learn the residual features that are not captured by the previous block.y represents the computation result of each group convolution block, α and γ represent the coefficients of the cross-level connections, K represents the convolution kernels of the blocks, b represents the normalization coefficients, Elu represents the activation function.Regarding the calculation formulas of the Cross-Level Residual Connections Module, as shown in the The module of deep supervision mechanism with spatial attention enhancement is proposed in the decoder stage, the spatial attention enhancement supervision function is proposed in the deep 3D residual network, which makes the network pay more attention to the important information in the input sequence to improve the performance of the model in the feature conversion stage, the spatial attention makes the network pay more attention to the important regions of the 3D data to improve the network perception in the decoder stage, the deep supervision mechanism can better learn the mapping relationship between the input 3D data to enhance the robustness of the model and improve the robustness in generative tasks.The decoder of the proposed model also consists of multiple convolutional blocks, each of which followed by batch normalization and ELU activation. The spatial attention module is incorporated in the decoder to provide additional guidance for the model to focus on the most informative regions of the feature maps. The spatial attention module is designed to assign different weights to different regions of the feature maps based on their importance for the segmentation task. The attention module takes the feature maps of the previous layer as input and produces a spatial attention map, which is multiplied element-wise with the feature maps to obtain the attended feature maps. The attended feature maps highlight the most informative regions of the feature maps, which can improve the accuracy and robustness of the segmentation results. The spatial attention-guided supervision is introduced by incorporating the attended feature maps into the loss function as an additional term. This term encourages the model to focus on the most informative regions of the feature maps during training and helps to reduce the influence of the irrelevant regions .Is represents the computation result of the spatial attention, Ic represents the result of the shallow skip mapping, I represents the result of the enhanced spatial attention, L represents the result of the deep supervision, Z represents the result of the enhanced spatial attention and shallow residual connection, Relu and Elu represent the activation functions.Regarding the calculation formulas of the Spatial Attention Guided Supervision module, as shown in the Cognitive function assessment coefficients are measures of people's cognitive abilities, including attention, memory, language, and spatial expression. Correlation analysis and multiple comparison analysis of cognitive function coefficients related to brain white matter can reveal the relationship between these indicators. Correlation analysis is a method to measure the strength of the relationship used for variables, usually using positive or negative correlation to determine the correlation, multiple comparison is a method used for differences between multiple variables. This paper uses SPSS software to conduct correlation analysis and multiple comparisons of cognitive coefficients in three cognitive states. The cognitive function coefficients involved in this paper are MMSE, MoCA, TMT-A, TMT-B, Stroop C-T, VFT, AVLT4, AVLT5, Rey-O, and BNT, with gender, age and education as covariates. The cognitive function assessment coefficients and their meanings in the statistics of this paper are as follows, MMSE: Mini-Mental State Examination, a standardized measurement tool to assess cognitive function, is commonly used to test the cognitive function of older adults. MoCA: Montreal Cognitive Assessment, a standardized measure of cognitive function, is also commonly used to test cognitive function in older adults. TMT-A and TMT-B: Trail Making Test A and B, a standardized measure to assess cognitive function, is also commonly used to test cognitive flexibility and executive function. TMT-A test requires subjects to connect numbers and TMT-B test requires subjects to connect numbers and letters alternately. Stroop C-T: The Stroop Color-Word Test, a standardized measure of cognitive function and inhibitory control, is a C-T task that requires subjects to quickly identify colors and inhibit word information that contradicts them. The VFT: Verbal Fluency Test, a standardized measure that assesses cognitive functioning and language ability, consists of two tasks: semantic category and letter category. AVLT4 and AVLT5: California Verbal Learning Test 4th and 5th Trials, a standardized measure to assess memory function. Rey-O: It represents Complex Figure Test, a standardized measure assessing visuospatial and memory functions. The BNT: Boston Naming Test, a standardized measure of verbal and lexical ability.This study visualized cognitive assessment parameters associated with brain white matter hyperintensity while considering gender, age and education level as covariates. The remaining 10 cognitive function coefficients were analyzed and presented, providing a clearer understanding of the distribution of cognitive assessment parameters among subjects. This information can serve as a reference for subsequent analysis and decision making regarding cognitive assessment parameters closely related to brain white matter hyperintensity. The cognitive assessment coefficients corresponding to the white matter high-signal dataset were first visualized with gender, age and education as covariates among the subjects, the remaining 10 variables were visualized with numerical density. As is shown in We evaluated the proposed the automatic 3D segmentation framework on a large public dataset for brain white matter hyperintensity segmentation, which contains MRI scans from patients with Alzheimer's disease, cognitive impairment and normal cognition. The dataset consists of 254 FLAIR images with manual annotations of brain white matter regions. Our experiments showed that the proposed the automatic 3D segmentation framework with cross-level residual connections in the encoder and spatial attention-guided supervision in the decoder achieved state of the art performance in terms of Segmentation accuracy. This dataset contains three kinds of data with different cognitive functions, dementia, MCI, NCI. The three automatic segmentation framework proposed in this paper is used to segment each of the three datasets. The segmentation results of dementia are shown in The above standardized measurement tools and tests can assess multiple domains of cognitive function, including attention, memory, orientation, computational skills, language skills, executive function and visuospatial abilities. In clinical and research settings, these tools can be used to assess impairment or improvement in cognitive function, diagnose cognitive dysfunction or neurodegenerative disorders and develop individualized rehabilitation plans and treatment protocols.In conducting the correlation experiment part, the correlation analysis was first performed on the cognitive function score coefficients related to cerebral white matter hyperintensity. The gender, age and education level of the subjects were used as covariates in the correlation analysis. In terms of gender, women would have better performance results than men on some cognitive assessments, age would also affect the decline in cognitive function, the level of education would also affect cognitive ability, these three factors would interfere with the study results and affect the accurate assessment of the relationship between cerebral white matter hyperintensity and cognitive function.p-values for MMSE and MoCA are <0.001, which indicates a significant association between the volume of white matter hyperintensity regions and cognitive function, p-values corresponding to these two cognitive assessment coefficients are <0.001 implying that the probability of observing a correlation as strong as the one found by chance in the study is <0.1%.After the correlation analysis of the cognitive assessment coefficients, the results are presented as demonstrated in The probability of observing a correlation as strong as the one found by chance in the study is <0.1%. That is the greater the white matter region of the brain in the subject, the smaller the corresponding MMSE and MoCA values, the two showed a negative correlation, suggesting an association between cognitive decline, neurodegenerative disorders and white matter hyperintensity, which clinically suggests the need for further examination and treatment, identifying which cognitive functions are affected in the patient, a more targeted approach to rehabilitation can be developed treatment approach. In the 3D segmentation experiments of three groups of brain white matter hyperintensity, the segmentation results of brain white matter hyperintensity obtained by training prediction of the automatic 3D segmentation framework proposed in this paper are shown in We propose an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The segmentation method proposed in this paper was compared with nnunet and nnunet-resnet for 3D segmentation volume of cerebral white matter as demonstrated in From the results in White matter hyperintensity is an abnormal signal in the white matter of the brain, clinically manifested as hyperintensity in MRI images. It is commonly associated with cerebral blood disease, small vessel disease and neurodegenerative disease. Accurate segmentation of these areas has greatly improved the treatment of cerebrovascular and small vessel diseases. Progress in the treatment of neurodegenerative diseases like dementia has also been made with the help of efficient segmentation techniques.This paper proposes a 3D segmentation method for the white matter hyperintensity region using a 3D encoder and 3D decoder. The method incorporates a residual network into the global framework. While traditional medical image segmentation has been based on two dimensional images, this method utilizes three dimensional characteristics for improved accuracy. This study proposes the Multi-layer Cross-connected Residual Mapping Module in the decoder stage for enhancing the feature extraction and representation abilities of the segmentation model for MRI images. The module combines low-level and high-level feature information using a multi-layer cross-connected residual structure. The study introduces the Spatial Attention Weighted Enhanced Supervision Module in the decoder section, which utilizes the spatial attention mechanism to capture boundary information and regional small pixel information during 3D reconstruction. This enhances the supervisory role of the mechanism and improves the segmentation accuracy and reconstruction capability of the model.This study analyzed the correlation of using cognitive assessment coefficients in the white matter hyperintensity segmentation experiment, taking into account the gender, age and education as covariates. The study then explored the correlation between the volume of the white matter hyperintensity area and the MMSE scores, MoCA scores. The results showed that a larger volume of the white matter hyperintensity was associated with lower MMSE and MoCA scores, indicating poorer cognitive function. This study conducted segmentation experiments on three different groups of white matter hyperintensity: Dementia, MCI, and NCI. By comparing the results of these experiments, the researchers found that the volume size of the white matter hyperintensity regions was largest in dementia, followed by MCI, NCI. The study also showed that the proposed three-dimensional segmentation framework had higher efficiency compared to other methods.The proposed 3D segmentation framework in this paper has some limitations. The model design parameters are too large, which can lead to longer training and inference times. The segmentation algorithm requires high performance equipment to run effectively. In this paper, we propose that the limitations of the automatic 3D brain white matter hyperintensity segmentation framework exist for incomplete segmentation of small volume of brain white matter hyperintensity tissue, the correlation between the volume of brain white matter hyperintensity corresponding to the three cognitive states and more cognitive assessment parameters can be explored in the future, the correlation between the brain white matter hyperintensity segmentation work carried out with more cerebrovascular diseases and cognitive state disorders.The contribution of this paper is to propose a three-dimensional automatic brain white matter hyperintensity volume segmentation framework, compared with nnunet and nnunet-resnet this proposed three-dimensional segmentation framework does not have incomplete segmentation and over-segmentation, this paper explored the correlation analysis between brain white matter hyperintensity volume and cognitive assessment coefficient MoCA, MMSE, and concluded that there is a negative correlation between the two.p-value of each functional coefficient was <0.001, which indicated a negative correlation between white matter hyperintensity volume and MMSE and MoCA scores, which indicated that larger volumes of brain white matter hyperintensity volume correlated with lower functional coefficients, such as MMSE and MOCA. This thesis concludes that the segmentation comparison of three different groups of cerebral white matter hyperintensity volume between Dementia, MCI and NCI in descending order of Dementia, MCI and NCI. To verify that the automatic 3D framework proposed in this paper has a high efficiency, the comparison with nnunet and nnunet-resnet, the results obtained show that the segmentation volumes of nnunet and nnunet-resnet are under-segmented and over-segmented, while the results of the automatic three-dimensional segmentation framework proposed in this paper are more accurate than the other two methods, through the comparison of the segmentation volume rate and segmentation accuracy in the results, the automatic three-dimensional segmentation framework proposed in this paper has a higher. The results of the automatic three-dimensional segmentation framework proposed in this paper are more accurate than the other two methods.The correlation analysis as well as multiple comparison analysis of the relevant cognitive functional coefficients affecting brain white matter was performed by SPSS. The results indicated that the The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Shenzhen Second People's Hospital. The patients/participants provided their written informed consent to participate in this study.BX and XZ proposed methods and completed the whole experiment. WY and YW completed paper writing and revised the paper. DZ, CT, and XL completed the organization of the graphs and tables in the paper. XC made modifications to the overall arrangement of the paper. All authors contributed to the article and approved the submitted version.


Understanding the relationships between genes is crucial to identify the genetic program of organisms. This research could potentially have implications for this understanding. This study aims to build a genetic network for the yeast genome by recognizing interacting genes. This is performed by incorporating different mathematical methodologies and classification techniques to predict relations between two genes. High accuracy is achieved across several tests to identify network interactions. Our findings provide new insights and potential interactions between genes in the yeast genome network. Our results are highly relevant to the field, as they emphasize the power of classification and its role in improving the current understanding of the yeast genome network.Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast  Reconstructing the causal relationships in genetic networks, including regulatory interactions, is important for understanding biological processes and disease mechanisms. Computational prediction of regulations from transcriptomics data can be useful for selecting and prioritizing candidate genes that can serve as biomarkers or potential drug targets. The two main technologies widely used for transcriptome-wide gene expression profiling are microarrays and next-generation RNA sequencing (RNA-seq). Here, we discuss an approach to reconstructing causal networks based on the analysis of transcription data from microarray and RNA-seq. While microarrays have been the preferred method due to cost-effectiveness and ease of data analysis ,2,3, RNAPrediction analysis is one of the compelling comparative methods used to identify the pros and cons of using RNA-seq and microarray-based models. Several studies show the effects of these data models on different organisms and various classification systems. A prediction analysis study for identifying cancer biomarkers presented a comparison between RNA-seq and microarray-based classifiers to identify the significance of each in the field of clinical practice . Other sSaccharomyces cerevisiae genes using microarray expression data derived from various experimental settings [S. cerevisiae genome as well. Each dataset (RNA-seq and microarray) represents a variety of experimental conditions to capture the nature of data and extract the prediction features. We will study the difference in prediction accuracy between microarray data and RNA-seq data. We will shed the light on the performance of these datasets across kernel classification models; one-class, two-class, and rare-event classifiers. Finally, we will study the impact of RNA-seq data normalization techniques on the prediction accuracy of S. cerevisiae gene pairs.In this work, we adopt a methodical approach to evaluate and compare the performance of models with various classifiers for regulatory network prediction using either RNA-seq or microarray-based input data. In our previous work, we detected regulatory associations for settings . In the S. cerevisiae) to study conserved regulatory relationships, or in human or human pathogens to identify drug targets or biomarkers. Our findings provide new insights into the interactions between genes in the yeast regulatory network. We have also identified new potential targets for transcription factors using both profiling technologies by inferring the network interactions. Our results emphasize the power of classification and its role to improve the current understanding of the yeast regulatory network.The practical strength of our approach is in improving the target selection process, making informed decisions on which genes or gene products are the most promising targets of experimental studies, and prioritizing the use of laboratory resources when only a limited number of genes can be tested experimentally. The approach can be used in model organisms  was used to obtain the ion data . We usedion data . We randprefetch and fastq-dump. Prefetch was used to download sequence files in the compressed SRA format using the SRA accession IDs obtained from the previous step. These sequence files were then used by the fastq-dump sub-tool to retrieve the SRA fastQ files. This process required a significant amount of time and disk space. At this point, all the files were prepared for mapping and transcript quantification to obtain the reads for each gene.SRA-Toolkit: This is another well-designed service by NCBI. SRA-Toolkit is a collection of tools to utilize SRA data . We mainrsem-prepare-reference and rsem-calculate-expression.RSEM tool: Finally, the fastQ files were fed to the RNA-seq by Expectation Maximization (RSEM) package tool . RSEM isThe reference was prepared by obtaining the yeast FASTA-formatted file from Ensembl genome browser release 82 . Then, RThe goal of this section is to create a list of genes each represented by normalized reads from different experiment conditions. The approach follows a typical RNA-seq workflow for differential expression (DE) analysis. The process of data collection is automated as a pipeline using the steps and tools listed below:It is worth noting that gene names are equalized to avoid any confusion between processes and to ensure data coherence. The gene names for all genes in File(N) and in the databases mentioned in As previously stated, the microarray data were acquired from our previous work . The datThe output of The outcome of this step is the data matrix that consists of 18,243,820 pairs of genes, each pair represented by a vector of 49 moments. All the moments are then standardized for all pairs of genes. We applied the Principal Component Analysis (PCA) to first convert the moments to principal components and then identify the significant moments . This reS. cerevisiae are found in various databases such as YeasTract [The training data consisted of two classes; related and unrelated pairs to aim at constructing the undirected gene regulatory network. The GRN data for easTract , YTRP [2easTract , HarbisoeasTract , YeastPaeasTract , and KEGeasTract . The lisDetermining the unrelated pairs presents a challenge to the prediction association since it is questionable to assume that any pair absent from all GRN database sources is unrelated. To tackle this, we set up the following assumption. The lowest correlated pairs of genes are to be considered in the negative class for training. Moment REWKLR is identified as a rare-event classifier where the unrelated pairs outnumber the related ones. This captures the natural dynamics of genetic data and considers the sparsity of the data. In particular, the data in this article exhibit the genetic data nature where the percentages of zeros (unrelated pairs) exceed that of ones (related pairs). REWKLR embeds the kernel in a typical Logistic Regression model to reflect the nonlinearity of data separation. The performance of REWKLR was shown to compete with other classifiers such as SVM ,26,27. TC), and the Gamma parameter . As for the one-class SVM classifier, we used only the 10,000 ones that are part of the training dataset. Several tests were attempted to find the best data fit and accuracy by tuning the kernel parameters in all classifiers. Details of training accuracy and parameters for each training dataset are discussed later in In this section, we address the data preparation and system specifications for the workflow setup shown in The pipeline unit was developed by utilizing an on-premise High-Performance Computer cluster that provides parallel job execution. It was essential particularly for the RNA-seq analysis since it required large computational power and memory. Converting each of the 1500 SRA files to fastQ and then calculating the count read from each fastQ file were parallelized. Jobs that were also run in parallel for a faster and more effective approach were calculating the moments and running the PCA analysis. The three datasets TPM, FPKM, and microarray were then fed to the classifiers. REWKLR is built on MatLab guided by . SVM andOverall, RNA-seq data achieves higher training accuracy over microarray. The training accuracy fluctuates across classification models for both normalization methods TPM and FPKM. We illustrate the Receiver Operating Characteristic (ROC) curve in The performance of each model was validated with the following databases: YeasTract (YT) , YTRP 2, YeastPaTo illustrate the complexity of the relationships predicted, we added density plots to compare the distributions of correlation values between linked and unlinked pairs. Next, we chose the recall accuracy measure to display the performance of all classifiers. The bar plot in Across all classifiers, it is apparent that on average the use of RNA-seq data yielded higher recall values of more than 80% with all datasets and more than 90% with the majority of datasets. On the hypothesis that the RNA-seq normalization technique might affect the prediction accuracy, we noticed that TPM normalization shows a slightly overall improved performance than FPKM normalization. However, it cannot be stated that normalization techniques have any effect on accuracy.Another appealing comparison measure is to compare the regulatory transcription networks constructed by Yeastract, RNA-seq using FPKM normalization, and microarray. We identified new potential targets for transcription factors using both profiling technologies, and we visualized the network modules using Cytoscape . Figure To evaluate our approach, we compared it to results reported previously  for two other GRN inference methods  for us study  where weFuture work will expand the study of normalization techniques, including exploring RPKM. The study will also be extended to inferring the whole regulatory transcription network for multiple transcription factors and comparing the results to other studies. To improve accuracy, the integration of various data types, such as gene expression, epigenetic marks, chromatin accessibility, and transcription factor binding sites will be incorporated into the analysis. Furthermore, machine learning algorithms could enhance the accuracy of gene–disease association predictions by applying various feature selection techniques and integrating multiple data types. Causal inference methods may also be explored to identify causal relationships between genes and diseases, and network-based drug discovery approaches may be developed to use predicted disease-associated genes as potential drug targets. Furthermore, the potential use for studying microbiomes, possibly in a metagenomic/metatranscriptomic analysis is an interesting concept that can be developed in the future. These future directions could provide valuable insights into the molecular basis of disease and enable the development of more effective and personalized treatments.