Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptional, Behavioral and Biochemical Profiling in the 3xTg-AD Mouse Model Reveals a Specific Signature of Amyloid Deposition and Functional Decline in Alzheimer’s Disease

Alzheimer’s disease (AD)-related degenerative decline is associated to the presence of amyloid beta (Aβ) plaque lesions and neuro fibrillary tangles (NFT). However, the precise molecular mechanisms linking Aβ deposition and neurological decline are still unclear. Here we combine genome-wide transcriptional profiling of the insular cortex of 3xTg-AD mice and control littermates from early through to late adulthood (2–14 months of age), with behavioral and biochemical profiling in the same animals to identify transcriptional determinants of functional decline specifically associated to build-up of Aβ deposits. Differential expression analysis revealed differentially expressed genes (DEGs) in the cortex long before observed onset of behavioral symptoms in this model. Using behavioral and biochemical data derived from the same mice and samples, we found that down but not up-regulated DEGs show a stronger average association with learning performance than random background genes in control not seen in AD mice. Conversely, these same genes were found to have a stronger association with Aβ deposition than background genes in AD but not in control mice, thereby identifying these genes as potential intermediaries between abnormal Aβ/NFT deposition and functional decline. Using a complementary approach, gene ontology analysis revealed a highly significant enrichment of learning and memory, associative, memory, and cognitive functions only among down-regulated, but not up-regulated, DEGs. Our results demonstrate wider transcriptional changes triggered by the abnormal deposition of Aβ/NFT occurring well before behavioral decline and identify a distinct set of genes specifically associated to abnormal Aβ protein deposition and cognitive decline.
Alteration of Proteotranscriptomic Landscape Reveals the Transcriptional Regulatory Circuits Controlling Key-Signaling Pathways and Metabolic Reprogramming During Tumor Evolution

The proteotranscriptomic landscape depends on the transcription, mRNA-turnover, translation, and regulated-destruction of proteins. Gene-specific mRNA-to-protein correlation is the consequence of the dynamic interplays of the different regulatory processes of proteotranscriptomic landscape. So far, the critical impact of mRNA and protein stability on their subsequent correlation on a global scale remained unresolved. Whether the mRNA-to-protein correlations are constrained by their stability and conserved across mammalian species including human is unknown. Moreover, whether the stability-dependent correlation pattern is altered in the tumor has not been explored. To establish the quantitative relationship between stability and correlation between mRNA and protein levels, we performed a multi-omics data integration study across mammalian systems including diverse types of human tissues and cell lines in a genome-wide manner. The current study illuminated an important aspect of the mammalian proteotranscriptomic landscape by providing evidence that stability-constrained mRNA-to-protein correlation follows a hierarchical pattern that remains conserved across different tissues and mammalian species. By analyzing the tumor and non-tumor tissues, we further illustrated that mRNA-to-protein correlations deviate in tumor tissues. By gene-centric analysis, we harnessed the hierarchical correlation patterns to identify altered mRNA-to-protein correlation in tumors and characterized the tumor correlation-enhancing and -repressing genes. We elucidated the transcriptional regulatory circuits controlling the correlation-enhancing and -repressing genes that are associated with metabolic reprogramming and cancer-associated pathways in tumor tissue. By tightly controlling the mRNA-to-protein correlation of specific genes, the transcriptional regulatory circuits may enable the tumor cells to evolve in varying tumor microenvironment. The mRNA-to-protein correlation analysis thus can serve as a unique approach to identify the pathways prioritized by the tumor cells at different clinical stages. The component of transcriptional regulatory circuits identified by the current study can serve as potential candidates for stage-dependent anticancer therapy.
Opening Up: Clients’ Inner Struggles in the Initial Phase of Therapy

Objective To explore how clients in clinical settings experience the process of opening up and sharing their inner experiences in the initial phase of therapy. Methods Two psychotherapy sessions of clients (N = 11) were videotaped and followed by interviews. Interpersonal process recall was used to obtain in-depth descriptions of clients’ immediate experiences in session. A follow-up interview was conducted 3 months later. The interviews were analyzed using thematic analysis. Results The data revealed how and why clients distanced themselves from inner experiences in the initial phase of therapy. The overarching theme was “Holding back and struggling to open up,” which included four subthemes: (a) fearing the intensity and consequences of negative emotions; (b) experiences of being incapable and bodily stuck; (c) being insecure about one’s worthiness and right to share inner experiences with the therapist; and (d) struggling with feeling disloyal to loved ones. Conclusion The participants held back because they feared different consequences of opening up. A range of concerns led participants to distance themselves from their inner experiences and/or to refrain from openly talking about them to the therapist. Concerns related to appropriate interpersonal conduct as client were especially important. This knowledge is highly relevant to clinicians when building safety for psychotherapeutic work.
Application Research of Intelligent Classification Technology in Enterprise Data Classification and Gradation System

Classification and gradation system adopts different security protection schemes for different types of data by implementing classification and gradation management of data, which is an important pretechnical means for data security protection and prevention of data leakage. This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. The generated training model can intelligently identify and classify file streams, improving work efficiency and accuracy of classification and gradation. At the same time, the differences, advantages, and disadvantages of K-NN (K-Nearest Neighbors), DT (Decision Tree), and LinearSVC algorithms are compared. The experimental data shows that LinearSVC algorithm is applicable to high-dimensional data, with discrete, sparse data features and large number of features, which is more suitable for classification of sensitive data of enterprises.
Adaptive Robust Dynamic Surface Integral Sliding Mode Control for Quadrotor UAVs under Parametric Uncertainties and External Disturbances

A robust adaptive fuzzy nonlinear controller based on dynamic surface and integral sliding mode control strategy (ADSISMC) is proposed to realize trajectory tracking for a class of quadrotor UAVs. In this study, the composite factors including parametric uncertainties and external disturbances are added to controller design, which make it more realistic. The quadrotor model is divided into two subsystems of attitude and position that make the control design become feasible. The main contributions of the proposed ADSISMC strategy are as follows: (1) The combination of dynamic surface and integral sliding mode makes the system always in sliding stage by finding the appropriate initial position compared with the common sliding mode, and the complexity of explosion in backstepping method is eliminated. (2) By introducing the fuzzy system, the unknown functions and uncertainties can be approximated which significantly improves the robustness and the tracking performance. (3) The switching control strategy is utilized to compensate for the errors between estimated and ideal inputs; the tracking performance of the whole system has been significantly improved. The simulation results show the effectiveness of the proposed control method.
Brain Functional Interaction of Acupuncture Effects in Diarrhea-Dominant Irritable Bowel Syndrome

Acupuncture is a traditional Chinese medicine treatment that has widely been used to modulate gastrointestinal dysfunction caused by irritable bowel syndrome (IBS) and to alleviate the resulting pain. Recent studies have shown that gastrointestinal dysfunction caused by IBS is associated with dysregulation of the brain's central and peripheral nervous system, while functional magnetic resonance imaging (fMRI) helps explore functional abnormality of the brain. However, previous studies rarely used fMRI to study the correlations between brain functional connection, interaction, or segregation (e.g., network degree and clustering coefficient) and acupuncture stimulation in IBS. To bridge this knowledge gap, we study the changed brain functional connection, interaction, and segregation before and after acupuncture stimulation for diarrhea-dominant IBS (IBS-D) with the help of complex network methods based on fMRI. Our results indicate that the abnormal functional connections (FCs) in the right hippocampus, right superior occipital gyrus, left lingual gyrus, left middle occipital gyrus, and the cerebellum, and abnormal network degree in right middle occipital gyrus, where normal controls are significantly different from IBS-D patients, are improved after acupuncture stimulation. These changed FCs and the network degree before and after acupuncture stimulation have significant correlations with the changed clinical information including IBS symptom severity score (r = −0.54, p = 0.0065) and IBS quality of life (r = 0.426, p = 0.038). We conclude that the changes of the brain functional connection, interaction, and segregation in the hippocampus, middle and superior occipital gyrus, cerebellum, and the lingual gyrus may be related to acupuncture stimulation. The abnormal functional connection, interaction, and segregation in IBS-D may be improved after acupuncture stimulation.
A Test Procedure Optimization Method for an Industrial Robot Servo System on an Integrated Testing Platform

A test procedure optimization method was proposed in this paper to improve the test efficiency of the industrial robot servo system (IRSS) to be tested on the IRSS integrated testing platform (IITP). First, an ordered sequence was used to define the IRSS test project when tested on the IITP. The ordered sequence consisted of execution subelements, which were a combination of control variable parameters of the IITP. Second, the optimization relationships among the IRSS test projects were dug out according to the IRSS test project sequences. Finally, by using the traveling salesmen problem (TSP), an optimization model was established based on the optimization relationships of the IRSS test projects, and the optimal test order of the IRSS test projects to be tested on the IITP was obtained by solving the model. A case analysis showed that the proposed method optimized the test procedure on the IITP effectively, and the test time when implementing the test on the IITP according to the optimized order was nearly 50% shorter than before optimization. The optimization effect was thus found to be significant.
Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach

Construction schedules play an important role in construction project management. However, during construction activities, risks may arise due to unexpected schedule changes, resulting in the ineffective delivery of projects. This study aims to reveal the law of schedule change risk propagation and to analyze the effects on the risk propagation through numerical simulations. First, construction projects are represented by activity-on-node (AON) networks. A model of risk propagation is then built based on a susceptible-infected (SI) model considering the effects of the nodal characteristics on the propagation process. Next, the model is tested on a real-world project to examine cascading failures with varying parameters. The experimental results demonstrate that the model is effective in identifying the activities most capable of affecting a project schedule and evaluating the impact of schedule change risk propagation. This study will provide a basis for enhancing the robustness of AON networks and controlling the propagation of schedule change risks.
Machine Learning Techniques for Quantification of Knee Segmentation from MRI

Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed.
Trenchcoat: Human-Computable Hashing Algorithms for Password Generation

The average user has between 90-130 online accounts, and around $3 \times 10^{11}$ passwords are in use this year. Most people are terrible at remembering"random"passwords, so they reuse or create similar passwords using a combination of predictable words, numbers, and symbols. Previous password-generation or management protocols have imposed so large a cognitive load that users have abandoned them in favor of insecure yet simpler methods (e.g., writing them down or reusing minor variants). We describe a range of candidate human-computable"hash"functions suitable for use as password generators - as long as the human (with minimal education assumptions) keeps a single, easily-memorizable"master"secret - and rate them by various metrics, including effective security. These functions hash master-secrets with user accounts to produce sub-secrets that can be used as passwords; $F_R($s$, w) \longrightarrow y$, takes a website $w$, produces a password $y$, parameterized by master secret $s$, which may or may not be a string. We exploit the unique configuration $R$ of each user's associative and implicit memory (detailed in section 2) to ensure that sources of randomness unique to each user are present in each master-secret $F_R$. An adversary cannot compute or verify $F_R$ efficiently since $R$ is unique to each individual; in that sense, our hash function is similar to a physically unclonable function. For the algorithms we propose, the user need only complete primitive operations such as addition, spatial navigation or searching. Critically, most of our methods are also accessible to neurodiverse, or cognitively or physically differently-abled persons. We present results from a survey (n=134 individuals) investigating real-world usage of these methods and how people currently come up with their passwords, we also survey 400 websites to collate current password advice.
Occurrence of IgE binding allergens during ripening of apple fruits

Apple allergy, as well as allergies to nuts, stone fruits and other fruits and vegetables, is very common in patients with hay fever caused by birch pollen. The observed clustering of hypersensitivities is due to cross‐reactions of allergen‐specific IgE antibodies. In the case of apple allergy, patients frequently report that the symptoms are more severe after ingestion of mature fruits. We therefore investigated whether changes of IgE binding properties arose during the development of Boskoop and Golden Delicious apples. Using one tree for each strain, extracts were prepared from fruits at different states of maturity. Sera of patients allergic to birch pollen and apples were studied by means of non‐competitive and competitive ELISA (EAST and EAST inhibition) as well as by immunoblotting followed by absorption/reflection densitometry. The results of the three methods agreed closely and revealed that the allergenic potency increased strongly during maturation of Golden Delicious apples and to a lesser deg.
Dynamic Multiobjective Optimization with Multiple Response Strategies Based on Linear Environment Detection

Dynamic multiobjective optimization problems (DMOPs) bring more challenges for multiobjective evolutionary algorithm (MOEA) due to its time-varying characteristic. To handle this kind of DMOPs, this paper presents a dynamic MOEA with multiple response strategies based on linear environment detection, called DMOEA-LEM. In this approach, different types of environmental changes are estimated and then the corresponding response strategies are activated to generate an efficient initial population for the new environment. DMOEA-LEM not only detects whether the environmental changes but also estimates the types of linear changes so that different prediction models can be selected to initialize the population when the environmental changes. To study the performance of DMOEA-LEM, a large number of test DMOPs are adopted and the experiments validate the advantages of our algorithm when compared to three state-of-the-art dynamic MOEAs.
The Formation and Evolution of Interorganisational Business Networks in Megaprojects: A Case Study of Chinese Skyscrapers

Megaprojects are implemented by different organisations, such as owners, consultants, and contractors. Gradually, these organisations and their connections can form business networks that influence both the market position of individual organisations and project performance. Previous research on large-scale projects mainly focused on static and homogeneous networks that were constructed by one individual project and/or carried out over one-off collaboration. However, this neglected the consideration of project network diversity, as well as repetitive, dynamic, cross-project coopetition relationship (i.e., collaboration and competition) and long-term business networks formed by key actors. Here, we chose Chinese skyscrapers over 300 meters that were built from 1996 to 2015 as typical megaproject cases and analysed the formation and evolution of megaproject business networks from the perspective of interorganisational coopetition. We identified the key actors involved and empirically studied their dynamic network positions over time. The main contributions of this study are threefold. First, we found that past collaboration experience has direct and dynamic impacts on the formation of megaproject business networks. Second, we identified key actors in the interorganisational business network and unveiled their dynamic positions with clear patterns. Third, we highlighted the temporal-spatial effect on the formation and development of business networks, alongside developments in the megaproject market. The findings of this study also provide practical applications for owners to choose collaboration partners and to build high-performance teams and for suppliers to enter and sustain the business in the megaprojects networks.
Adsorption Behavior of Quaternary Amine Types of Lignophenol Compounds for Some Precious Metals

Two kinds of quaternary amine type lignophenol derivatives were prepared and tested for the adsorption behavior of Au(III), Pd(II), Pt(IV), Cu(II), Fe(III), and Zn(II) in hydrochloric acid medium. Both the derivatives were found to exhibit excellent selectivity for precious metals with negligible adsorption for other metal ions. The adsorption isotherm study established the Langmuir type adsorption model, and from the respective adsorption isotherms, the maximum loading capacities for Au(III), Pd(II), and Pt(IV) were evaluated. Ionic interaction between the positive moiety of quaternary amine functional groups and the anionic chloride complexes formed by the precious metal ions in acidic chloride media was concluded as the main adsorption mechanism.
A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to develop a speaker recognition system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively.
Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain

Recommending the identity of bidders in public procurement auctions (tenders) has a significant impact in many areas of public procurement, but it has not yet been studied in depth. A bidders recommender would be a very beneficial tool because a supplier (company) can search appropriate tenders and, vice versa, a public procurement agency can discover automatically unknown companies which are suitable for its tender. This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest classifier. The bidders recommender is described theoretically, so it can be implemented or adapted to any particular situation. It has been successfully validated with a case study: an actual Spanish tender dataset (free public information) which has 102,087 tenders from 2014 to 2020 and a company dataset (nonfree public information) which has 1,353,213 Spanish companies. Quantitative, graphical, and statistical descriptions of both datasets are presented. The results of the case study were satisfactory: the winning bidding company is within the recommended companies group, from 24% to 38% of the tenders, according to different test conditions and scenarios.