id,title,description,date_created,date_modified,date_published,original_publication_date,publication_doi,provider,is_published,reviews_state,version,is_latest_version,preprint_doi,license,tags_list,tags_data,contributors_list,contributors_data,first_author,subjects_list,subjects_data,download_url,has_coi,conflict_of_interest_statement,has_data_links,has_prereg_links,prereg_links,prereg_link_info,last_updated
576ja_v1,Speech repression and threat narratives in politics: social goals and cognitive foundations,"Political movements often bind around mobilizing narratives, denouncing an evil or villains encroaching on a sacred prosocial value, such as national grandeur, faith, a class, racial, or gender equality. In their most devoted activists, this triggers moral motivations to protect the narrative from being argumentatively challenged, with accuracy and nuance as inevitable victims. Repressive reactions range from expressions of outrage or public shaming on social media to the “deplatforming” and “canceling” of controversial speakers to censorship and imprisonment of dissidents. Speech repression phenomena are puzzling because the ideological narratives activists try to protect are generally simplistic and inaccurate. Here, I argue that speech repression likely derives from three main socio-cognitive motivations. First, hyper-sensitive dispositions to detect threat, from hostile outgroups in particular. Second, motivations to try to keep people mobilized for moral causes and against dangerous groups, by controlling information flows and beliefs. Third, motivations to signal personal devotion to moral causes and ingroup to gain status. Political activists and leaders only need to believe that speech restriction will bring about desired effects to engage in it, not that their beliefs be true. Given selection pressures for genuine moral intuitions, speech restriction tactics may often spring from sincere ideological convictions.",2025-05-09T18:11:14.294301,2025-05-09T18:24:21.358086,2025-05-09T18:23:58.777825,,,osf,1,accepted,1,1,https://doi.org/10.31219/osf.io/576ja_v1,CC0 1.0 Universal,,[],Antoine Marie,"[{""id"": ""vjm9d"", ""name"": ""Antoine Marie"", ""index"": 0, ""orcid"": ""0000-0002-7958-0153"", ""bibliographic"": true}]",Antoine Marie,Social and Behavioral Sciences,"[{""id"": ""584240da54be81056cecac48"", ""text"": ""Social and Behavioral Sciences""}]",https://osf.io/download/681e455124166806ae700142,0,,not_applicable,not_applicable,[],,2025-05-10T00:11:34.189771
f7eu8_v1,First case of mpox due to monkeypox virus clade Ia infection detected outside Africa,"In February 2025, the first confirmed case of monkeypox virus (MPXV) clade Ia infection outside Africa was identified in a traveller returning to Ireland from the Democratic Republic of the Congo (DRC). We report the clinical, laboratory, and epidemiologic features of this case, including the stable disease course and absence of secondary transmission. We also present phylogenetic analysis and APOBEC3-associated mutation profiling that placed the virus obtained from this patient in an evolving cluster of MPXV clade Ia infections in Kinshasa, DRC, likely driven by sustained human-to-human transmission. This case highlights the evolving epidemiology of MPXV clade Ia and its potential for further international spread, reinforcing the need for vigilance and coordinated global surveillance.",2025-05-09T16:17:18.712059,2025-05-09T18:07:01.256877,2025-05-09T18:06:34.105897,,,osf,1,accepted,1,1,https://doi.org/10.31219/osf.io/f7eu8_v1,No license,Infectious Diseases; Microbiology; Monkeypox; Mpox; Public Health,"[""Infectious Diseases"", ""Microbiology"", ""Monkeypox"", ""Mpox"", ""Public Health""]",Cian Dowling-Cullen; Laura Fahey; Michael Carr; Brian Keogan; Josephine Hebert; Siti Mardhiah Muhamad Fauz; Jane Stapleton; Allison Deane; Elizabeth O'Donoghue; Colette Reilly,"[{""id"": ""e8w3s"", ""name"": ""Cian Dowling-Cullen"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""6fqxc"", ""name"": ""Laura Fahey"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""3mz6n"", ""name"": ""Michael Carr"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""h5j7y"", ""name"": ""Brian Keogan"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""kzahw"", ""name"": ""Josephine Hebert"", ""index"": 4, ""orcid"": null, ""bibliographic"": true}, {""id"": ""b7v2e"", ""name"": ""Siti Mardhiah Muhamad Fauz"", ""index"": 5, ""orcid"": null, ""bibliographic"": true}, {""id"": ""qdz6s"", ""name"": ""Jane Stapleton"", ""index"": 6, ""orcid"": null, ""bibliographic"": true}, {""id"": ""7t958"", ""name"": ""Allison Deane"", ""index"": 7, ""orcid"": null, ""bibliographic"": true}, {""id"": ""7nhds"", ""name"": ""Elizabeth O'Donoghue"", ""index"": 8, ""orcid"": null, ""bibliographic"": true}, {""id"": ""jzq2d"", ""name"": ""Colette Reilly"", ""index"": 9, ""orcid"": null, ""bibliographic"": true}]",Cian Dowling-Cullen,Infectious Disease; Clinical Epidemiology; Organisms; Public Health; Medicine and Health Sciences; Epidemiology; Medical Specialties; International Public Health; Viruses,"[{""id"": ""584240d954be81056ceca87b"", ""text"": ""Infectious Disease""}, {""id"": ""584240d954be81056ceca8ca"", ""text"": ""Clinical Epidemiology""}, {""id"": ""584240d954be81056cecaa4e"", ""text"": ""Organisms""}, {""id"": ""584240da54be81056cecaa96"", ""text"": ""Public Health""}, {""id"": ""584240da54be81056cecaaaa"", ""text"": ""Medicine and Health Sciences""}, {""id"": ""584240da54be81056cecab54"", ""text"": ""Epidemiology""}, {""id"": ""584240da54be81056cecab69"", ""text"": ""Medical Specialties""}, {""id"": ""584240da54be81056cecab9b"", ""text"": ""International Public Health""}, {""id"": ""584240da54be81056cecac5e"", ""text"": ""Viruses""}]",https://osf.io/download/681e2a9c392af38329da57e5,0,,not_applicable,not_applicable,[],,2025-05-10T00:11:34.198688
vpex7_v2,Energy-Space Physics and the Curvature-Aligned Transport System: A Framework for Time-Free Fundamental Physics and Deterministic Spatial Transition,"This dissertation presents a unified theoretical framework grounded in a time-free reformulation of physics in energy-space and introduces a Curvature-Aligned Transport System (CATS)—a novel model for deterministic spatial transitions that do not rely on classical displacement, velocity, or time-based evolution. In this framework, motion arises from entropy-gradient-driven flows embedded in a curved energy-space manifold. Extending concepts from general relativity, electromagnetism, and fluid dynamics, CATS defines a new class of transitions based on harmonic resonance, field alignment, and curvature-guided coherence. These dynamics are rigorously formulated using tensor calculus, differential geometry, and non-temporal variational principles. Comprehensive simulations validate the model’s predictions, demonstrating stable energy-space resonances, precise target localization, and field coherence. Scenario analyses, including Earth–Moon repositioning, suggest feasibility using structured electromagnetic fields and achievable energy budgets. These transitions occur through spatial realignment rather than kinematic propagation. An experimental roadmap is outlined, featuring vacuum-based transition tests, entropy-gradient diagnostics, and field-structuring strategies. Safety and scalability are evaluated for future applications. This research contributes a foundational extension to motion, causality, and transport in physics. By removing time as a fundamental variable and introducing entropy-guided spatial dynamics, the CATS framework provides a coherent, testable platform for nonlocal transitions and offers a new direction for advanced transitional-motion, quantum gravity studies, and unified physical theory.",2025-05-09T15:57:06.414798,2025-05-09T17:12:21.070155,2025-05-09T17:12:03.740983,,,osf,1,accepted,2,1,https://doi.org/10.31219/osf.io/vpex7_v2,No license,,[],Andrew P. Whittaker,"[{""id"": ""r2h8z"", ""name"": ""Andrew P. Whittaker"", ""index"": 0, ""orcid"": ""0009-0004-3584-6601"", ""bibliographic"": true}]",Andrew P. Whittaker,"Astrophysics and Astronomy; Physical Sciences and Mathematics; Cosmology, Relativity, and Gravity; Quantum Physics; Physics; Physical Processes; Elementary Particles and Fields and String Theory; Other Physics","[{""id"": ""584240d954be81056ceca8b5"", ""text"": ""Astrophysics and Astronomy""}, {""id"": ""584240d954be81056ceca9a1"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""584240d954be81056cecaa36"", ""text"": ""Cosmology, Relativity, and Gravity""}, {""id"": ""584240da54be81056cecabaf"", ""text"": ""Quantum Physics""}, {""id"": ""584240da54be81056cecabc4"", ""text"": ""Physics""}, {""id"": ""584240da54be81056cecac5a"", ""text"": ""Physical Processes""}, {""id"": ""584240da54be81056cecaca1"", ""text"": ""Elementary Particles and Fields and String Theory""}, {""id"": ""584240db54be81056cecace6"", ""text"": ""Other Physics""}]",https://osf.io/download/681e25f47966de012b5205e9,0,,not_applicable,not_applicable,[],,2025-05-10T00:11:34.198088
pf4nj_v1,Forkhead box M1 (FOXM1) : Machine learning discoveries of 2nd order synergy in Meningiomas,"Background : FOXM1 is a member of the FOX family of transcription factors, with
the defining feature of forkhead box, which is a sequence of 80 to 100 amino acids forming a motif (or winged helix) that binds to DNA. FOX proteins belong to a sub- group of the helix-turn-helix class of proteins. In 2010, it was assigned molecule of the year for its potential as a target for future cancer treatments. Recently, FOXM1 was ob- served as a key transcription factor for meningioma proliferation and a marker of poor clinical outcomes. Meningiomas are the most common intracranial primary neoplasm in adults. Patel et al. [1] analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data and using unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. Further, these groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Issue : Increasing evidence point to the fact that meningioma classification and grading, that is based on histopathology does not always accurately predict tumor aggressiveness and recurrence behaviour and knowledge of the underlying biology of the treatment resistant meningiomas and the impact of genetic alterations in these tumors, is lacking. At the current stage more genomic studies are required to unravel the role of other genes and their interations with other genetic factors. Resolution : In a recently published work Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Adapting this search engine to the Meningioma dataset, i present here 2nd order combinations of FOXM1, some of which have been known to exist via wet lab experiments, but many are yet to be tested. The reveals combinations might help oncologists/biologists test possible hypotheses that might be the causing factors in meningioma. Further, in my limited grasp, if proven true, the combinations revealed by the search engine might pave way for development of gene based therapies aimed at resolving pathological issues related to meningiomas.",2025-05-09T15:23:23.906406,2025-05-09T17:06:41.947999,2025-05-09T17:06:15.265441,,,osf,1,accepted,1,1,https://doi.org/10.31219/osf.io/pf4nj_v1,CC-By Attribution 4.0 International,,[],shriprakash sinha,"[{""id"": ""4muv5"", ""name"": ""shriprakash sinha"", ""index"": 0, ""orcid"": ""0000-0001-7027-5788"", ""bibliographic"": true}]",shriprakash sinha,Nervous System Diseases; Diseases; Medicine and Health Sciences; Life Sciences; Systems Biology; Computational Biology; Genetics and Genomics,"[{""id"": ""584240d954be81056ceca921"", ""text"": ""Nervous System Diseases""}, {""id"": ""584240d954be81056cecaa5d"", ""text"": ""Diseases""}, {""id"": ""584240da54be81056cecaaaa"", ""text"": ""Medicine and Health Sciences""}, {""id"": ""584240da54be81056cecaab0"", ""text"": ""Life Sciences""}, {""id"": ""584240da54be81056cecac2f"", ""text"": ""Systems Biology""}, {""id"": ""584240da54be81056cecac8d"", ""text"": ""Computational Biology""}, {""id"": ""584240db54be81056cecacea"", ""text"": ""Genetics and Genomics""}]",https://osf.io/download/681e1e054ca318846589eadc,0,,available,not_applicable,[],,2025-05-10T00:11:34.206027
wexa8_v1,"gap junction protein, alpha 3, 46kDa (connexin 46) (GJA3) : Machine learning discoveries of 2nd order synergy in Meningiomas","Background : GJA3 belongs to the group of connexins, or gap junction proteins, that are a family of structurally related transmembrane proteins that assemble to form vertebrate gap junctions, which are different from the innexins, form gap junctions in invertebrates. They are essential for many physiological processes, such as proper embryonic development, the coordinated depolarization of cardiac muscle, and the conducted response in microvasculature. For these reasons, mutations in connexin- encoding genes can lead to functional and developmental abnormalities. Meningiomas are the most common intracranial primary neoplasm in adults. Patel et al. [1] analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) us- ing clinical, gene expression, and sequencing data and using unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. Further, these groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Issue : Increasing evidence point to the fact that meningioma classification and grading, that is based on histopathology does not always accurately predict tumor aggressiveness and recurrence behaviour and knowledge of the underlying biology of the treatment resistant meningiomas and the impact of genetic alterations in these tumors, is lacking. At the current stage more genomic studies are required to unravel the role of other genes and their interations with other genetic factors. Resolution : In a recently published work Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Adapting this search engine to the Meningioma dataset, i present here 2nd order combinations of GJA3, some of which have been known to exist via wet lab experiments, but many are yet to be tested. The reveals combinations might help oncologists/biologists test possible hypotheses that might be the causing factors in meningioma. Further, in my limited grasp, if proven true, the combinations revealed by the search engine might pave way for development of gene based therapies aimed at resolving pathological issues related to meningiomas.",2025-05-09T15:06:32.200571,2025-05-09T16:55:01.341674,2025-05-09T16:54:42.113662,,,osf,1,accepted,1,1,https://doi.org/10.31219/osf.io/wexa8_v1,CC-By Attribution 4.0 International,,[],shriprakash sinha,"[{""id"": ""4muv5"", ""name"": ""shriprakash sinha"", ""index"": 0, ""orcid"": ""0000-0001-7027-5788"", ""bibliographic"": true}]",shriprakash sinha,Nervous System Diseases; Diseases; Medicine and Health Sciences; Life Sciences; Systems Biology; Computational Biology; Genetics and Genomics,"[{""id"": ""584240d954be81056ceca921"", ""text"": ""Nervous System Diseases""}, {""id"": ""584240d954be81056cecaa5d"", ""text"": ""Diseases""}, {""id"": ""584240da54be81056cecaaaa"", ""text"": ""Medicine and Health Sciences""}, {""id"": ""584240da54be81056cecaab0"", ""text"": ""Life Sciences""}, {""id"": ""584240da54be81056cecac2f"", ""text"": ""Systems Biology""}, {""id"": ""584240da54be81056cecac8d"", ""text"": ""Computational Biology""}, {""id"": ""584240db54be81056cecacea"", ""text"": ""Genetics and Genomics""}]",https://osf.io/download/681e1a2749885e100185e9f5,0,,available,not_applicable,[],,2025-05-10T00:11:34.203456
c4kfw_v2,Emergent Predictive Experience Theory (EPET): An Integrative Philosophy of Consciousness,"This paper introduces the Emergent Predictive Experience Theory (EPET), an integrative framework addressing the hard problem of consciousness from a non-reductive emergentist physicalist perspective. EPET synthesizes key insights from Predictive Processing (PP), Global Workspace Theory (GWT), embodied cognition, and core Buddhist philosophical principles (notably Anattā - no-self, and Paṭiccasamuppāda - dependent origination). The theory posits consciousness not as a substance or fundamental property, but as a real, emergent, dynamic process realized through integrated predictive modeling within an embodied, action-oriented system. **Offering a constitutive account**, it explains subjective quality (qualia) not as an epiphenomenal add-on, but as intrinsic properties of this modeling process itself, reflecting the system's ongoing assessment of interaction relevance for its own viability. The phenomenal sense of self is interpreted as a dynamic construct arising from recursive self-modeling within the predictive architecture, an account compatible with the Buddhist doctrine of Anattā. By integrating these scientific and philosophical perspectives, EPET aims to provide a coherent, naturalistic, and empirically grounded explanation of conscious experience, avoiding the pitfalls of substance dualism, panpsychism, and strong reductionism/illusionism, while offering a heuristically valuable framework for future interdisciplinary research and generating testable hypotheses.",2025-05-09T15:03:16.232759,2025-05-09T17:07:21.399273,2025-05-09T17:07:12.316757,,,osf,1,accepted,2,1,https://doi.org/10.31219/osf.io/c4kfw_v2,CC-By Attribution 4.0 International,Anatta; Buddhism; Buddhist philosophy; Paticcasamuppada; cognitive science; consciousness; dependent origination; embodied cognition; embodiment; emergence; emergentism; free energy principle; global workspace theory; hard problem; neuroscience; no-self; non-reductive physicalism; philosophy of mind; physicalism; predictive coding; predictive processing; qualia; self; self-model; sense of self,"[""Anatta"", ""Buddhism"", ""Buddhist philosophy"", ""Paticcasamuppada"", ""cognitive science"", ""consciousness"", ""dependent origination"", ""embodied cognition"", ""embodiment"", ""emergence"", ""emergentism"", ""free energy principle"", ""global workspace theory"", ""hard problem"", ""neuroscience"", ""no-self"", ""non-reductive physicalism"", ""philosophy of mind"", ""physicalism"", ""predictive coding"", ""predictive processing"", ""qualia"", ""self"", ""self-model"", ""sense of self""]",Andrey Kopnin,"[{""id"": ""vw9hu"", ""name"": ""Andrey Kopnin"", ""index"": 0, ""orcid"": ""0009-0006-9244-6764"", ""bibliographic"": true}]",Andrey Kopnin,Computational Neuroscience; Cognitive Neuroscience; Philosophy; Philosophy of Science; Buddhist Studies; Religion; Life Sciences; Arts and Humanities; Philosophy of Mind; Cognitive Psychology; Neuroscience and Neurobiology; Metaphysics; Social and Behavioral Sciences; Systems Neuroscience; Psychology,"[{""id"": ""584240d954be81056ceca905"", ""text"": ""Computational Neuroscience""}, {""id"": ""584240d954be81056ceca94c"", ""text"": ""Cognitive Neuroscience""}, {""id"": ""584240d954be81056ceca97a"", ""text"": ""Philosophy""}, {""id"": ""584240d954be81056ceca9d6"", ""text"": ""Philosophy of Science""}, {""id"": ""584240d954be81056cecaa4b"", ""text"": ""Buddhist Studies""}, {""id"": ""584240da54be81056cecaa9c"", ""text"": ""Religion""}, {""id"": ""584240da54be81056cecaab0"", ""text"": ""Life Sciences""}, {""id"": ""584240da54be81056cecaab4"", ""text"": ""Arts and Humanities""}, {""id"": ""584240da54be81056cecab46"", ""text"": ""Philosophy of Mind""}, {""id"": ""584240da54be81056cecab7e"", ""text"": ""Cognitive Psychology""}, {""id"": ""584240da54be81056cecabfd"", ""text"": ""Neuroscience and Neurobiology""}, {""id"": ""584240da54be81056cecac21"", ""text"": ""Metaphysics""}, {""id"": ""584240da54be81056cecac48"", ""text"": ""Social and Behavioral Sciences""}, {""id"": ""584240da54be81056cecac66"", ""text"": ""Systems Neuroscience""}, {""id"": ""584240da54be81056cecac68"", ""text"": ""Psychology""}]",https://osf.io/download/681e19436405ec5f7bda55f2,0,,not_applicable,not_applicable,[],,2025-05-10T00:11:34.193421
gt6mp_v1,DNA topoisomerase II α (TOP2A) : Machine learning discoveries of 2nd order synergy in Meningiomas,"Background : TOP2A belongs to the family of topoisomerases, which regulates the (un)winding of the DNA due to its double helical structure. The main function of TOP2A is to relieve the topological stress during DNA transcription, assist in separation of chromatids and condensation of chromosomes. Meningiomas are the most common intracranial primary neoplasm in adults. Patel et al. [1] analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data and using unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. Further, these groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Issue : Increasing evidence point to the fact that meningioma classification and grading, that is based on histopathology does not always accurately predict tumor aggressiveness and recurrence behaviour and knowledge of the underlying biology of the treatment resistant meningiomas and the impact of genetic alterations in these tumors, is lacking. At the current stage more genomic studies are required to unravel the role of other genes and their interations with other genetic factors. Resolution : In a recently published work Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Adapting this search engine to the Meningioma dataset, i present here 2nd order combinations of HJURP, some of which have been known to exist via wet lab experiments, but many are yet to be tested. The reveals combinations might help oncologists/biologists test possible hypotheses that might be the causing factors in meningioma. Further, in my limited grasp, if proven true, the combinations revealed by the search engine might pave way for development of gene based therapies aimed at resolving pathological issues related to meningiomas.",2025-05-09T14:55:08.566878,2025-05-09T16:55:40.972540,2025-05-09T16:55:25.030390,,,osf,1,accepted,1,1,https://doi.org/10.31219/osf.io/gt6mp_v1,CC-By Attribution 4.0 International,,[],shriprakash sinha,"[{""id"": ""4muv5"", ""name"": ""shriprakash sinha"", ""index"": 0, ""orcid"": ""0000-0001-7027-5788"", ""bibliographic"": true}]",shriprakash sinha,Nervous System Diseases; Diseases; Medicine and Health Sciences; Life Sciences; Systems Biology; Computational Biology; Genetics and Genomics,"[{""id"": ""584240d954be81056ceca921"", ""text"": ""Nervous System Diseases""}, {""id"": ""584240d954be81056cecaa5d"", ""text"": ""Diseases""}, {""id"": ""584240da54be81056cecaaaa"", ""text"": ""Medicine and Health Sciences""}, {""id"": ""584240da54be81056cecaab0"", ""text"": ""Life Sciences""}, {""id"": ""584240da54be81056cecac2f"", ""text"": ""Systems Biology""}, {""id"": ""584240da54be81056cecac8d"", ""text"": ""Computational Biology""}, {""id"": ""584240db54be81056cecacea"", ""text"": ""Genetics and Genomics""}]",https://osf.io/download/681e176cb0e1f91c9b89ec30,0,,available,not_applicable,[],,2025-05-10T00:11:34.204695
3k8ds_v1,cyclin dependent kinase inhibitor 2A (CDKN2A) : Machine learning discoveries of 2nd order synergy in Meningiomas,"Background : CDKN2A is located at chromosome 9, band p21.3 and it codes for two proteins, including the INK4 family member p16 (or p16INK4a) and p14arf, both of which act as tumor suppressors by regulating the cell cycle. p16 inhibits cyclin de- pendent kinases 4 and 6 (CDK-4/6), thus activating the retinoblastoma (Rb) family of proteins, which block traversal from G1 to S-phase, while p14ARF activates the p53 tumor suppressor. Meningiomas are the most common intracranial primary neoplasm in adults. Patel et al. [1] analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data and using unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. Further, these groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Issue : Increasing evidence point to the fact that meningioma classification and grading, that is based on histopathology does not always accurately predict tumor aggressiveness and recurrence behaviour and knowledge of the underlying biology of the treatment resistant meningiomas and the impact of genetic alterations in these tumors, is lacking. At the current stage more genomic studies are required to unravel the role of other genes and their interations with other genetic factors. Resolution : In a recently published work Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Adapting this search engine to the Meningioma dataset, i present here 2nd order combinations of CDKN2A, some of which have been known to exist via wet lab experiments, but many are yet to be tested. The reveals combinations might help oncologists/biologists test possible hypotheses that might be the causing factors in meningioma. Further, in my limited grasp, if proven true, the combinations revealed by the search engine might pave way for development of gene based therapies aimed at resolving pathological issues related to meningiomas.",2025-05-09T14:45:44.172151,2025-05-09T16:56:41.035955,2025-05-09T16:56:22.748279,,,osf,1,accepted,1,1,https://doi.org/10.31219/osf.io/3k8ds_v1,CC-By Attribution 4.0 International,,[],shriprakash sinha,"[{""id"": ""4muv5"", ""name"": ""shriprakash sinha"", ""index"": 0, ""orcid"": ""0000-0001-7027-5788"", ""bibliographic"": true}]",shriprakash sinha,Nervous System Diseases; Diseases; Medicine and Health Sciences; Life Sciences; Biology; Systems Biology; Computational Biology; Genetics and Genomics,"[{""id"": ""584240d954be81056ceca921"", ""text"": ""Nervous System Diseases""}, {""id"": ""584240d954be81056cecaa5d"", ""text"": ""Diseases""}, {""id"": ""584240da54be81056cecaaaa"", ""text"": ""Medicine and Health Sciences""}, {""id"": ""584240da54be81056cecaab0"", ""text"": ""Life Sciences""}, {""id"": ""584240da54be81056cecab01"", ""text"": ""Biology""}, {""id"": ""584240da54be81056cecac2f"", ""text"": ""Systems Biology""}, {""id"": ""584240da54be81056cecac8d"", ""text"": ""Computational Biology""}, {""id"": ""584240db54be81056cecacea"", ""text"": ""Genetics and Genomics""}]",https://osf.io/download/681e155e1cdef153127000b5,0,,available,not_applicable,[],,2025-05-10T00:11:34.212069
hnw5p_v1,A Constructive Solution to the Clay Millennium Yang–Mills Problem with a Mass Gap in Four Dimensions,"We present a constructive framework for a four-dimensional Yang–Mills quantum field theory with compact nonabelian gauge group SU(N), aiming to fulfill the Jaffe–Witten criteria of the Yang–Mills Millennium Problem. The approach proceeds through seven rigorously structured steps: lattice regularization, ultraviolet control, continuum limit, Lorentzian reconstruction, and derivation of a universal, strictly positive mass gap. Building on methods from Balaban’s renormalization group, cluster expansions, and infrared bounds à la Aizenman–Fröhlich–Spencer, the construction satisfies the Osterwalder–Schrader, Wightman, and Haag–Kastler axioms. While some technical results are assumed from prior literature (e.g., RG multiscale bounds), all steps are logically explicit and fully cross-referenced. This work offers a concrete candidate for solving the first Clay Millennium Problem from a mathematically constructive standpoint.",2025-05-09T14:42:09.913766,2025-05-09T16:58:01.748866,2025-05-09T16:57:47.525242,,,osf,1,accepted,1,1,https://doi.org/10.31219/osf.io/hnw5p_v1,No license,Axiomatic QFT; Cluster Expansion; Constructive Quantum Field Theory; Functional Integration; Gauge Invariance; Glueball Spectrum; Haag–Kastler Framework; Infrared Bounds; Lattice Gauge Theory; Mass Gap; Mathematical Physics; Millennium Prize Problems; Nonperturbative Methods; Osterwalder–Schrader Axioms; Quantum Field Theory; Reflection Positivity; Renormalization Group; SU(N) Gauge Theory; Wightman Axioms; Yang–Mills Theory,"[""Axiomatic QFT"", ""Cluster Expansion"", ""Constructive Quantum Field Theory"", ""Functional Integration"", ""Gauge Invariance"", ""Glueball Spectrum"", ""Haag\u2013Kastler Framework"", ""Infrared Bounds"", ""Lattice Gauge Theory"", ""Mass Gap"", ""Mathematical Physics"", ""Millennium Prize Problems"", ""Nonperturbative Methods"", ""Osterwalder\u2013Schrader Axioms"", ""Quantum Field Theory"", ""Reflection Positivity"", ""Renormalization Group"", ""SU(N) Gauge Theory"", ""Wightman Axioms"", ""Yang\u2013Mills Theory""]",David Gutierrez Ule,"[{""id"": ""s2cz4"", ""name"": ""David Gutierrez Ule"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}]",David Gutierrez Ule,Physical Sciences and Mathematics,"[{""id"": ""584240d954be81056ceca9a1"", ""text"": ""Physical Sciences and Mathematics""}]",https://osf.io/download/681e144c45b3be4306700255,0,,not_applicable,not_applicable,[],,2025-05-10T00:11:34.207827
avhy8_v1,cellular retinoic acid binding protein 1 (CRABP1) : Machine learning discoveries of 2nd order synergy in Meningiomas,"Background : CRABP1 is a cellular retinoic acid-binding protein, which is assumed to play an important role in retinoic acid-mediated differentiation and proliferation processes. The pleiotropic effects of retinoic acid (RA) in mammalian cells are mediated by two classes of proteins: the retinoic acid receptors (RAR) and CRABP-1/2. CRABP1 is known to be involved in multiple cancer proliferation pathways. Meningiomas are the most common intracranial primary neoplasm in adults. Patel et al. [1] analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data and using unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. Further, these groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Issue : Increasing evidence point to the fact that meningioma classification and grading, that is based on histopathology does not always accurately predict tumor aggressiveness and recurrence behaviour and knowledge of the underlying biology of the treatment resistant meningiomas and the impact of genetic alterations in these tumors, is lacking. At the current stage more genomic studies are required to unravel the role of other genes and their interations with other genetic factors. Resolution : In a recently published work Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Adapting this search engine to the Meningioma dataset, i present here 2nd order combinations of CRABP1, some of which have been known to exist via wet lab experiments, but many are yet to be tested. The reveals combinations might help oncologists/biologists test possible hypotheses that might be the causing factors in meningioma. Further, in my limited grasp, if proven true, the combinations revealed by the search engine might pave way for development of gene based therapies aimed at resolving pathological issues related to meningiomas.",2025-05-09T14:36:42.066602,2025-05-09T16:57:01.708466,2025-05-09T16:56:42.703860,,,osf,1,accepted,1,1,https://doi.org/10.31219/osf.io/avhy8_v1,CC-By Attribution 4.0 International,,[],shriprakash sinha,"[{""id"": ""4muv5"", ""name"": ""shriprakash sinha"", ""index"": 0, ""orcid"": ""0000-0001-7027-5788"", ""bibliographic"": true}]",shriprakash sinha,Nervous System Diseases; Diseases; Medicine and Health Sciences; Life Sciences; Systems Biology; Computational Biology; Genetics and Genomics,"[{""id"": ""584240d954be81056ceca921"", ""text"": ""Nervous System Diseases""}, {""id"": ""584240d954be81056cecaa5d"", ""text"": ""Diseases""}, {""id"": ""584240da54be81056cecaaaa"", ""text"": ""Medicine and Health Sciences""}, {""id"": ""584240da54be81056cecaab0"", ""text"": ""Life Sciences""}, {""id"": ""584240da54be81056cecac2f"", ""text"": ""Systems Biology""}, {""id"": ""584240da54be81056cecac8d"", ""text"": ""Computational Biology""}, {""id"": ""584240db54be81056cecacea"", ""text"": ""Genetics and Genomics""}]",https://osf.io/download/681e13141e46d765d87000a3,0,,available,not_applicable,[],,2025-05-10T00:11:34.205366